采用三维径向库什球采集和深度学习时空四维重建的高清运动分辨MRI。

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Victor Murray, Can Wu, Ricardo Otazo
{"title":"采用三维径向库什球采集和深度学习时空四维重建的高清运动分辨MRI。","authors":"Victor Murray, Can Wu, Ricardo Otazo","doi":"10.1088/1361-6560/ade195","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective.</i>To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung MRI.<i>Approach.</i>Free-breathing lung MRI was conducted on eight healthy volunteers and ten patients with lung tumors on a 3 T MRI scanner using a 3D radial kooshball sequence with half-spoke (ultrashort echo time, UTE, TE = 0.12 ms) and full-spoke (T1-weighted, TE = 1.55 ms) acquisitions. Data were motion-sorted using amplitude-binning on a respiratory motion signal. Two high-definition Movienet (HD-Movienet) deep learning models were proposed to reconstruct 3D radial kooshball data: slice-by-slice reconstruction in the coronal orientation using 2D convolutional kernels (2D-based HD-Movienet) and reconstruction on blocks of eight coronal slices using 3D convolutional kernels (3D-based HD-Movienet). Two applications were considered: (a) anatomical imaging at expiration and inspiration with four motion states and a scan time of 2 min, and (b) dynamic motion imaging with 10 motion states and a scan time of 4 min. The training was performed using XD-GRASP 4D images reconstructed from 4.5 min and 6.5 min acquisitions as references.<i>Main Results.</i>2D-based HD-Movienet achieved a reconstruction time of <6 s, significantly faster than the iterative XD-GRASP reconstruction (>10 min with GPU optimization) while maintaining comparable image quality to XD-GRASP with two extra minutes of scan time. The 3D-based HD-Movienet improved reconstruction quality at the expense of longer reconstruction times (<11 s).<i>Significance.</i>HD-Movienet demonstrates the feasibility of motion-resolved 4D MRI with isotropic 1.1 mm resolution and scan times of only 2 min for four motion states and 4 min for 10 motion states, marking a significant advancement in clinical free-breathing lung MRI.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.\",\"authors\":\"Victor Murray, Can Wu, Ricardo Otazo\",\"doi\":\"10.1088/1361-6560/ade195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Objective.</i>To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung MRI.<i>Approach.</i>Free-breathing lung MRI was conducted on eight healthy volunteers and ten patients with lung tumors on a 3 T MRI scanner using a 3D radial kooshball sequence with half-spoke (ultrashort echo time, UTE, TE = 0.12 ms) and full-spoke (T1-weighted, TE = 1.55 ms) acquisitions. Data were motion-sorted using amplitude-binning on a respiratory motion signal. Two high-definition Movienet (HD-Movienet) deep learning models were proposed to reconstruct 3D radial kooshball data: slice-by-slice reconstruction in the coronal orientation using 2D convolutional kernels (2D-based HD-Movienet) and reconstruction on blocks of eight coronal slices using 3D convolutional kernels (3D-based HD-Movienet). Two applications were considered: (a) anatomical imaging at expiration and inspiration with four motion states and a scan time of 2 min, and (b) dynamic motion imaging with 10 motion states and a scan time of 4 min. The training was performed using XD-GRASP 4D images reconstructed from 4.5 min and 6.5 min acquisitions as references.<i>Main Results.</i>2D-based HD-Movienet achieved a reconstruction time of <6 s, significantly faster than the iterative XD-GRASP reconstruction (>10 min with GPU optimization) while maintaining comparable image quality to XD-GRASP with two extra minutes of scan time. The 3D-based HD-Movienet improved reconstruction quality at the expense of longer reconstruction times (<11 s).<i>Significance.</i>HD-Movienet demonstrates the feasibility of motion-resolved 4D MRI with isotropic 1.1 mm resolution and scan times of only 2 min for four motion states and 4 min for 10 motion states, marking a significant advancement in clinical free-breathing lung MRI.</p>\",\"PeriodicalId\":20185,\"journal\":{\"name\":\"Physics in medicine and biology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physics in medicine and biology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6560/ade195\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics in medicine and biology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6560/ade195","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

目的:开发具有1.1mm各向同性分辨率的运动分辨率体积MRI,扫描时间为10分钟(GPU优化),同时保持与xD - grasp相当的图像质量,扫描时间为2分钟。基于3d的HD-Movienet以较长的重建时间为代价提高了重建质量(
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-definition motion-resolved MRI using 3D radial kooshball acquisition and deep learning spatial-temporal 4D reconstruction.

Objective.To develop motion-resolved volumetric MRI with 1.1 mm isotropic resolution and scan times <5 min using a combination of 3D radial kooshball acquisition and spatial-temporal deep learning 4D reconstruction for free-breathing high-definition (HD) lung MRI.Approach.Free-breathing lung MRI was conducted on eight healthy volunteers and ten patients with lung tumors on a 3 T MRI scanner using a 3D radial kooshball sequence with half-spoke (ultrashort echo time, UTE, TE = 0.12 ms) and full-spoke (T1-weighted, TE = 1.55 ms) acquisitions. Data were motion-sorted using amplitude-binning on a respiratory motion signal. Two high-definition Movienet (HD-Movienet) deep learning models were proposed to reconstruct 3D radial kooshball data: slice-by-slice reconstruction in the coronal orientation using 2D convolutional kernels (2D-based HD-Movienet) and reconstruction on blocks of eight coronal slices using 3D convolutional kernels (3D-based HD-Movienet). Two applications were considered: (a) anatomical imaging at expiration and inspiration with four motion states and a scan time of 2 min, and (b) dynamic motion imaging with 10 motion states and a scan time of 4 min. The training was performed using XD-GRASP 4D images reconstructed from 4.5 min and 6.5 min acquisitions as references.Main Results.2D-based HD-Movienet achieved a reconstruction time of <6 s, significantly faster than the iterative XD-GRASP reconstruction (>10 min with GPU optimization) while maintaining comparable image quality to XD-GRASP with two extra minutes of scan time. The 3D-based HD-Movienet improved reconstruction quality at the expense of longer reconstruction times (<11 s).Significance.HD-Movienet demonstrates the feasibility of motion-resolved 4D MRI with isotropic 1.1 mm resolution and scan times of only 2 min for four motion states and 4 min for 10 motion states, marking a significant advancement in clinical free-breathing lung MRI.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
×
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:604180095
Book学术官方微信