Although many studies of iterative based reconstruction algorithm have been performed on dose reduction in medical applications, there is little research on how to approach the systematical acquisition method to reduce the dose. In this study, we proposed a hybrid imaging technique using half ROI and full view scan as a new acquisition method. A prototype of the CT system (TVX-IL1500H, GERI, Korea) was used. The source-to-detector and the source-to-center of rotation distance were 1178 and 905 mm, respectively. A total of 720 projection data were obtained, from which full view CT reconstructed images were obtained as a reference image using a filtered back projection (FBP). Interior ROI CT images were reconstructed using the 720 truncated projection data. Proposed hybrid CT image was reconstructed using 360 truncated and variable number of nontruncated full-size projection data. We set a total of 6 acquisition parameters according to the number of full-size projection data and acquire hybrid images. Dose reduction can be achieved through half ROI and full view scan. As the number of nontruncated full-size projection data on the hybrid image increases, the CNR value increases. FOM values were also derived for each parameter. In conclusion, appropriate acquisition parameter for half ROI and full view scan was derived based on the FOM values.
Sparse view - computed tomography (CT) for low dose and photon counting detector (PCD) for spectral imaging have been studied for improvement of image quality and quantification in medical imaging. The sparse view–CT can reduce dose, but there is a limitation that cannot be completely restored yet and PCD with physical phenomena such as charge sharing, K-escape and material characteristic can be difficult to material quantification due to different distribution of noise characteristics in a specific energy band. In this study, we propose a deep running-based wavelet-CNN for the efficient reduction of physical factors such as noise and streak artifact generated by fusion of sparse view-CT and PCD. The physical phenomena of the spatio-energetic cross-talks were reflected in PCD. We obtained images with a total of four energy thresholds with limited angles and trained through the proposed method. The proposed method was evaluated for the image quality by the peak signal to noise ratio (PSNR), the normalized mean square error (NMSE), the structural similarity (SSIM), the multi-scale SSIM (MS-SSIM), and the feature similarity (FSIM). The experimental results demonstrated that the sparse view-CT with PCD using proposed deep running structure effectively removes the streak artifacts and improves the image quality.
The detection of microcalcification on mammograms is known as the most important feature. Microcalcifications are difficult to distinguish because they have low contrast in mammography due to size and breast density. The purpose of this study was to evaluate the feasibility of virtual monochromatic image (VMI) for quantitative assessment of the appropriate energy region for detection of malignant microcalcifications. The photon-counting spectral mammography system was modeled using Geant4 Application for Tomographic Emission (GATE) simulation tools. The breast phantom used a 50/50 ratio of adipose/glandular tissue and microcalcifications used calcium hydroxyapatite (Ca5(PO4)3(OH)), which is mainly malignant. Microcalcifications with various sizes ranging from 150 μm to 550 μm were embedded into the breast phantom. In this study, projection based VMI was used. This study quantitatively evaluates the appropriate energy region in terms of image quality using VMI technique. The results showed that VM images were optimized at an energy range of approximately 26 to 27 keV. In order to verify the usefulness of the results obtained from the VM images, the CNR was evaluated according to the microcalcification size using the bin images obtained by setting various energy thresholds based on the photon-counting detector. Compared to the results of VM images, the results of bin images showed a similar tendency. In this study, we investigated the optimum energy of monochromatic images for breast diagnostic applications. By setting the optimal energy range using VMI, we can identify microcalcifications better in mammography and expect to reduce the frequency of additional examination.
The segmentation of medical image applying in medical anatomy plays an important role in various application. So, the study of medical image processing is very important and necessary. Due to the presence of noise and complexity of structure, the existing methods have various shortcomings and the performances are not ideal. In this study, we propose a new method which based on back propagation (BP) neural network and AdaBoost algorithm. The BP neural network we created is 1-7-1 structure. then we trained the system by Gravitational search algorithm (Here, we use the segmented images which were obtained by classic fuzzy c-means algorithm as the ideal output data). Based on this, we established and trained 10 groups of BPNN (We also call it as weak classifier) by applying 10 groups of different data. subsequently, we adopted the AdaBoost algorithm to obtain the weight of each BPNN. Finally, we made up a new BPAdaboost system for image segmentation. In this experiment, we used one group of datasets: Brain MRI. A comparison with the conventional segmentation method through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results based on brain image segmentation.
