Bayesian Estimation of CBF Measured by DSC-MRI in Patients with Moyamoya Disease: Comparison with 15O-Gas PET and Singular Value Decomposition

Effect of Temporal Sampling Rate on Estimates of the Perfusion Parameters for Patients with Moyamoya Disease Assessed with Simultaneous Multislice Dynamic Susceptibility Contrast-enhanced MR Imaging

PURPOSE

The effect of temporal sampling rate (TSR) on perfusion parameters has not been fully investigated in Moyamoya disease (MMD); therefore, this study evaluated the influence of different TSRs on perfusion parameters quantitatively and qualitatively by applying simultaneous multi-slice (SMS) dynamic susceptibility contrast-enhanced MR imaging (DSC-MRI).

METHODS

DSC-MRI datasets were acquired from 28 patients with MMD with a TSR of 0.5 s. Cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (Tmax) were calculated for eight TSRs ranging from 0.5 to 4.0 s in 0.5-s increments that were subsampled from a TSR of 0.5 s datasets. Perfusion measurements and volume for chronic ischemic (Tmax ≥ 2 s) and non-ischemic (Tmax < 2 s) areas for each TSR were compared to measurements with a TSR of 0.5 s, as was visual perfusion map analysis.

RESULTS

CBF, CBV, and Tmax values tended to be underestimated, whereas MTT and TTP values were less influenced, with a longer TSR. Although Tmax values were overestimated in the TSR of 1.0 s in non-ischemic areas, differences in perfusion measurements between the TSRs of 0.5 and 1.0 s were generally minimal. The volumes of the chronic ischemic areas with a TSR ≥ 3.0 s were significantly underestimated. In CBF and CBV maps, no significant deterioration was noted in image quality up to 3.0 and 2.5 s, respectively. The image quality of MTT, TTP, and Tmax maps for the TSR of 1.0 s was similar to that for the TSR of 0.5 s but was significantly deteriorated for the TSRs of ≥ 1.5 s.

CONCLUSION

In the assessment of MMD by SMS DSC-MRI, application of TSRs of ≥ 1.5 s may lead to deterioration of the perfusion measurements; however, that was less influenced in TSRs of ≤ 1.0 s.

Spatial coefficient of variation of arterial spin labeling MRI for detecting hemodynamic disturbances measured with 15O-gas PET in patients with moyamoya disease

PURPOSE

The aim of this study was to investigate whether the spatial coefficient of variation of arterial spin labeling (ASL-CoV) acquired in clinical settings can be used to estimate the hemodynamic disturbances measured with ¹⁵O-gas positron emission tomography (PET), especially an increased oxygen extraction fraction (OEF), in patients with moyamoya disease.

METHODS

We evaluated 68 adult patients with moyamoya disease who underwent ASL (postlabeling delay (PLD) = 1525 ms and 2525 ms) and PET. Regional values were measured using the middle cerebral artery territorial atlas divided into proximal, middle, and distal regions based on the arterial transit time, and correlations of ASL-CoV with cerebral blood flow, cerebral blood volume, mean transit time, and OEF, as well as the relationship between increased OEF and ASL-CoV, were evaluated.

RESULTS

Regardless of the choice of region and PLD, ASL-CoV was significantly correlated with PET-measured parameters, including OEF (|ρ|= 0.30-0.80, P < 0.001). Regions with an increased OEF showed a significantly higher ASL-CoV than regions with a nonincreased OEF (P ≤ 0.03) regardless of the choice of region and PLD. The accuracy of identification of an increased OEF was highest when using a PLD of 1525 ms and the middle region (area under the curve = 0.750; using a cutoff value of 31.27, sensitivity = 97.4%, specificity = 41.7%, negative predictive value = 92.6%, and positive predictive value = 67.9%).

CONCLUSION

ASL-CoV may help identify patients with increased OEF.

