Recent advances in deep-learning-enhanced photoacoustic imaging

Jinge Yang; Seongwook Choi; Jiwoong Kim; Byullee Park; Chulhong Kim

doi:10.1117/1.APN.2.5.054001

24 July 2023 Recent advances in deep-learning-enhanced photoacoustic imaging

Jinge Yang, Seongwook Choi, Jiwoong Kim, Byullee Park, Chulhong Kim

Author Affiliations +

Advanced Photonics Nexus, Vol. 2, Issue 5, 054001 (July 2023). https://doi.org/10.1117/1.APN.2.5.054001

Abstract

Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound imaging. Despite PAI’s great potential to provide valuable biological information, its wide application has been hindered by technical limitations, such as hardware restrictions or lack of the biometric information required for image reconstruction. We first analyze the limitations of PAI and categorize them by seven key challenges: limited detection, low-dosage light delivery, inaccurate quantification, limited numerical reconstruction, tissue heterogeneity, imperfect image segmentation/classification, and others. Then, because deep learning (DL) has increasingly demonstrated its ability to overcome the physical limitations of imaging modalities, we review DL studies from the past five years that address each of the seven challenges in PAI. Finally, we discuss the promise of future research directions in DL-enhanced PAI.

1. Introduction

Photoacoustic imaging (PAI) is a noninvasive and radiation-free biomedical imaging modality that provides high spatial resolution, deep penetration, and great optical absorption contrast by synergistically combining optics and acoustics.¹ PAI is based on the photoacoustic (PA) effect, in which optical energy from a pulse laser is converted into acoustic energy waves by the light absorption characteristics of biomolecules.² The initial pressure of a generated PA wave can be calculated as

Eq. (1)

p_{0} = Γ μ_{a} η_{t h} F,

where

p_{0}

denotes the initial pressure,

Γ

is the Gruneisen coefficient,

μ_{a}

is the optical absorption coefficient,

η_{th}

is the efficiency of heat conversion from the optical absorption, and

F

is the optical fluence. Because acoustic scattering in biological tissues is several orders of magnitude lower than light scattering, PAI can obtain biomolecule information based on the absorption contrast of light at a depth of several centimeters.³^,⁴ PAI also extracts the concentrations of intrinsic chromophores, such as oxyhemoglobin (HbO), deoxyhemoglobin (HbR), melanin, water, and lipids, using multispectral image processing.⁵^–¹³ In particular, oxygen saturation (

{sO}_{2}

), an important index for evaluating various diseases, is calculated through HbO and HbR values.¹⁴ By exploiting spectral characteristics, PAI can analyze physiological functions such as

{sO}_{2}

, blood flow, and metabolic rates in preclinical and clinical research.⁴^,¹⁵^–¹⁷ For example, the high sensitivity of PAI to hemoglobin has made it valuable in preclinical studies of angiogenic diseases, tumor hypoxia, and cerebral hemodynamics.¹⁸^–²⁰ Further, the use of PAI is expanding into clinical research areas, such as thyroid and breast cancer screening, lymph node biopsy guidance, tissue examination, and melanoma staging.²¹^–²⁴ Not limited to endogenous chromophores, the high molecular sensitivity of PAI enables molecular imaging when exogenous contrast agents are administered.⁴ Biodistribution and pharmacokinetics in the body can be imaged in vivo through exogenous contrast agents that generate PA signals.²⁵^–²⁷ In this way, PAI is being used in diagnosing cancer and brain diseases and monitoring their therapies, in studying the organ accumulation of substances, and in tracking the dissemination of drugs.⁸^,²⁸^–³⁰

Photoacoustic microscopy (PAM) and photoacoustic computed tomography (PACT) are the main PAI modalities. PAM is subdivided into two types, depending on which of the two co-aligned acoustic and optical components is more tightly focused.³¹^–³⁶ Optical resolution PAM (OR-PAM), which implements focused optical illumination on the acoustic focal area, shows high spatial resolution (a few micrometers) and has been applied to investigate small biological structures.³⁷^,³⁸ Acoustic resolution PAM (AR-PAM) uses a less tightly focused optical beam than OR-PAM, but its acoustic focus is smaller than its laser focus.³⁹ AR-PAM achieves deeper light penetration (up to several centimeters, compared to the 1 mm depth in OR-PAM) despite its lower spatial resolution (tens/hundreds of micrometers), defined by the acoustic focus. PACT uses multiple detection positions to simultaneously reconstruct an image in 2D or 3D. It provides hundreds of images with micrometer-level spatial resolution at imaging depths ranging in the tens of millimeters. PACT uses a high-energy wide laser beam and an ultrasound (US) transducer array (e.g., linear, ring-shaped, arc-shaped, or hemispherical) to receive US waves generated by laser illumination.³^,²¹^,⁴⁰^–⁴² Images are created by reconstruction algorithms,⁴³ such as delay-and-sum (DAS),⁴⁴^,⁴⁵ delay-multiply-and-sum (DMAS),⁴⁶ backprojection (BP),⁴⁷ Fourier beam forming,⁴⁸ time reversal (TR),⁴⁹ and model-based methods.⁵⁰^,⁵¹

PAI has gained widespread recognition as a promising biomedical imaging modality for preclinical and clinical studies. However, to fully realize PAI’s great potential, the seven challenges listed in Table 1 and illustrated in Fig. 1 must be addressed to further enhance the image quality and expand PAI’s applications:

(1) The first challenge, overcoming limited detection capability, arises because most PAI systems are still constrained by such factors as restricted bandwidth, a limited detection view, and sampling sparsity.⁵⁹
(2) The second challenge is to compensate for low-dosage light delivery. The PACT systems based on LEDs or laser diodes are portable and cost-effective alternatives to bulky and expensive solid-state laser systems. However, their low-dosage light delivery provides only a low signal-to-noise ratio (SNR), which can affect image quality. In the case of OR-PAM, fast scanning with high repetition rates is necessary for certain applications such as recording brain-wide neuronal activities.⁶⁰ However, to ensure laser safety, low laser dosages are required, resulting in reduced SNRs and decreased image qualities.
(3) The third challenge is to improve the accuracy of quantitative PA imaging. Accurately determining physiological parameters remains a demanding task due to the complex and nonlinear nature of light absorption and scattering.⁶¹
(4) The fourth challenge is to optimize or replace current reconstruction methods, whose inherent limitations compromise their accuracy and effectiveness in generating high-quality images.
(5) The fifth challenge is to address the problems posed by tissue heterogeneity. Local variations in the acoustical properties of biological tissue can lead to inconsistencies in the reconstructed PA images, resulting in artifacts that degrade the accuracy of quantitative measurements derived from the images.⁶²
(6) The sixth challenge is to improve the classification and segmentation accuracy of PA images. The limited availability of annotated PAI data sets has hindered the development of automated image classification and segmentation, resulting in either continued reliance on manual delineation by expert physicians or the adaptation of traditional methods from other imaging modalities.
(7) In addition to the six challenges mentioned earlier, there are still specific issues, such as motion artifacts, limited spatial resolution, electrical noise, image misalignment, accelerating superresolution imaging, and achieving digital histologic staining, which are also important for PAI studies. To ensure a comprehensive understanding of the challenges in PAI, these specific issues are categorized as a seventh challenge. These seven challenges are summarized in Table 1.

Table 1

Summary of challenges facing PAI.

Section	Title	Challenges to be solved
3.1	Overcoming limited detection capabilities	Restricted bandwidth, limited detection view, sampling sparsity
3.2	Compensating for low-dosage light delivery	Low SNR in the low-dosage light-delivery system
3.3	Improving the accuracy of quantitative PA imaging	Inaccuracy in quantitative estimates ( ${sO}_{2}$ , optical absorption coefficient)
3.4	Optimizing or replacing conventional reconstruction algorithms	Limitations in conventional reconstruction algorithms
3.5	Addressing tissue heterogeneity	Acoustic reflection and imaging artifacts led by tissue heterogeneity
3.6	Improving the accuracy of image classification and segmentation	Inaccuracy and rough classification and segmentation of PA image
3.7	Overcoming other specified issues	Motion artifacts, limited spatial resolution, electrical noise and interference, image misalignment, accelerating superresolution imaging, achieving digital histologic staining

Fig. 1

Representations of seven major challenges in PAI, and DL-related methods to overcome them. DAS, delay-and-sum; DL, deep learning; BF-H&E, bright-field hematoxylin and eosin staining. The images are adapted with permission from Ref. 52, © 2021 Wiley-VCH GmbH; Ref. 53, © 2020 Optica; Ref. 54, © 2022 Optica; Ref. 55, © 2020 Elsevier GmbH; Ref. 56, CC-BY; Ref. 57, © 2021 Elsevier GmbH; and Ref. 58, © 2021 Elsevier GmbH.

Overcoming these challenges is important because relying solely on hardware improvements will not be enough to resolve them. It will require significant investments of time and resources to find effective solutions. Deep learning (DL) plays a crucial role in advancing the field of medical and bioimaging by not only addressing the inherent limitations of imaging systems but also by driving substantial improvements in classification and segmentation performance. In recent years, DL has gained significant traction in PAI research, leading to remarkable breakthroughs and achievements. This comprehensive review article provides an in-depth analysis of diverse methodologies and outcomes showcasing the utilization of DL techniques to effectively address the seven challenges encountered in PAI, as previously outlined.

2. Principles of DL Methods

DL is a subset of machine-learning algorithms that encompasses supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a modeling technique that establishes a correlation between input data and their corresponding ground truth (GT). This approach is commonly utilized in DL-enhanced medical imaging, where high- and low-quality images can be paired. On the other hand, unsupervised learning identifies specific patterns hidden within data, without the use of labeled examples or a priori known answers. Lastly, in reinforcement learning, an algorithm maximizes the final reward by learning through rewards obtained as a result of performing specific actions in a particular environment. A notable example of such a learning algorithm is AlphaGo, the first computer program to beat a human champion Go player.⁶³ Next, we explain the basic structure of DL network and the basic operating principle of DL training. In addition, we introduce the representative DL architectures that are most widely used in the field of image and video: convolution neural network (CNN), U-shaped neural network (U-Net), and generative adversarial network (GAN) architecture.

2.1.

Artificial Neural Networks

Artificial neural networks (ANNs) draw inspiration from biological neural networks, wherein different stimuli enter neurons through dendrites and are transmitted to other cells through axons once a threshold of activation is achieved [Fig. 2(a)]. In ANNs, an artificial neuron is a mathematical function conceived as a biological neuron. This function multiplies the various inputs by each weight, sums them, and adds the deviation.

Fig. 2

The concept of (a) a biological neural network and (b) an ANN derived from (a). (c) Schematics of a simple neural network and a DNN.

