PurposeContrast-enhanced digital breast tomosynthesis (CEDBT) highlights breast tumors with neo-angiogenesis. A recently proposed CEDBT system with a dual-layer (DL) flat-panel detector enables simultaneous acquisition of high-energy (HE) and low-energy (LE) projection images with a single exposure, which reduces acquisition time and eliminates motion artifacts. However, x-ray scatter degrades image quality and lesion detectability. We propose a practical method for accurate and robust scatter correction (SC) for DL-CEDBT.ApproachThe proposed hybrid SC method combines the advantages of a two-kernel iterative convolution method and an empirical interpolation strategy, which accounts for the reduced scatter from the peripheral breast region due to thickness roll-off and the scatter contribution from the region outside the breast. Scatter point spread functions were generated using Monte Carlo simulations with different breast glandular fractions, compressed thicknesses, and projection angles. Projection images and ground truth scatter maps of anthropomorphic digital breast phantoms were simulated to evaluate the performance of the proposed SC method and three other kernel- and interpolation-based methods. The mean absolute relative error (MARE) between scatter estimates and ground truth was used as the metric for SC accuracy.ResultsDL-CEDBT shows scatter characteristics different from dual-shot, primarily due to the two energy peaks of the incident spectrum and the structure of the DL detector. Compared with the other methods investigated, the proposed hybrid SC method showed superior accuracy and robustness, with MARE of ∼3.1% for all LE and HE projection images of different phantoms in both cranial-caudal and mediolateral-oblique views. After SC, cupping artifacts in the dual-energy image were removed, and the signal difference-to-noise ratio was improved by 82.0% for 8 mm iodine objects.ConclusionsA practical SC method was developed, which provided accurate and robust scatter estimates to improve image quality and lesion detectability for DL-CEDBT.
PurposeAccurate detection of microcalcifications (μCalcs) is crucial for the early detection of breast cancer. Some clinical studies have indicated that digital breast tomosynthesis (DBT) systems with a wide angular range have inferior μCalc detectability compared with those with a narrow angular range. This study aims to (1) provide guidance for optimizing wide-angle (WA) DBT for improving μCalcs detectability and (2) prioritize key optimization factors.ApproachAn in-silico DBT pipeline was constructed to evaluate μCalc detectability of a WA DBT system under various imaging conditions: focal spot motion (FSM), angular dose distribution (ADS), detector pixel pitch, and detector electronic noise (EN). Images were simulated using a digital anthropomorphic breast phantom inserted with 120 μm μCalc clusters. Evaluation metrics included the signal-to-noise ratio (SNR) of the filtered channel observer and the area under the receiver operator curve (AUC) of multiple-reader multiple-case analysis.ResultsResults showed that FSM degraded μCalcs sharpness and decreased the SNR and AUC by 5.2% and 1.8%, respectively. Non-uniform ADS increased the SNR by 62.8% and the AUC by 10.2% for filtered backprojection reconstruction with a typical clinical filter setting. When EN decreased from 2000 to 200 electrons, the SNR and AUC increased by 21.6% and 5.0%, respectively. Decreasing the detector pixel pitch from 85 to 50 μm improved the SNR and AUC by 55.6% and 7.5%, respectively. The combined improvement of a 50 μm pixel pitch and EN200 was 89.2% in the SNR and 12.8% in the AUC.ConclusionsBased on the magnitude of impact, the priority for enhancing μCalc detectability in WA DBT is as follows: (1) utilizing detectors with a small pixel pitch and low EN level, (2) allocating a higher dose to central projections, and (3) reducing FSM. The results from this study can potentially provide guidance for DBT system optimization in the future.
We report on an optimization-based image reconstruction algorithm for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm is designed to enable quantitative imaging of Iodine-based contrast agent by mitigating the depth blur artifact. The depth blurring is controlled by exploiting gradient sparsity of the contrast agent distribution. We find that minimization of directional total variation (TV) is particularly effective at exploiting gradient sparsity for the DBT scan configuration. In this initial work, the contrast agent imaging is performed by reconstructing images from DBT data acquired at source potentials of 30- and 49-kV, followed by weighted subtraction to suppress background glandular structure and isolate the contrast agent distribution. The algorithm is applied to DBT data, acquired with a Siemens Mammomat scanner, of a structured breast phantom with Iodine contrast agent inserts. Results for both in-plane and transverse-plane imaging for directional TV minimization are presented alongside images reconstructed by filtered back-projection for reference. It is seen that directional TV is able to substantially reduce depth blur for the Iodine-based contrast agent objects.
