Combined Reconstruction and Registration of Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) has the potential to en- hance breast cancer detection by reducing the confounding e ect of su- perimposed tissue associated with conventional mammography. In addi- tion the increased volumetric information should enable temporal datasets to be more accurately compared, a task that radiologists routinely apply to conventional mammograms to detect the changes associated with ma- lignancy. In this paper we address the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their reg- istration. Using a simple test object, and DBT simulations from in vivo breast compressions imaged using MRI, we demonstrate that this com- bined reconstruction and registration approach produces improvements in both the reconstructed volumes and the estimated transformation pa- rameters when compared to performing the tasks sequentially

Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity

Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering

Microcalcification clusters (MCs) are one of the most important biomarkers for breast cancer and Digital Breast Tomosynthesis (DBT) has consolidated its role in breast cancer imaging. As there are mixed observations about MCs detection using DBT, it is important to develop tools that improve this task. Furthermore, the visualization mode of MCs is also crucial, as their diagnosis is associated with their 3D morphology. In this work, DBT data from a public database were used to train a faster region-based convolutional neural network (R-CNN) to locate MCs in entire DBT. Additionally, the detected MCs were further analyzed through standard 2D visualization and 3D volume rendering (VR) specifically developed for DBT data. For MCs detection, the sensitivity of our Faster R-CNN was 60% with 4 false positives. These preliminary results are very promising and can be further improved. On the other hand, the 3D VR visualization provided important information, with higher quality and discernment of the detected MCs. The developed pipeline may help radiologists since (1) it indicates specific breast regions with possible lesions that deserve additional attention and (2) as the rendering of the MCs is similar to a segmentation, a detailed complementary analysis of their 3D morphology is possible

Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

Background: Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. Objective and methods: This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. Results: A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. Conclusion: Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT

Digital Breast Tomosynthesis: Outcomes and Tumor Characteristics in Women Recalled From Screening

Breast cancer is the most frequently diagnosed and second leading cause of cancer deaths in women, accounting for 25% of cancer diagnoses and 15.4% of cancer deaths in developed countries. Thus, early detection of breast cancer through screening has become increasingly important in mortality reduction efforts. Yet, mammography has faced considerable controversy in balancing the benefits and harms associated with screening. Digital breast tomosynthesis has emerged as an important imaging technique which, compared to standard mammography alone, reduces recall rates and false positives, and improves cancer detection. Additional cancers detected with tomosynthesis have been poorly characterized in the literature to date. To assess the effectiveness of screening with adjunct tomosynthesis, we propose to utilize our large database to characterize cancers detected in true positive recalls. Our findings will help clinicians make well-informed decisions for further management of women with mammographically suspicious or inconclusive findings, and contribute to future screening guidelines

Enhanced Digital Breast Tomosynthesis diagnosis using 3D visualization and automatic classification of lesions

Breast cancer represents the main cause of cancer-related deaths in women. Nonetheless, the mortality rate of this disease has been decreasing over the last three decades, largely due to the screening programs for early detection. For many years, both screening and clinical diagnosis were mostly done through Digital Mammography (DM). Approved in 2011, Digital Breast Tomosynthesis (DBT) is similar to DM but it allows a 3D reconstruction of the breast tissue, which helps the diagnosis by reducing the tissue overlap. Currently, DBT is firmly established and is approved as a stand-alone modality to replace DM. The main objective of this thesis is to develop computational tools to improve the visualization and interpretation of DBT data. Several methods for an enhanced visualization of DBT data through volume rendering were studied and developed. Firstly, important rendering parameters were considered. A new approach for automatic generation of transfer functions was implemented and two other parameters that highly affect the quality of volume rendered images were explored: voxel size in Z direction and sampling distance. Next, new image processing methods that improve the rendering quality by considering the noise regularization and the reduction of out-of-plane artifacts were developed. The interpretation of DBT data with automatic detection of lesions was approached through artificial intelligence methods. Several deep learning Convolutional Neural Networks (CNNs) were implemented and trained to classify a complete DBT image for the presence or absence of microcalcification clusters (MCs). Then, a faster R-CNN (region-based CNN) was trained to detect and accurately locate the MCs in the DBT images. The detected MCs were rendered with the developed 3D rendering software, which provided an enhanced visualization of the volume of interest. The combination of volume visualization with lesion detection may, in the future, improve both diagnostic accuracy and also reduce analysis time. This thesis promotes the development of new computational imaging methods to increase the diagnostic value of DBT, with the aim of assisting radiologists in their task of analyzing DBT volumes and diagnosing breast cancer

The impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings

Introduction: Mammography is the most used breast screening tool and was proven to reduce breast-cancer-associated mortality. The estimated sensitivity of mammography varies between 77% and 95%; however, sensitivity could be 26% lower in dense breasts than in entirely fatty breasts. The ability to represent the complex 3D breast architecture and early changes in anatomical structures in a 2D view is the biggest challenge for mammography. In Digital Breast Tomosynthesis (DBT), tomographic images are reconstructed from multiple projections acquired from different angles. This technique allows the generation of 3D data, reduction of tissue overlap and allows better evaluation of masses, architectural distortion, and asymmetries compared with conventional two-dimensional mammographic images.Objective: To evaluate the impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings.Methods: Prospective cohort for 180 women with mammographic non-mass findings who presented to Alexandria University Radio diagnosis Department either for screening or diagnostic purposes between July 2019 and August 2020 with mean age 51.44 ± 10.67 . Digital breast tomosynthesis and ultrasound was done for all patients. Lesions were evaluated on DM; DBT alone then combined DBT & DM. Comparison of results according to changes in BIRADS, diagnostic performance using histopathology as gold standard.Results: 208 non-mass findings were detected by conventional mammography (104 asymmetry, 35 architectural distortion, 69 micro calcifications), Tomosynthesis reduced the BIRADS 3 count by 32%, upgraded the count of BIRADS 4 lesions by 11.4% while upgraded the BIRADS 2 by 18.9% with consequent improvement of sensitivity and specificity, PPV, NPV and accuracy to 96%, 95%, 94%,97%, and 95.6%.Conclusion: Combined FFDM and DBT improved the diagnostic performance in evaluation of non-mass findings and proper BIRADS categorization