Aurélien Maillot, Soumaya Sridi, Xavier Pineau, Amandine André-Billeau, Stéphanie Hosteins, Jean-David Maes, Géraldine Montier, Marta Nuñez-Garcia, Bruno Quesson, Maxime Sermesant, Hubert Cochet, Matthias Stuber, Aurélien Bustin
{"title":"Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice.","authors":"Aurélien Maillot, Soumaya Sridi, Xavier Pineau, Amandine André-Billeau, Stéphanie Hosteins, Jean-David Maes, Géraldine Montier, Marta Nuñez-Garcia, Bruno Quesson, Maxime Sermesant, Hubert Cochet, Matthias Stuber, Aurélien Bustin","doi":"10.1007/s10334-023-01101-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection.</p><p><strong>Materials and methods: </strong>The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients' scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner.</p><p><strong>Results: </strong>Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss' kappa coefficient for automated-manual, intra-observer and inter-observer agreements were [Formula: see text]= 0.73, [Formula: see text] = 0.70 and [Formula: see text] = 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert.</p><p><strong>Discussion: </strong>Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.</p>","PeriodicalId":18067,"journal":{"name":"Magnetic Resonance Materials in Physics, Biology and Medicine","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667449/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic Resonance Materials in Physics, Biology and Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10334-023-01101-2","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/9 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection.
Materials and methods: The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients' scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner.
Results: Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss' kappa coefficient for automated-manual, intra-observer and inter-observer agreements were [Formula: see text]= 0.73, [Formula: see text] = 0.70 and [Formula: see text] = 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert.
Discussion: Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.
期刊介绍:
MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include:
advances in materials, hardware and software in magnetic resonance technology,
new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine,
study of animal models and intact cells using magnetic resonance,
reports of clinical trials on humans and clinical validation of magnetic resonance protocols.