Non-Isocentric Geometry for Next-Generation Tomosynthesis With Super-Resolution.

Our lab at the University of Pennsylvania (UPenn) is investigating novel designs for digital breast tomosynthesis. We built a next-generation tomosynthesis system with a non-isocentric geometry (superior-to-inferior detector motion). This paper examines four metrics of image quality affected by this design. First, aliasing was analyzed in reconstructions prepared with smaller pixelation than the detector. Aliasing was assessed with a theoretical model of r -factor, a metric calculating amplitudes of alias signal relative to input signal in the Fourier transform of the reconstruction of a sinusoidal object. Aliasing was also assessed experimentally with a bar pattern (illustrating spatial variations in aliasing) and 360°-star pattern (illustrating directional anisotropies in aliasing). Second, the point spread function (PSF) was modeled in the direction perpendicular to the detector to assess out-of-plane blurring. Third, power spectra were analyzed in an anthropomorphic phantom developed by UPenn and manufactured by Computerized Imaging Reference Systems (CIRS), Inc. (Norfolk, VA). Finally, calcifications were analyzed in the CIRS Model 020 BR3D Breast Imaging Phantom in terms of signal-to-noise ratio (SNR); i.e., mean calcification signal relative to background-tissue noise. Image quality was generally superior in the non-isocentric geometry: Aliasing artifacts were suppressed in both theoretical and experimental reconstructions prepared with smaller pixelation than the detector. PSF width was also reduced at most positions. Anatomic noise was reduced. Finally, SNR in calcification detection was improved. (A potential trade-off of smaller-pixel reconstructions was reduced SNR; however, SNR was still improved by the detector-motion acquisition.) In conclusion, the non-isocentric geometry improved image quality in several ways.

Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis.

OBJECTIVES: A virtual clinical trial (VCT) method is proposed to determine the limit of calcification detection in tomosynthesis. METHODS: Breast anatomy, focal findings, image acquisition, and interpretation (n = 14 readers) were simulated using screening data (n = 660 patients). Calcifications (0.2-0.4 mm3) were inserted into virtual breast phantoms. Digital breast tomosynthesis (DBT) acquisitions were simulated assuming various acquisition geometries: source motion (continuous and step-and-shoot), detector element size (140 and 70 µm), and reconstructed voxel size (35-140 µm). VCT results were estimated using multiple-reader multiple-case analyses and d' statistics. Signal-to-noise (SNR) analyses were also performed using BR3D phantoms. RESULTS: Source motion and reconstructed voxel size demonstrated significant changes in the performance of imaging systems. Acquisition geometries that use 70 µm reconstruction voxel size and step-and-shoot motion significantly improved calcification detection. Comparing 70 with 100 µm reconstructed voxel size for step-and-shoot, the ΔAUC was 0.0558 (0.0647) and d' ratio was 1.27 (1.29) for 140 µm (70 µm) detector element size. Comparing step-and-shoot with a continuous motion for a 70 µm reconstructed voxel size, the ΔAUC was 0.0863 (0.0434) and the d' ratio was 1.40 (1.19) for 140 µm (70 µm) detector element. Small detector element sizes (e.g., 70 µm) did not significantly improve detection. The SNR results with the BR3D phantom show that calcification detection is dependent upon reconstructed voxel size and detector element size, supporting VCT results with comparable agreement (ratios: d' = 1.16 ± 0.11, SNR = 1.34 ± 0.13). CONCLUSION: DBT acquisition geometries that use super-resolution (smaller reconstructed voxels than the detector element size) combined with step-and-shoot motion have the potential to improve the detection of calcifications. CLINICAL RELEVANCE: Calcifications may not always be discernable in tomosynthesis because of differences in acquisition and reconstruction methods. VCTs can identify strategies to optimize acquisition and reconstruction parameters for calcification detection in tomosynthesis, most notably through super-resolution in the reconstruction. KEY POINTS: • Super-resolution improves calcification detection and SNR in tomosynthesis; specifically, with the use of smaller reconstruction voxels. • Calcification detection using step-and-shoot motion is superior to that using continuous tube motion. • A detector element size of 70 µm does not provide better detection than 140 µm for small calcifications at the threshold of detectability.

