Bayesian estimation of cerebral perfusion using reduced-contrast-dose dynamic susceptibility contrast perfusion at 3T

Radiologists' Rating for Comparative Qualitative Assessment of Intravoxel Incoherent Motion Using Novel Analysis Methods

OBJECTIVE

The objective was to assess qualitative interpretability and quantitative precision and reproducibility of intravoxel incoherent motion ( IVIM) parametric images evaluated using novel IVIM analysis methods for diagnostic accuracy.

METHODS

Intravoxel incoherent motion datasets of 55 patients (male/female = 41:14; age = 17.8 ± 5.5 years) with histopathology-proven osteosarcoma were analyzed. Intravoxel incoherent motion parameters-diffusion coefficient ( D ), perfusion fraction ( f ), and perfusion coefficient ( D* )-were estimated using 5 IVIM analysis methods-(i) biexponential (BE) model, (ii) BE-segmented fitting 2-parameter (BESeg-2), (iii) BE-segmented fitting 1-parameter (BESeg-1), (iv) BE model with total variation penalty function (BE + TV), and (v) BE model with Huber penalty function (BE + HPF). Qualitative scoring in a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) was performed by 2 radiologists for 4 criteria: (a) tumor shape and margin, (b) morphologic correlation, (c) noise suppression, and (d) overall interpretability. Interobserver agreement was evaluated using Spearman rank-order correlation ( rs ). Precision and reproducibility were evaluated using within-subject coefficient of variation (wCV) and between-subject coefficient of variation (bCV).

RESULTS

BE + TV and BE + HPF produced significantly ( P < 10 -3 ) higher qualitative scores for D (fair-good [3.3-3.8]) than BE (poor [2.3]) and for D* (poor-fair [2.2-2.7]) and f (fair-good [3.2-3.8]) than BE, BESeg-2, and BESeg-1 ( D* : uninterpretable-poor [1.3-1.9] and f : poor-fair [1.5-3]). Interobserver agreement for qualitative scoring was rs = 0.48-0.59, P < 0.009. BE + TV and BE + HPF showed significantly ( P < 0.05) improved reproducibility in estimating D (wCV: 24%-31%, bCV: 21%-31% improvement) than the BE method and D* (wCV: 4%-19%, bCV: 5%-19% improvement) and f (wCV: 25%-49%, bCV: 25%-47% improvement) than BE, BESeg-2, and BESeg-1 methods.

CONCLUSIONS

BE + TV and BE + HPF demonstrated qualitatively and quantitatively improved IVIM parameter estimation and may be considered for clinical use further.

How to reduce the complication rate of multiple burr holes surgery in moyamoya angiopathy

PURPOSE

This study is aimed at analyzing clinical outcome, absence of stroke recurrence, revascularization, and complications and long-term follow-up in the surgical treatment of moyamoya angiopathy (MMA) using the multiple burr holes (MBH) technique with dura opening and arachnoid preservation as a single procedure. To the best of our knowledge, this is the first to describe an MBH technique with arachnoid preservation.

METHOD

We retrospectively reviewed all patients operated from June 2001 to March 2021, for a symptomatic and progressive MMA operated with opening of the dura but arachnoid preservation. Clinical examinations were obtained in all patients, and radiological monitoring was performed by cerebral 3D-magnetic resonance angiography (MRA) with perfusion or single-photon emission computed tomography (SPECT) with acetazolamide.

RESULTS

In total, 21 consecutive patients (6 children and 15 adults) were included with a mean age of 7.4 years in the pediatric group and 36.9 years in the adult group. Initial presentation was permanent ischemic stroke in 15 cases, transient ischemic attack (TIA) in 5 cases, and cerebral hemorrhage in one case. The MBH with dura opening and arachnoid preservation was performed bilaterally in 9 cases (43%) and unilaterally in 12 cases (57%). One patient died due to intraoperative bilateral ischemic stroke. Of the 20 other patients, 30% demonstrated clinical stability and 70% showed partial or complete recovery. Although one patient experienced a perioperative stroke, we did not observe any pseudomeningocele or postoperative ischemic stroke (IS) recurrence in all surviving cases during the average follow-up period of 55.5 months (range: 1-195). These outcomes emphasize the importance of preoperative monitoring to ensure the effectiveness and safety of the intervention. Postoperative angiography studies showed revascularization in 96.3% of treated hemispheres (100% in the adult group vs 80% in the pediatric group).

