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

A comparative study of limitedâ angle coneâ beam reconstruction methods for breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134781/1/mp7543.pd

X-ray Digital Tomosynthesis Imaging — Comparison of Reconstruction Algorithms in Terms of a Reduction in the Exposure Dose for Arthroplasty

Aims The purpose of this review was (1) to identify indications for volumetric X-ray digital tomosynthesis by using a conventional reconstruction technique [the filtered back-projection (FBP) algorithm] and modern reconstruction techniques [the maximum likelihood expectation maximization (MLEM) and simultaneous iterative reconstruction techniques (SIRT)] and (2) to compare the conventional and modern reconstruction techniques in terms of a reduction in the exposure dose

Application of boundary detection information in breast tomosynthesis reconstruction

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135006/1/mp1968.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

Multiscale bilateral filtering for improving image quality in digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135115/1/mp3283.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