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Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

Klaser, K; Varsavsky, T; Markiewicz, P; Atkinson, D; Thielemans, K; Hutton, B; Cardoso, M; (2019) Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning. In: Burgos, N and Gooya, A and Svoboda, D, (eds.) Simulation and Synthesis in Medical Imaging. SASHIMI 2019. Lecture Notes in Computer Science, vol 11827. Springer: Cham.

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Abstract

The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2 − norm between the ground truth CT and the pCT. However, given that the ultimate purpose is often to use the pCT as an attenuation map (µ-map) in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily optimal. The main objective should be to predict a pCT that, when used as µ-map, reconstructs a pseudo PET (pPET) which is as close as possible to the gold standard PET. To this end, we propose a novel multi-hypothesis deep learning framework that generates pCTs by minimising a combination of the pixel-wise error between pCT and CT and a proposed metric-loss that itself is represented by a convolutional neural network (CNN) and aims to minimise subsequent PET residuals. The model is trained on a database of 400 paired MR/CT/PET image slices. Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69.68HU) compared to a baseline CNN (66.25HU), but lead to significant improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u.

Type: Proceedings paper
Title: Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
Event: Simulation and Synthesis in Medical Imaging. SASHIMI 2019.
Location: Shenzhen, China
Dates: 13 October 2019 - 13 October 2019
DOI: 10.1007/978-3-030-32778-1_2
Publisher version: https://doi.org/10.1007/978-3-030-32778-1_2
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 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/10080027
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