Quantitative cerebral MR perfusion imaging: preliminary results in stroke.

PURPOSE: To evaluate quantitative cerebral blood flow (qCBF) with traditional time-based measurements or metrics of cerebral perfusion: time to peak (Tmax) and mean transit time (MTT) in stroke patients. MATERIALS AND METHODS: Nine ischemic stroke patients (four male, five female, 63 ± 16 years old) were included in the study which was Health Insurance Portability and Accountability Act compliant and institutional review board approved. Cerebral perfusion was quantified using the Bookend method. Mean values of qCBF, Tmax, and MTT were determined in regions of interest (ROIs). ROIs were drawn on diffusion weighted images in diffusion positive, critically ischemic (CI), in ipsilateral normal region immediately surrounding the critically ischemic region, the presumed penumbra (PP), and in contralateral diffusion negative control, presumed normal region (PN) of gray and white matter separately (GM and WM). RESULTS: In both GM and WM, qCBF measures distinguished the studied brain regions with the most markedly reduced values in regions corresponding to extent of likely ischemic injury. In planned comparisons, only qCBF measurements differed significantly between CI and PP tissues. ROC analysis supported the utility of qCBF for discriminating brain regions differing in the likely extent of ischemic injury (CI and PN regions - qCBF: area under the curve [AUC] = 0.96, Tmax: AUC = 0.96, MTT: AUC = 0.72). Importantly, qCBF afforded the best discrimination of CI and PP regions (qCBF: AUC = 0.82, Tmax: AUC = 0.65, MTT: AUC = 0.52). CONCLUSION: This initial evaluation indicates that quantitative MRI perfusion is feasible in ischemic stroke patients. qCBF derived with this strategy provide enhanced discrimination of CI and PP compared to time-based imaging metrics. This approach merits investigation in larger clinical studies.

Quantification of cerebral perfusion using the "bookend technique": an evaluation in CNS tumors.

We present a method of quantifying cerebral blood volume using dynamic susceptibility contrast. Our approach combines T(2)-weighted echo planar imaging (EPI) pulse sequences and reference scans that determine the parenchymal T(1) changes resulting from an injection of a gadolinium chelate. This combined T(2)- and T(1)-weighted approach (the "bookend" technique) has been shown to be effective in the quantification of gradient-echo (GRE) (T(2)*-weighted) perfusion images but has not been applied to spin-echo EPI (SE-EPI) (T(2)-weighted) images. The physics related to blood volume measurement based on T(2)- and T(2)*-weighted EPI sequences is known to be different, and there is a question as to whether the bookend approach is effective with SE-EPI. We have compared the quantitative SE-EPI with GRE-EPI in a series of patients with central nervous system (CNS) tumors. We found that quantitative cerebral blood volume (qCBV) values for SE-EPI and GRE-EPI are in agreement with each other and with historical reference values. A subjective evaluation of image quality showed that image quality in the SE-EPI scans was high and exhibited high interreader agreement. We conclude that measuring qCBV using the bookend technique with SE-EPI images is possible and may be a viable alternative to GRE-EPI in the evaluation of CNS tumors.

Method for rapid calculation of quantitative cerebral perfusion.

PURPOSE: To evaluate an algorithm based on algebraic estimation of T1 values (three-point estimation) in comparison with computational curve-fitting for the postprocessing of quantitative cerebral perfusion scans. MATERIALS AND METHODS: Computer simulations were performed to quantify the magnitude of the expected error on T1 and consequently cerebral perfusion using the three-point estimation technique on a Look-Locker (LL) EPI scan. In 50 patients, quantitative cerebral perfusion was calculated using the bookend method with three-point estimation and curve-fitting. The bookend method, a novel approach for calculating quantitative cerebral perfusion based on changes in T1 values after a contrast injection, is currently being validated. The number of computations was used as a measure of computation speed for each method. Student's paired t-test, Bland-Altman, and correlation analyses were performed to evaluate the accuracy of estimation. RESULTS: There was a 99.65% reduction in the number of computations with three-point estimation. Student's t-test showed no significant difference in cerebral perfusion (P=0.80, 0.49, paired t-test N=50, quantitative cerebral blood flow-white matter [qCBF-WM], qCBF-gray matter [qCBF-GM]) when compared to curve-fitting. The results of the two techniques were strongly correlated in patients (slope=0.99, intercept=1.58 mL/(100 g/minute), r=0.86) with a small systemic bias of -0.97 mL/(100 g/minute) in Bland-Altman analysis. CONCLUSION: The three-point estimation technique is adequate for rapid calculation of qCBF. The estimation scheme drastically reduces processing time, thus making the method feasible for clinical use.