{"title":"基于字典学习的去噪算法与扩散加权磁共振图像中的预期补丁对数似然法","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":"{\"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}","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
摘要
扩散加权成像(DWI)是磁共振成像(MRI)技术中对噪音最敏感的技术之一。随着获取 DWI 图像所用 b 值的增加,可获得弥散差异突出的图像。然而,增加 b 值的 DWI 图像不可避免地存在一个主要缺点,即噪声会被放大。因此,本研究建立了一种基于字典学习(DL)的去噪算法模型,并将其应用于 DWI 图像。所设计的算法是利用期望补丁对数似然建立的基于词典学习的算法模型。DWI 图像是通过将 b 值从 400 调整到 400 间隔来获得的。当将所提出的基于 DL 的去噪算法应用于 DWI 时,我们证实对比度-噪声比和变异系数与噪声图像相比分别提高了约 4.26 倍和 5.22 倍。总之,我们希望所提出的基于 DL 的去噪算法能高效获取高 b 值的 DWI 图像,这对观察急性脑梗塞和微血管疾病非常有用。
Dictionary learning-based denoising algorithm with expected patch log likelihood in diffusion-weighted magnetic resonance image
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.