Perfusion parameters derived from bolus-tracking perfusion imaging are immune to tracer recirculation.

PURPOSE: To investigate the impact of tracer recirculation on estimates of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). MATERIALS AND METHODS: The theoretical model used to derive CBF, CBV, and MTT was examined. CBF and CBV estimates with and without tracer recirculation were compared in computer simulations to examine the effects of tracer recirculation. RESULTS: The equations used to derive CBF, CBV, and MTT assume that the arterial input function and tissue tracer signals define the input and output signals, respectively, of a linear time-invariant system. As a result of the principle of superposition, these perfusion parameters are immune to tracer recirculation, which was confirmed by computer simulation. However, limited acquisition durations can lead to CBV and CBF errors of up to 50%. CONCLUSION: Tracer recirculation does not impact estimation of CBF, CBV, or MTT. However, previous approaches used to remove recirculation effects may be beneficial when used to compensate for limited acquisition durations in which the passage of the bolus is not adequately captured.

Cerebral blood flow estimation in vivo using local tissue reference functions.

PURPOSE: To evaluate the use of bolus signals obtained from tissue as reference functions (or local reference functions [LRFs]) rather than arterial input functions (AIFs) when deriving cross-calibrated cerebral blood flow (CBF(CC)) estimates via deconvolution. MATERIALS AND METHODS: AIF and white matter (WM) LRF CBF(CC) maps (cross-calibrated so that normal WM was 23.7 mL/minute/100 g) derived using singular value decomposition (SVD) were examined in 28 ischemic stroke patients. Median CBF(CC) estimates from normal gray matter (GM) and ischemic tissue were obtained. RESULTS: AIF and LRF median CBF(CC) estimates resembled one another for all 28 patients (average paired CBF(CC) difference 0.4 +/- 1.7 mL/minute/100 g and -0.4 +/- 1.4 mL/minute/100 g in GM and ischemic tissue, respectively). Wilcoxon signed-rank comparisons of patient median CBF(CC) measurements revealed no statistically significant differences between using AIFs and LRFs (P > 0.05). CONCLUSION: If CBF is quantified using a patient-specific cross-calibration factor, then LRF CBF estimates are at least as accurate as those from AIFs. Therefore, until AIF quantification is achievable in vivo, perfusion protocols tailored for LRFs would simplify the methodology and provide more reliable perfusion information.

Robust dynamic susceptibility contrast MR perfusion using 4D nonlinear noise filters.

PURPOSE: To investigate if 4D (simultaneous space and time) nonlinear filtering techniques can produce more robust cerebral blood flow (CBF) estimates by reducing noise in acquired dynamic susceptibility contrast (DSC) MR perfusion data. MATERIALS AND METHODS: A digital anthropomorphic brain perfusion phantom was constructed to analyze filter performance by: 1) deriving anthropomorphic tissue volume fractions from a human subject and 2) simulating DSC-MR perfusion signals for voxels with mixed tissue for various signal-to-noise ratios (SNRs). DSC-MR data for 11 acute ischemic stroke patients were also acquired at 3T. CBF maps cross-calibrated so that normal white matter CBF was 22 mL/minute/100 g were produced from DSC-MR data without filtering and from 4D-Gaussian and 4D-bilateral noise-filtered DSC-MR data. RESULTS: The nonlinear 4D-bilateral filter yielded the lowest CBF root-mean square error (RMSE) in the phantom experiments with noise (average RMSE across all tissues regions for no filtering, 4D-Gaussian, and 4D-bilateral was 5.3 mL/minute/100 g, 6.2 mL/minute/100 g, and 4.0 mL/minute/100 g, respectively) and had the best image quality in both the phantom and patient data. CONCLUSION: Nonlinear 4D noise filters are better suited to the 4D nature of DSC-MR data. Linear spatial filters are not appropriate and can produce larger CBF errors than without filtering.