A direct-indirect dual-layer flat-panel-detector (DI-DLFPD) was recently proposed to mitigate motion artifacts in dual-energy (DE) breast imaging. In this work, we developed a cascaded linear system model to predict the imaging performance of the DI-DLFPD and applied it to the lesion detection task in contrast-enhanced digital mammography. CsI scintillator was used in the back layer (BL) detector to acquire high-energy (HE) images, and the random variations of optical gain and blur in CsI were considered in noise propagation. The optical parameters were estimated from single x-ray imaging results previously obtained by our lab. Pre-sampling modular transfer function and normalized noise power spectrum in low-energy and HE images were modeled to derive spatial-frequency-dependent signal and noise in DE images. The detectability index (d') of iodinated objects was calculated as a function of the thicknesses of the Ag filter, front layer (FL) a-Se detector, and BL CsI scintillator. Reasonable agreement between modeled performance and measurements demonstrated the feasibility of applying the model to predict imaging performance. The results showed that employing a 100μm thick Ag filter with a 200μm thick a-Se in FL and a 400μm thick CsI in BL yields d' close to the optimum for this task. In the future, we will extend this model to more tasks like microcalcification detection to guide the optimization of the DI-DLFPD breast imaging system design.
Contrast enhanced digital mammography (CEDM) and contrast enhanced digital breast tomosynthesis (CEDBT) highlight the uptake of iodinated contrast agent in breast lesions in dual-energy (DE) subtracted images. In conventional methods, low-energy (LE) and high-energy (HE) images are acquired with two separate exposures, referred to as the dual-shot (DS) method. Patient motion between two exposures could result in residual breast tissue structure in DE images, which reduces iodinated lesion conspicuity. We propose to use a direct-indirect dual-layer flat-panel detector (DI-DLFPD) to acquire LE and HE images simultaneously, thereby eliminating the motion artifact. The DI-DLPFD system comprise a k-edge filter at the tube output, an amorphous-selenium (a-Se) direct detector as the front layer, and a cesium iodide (CsI) indirect detector as the back layer. This study presents the CEDM and CEDBT results from the first prototype DI-DLFPD. For comparison, CEDM and CEDBT images were also acquired with DS technique, with simulated 2mm patient motion between LE and HE exposures. The figure of merit (FOM) used to assess iodinated object detectability is the dose normalized signal difference to noise ratio squared. Our results showed that DI-DLFPD images exhibit complete cancellation of breast tissue structure, which led to significant improvement in iodinated object detectability and more accurate iodine quantification, compared to DS images with simulated patient motion.
KEYWORDS: Digital breast tomosynthesis, Breast, Autoregressive models, Imaging systems, Quantum reading, Diagnostics, Breast density, Quantum noise, Tissues, Cancer detection
Digital breast tomosynthesis (DBT) has been shown to improve both sensitivity and specificity for breast cancer detection compared to full-field digital mammography. However, its performance could be limited for patients with dense breasts. Clinical DBT systems vary in their system designs, one of which is the acquisition angular range (AR), which leads to varied performance for different imaging tasks. In this study, we aim to compare DBT systems with different AR. We used a previously validated cascaded linear system model to investigate the dependence of in-plane breast structural noise (BSN) and detectability of masses on AR. We conducted a pilot clinical study to compare the lesion conspicuity between clinical DBT systems with the narrowest and the widest AR. Patients called back for diagnostic imaging on suspicious findings were imaged with both narrow-angle (NA) and wide-angle (WA) DBT. We analyzed the BSN for clinical images using noise power spectrum (NPS) analysis. A 5-point Likert scale was used in the reader study to compare the lesion conspicuity. Our theoretical calculation results show that increasing AR leads to reduced BSN and improved mass detectability. The NPS analysis on clinical images shows the lowest BSN for WA DBT. The WA DBT provides better lesion conspicuity for masses and asymmetries and shows a greater advantage for non-microcalcification lesions in dense breasts. The NA DBT provides better characterizations for microcalcifications. The WA DBT can downgrade false-positive findings seen on NA DBT. In conclusion, WA DBT could improve the detection of masses and asymmetries for patients with dense breasts.
