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Ben Alaya I, Limam H, Kraiem T. Applications of artificial intelligence for DWI and PWI data processing in acute ischemic stroke: Current practices and future directions. Clin Imaging 2021; 81:79-86. [PMID: 34649081 DOI: 10.1016/j.clinimag.2021.09.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 09/05/2021] [Accepted: 09/22/2021] [Indexed: 11/03/2022]
Abstract
Multimodal Magnetic Resonance Imaging (MRI) techniques of Perfusion-Weighted Imaging (PWI) and Diffusion-Weighted Imaging (DWI) data are integral parts of the diagnostic workup in the acute stroke setting. The visual interpretation of PWI/DWI data is the most likely procedure to triage Acute Ischemic Stroke (AIS) patients who will access reperfusion therapy, especially in those exceeding 6 h of stroke onset. In fact, this process defines two classes of tissue: the ischemic core, which is presumed to be irreversibly damaged, visualized on DWI data and the penumbra which is the reversibly injured brain tissue around the ischemic tissue, visualized on PWI data. AIS patients with a large ischemic penumbra and limited infarction core have a high probability of benefiting from endovascular treatment. However, it is a tedious and time-consuming procedure. Consequently, it is subject to high inter- and intra-observer variability. Thus, the assessment of the potential risks and benefits of endovascular treatment is uncertain. Fast, accurate and automatic post-processing of PWI and DWI data is important for clinical diagnosis and is necessary to help the decision making for therapy. Therefore, an automated procedure that identifies stroke slices, stroke hemisphere, segments stroke regions in DWI, and measures hypoperfused tissue in PWI enhances considerably the reproducibility and the accuracy of stroke assessment. In this work, we draw an overview of several applications of Artificial Intelligence (AI) for the automation processing and their potential contributions in clinical practices. We compare the current approaches among each other's with respect to some key requirements.
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Affiliation(s)
- Ines Ben Alaya
- Tunis El Manar University, Higher Institute of Medical Technology of Tunis, Laboratory of Biophysics and Medical Technology, 1006 Tunis, Tunisia.
| | - Hela Limam
- Université de Tunis El Manar, Institut Supérieur d'Informatique, Institut Supérieur de Gestion de Tunis, Laboratoire BestMod, 1002 Tunis, Tunisie.
| | - Tarek Kraiem
- Tunis El Manar University, Higher Institute of Medical Technology of Tunis, Laboratory of Biophysics and Medical Technology, 1006 Tunis, Tunisia.
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Meier R, Lux P, Med B, Jung S, Fischer U, Gralla J, Reyes M, Wiest R, McKinley R, Kaesmacher J. Neural Network-derived Perfusion Maps for the Assessment of Lesions in Patients with Acute Ischemic Stroke. Radiol Artif Intell 2019; 1:e190019. [PMID: 33937801 DOI: 10.1148/ryai.2019190019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 06/03/2019] [Accepted: 06/14/2019] [Indexed: 11/11/2022]
Abstract
Purpose To perform a proof-of-concept study to investigate the clinical utility of perfusion maps derived from convolutional neural networks (CNNs) for the workup of patients with acute ischemic stroke presenting with a large vessel occlusion. Materials and Methods Data on endovascularly treated patients with acute ischemic stroke (n = 151; median age, 68 years [interquartile range, 59-75 years]; 82 of 151 [54.3%] women) were retrospectively extracted from a single-center institutional prospective registry (between January 2011 and December 2015). Dynamic susceptibility perfusion imaging data were processed by applying a commercially available reference method and in parallel by a recently proposed CNN method to automatically infer time to maximum of the tissue residue function (Tmax) perfusion maps. The outputs were compared by using quantitative markers of tissue at risk derived from manual segmentations of perfusion lesions from two expert raters. Results Strong correlations of lesion volumes (Tmax > 4 seconds, > 6 seconds, and > 8 seconds; R = 0.865-0.914; P < .001) and good spatial overlap of respective lesion segmentations (Dice coefficients, 0.70-0.85) between the CNN method and reference output were observed. Eligibility for late-window reperfusion treatment was feasible with use of the CNN method, with complete interrater agreement for the CNN method (Cohen κ = 1; P < .001), although slight discrepancies compared with the reference-based output were observed (Cohen κ = 0.609-0.64; P < .001). The CNN method tended to underestimate smaller lesion volumes, leading to a disagreement between the CNN and reference method in five of 45 patients (9%). Conclusion Compared with standard deconvolution-based processing of raw perfusion data, automatic CNN-derived Tmax perfusion maps can be applied to patients who have acute ischemic large vessel occlusion stroke, with similar clinical utility.© RSNA, 2019Supplemental material is available for this article.
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Affiliation(s)
- Raphael Meier
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Paula Lux
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - B Med
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Simon Jung
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Urs Fischer
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Jan Gralla
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Mauricio Reyes
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Richard McKinley
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
| | - Johannes Kaesmacher
- Support Center for Advanced Neuroimaging-University Institute of Diagnostic and Interventional Neuroradiology (R. Meier, P.L., J.G., R.W., R. McKinley, J.K.), Department of Neurology (S.J., U.F., J.K.), Institute for Surgical Technology and Biomechanics (M.R.), and Institute for Diagnostic, Interventional and Pediatric Radiology (J.K.), University Hospital Inselspital and University of Bern, Freiburgstrasse 4, 3010 Bern, Switzerland
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