CONCLUSIONS

Our results on this small cohort suggest that the MBH technique with opening of the dura and arachnoids preservation can prevent recurrent strokes and reduce the risk of pseudomeningocele.

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.

A Bayesian estimation method for cerebral blood flow measurement by area-detector CT perfusion imaging

PURPOSE

Bayesian estimation with advanced noise reduction (BEANR) in CT perfusion (CTP) could deliver more reliable cerebral blood flow (CBF) measurements than the commonly used reformulated singular value decomposition (rSVD). We compared the efficacy of CBF measurement by CTP using BEANR and rSVD, evaluating both relative to N-isopropyl-p-[(123) I]- iodoamphetamine (¹²³I-IMP) single-photon emission computed tomography (SPECT) as a reference standard, in patients with cerebrovascular disease.

METHODS

Thirty-one patients with suspected cerebrovascular disease underwent both CTP on a 320 detector-row CT system and SPECT. We applied rSVD and BEANR in the ischemic and contralateral regions to create CBF maps and calculate CBF ratios from the ischemic side to the healthy contralateral side (CBF index). The analysis involved comparing the CBF index between CTP methods and SPECT using Pearson's correlation and limits of agreement determined with Bland-Altman analyses, before comparing the mean difference in the CBF index between each CTP method and SPECT using the Wilcoxon matched pairs signed-rank test.

RESULTS

The CBF indices of BEANR and ¹²³I-IMP SPECT were significantly and positively correlated (r = 0.55, p < 0.0001), but there was no significant correlation between the rSVD method and SPECT (r = 0.15, p > 0.05). BEANR produced smaller limits of agreement for CBF than rSVD. The mean difference in the CBF index between BEANR and SPECT differed significantly from that between rSVD and SPECT (p < 0.001).

CONCLUSIONS

BEANR has a better potential utility for CBF measurement in CTP than rSVD compared to SPECT in patients with cerebrovascular disease.

Large Vessel Occlusion Sites Affect Agreement Between Outputs of Three Computed Tomography Perfusion Software Packages

OBJECTIVES

Computed tomography perfusion (CTP) data are important for hyperacute stroke decision making. Available comparisons between outputs of different CTP software packages show variable outcomes. Evaluation for factors associated with agreement between the volume estimates is limited. We assessed for differences in core and penumbra volume estimates of three CTP software packages - AutoMIStar, RAPID, and Vitrea - and analyzed factors associated with agreement between the volume estimates.

MATERIALS AND METHODS

Differences between software estimates of penumbra and core volumes were calculated for each patient with suspected acute ischemic stroke who underwent CTP. Exploratory hierarchical clustering and principal component analysis were performed to identify factors of decreased volume estimate agreement. Two-sample t-tests were performed, stratified by large vessel occlusion (LVO) location.

RESULTS

579 CTP studies were performed; 267 were normal, 139 artifacts, with 172 included in the final analysis. 79/172 had LVO of internal carotid artery (ICA, n = 20), M1 (n = 38) and proximal M2 (n = 21). LVO was the only factor associated with decreased software package agreement, and proximal LVO location was associated with general trend of increasing mean differences and standard deviations between software packages (range of mean differences [SD]: non-LVO, -17-6 [4-33] ml; M2, -40-13 [5-39] ml; M1, -43-26 [16-58] ml; ICA, -76-39 [22-97] ml).

CONCLUSIONS

Core and penumbra volume estimates can be affected by LVO location significantly between CTP software packages.

Predicting neuroimaging eligibility for extended-window endovascular thrombectomy

OBJECTIVE

Endovascular thrombectomy (EVT) and tissue plasminogen activator (tPA) are effective ischemic stroke treatments in the initial treatment window. In the extended treatment window, these treatments may offer benefit, but CT and MR perfusion may be necessary to determine patient eligibility. Many hospitals do not have access to advanced imaging tools or EVT capability, and further patient care would require transfer to a facility with these capabilities. To assist transfer decisions, the authors developed risk indices that could identify patients eligible for extended-window EVT or tPA.

