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- Title
Support vector machine classification of arterial volume-weighted arterial spin tagging images.
- Authors
Shah, Yash S.; Hernandez‐Garcia, Luis; Jahanian, Hesamoddin; Peltier, Scott J.
- Abstract
Introduction 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 (f MRI) 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. Methods 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. Results The AVAST technique outperforms traditional pseudocontinuous ASL, achieving classification accuracy comparable to that of BOLD contrast images. Conclusion 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.
- Subjects
MACHINE learning; SUPPORT vector machines; BLOOD volume; MAGNETIC resonance imaging; IMAGE analysis
- Publication
Brain & Behavior, 2016, Vol 6, Issue 12, pn/a
- ISSN
2162-3279
- Publication type
Article
- DOI
10.1002/brb3.549