KEYWORDS: Breast, Convolution, Interpolation, Digital breast tomosynthesis, Monte Carlo methods, X-rays, Error analysis, Imaging systems, X-ray detectors, Image compression
Contrast-enhanced digital breast tomosynthesis (CEDBT) utilizes iodinated contrast agents and spectral imaging techniques to highlight breast tumors with neo-angiogenesis. Conventional dual-shot (DS) CEDBT suffers from motion artifacts due to patient motion between two separate x-ray exposures for the sequential acquisitions of high- and low-energy (HE and LE) images. To mitigate motion artifacts, a new CEDBT system was designed with a direct-indirect dual-layer (DL) detector. It enables simultaneous acquisition of HE and LE images with a single exposure. Our previous study has demonstrated the feasibility of the DL detector to cancel normal breast tissue and provide lesion contrast comparable to DS. Due to the absence of anti-scatter grids, both DS- and DL-CEDBT suffer image quality degradation from scattered radiation, which introduces cupping artifacts and reduces lesion contrast. In this study, we are proposing a hybrid scatter correction method, which combines the straightforward kernel-based iterative convolution method and the empirical interpolation method. The hybrid method was applied to Monte Carlo (MC) simulated DL-DBT projection images of anthropomorphic digital breast phantoms of varying glandularity and compressed thickness. Results showed that the hybrid method improves scatter estimation accuracy compared to the single-kernel iterative convolution method. The hybrid method was applied to a clinical DS-CEDBT case for scatter correction. The results showed reduced cupping artifacts and improved lesion contrast.
KEYWORDS: Breast, Monte Carlo methods, Education and training, Muscles, Polymethylmethacrylate, Lead, Convolutional neural networks, Chest, Digital breast tomosynthesis, Picosecond phenomena
PurposeScatter radiation in contrast-enhanced digital breast tomosynthesis (CEDBT) reduces the image quality and iodinated lesion contrast. Monte Carlo simulation can provide accurate scatter estimation at the cost of computational burden. A model-based convolutional method trades off accuracy for processing speed. The purpose of this study is to develop a fast and robust deep-learning (DL) convolutional neural network (CNN)-based scatter correction method for CEDBT.ApproachProjection images and scatter maps of digital anthropomorphic breast phantoms were generated using Monte Carlo simulations. Experiments were conducted to validate the simulated scatter-to-primary ratio (SPR) at different locations of a breast phantom. Simulated projection images were used for CNN training and testing. Two separate U-Nets [low-energy (LE)-CNN and high-energy (HE)-CNN] were trained for LE and HE spectrum, respectively. CNN-based scatter correction was applied to a clinical case with a malignant iodinated mass to evaluate the influence on the lesion detection.ResultsThe average and standard deviation of mean absolute percentage error of LE-CNN and HE-CNN estimated scatter map are 2 % ± 0.4 % and 2.4 % ± 0.8 % , respectively. For clinical cases, the lesion signal difference to noise ratio average improvement was 190% after CNN-based scatter correction. To conduct scatter correction on clinical CEDBT images, the whole process of loading CNNs parameters and scatter correction for LE and HE images took <4 s, with 9 GB GPU computational cost. The SPR variation across the breast agrees between experimental measurements and Monte Carlo simulations.ConclusionsWe developed a CNN-based scatter correction method for CEDBT in both CC view and mediolateral-oblique view with high accuracy and fast speed.
We proposed a direct-indirect dual layer detector combination for CEDM and CEDBT to eliminate patient motion artifacts in dual energy images. Both physical experiments and Monte Carlo simulations were conducted to compare dual shot technique with the dual layer technique. The proposed direct-indirect dual layer detector combination uses a 200 µm direct a-Se as the front detector for LE images and a 400~1000 µm indirect CsI as the back detector for HE images. Results showed similar breast tissue cancellation between dual layer and dual shot techniques. Dual layer technique with direct-indirect detector has higher SDNR/MGD compared with dual shot technique.
