Chao Li, Jiahao Li, Jinwei Zhang, Eddy Solomon, Alexey V Dimov, Pascal Spincemaille, Thanh D Nguyen, Martin R Prince, Yi Wang
{"title":"使用隐式神经表征的导航器运动分辨率MR指纹识别：自由呼吸三维全肝多参数映射的可行性。","authors":"Chao Li, Jiahao Li, Jinwei Zhang, Eddy Solomon, Alexey V Dimov, Pascal Spincemaille, Thanh D Nguyen, Martin R Prince, Yi Wang","doi":"10.1002/mrm.70063","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To develop a multiparametric free-breathing three-dimensional, whole-liver quantitative maps of water T<sub>1</sub>, water T<sub>2</sub>, fat fraction (FF) and R<sub>2</sub>*.</p><p><strong>Methods: </strong>A multi-echo 3D stack-of-spiral gradient-echo sequence with inversion recovery and T<sub>2</sub>-prep magnetization preparations was implemented for multiparametric MRI. Fingerprinting and a neural network based on implicit neural representation (FINR) were developed to simultaneously reconstruct the motion deformation fields, the static images, perform water-fat separation, and generate T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF maps. FINR performance was evaluated in 10 healthy subjects by comparison with quantitative maps generated using conventional breath-holding imaging.</p><p><strong>Results: </strong>FINR consistently generated sharp images in all subjects free of motion artifacts. FINR showed minimal bias and narrow 95% limits of agreement for T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF values in the liver compared with conventional imaging. FINR training took about 3 h per subject, and FINR inference took less than 1 min to produce static images and motion deformation fields.</p><p><strong>Conclusions: </strong>FINR is a promising approach for 3D whole-liver T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF mapping in a single free-breathing continuous scan.</p>","PeriodicalId":18065,"journal":{"name":"Magnetic Resonance in Medicine","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.\",\"authors\":\"Chao Li, Jiahao Li, Jinwei Zhang, Eddy Solomon, Alexey V Dimov, Pascal Spincemaille, Thanh D Nguyen, Martin R Prince, Yi Wang\",\"doi\":\"10.1002/mrm.70063\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To develop a multiparametric free-breathing three-dimensional, whole-liver quantitative maps of water T<sub>1</sub>, water T<sub>2</sub>, fat fraction (FF) and R<sub>2</sub>*.</p><p><strong>Methods: </strong>A multi-echo 3D stack-of-spiral gradient-echo sequence with inversion recovery and T<sub>2</sub>-prep magnetization preparations was implemented for multiparametric MRI. Fingerprinting and a neural network based on implicit neural representation (FINR) were developed to simultaneously reconstruct the motion deformation fields, the static images, perform water-fat separation, and generate T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF maps. FINR performance was evaluated in 10 healthy subjects by comparison with quantitative maps generated using conventional breath-holding imaging.</p><p><strong>Results: </strong>FINR consistently generated sharp images in all subjects free of motion artifacts. FINR showed minimal bias and narrow 95% limits of agreement for T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF values in the liver compared with conventional imaging. FINR training took about 3 h per subject, and FINR inference took less than 1 min to produce static images and motion deformation fields.</p><p><strong>Conclusions: </strong>FINR is a promising approach for 3D whole-liver T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF mapping in a single free-breathing continuous scan.</p>\",\"PeriodicalId\":18065,\"journal\":{\"name\":\"Magnetic Resonance in Medicine\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Magnetic Resonance in Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/mrm.70063\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/mrm.70063","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.
Purpose: To develop a multiparametric free-breathing three-dimensional, whole-liver quantitative maps of water T1, water T2, fat fraction (FF) and R2*.
Methods: A multi-echo 3D stack-of-spiral gradient-echo sequence with inversion recovery and T2-prep magnetization preparations was implemented for multiparametric MRI. Fingerprinting and a neural network based on implicit neural representation (FINR) were developed to simultaneously reconstruct the motion deformation fields, the static images, perform water-fat separation, and generate T1, T2, R2*, and FF maps. FINR performance was evaluated in 10 healthy subjects by comparison with quantitative maps generated using conventional breath-holding imaging.
Results: FINR consistently generated sharp images in all subjects free of motion artifacts. FINR showed minimal bias and narrow 95% limits of agreement for T1, T2, R2*, and FF values in the liver compared with conventional imaging. FINR training took about 3 h per subject, and FINR inference took less than 1 min to produce static images and motion deformation fields.
Conclusions: FINR is a promising approach for 3D whole-liver T1, T2, R2*, and FF mapping in a single free-breathing continuous scan.
期刊介绍：
Magnetic Resonance in Medicine (Magn Reson Med) is an international journal devoted to the publication of original investigations concerned with all aspects of the development and use of nuclear magnetic resonance and electron paramagnetic resonance techniques for medical applications. Reports of original investigations in the areas of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.
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