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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. 2016. "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 = 2016,
month = 7
}
  • Purpose: To apply a novel self-gating k-space sorted 4-dimensional MRI (SG-KS-4D-MRI) method to overcome limitations due to anisotropic resolution and rebinning artifacts and to monitor pancreatic tumor motion. Methods and Materials: Ten patients were imaged using 4D-CT, cine 2-dimensional MRI (2D-MRI), and the SG-KS-4D-MRI, which is a spoiled gradient recalled echo sequence with 3-dimensional radial-sampling k-space projections and 1-dimensional projection-based self-gating. Tumor volumes were defined on all phases in both 4D-MRI and 4D-CT and then compared. Results: An isotropic resolution of 1.56 mm was achieved in the SG-KS-4D-MRI images, which showed superior soft-tissue contrast to 4D-CT and appeared to be free of stitchingmore » artifacts. The tumor motion trajectory cross-correlations (mean ± SD) between SG-KS-4D-MRI and cine 2D-MRI in superior–inferior, anterior–posterior, and medial–lateral directions were 0.93 ± 0.03, 0.83 ± 0.10, and 0.74 ± 0.18, respectively. The tumor motion trajectories cross-correlations between SG-KS-4D-MRI and 4D-CT in superior–inferior, anterior–posterior, and medial–lateral directions were 0.91 ± 0.06, 0.72 ± 0.16, and 0.44 ± 0.24, respectively. The average standard deviation of gross tumor volume calculated from the 10 breathing phases was 0.81 cm{sup 3} and 1.02 cm{sup 3} for SG-KS-4D-MRI and 4D-CT, respectively (P=.012). Conclusions: A novel SG-KS-4D-MRI acquisition method capable of reconstructing rebinning artifact–free, high-resolution 4D-MRI images was used to quantify pancreas tumor motion. The resultant pancreatic tumor motion trajectories agreed well with 2D-cine-MRI and 4D-CT. The pancreatic tumor volumes shown in the different phases for the SG-KS-4D-MRI were statistically significantly more consistent than those in the 4D-CT.« less
  • Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analyticalmore » noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. Conclusions: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.« less
  • Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analyticalmore » noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. Conclusions: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.« less
  • Purpose: To determine whether a relationship exists between the tumor volume (TV) or relative choline content determined using magnetic resonance spectroscopy imaging (MRSI) at 3T and the clinical prognostic parameters for patients with localized prostate cancer (PCa). Methods and Materials: A total of 72 men (mean age, 67.8 {+-} 6.2 years) were stratified as having low-risk (n = 26), intermediate-risk (n = 24), or high-risk (n = 22) PCa. MRSI was performed at 3T using a phased-array coil. Spectra are expressed as the total choline/citrate, total choline plus creatine/citrate, and total choline plus polyamines plus creatine/citrate ratios. The mean ratiomore » of the most pathologic voxels and the MRSI-based TV were also determined. Results: The mean values of the total choline/citrate, total choline plus creatine/citrate, and total choline plus polyamine plus creatine/citrate ratios were greater for Stage T2b or greater tumors vs. Stage T2a or less tumors: 7.53 {+-} 13.60 vs. 2.31 {+-} 5.65 (p = .018), 8.98 {+-} 14.58 vs. 2.56 {+-} 5.70 (p = .016), and 10.32 {+-} 15.47 vs. 3.55 {+-} 6.16 (p = .014), respectively. The mean MRSI-based TV for Stage T2b or greater and Stage T2a or less tumors was significantly different (2.23 {+-} 2.62 cm{sup 3} vs. 1.26 {+-} 2.06 cm{sup 3}, respectively; p = .030). This TV correlated with increased prostate-specific antigen levels (odds ratio, 1.293; p = .012). Patients with high-risk PCa had a larger TV than did the patients with intermediate-risk PCa. A similar result was found for the intermediate-risk group compared with the low-risk group (odds ratio, 1.225; p = .041). Conclusion: Biomarkers expressing the relative choline content and TV were significant parameters for the localization of PCa and could be helpful for determining the prognosis more accurately.« less
  • Objective: To determine whether changes in tumor volume occur during the course of conformal 3D radiotherapy of high-grade gliomas by use of magnetic resonance imaging (MRI) during treatment and whether these changes had an impact on tumor coverage. Methods and Materials: Between December 2000 and January 2004, 21 patients with WHO Grades 3 to 4 supratentorial malignant gliomas treated with 3D conformal radiotherapy (median dose, 70 Gy) were enrolled in a prospective clinical study. All patients underwent T1-weighted contrast-enhancing and T2-weighted and fluid-attenuated inversion recovery (FLAIR) imaging at approximately 1 to 2 weeks before radiotherapy, during radiotherapy (Weeks 1 andmore » 3), and at routine intervals thereafter. All MRI scans were coregistered to the treatment-planning CT. Gross tumor volume (GTV Pre-Rx) was defined from a postoperative T1-weighted contrast-enhancing MRI performed 1 to 2 weeks before start of radiotherapy. A second GTV (GTV Week 3) was defined by use of an MRI performed during Week 3 of radiotherapy. A uniform 0.5 cm expansion of the respective GTV, PTV (Pre-Rx), and PTV (Week 3) was applied to the final boost plan. Dose-volume histograms (DVH) were used to analyze any potential adverse changes in tumor coverage based on Week 3 MRI. Results: All MRI scans were reviewed independently by a neuroradiologist (DGH). Two patients were noted to have multifocal disease at presentation and were excluded from analysis. In 19 cases, changes in the GTV based on MRI at Week 3 during radiotherapy were as follows: 2 cases had an objective decrease in GTV ({>=}50%); 12 cases revealed a slight decrease in the rim enhancement or changes in cystic appearance of the GTV; 2 cases showed no change in GTV; and 3 cases demonstrated an increase in tumor volume. Both cases with objective decreases in GTV during treatment were Grade 3 tumors. No cases of tumor progression were noted in Grade 3 tumors during treatment. In comparison, three of 12 Grade 4 tumors had tumor progression, based on MRI obtained during Week 3 of radiotherapy. Median increase in GTV (Week 3) was 11.7 cc (range, 9.8-21.3). Retrospective DVH analysis of PTV (Pre-Rx) and PTV (Week 3) demonstrated a decrease in V{sub 95%}(PTV volume receiving 95% of the prescribed dose) in those 3 cases. Conclusions: Routine MR imaging during radiotherapy may be essential in ensuring tumor coverage if highly conformal radiotherapy techniques such as stereotactic boost and intensity-modulated radiotherapy are used in dose-escalation trials that utilize smaller treatment margins.« less