Assessing satisfaction of search in virtual mammograms for experienced and novice searchers.

Purpose: Satisfaction of search (SOS) is a phenomenon where searchers are more likely to miss a lesion/target after detecting a first lesion/target. Here, we investigated SOS for masses and calcifications in virtual mammograms with experienced and novice searchers to determine the extent to which: (1) SOS affects breast lesion detection, (2) similarity between lesions impacts detection, and (3) experience impacts SOS rates. Approach: The open virtual clinical trials framework was used to simulate the breast anatomy of patients, and up to two simulated masses and/or single-calcifications were inserted into the breast models. Experienced searchers (residents, fellows, and radiologists with breast imaging experience) and novice searchers (undergraduates who had no breast imaging experience) were instructed to search for up to two lesions (masses and calcifications) per image. Results: 2×2 mixed factors analysis of variances (ANOVAs) were run with: (1) single versus second lesion hit rates, (2) similar versus dissimilar second-lesion hit rates, and (3) similar versus dissimilar second-lesion response times as within-subject factors and experience as the between subject's factor. The ANOVAs demonstrated that: (1) experienced and novice searchers made a significant amount of SOS errors, (2) similarity had little impact on experienced searchers, but novice searchers were more likely to miss a dissimilar second lesion compared to when it was similar to a detected first lesion, (3) experienced and novice searchers were faster at finding similar compared to dissimilar second lesions. Conclusions: We demonstrated that SOS is a significant cause of lesion misses in virtual mammograms and that reader experience impacts detection rates for similar compared to dissimilar abnormalities. These results suggest that experience may impact strategy and/or recognition with theoretical implications for determining why SOS occurs.

Impact of Tomosynthesis Acquisition on 3D Segmentations of Breast Outline and Adipose/Dense Tissue with AI: A Simulation-Based Study.

Digital breast tomosynthesis (DBT) reconstructions introduce out-of-plane artifacts and false-tissue boundaries impacting the dense/adipose and breast outline (convex hull) segmentations. A virtual clinical trial method was proposed to segment both the breast tissues and the breast outline in DBT reconstructions. The DBT images of a representative population were simulated using three acquisition geometries: a left-right scan (conventional, I), a two-directional scan in the shape of a "T" (II), and an extra-wide range (XWR, III) left-right scan at a six-times higher dose than I. The nnU-Net was modified including two losses for segmentation: (1) tissues and (2) breast outline. The impact of loss (1) and the combination of loss (1) and (2) was evaluated using models trained with data simulating geometry I. The impact of the geometry was evaluated using the combined loss (1&2). The loss (1&2) improved the convex hull estimates, resolving 22.2% of the false classification of air voxels. Geometry II was superior to I and III, resolving 99.1% and 96.8% of the false classification of air voxels. Geometry III (Dice = (0.98, 0.94)) was superior to I (0.92, 0.78) and II (0.93, 0.74) for the tissue segmentation (adipose, dense, respectively). Thus, the loss (1&2) provided better segmentation, and geometries T and XWR improved the dense/adipose and breast outline segmentations relative to the conventional scan.

Multiclass Segmentation of Breast Tissue and Suspicious Findings: A Simulation-Based Study for the Development of Self-Steering Tomosynthesis.

In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirectional source motion with the intent to customize scanning motions around "suspicious findings". Customized acquisitions can improve the image quality in areas that require increased scrutiny, such as breast cancers, architectural distortions, and dense clusters. In this paper, virtual clinical trial techniques were used to analyze whether a finding or area at high risk of masking cancers can be detected in a single low-dose projection and thus be used for motion planning. This represents a step towards customizing the subsequent low-dose projection acquisitions autonomously, guided by the first low-dose projection; we call this technique "self-steering tomosynthesis." A U-Net was used to classify the low-dose projections into "risk classes" in simulated breasts with soft-tissue lesions; class probabilities were modified using post hoc Dirichlet calibration (DC). DC improved the multiclass segmentation (Dice = 0.43 vs. 0.28 before DC) and significantly reduced false positives (FPs) from the class of the highest risk of masking (sensitivity = 81.3% at 2 FPs per image vs. 76.0%). This simulation-based study demonstrated the feasibility of identifying suspicious areas using a single low-dose projection for self-steering tomosynthesis.

