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Title: MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence

Abstract

Purpose: To determine the feasibility of identifying cortical bone on MR images with a short-TE 3D fast-GRE sequence for attenuation correction of PET data in PET/MR. Methods: A water-fat-bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE{sub 1}/TE{sub 2}/TE{sub 3}=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast-GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm{sup 3}) was implemented along with a 3D Dixon sequence (TE{sub 1}/TE{sub 2}/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm{sup 3}) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm{sup 3}) and reconstructed voxel size (0.94×0.94×1.5mm{sup 3}) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast-GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separatedmore » using a threshold determined from the histogram. Results: There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short-TE MR image. The acquisition time for the 3D fast-GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water-fat-bone segmentation is visually similar compared to reference CT. Conclusion: Cortical bone and air can be separated based on intensity in MR image with a short-TE 3D fast-GRE sequence. Further research is required to optimize the strategy to reduce Gibbs artifacts.« less

Authors:
;  [1];  [2]
  1. The University of Texas MD Anderson Cancer Center, Houston, TX (United States)
  2. GE Healthcare, Houston, TX (United States)
Publication Date:
OSTI Identifier:
22409622
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 41; Journal Issue: 6; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; ATTENUATION; CORRECTIONS; FATS; IMAGE PROCESSING; NMR IMAGING; PHANTOMS; POSITRON COMPUTED TOMOGRAPHY; SKELETON; WATER

Citation Formats

Ai, H, Pan, T, The University of Texas Graduate School of Biomedical Science, Houston, TX, and Hwang, K. MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence. United States: N. p., 2014. Web. doi:10.1118/1.4889205.
Ai, H, Pan, T, The University of Texas Graduate School of Biomedical Science, Houston, TX, & Hwang, K. MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence. United States. https://doi.org/10.1118/1.4889205
Ai, H, Pan, T, The University of Texas Graduate School of Biomedical Science, Houston, TX, and Hwang, K. 2014. "MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence". United States. https://doi.org/10.1118/1.4889205.
@article{osti_22409622,
title = {MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence},
author = {Ai, H and Pan, T and The University of Texas Graduate School of Biomedical Science, Houston, TX and Hwang, K},
abstractNote = {Purpose: To determine the feasibility of identifying cortical bone on MR images with a short-TE 3D fast-GRE sequence for attenuation correction of PET data in PET/MR. Methods: A water-fat-bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE{sub 1}/TE{sub 2}/TE{sub 3}=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast-GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm{sup 3}) was implemented along with a 3D Dixon sequence (TE{sub 1}/TE{sub 2}/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm{sup 3}) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm{sup 3}) and reconstructed voxel size (0.94×0.94×1.5mm{sup 3}) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast-GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separated using a threshold determined from the histogram. Results: There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short-TE MR image. The acquisition time for the 3D fast-GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water-fat-bone segmentation is visually similar compared to reference CT. Conclusion: Cortical bone and air can be separated based on intensity in MR image with a short-TE 3D fast-GRE sequence. Further research is required to optimize the strategy to reduce Gibbs artifacts.},
doi = {10.1118/1.4889205},
url = {https://www.osti.gov/biblio/22409622}, journal = {Medical Physics},
issn = {0094-2405},
number = 6,
volume = 41,
place = {United States},
year = {Sun Jun 15 00:00:00 EDT 2014},
month = {Sun Jun 15 00:00:00 EDT 2014}
}