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Optimization of a Stationary Tomographic MBI System Including Non-Local Means Filtering

Erlandsson, K; Wirth, A; Thielemans, K; Baistow, I; Cherlin, A; Hutton, BF; (2024) Optimization of a Stationary Tomographic MBI System Including Non-Local Means Filtering. In: 2022 IEEE NSS/MIC RTSD - IEEE Nuclear Science Symposium, Medical Imaging Conference and Room Temperature Semiconductor Detector Conference. IEEE: Italy. Green open access

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Abstract

A novel stationary tomographic Molecular Breast Imaging (MBI) system is currently under development, with the aim of obtaining high image-quality with low dose and short scanning time. The system is based on dual opposing CZT detector arrays and multi-pinhole collimators. We have recently modified the iterative image reconstruction procedure by incorporating a novel relaxation scheme, in order to make the image contrast and noise properties more uniform throughout the field-of-view. In addition, we have introduced a post-reconstruction image denoising step based on the non-local means (NLM) filter. In view of the significant effect that these steps had on the image quality, we performed a new system parameter optimization. The parameters investigated were pinhole size, opening angle and separation, as well as the number of reconstruction iterations and the degree-of-smoothing parameter of the NLM filter. The optimization was performed based on simulated data, by maximizing the contrast-to-noise ratio (CNR) in the images. We found that the optimal system parameters were not so different with the new data processing steps as compared to previous results, while the CNR was improved by a factor > 3.

Type: Proceedings paper
Title: Optimization of a Stationary Tomographic MBI System Including Non-Local Means Filtering
Event: 2022 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
Dates: 5 Nov 2022 - 12 Nov 2022
ISBN-13: 9781665488723
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/NSS/MIC44845.2022.10398953
Publisher version: http://dx.doi.org/10.1109/nss/mic44845.2022.103989...
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: Image quality; Filtering;Tomography; Iterative methods; Optimization; Image reconstruction; Image denoising
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging
URI: https://discovery.ucl.ac.uk/id/eprint/10189344
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