Computerâ aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134920/1/mp0955.pd

Image quality of microcalcifications in digital breast tomosynthesis: Effects of projection-view distributions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98731/1/MPH005703.pd

Computerâ aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134897/1/mp2072.pd

Detection and diagnosis of breast lesions: Performance evaluation of digital breast tomosynthesis and magnetic resonance mammography

AbstractObjectiveTo assess the impact of digital breast tomosynthesis (DBT) and magnetic resonance mammography (MRM) in enhancing the performance of digital mammography (DM) in the detection and evaluation of different breast lesions.Patients and methodsIn this retrospective study, 98 patients with 103 breast lesions were assessed by DM, DBT and MRM. Mammography images were acquired using the “combo mode", where both DM and DBT scanned in the same compression. MRM was performed by 1T open system. Each lesion was assigned a blinded category in an individual performance for each modality. The resultant BI-RADS categories were correlated with reports of the pathology specimens or outcome of 18-month follow-up.ResultsBoth DBT and MRM showed equivalent sensitivity of 92%. The specificity for DBT and MRM was 80.7% and 89.7% respectively. The efficacy of DM was raised from 61% to 83.5% with DBT and 90.2% with MRM. The results of the three modalities and the final diagnosis revealed a significant correlation (p=0.035).The association between the results of DBT and those of MRM showed statistically significant difference between DBT and MRM for diagnosing breast lesions (p=0.001).ConclusionBoth MRM and DBT provide better performance than classic DM. Adding either of these modalities to the classic examination enhances diagnosis and precise disease distribution

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

Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134871/1/mp8694.pd

Digital breast tomosynthesis: A state of the art review

Digital Breast Tomosynthesis (DBT) is an X-ray mammography technique where multiple low-dose projection images of the breast are reconstructed in multiple tomographic images creating a semi-3D mammogram. This enables the visualization of a sequential set of thin sections of the breast, overcoming the masking effect of overlying fibroglandular breast tissue, then improving carcinoma detection and reducing false-positive cases. This review aims at describing current DBT technique, analyzing DBT in clinical practice and providing an overview of published studies on clinical experience with DBT in the screening and diagnostic settings