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Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality. [ABSTRACT FROM AUTHOR]

Background: X‐ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used. Purpose: To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters). Methods: Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast‐enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC‐GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons). Results: The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD. Conclusions: For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry. [ABSTRACT FROM AUTHOR]

Background: Breast computed tomography (CT) is an emerging breast imaging modality, and ongoing developments aim to improve breast CT's ability to detect microcalcifications. To understand the effects of different parameters on microcalcification detectability, a virtual clinical trial study was conducted using hybrid images and convolutional neural network (CNN)‐based model observers. Mathematically generated microcalcifications were embedded into breast CT data sets acquired at our institution, and parameters related to calcification size, calcification contrast, cluster diameter, cluster density, and image display method (i.e., single slices, slice averaging, and maximum‐intensity projections) were evaluated for their influence on microcalcification detectability. Purpose: To investigate the individual effects and the interplay of parameters affecting microcalcification detectability in breast CT. Methods: Spherical microcalcifications of varying diameters (0.04, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40 mm) and native intensities were computer simulated to portray the partial volume effects of the imaging system. Calcifications were mathematically embedded into 109 patient breast CT volume data sets as individual calcifications or as clusters of calcifications. Six numbers of calcifications (1, 3, 5, 7, 10, 15) distributed within six cluster diameters (1, 3, 5, 6, 8, 10 mm) were simulated to study the effect of cluster density. To study the role of image display method, 2D regions of interest (ROIs) and 3D volumes of interest (VOIs) were generated using single slice extraction, slice averaging, and maximum‐intensity projection (MIP). 2D and 3D CNNs were trained on the ROIs and VOIs, and receiver operating characteristic (ROC) curve analysis was used to evaluate detection performance. The area under the ROC curve (AUC) was used as the primary performance metric. Results: Detection performance decreased with increasing section thickness, and peak detection performance occurred using the native section thickness (0.2 mm) and MIP display. The MIP display method, despite using a single slice, yielded comparable performance to the native section thickness, which employed 50 slices. Reduction in slices did not sacrifice detection accuracy and provided significant computational advantages over multi‐slice image volumes. Larger cluster diameters resulted in reduced overall detectability, while smaller cluster diameters led to increased detectability. Additionally, we observed that the presence of more calcifications within a cluster improved the overall detectability, while fewer calcifications decreased it. Conclusions: As breast CT is still a relatively new breast imaging modality, there is an ongoing need to identify optimal imaging protocols. This work demonstrated the utility of MIP presentation for displaying image volumes containing microcalcification clusters. It is likely that human observers may also benefit from viewing MIPs compared to individual slices. The results of this investigation begin to elucidate how model observers interact with microcalcification clusters in a 3D volume, and will be useful for future studies investigating a broader set of parameters related to breast CT. [ABSTRACT FROM AUTHOR]

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp–Davis–Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation.

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp–Davis–Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation. [ABSTRACT FROM AUTHOR]

Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age. Methods: The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method. Results: The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast. Conclusions: The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features—BTV, MGV, and PBD, as a function of the patient's age. [ABSTRACT FROM AUTHOR]

Background: Radiopaque breast markers cause artifacts in dedicated spiral breast-computed tomography (SBCT). This study investigates the extent of artifacts in different marker types and the feasibility of reducing artifacts through a metal artifact reduction (MAR) algorithm. Methods: The pilot study included 18 women who underwent contrast-enhanced SBCT. In total, 20 markers of 4 different types were analyzed for artifacts. The extent of artifacts with and without MAR was measured via the consensus of two readers. Image noise was quantitatively evaluated, and the effect of MAR on the detectability of breast lesions was evaluated on a 3-point Likert scale. Results: Breast markers caused significant artifacts that impaired image quality and the detectability of lesions. MAR decreased artifact size in all analyzed cases, even in cases with multiple markers in a single slice. The median length of in-plain artifacts significantly decreased from 31 mm (range 11–51 mm) in uncorrected to 2 mm (range 1–5 mm) in corrected images (p ≤ 0.05). Artifact size was dependent on marker size. Image noise in slices affected by artifacts was significantly lower in corrected (13.6 ± 2.2 HU) than in uncorrected images (19.2 ± 6.8 HU, p ≤ 0.05). MAR improved the detectability of lesions affected by artifacts in 5 out of 11 cases. Conclusion: MAR is feasible in SBCT and improves the image quality and detectability of lesions. [ABSTRACT FROM AUTHOR]

