Mota, AM;
Clarkson, MJ;
Almeida, P;
Matela, N;
(2020)
An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization.
IEEE Transactions on Medical Imaging
, 39
(12)
pp. 4094-4101.
10.1109/TMI.2020.3013107.
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Abstract
Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.
| Type: | Article |
|---|---|
| Title: | An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TMI.2020.3013107 |
| Publisher version: | http://dx.doi.org/10.1109/TMI.2020.3013107 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deconvolution, Data visualization, Phantoms, Rendering (computer graphics), TV, Two dimensional displays, Three-dimensional displays |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10120784 |
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