Polychromatic X-ray in computed tomography (CT) can cause metal artifacts and beam hardening artifacts, which are limiting factors in the detection and diagnosis of lesions. Several groups have introduced virtual monochromatic imaging (VMI) techniques using dual-source CT to reduce these artifacts. However, the dual-source system with two exposures can increase the patient dose. The photon-counting detector with one exposure can replace a dual-source system. In this study, we investigated the feasibility of VMI in a photon-counting system. A prototype of the photon-counting CT system, which has 64 line-pixels Cadmium Zinc Telluride (CZT)-based photon-counting detector, was used. The source-to-detector distance and the source-to-center of rotation distance were 1,400 and 1,200 mm, respectively. Energy bins were set at 23 - 32, 33 - 42, 43 - 52, 53 - 62, and 63 - 90 keV. For comparison, the integrating mode was obtained by sum of five energy bins, which is assumed to polychromatic X-ray. Two copper (Cu) rods were inserted into PMMA cylinder phantom. As results, the VMI effectively removed metal artifacts. Noise and Signal-to-noise ratio (SNR) were evaluated and the optimal VMI was measured at 77 keV. Our results indicated that VMI in the prototype of the photon-counting system effectively eliminates the metal artifact and provides better image quality than integrating mode at 23 - 90 keV.
Region-of-interest (ROI) imaging is considered an effective method to reduce the exposure dose. We propose ROIbased beam modulation acquisition to restore the information outside of the ROI. The CT system and 3D voxelized abdominal phantom were simulated using the MATLAB R2017b program. A total of 360 projections were obtained and used for CT reconstruction with a filtered back projection (FBP) algorithm. Beam modulation CT images were reconstructed using 288 truncated and 72 full projections. An interpolation method and our proposed method based on a projection onto convex sets (POCS) algorithm corrected the truncated projections. The image quality of three ROIs was evaluated using the structural similarity index measure (SSIM). The reconstructed image obtained by beam modulation acquisition resulted in a much higher SSIM value for the external information than that obtained by the ROI scan. The proposed method based on a POCS algorithm provides the best image quality in beam modulation acquisition. In conclusion, we have verified the possibility of restoring the ROI external information using beam modulation acquisition.
The purpose of this study was to evaluate the feasibility of spectral mammography using the dual-energy method to noninvasively distinguish between type I (calcium oxalate, CO) and type II (calcium hydroxyapatite, HA) microcalcifications. Two types of microcalcifications are difficult to distinguish due to a similar linear attenuation coefficient. In order to improve the detection efficiency of microcalcifications, we used the photon counting detector with energy discrimination capability and microcalcifications were classified into optimal energy bins. Two energy bins were used to obtain dualenergy images. In this study, photon counting spectral mammography system was simulated using Geant4 Application for Tomographic Emission (GATE) simulation tools. The thickness of the breast phantom was 3 cm and microcalcifications of various sizes ranging from 130-550 μm were embedded into the breast phantom. Microcalcifications were classified as being calcium hydroxyapatite or calcium oxalate based on score calculation with the dual-energy images. According to the results, the measured CNR of calcium hydroxyapatite (HA) was higher than that of the calcium oxalate (CO) in conventional single-energy image. In addition, two types of microcalcifications were distinguished using dual-energy analysis method. This classification represents better performance with a high energy of 50 kVp and an energy threshold of 30 keV. These results indicate that the classification performance was improved when the difference in the low energy image and high energy image was used. This study demonstrated the feasibility of photon counting spectral mammography for classification of breast microcalcifications. We expect that dual-energy method can reduce the frequency of biopsy and discriminate microcalcifications in mammography. These results are expected to potentially improve the efficiency of early breast cancer diagnosis.
