Applying the transient error reconstruction algorithm in the assessment of the cerebral blood flow

The brain perfusion level, characterized by the cerebral blood flow (CBF) parameter, is a known indicator of blood supply in cerebral ischemic stroke. In magnetic resonance dynamic susceptibility contrast (DSC) perfusion studies the CBF parameter is estimated from the residue function obtained from deconvolving the tissue concentration curve by the arterial concentration curve. Deconvolution is a noise sensitive process and ensuring algorithmic stability leads to CBF biases. Distortions are introduced by noise reducing techniques in both the time-domain singular value decomposition (SVD) and frequency-domain based Fourier transform (FT) deconvolution approaches. We provide preliminary results of using the transient error reconstruction algorithm (TERA), an auto regressive moving average based technique, to compensate for these distortions. TERA is applied to determine the characteristics of the low-noise low frequency components of the residue function and then used to reconstruct the time-domain residue function. Results using noise-free signals indicate that the CBF estimates determined using TERA were less sensitive to the tissue mean transit time (MTT) than the time-domain SVD techniques. The difficulties encountered when applying TERA approach to signals with noise levels commonly found in MR perfusion studies are also discussed.