Background: In breast CT, scattered photons form a large portion of the acquired signal, adversely impacting image quality throughout the frequency response of the imaging system. Prior studies provided evidence for a new image acquisition design, dubbed Narrow Beam Breast CT (NB‐bCT), in preventing scatter acquisition. Purpose: Here, we report the design, implementation, and initial characterization of the first NB‐bCT prototype. Methods: The imaging system's apparatus is composed of two primary assemblies: a dynamic Fluence Modulator (collimator) and a photon‐counting line detector. The design of the assemblies enables them to operate in lockstep during image acquisition, converting sourced x‐rays into a moving narrow beam. During a projection, this narrow beam sweeps the entire fan angle coverage of the imaging system. The assemblies are each comprised of a metal housing, a sensory system, and a robotic system. A controller unit handles their relative movements. To study the impact of fluence modulation on the signal received in the detector, three physical breast phantoms, representative of small, average, and large size breasts, were developed and imaged, and acquired projections analyzed. The scatter acquisition in each projection as a function of breast phantom size was investigated. The imaging system's spatial resolution at the center and periphery of the field of view was measured. Results: Minimal acquisition of scattered rays occurs during image acquisition with NB‐bCT; results in minimal scatter to primary ratios in small, average, and large breast phantoms imaged were 0.05, 0.07, and 0.9, respectively. System spatial resolution of 5.2 lp/mm at 10% max MTF and 2.9 lp/mm at 50% max MTF at the center of the field of view was achieved, with minimal loss with the shift toward the corner (5.0 lp/mm at 10% max MTF and 2.5 lp/mm at 50% max MTF). Conclusion: The disclosed development, implementation, and characterization of a physical NB‐bCT prototype system demonstrates a new method of CT‐based image acquisition that yields high spatial resolution while minimizing scatter‐components in acquired projections. This methodology holds promise for high‐resolution CT‐imaging applications in which reduction of scatter contamination is desirable. [ABSTRACT FROM AUTHOR]

To assess the density values of breast lesions and breast tissue using non-contrast spiral breast CT (nc-SBCT) imaging. In this prospective study women undergoing nc-SBCT between April-October 2023 for any purpose were included in case of: histologically proven malignant lesion (ML); fibroadenoma (FA) with histologic confirmation or stability > 24 months (retrospectively); cysts with ultrasound correlation; and women with extremely dense breast (EDB) and no sonographic findings. Three regions of interest were placed on each lesion and 3 different area of EDB. The evaluation was performed by two readers (R1 and R2). Kruskal-Wallis test, intraclass correlation (ICC) and ROC analysis were used. 40 women with 12 ML, 10 FA, 15 cysts and 9 with EDB were included. Median density values and interquartile ranges for R1 and R2 were: 60.2 (53.3–67.3) and 62.5 (55.67–76.3) HU for ML; 46.3 (41.9–59.5) and 44.5 (40.5–59.8) HU for FA; 35.3 (24.3–46.0) and 39.7 (26.7–52.0) HU for cysts; and 28.7 (24.2–33.0) and 33.3 (31.7–36.8) HU for EDB. For both readers, densities were significantly different for ML versus EDB (p < 0.001) and cysts (p < 0.001) and for FA versus EDB (p=/<0.003). The AUC was 0.925 (95 %CI 0.858–0.993) for R1 and 0.942 (0.884–1.00) for R2 when comparing ML versus others and 0.792 (0.596–0.987) and 0.833 (0.659–1) when comparing ML versus FA. The ICC showed an almost perfect inter-reader (0.978) and intra-reader agreement (>0.879 for both readers). In nc-SBCT malignant lesions have higher density values compared to normal tissue and measurements of density values are reproducible between different readers. [ABSTRACT FROM AUTHOR]

Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient’s age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient’s age. Methods: The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method. Results: The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient’s age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast. Conclusions: The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features—BTV, MGV, and PBD, as a function of the patient’s age.