Dual-energy (DE) technology is useful in chest radiography because it can separate anatomical structures such as bone and soft tissue. The standard log subtraction (SLS), simple smoothing of the high-energy image (SSH), anti-correlated noise reduction (ACNR), and a general linear noise reduction algorithm (GLNR) are used as conventional DE techniques to separate bone and soft tissue. However, conventional DE techniques cannot accurately decompose the anatomical structures because these techniques are based on the assumptions that X-ray imaging is a linear relationship. This relationship can cause quantum noise as well as anatomical loss of normal tissue and difficulty in detecting lesions. In this study, we propose a non-linear DE technique which requires a step to calculate the coefficient in advance using a calibration phantom. The calibration phantom composed to aluminum and PMMA material to calculate non-linear coefficients using the quadratic fitting model for soft tissue and bone. The results demonstrated that a non-linear DE technique showed the higher contrast-to-noise ratio (CNR), signal to noise ratio (SNR) and figure of merit (FOM) at 60 /70 kVp and 130 kVp. In addition, it showed better performance and image quality than conventional DE technique in terms of material decomposition capability. In conclusion, a non-linear DE technique is expected to increase the diagnostic accuracy in chest radiography.
KEYWORDS: Breast, Prototyping, Imaging systems, Modulation transfer functions, Systems modeling, Digital breast tomosynthesis, Quantum electronics, Tumor growth modeling, Reconstruction algorithms, Health sciences
Quantitative imaging performance analysis has recently been the focus in medical imaging fields. It would not only provide objective information but also it could aid a patient diagnosis by giving optimized system parameters for various imaging tasks. However, the previous studies on task-based metric in breast tomosynthesis usually take into account a cascaded system modeling for generalized noise equivalent quanta. In this study, the authors have been focused on the experimental study for calculating task-based detectability index (d') in the prototype breast tomosynthesis system for different angular ranges. According to the summarized d' the authors observed that the highest d' could be found in the angular range of ±10.5° (1.5° angle step) among several cases for detection of 4.7 mm mass in our prototype breast tomosynthesis system. Our study would be easily applied in practical breast tomosynthesis for the quantitative performance analysis of imaging parameter is needed. More various imaging tasks with different parameter combinations would be conducted in the future for generalized optimization of breast tomosynthesis study.
In medical imaging field, various dose reduction techniques have been studied. We proposed shutter scan acquisition for region of interest (ROI) imaging to reduce the patient exposure dose in digital tomosynthesis system. Projections obtained by shutter scan acquisition is a combination of truncated projections and non-truncated projections. In this study, we call the number of truncated projections divided by the number of non-truncated projections as shutter weighting factor. The shutter scan acquisition parameters were optimized using 5 different acquisition sets with the shutter weighting factor (0.16, 0.35, 1.03, 3.05 and 7.1). A prototype CDT system (LISTEM, Korea) and the LUNGMAN phantom (Kyoto Kagaku, Japan) with an 8 mm lung nodule were used. A total of 81 projections with shutter scan acquisition were obtained in 5 sets according to shutter weighting factor. The image quality was investigated using the contrast noise ratio (CNR). We also calculated figure of merit (FOM) to determine optimal acquisition parameters for the shutter scan acquisition. The ROI of the reconstructed image with shutter scan acquisition showed enhanced contrast. The highest CNR and FOM value, shutter weighting factor 7.1, is the acquisition set consisting of 71 truncated projections and 10 non-truncated projections. In this study, we investigated the effects of composition ratio of the truncated and non-truncated projections on reconstructed images through the shutter scan acquisition. In addition, the optimal acquisition conditions for the shutter scan acquisition were determined by deriving the FOM values. In conclusion, we can suggest optimal shutter scan acquisition parameters on the lesion within the ROI to be diagnosed.
