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Proceedings Paper

Attenuation correction for PET/MRI using MRI-based pseudo CT
Author(s): Tonghe Wang; Yang Lei; Xue Dong; Kristin Higgins; Tian Liu; Walter J. Curran; Hui Mao; Jonathon A. Nye; Xiaofeng Yang
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Paper Abstract

Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based method to derive patient-specific pseudo computed tomography (PCT) maps from routine T1-weighted MRI in their native space for attenuation correction of brain PET. We developed a machine-learning-based method using a sequence of alternating random forests under the framework of an iterative refinement model. Anatomical feature selection is included in both training and predication stages to achieve excellent performance. To evaluate its accuracy, we retrospectively investigated 17 patients, each of which has been scanned by PET/CT and MR for brain. The PET images were corrected for attenuation on CT images as ground truth, as well as on PCT images generated from MR images. The side-by-side image comparisons and joint histograms demonstrated very good agreement of PET images after correction by PCT and CT. The mean differences of voxel values in selected VOIs were less than 4%, the mean absolute difference of all active area is around 2.5%. This work demonstrates a novel learning-based approach to automatically generate CT images from routine T1-weighted MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. Reconstructed PET images using the PCT exhibit errors well below accepted test/retest reliability of PET/CT indicating high quantitative equivalence.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131248 (16 March 2020); doi: 10.1117/12.2548158
Show Author Affiliations
Tonghe Wang, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Xue Dong, Emory Univ. (United States)
Kristin Higgins, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Hui Mao, Emory Univ. (United States)
Jonathon A. Nye, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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