Deep-Dixon: Deep-Learning frameworks for fusion of MR T1 images for fat and water extraction

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Snehal V. Laddha, Rohini S. Ochawar, Krushna Gandhi, Yu-Dong Zhang
{"title":"Deep-Dixon: Deep-Learning frameworks for fusion of MR T1 images for fat and water extraction","authors":"Snehal V. Laddha, Rohini S. Ochawar, Krushna Gandhi, Yu-Dong Zhang","doi":"10.1007/s11042-024-20255-2","DOIUrl":null,"url":null,"abstract":"<p>Medical image fusion plays a crucial role in understanding the necessity of medical procedures and it also assists radiologists in decision-making for surgical operations. Dixon has mathematically described a fat suppression technique that differentiates between fat and water signals by utilizing in-phase and out-of-phase MR imaging. The fusion of MR T1 images can be performed by adding or subtracting in-phase and out-phase images, respectively. The dataset used in this study was collected from the CHAOS grand challenge, comprising DICOM data sets from two different MRI sequences (T1 in-phase and out-phase). Our methodology involved training of deep learning models; VGG 19 and RESNET18 to extract features from this dataset to implement the Dixon technique, effectively separating the water and fat components. Using VGG19 and ResNet18 models, we were able to accomplish the image fusion accuracy for water-only images with EN as high as 5.70, 4.72, MI as 2.26, 2.21; SSIM as 0.97, 0.81; Qabf as 0.73, 0.72; Nabf as low as 0.18, 0.19 using VGG19 and ResNet18 models respectively. For fat-only images we have achieved EN as 4.17, 4.06; MI as 0.80, 0.77; SSIM as 0.45, 0.39; Qabf as 0.53, 0.48; Nabf as low as 0.22, 0.27. The experimental findings demonstrated the superior performance of our proposed method in terms of the enhanced accuracy and visual quality of water-only and fat-only images using several quantitative assessment parameters over other models experimented by various researchers. Our models are the stand-alone models for the implementation of the Dixon methodology using deep learning techniques. This model has experienced an improvement of 0.62 in EN, and 0.29 in Qabf compared to existing fusion models for different image modalities. Also, it can better assist radiologists in identifying tissues and blood vessels of abdominal organs that are rich in protein and understanding the fat content in lesions.</p>","PeriodicalId":18770,"journal":{"name":"Multimedia Tools and Applications","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Multimedia Tools and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11042-024-20255-2","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Medical image fusion plays a crucial role in understanding the necessity of medical procedures and it also assists radiologists in decision-making for surgical operations. Dixon has mathematically described a fat suppression technique that differentiates between fat and water signals by utilizing in-phase and out-of-phase MR imaging. The fusion of MR T1 images can be performed by adding or subtracting in-phase and out-phase images, respectively. The dataset used in this study was collected from the CHAOS grand challenge, comprising DICOM data sets from two different MRI sequences (T1 in-phase and out-phase). Our methodology involved training of deep learning models; VGG 19 and RESNET18 to extract features from this dataset to implement the Dixon technique, effectively separating the water and fat components. Using VGG19 and ResNet18 models, we were able to accomplish the image fusion accuracy for water-only images with EN as high as 5.70, 4.72, MI as 2.26, 2.21; SSIM as 0.97, 0.81; Qabf as 0.73, 0.72; Nabf as low as 0.18, 0.19 using VGG19 and ResNet18 models respectively. For fat-only images we have achieved EN as 4.17, 4.06; MI as 0.80, 0.77; SSIM as 0.45, 0.39; Qabf as 0.53, 0.48; Nabf as low as 0.22, 0.27. The experimental findings demonstrated the superior performance of our proposed method in terms of the enhanced accuracy and visual quality of water-only and fat-only images using several quantitative assessment parameters over other models experimented by various researchers. Our models are the stand-alone models for the implementation of the Dixon methodology using deep learning techniques. This model has experienced an improvement of 0.62 in EN, and 0.29 in Qabf compared to existing fusion models for different image modalities. Also, it can better assist radiologists in identifying tissues and blood vessels of abdominal organs that are rich in protein and understanding the fat content in lesions.

Abstract Image

Deep-Dixon:用于融合 MR T1 图像以提取脂肪和水分的深度学习框架
医学影像融合在理解医疗程序的必要性方面起着至关重要的作用,它还能帮助放射科医生做出外科手术决策。Dixon 用数学方法描述了一种脂肪抑制技术,该技术通过利用相内和相外磁共振成像来区分脂肪和水信号。核磁共振 T1 图像的融合可分别通过相内和相外图像的相加或相减来实现。本研究使用的数据集收集自 CHAOS 大挑战赛,包括来自两种不同磁共振成像序列(T1 相内和相外)的 DICOM 数据集。我们的方法包括训练深度学习模型(VGG 19 和 RESNET18),从该数据集中提取特征以实施 Dixon 技术,从而有效分离水和脂肪成分。使用 VGG19 和 ResNet18 模型,我们能够实现纯水图像的图像融合精度,EN 分别高达 5.70 和 4.72,MI 分别为 2.26 和 2.21,SSIM 分别为 0.97 和 0.81,Qabf 分别为 0.73 和 0.72,Nabf 分别为 0.18 和 0.19。对于纯脂肪图像,我们的 EN 值分别为 4.17、4.06;MI 值分别为 0.80、0.77;SSIM 值分别为 0.45、0.39;Qabf 值分别为 0.53、0.48;Nabf 值分别低至 0.22、0.27。实验结果表明,我们提出的方法在提高纯水图像和纯脂肪图像的准确性和视觉质量方面具有优越性能,其使用的几个定量评估参数优于其他研究人员实验过的模型。我们的模型是利用深度学习技术实现 Dixon 方法的独立模型。与不同图像模式的现有融合模型相比,该模型的EN值提高了0.62,Qabf值提高了0.29。此外,它还能更好地帮助放射科医生识别富含蛋白质的腹部器官组织和血管,了解病变部位的脂肪含量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信