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

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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.


Enheter & grupper
Externa organisationer
  • Chalmers Tekniska Högskola
  • University College London
  • Göteborgs universitet

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Medicinsk bildbehandling
  • Radiologi och bildbehandling
Titel på värdpublikationMedical Image Computing and Computer Assisted Intervention
Undertitel på gästpublikationMICCAI 2019 - 22nd International Conference, Proceedings
RedaktörerDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
FörlagSpringer Nature Switzerland AG
Antal sidor9
ISBN (elektroniskt)978-3-030-32245-8
ISBN (tryckt)9783030322441
StatusPublished - 2019 okt 10
Peer review utfördJa
Evenemang22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, Kina
Varaktighet: 2019 okt 132019 okt 17


NamnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volym11765 LNCS
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349


Konferens22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019