Automatic selection of arterial input function using cluster analysis

T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology

Background

This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma.

Methods

Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed.

Results

The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen's kappa = 0.75 [95% CI, 0.57-0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005).

Conclusions

This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.

Standardized acquisition and post-processing of dynamic susceptibility contrast perfusion in patients with brain tumors, cerebrovascular disease and dementia: comparability of post-processing software

OBJECTIVE

MR-perfusion post-processing still lacks standardization. This study evaluates the results of perfusion analysis with two established software solutions in a large series of patients with different diseases when a highly standardized processing workflow is ensured.

METHODS

Multicenter data of 260 patients (80 with brain tumors, 124 with cerebrovascular disease and 56 with dementia examined with the same MR protocol) were analyzed. Raw data sets were processed with two software suites: Olea sphere and NordicICE. Group differences were analyzed with paired t-tests and one-way ANOVA.

RESULTS

Perfusion metrics were significantly different for all examined diseases in the unaffected brain for both software suites [ratio cortex/white matter left hemisphere: mean transit time (MTT) 0.991 vs 0.847, p < 0.05; relative cerebral bloodflow (rBF) 3.23 vs 4.418, p < 0.001; relative cerebral bloodvolume (rBVc) 2.813 vs 3.884, p < 0.001; right hemisphere: MTT 1.079 vs 0.854, p < 0.05; rBF 3.262 vs 4.378, p < 0.001; rBVc 2.762 vs 3.935, p < 0.001)]. Perfusion results were also significantly different in patients with stroke (ratio cortex/white matter affected hemisphere: MTT 1.058 vs 0.784; p < 0.001), dementia (ratio cortex/white matter left hemisphere: rBVc 1.152 vs 1.795, p < 0.001; right hemisphere: rBVc 1.396 vs 1.662, p < 0.05) and brain tumors (ratio cortex/whole tumor rBVc: 0.778 vs 0.919, p < 0.001 and ratio cortex/tumor hotspot rBVc: 0.529 vs 0.512, p < 0.05).

CONCLUSION

Despite a highly standardized workflow, parametric perfusion maps are depended on the chosen software. Radiologists should consider software related variances when using dynamic susceptibility contrast perfusion for clinical imaging and research.

ADVANCES IN KNOWLEDGE

This multicenter study compared perfusion parameters calculated by two commercial dynamic susceptibility contrast perfusion post-processing software solutions in different central nervous system disorders with a large sample size and a highly standardized processing workflow. Despite, parametric perfusion maps are depended on the chosen software which impacts clinical imaging and research.

Automated ASPECTS in Acute Ischemic Stroke: A Comparative Analysis with CT Perfusion

BACKGROUND AND PURPOSE

Automated ASPECTS has the potential of reducing interobserver variability in the determination of early ischemic changes. We aimed to assess the performance of an automated ASPECTS software against the assessment of a neuroradiologist in a comparative analysis with concurrent CTP-based CBV ASPECTS.

MATERIALS AND METHODS

Patients with anterior circulation stroke who had baseline NCCT and CTP and underwent successful mechanical thrombectomy were included. NCCT-ASPECTS was assessed by 2 neuroradiologists, and discrepancies were resolved by consensus. CTP-CBV ASPECTS was assessed by a different neuroradiologist. Automated ASPECTS was provided by Brainomix software. ASPECTS was dichotomized (ASPECTS ≥6 or <6) and was also based on the time from onset (>6 or ≤6 hours).

RESULTS

A total of 58 patients were included. The interobserver agreement for NCCT ASPECTS was moderate (κ = 0.48) and marginally improved (κ = 0.64) for dichotomized data. Automated ASPECTS showed excellent agreement with consensus reads (κ = 0.84) and CTP-CBV ASPECTS (κ = 0.84). Intraclass correlation coefficients for ASPECTS across all 3 groups were 0.84 (95% CI, 0.76-0.90, raw scores) and 0.94 (95% CI, 0.91-0.96, dichotomized scores). Automated scores were comparable with consensus reads and CTP-CBV ASPECTS in patients when grouped on the basis of time from symptom onset (>6 or ≤6 hours). There was significant (P < .001) negative correlation with final infarction volume and the 3 ASPECTS groups (r = -0.52, consensus reads; -0.58, CTP-CBV; and -0.66, automated).

CONCLUSIONS

ASPECTS derived from an automated software performs equally as well as consensus reads of expert neuroradiologists and concurrent CTP-CBV ASPECTS and can be used to standardize ASPECTS reporting and minimize interpretation variability.

Oxygenation differs among white matter hyperintensities, intersected fiber tracts and unaffected white matter

White matter hyperintensities of presumed vascular origin are frequently observed on magnetic resonance imaging in normal aging. They are typically found in cerebral small vessel disease and suspected culprits in the etiology of complex age- and small vessel disease-related conditions, such as late-onset depression. White matter hyperintensities may interfere with surrounding white matter metabolic demands by disrupting fiber tract integrity. Meanwhile, risk factors for small vessel disease are thought to reduce tissue oxygenation, not only by reducing regional blood supply, but also by impairing capillary function. To address white matter oxygen supply–demand balance, we estimated voxel-wise capillary density as an index of resting white matter metabolism, and combined estimates of blood supply and capillary function to calculate white matter oxygen availability. We conducted a cross-sectional study with structural, perfusion- and diffusion-weighted magnetic resonance imaging in 21 patients with late-onset depression and 21 controls. We outlined white matter hyperintensities and used tractography to identify the tracts they intersect. Perfusion data comprised cerebral blood flow, blood volume, mean transit time and relative transit time heterogeneity—the latter a marker of capillary dysfunction. Based on these, white matter oxygenation was calculated as the steady state cerebral metabolic rate of oxygen under the assumption of normal tissue oxygen tension and vice versa. The number, volume and perfusion characteristics of white matter hyperintensities did not differ significantly between groups. Hemodynamic data showed white matter hyperintensities to have lower blood flow and blood volume, but higher relative transit time heterogeneity, than normal-appearing white matter, resulting in either reduced capillary metabolic rate of oxygen or oxygen tension. Intersected tracts showed significantly lower blood flow, blood volume and capillary metabolic rate of oxygen than normal-appearing white matter. Across groups, lower lesion oxygen tension was associated with higher lesion number and volume. Compared with normal-appearing white matter, tissue oxygenation is significantly reduced in white matter hyperintensities as well as the fiber tracts they intersect, independent of parallel late-onset depression. In white matter hyperintensities, reduced microvascular blood volume and concomitant capillary dysfunction indicate a severe oxygen supply–demand imbalance with hypoxic tissue injury. In intersected fiber tracts, parallel reductions in oxygenation and microvascular blood volume are consistent with adaptations to reduced metabolic demands. We speculate, that aging and vascular risk factors impair white matter hyperintensity perfusion and capillary function to create hypoxic tissue injury, which in turn affect the function and metabolic demands of the white matter tracts they disrupt.

Multi-stage automated local arterial input function selection in perfusion MRI

OBJECTIVE

Cerebral blood flow (CBF) quantification using dynamic-susceptibility contrast MRI can be achieved via model-independent deconvolution, with local arterial input function (AIF) deconvolution methods identifying multiple arterial regions with unique corresponding arterial input functions. The clinical application of local AIF methods necessitates an efficient and fully automated solution. To date, such local AIF methods have relied on the computation of a singular surrogate measure of bolus arrival time or custom arterial scoring functions to infer vascular supply origins. This paper aims to introduce a new local AIF method that alternatively utilises a multi-stage approach to perform AIF selection.