Cancerous masses are more conspicuous in wide-angle digital breast tomosynthesis (DBT) due to better depth resolution and tissue separation, while the detection of subtle microcalcifications (MC) is challenging. This study aims at providing guidance for a new DBT system design to enhance lesion detection through variable angular dose image acquisition and improved detector performance. Digital breast phantoms were generated, and projection images were simulated using the FDA VICTRE tool 1. Simulated masses and clusters of MC were inserted at different locations of the digital breast phantom. Projection images were simulated with 28 kVp W/Rh energy spectrum, 200 μm thick amorphous selenium (a- Se) direct active matrix flat panel imager (AMFPI), and 50° angular range with 25 views. The impact of a-Se detector performance, i.e., complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) versus AMFPI, with different electronic noise and pixel pitches, was investigated. Even and uneven angular dose distribution schemes (ADS) were designed to test the combined effect of image acquisition setting and detector performance on lesion detection. 2D Filtered Channel observer (FCO) and 3D Channelized Hotelling observer (CHO) were employed to evaluate lesion detectability under different scenarios. The results demonstrated the improvement of direct conversion CMOS APS on the detectability of small MC clusters by reducing FSM, improving DQE at a lower dose, and improve the sharpness of reconstructed MC. The uneven dose distribution benefits the detection of MC without compromising mass detection in FBP reconstructed volumes with slice thickness filter for wide angle DBT.
Breast density (BD) has been shown to be an independent risk factor for breast cancer, and it can be quantified by measuring the volumetric breast density (VBD). The longitudinal change in VBD has been used as a biomarker to assess the effect of chemoprevention drug for breast cancer risk reduction. However, the reproducibility of VBD measurement is hindered by breast compression, which causes temporal changes in the distribution of breast tissues, making it difficult to assess small longitudinal changes in VBD. Dual energy digital breast tomosynthesis (DE DBT) may provide more reproducible VBD measurements over different breast compressions. In this study, we aim to evaluate the reproducibility of VBD measurement using DE DBT. Digital breast phantoms were generated using a virtual clinical trial software (VICTRE) and were compressed multiple times into different clinical-relevant compressed breast thicknesses to emulate the change in repeated imaging of the same breast. The compressed thickness difference of 10 mm exaggerates the variation seen in clinical practice. DE DBT projection images of the phantoms were generated by Monte-Carlo simulations and included quantum noise and scatter radiation. A previously developed scatter correction method and a DE material decomposition technique were applied to obtain the BD map and calculate VBD. Our results show that DE DBT can provide reproducible VBD measurements for breasts under different compressions with the average absolute discrepancy of 2.3% ± 1.1% between two repeated measurements for all phantoms. The largest discrepancy is 3.9% for the extreme case with 20 mm compressed thickness difference.
The image quality of contrast-enhanced digital breast tomosynthesis (CEDBT) is degraded by scatter radiation. Scatter correction can improve the object contrast and reduce the cupping artifacts, but the image quality is limited by the increased image noise. In this study we investigate the effect of scatter correction on image noise in CEDBT. A scatter correction method based on image convolution with scatter-to-primary ratio kernel was applied. We analyzed the noise power spectrum (NPS) for CEDBT projection images before and after scatter correction using CIRS breast phantoms and evaluated the signal-difference-to-noise ratio (SDNR) of the iodine objects after image reconstruction. We applied image filtering to reduce image noise after scatter correction for phantom and clinical images. A deep learning based denoising technique was applied to further reduce the image noise for clinical images. Our results show that the scatter correction increases the image noise in dual-energy subtracted images, and the improvement in SDNR from scatter correction is limited. Noise reduction applied after scatter removal can regain the benefit in SDNR from scatter correction and further improve the visualization of contrast enhancement in CEDBT.
Purpose: There are currently five FDA approved commercial digital breast tomosynthesis (DBT) systems, all of which have varying geometry and exposure techniques. The aim of this work was to determine if an anthropomorphic breast phantom could be used to systematically compare performance of DBT, full field digital mammography (FFDM) and synthetic mammography (SM) across the systems. Methods: An anthropomorphic breast phantom was created through inkjet printing containing printed masses. The phantom was imaged using automatic exposure control (AEC) settings for that system. Thus, all phantom acquisition settings, and subsequent radiation dose levels, were dictated from the manufacturer settings. A four alternative forced choice reader study was conducted to assess reader performance. Results: Performance in detecting masses was higher with DBT than with FFDM or SM. The difference in proportion correct (PC) was statistically significant for most cases. Additionally, PC of the DBT systems trended with increased gantry span with lowest PC from Hologic and Fuji (both 15°), then both GE systems (25°), and highest for Siemens (50°). Conclusions: A phantom containing masses was imaged on five commercially available DBT systems across 3 states. A 4AFC study was performed to assess performance with FFDM, DBT, and SM across all systems. Overall detection was highest using DBT, with improvement as the gantry span increased. This study is the first of its kind to use an inkjet based physical anthropomorphic phantom to assess performance of all five commercially available breast imaging systems.
