Obtaining DSC MRI cerebral blood flow estimates without tissue specific errors

The singular value decomposition (SVD) deconvolution implementation is in common use in magnetic resonance (MR) dynamic susceptibility contrast (DSC) studies. The zdSVD SVD variant involves computationally manipulating DSC concentration signals to have zero arterial-tissue delay (ATD) prior to using SVD. Our proposed zdSVD improvements show how considering other signal time shifts leads to cerebral blood flow estimates whose accuracy shows a minimal dependency on the tissue mean transit times (MTT) values. This characteristic leads to greater absolute CBF accuracy across all MTT values after scaling MR studies to match PET flow values. A comparison is made between the modified zdSVD algorithm and other DSC-specific deconvolution algorithms.