This sum is then sent to a specific activation function and produces an output [Fig. 2(b)]. This artificial neuron is expressed as

Eq. (2)

y = σ \cdot ([x_{1} x_{2} \dots x_{n}] [\begin{matrix} w_{1} \\ \begin{matrix} w_{2} \\ \begin{matrix} ⋮ \\ w_{n} \end{matrix} \end{matrix} \end{matrix}] + b),

where

σ

is the activation function,

x

is the input,

w

is the weight,

b

is the variance, and

y

is the output. The calculated output acquires nonlinearity through such activation functions (

σ

) as sigmoid, hyperbolic tangent, and rectified linear unit.⁶⁴ Without such an activation function, it would be pointless to build a deep model, since a linear transformation would occur regardless of how many hidden layers are present.

ANNs typically have an input layer, a hidden layer, and an output layer, each comprising multiple units. The number of hidden layers between the input and output determines whether the ANN is a simple neural network or a deep neural network (DNN) [Fig. 2(c)]. Formulaically, the two networks in Fig. 2(c) are described as

Eq. (3)

y = w_{2} (σ (w_{1} \times x + b_{1})) + b_{2},

and

Eq. (4)

y = w_{4} (σ (w_{3} (σ (w_{2} (σ (w_{1} \times x + b_{1})) + b_{2})) + b_{3})) + b_{4} .

2.2.

Backpropagation

Backpropagation serves as the fundamental principle for training DL models. In order to grasp the concept of backpropagation, it is necessary to first understand forward propagation. Forward propagation involves sequentially passing an input value through multiple hidden layers to generate an output. For instance, given an input $x$ , a weight $w$ , a variance $b$ , and an activation function $σ$ , the DNNs’ forward propagation on Fig. 2(c) is represented as

Eq. (5)

y^{*} = w_{4} (σ (w_{3} (σ (w_{2} (σ (w_{1} \times x + b_{1})) + b_{2})) + b_{3})) + b_{4},

where

y^{*}

represents the value predicted by DNNs. The process of finding the optimized

w

and

b

variables that minimize the loss, which is the difference between the predicted result and the actual

y

, is called training. The loss is calculated for all training data sets. As loss-function metrics, image data sets such as PA images commonly employ the structural similarity index measure (SSIM) and peak-signal-to-noise ratio (PSNR). Backpropagation refers to the process of transmitting the loss back to the input stage using the chain rule.⁶⁵^,⁶⁶ This process determines the weight

w

that yields the minimal loss function value. Most DL models adopt the gradient descent technique throughout this procedure.⁶⁷ By calculating the gradient at a specific

w

and continually updating

w

, the loss function’s minima can be estimated using the following equation:

Eq. (6)

w_{t + 1} = w_{t} - gradient \times learning rate .

The learning rate, a hyperparameter that determines the variable’s update amount in proportion to the calculated slope, is set before the learning process and remains unchanged. The number of hidden layers and their dimensions are also hyperparameters. In the following section, we introduce representative ANN architectures commonly used in biomedical imaging, including PAI.

2.3.

CNN

CNNs, the most basic DL architecture, have received significant attention in the field of PAI due to their extensive use in image processing and computer vision. CNNs were developed to extract features or patterns in local areas of an image. A convolution operation is a mathematical process that measures the similarity between two functions. The convolution operation in image processing is the process of calculating how well a subsection of an image matches a filter (also referred to as a kernel) and is used for things such as edge filtering. In CNN, the network is trained by learning this filter, which is used to extract image features, as a weight. The encoder–decoder CNN architecture is a relatively simple network structure capable of performing image-to-image translation tasks [Fig. 3(a)].⁶⁸ Initially, the input image undergoes a series of downsampling and convolution operations. Throughout this process, the dimensions of the image progressively decrease while the number of image channels, representing an additional dimension, increases. This results in a bottleneck in the representation, which is subsequently reversed through a sequence of upsampling and convolutional operations. The bottleneck enforces the network to encode the image into a compact set of abstracted variables (also referred to as latent variables) along the channel dimension. The predicted image is synthesized by decoding these variables in the second half of the network.

Fig. 3

Three typical neural network architectures for biomedical imaging. (a) CNN, (b) U-Net, and (c) GAN.

2.4.

U-Net

A U-Net [Fig. 3(b)] is a CNN-based model that was originally proposed for image segmentation in the biomedical field.⁶⁹ The U-Net is composed of two symmetric networks: a network for obtaining overall context information of an image and a second network for accurate localization. The left part of the U-Net is the encoding process, which encodes the input image to obtain overall context information. The right part of U-Net is the decoding process, which decodes the encoded context information to generate a segmented image. The feature maps obtained during the encoding process are concatenated with up-convolved feature maps at each expanding step in the decoding process, using skip connections.⁷⁰ This enables the decoder to make more accurate predictions by directly conveying important information in the image. As a result, the U-Net architecture has shown excellent performance in several biomedical image segmentation tasks, even when trained on a very small amount of data, due to data augmentation techniques.

2.5.

GAN

A GAN is a type of generative model that learns data through a competition between a generator network and a discriminator network [Fig. 3(c)].⁷¹^,⁷² The generator network generates the fake data, and the discriminator network tries to distinguish the real data from the fake data. To deceive the discriminator, the generator aims to generate data that look as realistic as possible, while the discriminator attempts to distinguish the real data from the realistic fake data. Through this competition, both networks learn and improve iteratively, resulting in a generator that can generate increasingly realistic data. As a result, GANs have been successful in generating synthetic data that are very similar to real data, making them useful for applications such as data augmentation and image synthesis. These three representative networks are summarized in Table 2.

Table 2

Three representative networks.

Network	Key feature	Use case
CNN	Performs convolution operation for feature extraction.	Image enhancement
	Exhibits outstanding performance in feature extraction.	Image classification and object detection
	Captures spatial information of input data efficiently.	Image segmentation
U-Net	Comprises an encoder–decoder structure.	Image enhancement
	Utilizes skip connections to leverage high-resolution feature maps.	Image enhancement
	Demonstrates strong performance even with small data sets.	Image segmentation
	Excels in segmentation tasks.	Image segmentation
GAN	Consists of a generator network and a discriminator network.	Image generation
	Generates data that closely resembles real input data (generator).	Image generation
	Discriminates between generated data and real data (discriminator).	Image style transfer
	Engages in competitive training between the generator and discriminator.	Image/data augmentation
	Applies for generating new data.	Image/data augmentation

3. Challenges in PAI and Solutions through DL

3.1.

Overcoming Limited Detection Capabilities

In PAI, optimal image quality requires a broadband US transducer and dense spatial sampling to enclose the target.⁴³^,⁶¹^,⁷³ However, real-world scenarios introduce limitations, such as limited bandwidth, limited view, and data sparsity. DL methods have been used as postprocessing techniques to overcome these limitations and enhance the PA signals or images, reducing artifacts. This section provides an overview of studies utilizing DL methods as PAI postprocessing methods. The DL-based image reconstruction studies are discussed separately in Sec. 3.4.

3.1.1.

Limited bandwidth

The bandwidth of US transducer arrays is limited compared to the natural broadband PA signal (from tens of kilohertz to a hundred megahertz).⁷⁴ Although optical detectors of PA waves have expanded the detection bandwidth, manufacturing high-density optical detector arrays and adopting them to PACT remains a technical challenge.⁷⁵

To solve the limited-bandwidth problem, Gutte et al. proposed a DNN with five fully connected layers to enhance the PA bandwidth [Fig. 4(a)].⁷⁶ The network takes a limited-bandwidth signal as input and outputs an enhanced bandwidth signal, which is then used for PA image reconstruction using DAS. To train the network, the authors generated numerical phantoms using the k-Wave toolbox⁷⁹ to create pairs of full-bandwidth and limited-bandwidth PA signals. The synthesized results from the numerical phantoms demonstrated an enhanced bandwidth that is like that of images obtained from the full-bandwidth signal [Fig. 4(a)].

Fig. 4

Representative studies using DL methods to overcome limited-detection capabilities. (a) A DNN with five fully connected layers enhances bandwidth. (b) LV-GAN for addressing the limited-view problem. (c) A Y-Net generates the PA images by optimizing both raw data and reconstructed images from the traditional method. (d) A 3D progressive U-Net (3D-pUnet) to diminish the effects of limited-view artifacts and sparsity arising from cluster view detection. The images are adapted with permission from Ref. 76, © 2017 SPIE; Ref. 77, © 2020 Wiley-VCH GmbH; Ref. 78, © 2020 Elsevier GmbH; and Ref. 52, © 2021 Wiley-VCH GmbH. BW, bandwidth; DNN, deep neural network; DAS, delay-and-sum; cluster, cluster view detection; full, full view detection.

3.1.2.

Limited view

PA image quality is reduced by the scant information provided by the limited coverage angle of the PA signals detected by the US transducer.³ This problem is commonly encountered in PACT systems, particularly in linear US array-based systems and is referred to as the “limited view” problem.⁷⁵^,⁸⁰ Researchers have addressed this problem to some extent by developing reconstruction methods with iterative methods.⁸⁰ Recent studies based on the fluctuation of the PA signal of blood flow or microbubbles⁸¹^,⁸² show another effective solution, but they need a number of single images to reconstruct one fluctuation image, which compromises the temporal resolution.

Deng et al.⁸³ developed DL methods using U-Net and principal component analysis processed very deep convolutional networks (PCA-VGG)⁸⁴ while Zhang et al.⁸⁵ designed a dual domain U-Net (DuDoUnet) incorporating reconstructed images and frequency domain information. In addition to utilizing the U-Net architecture, researchers have also explored the use of GAN networks, which have garnered attention due to their ability to preserve high-frequency features and prevent oversmoothing in images. Lu et al. proposed a GAN-based network called the limited view GAN (LV-GAN).⁷⁷ Figure 4(b) shows the architecture of LV-GAN, which consists of two networks: the generator network responsible for generating high-quality PA images from limited-view images, and the discriminator network designed to distinguish the generated images from the GT. To ensure accurate and generalizable results, the LV-GAN was trained using both simulated data generated by the k-Wave toolbox and experimental data obtained from a custom-made PA system. The results presented in Fig. 4(b), using ex vivo data, demonstrate the ability of LV-GAN to successfully reconstruct high-quality PA images in limited-view scenarios. The quantitative analysis further confirms that LV-GAN outperforms the U-Net framework, achieving the highest retrieval accuracy.

The combination of a postprocessing method with direct processing using PA signals is considered as another approach to reduce artifacts in limited-view scenarios. Lan et al.⁷⁸ designed a new network architecture, called Y-net, which reconstructs PA images by optimizing both raw data and reconstructed images from the traditional method [Fig. 4(c)]. This network has two inputs, one from raw PA data and the other from the traditional reconstruction. It combines two encoders, each corresponding to one of the input paths, with a shared decoder path. The training data were generated by the k-Wave toolbox with a linear array setup. The public vascular data set⁸⁶ was used to generate PA signals. They compared the proposed method with conventional reconstruction methods [e.g., DAS and time reversal (TR)] and other DL methods such as U-Net. In in vitro and in vivo experiments, the proposed method showed superior performance to the other methods, with the best spatial resolution.