MATERIAL AND METHODS

A fully automated, multi-stage local AIF method is proposed, leveraging both signal-based cluster analysis and priority flooding to define arterial regions and their corresponding vascular supply origins. The introduced method was applied to data from four patients with cerebrovascular disease who showed significant artefacts when using a prevailing automated local AIF method.

RESULTS

The immediately apparent image artefacts found using the pre-existing method due to poor AIF selection were found to be absent when using the proposed method.

CONCLUSION

The results suggest the proposed solution provides a more robust approach to perfusion quantification than currently available fully automated local AIF methods.

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.

Neural Network-derived Perfusion Maps for the Assessment of Lesions in Patients with Acute Ischemic Stroke

Purpose

To perform a proof-of-concept study to investigate the clinical utility of perfusion maps derived from convolutional neural networks (CNNs) for the workup of patients with acute ischemic stroke presenting with a large vessel occlusion.

Materials and Methods

Data on endovascularly treated patients with acute ischemic stroke (n = 151; median age, 68 years [interquartile range, 59-75 years]; 82 of 151 [54.3%] women) were retrospectively extracted from a single-center institutional prospective registry (between January 2011 and December 2015). Dynamic susceptibility perfusion imaging data were processed by applying a commercially available reference method and in parallel by a recently proposed CNN method to automatically infer time to maximum of the tissue residue function (Tmax) perfusion maps. The outputs were compared by using quantitative markers of tissue at risk derived from manual segmentations of perfusion lesions from two expert raters.

Results

Strong correlations of lesion volumes (Tmax > 4 seconds, > 6 seconds, and > 8 seconds; R = 0.865-0.914; P < .001) and good spatial overlap of respective lesion segmentations (Dice coefficients, 0.70-0.85) between the CNN method and reference output were observed. Eligibility for late-window reperfusion treatment was feasible with use of the CNN method, with complete interrater agreement for the CNN method (Cohen κ = 1; P < .001), although slight discrepancies compared with the reference-based output were observed (Cohen κ = 0.609-0.64; P < .001). The CNN method tended to underestimate smaller lesion volumes, leading to a disagreement between the CNN and reference method in five of 45 patients (9%).

Conclusion

Compared with standard deconvolution-based processing of raw perfusion data, automatic CNN-derived Tmax perfusion maps can be applied to patients who have acute ischemic large vessel occlusion stroke, with similar clinical utility.© RSNA, 2019Supplemental material is available for this article.

Accelerated whole-brain perfusion imaging using a simultaneous multislice spin-echo and gradient-echo sequence with joint virtual coil reconstruction

PURPOSE

Dynamic susceptibility contrast imaging requires high temporal sampling, which poses limits on achievable spatial coverage and resolution. Additionally, more encoding-intensive multi-echo acquisitions for quantitative imaging are desired to mitigate contrast leakage effects, which further limits spatial encoding. We present an accelerated sequence that provides whole-brain coverage at an improved spatio-temporal resolution, to allow for dynamic quantitative R2 and R2 * mapping during contrast-enhanced imaging.

METHODS

A multi-echo spin and gradient-echo sequence was implemented with simultaneous multislice acquisition. Complementary k-space sampling between repetitions and joint virtual coil reconstruction were used along with a dynamic phase-matching technique to achieve high-quality reconstruction at 9-fold acceleration, which enabled 2 × 2 × 5 mm whole-brain imaging at TR of 1.5 to 1.7 seconds. The multi-echo images from this sequence were fit to achieve quantitative R2 and R2 * maps for each repetition, and subsequently used to find perfusion measures including cerebral blood flow and cerebral blood volume.

RESULTS

Images reconstructed using joint virtual coil show improved image quality and g-factor compared with conventional reconstruction methods, resulting in improved quantitative maps with a 9-fold acceleration factor and whole-brain coverage during the dynamic perfusion acquisition.

CONCLUSION

The method presented shows the advantage of using a joint virtual coil-GRAPPA reconstruction to allow for high acceleration factors while maintaining reliable image quality for quantitative perfusion mapping, with the potential to improve tumor diagnostics and monitoring.

Defining Ischemic Core in Acute Ischemic Stroke Using CT Perfusion: A Multiparametric Bayesian-Based Model

BACKGROUND AND PURPOSE

The Bayesian probabilistic method has shown promising results to offset noise-related variability in perfusion analysis. Using CTP, we aimed to find optimal Bayesian-estimated thresholds based on multiparametric voxel-level models to estimate the ischemic core in patients with acute ischemic stroke.

MATERIALS AND METHODS

Patients with anterior circulation acute ischemic stroke who had baseline CTP and achieved successful recanalization were included. In a subset of patients, multiparametric voxel-based models were constructed between Bayesian-processed CTP maps and follow-up MRIs to identify pretreatment CTP parameters that were predictive of infarction using robust logistic regression. Subsequently CTP-estimated ischemic core volumes from our Bayesian model were compared against routine clinical practice oscillation singular value decomposition-relative cerebral blood flow <30%, and the volumetric accuracy was assessed against final infarct volume.

RESULTS

In the constructed multivariate voxel-based model, 4 variables were identified as independent predictors of infarction: TTP, relative CBF, differential arterial tissue delay, and differential mean transit time. At an optimal cutoff point of 0.109, this model identified infarcted voxels with nearly 80% accuracy. The limits of agreement between CTP-estimated ischemic core and final infarct volume ranged from -25 to 27 mL for the Bayesian model, compared with -61 to 52 mL for oscillation singular value decomposition-relative CBF.

CONCLUSIONS

We established thresholds for the Bayesian model to estimate the ischemic core. The described multiparametric Bayesian-based model improved consistency in CTP estimation of the ischemic core compared with the methodology used in current clinical routine.

Comparison of T1 mapping and fixed T1 method for dynamic contrast-enhanced MRI perfusion in brain gliomas

OBJECTIVES

To compare dynamic contrast-enhanced MRI (DCE-MRI) data obtained using different prebolus T1 values in glioma grading and molecular profiling.

METHODS

We retrospectively reviewed 83 cases of gliomas: 46 lower-grade gliomas (LGG; grades II and III) and 37 high-grade gliomas (HGG; grade IV). DCE-MRI maps of plasma volume fraction (Vp), extravascular-extracellular volume fraction (Ve), and tracer transfer constant from plasma to tissue (K^trans) were obtained using a fixed T1 value of 1400 ms and a measured T1 obtained with variable flip angle (VFA). Tumour segmentations were performed and first-order histogram parameters were extracted from volumes of interest (VOIs) after co-registration with the perfusion maps. The two methods were compared using Wilcoxon matched-pairs signed-rank test and Bland-Altman analysis. Diagnostic accuracy was obtained and compared using ROC curve analysis and DeLong's test.

RESULTS

Perfusion parameters obtained with the fixed T1 value were significantly higher than those obtained with the VFA. As regards diagnostic accuracy, there were no significant differences between the two methods both for glioma grading and molecular classification, except for few parameters of both methods.

CONCLUSIONS

DCE-MRI data obtained with different prebolus T1 are not comparable and the definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI.

KEY POINTS

• DCE-MRI data obtained with different prebolus T1 are significantly different, thus not comparable. • The definition of a prebolus T1 by T1 mapping is not mandatory since it does not improve the diagnostic accuracy of DCE-MRI for glioma grading. • The use of a fixed T1 value represents a valid alternative to T1 mapping for DCE-MRI analysis.

Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images

Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871 ± 0.024 , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.