METHODS

The authors retrospectively identified stroke patients who had concurrent CTA and perfusion and evaluated three potential outcomes that would suggest a benefit from patient transfer. The first outcome was large-vessel occlusion (LVO) and target mismatch (TM) in patients 5-23 hours from last known normal (LKN). The second outcome was TM in patients 5-15 hours from LKN with known LVO. The third outcome was TM in patients 4.5-12 hours from LKN. The authors created multivariable models using backward stepping with an α-error criterion of 0.05 and assessed them using C statistics.

RESULTS

The final predictors included the National Institutes of Health Stroke Scale (NIHSS), the Alberta Stroke Program Early CT Score (ASPECTS), and age. The prediction of the first outcome had a C statistic of 0.71 (n = 145), the second outcome had a C statistic of 0.85 (n = 56), and the third outcome had a C statistic of 0.86 (n = 54). With 1 point given for each predictor at different cutoffs, a score of 3 points had probabilities of true positive of 80%, 90%, and 94% for the first, second, and third outcomes, respectively.

CONCLUSIONS

Despite the limited sample size, compared with perfusion-based examinations, the clinical variables identified in this study accurately predicted which stroke patients would have salvageable penumbra (C statistic 71%-86%) in a range of clinical scenarios and treatment cutoffs. This prediction improved (C statistic 85%-86%) when utilized in patients with confirmed LVO or a less stringent tissue mismatch (TM < 1.2) cutoff. Larger patient registries should be used to validate and improve the predictive ability of these models.

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Purpose

Computed tomography perfusion (CTP) is used to diagnose ischemic strokes through contralateral hemisphere comparisons of various perfusion parameters. Various perfusion parameter thresholds have been utilized to segment infarct tissue due to differences in CTP software and patient baseline hemodynamics. This study utilized a convolutional neural network (CNN) to eliminate the need for non-universal parameter thresholds to segment infarct tissue.

Methods

CTP data from 63 ischemic stroke patients was retrospectively collected and perfusion parameter maps were generated using Vitrea CTP software. Infarct ground truth labels were segmented from diffusion-weighted imaging (DWI) and CTP and DWI volumes were registered. A U-net based CNN was trained and tested five separate times using each CTP parameter (cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak (TTP), mean-transit-time (MTT), delay time). 8,352 infarct slices were utilized with a 60:30:10 training:testing:validation split and Monte Carlo cross-validation was conducted using 20 iterations. Infarct volumes were reconstructed following segmentation from each CTP slice. Infarct spatial and volumetric agreement was compared between each CTP parameter and DWI.

Results

Spatial agreement metrics (Dice coefficient, positive predictive value) for each CTP parameter in predicting infarct volumes are: CBF=(0.67, 0.76), CBV=(0.44, 0.62), TTP=(0.60, 0.67), MTT=(0.58, 0.62), delay time=(0.57, 0.60). 95% confidence intervals for volume differences with DWI infarct are: CBF=14.3±11.5 mL, CBV=29.6±21.2 mL, TTP=7.7±15.2 mL, MTT=-10.7±18.6 mL, delay time=-5.7±23.6 mL.

Conclusions

CBF is the most accurate CTP parameter in segmenting infarct tissue. Segmentation of infarct using a CNN has the potential to eliminate non-universal CTP contralateral hemisphere comparison thresholds.

Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke

PURPOSE

A variety of postprocessing algorithms for CT perfusion are available, with substantial differences in terms of quantitative maps. Although potential advantages of a Bayesian estimation algorithm are suggested, direct comparison with other algorithms in clinical settings remains scarce. We aimed to compare performance of a Bayesian estimation algorithm and singular value decomposition (SVD) algorithms for the assessment of acute ischemic stroke using an 80-detector row CT perfusion.