Progression in Moyamoya Disease: Clinical Feature, Neuroimaging Evaluation and Treatment

Moyamoya disease (MMD) is a chronic cerebrovascular disease characterized by progressive stenosis of the arteries of the circle of Willis, with the formation of collateral vascular network at the base of the brain. Its clinical manifestations are complicated. Numerous studies have attempted to clarify the clinical features of MMD, including its epidemiology, genetic characteristics, and pathophysiology. With the development of neuroimaging techniques, various neuroimaging modalities with different advantages have deepened the understanding of MMD in terms of structural, functional, spatial, and temporal dimensions. At present, the main treatment for MMD focuses on neurological protection, cerebral blood flow reconstruction, and neurological rehabilitation, such as pharmacological treatment, surgical revascularization, and cognitive rehabilitation. In this review, we discuss recent progress in understanding the clinical features, in the neuroimaging evaluation and treatment of MMD.

Time to peak and full width at half maximum in MR perfusion: valuable indicators for monitoring moyamoya patients after revascularization

Moyamoya disease (MMD) is a chronic, steno-occlusive cerebrovascular disorder of unknown etiology. Surgical treatment is the only known effective method to restore blood flow to affected areas of the brain. However, there are lack of generally accepted noninvasive tools for therapeutic outcome monitoring. As dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) is the standard MR perfusion imaging technique in the clinical setting, we investigated a dataset of nineteen pediatric MMD patients with one preoperational and multiple periodic DSC MRI examinations for four to thirty-eight months after indirect revascularization. A rigid gamma variate model was used to derive two nondeconvolution-based perfusion parameters: time to peak (TTP) and full width at half maximum (FWHM) for monitoring transitional bolus delay and dispersion changes respectively. TTP and FWHM values were normalized to the cerebellum. Here, we report that 74% (14/19) of patients improve in both TTP and FWHM measurements, and whereof 57% (8/14) improve more noticeably on FWHM. TTP is in good agreement with Tmax in estimating bolus delay. Our study data also suggest bolus dispersion estimated by FWHM is an additional, informative indicator in pediatric MMD monitoring.

DSC Brain Perfusion Using Advanced Deconvolution Models in the Diagnostic Work-up of Dementia and Mild Cognitive Impairment: A Semiquantitative Comparison with HMPAO-SPECT-Brain Perfusion

BACKGROUND

SPECT (single-photon emission-computed tomography) is used for the detection of hypoperfusion in cognitive impairment and dementia but is not widely available and related to radiation dose exposure. We compared the performance of DSC (dynamic susceptibility contrast) perfusion using semi- and fully adaptive deconvolution models to HMPAO-SPECT (99mTc-hexamethylpropyleneamine oxime-SPECT).

MATERIAL AND METHODS

Twenty-seven patients with dementia of different subtypes including frontotemporal dementia (FTD) and mild cognitive impairment (MCI) received a multimodal diagnostic work-up including DSC perfusion at a clinical 3T high-field scanner and HMPAO-SPECT. Nineteen healthy control individuals received DSC perfusion. For calculation of the hemodynamic parameter maps, oscillation-index standard truncated singular value decomposition (oSVD, semi-adaptive) as well as Bayesian parameter estimation (BAY, fully adaptive) were performed.

RESULTS

Patients showed decreased cortical perfusion in the left frontal lobe compared to controls (relative cerebral blood volume corrected, rBVc: 0.37 vs 0.27, p = 0.048, adjusted for age and sex). Performance of rBVc (corrected for T1 effects) was highest compared to SPECT for detection of frontal hypoperfusion (sensitivity 83%, specificity 80% for oSVD and BAY, area under curve (AUC) = 0.833 respectively, p < 0.05) in FTD and MCI. For nonleakage-corrected rBV and for rBF (relative cerebral blood flow), sensitivity of frontal hypoperfusion was above 80% for oSVD and for BAY (rBV: sensitivity 83%, specificity 75%, AUC = 0.908 for oSVD and 0.917 for BAY, p < 0.05 respectively; rBF: sensitivity 83%, specificity 65%, AUC = 0.825, p < 0.05 for oSVD).

CONCLUSION

Advanced deconvolution DSC can reliably detect pathological perfusion alterations in FTD and MCI. Hence, this widely accessible technique has the potential to improve the diagnosis of dementia and MCI as part of an interdisciplinary multimodal imaging work-up. Advances in knowledge: Advanced DSC perfusion has a high potential in the work-up of suspected dementia and correlates with SPECT brain perfusion results in dementia and MCI.