Contrast enhanced digital mammography (CEDM) using dual energy technique has been studied due to its ability of emphasizing breast cancer. However, when using CEDM the patient dose and the toxicity of iodine should be considered. A photon counting detector (PCD), which has the ability of energy discrimination, has been regarded as an alternative technique to resolve the problem of excessive patient dose. The purpose of this study was to confirm the feasibility of CEDM based on the PCD by using a projection-based energy weighting technique. We used Geant4 Application for Tomographic Emission (GATE) version 6.0. We simulated two different types of PCD which were constructed with silicon (Si) and cadmium zinc telluride (CZT). Each inner cylinder filled with four iodine with different low concentrations and thicknesses in cylindrical shape of breast phantom. For comparison, we acquired a convention integrating mode image and five bin images based on PCD system by projection-based weighting technique. The results demonstrated that CEDM based on the PCD significantly improved contrast to noise ratio (CNR) compared to conventional integrating mode. As a result of applying the dual energy technique to the projection-based weighing image, the CNR of low concentration iodine was improved. In conclusion, the CEDM based on PCD with projection-based weighting technique has improved a detection capability of low concentration iodine than integrating mode.
During breast image acquisition from the mammography, the inner regions of the breast are relatively thicker and denser than the peripheral areas, which can lead to overexposure to the periphery. Some images show low visibility of tissue structures in the breast peripheral areas due to the intensity change. It has a negative effect on diagnosis for breast cancer detection. To improve image quality, we have proposed pre-processing technique based on distance transformation to enhance the visibility of peripheral areas. The distance transform method aims to calculate the distance between each zero pixel and the nearest nonzero pixel in the binary images. For each pixel with the distance to the skin-line, the intensity of pixel is iteratively corrected by multiplying a propagation ratio. To evaluate the quality of processed images, the texture features were extracted using gray-level co-occurrence matrices (GLCM). And the breast density is quantitatively calculated. According to the results, the structure of breast tissues in the overexposed peripheral areas was well observed. The processed images showed more complexity and improved contrast. On the other hand, the homogeneity tended to be similar to the original images. The pixel values of peripheral areas were normalized without losing information and weighted to reduce the intensity variation. In this study, the pre-processing technique based on distance transformation was used to overcome the problem of overexposed peripheral areas in the breast images. The results demonstrated that appropriate pre-processing techniques are useful for improving image quality and accuracy of density measurement.
Chest digital tomosynthesis (CDT) is a new 3D imaging technique that can be expected to improve the detection of subtle lung disease over conventional chest radiography. Algorithm development for CDT system is challenging in that a limited number of low-dose projections are acquired over a limited angular range. To confirm the feasibility of algebraic reconstruction technique (ART) method under variations in key imaging parameters, quality metrics were conducted using LUNGMAN phantom included grand-glass opacity (GGO) tumor. Reconstructed images were acquired from the total 41 projection images over a total angular range of ±20°. We evaluated contrast-to-noise ratio (CNR) and artifacts spread function (ASF) to investigate the effect of reconstruction parameters such as number of iterations, relaxation parameter and initial guess on image quality. We found that proper value of ART relaxation parameter could improve image quality from the same projection. In this study, proper value of relaxation parameters for zero-image (ZI) and back-projection (BP) initial guesses were 0.4 and 0.6, respectively. Also, the maximum CNR values and the minimum full width at half maximum (FWHM) of ASF were acquired in the reconstructed images after 20 iterations and 3 iterations, respectively. According to the results, BP initial guess for ART method could provide better image quality than ZI initial guess. In conclusion, ART method with proper reconstruction parameters could improve image quality due to the limited angular range in CDT system.
Spectral computed tomography (SCT) is a promising technique for obtaining enhanced image with contrast agent and distinguishing different materials. We focused on developing the analytic reconstruction algorithm in material decomposition technique with lower radiation exposure and shorter acquisition time. Sparse-angular sampling can reduce patient dose and scanning time for obtaining the reconstruction images. In this study, the sinogram interpolation method was used to improve the quality of material decomposed images in sparse angular sampling. A prototype of spectral CT system with 64 pixels CZT-based photon counting detector was used. The source-to-detector distance and the source-tocenter of rotation distance were 1200 and 1015 mm, respectively. The x-ray spectrum at 90 kVp with a tube current of 110 μA was used. Two energy bins (23-33 keV and 34-44 keV) were set to obtain the two images for decomposed iodine and calcification. We used PMMA phantom and its height and radius were 50 mm and 17.5 mm, respectively. The phantom contained 4 materials including iodine, gadolinium, calcification, and liquid state lipid. We evaluated the signal to noise ratio (SNR) of materials to examine the significance of sinogram interpolation method. The decomposed iodine and calcification images were obtained by projection based subtraction method using two energy bins with 36 projection data. The SNR in decomposed images were improved by using sinogram interpolation method. And these results indicated that the signal of decomposed material was increased and the noise of decomposed material was reduced. In conclusion, the sinogram interpolation method can be used in material decomposition method with sparse-angular sampling.
