I can measure blood flow, blood volume, mean transit time and is compatible with different hardware systems

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Summary

Perfusion weighted imaging (PWI) using magnetic resonance imaging (MRI) may be used for measuring blood flow, capillary blood flow, blood velocity and blood oxygenation as a diagnostic indicator across many conditions, including intracranial tumors, stroke, and neurodegenerative diseases. There exist various algorithms for processing PWI data; however, current methods have two main drawbacks: (1) current deconvolution methods can introduce distortions that may lead to inaccurate measurements of blood flow parameters; and (2) different algorithms are compatible with different PWI hardware systems and can give different parameters, which can lead to discrepancies in diagnosis.

UCLA researchers have developed a novel generalized algorithm for processing PWI data that utilizes a spatiotemporal deep learning framework. Their method accurately measures blood flow parameters, and is compatible with different hardware and protocols. Such a generalized approach eliminates the drawbacks of current methods and helps clinicians make an accurate assessment of the perfusion parameters.

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