MaasPenn Radiomics Reproducibility Score: A Novel Quantitative Measure for Evaluating the Reproducibility of CT-Based Handcrafted Radiomic Features.

The reproducibility of handcrafted radiomic features (HRFs) has been reported to be affected by variations in imaging parameters, which significantly affect the generalizability of developed signatures and translation to clinical practice. However, the collective effect of the variations in imaging parameters on the reproducibility of HRFs remains unclear, with no objective measure to assess it in the absence of reproducibility analysis. We assessed these effects of variations in a large number of scenarios and developed the first quantitative score to assess the reproducibility of CT-based HRFs without the need for phantom or reproducibility studies. We further assessed the potential of image resampling and ComBat harmonization for removing these effects. Our findings suggest a need for radiomics-specific harmonization methods. Our developed score should be considered as a first attempt to introduce comprehensive metrics to quantify the reproducibility of CT-based handcrafted radiomic features. More research is warranted to demonstrate its validity in clinical contexts and to further improve it, possibly by the incorporation of more realistic situations, which better reflect real patients' situations.

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials.

PURPOSE: Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. METHODS: Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I-IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II-IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. RESULTS: The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. CONCLUSIONS: For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs.

Reply to Orlhac, F.; Buvat, I. Comment on "Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848".

We would like to thank Orlhac and Buvat [...].

VIRTUAL CLINICAL TRIALS IN MEDICAL IMAGING SYSTEM EVALUATION AND OPTIMISATION.

Virtual clinical trials (VCTs) can be used to evaluate and optimise medical imaging systems. VCTs are based on computer simulations of human anatomy, imaging modalities and image interpretation. OpenVCT is an open-source framework for conducting VCTs of medical imaging, with a particular focus on breast imaging. The aim of this paper was to evaluate the OpenVCT framework in two tasks involving digital breast tomosynthesis (DBT). First, VCTs were used to perform a detailed comparison of virtual and clinical reading studies for the detection of lesions in digital mammography and DBT. Then, the framework was expanded to include mechanical imaging (MI) and was used to optimise the novel combination of simultaneous DBT and MI. The first experiments showed close agreement between the clinical and the virtual study, confirming that VCTs can predict changes in performance of DBT accurately. Work in simultaneous DBT and MI system has demonstrated that the system can be optimised in terms of the DBT image quality. We are currently working to expand the OpenVCT software to simulate MI acquisition more accurately and to include models of tumour growth. Based on our experience to date, we envision a future in which VCTs have an important role in medical imaging, including support for more imaging modalities, use with rare diseases and a role in training and testing artificial intelligence (AI) systems.

Computational Breast Anatomy Simulation Using Multi-Scale Perlin Noise.

Virtual clinical trials (VCTs) of medical imaging require realistic models of human anatomy. For VCTs in breast imaging, a multi-scale Perlin noise method is proposed to simulate anatomical structures of breast tissue in the context of an ongoing breast phantom development effort. Four Perlin noise distributions were used to replace voxels representing the tissue compartments and Cooper's ligaments in the breast phantoms. Digital mammography and tomosynthesis projections were simulated using a clinical DBT system configuration. Power-spectrum analyses and higher-order statistics properties using Laplacian fractional entropy (LFE) of the parenchymal texture are presented. These objective measures were calculated in phantom and patient images using a sample of 140 clinical mammograms and 500 phantom images. Power-law exponents were calculated using the slope of the curve fitted in the low frequency [0.1, 1.0] mm-1 region of the power spectrum. The results show that the images simulated with our prior and proposed Perlin method have similar power-law spectra when compared with clinical mammograms. The power-law exponents calculated are -3.10, -3.55, and -3.46, for the log-power spectra of patient, prior phantom and proposed phantom images, respectively. The results also indicate an improved agreement between the mean LFE estimates of Perlin-noise based phantoms and patients than our prior phantoms and patients. Thus, the proposed method improved the simulation of anatomic noise substantially compared to our prior method, showing close agreement with breast parenchyma measures.

The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization.

