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

Alvén, Jennifer ; Heurling, Kerstin ; Smith, Ruben LU ; Strandberg, Olof LU ; Schöll, Michael LU ; Hansson, Oskar LU and Kahl, Fredrik (2019) 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11765 LNCS. p.355-363
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... (More)

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.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Medical Image Computing and Computer Assisted Intervention : MICCAI 2019 - 22nd International Conference, Proceedings - MICCAI 2019 - 22nd International Conference, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Shen, Dinggang ; Yap, Pew-Thian ; Liu, Tianming ; Peters, Terry M. ; Khan, Ali ; Staib, Lawrence H. ; Essert, Caroline ; Zhou, Sean ; ; ; ; ; ; ; and
volume
11765 LNCS
pages
9 pages
publisher
Springer Nature Switzerland AG
conference name
22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
conference location
Shenzhen, China
conference dates
2019-10-13 - 2019-10-17
external identifiers
  • scopus:85075683154
ISSN
0302-9743
1611-3349
ISBN
9783030322441
978-3-030-32245-8
DOI
10.1007/978-3-030-32245-8_40
language
English
LU publication?
yes
id
f1d90480-d6df-4b9a-b70d-e4f7f86f9790
date added to LUP
2019-12-16 16:21:57
date last changed
2020-01-13 02:36:35
@inproceedings{f1d90480-d6df-4b9a-b70d-e4f7f86f9790,
  abstract     = {<p>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.</p>},
  author       = {Alvén, Jennifer and Heurling, Kerstin and Smith, Ruben and Strandberg, Olof and Schöll, Michael and Hansson, Oskar and Kahl, Fredrik},
  booktitle    = {Medical Image Computing and Computer Assisted Intervention  : MICCAI 2019 - 22nd International Conference, Proceedings},
  editor       = {Shen, Dinggang and Yap, Pew-Thian and Liu, Tianming and Peters, Terry M. and Khan, Ali and Staib, Lawrence H. and Essert, Caroline and Zhou, Sean},
  isbn         = {9783030322441},
  issn         = {0302-9743},
  language     = {eng},
  month        = {10},
  pages        = {355--363},
  publisher    = {Springer Nature Switzerland AG},
  series       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  title        = {A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images},
  url          = {http://dx.doi.org/10.1007/978-3-030-32245-8_40},
  doi          = {10.1007/978-3-030-32245-8_40},
  volume       = {11765 LNCS},
  year         = {2019},
}