3.1.3.

Sparsity

To achieve the best image quality, the interval between two adjacent positions of the transducer or array elements must be less than half of the lowest detectable acoustic wavelength, according to the Nyquist sampling criterion.⁸⁷ In sparse sampling, the actual detector density is lower than this requirement, introducing streak-shaped artifacts in images.⁷⁴ Sparse sampling can also result from a trade-off between image quality and temporal resolution, which is sometimes driven by system cost and hardware limitations.⁸⁸

To remove artifacts caused by data sparsity, Guan et al.⁸⁹ added additional dense connectivity into the contracting and expanding paths of a U-Net. Farnia et al.⁹⁰ combined a TR method with a U-Net by inserting it in the first layer. Guo et al.⁹¹ built a network containing a signal-processing method and an attention-steered network (AS-Net). Lan et al.⁹² proposed a knowledge infusion GAN (Ki-GAN) architecture that combines DAS and PA signals for reconstruction from sparsely sampled data. DiSpirito et al.⁹³ compared various CNN architectures for PAM image recovery from undersampled data of in vivo mouse brains.⁹⁴ They chose a fully dense U-Net (FD U-Net) with a dense block, allowing PAM image reconstruction using just 2% of the original pixels. Later, they proposed a new method based on a deep image prior (DIP) method⁹⁵ to solve this problem without pretraining or GT data.

3.1.4.

Combinational limited-detection problems

Previous studies have primarily tackled individual issues in isolation, neglecting the simultaneous occurrence of multiple limited-detection challenges in PA systems.⁷⁴ However, researchers have recently focused on utilizing a single NN to address two or three limited-detection problems concurrently, leading to promising advancements in this area.

For linear array, Godefroy et al.⁹⁶ incorporated dropout layers⁹⁷ into a modified U-Net and further built a Bayesian NN to improve the PA image quality. Vu et al.⁹⁸ built a Wasserstein GAN (WGAN-GP) that combined a U-Net and a deep convolutional GAN (DCGAN).⁹⁹ The network reduced limited-view and limited-bandwidth artifacts in PACT images. For a ring-shaped array, Zhang et al.¹⁰⁰ developed a 10-layer CNN, termed a ring-array DL network (RADL-net), to eliminate limited-view and under-sampling artifacts in photoacoustic tomography (PAT, also known as PACT) images. Davoudi et al.¹⁰¹ proposed a U-Net network to improve the image quality from sparsely sampled data from a full-ring transducer array. They later updated their U-Net architecture¹⁰² to operate on both images and PA signals. Awasthi et al.¹⁰³ proposed a U-Net architecture to achieve superresolution, denoising, and bandwidth enhancements. They replaced the softmax activation function in the final two layers of the U-Net for segmentation with an exponential linear unit.¹⁰⁴ Schwab et al. proposed a network that combined the BP with dynamic aperture length (DAL) correction, which they called DALnet¹⁰⁵ to address the limited-view and undersampling issues in the 3D imaging PACT system.

One of the notable achievements in applying DL to the 3D-PACT system was made by Choi et al.⁵² They introduced a 3D progressive U-Net (3D-pUnet) as a solution to address limited-view artifacts and sparsity caused by clustered-sampling detection, as shown in Fig. 4(d). The design of their network was inspired by the progressive growth GAN,¹⁰⁶ which utilizes a progressively increasing procedure to optimize a U-Net. In their 3D-pUnet, subnetworks were trained sequentially using downsampled data from the original high-resolution volume data, gradually transferring knowledge obtained from each progressive step.

The training data set consisted of in vivo experimental data from rats, and the results demonstrated superior performance compared with the conventional 3D-U-Net method. Interestingly, they demonstrated that the 3D-pUnet trained cluster-sampled data set also works in sparsely sampled data sets. The proposed approach was also applied to predict dynamic contrast-enhanced images and functional neuroimaging in rats, achieving increased imaging speed while preserving high image quality. In addition, they demonstrated the ability to accurately measure physiological phenomena and enhance structural information in untrained subjects, including tumor-bearing mice and humans.

All the research reviewed in this section is summarized in Table 3.

Table 3

Summary of overcoming the limited detection capabilities with DL approaches.

Author	Neural network architecture	Basic network	Training data set (if specified, validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Data amount	Test data set	Specified task	Representative evaluation results
Gutte et al.⁷⁶	FC-DNN	CNN	Simulation of the breast phantom	286,300 slices (from 2863 volumes)	Simulation/in vitro phantom	Reduce limited-bandwidth artifacts	CNR (versus DAS) 0.01 → 2.54
Gutte et al.⁷⁶	FC-DNN	CNN	Simulation of the breast phantom	286,300 slices (from 2863 volumes)	Simulation/in vitro phantom	Reduce limited-bandwidth artifacts	PC 0.22 → 0.75
Deng et al.⁸³	U-Net and VGG	U-Net	In vivo mouse liver	50	Numerical simulation data/in vitro phantom/in vivo data	Reduce limited-view artifacts from the circular US array	SSIM (versus DAS) 0.39 → 0.91
Deng et al.⁸³	U-Net and VGG	U-Net	In vivo mouse liver	50	Numerical simulation data/in vitro phantom/in vivo data	Reduce limited-view artifacts from the circular US array	PSNR 7.54 → 24.34
Zhang et al.⁸⁵	DuDoUnet	U-Net	k-Wave simulation	1500	k-Wave simulation	Reduce limited-view artifacts from the linear US array	SSIM (versus U-Net) 0.909 → 0.935
Zhang et al.⁸⁵	DuDoUnet	U-Net	k-Wave simulation	1500	k-Wave simulation	Reduce limited-view artifacts from the linear US array	PSNR 19.4 → 20.8
Lu et al.⁷⁷	LV-GAN	GAN	k-Wave simulation of absorbers and vessels/in vitro phantom of microsphere and vessel structure	793 pairs (absorbers)/1600 pairs (vessels)/	k-Wave simulation of absorbers and vessels/in vitro phantom (microsphere and vessel structure)	Reduce limited-view artifacts from the circular US array	SSIM (versus DAS) 0.135 → 0.871
				30 pairs (microsphere)			PSNR 9.41 → 30.38
				30 pairs (microsphere)			CNR 22.72 → 43.41
				22 pairs (vessel structures)			CNR 22.72 → 43.41
Lan et al.⁷⁸	Y-Net	—	k-Wave simulation of segmented blood vessels from DRIVE data set	4700	k-Wave simulation/in vitro phantom/in vivo human palm	Reduce limited-view artifacts from the linear US array	SSIM (versus DAS) 0.203 → 0.911
							PSNR 17.36 → 25.54
							SNR 1.74 → 9.92
Guan et al.⁸⁹	FD-UNet	U-Net	k-Wave simulation:/k-Wave simulation of realistic vasculature phantom from micro-CT images of mouse brain	1000 simulation/1000 (realistic vasculature)	k-Wave simulation:/k-Wave simulation of realistic vasculature phantom (micro-CT images of the mouse brain)	Reduce artifacts from sparse data in the circular US array	SSIM (versus DAS) 0.75 → 0.87
Guan et al.⁸⁹	FD-UNet	U-Net		1000 simulation/1000 (realistic vasculature)		Reduce artifacts from sparse data in the circular US array	PSNR 32.48 → 44.84
Farnia et al.⁹⁰	U-Net	U-Net	k-Wave simulation from the DRIVE data set	3200	k-Wave simulation from DRIVE data set/in vivo mouse brain	Reduce artifacts from sparse data in the circular US array	SSIM (versus DAS) 0.81 → 0.97
							PSNR 29.1 → 35.3
							SNR 11.8 → 14.6
							EPI 0.68 → 0.90
Guo et al.⁹¹	AS-Net	Non	k-Wave simulation of human fundus culi vessel/in vivo fish/in vivo mouse	3600/1744/1046	k-Wave simulation of human fundus culi vessel/in vivo fish/in vivo mouse	Reduce artifacts from sparse data and speed up reconstruction from the circular US array	SSIM (versus DAS) 0.113 → 0.985
Guo et al.⁹¹	AS-Net	Non		3600/1744/1046			PSNR 8.64 → 19.52
Lan et al.⁹²	Ki-GAN	GAN	k-Wave simulation of retinal vessels from public data set	4300	k-Wave simulation of retinal vessels from public data set	Remove artifacts from sparse data from the circular US array	SSIM (versus DAS) 0.215 → 0.928
							PSNR 15.61 → 25.51
							SNR 1.63 → 11.52
DiSpirito et al.⁹³	FD U-Net	U-Net	In vivo mouse brain	304	In vivo mouse brain	Improve the image quality of undersampled PAM images	SSIM (versus zero fill) 0.510 → 0.961
							PSNR 16.94 → 34.04
							MS-SSIM 0.585 → 0.990
							MAE 0.0701 → 0.0084
							MSE 0.0027 → 0.00044
Vu et al.⁹⁵	DIP	CNN	In vivo blood vessels	—	In vivo blood vessels/non-vascular data	Improve the image quality of undersampled PAM images	SSIM (versus bilinear) 0.851 → 0.928
Vu et al.⁹⁵	DIP	CNN	In vivo blood vessels	—	In vivo blood vessels/non-vascular data	Improve the image quality of undersampled PAM images	PSNR 25.6 → 31.0
Godefroy et al.⁹⁶	U-Net/Bayesian NN	U-Net	Pairs of PAI and photographs of leaves/Corresponded numerical simulation	500	PAI and photographs of leaves/numerical simulation	Reduce limited-view and limited-bandwidth artifacts from the linear US array	NCC (versus DAS) 0.31 → 0.89
Godefroy et al.⁹⁶	U-Net/Bayesian NN	U-Net		500	PAI and photographs of leaves/numerical simulation		SSIM 0.29 → 0.87
Vu et al.⁹⁸	WGAN-GP	GAN	k-Wave simulation: disk phantom and TPM vascular data	4000 (disk)/7200 (vascular)	k-Wave simulation: disk phantom and TPM vascular data/tube phantom/in vivo mouse skin	Reduce limited-view and limited-bandwidth artifacts from the linear US array	SSIM (versus U-Net) 0.62 → 0.65
Vu et al.⁹⁸	WGAN-GP	GAN	k-Wave simulation: disk phantom and TPM vascular data	4000 (disk)/7200 (vascular)			PSNR 25.7 → 26.5
Zhang et al.¹⁰⁰	RADL-net	CNN	k-Wave simulation	161,000 (including augmentation and cropping from 126 vascular images)	k-Wave simulation/vascular structure phantom/in vivo mouse brain	Reduce limited-view and sparsity artifacts from the ring-shaped US array	SSIM (versus DAS) 0.11 → 0.93
Zhang et al.¹⁰⁰	RADL-net	CNN	k-Wave simulation				PSNR 17.5 → 23.3
Davoudi et al.¹⁰¹	U-Net	U-Net	Simulation: planar parabolic absorber and mouse/in vitro circular phantom/in vitro vessel-structure phantom/in vivo mouse	Not mentioned/28/33/420	Simulation: planar parabolic absorber and mouse/in vitro circular phantom/in vitro vessel-structure phantom/in vivo mouse	Reduce limited-view and sparsity artifacts from the circular US array	SSIM (versus input) 0.281 → 0.845
Davoudi et al.¹⁰²	U-Net	U-Net	In vivo human finger from seven healthy volunteers	4109 (including validation)	In vivo human finger	Reduce the limited-view and sparsity artifacts from the US circular array	SSIM (versus U-Net) 0.845 → 0.944
							PSNR 14.3 → 19.0
							MSE 0.04 → 0.014
							NRMSE 0.818 → 0.355
Awasthi et al.¹⁰³	Hybrid end-to-end U-Net	U-Net	k-Wave simulation from breast sinogram images	1000	k-Wave simulation of the numerical phantom, blood vessel, and breast/ horsehair phantoms/ in vivo rat brain	Super-resolution, denoising, and bandwidth enhancement of the PA signal from the circular US array	PC (versus DAS) 0.307 → 0.730
							SSIM 0.272 → 0.703
							RMSE 0.107 → 0.0617
Schwab et al.¹⁰⁵	DALnet	CNN	Numerical simulation of 200 projection images from 3D lung blood vessel data	3000 (after cropping)	Numerical simulation/in vivo human finger	Reduce limited-view, sparsity and limited bandwidth artifacts	SSIM (versus input) 0.305 → 0.726
Schwab et al.¹⁰⁵	DALnet	CNN		3000 (after cropping)	Numerical simulation/in vivo human finger		Correlation 0.382 → 0.933
Choi et al.⁵²	3D-pUnet	U-Net	In vivo rat	1089	In vivo rat/in vivo mouse/in vivo human	Reduce limited-view and sparsity artifacts	MS-SSIM (versus input) 0.83 → 0.94
							PSNR 32.0 → 34.8
							RMSE 0.025 → 0.019

