Initial investigation of reading efficiency from experienced radiologists interpreting digital breast tomosynthesis (DBT) images

Initial investigation of reading efficiency from experienced radiologists interpreting digital breast tomosynthesis (DBT) image

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

Characterisation of breast lesions: comparison of digital breast tomosynthesis and ultrasonography

Background: The main aim of the study was to characterize breast lesions using digital breast tomosynthesis and ultrasound and compare the detection and characterization of lesions between both the modalities.Methods: This was a cross-sectional, observational study that included 150 women who were screened with mammography followed by digital breast tomosynthesis and ultrasound for breast cancer. Patients approaching willingly for screening as per the inclusion criteria underwent mammography and digital breast tomosynthesis followed by ultrasonography. In lesion showing characteristics of malignancy biopsy correlation was done.Results: About half of the patients had type C tissue composition of the breast (50.67%). Most patients were of age between 35 to 44 years (46%). Malignant lesions were similarly detected by both the modalities. Almost all benign cases were also similarly diagnosed. Sensitivity and specificity of ultrasonography was 94.85% and 92%. Sensitivity and specificity for tomosynthesis was 91.86% and 88.24%. Combined testing showed 100% sensitivity, 97.8% specificity, 98.36% positive predictive value and 100% negative predictive value.Conclusions: Combining use of tomosynthesis and ultrasonography can make it possible to detect any small lesion, malignancy in its earliest stage (in situ) as well as few premalignant conditions like atypical ductal hyperplasia, as in few conditions there is possible sonography negative and mammogram positive calcifications are found

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

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