METHODS

CT perfusion data of 36 patients with acute ischemic stroke were analyzed using the Vitrea implemented a standard SVD algorithm, a reformulated SVD algorithm and a Bayesian estimation algorithm. Correlations and statistical differences between affected and contralateral sides of quantitative parameters (cerebral blood volume [CBV], cerebral blood flow [CBF], mean transit time [MTT], time to peak [TTP] and delay) were analyzed. Agreement of the CT perfusion-estimated and the follow-up diffusion-weighted imaging-derived infarct volume were evaluated by nonparametric Passing-Bablok regression analysis.

RESULTS

CBF and MTT of the Bayesian estimation algorithm were substantially different and showed a better correlation with the standard SVD algorithm (ρ = 0.78 and 0.80, p < 0.001) than with the reformulated SVD algorithm (ρ = 0.59 and 0.39, p < 0.001). There is no significant difference in MTT only when using the reformulated SVD algorithm (p = 0.217). Regarding the regression lines, the slope and intercept were nearly ideal with the Bayesian estimation algorithm (y = 2.42 x-6.51; ρ = 0.60, p < 0.001) in comparison with the SVD algorithms.

CONCLUSIONS

The Bayesian estimation algorithm can lead to a better performance compared with the SVD algorithms in the assessment of acute ischemic stroke because of better delineation of abnormal perfusion areas and accurate estimation of infarct volume.

Effect of computed tomography perfusion post-processing algorithms on optimal threshold selection for final infarct volume prediction

In acute ischemic stroke (AIS) patients, eligibility for endovascular intervention is commonly determined through computed tomography perfusion (CTP) analysis by quantifying ischemic tissue using perfusion parameter thresholds. However, thresholds are not uniform across all analysis methods due to dependencies on patient demographics and computational algorithms. This study aimed to investigate optimal perfusion thresholds for quantifying infarct and penumbra volumes using two post-processing CTP algorithms: Vitrea Bayesian and singular value decomposition plus (SVD+). We utilized 107 AIS patients (67 non-intervention patients and 40 successful reperfusion of thrombolysis in cerebral infarction (2b/3) patients). Infarct volumes were predicted for both post-processing algorithms through contralateral hemisphere comparisons using absolute time-to-peak (TTP) and relative regional cerebral blood volume (rCBV) thresholds ranging from +2.8 seconds to +9.3 seconds and -0.23 to -0.56 respectively. Optimal thresholds were determined by minimizing differences between predicted CTP and 24-hour fluid-attenuation inversion recovery magnetic resonance imaging infarct. Optimal thresholds were tested on 60 validation patients (30 intervention and 30 non-intervention) and compared using RAPID CTP software. Among the 67 non-intervention and 40 intervention patients, the following optimal thresholds were determined: intervention Bayesian: TTP = +4.8 seconds, rCBV = -0.29; intervention SVD+: TTP = +5.8 seconds, rCBV = -0.29; non-intervention Bayesian: TTP = +5.3 seconds, rCBV = -0.32; non-intervention SVD+: TTP = +6.3 seconds, rCBV = -0.26. When comparing SVD+ and Bayesian post-processing algorithms, optimal thresholds for TTP were significantly different for intervention and non-intervention patients. rCBV optimal thresholds were equal for intervention patients and significantly different for non-intervention patients. Comparison with commercially utilized software indicated similar performance.

Assessment of a Bayesian Vitrea CT Perfusion Analysis to Predict Final Infarct and Penumbra Volumes in Patients with Acute Ischemic Stroke: A Comparison with RAPID

BACKGROUND AND PURPOSE

Brain CTP is used to estimate infarct and penumbra volumes to determine endovascular treatment eligibility for patients with acute ischemic stroke. We aimed to assess the accuracy of a Bayesian CTP algorithm in determining penumbra and final infarct volumes.

MATERIALS AND METHODS

Data were retrospectively collected for 105 patients with acute ischemic stroke (55 patients with successful recanalization [TICI 2b/2c/3] and large-vessel occlusions and 50 patients without interventions). Final infarct volumes were calculated using DWI and FLAIR 24 hours following CTP imaging. RAPID and the Vitrea Bayesian CTP algorithm (with 3 different settings) predicted infarct and penumbra volumes for comparison with final infarct volumes to assess software performance. Vitrea settings used different combinations of perfusion maps (MTT, TTP, CBV, CBF, delay time) for infarct and penumbra quantification. Patients with and without interventions were included for assessment of predicted infarct and penumbra volumes, respectively.