Perfusion weighted imaging using combined gradient/spin echo EPIK: Brain tumour applications in hybrid MR-PET

Advanced perfusion-weighted imaging (PWI) methods that combine gradient echo (GE) and spin echo (SE) data are important tools for the study of brain tumours. In PWI, single-shot, EPI-based methods have been widely used due to their relatively high imaging speed. However, when used with increasing spatial resolution, single-shot EPI methods often show limitations in whole-brain coverage for multi-contrast applications. To overcome this limitation, this work employs a new version of EPI with keyhole (EPIK) to provide five echoes: two with GEs, two with mixed GESE and one with SE; the sequence is termed "GESE-EPIK." The performance of GESE-EPIK is evaluated against its nearest relative, EPI, in terms of the temporal signal-to-noise ratio (tSNR). Here, data from brain tumour patients were acquired using a hybrid 3T MR-BrainPET scanner. GESE-EPIK resulted in reduced susceptibility artefacts, shorter TEs for the five echoes and increased brain coverage when compared to EPI. Moreover, compared to EPI, EPIK achieved a comparable tSNR for the first and second echoes and significantly higher tSNR for other echoes. A new method to obtain multi-echo GE and SE data with shorter TEs and increased brain coverage is demonstrated. As proposed here, the workflow can be shortened and the integration of multimodal clinical MR-PET studies can be facilitated.

An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors

INTRODUCTION

Automatic and accurate arterial input function (AIF) selection has an essential role for quantification of cerebral perfusion hemodynamic parameters using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). The purpose of this study is to develop an optimal automatic method for arterial input function determination in DSC-MRI of glioma brain tumors by using a new preprocessing method.

MATERIAL AND METHODS

For this study, DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed AIF selection framework consisted an effcient pre-processing step, through which non-arterial curves such as tumorous, tissue, noisy and partial-volume affected curves were excluded, followed by AIF selection through agglomerative hierarchical (AH) clustering method. The performance of automatic AIF clustering was compared with manual AIF selection performed by an experienced radiologist, based on curve shape parameters, i.e. maximum peak (MP), full-width-at-half-maximum (FWHM), M (=MP/ (TTP × FWHM)) and root mean square error (RMSE).

RESULTS

Mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by two-tailed paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, approving the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and coefficients of variation percent showed a better agreement between manual AIF and our proposed AIF selection than previously proposed methods.

CONCLUSION

The results of current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears promising for efficient and accurate clinical applications.

Sampling arterial input function (AIF) from peripheral arteries: Comparison of a temporospatial-feature based method against conventional manual method

It is often difficult to accurately localize small arteries in images of peripheral organs, and even more so with vascular abnormality vasculatures, including collateral arteries, in peripheral artery disease (PAD). This poses a challenge for manually sampling arterial input function (AIF) in quantifying dynamic contrast-enhanced (DCE) MRI data of peripheral organs. In this study, we designed a multi-step screening approach that utilizes both the temporal and spatial information of the dynamic images, and is presumably suitable for localizing small and unpredictable peripheral arteries. In 41 DCE MRI datasets acquired from human calf muscles, the proposed method took <5 s on average for sampling AIF for each case, much more efficient than the manual sampling method; AIFs by the two methods were comparable, with Pearson's correlation coefficient of 0.983 ± 0.004 (p-value < 0.01) and relative difference of 2.4% ± 2.6%. In conclusion, the proposed temporospatial-feature based method enables efficient and accurate sampling of AIF from peripheral arteries, and would improve measurement precision and inter-observer consistency for quantitative DCE MRI of peripheral tissues.

Transit time homogenization in ischemic stroke - A novel biomarker of penumbral microvascular failure?

Cerebral ischemia causes widespread capillary no-flow in animal studies. The extent of microvascular impairment in human stroke, however, is unclear. We examined how acute intra-voxel transit time characteristics and subsequent recanalization affect tissue outcome on follow-up MRI in a historic cohort of 126 acute ischemic stroke patients. Based on perfusion-weighted MRI data, we characterized voxel-wise transit times in terms of their mean transit time (MTT), standard deviation (capillary transit time heterogeneity - CTH), and the CTH:MTT ratio (relative transit time heterogeneity), which is expected to remain constant during changes in perfusion pressure in a microvasculature consisting of passive, compliant vessels. To aid data interpretation, we also developed a computational model that relates graded microvascular failure to changes in these parameters. In perfusion-diffusion mismatch tissue, prolonged mean transit time (>5 seconds) and very low cerebral blood flow (≤6 mL/100 mL/min) was associated with high risk of infarction, largely independent of recanalization status. In the remaining mismatch region, low relative transit time heterogeneity predicted subsequent infarction if recanalization was not achieved. Our model suggested that transit time homogenization represents capillary no-flow. Consistent with this notion, low relative transit time heterogeneity values were associated with lower cerebral blood volume. We speculate that low RTH may represent a novel biomarker of penumbral microvascular failure.

Perfusion Magnetic Resonance Imaging Changes in Normal Appearing Brain Tissue after Radiotherapy in Glioblastoma Patients may Confound Longitudinal Evaluation of Treatment Response

Background

The aim of this study was assess acute and early delayed radiation-induced changes in normal-appearing brain tissue perfusion as measured with perfusion magnetic resonance imaging (MRI) and the dependence of these changes on the fractionated radiotherapy (FRT) dose level.

Patients and methods

Seventeen patients with glioma WHO grade III-IV treated with FRT were included in this prospective study, seven were excluded because of inconsistent FRT protocol or missing examinations. Dynamic susceptibility contrast MRI and contrast-enhanced 3D-T1-weighted (3D-T1w) images were acquired prior to and in average (standard deviation): 3.1 (3.3), 34.4 (9.5) and 103.3 (12.9) days after FRT. Pre-FRT 3D-T1w images were segmented into white- and grey matter. Cerebral blood volume (CBV) and cerebral blood flow (CBF) maps were calculated and co-registered patient-wise to pre-FRT 3D-T1w images. Seven radiation dose regions were created for each tissue type: 0-5 Gy, 5-10 Gy, 10-20 Gy, 20-30 Gy, 30-40 Gy, 40-50 Gy and 50-60 Gy. Mean CBV and CBF were calculated in each dose region and normalised (nCBV and nCBF) to the mean CBV and CBF in 0-5 Gy white- and grey matter reference regions, respectively.

Results

Regional and global nCBV and nCBF in white- and grey matter decreased after FRT, followed by a tendency to recover. The response of nCBV and nCBF was dose-dependent in white matter but not in grey matter.

Conclusions

Our data suggest that radiation-induced perfusion changes occur in normal-appearing brain tissue after FRT. This can cause an overestimation of relative tumour perfusion using dynamic susceptibility contrast MRI, and can thus confound tumour treatment evaluation.

Arterial input function in a dedicated slice for cerebral perfusion measurements in humans

OBJECT

We aimed to modify our previously published method for arterial input function measurements for evaluation of cerebral perfusion (dynamic susceptibility contrast MRI) such that it can be applied in humans in a clinical setting.

MATERIALS AND METHODS

Similarly to our previous work, a conventional measurement sequence for dynamic susceptibility contrast MRI is extended with an additional measurement slice at the neck. Measurement parameters at this slice were optimized for the blood signal (short echo time, background suppression, magnitude and phase images). Phase-based evaluation of the signal in the carotid arteries is used to obtain quantitative arterial input functions.

RESULTS

In all pilot measurements, quantitative arterial input functions were obtained. The resulting absolute perfusion parameters agree well with literature values (gray and white matter mean values of 46 and 24 mL/100 g/min, respectively, for cerebral blood flow and 3.0% and 1.6%, respectively, for cerebral blood volume).

CONCLUSIONS

The proposed method has the potential to quantify arterial input functions in the carotid arteries from a direct measurement without any additional normalization.

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning

Background and Purpose—

Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume.

