Support vector machine classification of arterial volume-weighted arterial spin tagging images

In recent years, machine-learning techniques have gained growing popularity in medical image analysis. Temporal brain-state classification is one of the major applications of machine-learning techniques in functional magnetic resonance imaging (fMRI) brain data. This article explores the use of support vector machine (SVM) classification technique with motor-visual activation paradigm to perform brain-state classification into activation and rest with an emphasis on different acquisition techniques.

Images were acquired using a recently developed variant of traditional pseudocontinuous arterial spin labeling technique called arterial volume-weighted arterial spin tagging (AVAST). The classification scheme is also performed on images acquired using blood oxygenation-level dependent (BOLD) and traditional perfusion-weighted arterial spin labeling (ASL) techniques for comparison.

The AVAST technique outperforms traditional pseudocontinuous ASL, achieving classification accuracy comparable to that of BOLD contrast images.

This study demonstrates that AVAST has superior signal-to-noise ratio and improved temporal resolution as compared with traditional perfusion-weighted ASL and reduced sensitivity to scanner drift as compared with BOLD. Owing to these characteristics, AVAST lends itself as an ideal choice for dynamic fMRI and real-time neurofeedback experiments with sustained activation periods.