Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts.

Autor:	Mahmutoglu MA; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Preetha CJ; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Meredig H; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Tonn JC; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Weller M; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Wick W; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Bendszus M; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Brugnara G; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)., Vollmuth P; From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.).
Jazyk:	angličtina
Zdroj:	Radiology. Artificial intelligence [Radiol Artif Intell] 2024 Jan; Vol. 6 (1), pp. e230095.
DOI:	10.1148/ryai.230095
Abstrakt:	Purpose To develop a fully automated device- and sequence-independent convolutional neural network (CNN) for reliable and high-throughput labeling of heterogeneous, unstructured MRI data. Materials and Methods Retrospective, multicentric brain MRI data (2179 patients with glioblastoma, 8544 examinations, 63 327 sequences) from 249 hospitals and 29 scanner types were used to develop a network based on ResNet-18 architecture to differentiate nine MRI sequence types, including T1-weighted, postcontrast T1-weighted, T2-weighted, fluid-attenuated inversion recovery, susceptibility-weighted, apparent diffusion coefficient, diffusion-weighted (low and high b value), and gradient-recalled echo T2*-weighted and dynamic susceptibility contrast-related images. The two-dimensional-midsection images from each sequence were allocated to training or validation (approximately 80%) and testing (approximately 20%) using a stratified split to ensure balanced groups across institutions, patients, and MRI sequence types. The prediction accuracy was quantified for each sequence type, and subgroup comparison of model performance was performed using χ 2 tests. Results On the test set, the overall accuracy of the CNN (ResNet-18) ensemble model among all sequence types was 97.9% (95% CI: 97.6, 98.1), ranging from 84.2% for susceptibility-weighted images (95% CI: 81.8, 86.6) to 99.8% for T2-weighted images (95% CI: 99.7, 99.9). The ResNet-18 model achieved significantly better accuracy compared with ResNet-50 despite its simpler architecture (97.9% vs 97.1%; P ≤ .001). The accuracy of the ResNet-18 model was not affected by the presence versus absence of tumor on the two-dimensional-midsection images for any sequence type ( P > .05). Conclusion The developed CNN ( www.github.com/neuroAI-HD/HD-SEQ-ID ) reliably differentiates nine types of MRI sequences within multicenter and large-scale population neuroimaging data and may enhance the speed, accuracy, and efficiency of clinical and research neuroradiologic workflows. Keywords: MR-Imaging, Neural Networks, CNS, Brain/Brain Stem, Computer Applications-General (Informatics), Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2023.
Databáze:	MEDLINE
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