Methods—

Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNN deep ) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNN deep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNN Tmax ), a shallow CNN based on a combination of 9 different biomarkers (CNN shallow ), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10 −6 mm 2 /s (ADC thres ). To assess whether CNN deep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNN deep, −rtpa to access a treatment effect. The networks’ performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast.

Results—

CNN deep yields significantly better performance in predicting final outcome (AUC=0.88±0.12) than generalized linear model (AUC=0.78±0.12; P =0.005), CNN Tmax (AUC=0.72±0.14; P <0.003), and ADC thres (AUC=0.66±0.13; P <0.0001) and a substantially better performance than CNN shallow (AUC=0.85±0.11; P =0.063). Measured by contrast, CNN deep improves the predictions significantly, showing superiority to all other methods ( P ≤0.003). CNN deep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different ( P =0.048).

Conclusions—

The considerable prediction improvement accuracy over current state of the art increases the potential for automated decision support in providing recommendations for personalized treatment plans.

Detection of severity in Alzheimer's disease (AD) using computational modeling

The prevalent cause of dementia - Alzheimer's disease (AD) is characterized by an early cholinergic deficit that is in part responsible for the cognitive deficits (especially memory and attention defects). Prolonged AD leads to moderate-to-severe AD, which is one of the leading causes of death. Placebo-controlled, randomized clinical trials have shown significant effects of Acetyl cholin esterase inhibitors (ChEIs) on function, cognition, activities of daily living (ADL) and behavioral symptoms in patients. Studies have shown comparable effects for ChEIs in patients with moderate-to-severe or mild AD. Setting a fixed measurement (e.g. a Mini-Mental State Examination score, as a 'when to stop treatment limit) for the disease is not clinically rational. Detection of changed regional cerebral blood flow in mild cognitive impairment and early AD by perfusion-weighted magnetic resonance imaging has been a challenge. The utility of perfusion-weighted magnetic resonance imaging (PW-MRI) for detecting changes in regional cerebral blood flow (rCBF) in patients with mild cognitive impairment (MCI) and early AD was evaluated. We describe a computer aided prediction model to determine the severity of AD using known data in literature. We designed an automated system for the determination of AD severity. It is used to predict the clinical cases and conditions with disagreements from specialist. The model described is useful in clinical practice to validate diagnosis.

The effect of carotid artery stenting on capillary transit time heterogeneity in patients with carotid artery stenosis

Introduction

Carotid revascularisation improves haemodynamic compromise in cerebral circulation as an additional benefit to the primary goal of reducing future thromboembolic risk. We determined the effect of carotid artery stenting on cerebral perfusion and oxygenation using a perfusion-weighted MRI algorithm that is based on assessment of capillary transit-time heterogeneity together with other perfusion and metabolism-related metrics.

Patients and methods

A consecutive series of 33 patients were evaluated by dynamic susceptibility contrast perfusion-weighted MRI prior to and within 24 h of the endovascular procedure. The level of relative change induced by stenting, and relationship of these changes with respect to baseline stenosis degree were analysed.

Results

Stenting led to significant increase in cerebral blood flow (p < 0.001), and decrease in cerebral blood volume (p = 0.001) and mean transit time (p < 0.001); this was accompanied by reduction in oxygen extraction fraction (p < 0.001) and capillary transit-time heterogeneity (p < 0.001), but an overall increase in relative capillary transit-time heterogeneity (RTH: CTH divided by MTT; p = 0.008). No significant change was observed with respect to cerebral metabolic rate of oxygen. The median volume of tissue with MTT > 2s decreased from 24 ml to 12 ml (p = 0.009), with CTH > 2s from 29 ml to 19 ml (p = 0.041), and with RTH < 0.9 from 61 ml to 39 ml (p = 0.037) following stenting. These changes were correlated with the baseline degree of stenosis.Discussion: Stenting improved the moderate stage of haemodynamic compromise at baseline in our cohort. The decreased relative transit-time heterogeneity, which increases following stenting, is probably a reflection of decreased functional capillary density secondary to chronic hypoperfusion induced by the proximal stenosis.Conclusion: Carotid artery stenting, is not only important for prophylaxis of future vascular events, but also is critical for restoration of microvascular function in the cerebral tissue.

Cerebellar Hypoperfusion in Migraine Attack: Incidence and Significance

BACKGROUND AND PURPOSE

Patients diagnosed with migraine with aura have an increased lifetime risk of ischemic stroke. It is not yet clear whether prolonged cortical hypoperfusion during an aura increases the immediate risk of cerebellar infarction because it may induce crossed cerebellar diaschisis and subsequent tissue damage. To address this question, we retrospectively analyzed potential relationships between cortical oligemia and cerebellar hypoperfusion in patients with migraine with aura and their potential relation to small infarct-like cerebellar lesions.

MATERIALS AND METHODS

One hundred six migraineurs who underwent MR imaging, including DSC perfusion, were included in the study. In patients with apparent perfusion asymmetry, we used ROI analysis encompassing 18 infra- and supratentorial ROIs to account for differences in regional cerebral blood flow and volume. The presence of cerebellar hypoperfusion was calculated using an asymmetry index, with values of >10% being considered significant.

RESULTS

We observed perfusion asymmetries in 23/106 patients, 22 in patients with migraine with aura (20.8%). Cerebellar hypoperfusion was observed in 12/23 patients (52.2%), and crossed cerebellar diaschisis, in 9/23 patients (39.1%) with abnormal perfusion. In none of the 106 patients were DWI restrictions observed during migraine with aura.

CONCLUSIONS

Cerebellar hypoperfusion and crossed cerebellar diaschisis are common in patients with migraine with aura and cortical perfusion abnormalities. Crossed cerebellar diaschisis in migraine with aura may be considered a benign phenomenon because we observed no association with DWI restriction or manifest cerebellar infarctions, even in patients with prolonged symptom-related perfusion abnormalities persisting for up to 24 hours.

A tool to visualize and analyze perfusion data: Development and application of the R package "CTP"

BACKGROUND AND OBJECTIVE

Computed tomography perfusion (CTP) is a widely used imaging modality especially in neuroimaging. Despite this, CTP is often prohibitive due to the dearth of free/open-source software. This could have wide-ranging implications for instruction and research. We have implemented an online-available CTP tool built and run completely within the R computing environment.

METHODS

Called from within R, the user can select one of four different methods to construct a cerebral blood flow (CBF) map: (1) max-slope (2) singular value decomposition (3) block circulant singular value decomposition or (4) oscillation minimization singular value decomposition. The four methods are compared against a digital CBF phantom.

RESULTS

All four methods generate a CBF map, with the oscillation minimization technique giving the most accurate map.

CONCLUSIONS

We have constructed an easily accessible teaching and research tool to create a CBF map and made it freely available. We hope this tool will help facilitate understanding of the methods involved in constructing perfusion maps and be a valuable resource to future researchers.

Comparison between the Prebolus T1 Measurement and the Fixed T1 Value in Dynamic Contrast-Enhanced MR Imaging for the Differentiation of True Progression from Pseudoprogression in Glioblastoma Treated with Concurrent Radiation Therapy and Temozolomide Chemotherapy

BACKGROUND AND PURPOSE

Glioblastoma is the most common primary brain malignancy and differentiation of true progression from pseudoprogression is clinically important. Our purpose was to compare the diagnostic performance of dynamic contrast-enhanced pharmacokinetic parameters using the fixed T1 and measured T1 on differentiating true from pseudoprogression of glioblastoma after chemoradiation with temozolomide.