RESULTS

RAPID and Vitrea default setting had the most accurate final infarct volume prediction in patients with interventions ([Spearman correlation coefficient, mean infarct difference] default versus FLAIR: [0.77, 4.1 mL], default versus DWI: [0.72, 4.7 mL], RAPID versus FLAIR: [0.75, 7.5 mL], RAPID versus DWI: [0.75, 6.9 mL]). Default Vitrea and RAPID were the most and least accurate in determining final infarct volume for patients without an intervention, respectively (default versus FLAIR: [0.76, -0.4 mL], default versus DWI: [0.71, -2.6 mL], RAPID versus FLAIR: [0.68, -49.3 mL], RAPID versus DWI: [0.65, -51.5 mL]).

CONCLUSIONS

Compared with RAPID, the Vitrea default setting was noninferior for patients with interventions and superior in penumbra estimation for patients without interventions as indicated by mean infarct differences and correlations with final infarct volumes.

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

BACKGROUND AND PURPOSE

CBF analysis of DSC perfusion using the singular value decomposition algorithm is not accurate in patients with Moyamoya disease. This study compared the Bayesian estimation of CBF against the criterion standard PET and singular value decomposition methods in patients with Moyamoya disease.

MATERIALS AND METHODS

Nineteen patients with Moyamoya disease (10 women; 22-52 years of age) were evaluated with both DSC and ¹⁵O-gas PET within 60 days. DSC-CBF maps were created using Bayesian analysis and 3 singular value decomposition analyses (standard singular value decomposition, a block-circulant deconvolution method with a fixed noise cutoff, and a block-circulant deconvolution method that adopts an occillating noise cutoff for each voxel according to the strength of noise). Qualitative and quantitative analyses of the Bayesian-CBF and singular value decomposition-CBF methods were performed against ¹⁵O-gas PET and compared with each other.

RESULTS

In qualitative assessments of DSC-CBF maps, Bayesian-CBF maps showed better visualization of decreased CBF on PET (sensitivity = 62.5%, specificity = 100%, positive predictive value = 100%, negative predictive value = 78.6%) than a block-circulant deconvolution method with a fixed noise cutoff and a block-circulant deconvolution method that adopts an oscillating noise cutoff for each voxel according to the strength of noise (P < .03 for all except for specificity). Quantitative analysis of CBF showed that the correlation between Bayesian-CBF and PET-CBF values (ρ = 0.46, P < .001) was similar among the 3 singular value decomposition methods, and Bayesian analysis overestimated true CBF (mean difference, 47.28 mL/min/100 g). However, the correlation between CBF values normalized to the cerebellum was better in Bayesian analysis (ρ = 0.56, P < .001) than in the 3 singular value decomposition methods (P < .02).

CONCLUSIONS

Compared with previously reported singular value decomposition algorithms, Bayesian analysis of DSC perfusion enabled better qualitative and quantitative assessments of CBF in patients with Moyamoya disease.

Estimation of Ischemic Core Volume Using Computed Tomographic Perfusion

Background and Purpose- Estimation of infarction based on computed tomographic perfusion (CTP) has been challenging, mainly because of noise associated with CTP data. The Bayesian method is a robust probabilistic method that minimizes effects of oscillation, tracer delay, and noise during residue function estimation compared with other deconvolution methods. This study compares CTP-estimated ischemic core volume calculated by the Bayesian method and by the commonly used block-circulant singular value deconvolution technique. Methods- Patients were included if they had (1) anterior circulation ischemic stroke, (2) baseline CTP, (3) successful recanalization defined by thrombolysis in cerebral infarction ≥IIb, and (4) minimum infarction volume of >5 mL on follow-up magnetic resonance imaging (MRI). CTP data were processed with circulant singular value deconvolution and Bayesian methods. Two established CTP methods for estimation of ischemic core volume were applied: cerebral blood flow (CBF) method (relative CBF, <30% within the region of delay >2 seconds) and cerebral blood volume method (<2 mL per 100 g within the region of relative mean transit time >145%). Final infarct volume was determined on MRI (fluid-attenuated inversion recovery images). CTP and MRI-derived ischemic core volumes were compared by univariate and Bland-Altman analysis. Results- Among 35 patients included, the mean/median (mL) difference for CTP-estimated ischemic core volume against MRI was -4/-7 for Bayesian CBF ( P=0.770), 20/12 for Bayesian cerebral blood volume ( P=0.041), 21/10 for circulant singular value deconvolution CBF ( P=0.006), and 35/18 for circulant singular value deconvolution cerebral blood volume ( P<0.001). Among all methods, Bayesian CBF provided the narrowest limits of agreement (-28 to 19 mL) in comparison with MRI. Conclusions- Despite existing variabilities between CTP postprocessing methods, Bayesian postprocessing increases accuracy and limits variability in CTP estimation of ischemic core.

Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps

PURPOSE

The purpose of this work is to investigate if the curve-fitting algorithm in Dynamic Contrast Enhanced (DCE) MRI experiments influences the diagnostic quality of calculated parameter maps.

MATERIAL AND METHODS

We compared the Levenberg-Marquardt (LM) and a Bayesian method (BM) in DCE data of 42 glioma patients, using two compartmental models (extended Toft's and 2-compartment-exchange model). Logistic regression and an ordinal linear mixed model were used to investigate if the image quality differed between the curve-fitting algorithms and to quantify if image quality was affected for different parameters and algorithms. The diagnostic performance to discriminate between high-grade and low-grade gliomas was compared by applying a Wilcoxon signed-rank test (statistical significance p>0.05). Two neuroradiologists assessed different qualitative imaging features.

RESULTS

Parameter maps based on BM, particularly those describing the blood-brain barrier, were superior those based on LM. The image quality was found to be significantly improved (p<0.001) for BM when assessed through independent clinical scores. In addition, given a set of clinical scores, the generating algorithm could be predicted with high accuracy (area under the receiver operating characteristic curve between 0.91 and 1). Using linear mixed models, image quality was found to be improved when applying the 2-compartment-exchange model compared to the extended Toft's model, regardless of the underlying fitting algorithm. Tumor grades were only differentiated reliably on plasma volume maps when applying BM. The curve-fitting algorithm had, however, no influence on grading when using parameter maps describing the blood-brain barrier.

CONCLUSION

The Bayesian method has the potential to increase the diagnostic reliability of Dynamic Contrast Enhanced parameter maps in brain tumors. In our data, images based on the 2-compartment-exchange model were superior to those based on the extended Toft's model.

Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload

BACKGROUND AND PURPOSE

Several leakage correction algorithms for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI)-based cerebral blood volume (CBV) measurement have been proposed, and combination with a preload of contrast agent is generally recommended. A single bolus application scheme would largely simplify and facilitate standardized clinical applications, while reducing contrast agent (CA) dose. The aim of this study was, therefore, to investigate whether appropriate leakage correction redundantizes prebolus application by comparing normalized DSC-based CBV (nCBV) measures of two consecutive CA boli.

MATERIALS AND METHODS

Twenty-seven patients with suspected glioblastoma (WHO-grade-IV) underwent DSC-MRI during two consecutive boli of Gd-based CA. Four variants of two post-processing leakage correction techniques were compared with respect to nCBV in contrast enhancing tumor tissue. First, a reference curve approach with first pass and full integration of corrected ΔR2*(t), and second, a deconvolution-based approach using singular value decomposition (SVD) with a standard noise-dependent cutoff or Tikhonov regularization.

RESULTS

Compared to respective uncorrected values, all leakage correction techniques increased nCBV for data acquired without prebolus, while there was no consistent trend for data acquired with prebolus. The best agreement between corrected nCBV values in contrast enhancing tumor, obtained in the same patients without and with prebolus, respectively, was obtained for the reference curve-based correction approach with either first pass or full integration.

CONCLUSION

The reference curve-based leakage correction approach with integration-based nCBV calculation yielded a high accordance between nCBV values without and with prebolus, respectively. Thus, it appears possible to obtain valid nCBV in glioblastoma with a single CA injection.

