Enhancing Clinical MRI Perfusion Maps with Data-Driven Maps of Complementary Nature for Lesion Outcome Prediction

Pinto, Adriano; Pereira, Sérgio; Meier, Raphael; Alves, Victor; Wiest, Roland; Silva, Carlos A.; Reyes, Mauricio (2018). Enhancing Clinical MRI Perfusion Maps with Data-Driven Maps of Complementary Nature for Lesion Outcome Prediction. In: Frangi, Alejandro F.; Schnabel, Julia A.; Davatzikos, Christos; Alberola-López, Carlos; Fichtinger, Gabor (eds.) Medical Image Computing and Computer Assisted Interventions. Lecture Notes in Computer Science: Vol. 11072 (pp. 107-115). Cham: Springer International Publishing 10.1007/978-3-030-00931-1_13

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Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life. To perform the revascularization procedure, the decision making of physicians considers its risks and benefits based on multi-modal MRI and clinical experience. Therefore, automatic prediction of the ischemic stroke lesion outcome has the potential to assist the physician towards a better stroke assessment and information about tissue outcome. Typically, automatic methods consider the information of the standard kinetic models of diffusion and perfusion MRI (e.g. Tmax, TTP, MTT, rCBF, rCBV) to perform lesion outcome prediction. In this work, we propose a deep learning method to fuse this information with an automated data selection of the raw 4D PWI image information, followed by a data-driven deep-learning modeling of the underlying blood flow hemodynamics. We demonstrate the ability of the proposed approach to improve prediction of tissue at risk before therapy, as compared to only using the standard clinical perfusion maps, hence suggesting on the potential benefits of the proposed data-driven raw perfusion data modelling approach.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Meier, Raphael; Wiest, Roland and Reyes, Mauricio

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISBN:

978-3-030-00931-1

Series:

Lecture Notes in Computer Science

Publisher:

Springer International Publishing

Language:

English

Submitter:

Mauricio Antonio Reyes Aguirre

Date Deposited:

02 Oct 2019 17:43

Last Modified:

24 Oct 2019 03:50

Publisher DOI:

10.1007/978-3-030-00931-1_13

BORIS DOI:

10.7892/boris.132345

URI:

https://boris.unibe.ch/id/eprint/132345

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