1 research outputs found
Comparison of different image reconstruction algorithms for Digital Breast Tomosynthesis and assessment of their potential to reduce radiation dose
Tese de mestrado, Engenharia FÃsica, 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