CNR, contrast-to-noise ratio; PC, Pearson’s correlation coefficient; SSIM, structural similarity index; PSNR, peak signal-to-noise ratio; EPI, edge-preserving index; MS-SSIM, multiscale SSIM; MAE, mean absolute value; MSE, mean squared error; NCC, normalized 2D cross-correlation; TPM, three photon microscopy; sSSIM, shifted structured similarity index; NRMSE, normalized root mean squared error.

3.2.

Compensating for Low-Dosage Light Delivery

Pulsed laser sources, such as an optical parametric oscillator laser system with a Nd:YAG pumped laser, are commonly used in PACT systems to achieve deep penetration with a high SNR, but those laser systems are bulky and expensive.²^,¹⁰⁷ In recent years, researchers have explored compact and less expensive alternatives, such as pulsed-laser diodes¹⁰⁷ and light-emitting diodes (LEDs).¹⁰⁸ While these alternatives have shown promising results, their low pulse energy results in a low SNR, requiring frame averaging to increase image quality. Unfortunately, this method comes at a cost, as it reduces imaging speed. Furthermore, in dynamic imaging, frame averaging can cause blurring or ghosting due to the movement of the object being imaged. To address these problems, DL methods can be applied to enhance image quality in situations where the light intensity is low.

One of the representative works for LED-based systems was achieved by Hariri et al.⁵³ They proposed a multilevel wavelet-convolutional NN (MWCNN) that could map the low-fluence PA images to high-fluence PA images from an Nd:YAG laser system. This approach helps to eliminate the background noise while preserving the structures of the target, as shown in Fig. 5(a). Phantom and in vivo studies were conducted to assess the performance of their model. The MWCNN demonstrated a significant improvement in contrast-to-noise ratio (CNR) with up to a 4.3-fold enhancement in the phantom study and a 1.76-fold enhancement in the in vivo study. These results highlight the practicality of the proposed method in real-world scenarios. Singh et al.¹¹¹ and Anas et al.¹¹² proposed a U-Net and a deep CNN-based approach to improve the image quality with a similar system setup. Anas et al. later introduced a recurrent neural network (RNN)¹¹³ to further improve the system’s performance.¹¹⁴

Fig. 5

Representative DL approaches compensate for low laser dosage. (a) An MWCNN that generates high-quality PA images from low-fluence PA images. (b) An HD-UNet that enhances the image quality in a pulsed-laser diode PACT system. (c) An MT-RDN that performs image denoising, superresolution, and vascular enhancement. The images are adapted with permission from Ref. 53, © 2020 Optica; Ref. 109, © 2022 SPIE; and Ref. 110, © 2020 Wiley-VCH GmbH. MWCNN, multi-level wavelet-convolutional neural network; HD-UNet, hybrid dense U-Net; MT-RDN, multitask residual dense network.

To enhance the image quality in a pulsed-laser-diode PA system, Rajendran et al.¹⁰⁹ proposed a hybrid dense U-Net (HD-UNet) [Fig. 5(b)]. To train the network, they generated simulated data using the k-Wave toolbox, and evaluated the model with both single- and multi-US transducer (1-UST and multi-UST-PLD) PACT systems, using both phantom and in vivo images. Compared with their previous system, the HD-UNet improved the imaging speed by approximately 6 times in the 1-UST system and 2 times in the multi-UST-PLD system. To address the challenges of balancing laser dosage, imaging speed, and image quality in OR-PAM, Zhao et al.¹¹⁰ proposed a multitask residual dense network (MT-RDN) that performs image denoising, superresolution, and vascular enhancement [Fig. 5(c)]. The network comprises three subnetworks, each using an independent RDN framework and assigned a supervised learning task. The first subnetwork processes the data of input 1 (i.e., 532 nm data) to obtain output 1, and the second subnetwork processes the data of input 2 (i.e., 560 nm data) to obtain output 2. These outputs are then combined and processed by subnetwork 3, and the differences between the outputs and the GT are compared.

To train the network, input images were undersampled at half-per-pulse laser energy of the GT, while the GT images were sampled at the full ANSI per-pulse fluence limit. To evaluate the performance of the proposed method, U-Net and RDN were used. The MT-RDN method achieved a 16-fold reduction in laser dosage at 2 times data undersampling and a 32-fold reduction in dosage at 4 times undersampling compared to the GT images.

All the research reviewed in this section is summarized in Table 4.

Table 4

Summary of studies on compensating for low-dosage light delivery.

Author	Neural network architecture	Basic network	Training data set (if specified, validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Data amount	Test data set	Specified task	Representative evaluation results
Hariri et al.⁵³	MWCNN	U-Net	Agarose hydrogel phantom: LED-based PA image and Nd:YAG-based PA image	229	Agarose hydrogel phantom of LED-based PA image/in vivo mouse	Denoise PA images from low-dosage system	SSIM (versus input) 0.63 → 0.93
Hariri et al.⁵³	MWCNN	U-Net		229		Denoise PA images from low-dosage system	PSNR 15.58 → 53.88
Singh et al.¹¹¹	U-Net	U-Net	LED-based and Nd:YAG-based tube phantom	150	LED-based phantom using ICG and MB	Reduce the frame averaging	SNR 14 → 20
Anas et al.¹¹²	—	CNN	In vitro phantom	4536	In vivo fingers	Improve the quality of PA images	SSIM (versus average) 0.654 → 0.885
Anas et al.¹¹²	—	CNN	In vitro phantom	4536	In vivo fingers	Improve the quality of PA images	PSNR 28.3 → 36.0
Anas et al.¹¹⁴	—	CNN and LSTM	In vitro wire phantom/in vitro nanoparticle phantom	352,000	In vitro phantom/in vivo human fingers	Improve the quality of PA images	SSIM (versus input) 0.86→0.96
Anas et al.¹¹⁴	—	CNN and LSTM	In vitro wire phantom/in vitro nanoparticle phantom	352,000	In vitro phantom/in vivo human fingers	Improve the quality of PA images	PSNR 32.3 → 37.8
Rajendran et al.¹⁰⁹	HD-Unet	U-Net	k-Wave simulation	450	In vitro phantom/in vivo rat	Improve the frame rate	SSIM (versus U-Net) 0.92 → 0.98
							PSNR 28.6 → 32.9
							MAE 0.025 → 0.017
Zhao et al.¹¹⁰	MT-RDN	—	In vivo mouse brain and ear	6696	In vivo mouse brain and ear	Improve the quality from low dosage laser and downsampled data	SSIM (versus input) 0.64 → 0.79
Zhao et al.¹¹⁰	MT-RDN	—	In vivo mouse brain and ear	6696	In vivo mouse brain and ear		PSNR 21.9 → 25.6

ICG, indocyanine green; MB, methylene blue; LSTM, long short-term memory.

3.3.

Improving the Accuracy of Quantitative PAI

Quantitative photoacoustic imaging (qPAI) quantifies molecular concentrations in biological tissue using multiwavelength PA images, enabling the estimation of various endogenous and exogenous contrast agents and physiological parameters, such as ${sO}_{2}$ .⁶¹ However, qPAI presents significant challenges due to the wavelength-dependent nature of light absorption and scattering, leading to varying levels of light attenuation across different wavelengths.²^,¹¹⁵ Thus, it is hard to accurately determine the fluence distribution, which is nonlinear and complex in biological tissues. Early research in qPAI assumed constant optical properties of biological tissue and uniform parameters such as the scattering coefficient throughout the imaging field.⁶¹ However, recent studies have shown that these assumptions lead to errors, especially in deep-tissue imaging.¹¹⁶ Model-based iterative optimization methods have been developed to address this issue and provide more accurate solutions.¹¹⁷ But these methods are time-consuming and sensitive to quantification errors.¹¹⁸ A new approach called eigenspectral multispectral optoacoustic tomography (eMSOT) has been proposed to improve qPAI accuracy.¹¹⁶ eMSOT formulates light fluence in tissues as an affine function of reference base spectra, leading to improved accuracy in qPAI. However, it requires ad hoc inversion and has limitations in scale invariance.

Researchers have pursued multiple avenues to extract fluence distribution information from multiwavelength PA images using DL architectures. Cai et al.¹¹⁹ introduced ResU-Net, which adds a residual learning mechanism to the U-Net. Chang et al.¹²⁰ developed DR2U-Net, a fine-tuned deep residual recurrent U-Net. Luke et al.¹²¹ combined two U-Nets to create a new network called O-Net, which segments blood vessels and estimates ${sO}_{2}$ . A novel DL architecture that contains an encoder, decoder, and aggregator was introduced by Yang et al.¹²² termed called EDA-Net. The encoder and decoder paths both feature a dense block, while the aggregator path incorporates an aggregation block. Gröhl et al.¹²³ designed a nine-layer fully connected NN that directly estimates ${sO}_{2}$ from PA images. All showed much high accuracy in estimating ${sO}_{2}$ distributions or other molecular concentrations compared with linear unmixing.

