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Title: Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation

Abstract

Purpose: To denoise self-gated k-space sorted 4-dimensional magnetic resonance imaging (SG-KS-4D-MRI) by applying a nonlocal means denoising filter, block-matching and 3-dimensional filtering (BM3D), to test its impact on the accuracy of 4D image deformable registration and automated tumor segmentation for pancreatic cancer patients. Methods and Materials: Nine patients with pancreatic cancer and abdominal SG-KS-4D-MRI were included in the study. Block-matching and 3D filtering was adapted to search in the axial slices/frames adjacent to the reference image patch in the spatial and temporal domains. The patches with high similarity to the reference patch were used to collectively denoise the 4D-MRI image. The pancreas tumor was manually contoured on the first end-of-exhalation phase for both the raw and the denoised 4D-MRI. B-spline deformable registration was applied to the subsequent phases for contour propagation. The consistency of tumor volume defined by the standard deviation of gross tumor volumes from 10 breathing phases (σ-GTV), tumor motion trajectories in 3 cardinal motion planes, 4D-MRI imaging noise, and image contrast-to-noise ratio were compared between the raw and denoised groups. Results: Block-matching and 3D filtering visually and quantitatively reduced image noise by 52% and improved image contrast-to-noise ratio by 56%, without compromising soft tissue edge definitions. Automatic tumormore » segmentation is statistically more consistent on the denoised 4D-MRI (σ-GTV = 0.6 cm{sup 3}) than on the raw 4D-MRI (σ-GTV = 0.8 cm{sup 3}). Tumor end-of-exhalation location is also more reproducible on the denoised 4D-MRI than on the raw 4D-MRI in all 3 cardinal motion planes. Conclusions: Block-matching and 3D filtering can significantly reduce random image noise while maintaining structural features in the SG-KS-4D-MRI datasets. In this study of pancreatic tumor segmentation, automatic segmentation of GTV in the registered image sets is shown to be more consistent on the denoised 4D-MRI than on the raw 4D-MRI.« less

Authors:
 [1];  [2];  [3];  [2]; ;  [3];  [2];  [3];  [2];  [1];  [4];  [1];  [2]
  1. Department of Computer Science, Xidian University, Xi'An (China)
  2. Department of Radiation Oncology, Cedars Sinai Medical Center, Los Angeles, California (United States)
  3. Department of Biomedical Sciences, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California (United States)
  4. Department of Radiation Oncology, University of California at Los Angeles, Los Angeles, California (United States)
Publication Date:
OSTI Identifier:
22648720
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Radiation Oncology, Biology and Physics; Journal Volume: 95; Journal Issue: 3; Other Information: Copyright (c) 2016 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; BIOMEDICAL RADIOGRAPHY; FILTERS; IMAGES; NEOPLASMS; NMR IMAGING; NOISE; PANCREAS; THREE-DIMENSIONAL CALCULATIONS

Citation Formats

Jin, Jun, McKenzie, Elizabeth, Fan, Zhaoyang, Tuli, Richard, Deng, Zixin, Pang, Jianing, Fraass, Benedick, Li, Debiao, Sandler, Howard, Yang, Guang, Sheng, Ke, Gou, Shuiping, and Yang, Wensha, E-mail: wensha.yang@cshs.org. Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation. United States: N. p., 2016. Web. doi:10.1016/J.IJROBP.2016.02.006.
Jin, Jun, McKenzie, Elizabeth, Fan, Zhaoyang, Tuli, Richard, Deng, Zixin, Pang, Jianing, Fraass, Benedick, Li, Debiao, Sandler, Howard, Yang, Guang, Sheng, Ke, Gou, Shuiping, & Yang, Wensha, E-mail: wensha.yang@cshs.org. Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation. United States. doi:10.1016/J.IJROBP.2016.02.006.
Jin, Jun, McKenzie, Elizabeth, Fan, Zhaoyang, Tuli, Richard, Deng, Zixin, Pang, Jianing, Fraass, Benedick, Li, Debiao, Sandler, Howard, Yang, Guang, Sheng, Ke, Gou, Shuiping, and Yang, Wensha, E-mail: wensha.yang@cshs.org. Fri . "Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation". United States. doi:10.1016/J.IJROBP.2016.02.006.
@article{osti_22648720,
title = {Nonlocal Means Denoising of Self-Gated and k-Space Sorted 4-Dimensional Magnetic Resonance Imaging Using Block-Matching and 3-Dimensional Filtering: Implications for Pancreatic Tumor Registration and Segmentation},
author = {Jin, Jun and McKenzie, Elizabeth and Fan, Zhaoyang and Tuli, Richard and Deng, Zixin and Pang, Jianing and Fraass, Benedick and Li, Debiao and Sandler, Howard and Yang, Guang and Sheng, Ke and Gou, Shuiping and Yang, Wensha, E-mail: wensha.yang@cshs.org},
abstractNote = {Purpose: To denoise self-gated k-space sorted 4-dimensional magnetic resonance imaging (SG-KS-4D-MRI) by applying a nonlocal means denoising filter, block-matching and 3-dimensional filtering (BM3D), to test its impact on the accuracy of 4D image deformable registration and automated tumor segmentation for pancreatic cancer patients. Methods and Materials: Nine patients with pancreatic cancer and abdominal SG-KS-4D-MRI were included in the study. Block-matching and 3D filtering was adapted to search in the axial slices/frames adjacent to the reference image patch in the spatial and temporal domains. The patches with high similarity to the reference patch were used to collectively denoise the 4D-MRI image. The pancreas tumor was manually contoured on the first end-of-exhalation phase for both the raw and the denoised 4D-MRI. B-spline deformable registration was applied to the subsequent phases for contour propagation. The consistency of tumor volume defined by the standard deviation of gross tumor volumes from 10 breathing phases (σ-GTV), tumor motion trajectories in 3 cardinal motion planes, 4D-MRI imaging noise, and image contrast-to-noise ratio were compared between the raw and denoised groups. Results: Block-matching and 3D filtering visually and quantitatively reduced image noise by 52% and improved image contrast-to-noise ratio by 56%, without compromising soft tissue edge definitions. Automatic tumor segmentation is statistically more consistent on the denoised 4D-MRI (σ-GTV = 0.6 cm{sup 3}) than on the raw 4D-MRI (σ-GTV = 0.8 cm{sup 3}). Tumor end-of-exhalation location is also more reproducible on the denoised 4D-MRI than on the raw 4D-MRI in all 3 cardinal motion planes. Conclusions: Block-matching and 3D filtering can significantly reduce random image noise while maintaining structural features in the SG-KS-4D-MRI datasets. In this study of pancreatic tumor segmentation, automatic segmentation of GTV in the registered image sets is shown to be more consistent on the denoised 4D-MRI than on the raw 4D-MRI.},
doi = {10.1016/J.IJROBP.2016.02.006},
journal = {International Journal of Radiation Oncology, Biology and Physics},
number = 3,
volume = 95,
place = {United States},
year = {Fri Jul 01 00:00:00 EDT 2016},
month = {Fri Jul 01 00:00:00 EDT 2016}
}