MATERIALS AND METHODS

This retrospective study included 37 patients with histopathologically confirmed glioblastoma with new enhancing lesions after temozolomide chemoradiation defined as true progression (n = 15) or pseudoprogression (n = 22). Dynamic contrast-enhanced pharmacokinetic parameters, including the volume transfer constant, the rate transfer constant, the blood plasma volume per unit volume, and the extravascular extracellular space per unit volume, were calculated by using both the fixed T1 of 1000 ms and measured T1 by using the multiple flip-angle method. Intra- and interobserver reproducibility was assessed by using the intraclass correlation coefficient. Dynamic contrast-enhanced pharmacokinetic parameters were compared between the 2 groups by using univariate and multivariate analysis. The diagnostic performance was evaluated by receiver operating characteristic analysis and leave-one-out cross validation.

RESULTS

The intraclass correlation coefficients of all the parameters from both T1 values were fair to excellent (0.689-0.999). The volume transfer constant and rate transfer constant from the fixed T1 were significantly higher in patients with true progression (P = .048 and .010, respectively). Multivariate analysis revealed that the rate transfer constant from the fixed T1 was the only independent variable (OR, 1.77 × 10⁵) and showed substantial diagnostic power on receiver operating characteristic analysis (area under the curve, 0.752; P = .002). The sensitivity and specificity on leave-one-out cross validation were 73.3% (11/15) and 59.1% (13/20), respectively.

CONCLUSIONS

The dynamic contrast-enhanced parameter of rate transfer constant from the fixed T1 acted as a preferable marker to differentiate true progression from pseudoprogression.

Slice-accelerated gradient-echo echo planar imaging dynamic susceptibility contrast-enhanced MRI with blipped CAIPI: effect of increasing temporal resolution

PURPOSE

To assess the influence of high temporal resolution on the perfusion measurements and image quality of perfusion maps, by applying simultaneous-multi-slice acquisition (SMS) dynamic susceptibility contrast-enhanced (DSC) magnetic resonance imaging (MRI).

MATERIALS AND METHODS

DSC-MRI data using SMS gradient-echo echo planar imaging sequences in 10 subjects with no intracranial abnormalities were retrospectively analyzed. Three additional data sets with temporal resolution of 1.0, 1.5, and 2.0 s were created from the raw data sets of 0.5 s. Cerebral blood flow (CBF), cerebral blood volume, mean transit time (MTT), time to peak (TTP), and time to maximum tissue residue function (T _max) measurements were performed, as was visual perfusion map analysis. The perfusion parameter for temporal resolution of 0.5 s (reference) was compared with each synthesized perfusion parameter.

RESULTS

CBF, MTT, and TTP values at temporal resolutions of 1.5 and 2.0 s differed significantly from the reference. The image quality of MTT, TTP, and T _max maps deteriorated with decreasing temporal resolution.

CONCLUSION

The temporal resolution of DSC-MRI influences perfusion parameters and SMS DSC-MRI provides better image quality for MTT, TTP, and T _max maps.

A data-oriented self-calibration and robust chemical-shift encoding by using clusterization (OSCAR): Theory, optimization and clinical validation in neuromuscular disorders

Multi-echo Chemical Shift-Encoded (CSE) methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water/fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant (such as for neuromuscular disorders) because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. Results established that the presented algorithm is able to identify at least 4 different partitions from MRI dataset under which to perform independent self-calibration routines and was found robust in NMD imaging studies (as evaluated on a cohort of 24 subjects) against latest CSE techniques with either calibrated or non-calibrated approaches. Particularly, the PDFF of the thigh was more reproducible for the quantitative estimation of pathological muscular fat infiltrations, which may be promising to evaluate disease progression in clinical practice.

Optimization of DSC MRI Echo Times for CBV Measurements Using Error Analysis in a Pilot Study of High-Grade Gliomas

BACKGROUND AND PURPOSE

The optimal TE must be calculated to minimize the variance in CBV measurements made with DSC MR imaging. Simulations can be used to determine the influence of the TE on CBV, but they may not adequately recapitulate the in vivo heterogeneity of precontrast T2*, contrast agent kinetics, and the biophysical basis of contrast agent-induced T2* changes. The purpose of this study was to combine quantitative multiecho DSC MRI T2* time curves with error analysis in order to compute the optimal TE for a traditional single-echo acquisition.

MATERIALS AND METHODS

Eleven subjects with high-grade gliomas were scanned at 3T with a dual-echo DSC MR imaging sequence to quantify contrast agent-induced T2* changes in this retrospective study. Optimized TEs were calculated with propagation of error analysis for high-grade glial tumors, normal-appearing white matter, and arterial input function estimation.

RESULTS

The optimal TE is a weighted average of the T2* values that occur as a contrast agent bolus transverses a voxel. The mean optimal TEs were 30.0 ± 7.4 ms for high-grade glial tumors, 36.3 ± 4.6 ms for normal-appearing white matter, and 11.8 ± 1.4 ms for arterial input function estimation (repeated-measures ANOVA, P < .001).

CONCLUSIONS

Greater heterogeneity was observed in the optimal TE values for high-grade gliomas, and mean values of all 3 ROIs were statistically significant. The optimal TE for the arterial input function estimation is much shorter; this finding implies that quantitative DSC MR imaging acquisitions would benefit from multiecho acquisitions. In the case of a single-echo acquisition, the optimal TE prescribed should be 30-35 ms (without a preload) and 20-30 ms (with a standard full-dose preload).

Utilization of MR angiography in perfusion imaging for identifying arterial input function

OBJECTIVE

This research utilizes magnetic resonance angiography (MRA) to identify arterial locations during the parametric evaluation of concentration time curves (CTCs), and to prevent shape distortions in arterial input function (AIF).

MATERIALS AND METHODS

We carried out cluster analysis with the CTC parameters of voxels located within and around the middle cerebral artery (MCA). Through MRA, we located voxels that meet the AIF criteria and those with distorted CTCs. To minimize partial volume effect, we re-scaled the time integral of CTCs by the time integral of venous output function (VOF). We calculated the steady-state value to area under curve ratio (SS:AUC) of VOF and used it as a reference in selecting AIF. CTCs close to this reference value (selected AIF) and those far from it were used (eliminated AIF) to compute cerebral blood flow (CBF).

RESULTS

Eliminated AIFs were found to be either on or anterior to MCA, whereas selected AIFs were located superior, inferior, posterior, or anterior to MCA. If the SS:AUC of AIF was far from the reference value, CBF was either under- or over-estimated by a maximum of 41.1 ± 14.3 and 36.6 ± 19.2%, respectively.

CONCLUSION

MRA enables excluding voxels on the MCA during cluster analysis, and avoiding the risk of shape distortions.

Capillary dysfunction is associated with symptom severity and neurodegeneration in Alzheimer's disease

INTRODUCTION

We examined whether cortical microvascular blood volume and hemodynamics in Alzheimer's disease (AD) are consistent with tissue hypoxia and whether they correlate with cognitive performance and the degree of cortical thinning.

METHODS

Thirty-two AD patients underwent cognitive testing, structural magnetic resonance imaging (MRI), and perfusion MRI at baseline and after 6 months. We measured cortical thickness, microvascular cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and capillary transit time heterogeneity (CTH) and estimated tissue oxygen tension (P_tO₂).

RESULTS

At baseline, poor cognitive performance and regional cortical thinning correlated with lower CBF and CBV, with higher MTT and CTH and with low P_tO₂ across the cortex. Cognitive decline over time was associated with increasing whole brain relative transit time heterogeneity (RTH = CTH/MTT).

DISCUSSION

Our results confirm the importance of microvascular pathology in AD. Deteriorating microvascular hemodynamics may cause hypoxia, which is known to precipitate amyloid retention.