One of the representative results is from Ref. 124. Researchers built two separated convolutional encoder–decoder type networks with skip connections, termed EDS to solve this problem in 3D conditions [Fig. 6(a)]. One network was trained to output images of ${sO}_{2}$ from 3D-image data and the other network was trained to segment vessels. By leveraging the spatial information present in the 3D images, the 3D fully convolutional networks could produce precise ${sO}_{2}$ maps. Besides getting more accurate ${sO}_{2}$ results, these networks were able to handle limited-detection capabilities, such as limited-view artifacts, and showed promise for producing accurate estimates in vivo.

Fig. 6

Representative studies to improve the accuracy of quantitative PAI by DL. (a) Convolutional encoder–decoder type network with skip connections (EDS) to produce accurate estimates of ${sO}_{2}$ in a 3D data set. (b) Dual-path network based on U-Net (QPAT-Net) to reconstruct images of the absorption coefficient for deep tissues. (c) US-enhanced U-Net model (US-UNet) to reconstruct the optical absorption distribution. The images are adapted with permission from Ref. 124, © 2020 SPIE; Ref. 54, © 2022 Optica; and Ref. 125, © 2022 Elsevier GmbH.

Researchers have also employed DL methods to recover the absorption coefficient from reconstructed PA images. Chen et al.¹²⁶ proposed a U-Net-based DL network to recover the optical absorption coefficient and Grohl et al.¹²⁷ adapted a U-Net to compute error estimates for optical parameter estimations. A notable contribution was made by Li et al. in a recent study.⁵⁴ They addressed the challenge of insufficient data-label pairs in qPAI by introducing two DNNs, depicted in Fig. 6(b). First, they introduced a simulation-to-experiment end-to-end data translation network (SEED-Net) that provides GT images for experimental images through unsupervised data translation from a simulation data set. They then designed a dual-path network based on U-Net (QPAT-Net) to reconstruct images of the absorption coefficient for deep tissues. The QPAT-Net outperformed the previous QPAT method¹²⁸ in simulation, ex vivo, and in vivo, with more accurate absorption information and relatively few errors.

Another seminal study was done by Zou et al.¹²⁵ They developed the US-enhanced U-Net model (US-Unet), which combines information from US images and PA images to reconstruct the optical absorption distribution [Fig. 6(c)]. They implemented a pretrained ResNet-18 to extract features from US images of ovarian lesions.

This feature information was incorporated into a U-Net structure designed to reconstruct the optical absorption coefficient. The U-Net was trained on simulation data and subsequently tested on a phantom, blood tubes, and clinical data from 35 patients. The US-Unet outperformed both the U-Net model without US features and the standard DAS method in phantom and clinical studies, demonstrating its potential for improving accuracy in clinical PAI applications.

Compensating for the distribution of light fluence can improve the accuracy of qPAI.¹²⁹^–¹³¹ To this end, Madasamy et al.¹³² compared the compensation performance of different DL models. The models tested included U-Net,⁶⁹ FD U-Net,⁸⁹ Y-Net, FD Y-Net,⁷⁸ deep residual U-Net (deep ResU-Net),¹³³ and GAN.¹³⁴ Results showed the robustness of all DL models to noise and their effectiveness; FD U-Net showed the best performance. qPAI requires an unmixing process, which can be achieved through linear or model-based methods.¹¹⁵ Durairaj et al.¹³⁵ proposed an unsupervised learning approach using an initialization network and an unmixing network. Olefir et al.¹³⁶ introduced DL-eMSOT, combining eMSOT with a bidirectional RNN and CNN blocks for accurate ${sO}_{2}$ estimation and faster calculations.

All the research reviewed in this section is summarized in Table 5.

Table 5

Summary of studies to improve the accuracy of quantitative PAI.

Author	Neural network architecture	Basic network	Training data set (if specified, validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Data amount	Test data set	Specified task	Representative evaluation results
Cai et al.¹¹⁹	ResU-net	U-Net	Numerical simulation	2048	Numerical simulation	Extract information from multispectral PA images	Relative errors (versus linear unmixing) 36.9% → 0.76%
Chang. et al.¹²⁰	DR2U-net	U-Net	Monte Carlo simulation of simulated tissue structure	2560	Monte Carlo simulation of simulated tissue structure	Extract fluence distribution from optical absorption images	Relative Errors (versus linear unmixing) 48.76% → 1.27%
Luke et al.¹²¹	O-Net:	U-Net	Monte Carlo simulation of epidermis, dermis, and breast tissue	1600 pairs (one pair has two-wavelength PA data)	Monte Carlo simulation of epidermis, dermis, and breast tissue	Estimate the oxygen saturation and segment	Relative errors (versus linear unmixing) 43.7% → 5.15%
Yang et al.¹²²	EDA-net	—	Monte Carlo and k-Wave simulation from female breast phantom	4888	Monte Carlo and k-Wave simulation based on clinically obtained female breast phantom	Extract the information from the multi-wavelength PA images	Relative errors (versus linear unmixing) 41.32% → 4.78%
Gröhl et al.¹²³	Nine-layer fully connected NN	CNN	Monte Carlo simulation of in silico vessel phantoms	776	In vivo porcine brain and human forearm	Obtain quantitative estimates for blood oxygenation	No statistical results
Bench et al.¹²⁴	EDS	U-Net	k-Wave simulation of human lung from lung CT scans/k-Wave simulation of three-layer skin model	—	k-Wave simulation	Produce 3D maps of vascular ${sO}_{2}$ and vessel positions	Mean difference (versus linear unmixing) 6.6% → 0.3%
Chen et al.¹²⁶	U-Net	U-Net	Monte Carlo simulation	2880	In vitro phantom	Recover the optical absorption coefficient	Relative error less than 10%
Gröhl et.al¹²⁷	U-Net	U-Net	Monte Carlo and k-Wave simulations of in silico tissue	3600	Monte Carlo and k-Wave simulation of in silico	Improve optical absorption coefficient estimation	Estimation error (versus linear unmixing) 58.3% → 3.1%
Li et al.⁵⁴	Two GANs: SEED-Net and QOAT-Net	GAN	Numerical simulation of phantom, mouse, and human brain/experimental data of phantom, ex vivo, and in vivo mouse	3040, 2560, and 2560/2916, 3200, and 3800	Ex vivo porcine tissue, mouse liver, and kidney/In vivo mouse	Improve optical absorption coefficient estimation	Relative errors (versus linear unmixing) 8.00% → 4.82%
Li et al.⁵⁴	Two GANs: SEED-Net and QOAT-Net	GAN		3040, 2560, and 2560/2916, 3200, and 3800		Improve optical absorption coefficient estimation	Relative errors 8.00% → 4.82%
Zou et al.¹²⁵	US-Unet	U-Net	Monte Carlo and k-Wave simulation/in vitro phantom	2000/480	In vitro blood tube/in vivo clinical data set	Improve optical absorption coefficient estimation	Accuracy (versus linear unmixing) 0.71 → 0.89
Madasamy et al.¹³²	Network comparing: U-Net, FD U-Net, Y-Net, FD Y-Net, Deep ResU-Net, and GAN	—	2D numerical simulation of retinal fundus (from Kaggle and RFMID)/3D numerical simulation of breast phantom	1858 (before augmentation)/5 3D volumes (12,288 slices after augmentation)	2D numerical blood vessel/3D numerical breast phantom	Fluence correction	PSNR (versus linear unmixing) 37.9 → 45.8
Madasamy et al.¹³²		—			2D numerical blood vessel/3D numerical breast phantom	Fluence correction	SSIM 0.80 → 0.96
Durairaj et al.¹³⁵	Two networks: initialization network and unmixing network	—	NIRFAST and k-Wave simulation	Not mentioned	NIRFAST and k-Wave simulation	Unmix the spectral information	Regardless of prior spectral information
Olefir et al.¹³⁶	DL-eMSOT: bi-directional RNN with two LSTMs	—	Monte Carlo simulation	10,944	In vitro phantom/in vivo mouse	Replace inverse problem of eMSOT	Mean error (versus eMSOT) 4.9% → 1.4%
							Median error 3.5% → 0.9%
							Standard deviation 4.8% → 1.5%

3.4.

Optimizing or Replacing Conventional Reconstruction Algorithms

In PACT, the acoustic inverse problem involves reconstructing the PA initial pressure from raw data. Several reconstruction methods have been developed, including BP,⁴⁷ FB,⁴⁸ DAS,⁴⁴ DMAS,⁴⁶ TR,⁴⁹ and model-based methods.⁵¹ However, each method has limitations, and either to enhance existing reconstruction techniques or to directly reconstruct PA images using NNs, researchers have turned to DL methods.

Various DL methods have been developed to convert PA raw data into images. One such method, called Pixel-DL, proposed by Guan et al.,¹³⁷ uses pixel-wise interpolation followed by an FD U-Net for limited-view and sparse PAT image reconstruction [Fig. 7(a)]. The Pixel-DL model was trained and tested using simulated PA data from synthetic, mouse brain, lung, and fundus vasculature phantoms. It achieved comparable or better performance than iterative methods and consistently outperformed other CNN-based approaches for correcting artifacts.

Fig. 7

Representative studies to optimize conventional reconstruction algorithms or replace them with DL. (a) Pixel-wise interpolation approach followed by an FD-UNet for limited-view and sparse PAT image reconstruction. (b) End-to-end U-Net with residual blocks to reconstruct PA images. (c) Two-step PA image reconstruction process with FPnet and U-Net. The images are adapted with permission from Ref. 137, © 2020 Nature Publishing Group; Ref. 138, © 2020 Optica; and Ref. 55, © 2020 Elsevier GmbH.

To direct reconstruct PA images, Waibel et al.¹³⁹ introduced a modified U-Net that includes additional convolutional layers in each skip connection. Antholzer et al.¹⁴⁰ proposed a direct reconstruction process, based on a U-Net and a simple CNN, that can resolve limited-view and sparse-sampling issues. Lan et al.¹⁴¹ proposed a modified U-Net, termed DU-Net, to reconstruct PA images using multifrequency US-sensor raw data. A noteworthy study of this topic is an end-to-end reconstruction network developed by Feng et al.,¹³⁸ termed Res-U-Net [Fig. 7(b)]. They integrated residual blocks into the contracting and symmetrically expanding path of U-Net and added a skip connection between the input of raw data and the output of images. The training, validation, and test data sets were synthesized using the k-Wave toolbox. In digital phantom experiments, the Res-UNet showed performance superior to other reconstruction methods [Fig. 7(b)].