Assessment of the Agreement between Cerebral Hemodynamic Indices Quantified Using Dynamic Susceptibility Contrast and Dynamic Contrast-enhanced Perfusion Magnetic Resonance Imagings

Background:

Brain tumor is one of the most common tumors. A successful treatment might be achieved with an early identification. Pathological investigation as the gold standard method for tumor identification has some limitations. Noninvasive assessment of tumor specifications may be possible using perfusion-weighted magnetic resonance imaging (MRI). Cerebral blood volume (CBV) and cerebral blood flow (CBF) could be calculated based on dynamic contrast-enhanced MRI (DCE-MRI) in addition to dynamic susceptibility contrast MRI (DSC-MRI) modality. Each category of the cerebral hemodynamic and permeability indices revealed the specific tumor characteristics and their collection could help for better identification of the tumor. Some mathematical methods were developed to determine both cerebral hemodynamic and permeability indices based on a single-dose DCE perfusion MRI. There are only a few studies available on the comparison of DSC- and DCE-derived cerebral hemodynamic indices such as CBF and CBV.

Aim:

The objective of the study was to validate first-pass perfusion parameters derived from T1-based DCE method in comparison to the routine T2*-based DSC protocol.

Materials and Methods:

Twenty-nine patients with brain tumor underwent DCE- and DSC-MRIs to evaluate the agreement between DSC- and DCE-derived cerebral hemodynamic parameters. Agreement between DSC- and DCE-derived cerebral hemodynamic indices was determined using the statistical method described by Bland and Altman. The reliability between DSC- and DCE-derived cerebral hemodynamic indices was measured using the intraclass correlation analysis.

Results:

The achieved magnitudes for DCE-derived CBV (gray matter [GM]: 5.01 ± 1.40 mL/100 g vs. white matter [WM]: 1.84 ± 0.74 mL/100 g) and DCE-derived CBF (GM: 60.53 ± 12.70 mL/100 g/min vs. WM: 32.00 ± 6.00 mL/100 g/min) were in good agreement with other studies. The intraclass correlation coefficients showed that the cerebral hemodynamic indices could accurately be estimated based on the DCE-MRI using a single-compartment model (>0.87), and DCE-derived cerebral hemodynamic indices are significantly similar to the magnitudes achieved based on the DSC-MRI (P < 0.001). Furthermore, an acceptable agreement was observed between DSC- and DCE-derived cerebral hemodynamic indices.

Conclusion:

Based on the measurement of the cerebral hemodynamic and blood–brain barrier permeability using DCE-MRI, a more comprehensive collection of the physiological parameters cloud be achieved for tumor evaluations.

Determinants of the impact of blood pressure variability on neurological outcome after acute ischaemic stroke

INTRODUCTION

Increased blood pressure variability (BPV) is detrimental after acute ischaemic stroke, but the interaction between BPV and neuroimaging factors that directly influence stroke outcome has not been explored.

METHODS

We retrospectively reviewed inpatients from 2007 to 2014 with acute anterior circulation ischaemic stroke, CT perfusion and angiography at hospital admission, and a modified Rankin Scale (mRS) 30-365 days after stroke onset. BPV indices included SD, coefficient of variation and successive variation of the systolic blood pressure between 0 and 120 hours after admission. Ordinal logistic regression models were fitted to mRS with predictor variables of BPV indices. Models were further stratified by CT perfusion volumetric measurements, proximal vessel occlusion and collateral score.

RESULTS

110 patients met the inclusion criteria. The likelihood of a 1-point rise in the mRS increased with every 10 mm Hg increase in BPV (OR for the 3 BPV indices ranged from 2.27 to 5.54), which was more pronounced in patients with larger ischaemic core volumes (OR 8.37 to 18.0) and larger hypoperfused volumes (OR 6.02 to 15.4). This association also held true for patients with larger mismatch volume, proximal vessel occlusion and good collateral vessels.

CONCLUSIONS

These results indicate that increased BPV is associated with worse neurological outcome after stroke, particularly in patients with a large lesion core volume, concurrent viable ischaemic penumbra, proximal vessel occlusion and good collaterals. This subset of patients, who are often not candidates for or fail acute stroke therapies such as intravenous tissue plasminogen activator or endovascular thrombectomy, may benefit from interventions aimed at reducing BPV.

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.