Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI.
OBJECTIVE
To develop a multivariate machine learning classification-based cerebral blood flow (CBF) quantification method for arterial spin labeling (ASL) perfusion MRI.


METHODS
The label and control images of ASL MRI were separated using a machine-learning algorithm, the support vector machine (SVM). The perfusion-weighted image was subsequently extracted from the multivariate (all voxels) SVM classifier. Using the same pre-processing steps, the proposed method was compared with standard ASL CBF quantification method using synthetic data and in-vivo ASL images.


RESULTS
As compared with the conventional univariate approach, the proposed ASL CBF quantification method significantly improved spatial signal-to-noise-ratio (SNR) and image appearance of ASL CBF images.


CONCLUSIONS
the multivariate machine learning-based classification is useful for ASL CBF quantification.
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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D000328:Adult; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001158:Arteries; D001921:Brain / Q000098:blood supply* / Q000473:pathology; D001931:Brain Mapping; D002560:Cerebrovascular Circulation / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D018810:Magnetic Resonance Angiography; D008297:Male; D009072:Moyamoya Disease / Q000473:pathology; D059629:Signal-To-Noise Ratio; D013113:Spin Labels; D060388:Support Vector Machine; D055815:Young Adult
D013113:Spin Labels
arterial spin labeling; cerebral blood flow; support vector machine