{"title":"Dictionary learning-based denoising algorithm with expected patch log likelihood in diffusion-weighted magnetic resonance image","authors":"Kyuseok Kim, Hyun-Woo Jeong, Youngjin Lee","doi":"10.1007/s40042-024-01167-8","DOIUrl":null,"url":null,"abstract":"<div><p>Diffusion-weighted imaging (DWI) is one of the most sensitive techniques to noise among magnetic resonance imaging (MRI) techniques. As the <i>b</i>-value used to acquire the DWI image increases, an image in which the difference in diffusion is emphasized can be obtained. However, DWI images with increased <i>b</i>-values inevitably have a major drawback in that noise is amplified. Thus, in this study, a dictionary learning (DL)-based denoising algorithm was modeled and applied to DWI images. The designed algorithm was modeled as a DL-based algorithm using the expected patch log likelihood. The DWI images were obtained by adjusting the <i>b</i>-value from 400 to 400 intervals. When the proposed DL-based denoising algorithm was applied to DWI, we confirmed that the contrast-to-noise ratio and coefficient of variation were improved by approximately 4.26 and 5.22 times, respectively, compared with noisy images. In conclusion, we expect that the proposed DL-based denoising algorithm will be highly efficient in acquiring DWI images using a high b-value, which is useful for observing acute cerebral infarction and microvascular disease.</p></div>","PeriodicalId":677,"journal":{"name":"Journal of the Korean Physical Society","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Physical Society","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s40042-024-01167-8","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Diffusion-weighted imaging (DWI) is one of the most sensitive techniques to noise among magnetic resonance imaging (MRI) techniques. As the b-value used to acquire the DWI image increases, an image in which the difference in diffusion is emphasized can be obtained. However, DWI images with increased b-values inevitably have a major drawback in that noise is amplified. Thus, in this study, a dictionary learning (DL)-based denoising algorithm was modeled and applied to DWI images. The designed algorithm was modeled as a DL-based algorithm using the expected patch log likelihood. The DWI images were obtained by adjusting the b-value from 400 to 400 intervals. When the proposed DL-based denoising algorithm was applied to DWI, we confirmed that the contrast-to-noise ratio and coefficient of variation were improved by approximately 4.26 and 5.22 times, respectively, compared with noisy images. In conclusion, we expect that the proposed DL-based denoising algorithm will be highly efficient in acquiring DWI images using a high b-value, which is useful for observing acute cerebral infarction and microvascular disease.
期刊介绍:
The Journal of the Korean Physical Society (JKPS) covers all fields of physics spanning from statistical physics and condensed matter physics to particle physics. The manuscript to be published in JKPS is required to hold the originality, significance, and recent completeness. The journal is composed of Full paper, Letters, and Brief sections. In addition, featured articles with outstanding results are selected by the Editorial board and introduced in the online version. For emphasis on aspect of international journal, several world-distinguished researchers join the Editorial board. High quality of papers may be express-published when it is recommended or requested.