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



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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