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A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
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- Alvén, Jennifer, 1989 (author)
- Chalmers University of Technology,Chalmers tekniska högskola
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- Heurling, Kerstin (author)
- Gothenburg University,University of Gothenburg,Göteborgs universitet,Institutionen för neurovetenskap och fysiologi, sektionen för psykiatri och neurokemi,Wallenberg Centre for Molecular and Translational Medicine,Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry
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- Smith, Ruben (author)
- Lund University,Lunds universitet,Klinisk minnesforskning,Forskargrupper vid Lunds universitet,Clinical Memory Research,Lund University Research Groups
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- Shen, Dinggang (editor)
- Yap, Pew-Thian (editor)
- Liu, Tianming (editor)
- Peters, Terry M. (editor)
- Khan, Ali (editor)
- Staib, Lawrence H. (editor)
- Essert, Caroline (editor)
- Zhou, Sean (editor)
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- (creator_code:org_t)
- 2019-10-10
- 2019
- English 9 s.
- In: Medical Image Computing and Computer Assisted Intervention : MICCAI 2019 - 22nd International Conference, Proceedings - MICCAI 2019 - 22nd International Conference, Proceedings. - Cham : Springer International Publishing. - 0302-9743 .- 1611-3349. - 9783030322458 - 9783030322441 ; 11765 LNCS, s. 355-363
- Conference paper (peer-reviewed)
Abstract
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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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
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