A PET-Guided Framework Supports a Multiple Arterial Input Functions Approach in DSC-MRI in Acute Stroke

BACKGROUND AND PURPOSE

In acute stroke, arterial-input-function (AIF) determination is essential for obtaining perfusion estimates with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging (DSC-MRI). Standard DSC-MRI postprocessing applies single AIF selection, ie, global AIF. Physiological considerations, however, suggest that a multiple AIFs selection method would improve perfusion estimates to detect penumbral flow. In this study, we developed a framework based on comparable DSC-MRI and positron emission tomography (PET) images to compare the two AIF selection approaches and assess their performance in penumbral flow detection in acute stroke.

METHODS

In a retrospective analysis of 17 sub(acute) stroke patients with consecutive MRI and PET scans, voxel-wise optimized AIFs were calculated based on the kinetic model as derived from both imaging modalities. Perfusion maps were calculated based on the optimized-AIF using two methodologies: (1) Global AIF and (2) multiple AIFs as identified by cluster analysis. Performance of penumbral-flow detection was tested by receiver-operating characteristics (ROC) curve analysis, ie, the area under the curve (AUC).

RESULTS

Large variation of optimized AIFs across brain voxels demonstrated that there is no optimal single AIF. Subsequently, the multiple-AIF method (AUC range over all maps: .82-.90) outperformed the global AIF methodology (AUC .72-.85) significantly.

CONCLUSIONS

We provide PET imaging-based evidence that a multiple AIF methodology is beneficial for penumbral flow detection in comparison with the standard global AIF methodology in acute stroke.

Assessment of tumor oxygenation and its impact on treatment response in bevacizumab-treated recurrent glioblastoma

Antiantiogenic therapy with bevacizumab in recurrent glioblastoma is currently understood to both reduce microvascular density and to prune abnormal tumor microvessels. Microvascular pruning and the resulting vascular normalization are hypothesized to reduce tumor hypoxia and increase supply of systemic therapy to the tumor; however, the underlying pathophysiological changes and their timing after treatment initiation remain controversial. Here, we use a novel dynamic susceptibility contrast MRI-based method, which allows simultaneous assessment of tumor net oxygenation changes reflected by the tumor metabolic rate of oxygen and vascular normalization represented by the capillary transit time heterogeneity. We find that capillary transit time heterogeneity, and hence the oxygen extraction fraction combine with the tumoral blood flow (cerebral blood flow) in such a way that the overall tumor oxygenation appears to be worsened despite vascular normalization. Accordingly, hazards for both progression and death are found elevated in patients with a greater reduction of tumor metabolic rate of oxygen in response to bevacizumab and patients with higher intratumoral tumor metabolic rate of oxygen at baseline. This implies that tumors with a higher degree of angiogenesis prior to bevacizumab-treatment retain a higher level of angiogenesis during therapy despite a greater antiangiogenic effect of bevacizumab, hinting at evasive mechanisms limiting bevacizumab efficacy in that a reversal of their biological behavior and relative prognosis does not occur.

Stable spline deconvolution for dynamic susceptibility contrast MRI

PURPOSE

To present the stable spline (SS) deconvolution method for the quantification of the cerebral blood flow (CBF) from dynamic susceptibility contrast MRI.

METHODS

The SS method was compared with both the block-circulant singular value decomposition (oSVD) and nonlinear stochastic regularization (NSR) methods. oSVD is one of the most popular deconvolution methods in dynamic susceptibility contrast MRI (DSC-MRI). NSR is an alternative approach that we proposed previously. The three methods were compared using simulated data and two clinical data sets.

RESULTS

The SS method correctly reconstructed the dispersed residue function and its peak in presence of dispersion, regardless of the delay. In absence of dispersion, SS performs similarly to oSVD and does not correctly reconstruct the residue function and its peak. SS and NSR better differentiate healthy and pathologic CBF values compared with oSVD in all simulated conditions. Using acquired data, SS and NSR provide more clinically plausible and physiological estimates of the residue function and CBF maps compared with oSVD.

CONCLUSION

The SS method overcomes some of the limitations of oSVD, such as unphysiological estimates of the residue function and NSR, the latter of which is too computationally expensive to be applied to large data sets. Thus, the SS method is a valuable alternative for CBF quantification using DSC-MRI data. Magn Reson Med 78:1801-1811, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Reproducibility of dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI in the study of brain gliomas: a comparison of data obtained using different commercial software

PURPOSE

Dynamic susceptibility contrast MRI (DSC) and dynamic contrast-enhanced MRI (DCE) are useful tools in the diagnosis and follow-up of brain gliomas; nevertheless, both techniques leave the open issue of data reproducibility. We evaluated the reproducibility of data obtained using two different commercial software for perfusion maps calculation and analysis, as one of the potential sources of variability can be the software itself.

METHODS

DSC and DCE analyses from 20 patients with gliomas were tested for both the intrasoftware (as intraobserver and interobserver reproducibility) and the intersoftware reproducibility, as well as the impact of different postprocessing choices [vascular input function (VIF) selection and deconvolution algorithms] on the quantification of perfusion biomarkers plasma volume (Vp), volume transfer constant (K ^trans) and rCBV. Data reproducibility was evaluated with the intraclass correlation coefficient (ICC) and Bland-Altman analysis.

RESULTS

For all the biomarkers, the intra- and interobserver reproducibility resulted in almost perfect agreement in each software, whereas for the intersoftware reproducibility the value ranged from 0.311 to 0.577, suggesting fair to moderate agreement; Bland-Altman analysis showed high dispersion of data, thus confirming these findings. Comparisons of different VIF estimation methods for DCE biomarkers resulted in ICC of 0.636 for K ^trans and 0.662 for Vp; comparison of two deconvolution algorithms in DSC resulted in an ICC of 0.999.

CONCLUSIONS

The use of single software ensures very good intraobserver and interobservers reproducibility. Caution should be taken when comparing data obtained using different software or different postprocessing within the same software, as reproducibility is not guaranteed anymore.

Effect of electrical forepaw stimulation on capillary transit-time heterogeneity (CTH)

Functional hyperemia reduces oxygen extraction efficacy unless counteracted by a reduction of capillary transit-time heterogeneity of blood. We adapted a bolus tracking approach to capillary transit-time heterogeneity estimation for two-photon microscopy and then quantified changes in plasma mean transit time and capillary transit-time heterogeneity during forepaw stimulation in anesthetized mice (C57BL/6NTac). In addition, we analyzed transit time coefficient of variance = capillary transit-time heterogeneity/mean transit time, which we expect to remain constant in passive, compliant microvascular networks. Electrical forepaw stimulation reduced, both mean transit time (11.3% ± 1.3%) and capillary transit-time heterogeneity (24.1% ± 3.3%), consistent with earlier literature and model predictions. We observed a coefficient of variance reduction (14.3% ± 3.5%) during functional activation, especially for the arteriolar-to-venular passage. Such coefficient of variance reduction during functional activation suggests homogenization of capillary flows beyond that expected as a passive response to increased blood flow by other stimuli. This finding is consistent with an active neurocapillary coupling mechanism, for example via pericyte dilation. Mean transit time and capillary transit-time heterogeneity reductions were consistent with the relative change inferred from capillary hemodynamics (cell velocity and flux). Our findings support the important role of capillary transit-time heterogeneity in flow-metabolism coupling during functional activation.

Prognostic and predictive values of diffusion and perfusion MRI in paediatric intracranial ependymomas in a large national study

OBJECTIVE

To assess the relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) derived, respectively, from perfusion and diffusion pre-operative MRI of intracranial ependymomas and their predictive and prognostic values.

