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de Villedon de Naide V, Maes JD, Villegas-Martinez M, Ribal I, Maillot A, Ozenne V, Montier G, Boullé T, Sridi S, Gut P, Küstner T, Stuber M, Cochet H, Bustin A. Fully automated contrast selection of joint bright- and black-blood late gadolinium enhancement imaging for robust myocardial scar assessment. Magn Reson Imaging 2024; 109:256-263. [PMID: 38522623 DOI: 10.1016/j.mri.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 03/14/2024] [Accepted: 03/21/2024] [Indexed: 03/26/2024]
Abstract
PURPOSE Joint bright- and black-blood MRI techniques provide improved scar localization and contrast. Black-blood contrast is obtained after the visual selection of an optimal inversion time (TI) which often results in uncertainties, inter- and intra-observer variability and increased workload. In this work, we propose an artificial intelligence-based algorithm to enable fully automated TI selection and simplify myocardial scar imaging. METHODS The proposed algorithm first localizes the left ventricle using a U-Net architecture. The localized left cavity centroid is extracted and a squared region of interest ("focus box") is created around the resulting pixel. The focus box is then propagated on each image and the sum of the pixel intensity inside is computed. The smallest sum corresponds to the image with the lowest intensity signal within the blood pool and healthy myocardium, which will provide an ideal scar-to-blood contrast. The image's corresponding TI is considered optimal. The U-Net was trained to segment the epicardium in 177 patients with binary cross-entropy loss. The algorithm was validated retrospectively in 152 patients, and the agreement between the algorithm and two magnetic resonance (MR) operators' prediction of TI values was calculated using the Fleiss' kappa coefficient. Thirty focus box sizes, ranging from 2.3mm2 to 20.3cm2, were tested. Processing times were measured. RESULTS The U-Net's Dice score was 93.0 ± 0.1%. The proposed algorithm extracted TI values in 2.7 ± 0.1 s per patient (vs. 16.0 ± 8.5 s for the operator). An agreement between the algorithm's prediction and the MR operators' prediction was found in 137/152 patients (κ= 0.89), for an optimal focus box of size 2.3cm2. CONCLUSION The proposed fully-automated algorithm has potential of reducing uncertainties, variability, and workload inherent to manual approaches with promise for future clinical implementation for joint bright- and black-blood MRI.
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Affiliation(s)
| | - Jean-David Maes
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | | | - Indra Ribal
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France
| | - Aurélien Maillot
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France
| | - Valéry Ozenne
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France
| | - Géraldine Montier
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Thibaut Boullé
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Soumaya Sridi
- CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Pauline Gut
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, 72076 Tübingen, Germany
| | - Matthias Stuber
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Hubert Cochet
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France
| | - Aurélien Bustin
- Université de Bordeaux, INSERM, CRCTB, U 1045, IHU Liryc, F-33000 Bordeaux, France; CHU de Bordeaux, Department of Cardiovascular Imaging, INSERM, U 1045, F-33000 Bordeaux, France; Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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