PurposeWe characterize the flying focal spot (FFS) technology in digital breast tomosynthesis (DBT), designed to overcome source motion blurring.ApproachA wide-angle DBT system with continuous gantry and focus motion (“uncompensated focus”) and a system with FFS were compared for image sharpness and lesion detectability. The modulation transfer function (MTF) was assessed as a function of height in the projections and reconstructed images, along with lesion detectability using the contrast detail phantom for mammography (CDMAM) and the L1 phantom.ResultsFor the uncompensated focus system, the spatial frequency for 25% MTF value (f25%) measured at 2, 4, and 6 cm in DBT projections fell by 35%, 49%, and 59%, respectively in the tube-travel direction compared with the FFS system. There was no significant difference in f25% for the front-back and tube-travel directions for the FFS unit. The in-plane MTF in the tube-travel direction also improved with the FFS technology.The threshold gold thickness (Tt) for the 0.16-mm diameter discs of contrast detail phantom for mammography (CDMAM) improved for the FFS system in DBT mode, especially at greater heights above the table; Tt at 45 and 65 mm improved by 16% and 24%, respectively, compared with the uncompensated focus system. In addition, improvements in calcification and mass detection in a structured background were observed for DBT and synthetic mammography. The FFS system demonstrated faster scan times (4.8 s versus 21.7 s), potentially reducing patient motion artifacts.ConclusionsThe FFS technology offers isotropic resolution, improved small detail detectability, and faster scan times in DBT mode compared with the traditional continuous gantry and focus motion approach.
KEYWORDS: Digital breast tomosynthesis, Breast, Visibility, Breast density, Cancer detection, Cancer, Breast cancer, Magnetic resonance imaging, X-ray imaging, Ultrasonography
Breast cancer (BC) detectability depends on many factors: the type of cancer, breast tissue related factors, the choice and use of technology and human factors. New imaging techniques should provide higher accuracy and less false negatives. To tailor any future virtual imaging trial (VIT), a detailed description of invasive BC lesions was undertaken. In this single-institution retrospective study, imaging characteristics of 100 consecutive invasive BCs diagnosed in our hospital were assessed in terms of a visibility score, BI-RADS descriptors, breast density, lesion size and location on all breast x-ray imaging techniques and ultrasound (US). Seventy-seven out of these 100 invasive BCs were diagnosed using DBT in addition to FFDM and US and in 29 cases MRI was performed. Not all imaging modalities are equally well performing regarding visualization of invasive BC; 29 out of 77 lesions were poorly visible on FFDM, 9 on DBT, 34 on SM, and 11 on US. Four lesions were poorly visible on all these modalities, but fortunately clearly visible on MRI. The studied invasive lesions that are well visible on all modalities are mostly irregular spiculated lesions with a high density, and have in this study a median size of 18mm. The poorly visible lesions are also mostly irregularly shaped, show more variations in their margins and have a smaller median size of 12.5mm. They are equally or highly dense compared to the background tissue and are in general present in slightly denser breasts. Two lesions are not visible on mammography due to the peripheral location of the invasive breast cancer, one was located sternal and one very peripheral in the axillary tail. Both lesions were visible on ultrasound. This database provides detailed information on the imaging characteristics of invasive BCs which could be a valuable input for VITs.
The required realism of virtual breast phantoms is likely to depend on the imaging modality and the task. This work investigates the extent to which the VICTRE breast models are suitable for the evaluation of synthetic mammography (SM) in terms of statistical texture properties and microcalcification detection performance. First, a power spectrum analysis was performed on digital breast tomosynthesis (DBT) and SM images of patients and virtual phantoms, including all four breast density categories. The fitted power law exponent 𝛽 was used to characterize breast texture. Next, calcification clusters were simulated in patient and phantom backgrounds acquired with three different DBT dose distributions applied over the projections. A human observer detectability study was performed. The power spectrum analysis showed slightly lower power law exponents for patients compared to virtual breast phantoms. The trend of 𝛽 across different density categories is similar for patient and phantom SM images. Additionally, trends in the detectability study with virtual phantoms were similar to those in the patient study, however, the absolute performance values and level of significance between the different dose distributions were not identical. Nevertheless, this suggests that the VICTRE breast phantoms are potentially valuable replacements for patients in system optimization studies for microcalcification detection in SM and DBT.
There are two main acquisition modes for acquiring digital breast tomosynthesis (DBT) projection data: continuous mode and step-and-shoot mode. This work characterizes a new x-ray tube with flying focal spot (FFS) technology, designed to compensate for the continuous focus motion during exposure. The image sharpness of an established wide-angle DBT system with moving focus during exposure (the current standard) and a newly introduced FFS x-ray tube was assessed using the modulation transfer function (MTF). The spatial frequency for the 25% MTF value (f25%) was measured at 2, 4 and 6cm above the table. The impact on the visibility of calcification-like test objects was investigated with the CDMAM phantom that was positioned at various heights above the table. The threshold gold thickness (Tt) was calculated for the 0.1 mm discs. For the moving focus system, f25% measured at 2, 4 and 6cm in the 0° DBT projection fell by 35%, 49% and 59% in the tube-travel direction compared to figures of 7.6, 6.8, and 6.6 mm-1 for the FFS system. In the frontback direction, f25% was 7.4, 7.3 and 7.2 mm-1 at 2, 4 and 6cm, respectively for the moving focus unit while for the FFS system, these figures were 7.1, 6.7 and 5.9 mm-1. There was no significant difference in f25% for the front-back and tube-travel directions for the FFS unit (p<0.04). The Tt values at 4cm and 6cm above the table in DBT mode improved by 33% and 52%, respectively for the FFS system versus the moving focus system. The improvement in Tt increased as object height above the table increased. The FFS system has a reduced DBT scan time compared to the moving focus system (4.8s vs 21.7s) which is expected to reduce patient motion artefacts. The FFS system has in DBT mode an improved isotropic resolution, improved small detail detectability and faster total scan times. Therefore, this new DBT system has the potential for increased microcalcification detection and patient throughput compared to its predecessors.