Another representative work was done by Tong et al.⁵⁵ They proposed a novel two-step reconstruction process with a feature projection network (FPnet) and a U-Net [Fig. 7(c)]. The FPnet converts PA signals to images and contains several convolutional layers to extract features. There is one max pooling layer for downsampling and one full connection layer for domain transformation. The U-Net performs postprocessing to improve image quality. The resulting network, trained using numerical simulations and in vivo experimental data, outperformed other approaches to handle limited-view and sparsely sampled experimental data, exhibiting superior performance on in vivo experiments [Fig. 7(c)].

In addition, Yang et al.¹⁴² introduced recurrent inference machines (RIM), an iterative PAT reconstruction method using convolution layers. Kim et al.¹⁴³ employed upgUNET, a U-Net model with 3D transformed arrays for image reconstruction. Hauptmann et al.¹⁴⁴ proposed DGD, a deep gradient descent algorithm, outperforming U-Net and other model-based methods. They also introduced fast-forward PAT (FF-PAT), a modified version of DGD, which addressed artifacts using a small multiscale network.¹⁴⁵

All the research reviewed in this section is summarized in Table 6.

Table 6

Summary of methods to optimize or replace conventional reconstruction algorithms.

Author	Neural network architecture	Basic network	Training data set (if specified, validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Data amount	Test data set	Specified task	Representative evaluation results
Guan et al.¹³⁷	Pixel-DL	U-Net	k-Wave simulation: circles, Shepp-Logan, and vasculature, vasculature phantom from micro-CT images of mouse brain	1000 (circles),/1000 (realistic vasculature)	k-Wave simulation: circles, Shepp-Logan, and vasculature, vasculature phantom from micro-CT images of mouse brain	Reconstruct PA images from PA signal	PSNR (versus TR) 17.49 → 24.57
Guan et al.¹³⁷	Pixel-DL	U-Net		1000 (circles),/1000 (realistic vasculature)		Reconstruct PA images from PA signal	SSIM 0.52 → 079
Waibel et al.¹³⁹	U-Net	U-Net	Monte Carlo and k-Wave simulation	2304	Monte Carlo and k-Wave simulation	Reconstruct PA images from PA signal	IQR (versus DAS) 98% → 10%
Antholzer et al.¹⁴⁰	U-Net	U-Net	Numerical simulation of ring-shaped phantoms	1000	Numerical simulation of ring-shaped phantoms	Reconstruct PA images from PA signal	MSE (versus general CNN) 0.33 → 0.026
Lan et al.¹⁴¹	DU-Net	U-Net	k-Wave simulation: disc phantom and segmented fundus oculi/vessels CT	4000	k-Wave simulation: disc phantom and segmented fundus oculi/ vessels CT	Reconstruct PA images from PA signal	PSNR (versus DAS) 26.843 → 44.47
Lan et al.¹⁴¹	DU-Net	U-Net		4000		Reconstruct PA images from PA signal	SSIM 0.394 → 0.994
Feng et al.¹³⁸	Res-UNet	U-Net	k-Wave simulation: disc bread, spider (from “quick draw”), simple wires, logos, natural phantom	58,126 (80% of 27,000, 13,000, 10,800, 6000, 240, 15,000)	k-Wave simulation: disc, PAT, vessel/in vitro phantom	Reconstruct PA images from PA signal	Vessel phantom
							PC (versus MRR) 0.41 → 0.80
							PSNR 6.57 → 13.29
Tong et al.⁵⁵	FPnet+ U-Net	U-Net	Numerical simulation: brain from MRI, abdomen from MRI, vessel from DRIVE data set/in vivo mouse brain and abdomen	15,757: 2211 (brain), 8273 (abdomen), 4000 (vessel)/698 (mouse brain), 575 (mouse abdomen)	Numerical simulation: brain, abdomen and liver cancer from MRI, vessel/ in vivo mouse brain and abdomen	Reconstruct PA images from PA signal	MSOT-Abdomen
							PSNR (versus FBP) 16.0532 → 30.3972
							SSIM 0.2647 → 0.9073
							RMSE 0.4771 → 0.0910
Yang et al.¹⁴²	RIM	—	k-Wave simulation of segmented blood vessels from DRIVE data set	2400	k-Wave simulation of segmented blood vessels from DRIVE	Reconstruct PA images from PA signal	PSNR (versus DGD) 42.37 → 44.26
Kim et al.¹⁴³	upgUNET	U-Net	Monte Carlo simulation	128,000 (after augmentation)	Monte Carlo simulation/ in vitro metal-wire phantom/ in vivo human finger	Reconstruct PA images from PA signal	PSNR (versus DAS) 20.97 → 27.73
Kim et al.¹⁴³	upgUNET	U-Net	Monte Carlo simulation	128,000 (after augmentation)		Reconstruct PA images from PA signal	SSIM 0.208 → 0.754
Hauptmann et al.¹⁴⁴	Updated DGD (deep gradient descent)	DGD (deep gradient descent)	k-Wave simulation pf human lung from 50 whole-lung CT scans	1024 (from 50 CT scans)	k-Wave simulation pf human lung from 50 whole-lung CT scans/ in vivo human palm	Reconstruct PA images from PA signal	PSNR (versus U-Net) 40.81 → 41.40
Hauptmann et al.¹⁴⁴	Updated DGD (deep gradient descent)	DGD (deep gradient descent)	k-Wave simulation pf human lung from 50 whole-lung CT scans	1024 (from 50 CT scans)		Reconstruct PA images from PA signal	SSIM 0.933 → 0.945
Hauptmann et al.¹⁴⁵	FF-PAT	U-Net	k-Wave simulation of human lung from lung CT scans	1024 (from 50 CT scans)	k-Wave simulation of human lung/in vivo data	Reconstruct PA images from PA signal	PSNR (versus BP) 33.5672 → 42.1749

MRR, model-resolution-based regularization algorithm.

3.5.

Addressing Tissue Heterogeneity

Biological tissues are acoustically nonuniform, making it crucial to use a locally appropriate speed of sound (SoS) value for accurate PA reconstruction. SoS mismatch or a discontinuity in hard textured tissue can create acoustic reflection and imaging artifacts⁷⁴ that make it hard to detect the source of the PA signal, which is especially troublesome for interventional applications. DL methods have been used to detect point sources, remove reflections, and mitigate the difficulties presented by acoustic heterogeneity.

Highly echogenic structures can cause a reflection of a PA wave to appear to be a true signal,¹⁴⁶ which makes it hard to find point targets or real sources in PAI. Reiter et al.¹⁴⁷ trained a CNN to identify and remove reflection noise, locate point targets, and calculate absorber sizes in PAI. Later, Allman et al.¹⁴⁸ found Fast-RCNN¹⁴⁹ to be more effective than VGG16⁸⁴ for source detection and artifact elimination. Shan et al.¹⁵⁰ incorporated a DNN into an iterative algorithm to correct reflection artifacts, achieving superior results compared with other methods.¹⁵¹^,¹⁵²

Jeon et al.⁵⁶ proposed a generalized solution to mitigate SoS aberration in heterogeneous tissue by DL. They proposed a hybrid DNN model, named SegU-net, based on U-Net and SegNet¹⁵³ [Fig. 8(a)]. The architecture is similar to SegNet, but has an additional connection between the encoder and decoder through concatenation layers, like U-Net. The training data were generated using the k-Wave toolbox with different SoS values. They tested the model with phantoms with homogeneous media and in heterogeneous media. The proposed method showed better results than the multistencil fast marching¹⁵⁵ method and automatic SoS selection.¹⁵⁶ It not only resolved the SoS aberration but also removed streak artifacts in images of healthy human limbs and melanoma.

Fig. 8

Representative DL studies to correct the SoS and improve the accuracy of image classification and segmentation. (a) Hybrid DNN model including U-Net and Segnet to mitigate SOS aberration in heterogeneous tissue. (b) Sparse-UNet (S-UNet) for automatic vascular segmentation in MSOT images. The images are adapted with permission from Ref. 153, CC-BY; Ref. 154, © Elsevier GmbH.

All the research reviewed in this section is summarized in Table 7.

Table 7

Summary of methods for addressing tissue heterogeneity.

Author	Neural network architecture	Basic network	Training data set (if specified, validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Data amount	Test data set	Specified task	Representative evaluation results
Reiter et al.¹⁴⁷	CNN	CNN	k-Wave simulation	19,296	k-Wave simulation/in vitro vessel-mimicking target phantom	Identify point source	—
Allman et al.¹⁴⁸	CNN consisting of VGG16/Fast R-CNN	CNN	k-Wave simulation	15,993	k-Wave simulation/in vivo data	Identify and remove reflection artifacts	Precision, recall, and AUC > 0.96
Allman et al.¹⁵⁷	CNN consisting of VGG16/fast R-CNN	CNN	k-Wave simulation	15,993	In vitro phantom	Correct reflection artifact	Accuracy (phantom) 74.36%
Shan et al.¹⁵⁰	U-Net	U-Net	Numerical simulation from 3 cadaver CT	64,000	Numerical simulation from 1 cadaver CT	Correct reflection artifacts	PSNR (versus TR) 9 → 29
Shan et al.¹⁵⁰	U-Net	U-Net	Numerical simulation from 3 cadaver CT	64,000	Numerical simulation from 1 cadaver CT	Correct reflection artifacts	SSIM (versus TR) 0.2 → 0.9
Jeon et al.⁵⁶	SegU-net	U-Net	k-Wave simulation of in silico phantom	270	k-Wave simulation of in silico phantom/in vivo human forearm and foot	Reduce speed-of-sound aberrations	In silico phantom
Jeon et al.⁵⁶	SegU-net	U-Net	k-Wave simulation of in silico phantom	270		Reduce speed-of-sound aberrations	SSIM (versus pre-corrected) + 0.24

3.6.

Improving the Accuracy of Image Classification and Segmentation

As PAI gains increasing attention in clinical studies, more accurate classification and segmentation methods are necessary to improve the interpretation of PA images. Image segmentation extracts the outline of objects within an image and identifies the distinct parts of the image that correspond to these objects.¹⁵⁸^,¹⁵⁹ Image classification predicts a label for an image, identifying its content. In this section, we focus on segmentation and classification techniques.¹⁵⁸

Segmentation and classification are widely used in image postprocessing, and transfer learning has been utilized to take advantage of pretrained DNNs. Zhang et al.¹⁶⁰ used DL models AlexNet¹⁶¹ and GoogLeNet¹⁶² for PA image classification, outperforming support vector machine (SVM). Jnawali et al.¹⁶³ employed transfer learning with Inception-ResNet-V2¹⁶⁴ for thyroid cancer detection and introduced a deep 3D CNN for cancer detection in multispectral photoacoustic data sets.¹⁶⁵ Moustakidis et al.¹⁶⁶ developed SkinSeg for identifying skin layers in raster-scan optoacoustic mesoscopy (RSOM) images, evaluating decision trees,¹⁶⁷ SVM,¹⁶⁸ and DL algorithms. Nitkunanantharajah et al.¹⁶⁹ achieved good classification performance with ResNet18¹⁶⁴ on RSOM nail fold images.