While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs. The reproducibility of HRFs was significantly affected by variations in IPR, with pairwise concordant HRFs, as measured by the concordance correlation coefficient (CCC), ranging from 42% to 95%. The number of concordant HRFs (CCC > 0.9) after resampling varied depending on (i) the scanner model, (ii) the IM, and (iii) the NUIR. The number of concordant HRFs after ComBat harmonization depended on the variations between the batches harmonized. The majority of IMs resulted in a higher number of concordant HRFs compared to ComBat harmonization, and the combination of IMs and ComBat harmonization did not yield a significant benefit. Our developed framework can be used to assess the reproducibility and harmonizability of RFs.

Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets.

Advances in 3D imaging technology are transforming how radiologists search for cancer1,2 and how security officers scrutinize baggage for dangerous objects.3 These new 3D technologies often improve search over 2D images4,5 but vastly increase the image data. Here, we investigate 3D search for targets of various sizes in filtered noise and digital breast phantoms. For a Bayesian ideal observer optimally processing the filtered noise and a convolutional neural network processing the digital breast phantoms, search with 3D image stacks increases target information and improves accuracy over search with 2D images. In contrast, 3D search by humans leads to high miss rates for small targets easily detected in 2D search, but not for larger targets more visible in the visual periphery. Analyses of human eye movements, perceptual judgments, and a computational model with a foveated visual system suggest that human errors can be explained by interaction among a target's peripheral visibility, eye movement under-exploration of the 3D images, and a perceived overestimation of the explored area. Instructing observers to extend the search reduces 75% of the small target misses without increasing false positives. Results with twelve radiologists confirm that even medical professionals reading realistic breast phantoms have high miss rates for small targets in 3D search. Thus, under-exploration represents a fundamental limitation to the efficacy with which humans search in 3D image stacks and miss targets with these prevalent image technologies.

Evaluating attenuation correction strategies in a dedicated, single-gantry breast PET-tomosynthesis scanner.

We are developing a dedicated, combined breast positron emission tomography (PET)-tomosynthesis scanner. Both the PET and digital breast tomosynthesis (DBT) scanners are integrated in a single gantry to provide spatially co-registered 3D PET-tomosynthesis images. The DBT image will be used to identify the breast boundary and breast density to improve the quantitative accuracy of the PET image. This paper explores PET attenuation correction (AC) strategies that can be performed with the combined breast PET-DBT scanner to obtain more accurate, quantitative high-resolution 3D PET images. The PET detector is comprised of a 32 × 32 array of 1.5 × 1.5 × 15 mm3 LYSO crystals. The PET scanner utilizes two detector heads separated by either 9 or 11 cm, with each detector head having a 4 × 2 arrangement of PET detectors. GEANT4 Application for Tomographic Emission simulations were performed using an anthropomorphic breast phantom with heterogeneous attenuation under clinical DBT-compression. FDG-avid lesions, each 5 mm in diameter with 8:1 uptake, were simulated at four locations within the breast. Simulations were performed with a scan time of 2 min. PET AC was performed using the actual breast simulation model as well as DBT reconstructed volumetric images to derive the breast outline. In addition to using the known breast density as defined by the breast model, we also modeled it as uniform patient-independent soft-tissue, and as a uniform patient-specific material derived from breast tissue composition. Measured absolute lesion uptake was used to evaluate the quantitative accuracy of performing AC using the various strategies. This study demonstrates that AC is necessary to obtain a closer estimate of the true lesion uptake and background activity in the breast. The DBT image dataset assists in measuring lesion uptake with low bias by facilitating accurate breast delineation as well as providing accurate information related to the breast tissue composition. While both the uniform soft-tissue and patient-specific material approaches provides a close estimate to the ground truth, <5% bias can be achieved by using a uniform patient-specific material to define the attenuation map.

Characterization of the imaging settings in screening mammography using a tracking and reporting system: A multi-center and multi-vendor analysis.