Background: Radiopaque breast markers cause artifacts in dedicated spiral breast-computed tomography (SBCT). This study investigates the extent of artifacts in different marker types and the feasibility of reducing artifacts through a metal artifact reduction (MAR) algorithm. Methods: The pilot study included 18 women who underwent contrast-enhanced SBCT. In total, 20 markers of 4 different types were analyzed for artifacts. The extent of artifacts with and without MAR was measured via the consensus of two readers. Image noise was quantitatively evaluated, and the effect of MAR on the detectability of breast lesions was evaluated on a 3-point Likert scale. Results: Breast markers caused significant artifacts that impaired image quality and the detectability of lesions. MAR decreased artifact size in all analyzed cases, even in cases with multiple markers in a single slice. The median length of in-plain artifacts significantly decreased from 31 mm (range 11–51 mm) in uncorrected to 2 mm (range 1–5 mm) in corrected images (p ≤ 0.05). Artifact size was dependent on marker size. Image noise in slices affected by artifacts was significantly lower in corrected (13.6 ± 2.2 HU) than in uncorrected images (19.2 ± 6.8 HU, p ≤ 0.05). MAR improved the detectability of lesions affected by artifacts in 5 out of 11 cases. Conclusion: MAR is feasible in SBCT and improves the image quality and detectability of lesions.

Purpose: Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft‐tissue contrast. Grating interferometry breast computed tomography (GI‐BCT) is a promising X‐ray phase contrast modality that could overcome these limitations by offering high soft‐tissue contrast and excellent three‐dimensional resolution. To enable the transition of this technology to clinical practice, dedicated data‐processing algorithms must be developed in order to effectively retrieve the signals of interest from the measured raw data. Methods: This article proposes a novel denoising algorithm that can cope with the high‐noise amplitudes and heteroscedasticity which arise in GI‐BCT when operated in a low‐dose regime to effectively regularize the ill‐conditioned GI‐BCT inverse problem. We present a data‐driven algorithm called INSIDEnet, which combines different ideas such as multiscale image processing, transform‐domain filtering, transform learning, and explicit orthogonality to build an Interpretable NonexpanSIve Data‐Efficient network (INSIDEnet). Results: We apply the method to simulated breast phantom datasets and to real data acquired on a GI‐BCT prototype and show that the proposed algorithm outperforms traditional state‐of‐the‐art filters and is competitive with deep neural networks. The strong inductive bias given by the proposed model's architecture allows to reliably train the algorithm with very limited data while providing high model interpretability, thus offering a great advantage over classical convolutional neural networks (CNNs). Conclusions: The proposed INSIDEnet is highly data‐efficient, interpretable, and outperforms state‐of‐the‐art CNNs when trained on very limited training data. We expect the proposed method to become an important tool as part of a dedicated plug‐and‐play GI‐BCT reconstruction framework, needed to translate this promising technology to the clinics. [ABSTRACT FROM AUTHOR]

Purpose: We aim to determine the combination of X-ray spectrum and detector scintillator thickness that maximizes the detectability of microcalcification clusters in dedicated cone-beam breast CT., Approach: A cascaded linear system analysis was implemented in the spatial frequency domain and was used to determine the detectability index using numerical observers for the imaging task of detecting a microcalcification cluster with 0.17 mm diameter calcium carbonate spheres. The analysis considered a thallium-doped cesium iodide scintillator coupled to a complementary metal-oxide semiconductor detector and an analytical filtered-back-projection reconstruction algorithm. Independent system parameters considered were the scintillator thickness, applied X-ray tube voltage, and X-ray beam filtration. The combination of these parameters that maximized the detectability index was considered optimal., Results: Prewhitening, nonprewhitening, and nonprewhitening with eye filter numerical observers indicate that the combination of 0.525 to 0.6 mm thick scintillator, 70 kV, and 0.25 to 0.4 mm added copper filtration maximized the detectability index at a mean glandular dose (MGD) of 4.5 mGy., Conclusion: Using parallel cascade systems' analysis, the combination of parameters that could maximize the detection of microcalcifications was identified. The analysis indicates that a harder beam than that used in current practice may be beneficial for the task of detecting microcalcifications at an MGD suitable for breast cancer screening., (© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp-Davis-Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm 3 ). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods ( p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction ( p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation.