PACT has demonstrated its great potential for human vascular imaging in several clinical studies.¹⁵ However, segmentation of the vascular structures, particularly the vascular lumen, is still accomplished through manual delineation by expert physicians, which is not only time-consuming but also subjective. To address this issue, Chlis et al.¹⁵⁴ proposed a sparse UNet (S-UNet) for automatic vascular segmentation on MSOT images. GT is obtained from binary images extracted from MSOT images, based on consensus between two clinical experts [Fig. 8(b)]. The MSOT raw data from six healthy humans’ vasculature were acquired using a handheld MSOT system, and they were split into training, validation, and test sets. The S-Unet showed performance similar to other U-Net methods, but with smaller parameter sizes and the ability to select wavelengths, indicating its potential for clinical application.¹⁷⁰

In addition, Lafci et al.¹⁷¹ proposed a U-Net architecture to accurately segment animal boundaries in hybrid PA and US (PAUS) images. Boink et al.¹⁷² proposed a learned primal-dual (L-PD) algorithm based on a CNN to solve the reconstruction and segmentation problem simultaneously. Ly et al.⁵⁷ introduced a modified U-Net DL model for automatic skin and vessel segmentation in in vivo PAI. The U-Net architecture showed the best performance.

All the research reviewed in this section is summarized in Table 8.

Table 8

Summary of methods for improving the accuracy of image classification and segmentation.

Author	Neural network architecture	Basic network	Training data set (if specified, validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Data amount	Test data set	Specified task	Representative evaluation results
Zhang et al.¹⁶⁰	AlexNet/ GoogLeNet	CNN	k-Wave simulation from in vivo human breast (normal, cancer)	98 (normal)/ 75 (patient)	k-Wave simulation from in vivo human breast	Classify and segment images	BI-RADS rating accuracy: 83% to 96%
Jnawali et al.¹⁶³	Inception- Resnet-V2	—	Ex vivo human thyroid (normal, benign, cancer)	73	Ex vivo human thyroid	Detect cancer tissue	AUCs for cancer, benign, and normal: 0.73, 0.81, and 0.88
Jnawali et al.¹⁶⁵	3D CNN	CNN	Thyroid cancer tissue	74 (thyroid)/ 74 (prostate)	Thyroid and prostate cancer tissue	Detect cancer tissue	AUC, 0.72
Moustakidis et al.¹⁶⁶	SkinSeg	—	In vivo human	About 26,190 (unclear description)	In vivo human	Identify skin morphology	Per-class accuracy: 84.95%
Nitkunanantharajah et al.¹⁶⁹	ResNet18	—	In vivo human nailfold (SSc. normal)	990 (from 33 subjects)	In vivo human nailfold	Classify images	Intra-class correlation: 0.902. AUC: 0.897
Nitkunanantharajah et al.¹⁶⁹	ResNet18	—	In vivo human nailfold (SSc. normal)	990 (from 33 subjects)	In vivo human nailfold	Classify images	Sensitivity and specificity: 0.783 and 0.895
Chlis et al.¹⁵⁴	S-Unet	U-Net	In vivo human vasculature	98 pairs (one pair has 28-wavelength PA data)	In vivo human vasculature	Segment human vasculature	Dice coeff. (versus U-Net): 0.75 → 0.86
Schellenberg et al.¹⁷⁰	nnU-Net	U-Net/ CNN	PA/US images of forearm, calf, and neck from 10 volunteers	144 PA and US image pairs	36 images for validation and 108 images from six volunteers	Segment images	Dice coeff. (versus FCNN): 0.66 → 0.85.
Schellenberg et al.¹⁷⁰	nnU-Net	U-Net/ CNN	PA/US images of forearm, calf, and neck from 10 volunteers	144 PA and US image pairs	36 images for validation and 108 images from six volunteers	Segment images	Normalized surface distance: 0.61 → 0.89
Lafci et al.¹⁷¹	U-Net	U-Net	In vivo mice brain, kidney, and liver	174 images from 12 mice (brain)/ 97 images from 13 mice (kidney)/ 108 images from 14 mice (liver)	In vivo mice brain, kidney, and liver	Segment hybrid PA/US image	Dice coeff. 0.95
Boink et al.¹⁷²	L-PD	—	Retinal blood vessels from DRIVE data set	768	Retinal blood vessels from DRIVE dataset/ in vitro phantom	Reconstruct and segment images	PSNR (versus FBP): 34 → 42.5
Ly et al.⁵⁷	Modified U-Net	U-Net	In vivo human palm	800	In vivo human palm	Segment blood vessels profile	Global accuracy: 0.9938 (SegNet-5), 0.9920 (FCN-8) → 0.9953
Ly et al.⁵⁷	Modified U-Net	U-Net	In vivo human palm	800	In vivo human palm	Segment blood vessels profile	Sensitivity: 0.6406 (SegNet-5), 0.6220 (FCN-8) → 0.8084

AUC, area under the receiver operating characteristic (ROC) curve.

3.7.

Overcoming Other Specified Issues

In addition to the general challenges to PAI mentioned above, researchers encounter several more specific problems with their imaging systems. Collectively, these specific issues constitute a seventh challenge, and among them we have identified six representative categories: motion artifacts, limited spatial resolution, electrical noise and interference, image misalignment, slow accelerating superresolution imaging, and achieving digital histologic staining.

Motion artifacts caused by breathing or heartbeats can significantly reduce image quality in PAM and PA endoscopy (PAE or intravascular PA, IVPA). To address this issue, researchers have presented various breathing artifact removal methods,¹⁷³^,¹⁷⁴ and DL methods have recently been proposed as a potential solution. Chen et al.¹⁷⁵ introduced a CNN approach with three convolutional layers to address motion artifacts and pixel dislocation in in vivo rat brain images. Zheng et al.¹⁷⁶ proposed MAC-Net, a network based on VGG16 GAN¹³⁴ and spatial transformer networks (STN),¹⁷⁷ to suppress motion artifacts in IVPA. Both methods demonstrated successful improvement in image quality.

OR-PAM can penetrate $\sim 1 mm$ deep in biological tissue, limited by light scattering. AR-PAM, which does not use focused light, can penetrate up to several centimeters, but it has a lower spatial resolution than OR-PAM. Researchers have applied DL to enhance the spatial resolution of AR-PAM to match that of OR-PAM. Cheng et al.¹⁷⁸ proposed a GAN-based framework called Wasserstein GAN¹⁷⁹ [Fig. 9(a)]. An integrated OR- and AR-PAM system was built for data acquisition and network training. The generator network takes an AR-PAM image as input and generates a high-resolution image, while the discriminator network evaluates the similarity between the generator’s output and the GT image obtained from OR-PAM. Using in vivo mouse ear vascular images, the proposed method was first compared with the blind deconvolution method, and it improved the spatial resolution and produced superior microvasculature images. Furthermore, the proposed method was shown to be applicable to other types of tissues (e.g., brain vessels) and deep tissues (e.g., a chicken breast tissue slice of $1700 μ m$ thickness) that are not easily accessible by OR-PAM. A similar study was implemented by Zhang et al.,¹⁸¹ who combined a physical model and a learning-based algorithm, termed MultiResU-Net.

Fig. 9

Representative studies using DL to solve specific issues. (a) GAN-based framework (Wasserstein GAN) to enhance the spatial resolution of AR-PAM. (b) GAN with U-Net to reconstruct superresolution images from raw image frames. (c) Deep-PAM generates virtually stained histological images for both thin sections and thick fresh tissue specimens. The images are adapted with permission from Ref. 178, © 2021 Elsevier GmbH; Ref. 180, © 2022 Springer Nature; and Ref. 58, © 2021 Elsevier GmbH. BF-H&E, brightfield hematoxylin and eosin staining; DNN, deep neural network.

DL methods have been applied to address noise and interference issues in PA imaging. Dehner et al.¹⁸² developed a discriminative DNN using a U-Net architecture to separate electrical noise from PA signals, improving PA image contrast and spectral unmixing performance. He et al.¹⁸³ proposed an attention-enhanced GAN with a modified U-Net generator to remove noise from PAM images, prioritizing fine-feature restoration. Gulenko et al.¹⁸⁴ evaluated different CNN architectures and found that U-Net demonstrated higher efficiency and accuracy in removing electromagnetic interference noise from PAE systems.

To address image misalignment in PAM, Kim et al.¹⁸⁵ utilized a U-Net framework. Their method effectively addressed nonlinear mismatched cross-sectional B-scan PA images during bidirectional raster scanning, resulting in a significant improvement in imaging speed, doubling the speed compared to conventional approaches.

To improve the temporal resolution of superresolution localization imaging,¹⁸⁶^,¹⁸⁷ hundreds of thousands of overlapping images are traditionally required. However, this process can be time-consuming. To address this problem, Kim et al.¹⁸⁰ proposed a GAN with U-Net based on pix2pix¹⁸⁸ to reconstruct superresolution images from raw image frames [Fig. 9(b)]. The proposed network can be applied to both 3D label-free localization OR-PAM and 2D labeled localization PACT. The authors trained and validated the network with in vivo data from 3D OR-PAM and 2D PACT images. The proposed method reduced the required number of raw frames by 10-fold for OR-PAM and 12-fold for PACT, resulting in a significant improvement in temporal resolution.

Ultraviolet PAM (UV-PAM) takes advantage of the optical absorption contrast of UV light to highlight cell nuclei, generating PA contrast images similar to hematoxylin and eosin (H&E) labeling.¹⁸⁹ DL techniques can be used to digitally generate histological stains using trained NNs based on UV-PAM images, providing label-free alternatives to standard chemical staining methods.¹⁹⁰

Boktor et al.¹⁹¹ utilized a DL approach based on GANs to digitally stain total-absorption PA remote sensing (TA-PARS) images, achieving high agreement with the gold standard of histological staining. Cao et al.¹⁹² employed a cycle-consistent adversarial network (CycleGAN)¹⁹³ model to virtually stain UV-PAM images, producing pseudo-color PAM images that matched the details of corresponding H&E histology images.

In a recent study, Kang et al.⁵⁸ combined UV-PAM with DL to generate rapid and label-free histological images [Fig. 9(c)]. Their proposed method, termed deep-PAM, can generate virtually stained histological images for both thin sections and thick fresh tissue specimens. By utilizing an unpaired image-to-image translation network, a CycleGAN, they were able to process GM-UV-PAM images and instantly produce H&E-equivalent images of unprocessed tissues. This groundbreaking approach has significant implications for the field of histology and may offer an alternative to traditional staining methods.

