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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.

Details

Authors
Organisations
External organisations
  • Chalmers University of Technology
  • University College London
  • University of Gothenburg
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Image Processing
  • Radiology, Nuclear Medicine and Medical Imaging
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention
Subtitle of host publicationMICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer Nature Switzerland AG
Pages355-363
Number of pages9
ISBN (Electronic)978-3-030-32245-8
ISBN (Print)9783030322441
Publication statusPublished - 2019 Oct 10
Publication categoryResearch
Peer-reviewedYes
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11765 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period2019/10/132019/10/17