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Article

End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT

by
Andreas Mittermeier
1,*,
Paul Reidler
1,
Matthias P. Fabritius
1,
Balthasar Schachtner
1,2,
Philipp Wesp
1,
Birgit Ertl-Wagner
3,
Olaf Dietrich
1,
Jens Ricke
1,
Lars Kellert
4,
Steffen Tiedt
5,
Wolfgang G. Kunz
1 and
Michael Ingrisch
1
1
Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany
2
Comprehensive Pneumology Center (CPC-M), German Center for Lung Research (DZL), 81377 Munich, Germany
3
Department of Diagnostic Imaging, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada
4
Department of Neurology, University Hospital, LMU Munich, 81377 Munich, Germany
5
Institute for Stroke and Dementia Research, University Hospital, LMU Munich, 81377 Munich, Germany
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(5), 1142; https://doi.org/10.3390/diagnostics12051142
Submission received: 8 March 2022 / Revised: 26 April 2022 / Accepted: 3 May 2022 / Published: 5 May 2022
(This article belongs to the Special Issue Artificial Intelligence in Stroke Imaging)
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Abstract

(1) Background: CT perfusion (CTP) is used to quantify cerebral hypoperfusion in acute ischemic stroke. Conventional attenuation curve analysis is not standardized and might require input from expert users, hampering clinical application. This study aims to bypass conventional tracer-kinetic analysis with an end-to-end deep learning model to directly categorize patients by stroke core volume from raw, slice-reduced CTP data. (2) Methods: In this retrospective analysis, we included patients with acute ischemic stroke due to proximal occlusion of the anterior circulation who underwent CTP imaging. A novel convolutional neural network was implemented to extract spatial and temporal features from time-resolved imaging data. In a classification task, the network categorized patients into small or large core. In ten-fold cross-validation, the network was repeatedly trained, evaluated, and tested, using the area under the receiver operating characteristic curve (ROC-AUC). A final model was created in an ensemble approach and independently validated on an external dataset. (3) Results: 217 patients were included in the training cohort and 23 patients in the independent test cohort. Median core volume was 32.4 mL and was used as threshold value for the binary classification task. Model performance yielded a mean (SD) ROC-AUC of 0.72 (0.10) for the test folds. External independent validation resulted in an ensembled mean ROC-AUC of 0.61. (4) Conclusions: In this proof-of-concept study, the proposed end-to-end deep learning approach bypasses conventional perfusion analysis and allows to predict dichotomized infarction core volume solely from slice-reduced CTP images without underlying tracer kinetic assumptions. Further studies can easily extend to additional clinically relevant endpoints.
Keywords: CT perfusion; stroke; deep learning; contrast-enhanced perfusion imaging; convolutional neural networks; end-to-end modeling CT perfusion; stroke; deep learning; contrast-enhanced perfusion imaging; convolutional neural networks; end-to-end modeling

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MDPI and ACS Style

Mittermeier, A.; Reidler, P.; Fabritius, M.P.; Schachtner, B.; Wesp, P.; Ertl-Wagner, B.; Dietrich, O.; Ricke, J.; Kellert, L.; Tiedt, S.; et al. End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT. Diagnostics 2022, 12, 1142. https://doi.org/10.3390/diagnostics12051142

AMA Style

Mittermeier A, Reidler P, Fabritius MP, Schachtner B, Wesp P, Ertl-Wagner B, Dietrich O, Ricke J, Kellert L, Tiedt S, et al. End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT. Diagnostics. 2022; 12(5):1142. https://doi.org/10.3390/diagnostics12051142

Chicago/Turabian Style

Mittermeier, Andreas, Paul Reidler, Matthias P. Fabritius, Balthasar Schachtner, Philipp Wesp, Birgit Ertl-Wagner, Olaf Dietrich, Jens Ricke, Lars Kellert, Steffen Tiedt, and et al. 2022. "End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT" Diagnostics 12, no. 5: 1142. https://doi.org/10.3390/diagnostics12051142

APA Style

Mittermeier, A., Reidler, P., Fabritius, M. P., Schachtner, B., Wesp, P., Ertl-Wagner, B., Dietrich, O., Ricke, J., Kellert, L., Tiedt, S., Kunz, W. G., & Ingrisch, M. (2022). End-to-End Deep Learning Approach for Perfusion Data: A Proof-of-Concept Study to Classify Core Volume in Stroke CT. Diagnostics, 12(5), 1142. https://doi.org/10.3390/diagnostics12051142

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