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Deep Boosted Regression for MR to CT Synthesis

Kläser, K; Markiewicz, P; Ranzini, M; Li, W; Modat, M; Hutton, BF; Atkinson, D; ... Ourselin, S; + view all (2018) Deep Boosted Regression for MR to CT Synthesis. In: Gooya, A and Goksel, O and Oguz, I and Burgos, N, (eds.) SASHIMI 2018: Simulation and Synthesis in Medical Imaging: Proceedings. (pp. pp. 61-70). Springer: Cham, Switzerland. Green open access

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Abstract

Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction is particularly challenging for PET-MRI as neither PET nor magnetic resonance imaging (MRI) can directly image tissue attenuation properties. MRI-based computed tomography (CT) synthesis has been proposed as an alternative to physics based and segmentation-based approaches that assign a population-based tissue density value in order to generate an attenuation map. We propose a novel deep fully convolutional neural network that generates synthetic CTs in a recursive manner by gradually reducing the residuals of the previous network, increasing the overall accuracy and generalisability, while keeping the number of trainable parameters within reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT pairs and a four-fold random bootstrapped validation with a 80:20 split is performed. Quantitative results show that the proposed framework outperforms a state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE) from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction error from 14.3% to 7.2%.

Type: Proceedings paper
Title: Deep Boosted Regression for MR to CT Synthesis
Event: SASHIMI 2018, Third International Workshop, held in conjunction with MICCAI 2018, 16 September 2018, Granada, Spain
ISBN-13: 978-3-030-00535-1
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00536-8_7
Publisher version: https://doi.org/10.1007/978-3-030-00536-8_7
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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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 > Metabolism and Experi Therapeutics
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 Chemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10060314
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