This paper implements a generative adversarial deep learning network (GAN) to automate the generation of realistic and representative 3D cancer models to investigate digital breast tomosynthesis (DBT) with virtual clinical trials (VCT). Initially, a series of mass lesions from wide-angle DBT cancer cases were manually segmented. We trained a 3D-GAN in two phases: the first phase utilized 105 manually created models of invasive ductal carcinoma (IDA) (including both microlobulated and spiculated lesions) as a training dataset; the second phase focused on enhancing the details of the borders of the GAN-models by using a smaller training set of 42 highly spiculated segmentations. To improve the realism of the generated models in VCTs from both phases, post-processing was carried out by removing disconnected pixels, filling holes, smoothing the borders and elongating existing spicules. Fifteen generated lesions were then simulated in acquired patient images and their realism was validated by a radiologist. 80% of the simulated cases received at least a realism score of 3 out of 5. While the average realism score of the generated voxel models was slightly lower than the average score of manually segmented lesions, the 3D-GAN successfully generated breast cancer models of spiculated masses even when trained with a limited dataset. This same method could be applied to generate other mass models of certain subgroups to allow lesion specific simulations, increasing the efficiency of the process to produce representative lesion models for VCTs.
This work proposes an objective and automated procedure to obtain realistic digital breast tomosynthesis (DBT) images for virtual clinical trials (VCT) using the hybrid approach (simulating lesions in acquired patient images). Based on extensive feedback from a radiologist, we have implemented an automatic selection of an appropriate insertion position that (1) is located in the interior region of the breast, (2) contains sufficient glandular tissue, and (3) has the lowest variance to cope with the presence of prominent background structure and contains no Sobel edge detected blood vessels. Next, the lesion is rotated to align with the breast structures using the histogram of oriented gradient feature descriptor. The spicules of the lesion are extended to improve the fine details of the manually segmented mass models. To reduce the pronounced shadow artefact surrounding the mass, the lesion template is modified with a fitted 2D gaussian to create a softer transition between background and lesion. The realism of 20 simulated lesions using the established automated procedure was scored; 70% of the simulated cases received at least a realism score of 4 out of 5. This means an improvement in realism compared to when lesions without processing are inserted at random locations in the patient background images. Additionally, the automated method eliminates the dependency on the researcher performing the VCT.
Aim: Compare an in-house developed hybrid simulation framework and the FDA’s total simulation framework VICTRE in an exercise to simulate realistic DBT images. Methods: Three different set-ups were investigated in increasing order of difficulty: (1) A simple object insert simulated in homogeneous backgrounds. (2) The same simple test object in a breast phantom to investigate the impact of a non-homogeneous background. (3) The simple test object replaced with clinically relevant lesions (spiculated and non-spiculated masses, calcification clusters) to test the frameworks in their entirety. Next to a visual analysis, a quantitative comparison based on contrast and signal-difference-to-noise (SDNR) measurements was performed. Results: Similar contrast and SDNR values in the ‘for processing’ images are obtained for a glandular test object when simulated with VICTRE and the Leuven platform, for both homogeneous as well as structured backgrounds (e.g. structured background; contrast: 0.13 and 0.12, SDNR: 8.99 and 8.56 for the Leuven platform and VICTRE respectively). The reconstruction algorithms of both frameworks differ, but the input of VICTRE images in an offline reconstruction tool like in the Leuven framework leads to similar results. In DBT reconstructed slices, the simulated mass models looked similar to the real lesions from which the model was derived. The simulated calcification clusters are more subtle when using the VICTRE framework, while all clusters appear to be realistic. This illustrates the need of full characterization of the methods. Conclusion: The step-by-step comparison of two very different frameworks was successful. Both frameworks are able to simulate objects with the same characteristics (contrast, SDNR, shape) and can create images with realistic lesions.
KEYWORDS: Digital breast tomosynthesis, Tumor growth modeling, Clinical trials, Breast cancer, Databases, Data modeling, Cancer, Systems modeling, Visual process modeling, Image segmentation
Aim: To develop, validate and apply a pipeline for breast cancer voxel model generation from patient digital breast tomosynthesis (DBT) cases for cancer type specific virtual clinical trials (VCT). Methods: Input cancer cases were retrieved from wide-angle DBT systems. Three aspects of the creation process were investigated: (1) The impact of the limited z-resolution of DBT on the shape of the voxel model using circularity measurements (i.e. ratio of diameters between input and result after simulation test), DICE coefficient and artefact spread function. (2) The possibility to speed up and automate lesion segmentation with a deep learning network. (3) The ultimate realism of the voxel models in a VCT application, visually scored by a radiologist and a medical physicist. Results: Deviations between ground truth and segmented voxel models due to the pseudo-3D characteristics of DBT were limited, with circularity changes smaller than 8%. A 4-layer U-net deep learning network with a multiplication of the DICE loss and the implemented boundary loss as loss function is capable to produce segmentations within the variability of manual segmentations (DICE coefficient = 0.80). A reader study of the VCT application showed an average realism score of 3.4 on a scale of 1 to 5 for the simulated lesion manually segmented, compared to an average of 4.3 for the real lesions. An initial total of 25 invasive cancer models (9 non-spiculated, 16 spiculated masses) was successfully created and validated. Conclusion: Segmentation from an object with limited z-resolution induces an acceptable deformation. Voxel models created from DBT images can be used to mimic realistic DBT cancer cases. The use of AI techniques has facilitated the cumbersome manual segmentation task.
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