NDLI: Predicting final extent of ischemic infarction using an artificial neural network analysis of multiparametric MRI in patients with stroke

Content Provider	IEEE Xplore Digital Library
Author	Bagher-Ebadian, H. Jafari-Khouzani, K. Mitsias, P.D. Soltanian-Zadeh, H. Chopp, M. Ewing, J.R.
Copyright Year	2009
Description	Author affiliation: Dept. Neurology, Henry Ford Hospital, Physics Dept., Oakland U, USA (Chopp, M.) \|\| Dept. Diagnostic Radiology, Henry Ford Hospital, USA (Jafari-Khouzani, K.; Soltanian-Zadeh, H.) \|\| Neurology, Henry Ford Hospital, Depts. Physiology and Neurology, Wayne State University, USA (Ewing, J.R.) \|\| Dept. Neurology, Henry Ford Hospital, Wayne State University, Detroit MI, USA (Mitsias, P.D.) \|\| Dept. Neurology, Henry Ford Hospital, Detroit, MI 48202, USA (Bagher-Ebadian, H.)
Abstract	In ischemic stroke, the extent of ischemic lesion recovery is one of the most important correlate of functional recovery in brain. Using a set of acute phase MR images (Diffusion-Weighted - DWI, T-Weighted - T1WI, T-Weighted T2WI, and proton density weighted - PDWI) for inputs, and the chronic T2WI at 3 months as an outcome measure, an Artificial Neural Network (ANN) was trained to predict the 3-month outcome in the form of a pixel-by-pixel forecast of the chronic T2WI. The ANN was trained and tested using 14 slices from 3 subjects using a K-Folding Cross-Validation (KFCV) method with 14 folds. The Area Under the Receiver Operator Characteristic Curve (AUROC) for 14 folds was used for training, testing and optimization of the ANN. After training and optimization, the ANN produced a map that was well correlated (r = 0.88, p ≪ 0.0001) with the T2WI at 3 months. To confirm that the trained ANN performed well against a new dataset, 13 slices from 4 other patients were shown to the trained ANN. The prediction made by the ANN had an excellent overall performance (AUROC = 0.82), and was very well correlated to the 3-month ischemic lesion on T2-Weighted image.
Starting Page	229
Ending Page	235
File Size	4806790
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178883
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Magnetic resonance imaging Nervous system Hospitals Lesions Physics Testing Biological neural networks Protons Density measurement
Content Type	Text
Resource Type	Article

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