Is correction necessary when clinically determining quantitative cerebral perfusion parameters from multi-slice dynamic susceptibility contrast MR studies?

Several groups have modified the standard singular value decomposition (SVD) algorithm to produce delay-insensitive cerebral blood flow (CBF) estimates from dynamic susceptibility contrast (DSC) perfusion studies. However, new dependences of CBF estimates on bolus arrival times and slice position in multi-slice studies have been recently recognized. These conflicting findings can be reconciled by accounting for several experimental and algorithmic factors. Using simulation and clinical studies, the non-simultaneous measurement of arterial and tissue concentration curves (relative slice position) in a multi-slice study is shown to affect time-related perfusion parameters, e.g. arterial-tissue-delay measurements. However, the current clinical impact of relative slice position on amplitude-related perfusion parameters, e.g. CBF, can be expected to be small unless any of the following conditions are present individually or in combination: (a) high concentration curve signal-to-noise ratios, (b) small tissue mean transit times, (c) narrow arterial input functions or (d) low temporal resolution of the DSC image sequence. Recent improvements in magnetic resonance (MR) technology can easily be expected to lead to scenarios where these effects become increasingly important sources of inaccuracy for all perfusion parameter estimates. We show that using Fourier interpolated (high temporal resolution) residue functions reduces the systematic error of the perfusion parameters obtained from multi-slice studies.

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.