METHODS

Pre-operative MRI and clinical data for intracranial ependymomas diagnosed between January 2000 and December 2013 were retrospectively retrieved from a web-based national database. MRI data included diffusion (62 patients) and perfusion (20 patients) MRI. Patient age, histopathological diagnosis, tumour location, ADC, relative ADC (rADC) and rCBV were considered as potential factors in a survival analysis. Survival rates were estimated using the Kaplan-Meier method. Univariate analyses were performed using the log-rank test to compare groups. We also performed a multivariate analysis, applying the Cox proportional hazards model.

RESULTS

ADC and rADC values within hypointense regions differed significantly between grades II and III (p = 0.01). The 75th percentile of ADC within hypointense regions and the 25th percentile of rCBV within non-enhancing lesions were prognostic of disease-free survival (p = 0.004, p = 0.05). A significant correlation was found between the 75th percentile of rCBV and the 25th percentile of rADC (p = 0.01) in enhancing regions of grade-III tumours.

CONCLUSION

Pre-operative rADC and rCBV could be used as prognostic factors for clinical outcome and to predict histological grade in paediatric ependymomas.

ADVANCES IN KNOWLEDGE

Prognostic value of diffusion and perfusion MRI in paediatric ependymoma was found and may play a role in the prognostic classification of patients in order to design more tailored treatment strategies.

Capillary Transit Time Heterogeneity Is Associated with Modified Rankin Scale Score at Discharge in Patients with Bilateral High Grade Internal Carotid Artery Stenosis

Background and Purpose

Perfusion weighted imaging (PWI) is inherently unreliable in patients with severe perfusion abnormalities. We compared the diagnostic accuracy of a novel index of microvascular flow-patterns, so-called capillary transit time heterogeneity (CTH) to that of the commonly used delay parameter T_max in patients with bilateral high grade internal carotid artery stenosis (ICAS).

Methods

Consecutive patients with bilateral ICAS ≥ 70%^NASCET who underwent PWI were retrospectively examined. Maps of CTH and T_max were analyzed with a volumetric approach using several thresholds. Predictors of favorable outcome (modified Rankin scale at discharge 0–2) were identified using univariate and receiver operating characteristic (ROC) curve analysis.

Results

Eighteen patients were included. CTH ≥ 30s differentiated best between patients with favorable and unfavorable outcome when both hemispheres were taken into account (sensitivity 83%, specificity 73%, area under the curve [AUC] 0.833 [confidence interval (CI) 0.635; 1.000]; p = 0.027). The best discrimination using T_max was achieved with a threshold of ≥ 4s (sensitivity 83%, specificity 64%, AUC 0.803 [CI 0.585;1.000]; p = 0.044). The highest AUC was found for left sided volume with CTH ≥ 15s (sensitivity 83%, specificity 91%, AUC 0.924 [CI 0.791;1.000]; p = 0.005).

Conclusion

The study suggests that CTH is superior to T_max in discriminating ICAS patients with favorable from non-favorable outcome. This finding may reflect the simultaneous involvement of large vessels and microvessels in ICAS and underscore the need to diagnose and manage both aspects of the disease.

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters. Results SPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637). Conclusion An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated. (©) RSNA, 2016 Online supplemental material is available for this article.

Time-dependent parameter of perfusion imaging as independent predictor of clinical outcome in symptomatic carotid artery stenosis

BACKGROUND

Carotid artery stenosis is a frequent cause of ischemic stroke. While any degree of stenosis can cause embolic stroke, a higher degree of stenosis can also cause hemodynamic infarction. The hemodynamic effect of a stenosis can be assessed via perfusion weighted MRI (PWI). Our aim was to investigate the ability of PWI-derived parameters such as TTP (time-to-peak) and T(max) (time to the peak of the residue curve) to predict outcome in patients with unilateral acute symptomatic internal carotid artery (sICA) stenosis.

METHODS

Patients with unilateral acute sICA stenosis (≥50% according to NASCET), without intracranial stenosis or occlusion, who underwent PWI, were included. Clinical characteristics, volume of restricted diffusion, volume of prolonged TTP and T(max) were retrospectively analyzed and correlated with outcome represented by the modified Rankin Scale (mRS) score at discharge. TTP and T(max) volumes were dichotomized using a ROC curve analysis. Multivariate analysis was performed to determine which PWI-parameter was an independent predictor of outcome.

RESULTS

Thirty-two patients were included. Degree of stenosis, volume of visually assessed TTP and volume of TTP ≥2 s did not distinguish patients with favorable (mRS 0-2) and unfavorable (mRS 3-6) outcome. In contrast, patients with unfavorable outcome had higher volumes of TTP ≥4 s (9.12 vs. 0.87 ml; p = 0.043), TTP ≥6 s (6.70 vs. 0.20 ml; p = 0.017), T(max) ≥4 s (25.27 vs. 0.00 ml; p = 0.043), T(max) ≥6 s (9.21 vs. 0.00 ml; p = 0.017), T(max) ≥8 s (6.86 vs. 0.00 ml; p = 0.011) and T(max) ≥10s (5.94 vs. 0.00 ml; p = 0.025) in univariate analysis. Multivariate logistic regression showed that NIHSS score on admission (Odds Ratio (OR) 0.466, confidence interval (CI) [0.224;0.971], p = 0.041), T(max) ≥8 s (OR 0.025, CI [0.001;0.898] p = 0.043) and TTP ≥6 s (OR 0.025, CI [0.001;0.898] p = 0.043) were independent predictors of clinical outcome.

CONCLUSION

As they stood out in multivariate regression and are objective and reproducible parameters, PWI-derived volumes of T(max) ≥8 s and TTP ≥6 s might be superior to degree of stenosis and visually assessed TTP maps in predicting short term patient outcome. Future studies should assess if perfusion weighted imaging might guide the selection of patients for recanalization procedures.

Leakage decrease detected by dynamic susceptibility-weighted contrast-enhanced perfusion MRI predicts survival in recurrent glioblastoma treated with bevacizumab

BACKGROUND AND PURPOSE

In glioblastoma, tumor progression appears to be triggered by expression of VEGF, a regulator of blood vessel permeability. Bevacizumab is a monoclonal antibody that inhibits angiogenesis by clearing circulating VEGF, resulting in a decline in the contrast-enhancing tumor, which does not always correlate with treatment response. Our objectives were: (1) to evaluate whether changes in DSC perfusion MRI-derived leakage could predict survival in recurrent glioblastoma, and (2) to estimate whether leakage at baseline was related to treatment outcome.

MATERIALS AND METHODS

We retrospectively analyzed DSC perfusion MRI in 24 recurrent glioblastomas treated with bevacizumab as second line chemotherapy. Leakage at baseline and changes in maximum leakage between baseline and the first follow-up after treatment were selected for quantitative analysis. Survival univariate analysis was made constructing survival curves using Kaplan-Meier method and comparing subgroups by log rank probability test.

RESULTS

Leakage reduction at 8 weeks after initiation of bevacizumab treatment had a significant influence on overall survival (OS) and progression-free survival (PFS). Median OS and PFS were 2.4 and 2.8 months longer for patients with leakage reduction at the first follow-up. Higher leakage at baseline was associated with leakage reduction after treatment. Odds ratio of treatment response was 9 for patients with maximum leakage at baseline >5.

CONCLUSIONS

Leakage decrease may predict OS and PFS in recurrent glioblastomas treated with bevacizumab. Leakage reduction postulates as a potential biomarker for treatment response evaluation. Leakage at baseline seems to predict response to treatment, but was not independently associated with survival.

MR Perfusion-derived Hemodynamic Parametric Response Mapping of Bevacizumab Efficacy in Recurrent Glioblastoma

PURPOSE

To better understand the effect of bevacizumab therapy on tumor blood flow and oxygenation status in patients with recurrent glioblastoma.