• Phase-contrast CT addresses poor soft-tissue contrast in X-ray breast imaging. • Interactive 3D breast virtual visualizations available at different resolutions. • Spatial resolution bridging from radiological level to virtual histology level. • 2D and 3D images may be combinedly used in a multidisciplinary clinical context. • Discernibility of breast structures and microcalcifications is enhanced. Phase-contrast imaging techniques address the issue of poor soft-tissue contrast encountered in traditional X-ray imaging. This can be accomplished with the propagation-based phase-contrast technique by employing a coherent photon beam, which is available at synchrotron facilities, as well as long sample-to-detector distances. This study demonstrates the optimization of propagation-based phase-contrast computed tomography (CT) techniques for multiscale X-ray imaging of the breast at the Elettra synchrotron facility (Trieste, Italy). Two whole breast mastectomy samples were acquired with propagation-based breast-CT using a monochromatic synchrotron beam at a pixel size of 60 µm. Paraffin-embedded blocks sampled from the same tissues were scanned with propagation-based micro-CT imaging using a polychromatic synchrotron beam at a pixel size of 4 µm. Images of both methodologies and of the same sample were spatially registered. The resulting images showed the transition from whole breast imaging with propagation-based breast-CT methodology to virtual histology with propagation-based micro-CT imaging of the same sample. Additionally, conventional histological images were matched to virtual histology images. Phase-contrast images offer a high resolution with low noise, which allows for a highly precise match between virtual and conventional histology. Furthermore, those techniques allow a clear discernment of breast structures, lesions, and microcalcifications, being a promising clinically-compatible tool for breast imaging in a multiscale approach, to either assist in the detection of cancer in full volume breast samples or to complement structure identification in paraffin-embedded breast tissue samples. [ABSTRACT FROM AUTHOR]

Objective. This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains. Approach. The proposed objective function, named beta loss function, is based on the fact that x-ray-based breast images follow the power-law spectrum. Accordingly, the exponent of the power-law spectrum (beta value) for breast CT images is approximately two. The beta loss function is defined in terms of L1 distance between the beta value of synthetic images and validation samples. To compare the GAN performances for transfer learning datasets from different image domains, ImageNet and anatomical noise images are used in the transfer learning dataset. We employ styleGAN2 as the backbone network and add the proposed beta loss function. The patient-derived breast CT dataset is used as the training and validation dataset; 7355 and 212 images are used for network training and validation, respectively. We use the beta value evaluation and Fréchet inception distance (FID) score for quantitative evaluation. Main results. For qualitative assessment, we attempt to replicate the images from the validation dataset using the trained GAN. Our results show that the proposed beta loss function achieves a more similar beta value to real images and a lower FID score. Moreover, we observe that the GAN pretrained with anatomical noise images achieves better equality than ImageNet for beta value evaluation and FID score. Finally, the beta loss function with anatomical noise as the transfer learning dataset achieves the lowest FID score. Significance. Overall, the GAN using the proposed beta loss function with anatomical noise images as the transfer learning dataset provides the lowest FID score among all tested cases. Hence, this work has implications for developing GAN-based breast image synthesis methods for medical imaging applications., (© 2023 Institute of Physics and Engineering in Medicine.)

Objective. In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer using transfer learning (TL-CNN) to reduce the required number of training samples. Approach. To train model observers, we generate simulated breast CT image volumes that are reconstructed using the FeldkampDavisKress algorithm with a ramp and Hanning-weighted ramp filter. The observer performance is evaluated on the background-known-statistically (BKS)/signal-known-exactly task with a spherical signal, and the BKS/signal-known-statistically task with random signal generated by the stochastic grown method. We compare the detectability of the CNN-based model observer with that of conventional linear model observers for multi-slice images (i.e. a multi-slice channelized Hotelling observer (CHO) and volumetric CHO). We also analyze the detectability of the TL-CNN for different numbers of training samples to examine its performance robustness to a limited number of training samples. To further analyze the effectiveness of transfer learning, we calculate the correlation coefficients of filter weights in the CNN-based multi-slice model observer. Main results. When using transfer learning for the CNN-based multi-slice ideal model observer, the TL-CNN provides the same performance with a 91.7% reduction in the number of training samples compared to that when transfer learning is not used. Moreover, compared to the conventional linear model observer, the proposed CNN-based multi-slice model observers achieve 45% higher detectability in the signal-known-statistically detection tasks and 13% higher detectability in the SKE detection tasks. In correlation coefficient analysis, it is observed that the filters in most of the layers are highly correlated, demonstrating the effectiveness of the transfer learning for multi-slice model observer training. Significance. Deep learning-based model observers require large numbers of training samples, and the required number of training samples increases as the dimensions of the image (i.e. the number of slices) increase. With applying transfer learning, the required number of training samples is significantly reduced without performance drop., (© 2023 Institute of Physics and Engineering in Medicine.)