KEYWORDS: Breast, Monte Carlo methods, Digital breast tomosynthesis, Dual energy imaging, Point spread functions, Computer simulations, Imaging systems, Convolutional neural networks
Dual energy contrast-enhanced digital breast tomosynthesis (CEDBT) uses weighted subtraction of two energy spectra to highlight tumor angiogenesis with uptake of iodinated contrast agent. The high energy scan contains more severe scatter radiation than regular low energy DBT. The purpose of this study is to develop a convolutional neural network (CNN) based scatter correction method for dual energy CEDBT in both craniocaudal (CC) view and mediolateral oblique (MLO) view. Anthropomorphic digital breast phantoms with various glandularity and 3D shape were generated using the VICTRE software tool developed by the FDA. The pectoralis muscle layer was inserted into the phantoms for MLO view. Projection images with and without scatter radiation were simulated using Monte Carlo (MC) simulation code of VICTRE, meeting the prototype Siemens Mammomat Inspiration CEDBT system with 300 μm thick a-Se detector, 25 projections within 46-degree angular range. Scatter radiation ground truth was generated from MC simulated projection images to train CNN. Two separate U-net CNNs were trained to predict scatter radiation maps. Mean absolute percentage error (MAPE) was used as the loss function. The average MAPE of this method is less than 3 % from the ground truth of MC simulation. The proposed scatter correction method was then applied to clinical cases, demonstrating the reduction of cupping artifact and the improvement in contrast object conspicuity.
KEYWORDS: Digital breast tomosynthesis, CMOS sensors, Breast, Monte Carlo methods, Visibility, Tissues, Modulation transfer functions, Image resolution, Signal detection
Wide-angle digital breast tomosynthesis (DBT) gives better depth resolution and tissue separation while the microcalcification (MC) detectability is impacted by many factors, such as detector performance, focal spot motion (FSM), increased noise due to scatter radiation, angular dose distribution and image reconstruction methods. This study aims at developing an in-silico experimental pipeline to compare these factors’ influence on the MC detectability, so that new system design can be proposed to improve the performance. The VICTRE tool developed by Food and Drug Administration was used to generate a 49 mm-thick anthropomorphic digital breast phantom and the projection images using Monte Carlo simulation. We inserted 38 MC clusters with size of 120 μm at 40 mm from the bottom of the phantom. The Monte Carlo simulation used parameters of a clinical wide-angle DBT system, with 25 projections over 50-degree angular range, and 28 kVp W/Rh energy spectrum. Projection images were simulated under different scenarios: uniform/nonuniform angular dose distribution, with/without FSM of 2 mm, with/without scatter radiation, and TFT/CMOS detector types. A four-alternative forced choice (4AFC) methodology was employed to evaluate the MC detectability. The percentage correct and visibility score under all scenarios were compared. The results show that MC detectability decreases with the presence of either increased noise due to scattered radiation, or image blur due to FSM. Nonuniform angular dose distribution improves the detectability of MCs when using FBP reconstruction with narrow slice thickness filter. MC sharpness is improved with CMOS detector with smaller pixel size and lower electronic noise.
Dense breast tissue has been shown to reduce the sensitivity and specificity of breast cancer detection. Estimation of breast density (BD) in digital breast tomosynthesis (DBT) may provide masking risk assessment in quasi three dimensions (3D). Dual energy (DE) mammography has been used for breast tissue decomposition, which may provide more accurate BD map for DBT projections. This study aims to investigate the feasibility of DE DBT for volumetric BD estimation and 3D masking risk assessment. A compressed digital breast phantom (with ground truth for BD) was simulated using a virtual clinical trial software (VICTRE). Low and high energy projection images of the phantom were simulated emulating the Siemens Mammomat Inspiration DBT system. DE decomposition was performed for each projection angle using a library of calibrated DE attenuation data for various combinations of breast thickness and glandularity. The resulted BD maps for all projection angles were used for simple-back-projection image reconstruction, and the dense regions for each image slice were segmented based on the reconstructed voxel values. This method was applied to clinical images from DE DBT acquisition after scatter correction. Our results show that DE decomposition provides consistent BD map for each projection angle and throughout the entire breast (including the periphery with thickness roll-off), and consistent measurements of the total breast volume, fibroglandular tissue volume and volumetric BD. The 3D segmented fibroglandular tissue map can be used to assess the masking risk for the DBT image volume.