A tracking and reporting system was developed to monitor radiation dose in X-ray breast imaging. We used our tracking system to characterize and compare the mammographic practices of five breast imaging centers located in the United States and Brazil. Clinical data were acquired using eight mammography systems comprising three modalities: computed radiography (CR), full-field digital mammography (FFDM), and digital breast tomosynthesis (DBT). Our database consists of metadata extracted from 334,234 images. We analyzed distributions and correlations of compressed breast thickness (CBT), compression force, target-filter combinations, X-ray tube voltage, and average glandular dose (AGD). AGD reference curves were calculated based on AGD distributions as a function of CBT. These curves represent an AGD reference for a particular population and system. Differences in AGD and imaging settings were attributed to a combination of factors, such as improvements in technology, imaging protocol, and patient demographics. The tracking system allows the comparison of various imaging settings used in screening mammography, as well as the tracking of patient- and population-specific breast data collected from different populations.

Calculation of Radiomic Features to Validate the Textural Realism of Physical Anthropomorphic Phantoms for Digital Mammography.

In this paper, radiomic features are used to validate the textural realism of two anthropomorphic phantoms for digital mammography. One phantom was based off a computational breast model; it was 3D printed by CIRS (Computerized Imaging Reference Systems, Inc., Norfolk, VA) under license from the University of Pennsylvania. We investigate how the textural realism of this phantom compares against a phantom derived from an actual patient's mammogram ("Rachel", Gammex 169, Madison, WI). Images of each phantom were acquired at three kV in 1 kV increments using auto-time technique settings. Acquisitions at each technique setting were repeated twice, resulting in six images per phantom. In the raw ("FOR PROCESSING") images, 341 features were calculated; i.e., gray-level histogram, co-occurrence, run length, fractal dimension, Gabor Wavelet, local binary pattern, Laws, and co-occurrence Laws features. Features were also calculated in a negative screening population. For each feature, the middle 95% of the clinical distribution was used to evaluate the textural realism of each phantom. A feature was considered realistic if all six measurements in the phantom were within the middle 95% of the clinical distribution. Otherwise, a feature was considered unrealistic. More features were actually found to be realistic by this definition in the CIRS phantom (305 out of 341 features or 89.44%) than in the phantom derived from a specific patient's mammogram (261 out of 341 features or 76.54%). We conclude that the texture is realistic overall in both phantoms.

Proposing Rapid Source Pulsing for Improved Super-Resolution in Digital Breast Tomosynthesis.

Our previous work showed that digital breast tomosynthesis (DBT) systems are capable of super-resolution, or subpixel resolution relative to the detector. Using a bar pattern phantom, it is possible to demonstrate that there are anisotropies in super-resolution throughout the reconstruction. These anisotropies are lessened in acquisition geometries with narrow spacing between source positions. This paper demonstrates that by re-arranging the source positions in the scan, the anisotropies can be minimized even further. To this end, a theoretical model of the reconstruction of a high-frequency sinusoidal test object was developed from first principles. We modeled the effect of clustering additional source positions around each conventional source position in fine increments (submillimeter). This design can be implemented by rapidly pulsing the source during a continuous sweep of the x-ray tube. It is shown that it is not possible to eliminate the anisotropies in a conventional DBT system with uniformly-spaced source positions, even if the increments of spacing are narrower than those used clinically. However, super-resolution can be achieved everywhere if the source positions are re-arranged in clusters with submillimeter spacing. Our previous work investigated a different approach for optimizing super-resolution through the use of detector motion perpendicular to the breast support. The advantage of introducing rapid source pulsing is that detector motion is no longer required; this mitigates the need for a thick detector housing, which may be cumbersome for patient positioning.

Technical Note: Noise models for virtual clinical trials of digital breast tomosynthesis.

PURPOSE: To investigate the use of an affine-variance noise model, with correlated quantum noise and spatially dependent quantum gain, for the simulation of noise in virtual clinical trials (VCT) of digital breast tomosynthesis (DBT). METHODS: Two distinct technologies were considered: an amorphous-selenium (a-Se) detector with direct conversion and a thallium-doped cesium iodide (CsI(Tl)) detector with indirect conversion. A VCT framework was used to generate noise-free projections of a uniform three-dimensional simulated phantom, whose geometry and absorption match those of a polymethyl methacrylate (PMMA) uniform physical phantom. The noise model was then used to generate noisy observations from the simulated noise-free data, while two clinically available DBT units were used to acquire projections of the PMMA physical phantom. Real and simulated projections were then compared using the signal-to-noise ratio (SNR) and normalized noise power spectrum (NNPS). RESULTS: Simulated images reported errors smaller than 4.4% and 7.0% in terms of SNR and NNPS, respectively. These errors are within the expected variation between two clinical units of the same model. The errors increase to 65.8% if uncorrelated models are adopted for the simulation of systems featuring indirect detection. The assumption of spatially independent quantum gain generates errors of 11.2%. CONCLUSIONS: The investigated noise model can be used to accurately reproduce the noise found in clinical DBT. The assumption of uncorrelated noise may be adopted if the system features a direct detector with minimal pixel crosstalk.