Purpose: To intra-individually compare patient comfort of spiral breast computed tomography (SBCT) versus digital mammography (DM).Method: This prospective IRB approved study included 79 patients undergoing both SBCT and DM for the assessment of BI-RADS 4 - 6 lesions. Following SBCT and DM patients answered a standardized questionnaire regarding "Overall patient comfort" and "Pain" on a 5-point Likert Scale. On the same Likert Scale, experienced radiologic technicians rated the workflow of the SBCT regarding patients' "Mobility", ease of patient "Positioning", patients' adherence to the examination ("Compliance") and expected image quality. Visibility of fibroglandular tissue in SBCT was independently rated by two breast radiologists on a 10-point Likert Scale. Subgroups stratified by menopausal status and body mass index (BMI) were analyzed.Results: Patients reported significantly lower pain during SBCT (4.73 ± 0.57) compared to DM (4.09 ± 0.90; P < 0.01). This effect was independent from BMI. However, pain reduction by SBCT was most pronounced in premenopausal (SBCT vs. DM: 4.79 ± 0.50 vs. 3.89 ± 0.99) compared to postmenopausal patients (4.71 ± 0.77 vs. 4.20 ± 0.89). Overall patient comfort in premenopausal patients tended to be higher in SBCT compared to DM (P = 0.08). Radiologic technicians rated the SBCT procedure generally as positive (average: 4.62 ± 0.56). Coverage of fibroglandular tissue in SBCT was generally high (9.82 ± 0.43) and interrater agreement was good (κ = 0.77).Conclusions: Patients experience less pain during spiral breast computed tomography compared to DM, especially in premenopausal women. Imaging is feasible at a high level of anatomical breast coverage and without problems with the clinical workflow. [ABSTRACT FROM AUTHOR]

Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image 'patchiness', the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation., (© 2022 Institute of Physics and Engineering in Medicine.)

Objective. A dedicated cone-beam breast computed tomography (BCT) using a high-resolution, low-noise detector operating in offset-detector geometry has been developed. This study investigates the effects of varying detector offsets and image reconstruction algorithms to determine the appropriate combination of detector offset and reconstruction algorithm. Approach. Projection datasets (300 projections in 360°) of 30 breasts containing calcified lesions that were acquired using a prototype cone-beam BCT system comprising a 40 × 30 cm flat-panel detector with 1024 × 768 detector pixels were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. The projection datasets were retrospectively truncated to emulate cone-beam datasets with sinograms of 768×768 and 640×768 detector pixels, corresponding to 5 cm and 7.5 cm lateral offsets, respectively. These datasets were reconstructed using the FDK algorithm with appropriate weights and an ASD-POCS-based Fast, total variation-Regularized, Iterative, Statistical reconstruction Technique (FRIST), resulting in a total of 4 offset-detector reconstructions (2 detector offsets × 2 reconstruction methods). Signal difference-to-noise ratio (SDNR), variance, and full-width at half-maximum (FWHM) of calcifications in two orthogonal directions were determined from all reconstructions. All quantitative measurements were performed on images in units of linear attenuation coefficient (1/cm). Results. The FWHM of calcifications did not differ ( P  > 0.262) among reconstruction algorithms and detector formats, implying comparable spatial resolution. For a chosen detector offset, the FRIST algorithm outperformed FDK in terms of variance and SDNR ( P  < 0.0001). For a given reconstruction method, the 5 cm offset provided better results. Significance. This study indicates the feasibility of using the compressed sensing-based, FRIST algorithm to reconstruct sinograms from offset-detectors. Among the reconstruction methods and detector offsets studied, FRIST reconstructions corresponding to a 30 cm × 30 cm with 5 cm lateral offset, achieved the best performance. A clinical prototype using such an offset geometry has been developed and installed for clinical trials., (© 2022 Institute of Physics and Engineering in Medicine.)