Contrast-Enhanced Digital Breast Tomosynthesis (CEDBT) provides quasi three-dimensional contrast enhancement of breast lesions and has been investigated for breast cancer detection and lesion assessment. The acquisition geometry of CEDBT may affect its ability to detect and assess contrast-enhanced lesions. In this study, we investigate the effects of angular range of CEDBT on lesion margin assessment. The CIRS BR3D phantom with iodine inserts was imaged for four angular ranges between 15 and 45 degrees with same total glandular dose using a prototype CEDBT system. The artifact spread functions of iodine objects with various sizes were measured. The detectability of iodine objects overlaid in the depth direction with different separation distances was evaluated using signal-difference-to-noise ratio. Clinical images of malignant lesions were acquired with 25 projections over approximately 50 degrees, and CEDBT for various angular ranges were generated using a subset or all of the projection images and were assessed for lesion margins. Our results show that increasing angular range of CEDBT improves the separation of overlapping iodine signals in phantom images, and the margins of malignant mass lesions are better identified. In conclusion, CEDBT with wide angular range may improve lesion characterizations, e.g. lesion size, morphology and location, and provide better performance than contrast enhanced digital mammography (CEDM) for applications such as guidance of biopsy and evaluation of treatment response.
Contrast-enhanced digital mammography (CEDM) reveals neovasculature of breast lesions in a two-dimensional contrast enhancement map. Contrast-enhanced digital breast tomosynthesis (CEDBT) provides contrast enhancement in three dimensions, which may improve lesion characterization and localization. We aim to compare CEDM and CEDBT for lesion assessment. Women with breast imaging-reporting and data system 4 or 5 suspicious breast lesion(s) were recruited in our study and were imaged with CEDM and CEDBT in succession under one breast compression. Two radiologists assessed CEDM and CEDBT with both images displayed side-by-side and compared (1) contrast enhancement of lesions and (2) lesion margin using a five-point scale ranging from −2 (CEDM much better) to +2 (CEDBT much better). Biopsy identified 19 malignant lesions with contrast enhancement. Our results show that CEDBT provides better lesion margins than CEDM with limited reduction in contrast enhancement. CEDBT delivers less radiation dose compared to CEDM + DBT. Synthetic CEDM can be generated from CEDBT data and provides lesion contrast enhancement comparable to CEDM. CEDBT has potential for clinical applications, such as treatment response monitoring and guidance for biopsy.
The detection of cancerous mass lesions using digital breast tomosynthesis (DBT) has been shown to be limited in patients with dense breasts. Detection may potentially be improved by increasing the DBT angular range (AR), which reduces breast structural noise and increases object contrast in the reconstructed slice. We investigate the impact of DBT AR on the detection of masses in a simulation study using a cascaded linear system model (CLSM) for DBT. We compare the mass conspicuity between wide- and narrow-AR DBT system in a clinical pilot study. The simulation results show reduced in-plane breast structural noise and increased in-plane detectability of masses with increasing AR. The clinical results show that masses are more conspicuous in wide-AR DBT than narrow-AR DBT. Our study indicates that the detection of mass lesions in dense breasts can be improved by increasing DBT AR.
Contrast-Enhanced Digital Breast Tomosynthesis (CEDBT) provides a three-dimensional (3D) contrast-enhancement map with co-registered anatomical information from low-energy DBT. It combines the benefits from Contrast-Enhanced Digital Mammography (CEDM) and Digital Breast Tomosynthesis (DBT), and may improve breast cancer detection and assessment of lesion morphology. We investigate the efficacy of CEDBT in the assessment of lesion contrast enhancement and margin identification, and evaluate the dose efficiency. We generate synthetic CEDM images from CEDBT data, similar to synthesis of 2D mammograms from DBT data, which may facilitate overall lesion assessment without additional radiation dose. Preliminary results from a patient study show that CEDBT depicts lesion margins better compared to CEDM, while the contrast-enhancement level for in-plane slice is not as high as in CEDM. CEDBT delivers less radiation dose compared to CEDM + DBT. Synthetic CEDM is able to provide lesion contrast-enhancement level comparable to CEDM.
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