Design and implementation of a radiation dose tracking and reporting system for mammography and digital breast tomosynthesis.

A tracking system has been implemented to monitor radiation dose for digital mammography (DM) and digital breast tomosynthesis (DBT). This system communicates with a PACS through DICOM messages that allow the image metadata to be stored in a relational database. The tracking system accepts X-ray breast images, maps the image metadata into a SQL database, and allows a client-side application to report the data using a business intelligence framework. The database contains the DICOM information of 54,244 studies (235,225 images) acquired from four Selenia Dimensions systems. The average time to receive the images, and then extract and write the metadata into the database is 2.28 s for a DM and 3.84 s for a DBT image. Using the stored metadata, physics reports are generated based on chosen criteria (i.e., system model, mammography unit, breast data, acquisition techniques, physician, etc.). Our results show that the mean average glandular dose (AGD) varies significantly with compressed breast thickness and age. We observed an overall dose increase of 25.6% between DM and DBT (1.76 vs 2.21 mGy).

Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain.

Digital breast tomosynthesis (DBT) is an imaging technique created to visualize 3-D mammary structures for the purpose of diagnosing breast cancer. This imaging technique is based on the principle of computed tomography. Due to the use of a dangerous ionizing radiation, the "as low as reasonably achievable" (ALARA) principle should be respected, aiming at minimizing the radiation dose to obtain an adequate examination. Thus, a noise filtering method is a fundamental step to achieve the ALARA principle, as the noise level of the image increases as the radiation dose is reduced, making it difficult to analyze the image. In our work, a double denoising approach for DBT is proposed, filtering in both projection (prereconstruction) and image (postreconstruction) domains. First, in the prefiltering step, methods were used for filtering the Poisson noise. To reconstruct the DBT projections, we used the filtered backprojection algorithm. Then, in the postfiltering step, methods were used for filtering Gaussian noise. Experiments were performed on simulated data generated by open virtual clinical trials (OpenVCT) software and on a physical phantom, using several combinations of methods in each domain. Our results showed that double filtering (i.e., in both domains) is not superior to filtering in projection domain only. By investigating the possible reason to explain these results, it was found that the noise model in DBT image domain could be better modeled by a Burr distribution than a Gaussian distribution. Finally, this important contribution can open a research direction in the DBT denoising problem.

Personalization of X-Ray Tube Motion in Digital Breast Tomosynthesis Using Virtual Defrise Phantoms.

In digital breast tomosynthesis (DBT), projection images are acquired as the x-ray tube rotates in the plane of the chest wall. We constructed a prototype next-generation tomosynthesis (NGT) system that has an additional component of tube motion in the perpendicular direction (i.e., posteroanterior motion). Our previous work demonstrated the advantages of the NGT system using the Defrise phantom. The reconstruction shows higher contrast and fewer blurring artifacts. To expand upon that work, this paper analyzes how image quality can be further improved by customizing the motion path of the x-ray tube based on the object being imaged. In simulations, phantoms are created with realistic 3D breast outlines based on an established model of the breast under compression. The phantoms are given an internal structure similar to a Defrise phantom. Two tissue types (fibroglandular and adipose) are arranged in a square-wave pattern. The reconstruction is analyzed as a binary classification task using thresholding to segment the two tissue types. At various thresholds, the classification of each voxel in the reconstruction is compared against the phantom, and receiver operating characteristic (ROC) curves are calculated. It is shown that the area under the ROC curve (AUC) is dependent on the x-ray tube trajectory. The trajectory that maximizes AUC differs between phantoms. In conclusion, this paper demonstrates that the acquisition geometry in DBT should be personalized to the object being imaged in order to optimize the image quality.