KEYWORDS: Photon counting, Dual energy imaging, Sensors, Breast, Imaging systems, Iodine, Mammography, Monte Carlo methods, Windows, Signal attenuation, Tissues, X-rays
The photon counting detector with energy discrimination capabilities provides the spectral information and energy of each photon with single exposure. The energy-resolved photon counting detector makes it possible to improve the visualization of contrast agent by selecting the appropriate energy window. In this study, we simulated the photon counting spectral mammography system using a Monte Carlo method and compared three contrast enhancement methods (K-edge imaging, projection-based energy weighting imaging, and dual energy subtraction imaging). For the quantitative comparison, we used the homogeneous cylindrical breast phantom as a reference and the heterogeneous XCAT breast phantom. To evaluate the K-edge imaging methods, we obtained images by increasing the energy window width based on K-edge absorption energy of iodine. The iodine which has the K-edge discontinuity in the attenuation coefficient curve can be separated from the background. The projection-based energy weighting factor was defined as the difference in the transmissions between the contrast agent and the background. Each weighting factor as a function of photon energy was calculated and applied to the each energy bin. For the dual energy subtraction imaging, we acquired two images with below and above the iodine K-edge energy using single exposure. To suppress the breast tissue in high energy images, the weighting factor was applied as the ratio of the linear attenuation coefficients of the breast tissue at high and low energies. Our results demonstrated the CNR improvement of the K-edge imaging was the highest among the three methods. These imaging techniques based on the energy-resolved photon counting detector improved image quality with the spectral information.
Chest digital tomosynthesis (CDT) is a recently introduced new imaging modality for better detection of high- and smallcontrast lung nodules compared to conventional X-ray radiography. In CDT system, several projection views need to be acquired with limited angular range. The acquisition of insufficient number of projection data can degrade the reconstructed image quality. This image degradation easily affected by acquisition parameters such as angular dose distribution, number of projection views and reconstruction algorithm. To investigate the imaging characteristics, we evaluated the impact of the angular dose distribution on image quality by simulation studies with Geant4 Application for Tomographic Emission (GATE). We designed the different angular dose distribution conditions. The results showed that the contrast-to-noise ratio (CNR) improves when exposed the higher dose at central projection views than peripheral views. While it was found that increasing angular dose distribution at central views improved lung nodule detectability, although both peripheral regions slightly suffer from image noise due to low dose distribution. The improvements of CNR by using proposed image acquisition technique suggest possible directions for further improvement of CDT system for lung nodule detection with high quality imaging capabilities.
Chest digital tomosynthesis (CDT) system has recently been introduced and studied. This system offers the potential to be a substantial improvement over conventional chest radiography for the lung nodule detection and reduces the radiation dose with limited angles. PC-based Monte Carlo program (PCXMC) simulation toolkit (STUK, Helsinki, Finland) is widely used to evaluate radiation dose in CDT system. However, this toolkit has two significant limits. Although PCXMC is not possible to describe a model for every individual patient and does not describe the accurate X-ray beam spectrum, Geant4 Application for Tomographic Emission (GATE) simulation describes the various size of phantom for individual patient and proper X-ray spectrum. However, few studies have been conducted to evaluate effective dose in CDT system with the Monte Carlo simulation toolkit using GATE.
The purpose of this study was to evaluate effective dose in virtual infant chest phantom of posterior-anterior (PA) view in CDT system using GATE simulation. We obtained the effective dose at different tube angles by applying dose actor function in GATE simulation which was commonly used to obtain the medical radiation dosimetry. The results indicated that GATE simulation was useful to estimate distribution of absorbed dose. Consequently, we obtained the acceptable distribution of effective dose at each projection. These results indicated that GATE simulation can be alternative method of calculating effective dose in CDT applications.
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