540 research outputs found
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
Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd
Comparison of different image reconstruction algorithms for Digital Breast Tomosynthesis and assessment of their potential to reduce radiation dose
Tese de mestrado, Engenharia Física, 2022, Universidade de Lisboa, Faculdade de CiênciasDigital Breast Tomosynthesis is a three-dimensional medical imaging technique that allows the
view of sectional parts of the breast. Obtaining multiple slices of the breast constitutes an advantage
in contrast to conventional mammography examination in view of the increased potential in breast
cancer detectability. Conventional mammography, despite being a screening success, has undesirable
specificity, sensitivity, and high recall rates owing to the overlapping of tissues. Although this new
technique promises better diagnostic results, the acquisition methods and image reconstruction
algorithms are still under research.
Several articles suggest the use of analytic algorithms. However, more recent articles highlight the
iterative algorithm’s potential for increasing image quality when compared to the former. The scope
of this dissertation was to test the hypothesis of achieving higher quality images using iterative
algorithms acquired with lower doses than those using analytic algorithms.
In a first stage, the open-source Tomographic Iterative GPU-based Reconstruction (TIGRE)
Toolbox for fast and accurate 3D x-ray image reconstruction was used to reconstruct the images
acquired using an acrylic phantom. The algorithms used from the toolbox were the Feldkamp, Davis,
and Kress, the Simultaneous Algebraic Reconstruction Technique, and the Maximum Likelihood
Expectation Maximization algorithm.
In a second and final state, the possibility of further reducing the radiation dose using image
postprocessing tools was evaluated. A Total Variation Minimization filter was applied to the images
reconstructed with the TIGRE toolbox algorithm that provided the best image quality. These were then
compared to the images of the commercial unit used for the image acquisitions.
With the use of image quality parameters, it was found that the Maximum Likelihood Expectation
Maximization algorithm performance was the best of the three for lower radiation doses, especially
with the filter. In sum, the result showed the potential of the algorithm in obtaining images with quality
for low doses
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
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