Stephanie A. Soderlund , Abdullah S. Bdaiwi , Joseph W. Plummer , Jason C. Woods , Laura L. Walkup , Zackary I. Cleveland
{"title":"利用基于补丁的高阶奇异值分解去噪改进扩散加权超极化 129Xe 肺部 MRI。","authors":"Stephanie A. Soderlund , Abdullah S. Bdaiwi , Joseph W. Plummer , Jason C. Woods , Laura L. Walkup , Zackary I. Cleveland","doi":"10.1016/j.acra.2024.06.029","DOIUrl":null,"url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div><span>Hyperpolarized xenon (</span><sup>129</sup><span>Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging—commonly the apparent diffusion coefficient<span> (ADC)—can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease<span>. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.</span></span></span></div></div><div><h3>Materials and Methods</h3><div><span>GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted </span><sup>129</sup><span><span>Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis<span> (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed </span></span>diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.</span></div></div><div><h3>Results</h3><div>Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).</div></div><div><h3>Conclusion</h3><div>When applied to diffusion-weighted <sup>129</sup><span><span>Xe images, GLHOSVD improved image quality and allowed </span>airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.</span></div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5289-5299"},"PeriodicalIF":3.8000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising\",\"authors\":\"Stephanie A. Soderlund , Abdullah S. Bdaiwi , Joseph W. Plummer , Jason C. Woods , Laura L. Walkup , Zackary I. Cleveland\",\"doi\":\"10.1016/j.acra.2024.06.029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Rationale and Objectives</h3><div><span>Hyperpolarized xenon (</span><sup>129</sup><span>Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging—commonly the apparent diffusion coefficient<span> (ADC)—can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease<span>. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.</span></span></span></div></div><div><h3>Materials and Methods</h3><div><span>GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted </span><sup>129</sup><span><span>Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis<span> (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed </span></span>diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.</span></div></div><div><h3>Results</h3><div>Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).</div></div><div><h3>Conclusion</h3><div>When applied to diffusion-weighted <sup>129</sup><span><span>Xe images, GLHOSVD improved image quality and allowed </span>airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.</span></div></div>\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":\"31 12\",\"pages\":\"Pages 5289-5299\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S107663322400388X\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S107663322400388X","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising
Rationale and Objectives
Hyperpolarized xenon (129Xe) MRI is a noninvasive method to assess pulmonary structure and function. To measure lung microstructure, diffusion-weighted imaging—commonly the apparent diffusion coefficient (ADC)—can be employed to map changes in alveolar-airspace size resulting from normal aging and pulmonary disease. However, low signal-to-noise ratio (SNR) decreases ADC measurement certainty, and biases ADC to spuriously low values. Further, these challenges are most severe in regions of the lung where alveolar simplification or emphysematous remodeling generate abnormally high ADCs. Here, we apply Global Local Higher Order Singular Value Decomposition (GLHOSVD) denoising to enhance image SNR, thereby reducing uncertainty and bias in diffusion measurements.
Materials and Methods
GLHOSVD denoising was employed in simulated images and gas phantoms with known diffusion coefficients to validate its effectiveness and optimize parameters for analysis of diffusion-weighted 129Xe MRI. GLHOSVD was applied to data from 120 subjects (34 control, 39 cystic fibrosis (CF), 27 lymphangioleiomyomatosis (LAM), and 20 asthma). Image SNR, ADC, and distributed diffusivity coefficient (DDC) were compared before and after denoising using Wilcoxon signed-rank analysis for all images.
Results
Denoising significantly increased SNR in simulated, phantom, and in-vivo images, showing a greater than 2-fold increase (p < 0.001) across diffusion-weighted images. Although mean ADC and DDC remained unchanged (p > 0.05), ADC and DDC standard deviation decreased significantly in denoised images (p < 0.001).
Conclusion
When applied to diffusion-weighted 129Xe images, GLHOSVD improved image quality and allowed airspace size to be quantified in high-diffusion regions of the lungs that were previously inaccessible to measurement due to prohibitively low SNR, thus providing insights into disease pathology.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.