提供实用指导,以识别和排除不理想的 DSC-MRI 结果。

Frontiers in radiology Pub Date : 2024-02-20 eCollection Date: 2024-01-01 DOI:10.3389/fradi.2024.1307586
Melissa A Prah, Kathleen M Schmainda
{"title":"提供实用指导,以识别和排除不理想的 DSC-MRI 结果。","authors":"Melissa A Prah, Kathleen M Schmainda","doi":"10.3389/fradi.2024.1307586","DOIUrl":null,"url":null,"abstract":"<p><p>Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"4 ","pages":"1307586"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10913595/pdf/","citationCount":"0","resultStr":"{\"title\":\"Practical guidance to identify and troubleshoot suboptimal DSC-MRI results.\",\"authors\":\"Melissa A Prah, Kathleen M Schmainda\",\"doi\":\"10.3389/fradi.2024.1307586\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.</p>\",\"PeriodicalId\":73101,\"journal\":{\"name\":\"Frontiers in radiology\",\"volume\":\"4 \",\"pages\":\"1307586\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10913595/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in radiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fradi.2024.1307586\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fradi.2024.1307586","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

由动态易感性对比(DSC)灌注磁共振成像(pMRI)得出的相对脑血量(rCBV)已被证明是神经放射肿瘤负荷的可靠标记物。最近就 pMRI 采集策略达成的共识建议为将 pMRI 纳入不同规模或经验的患者治疗中心提供了途径。然而,即使正确实施和执行了 DSC-MRI 方案,仍会出现一些问题,许多中心可能不容易识别或意识到这些问题。此外,漏掉的 pMRI 问题并不总能在生成的 rCBV 图像中显现出来,从而导致放射诊断不准确或漏诊。因此,我们从我们的 DSC-MRI 数据集数据库中收集了一些真实的例子,展示了在采集、后处理和解读过程中出现的问题,并在可能的情况下提供了适当的缓解策略。所涉及的 pMRI 问题包括与图像采集和后处理相关的问题,重点是造影剂给药、时间和速率、信噪比质量以及易感伪影。这项工作的目标是为尽量减少和识别 pMRI 问题提供指导,以确保只能解读高质量的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Practical guidance to identify and troubleshoot suboptimal DSC-MRI results.

Relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) perfusion MR imaging (pMRI) has been shown to be a robust marker of neuroradiological tumor burden. Recent consensus recommendations in pMRI acquisition strategies have provided a pathway for pMRI inclusion in diverse patient care centers, regardless of size or experience. However, even with proper implementation and execution of the DSC-MRI protocol, issues will arise that many centers may not easily recognize or be aware of. Furthermore, missed pMRI issues are not always apparent in the resulting rCBV images, potentiating inaccurate or missed radiological diagnoses. Therefore, we gathered from our database of DSC-MRI datasets, true-to-life examples showcasing the breakdowns in acquisition, postprocessing, and interpretation, along with appropriate mitigation strategies when possible. The pMRI issues addressed include those related to image acquisition and postprocessing with a focus on contrast agent administration, timing, and rate, signal-to-noise quality, and susceptibility artifact. The goal of this work is to provide guidance to minimize and recognize pMRI issues to ensure that only quality data is interpreted.

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
CiteScore
1.20
自引率
0.00%
发文量
0
期刊最新文献
Language task-based fMRI analysis using machine learning and deep learning. Case Report: Diffuse cerebral lymphomatosis with superimposed multifocal primary CNS lymphoma. Diffusion-weighted MRI in the identification of renal parenchymal involvement in children with a first episode of febrile urinary tract infection. SenseCare: a research platform for medical image informatics and interactive 3D visualization. Editorial: Artificial intelligence and multimodal medical imaging data fusion for improving cardiovascular disease care.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1