Evaluating the Role of Leakage Correction of Hemodynamic Parameters Derived from Dynamic Contrast Enhanced MRI for Glioma Grading

Dinil Sasi Sankaralayam,Anandh K. Ramaniharan,Rakesh Kumar Gupta,Rana Patir,Sunita Ahlawat,Sandeep Vaishya,Anup Singh

Journal of Magnetic Resonance Imaging（2024）

Indian Inst Technol Delhi | Philips Healthcare India Pvt Ltd | Fortis Mem Res Inst

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Journal of Magnetic Resonance ImagingEarly View Letter to the Editor Evaluating the Role of Leakage Correction of Hemodynamic Parameters derived from Dynamic Contrast Enhanced MRI for Glioma Grading Dinil Sasi Sankaralayam PhD, Dinil Sasi Sankaralayam PhD [email protected] orcid.org/0000-0002-6669-5584 Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USASearch for more papers by this authorAnandh K. Ramaniharan PhD, Anandh K. Ramaniharan PhD Philips Innovation Campus, Philips Healthcare India Private Limited, Bangalore, Karnataka, IndiaSearch for more papers by this authorRakesh Kumar Gupta MD, Rakesh Kumar Gupta MD orcid.org/0000-0001-6047-3115 Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorRana Patir MCh, Rana Patir MCh Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorSunita Ahlawat MD, Sunita Ahlawat MD SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorSandeep Vaishya MCh, Sandeep Vaishya MCh Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorAnup Singh PhD, Corresponding Author Anup Singh PhD [email protected] orcid.org/0000-0001-6744-8326 Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India Address reprint requests to: A.S., Room No: 299, Block–II, Indian Institute of Technology Delhi, 110 016 New Delhi, India. Email: [email protected]Search for more papers by this author Dinil Sasi Sankaralayam PhD, Dinil Sasi Sankaralayam PhD [email protected] orcid.org/0000-0002-6669-5584 Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USASearch for more papers by this authorAnandh K. Ramaniharan PhD, Anandh K. Ramaniharan PhD Philips Innovation Campus, Philips Healthcare India Private Limited, Bangalore, Karnataka, IndiaSearch for more papers by this authorRakesh Kumar Gupta MD, Rakesh Kumar Gupta MD orcid.org/0000-0001-6047-3115 Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorRana Patir MCh, Rana Patir MCh Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorSunita Ahlawat MD, Sunita Ahlawat MD SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorSandeep Vaishya MCh, Sandeep Vaishya MCh Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurugram, Haryana, IndiaSearch for more papers by this authorAnup Singh PhD, Corresponding Author Anup Singh PhD [email protected] orcid.org/0000-0001-6744-8326 Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India Address reprint requests to: A.S., Room No: 299, Block–II, Indian Institute of Technology Delhi, 110 016 New Delhi, India. Email: [email protected]Search for more papers by this author First published: 10 March 2024 https://doi.org/10.1002/jmri.29338 Level of Evidence: 4 Technical Efficacy: Stage 2 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Supporting Information Filename Description jmri29338-sup-0001-Supinfo.docxWord 2007 document , 950.6 KB Data S1. Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. References 1Lüdemann L, Grieger W, Wurm R, Budzisch M, Hamm B, Zimmer C. Comparison of dynamic contrast-enhanced MRI with WHO tumor grading for gliomas. Eur Radiol 2001; 11: 1231-1241. 10.1007/s003300000748 CASPubMedWeb of Science®Google Scholar 2Zhao M, Guo LL, Huang N, et al. Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging. Oncol Lett 2017; 14: 5418-5426. PubMedWeb of Science®Google Scholar 3Singh A, Rathore RK, Haris M, Verma SK, Husain N, Gupta RK. Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRI. J Magn Reson Imaging 2009; 29(1): 166-176. 10.1002/jmri.21624 PubMedWeb of Science®Google Scholar 4Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K. Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med 2009; 62: 205-217. 10.1002/mrm.22005 CASPubMedWeb of Science®Google Scholar 5Li KL, Jackson A. New hybrid technique for accurate and reproducible quantitation of dynamic contrast-enhanced MRI data. Magn Reson Med 2003; 50: 1286-1295. 10.1002/mrm.10652 CASPubMedWeb of Science®Google Scholar 6Sasi SD, Ramaniharan AK, Bhattacharjee R, et al. Evaluating feasibility of high resolution T1-perfusion MRI with whole brain coverage using compressed SENSE: Application to glioma grading. Eur J Radiol 2020; 129:109049. Web of Science®Google Scholar 7Sasi SD, Gupta RK, Patir R, Ahlawat S, Vaishya S, Singh A. A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma. Magn Reson Imaging 2021; 83: 77-88. 10.1016/j.mri.2021.07.005 PubMedWeb of Science®Google Scholar 8Heye AK, Thrippleton MJ, Armitage PA, et al. Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability. Neuroimage 2016; 125: 446-455. 10.1016/j.neuroimage.2015.10.018 PubMedWeb of Science®Google Scholar 9Sahoo P, Gupta RK, Gupta PK, et al. Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1-weighted DCE-MRI. Magn Reson Imaging 2017; 44: 32-37. 10.1016/j.mri.2017.08.003 PubMedWeb of Science®Google Scholar 10Sourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med 2011; 66: 735-745. 10.1002/mrm.22861 PubMedWeb of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation

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Dynamic Contrast-Enhanced MRI,Diagnostic Accuracy,Functional MRI,Perfusion Imaging

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
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