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A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images

Alvén, J; Heurling, K; Smith, R; Strandberg, O; Schöll, M; Hansson, O; Kahl, F; (2019) A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images. In: Shen, D and Liu, T and Peters, TM and Staib, LH and Essert, C and Zhou, S and Yap, PT and Khan, A, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II. (pp. pp. 355-363). Springer: Cham, Switzerland. Green open access

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Abstract

The procedure of aligning a positron emission tomography (PET) image with a common coordinate system, spatial normalization, typically demands a corresponding structural magnetic resonance (MR) image. However, MR imaging is not always available or feasible for the subject, which calls for enabling spatial normalization without MR, MR-less spatial normalization. In this work, we propose a template-free approach to MR-less spatial normalization for [18F]flortaucipir tau PET images. We use a deep neural network that estimates an aligning transformation from the PET input image, and outputs the spatially normalized image as well as the parameterized transformation. In order to do so, the proposed network iteratively estimates a set of rigid and affine transformations by means of convolutional neural network regressors as well as spatial transformer layers. The network is trained and validated on 199 tau PET volumes with corresponding ground truth transformations, and tested on two different datasets. The proposed method shows competitive performance in terms of registration accuracy as well as speed, and compares favourably to previously published results.

Type: Proceedings paper
Title: A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
Event: 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2019)
ISBN-13: 9783030322441
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-32245-8_40
Publisher version: https://doi.org/10.1007/978-3-030-32245-8_40
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 > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
URI: https://discovery.ucl.ac.uk/id/eprint/10089228
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