Yang Chen, Jiayu Xiao, Steven Cen, Zhehao Hu, Junzhou Chen, Mark S Shiroishi, Frances E Chow, Jason C Ye, David D Tran, Kyle Hurth, Gabriel Zada, Hsu-Lei Lee, Anthony G Christodoulou, Debiao Li, Eric Chang, Zhaoyang Fan
Purpose To develop a multiparametric dynamic contrast imaging (mpDYCI) technique that enables simultaneous quantification of brain tissue perfusion, microvasculature permeability, transmembrane water efflux, and susceptibility and can be integrated into the routine brain tumor imaging protocol for voxelwise multifaceted quantitative brain tumor assessment. Materials and Methods In this prospective study conducted from March 2023 to April 2024, the mpDYCI technique was evaluated. The mpDYCI technique builds on an MR Multitasking-based dynamic T1 and T2* mapping method and incorporates several technical optimizations in pulse sequence, image reconstruction, T1/T2* quantification, and quantitative metric estimation to achieve robust whole-brain multiparametric quantification. The intersession repeatability and accuracy of mpDYCI metrics were assessed, using intraclass correlation coefficient (ICC), through digital phantom and in vivo experiments involving healthy individuals and individuals with brain tumors. The feasibility of integrating mpDYCI into the routine brain imaging protocol and its clinical utilities based on complementary information from intrinsically coregistered multiple quantitative metrics were also explored. Results In vivo experiments were performed in six healthy participants (mean age, 33 years; range, 27-48 years; three female) and 55 participants with brain tumors (mean age, 56 years; range, 24-81 years; 36 female). Quantitative metrics derived from mpDYCI demonstrated good to excellent repeatability (ICC ≥ 0.80) and excellent agreement with reference standards (range, 6.86%-15.21% percentage error or ICC ≥ 0.93). Histogram analysis, voxel clustering, and histologic validation confirmed the capability of mpDYCI to capture the intratumoral heterogeneity. Low voxelwise correlations between each pair of mpDYCI metrics (correlation coefficient ≤ 0.33 except for one pair) suggested that each metric provides complementary information. Furthermore, mpDYCI exhibited the potential to help differentiate treatment-related effects from true tumor progression in brain metastases. Conclusion With a single 7.5-minute scan and single-dose contrast media injection, mpDYCI can simultaneously quantify perfusion, permeability, water efflux, and susceptibility, thereby enabling comprehensive voxelwise characterization of brain tumors. Keywords: MR Perfusion, CNS, Brain/Brain Stem, Tumor Immune Microenvironment, Reconstruction Algorithms, MR-Dynamic Contrast Enhanced, MR Imaging, Brain Tumor Heterogeneity, Multiparametric MR Imaging, Dynamic Contrast-enhanced MRI, Dynamic Susceptibility Contrast MRI, Quantitative Susceptibility Mapping Supplemental material is available for this article. © RSNA, 2025.
下载PDF
{"title":"Multiparametric Dynamic Contrast Imaging for Voxelwise Quantitative Assessment of Brain Tumors.","authors":"Yang Chen, Jiayu Xiao, Steven Cen, Zhehao Hu, Junzhou Chen, Mark S Shiroishi, Frances E Chow, Jason C Ye, David D Tran, Kyle Hurth, Gabriel Zada, Hsu-Lei Lee, Anthony G Christodoulou, Debiao Li, Eric Chang, Zhaoyang Fan","doi":"10.1148/rycan.250049","DOIUrl":"10.1148/rycan.250049","url":null,"abstract":"<p><p>Purpose To develop a multiparametric dynamic contrast imaging (mpDYCI) technique that enables simultaneous quantification of brain tissue perfusion, microvasculature permeability, transmembrane water efflux, and susceptibility and can be integrated into the routine brain tumor imaging protocol for voxelwise multifaceted quantitative brain tumor assessment. Materials and Methods In this prospective study conducted from March 2023 to April 2024, the mpDYCI technique was evaluated. The mpDYCI technique builds on an MR Multitasking-based dynamic T1 and T2* mapping method and incorporates several technical optimizations in pulse sequence, image reconstruction, T1/T2* quantification, and quantitative metric estimation to achieve robust whole-brain multiparametric quantification. The intersession repeatability and accuracy of mpDYCI metrics were assessed, using intraclass correlation coefficient (ICC), through digital phantom and in vivo experiments involving healthy individuals and individuals with brain tumors. The feasibility of integrating mpDYCI into the routine brain imaging protocol and its clinical utilities based on complementary information from intrinsically coregistered multiple quantitative metrics were also explored. Results In vivo experiments were performed in six healthy participants (mean age, 33 years; range, 27-48 years; three female) and 55 participants with brain tumors (mean age, 56 years; range, 24-81 years; 36 female). Quantitative metrics derived from mpDYCI demonstrated good to excellent repeatability (ICC ≥ 0.80) and excellent agreement with reference standards (range, 6.86%-15.21% percentage error or ICC ≥ 0.93). Histogram analysis, voxel clustering, and histologic validation confirmed the capability of mpDYCI to capture the intratumoral heterogeneity. Low voxelwise correlations between each pair of mpDYCI metrics (correlation coefficient ≤ 0.33 except for one pair) suggested that each metric provides complementary information. Furthermore, mpDYCI exhibited the potential to help differentiate treatment-related effects from true tumor progression in brain metastases. Conclusion With a single 7.5-minute scan and single-dose contrast media injection, mpDYCI can simultaneously quantify perfusion, permeability, water efflux, and susceptibility, thereby enabling comprehensive voxelwise characterization of brain tumors. <b>Keywords:</b> MR Perfusion, CNS, Brain/Brain Stem, Tumor Immune Microenvironment, Reconstruction Algorithms, MR-Dynamic Contrast Enhanced, MR Imaging, Brain Tumor Heterogeneity, Multiparametric MR Imaging, Dynamic Contrast-enhanced MRI, Dynamic Susceptibility Contrast MRI, Quantitative Susceptibility Mapping <i>Supplemental material is available for this article.</i> © RSNA, 2025.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"7 5","pages":"e250049"},"PeriodicalIF":5.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145150732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
引用
批量引用