MATERIALS AND METHODS

Retrospective data evaluation was approved by the local ethics committee of the University of Heidelberg (ethics approval number, S-320/2012), and informed consent was waived. A total of 71 patients who received a diagnosis of recurrent glioblastoma underwent conventional anatomic magnetic resonance (MR) imaging and dynamic susceptibility contrast material-enhanced MR imaging at baseline and at the first follow-up examination after initiation of bevacizumab therapy. Parametric response maps (PRMs) were created with multistep (nonlinear) registration of patients' post- to pretreatment images and voxel-wise subtraction between Gaussian-normalized relative cerebral blood volume (nrCBV) and Gaussian-normalized relative cerebral blood flow (nrCBF) maps. Intratumor voxels were stratified as being increased (PRM[+]) or decreased (PRM[-]) if they exceeded a threshold that represented the 95% confidence interval in the normal-appearing brain. Correlation with progression-free and overall survival was performed with Cox proportional hazards models.

RESULTS

The risks for disease progression and death significantly increased with (a) higher baseline nrCBV (hazard ratio [HR] = 1.86, P < .01; HR = 1.52, P < .01) and nrCBF (HR = 1.78, P < .01; HR = 1.86, P < .01) values and (b) higher PRM(-) of nrCBV (HR = 1.03, P = .01; HR = 1.02, P = .03) and nrCBF (HR = 1.04, P < .01; HR = 1.03, P < .01), but not with higher PRM(+) of nrCBV and nrCBF, and not for the relative change in mean nrCBV and nrCBF, confirming the superiority of the PRM approach. The magnitude of PRM(-) for both nrCBV and nrCBF significantly increased for higher baseline values (P < .01).

CONCLUSION

Pretreatment hemodynamic parameters are the principal determinant of response to bevacizumab therapy in patients with recurrent glioblastoma. Although the magnitude of PRM(-) is a function of the corresponding pretreatment parameter, the finding of higher PRM(-) and a lack of change in PRM(+) in nonresponders to bevacizumab therapy implies that tumors with a high degree of angiogenesis before bevacizumab therapy retain a higher level of angiogenesis during therapy, despite a greater antiangiogenic effect of bevacizumab, such that a reversal of the biologic behavior and relative prognosis of these tumors does not occur.

IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma

The recent identification of IDH mutations in gliomas and several other cancers suggests that this pathway is involved in oncogenesis; however effector functions are complex and yet incompletely understood. To study the regulatory effects of IDH on hypoxia-inducible-factor 1-alpha (HIF1A), a driving force in hypoxia-initiated angiogenesis, we analyzed mRNA expression profiles of 288 glioma patients and show decreased expression of HIF1A targets on a single-gene and pathway level, strong inhibition of upstream regulators such as HIF1A and downstream biological functions such as angio- and vasculogenesis in IDH mutant tumors. Genotype/imaging phenotype correlation analysis with relative cerebral blood volume (rCBV) MRI - a robust and non-invasive estimate of tumor angiogenesis - in 73 treatment-naive patients with low-grade and anaplastic gliomas showed that a one-unit increase in rCBV corresponded to a two-third decrease in the odds for an IDH mutation and correctly predicted IDH mutation status in 88% of patients. Together, these findings (1) show that IDH mutation status is associated with a distinct angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging and (2) highlight the potential future of radiogenomics (i.e. the correlation between cancer imaging and genomic features) towards a more accurate diagnostic workup of brain tumors.

Arterial input function and gray matter cerebral blood volume measurements in children

Purpose

To investigate how arterial input functions (AIFs) vary with age in children and compare the use of individual and population AIFs for calculating gray matter CBV values. Quantitative measures of cerebral blood volume (CBV) using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) require measurement of an AIF. AIFs are affected by numerous factors including patient age. Few data presenting AIFs in the pediatric population exists.

Materials and Methods

Twenty‐two previously treated pediatric brain tumor patients (mean age, 6.3 years; range, 2.0–15.3 years) underwent DSC‐MRI scans on a 3T MRI scanner over 36 visits. AIFs were measured in the middle cerebral artery. A functional form of an adult population AIF was fitted to each AIF to obtain parameters reflecting AIF shape. The relationship between parameters and age was assessed. Correlations between gray matter CBV values calculated using the resulting population and individual patient AIFs were explored.

Results

There was a large variation in individual patient AIFs but correlations between AIF shape and age were observed. The center (r = 0.596, P < 0.001) and width of the first‐pass peak (r = 0.441, P = 0.007) were found to correlate significantly with age. Intrapatient coefficients of variation were significantly lower than interpatient values for all parameters (P < 0.001). Differences in CBV values calculated with an overall population and age‐specific population AIF compared to those calculated with individual AIFs were 31.3% and 31.0%, respectively.

Conclusion

Parameters describing AIF shape correlate with patient age in line with expected changes in cardiac output. In pediatric DSC‐MRI studies individual patient AIFs are recommended. J. Magn. Reson. Imaging 2016;43:981–989

Analysis of perfusion MRI in stroke: To deconvolve, or not to deconvolve

PURPOSE

There is currently controversy regarding the benefits of deconvolution-based parameters in stroke imaging, with studies suggesting a similar infarct prediction using summary parameters. We investigate here the performance of deconvolution-based parameters and summary parameters for dynamic-susceptibility contrast (DSC) MRI analysis, with particular emphasis on precision.

METHODS

Numerical simulations were used to assess the contribution of noise and arterial input function (AIF) variability to measurement precision. A realistic AIF range was defined based on in vivo data from an acute stroke clinical study. The simulated tissue curves were analyzed using two popular singular value decomposition (SVD) based algorithms, as well as using summary parameters.

RESULTS

SVD-based deconvolution methods were found to considerably reduce the AIF-dependency, but a residual AIF bias remained on the calculated parameters. Summary parameters, in turn, show a lower sensitivity to noise. The residual AIF-dependency for deconvolution methods and the large AIF-sensitivity of summary parameters was greatly reduced when normalizing them relative to normal tissue.

CONCLUSION

Consistent with recent studies suggesting high performance of summary parameters in infarct prediction, our results suggest that DSC-MRI analysis using properly normalized summary parameters may have advantages in terms of lower noise and AIF-sensitivity as compared to commonly used deconvolution methods. Magn Reson Med 76:1282-1290, 2016. © 2015 Wiley Periodicals, Inc.

Reduced perfusion in white matter lesions in multiple sclerosis

OBJECTIVE

To investigate dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI) in white matter lesions (WML) in patients with multiple sclerosis (MS), using automatically generated binary masks of brain tissue.

BACKGROUND

WML in MS have in some studies demonstrated perfusion abnormalities compared to normal appearing white matter (NAWM), however perfusion changes in WML in MS have in general not been well documented.

METHODS

DSC PWI was performed at 1.5 Tesla in 69 newly diagnosed MS patients. Parametric perfusion maps representing cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) were obtained. Binary masks of WML, white matter (WM) and grey matter (GM) were automatically generated and co-registered to the perfusion maps. The WML mask was manually edited and modified to correct for errors in the automatic lesion detection. Perfusion parameters were derived both from WML and NAWM using the manually modified WML mask, and using the original non-modified WML mask (with and without GM exclusion mask). Differences in perfusion measures between WML and NAWM were analyzed.

RESULTS

CBF was significantly lower (p<0.001) and MTT significantly higher (p<0.001) in WML compared to NAWM. CBV did not show significant difference between WML and NAWM. The non-modified WML mask gave similar results as manually modified WML mask if the GM exclusion mask was used in the analysis.

CONCLUSIONS

DSC PWI revealed lower CBF and higher MTT, consistent with reduced perfusion, in WML compared to NAWM in patients with early MS. Automatically generated binary masks are a promising tool in perfusion analysis of WML.