All the research reviewed in this section is summarized in Table 9.

Table 9

Summary of methods for addressing other specified issues.

Author	Neural network architecture	Basic network	Training data set (if specified; validation is excluded)		Test data set	Specified task	Representative evaluation results
Author	Neural network architecture	Basic network	Source	Amount	Test data set	Specified task	Representative evaluation results
Chen et al.¹⁷⁵	CNN	CNN	—	—	Simulation/in vivo rat brain	Correct motion artifact	—
Zheng et al.¹⁷⁶	MAC-Net	GAN	Simulation	7680	Simulation/in vivo IVUS and IVOCT	Correct motion artifact	AIFD (versus pre-corrected) 0.1007 → 0.0075
Cheng et al.¹⁷⁸	WGAN-GP	GAN	In vivo mouse ear	528	In vivo mouse ear	Transform AR-PAM images into OR-PAM	PSNR (versus blind deconv.) 18.05 → 20.02
							SSIM 0.27 → 0.61
							PC 0.76 → 0.78
Zhang et al.¹⁸¹	AOTDL-GAN/ MultiResU-Net	GAN/U-Net	Simulation/in vitro phantom/in vivo mouse	3500	Simulation/in vitro phantom/in vivo mouse	Transform AR-PAM images into OR-PAM	SNR (versus Deconv) 4.853 → 5.70
							CNR 6.93 → 12.50
							Lateral resolution $45 μ m$ → $15 μ m$
Dehner et al.¹⁸²	U-Net	U-Net	Simulated PA image/Pure electrical noise/Simulated white Gaussian noise	3000/2110/—	Simulated PA image/Pure electrical noise/phantom/in vivo human breast/simulated white Gaussian noise	Remove noise	SNR of sinograms (versus pre-corrected) + 10.9 dB
He et al.¹⁸³	GAN	GAN	Leaf phantom/in vivo mouse ear	236/149	Leaf phantom/in vivo mouse ear	Remove noise	SNR (versus input) 29.08 → 90.73
He et al.¹⁸³	GAN	GAN	Leaf phantom/in vivo mouse ear	236/149	Leaf phantom/in vivo mouse ear	Remove noise	CNR 4.80 → 7.63
Kim et al.¹⁸⁵	MS-FD-U-Net	GAN	In vivo mouse ear	830	In vivo mouse ear	Align bidirectional raster scanning	SSIM (versus input) 0.993 → 0.994
							PSNR 50.22 → 50.62
							MSE 1.09 → 0.99
Gulenko et al.¹⁸⁴	Modified U-Net	U-Net	In vivo rat colorectum/ in vivo rabbit transurethral	700	In vivo rat colorectum/in vivo rabbit transurethral	Remove noise	Log(RMSE) (versus Segnet) 2.9 → 2.5
							Log(SSIM) −2.2 → -0.25
							Log(MAE) 2.6 → 2.0
Kim et al.¹⁸⁰	U-Net	U-Net	In vivo OR-PAM mouse ear/in vivo PACT mouse brain	3000/500	In vivo OR-PAM mouse ear/in vivo PACT mouse brain	Accelerate localization process	PSNR (versus input) 38.47 → 40.70
Kim et al.¹⁸⁰	U-Net	U-Net	In vivo OR-PAM mouse ear/in vivo PACT mouse brain	3000/500	In vivo OR-PAM mouse ear/in vivo PACT mouse brain	Accelerate localization process	MS-SSIM 0.89 → 0.97
Boktor et al.¹⁹¹	Pix2Pix GAN	GAN	Experiments	15,000	Experiments	Perform virtual staining	SSIM between H&E and UV-PAM: 0.91
Cao et al.¹⁹²	CycleGAN	GAN	Experiments	17,940 (UV-PAM)/26,565 (H&E)	Experiments	Perform virtual staining	H&E versus UV-PAM
							Cell count: 5549 and 5423.
							Nuclear area ( $μ m^{2}$ ): 24.2 and 22.0.
							Internuclear dist.: 10.14 and 10.18
Kang et al.⁵⁸	CycleGAN	GAN	Experiments	400 (thin section)/800 (thick and fresh tissue)	Experiments	Perform virtual staining	H&E versus UV-PAM
							Cell count: 289 and 283.
							Nuclear area ( $μ m^{2}$ ): 70.66 and 72.75

4. Discussion and Conclusion

PAI is a rapidly growing biomedical imaging modality that utilizes endogenous chromophores to noninvasively provide biometric information, such as vascular structure and ${sO}_{2}$ . However, as shown in Fig. 1, PAI still faces seven significant challenges: (1) overcoming limited detection capabilities, (2) compensating for low-dosage light delivery, (3) improving the accuracy of quantitative PA imaging, (4) optimizing or replacing conventional reconstruction methods, (5) addressing tissue heterogeneity, (6) improving the accuracy of image classification and segmentation, and (7) overcoming other specified issues. In this review paper, we have summarized DL studies over the past five years that have addressed these general challenges in PAI. Further, we have discussed how DL can be used to solve several more specific problems in PAI.

CNN, U-Net, and GAN have been the most representative networks used in PAI-related research. While some studies use basic architectures to achieve their goals, others modify or develop new architectures to solve particular problems in PAI. These networks can be used in various ways, such as postprocessing reconstructed images with different types of noise or directly reconstructing PA images from the time domain in the image domain.

Furthermore, recent research has aimed to extract more accurate quantitative information by using multiple networks, rather than solely focusing on enhancing image quality with one network. This approach can provide more comprehensive and detailed information, improving the overall performance of PAI. While SSIM is commonly used as the loss function, other metrics, such as PSNR and the Pearson correlation, may be added to improve information extraction and convergence speed. The continued exploration and refinement of these network architectures and loss functions will likely contribute to continued advancements in PAI.

Several obstacles remain. The success of DL approaches in PAI is highly dependent on the availability of high-quality data sets, and there is a scarcity of experimental training data. DL approaches in PAI also lack a standardized PA-image format and publicly available data that are accessible to all groups. Consequently, researchers rely on data generated from experiments or simulations, and even publishing PA data is difficult because there is no standard format. The k-Wave⁷⁹ toolbox is the most generally used to generate the PA initial pressure, along with other light transport simulators, such as mcxlab,¹⁹⁴ to generate the light distribution. However, creating reliable simulation data requires GT data from the real world. Commonly, x-ray CT or MRI images of blood vessels and organs are used for PA simulation. Fortunately, there are public data sets of x-ray CT and MRI images, and many groups have used these open data sets to generate PA GTs. However, the varying information obtained by different imaging modalities may not align with PAI. Recently, the International Photoacoustic Standardization Consortium (IPASC)¹⁹⁵ has been working to overcome this challenge by bringing together researchers, device developers, and government regulators to achieve standardization of PAI through community-led consensus building. With the efforts and involvement of IPASC, the generalization ability of DL, which is the fundamental problem in medical imaging field, will increase.

While DL has shown improved image qualities in PAI, there are still concerns regarding its applications in biomedical images. Therefore, the efforts of researchers who aim to advance PAI without applying DL are still valuable. For examples, new restoration algorithms¹⁹⁶ are being developed to enhance the image quality affected by limited-detection capabilities. The development of ultrawide detection bandwidth transducers¹⁹⁷ aims to mitigate the limited bandwidth of traditional US transducers, thereby improving the overall sensitivity and resolution of PAI. Furthermore, specially designed PACT systems with fast-sweep laser scanning techniques offer automatic fluence compensation and motion correction.¹²⁹ Combining PACT with transmission-mode US-computed tomography enables the mapping of the distribution of SoS, further enhancing PACT image quality.¹⁹⁸ Moreover, a wide range of exogenous contrast agents has been developed to improve the SNR of PAI or to overcome the resolution limitations.⁴

Despite the challenges faced in applying DL to PAI, there is no doubt that DL will have a great impact on the biomedical imaging field, well beyond PAI.¹⁹⁹^–²⁰⁷ PAI’s fundamental problems, caused by hardware limitations and the lack of tissue information, are ripe for solution by the information extraction, convergence, and high-speed processing enabled by DL. The result will be new opportunities for PAI to take off as a major imaging modality, opening an exciting era of DL-based PAI.

Acknowledgments

This work was supported in part by a grant from the National Research Foundation (NRF) of Korea, funded by the Ministry of Science and ICT (Grant Nos. 2023R1A2C3004880, 2021M3C1C3097624); a grant from the NRF, funded by the Ministry of Education (Grant No. 2019H1A2A1076500); a grant from the Korea Medical Device Development Fund, funded by the Ministry of Trade, Industry and Energy (Grant Nos. 9991007019, KMDF_PR_20200901_0008); a grant from the Basic Science Research Program, through the NRF, funded by the Ministry of Education (Grant No. 2020R1A6A1A03047902); a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Korea government (MSIT) [Grant No. 2019-0-01906, Artificial Intelligence Graduate School Program (POSTECH)]; a grant from the Korea Evaluation Institute of Industrial Technology (KEIT), funded by the Korea government (MOTIE); and by the BK21 FOUR (Fostering Outstanding Universities for Research) project.

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Jinge Yang, Seongwook Choi, Jiwoong Kim, Byullee Park, and Chulhong Kim "Recent advances in deep-learning-enhanced photoacoustic imaging," Advanced Photonics Nexus 2(5), 054001 (24 July 2023). https://doi.org/10.1117/1.APN.2.5.054001

Received: 30 May 2023; Accepted: 5 July 2023; Published: 24 July 2023

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KEYWORDS

Image segmentation

In vivo imaging

Image restoration

Monte Carlo methods

Education and training

Gallium nitride

Network architectures

1.

Introduction

Eq. (1)

Table 1

Fig. 1

2.

Principles of DL Methods

2.1.

Artificial Neural Networks

Fig. 2

Eq. (2)

Eq. (3)

Eq. (4)

2.2.

Backpropagation

Eq. (5)

Eq. (6)

2.3.

CNN

Fig. 3

2.4.

U-Net

2.5.

GAN

Table 2

3.

Challenges in PAI and Solutions through DL

3.1.

Overcoming Limited Detection Capabilities

3.1.1.

Limited bandwidth

Fig. 4

3.1.2.

Limited view

3.1.3.

Sparsity

3.1.4.

Combinational limited-detection problems

Table 3

3.2.

Compensating for Low-Dosage Light Delivery

Fig. 5

Table 4

3.3.

Improving the Accuracy of Quantitative PAI

Fig. 6

Table 5

3.4.

Optimizing or Replacing Conventional Reconstruction Algorithms

Fig. 7

Table 6

3.5.

Addressing Tissue Heterogeneity

Fig. 8

Table 7

3.6.

Improving the Accuracy of Image Classification and Segmentation

Table 8

3.7.

Overcoming Other Specified Issues

Fig. 9

Table 9

4.

Discussion and Conclusion

Acknowledgments

References

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