Bayesian hemodynamic parameter estimation by bolus tracking perfusion weighted imaging

A deep learning approach for quantifying CT perfusion parameters in stroke

Objective. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residue function and underestimation of tissue perfusion. In addition, the use of global arterial input function (AIF) potentially leads to erroneous parameter estimates. We aim to develop a method for accurately estimating physiological parameters from CTP images.Approach. We introduced a Transformer-based network to learn voxel-wise temporal features of CTP images. With global AIF and concentration time curve (CTC) of brain tissue as inputs, the network estimated local AIF and flow-scaled residue function. The derived parameters, including cerebral blood flow (CBF) and bolus arrival delay (BAD), were validated on both simulated and patient data (ISLES18 dataset), and were compared with multiple SVD-based methods, including standard SVD (sSVD), block-circulant SVD (cSVD) and oscillation-index SVD (oSVD).Main results.On data simulating multiple scenarios, local AIF estimated by the proposed method correlated with true AIF with a coefficient of 0.97 ± 0.04 (P < 0.001), estimated CBF with a mean error of 4.95 ml/100 g min-1, and estimated BAD with a mean error of 0.51 s; the latter two errors were significantly lower than those of the SVD-based methods (P < 0.001). The CBF estimated by the SVD-based methods were underestimated by 10% ∼ 15%. For patient data, the CBF estimates of the proposed method were significantly higher than those of the sSVD method in both normally perfused and ischemic tissues, by 13.83 ml/100 g min-1or 39.33% and 8.55 ml/100 g min-1or 57.73% (P < 0.001), respectively, which was in agreement with the simulation results.Significance. The proposed method is capable of estimating local AIF and perfusion parameters from CTP images with high accuracy, potentially improving CTP's performance and efficiency in diagnosing and treating ischemic stroke.

Assessment of Perfusion Volumes by a New Automated Software for Computed Tomography Perfusion

INTRODUCTION

To compare the perfusion volumes assessed by a new automated CT perfusion (CTP) software iStroke with the circular singular value decomposition software RAPID and determine its predictive value for functional outcome in patients with acute ischaemic stroke (AIS) who underwent endovascular treatment (EVT).

METHODS

Data on patients with AIS were collected from four hospitals in China. All patients received CTP followed by EVT with complete recanalisation within 24 hours of symptom onset. We evaluated the agreement of CTP measures between the two softwares by Spearman's rank correlation tests and kappa tests. Bland-Altman plots were used to evaluate the agreement of infarct core volume (ICV) on CTP and ground truth on diffusion-weighted imaging (DWI). Logistic regression models were used to test the association between ICV on these two softwares and functional outcomes.

RESULTS

Among 326 patients, 228 had DWI examinations and 40 of them had infarct volume >70 mL. In all patients, the infarct core and hypoperfusion volumes on iStroke had a strong correlation with those on RAPID (ρ=0.68 and 0.66, respectively). The agreement of large infarct core (volume >70 mL) was substantial (kappa=0.73, p<0.001) between these two softwares. The ICV measured by iStroke and RAPID was significantly correlated with independent functional outcome at 90 days (p=0.009 and p<0.001, respectively). In patients with DWI examinations and those with an ICV >70 mL, the ICV of iStroke and RAPID was comparable on individual agreement with ground truth.

CONCLUSION

The automatic CTP software iStroke is a reliable tool for assessing infarct core and mismatch volumes, making it clinically useful for selecting patients with AIS for acute reperfusion therapy in the extended time window.

A Novel Self-Supervised Learning-Based Method for Dynamic CT Brain Perfusion Imaging

Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images.

[Noise Characteristics of Summary Maps for Brain CT Perfusion: A Simulation Study Using a Digital Phantom and Clinical Images]

PURPOSE

Cerebral CT perfusion (CTP) summary maps classify the ischemic core, penumbra, and normal tissue from traditional parametric maps, which is a criterion for indicating thrombectomy. Since perfusion maps change when the CTP radiation dose is reduced, summary maps also might change. This study aimed to assess the noise characteristics of a summary map in simulation experiments.

METHODS

We added various amounts of noise to a digital phantom and clinical CTPs, used Vitrea (Canon Medical Systems, Tochigi, Japan) to perform perfusion analysis, and assessed the relationship between the noise and cerebral blood volume (CBV), time to maximum (Tmax), and ischemic core and penumbra volumes.

RESULTS

As the noise increased, the obtained CBV increased, the obtained Tmax shortened, and the obtained ischemic core and penumbra volumes decreased, which depended on the tissue's CBV and Tmax.

CONCLUSION

Under low-dose conditions, the ischemic core and penumbra volumes decreased, so the criteria for thrombectomy may differ from those for standard doses.

Effect of singular value decomposition on removing injection variability in 2D quantitative angiography: An in silico and in vitro phantoms study

BACKGROUND

Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD-based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations.

PURPOSE

Building on the identified limitations in QA, the study aims to adapt SVD-based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two-dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics.

MATERIALS AND METHODS

The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time-density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient-specific internal carotid artery phantom. The DSA showcases factors like x-ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data.

RESULTS

The different SVD algorithm variants showed strong correlations between flow and deconvolution-adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment.

CONCLUSION

Implementing SVD-based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.

Deep learning-based correction for time truncation in cerebral computed tomography perfusion

Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.

CT perfusion in stroke: Comparing conventional and RAPID automated software

CT perfusion (CTP) imaging is increasingly used for routine evaluation of acute ischemic stroke. Knowledge about the different types of CTP software, imaging acquisition and post-processing, and interpretation is crucial for appropriate patient selection for reperfusion therapy. Conventional vendor-provided CTP software differentiates between ischemic penumbra and core infarct using the tiebreaker of critically reduced cerebral blood volume (CBV) values within brain regions showing abnormally elevated time parameters like mean transit time (MTT) or time to peak (TTP). On the other hand, RAPID automated software differentiates between ischemic penumbra and core infarct using the tiebreaker of critically reduced cerebral blood flow (CBF) values within brain regions showing abnormally elevated time to maximum (Tmax). Additionally, RAPID calculates certain indices that confer prognostic value, such as the hypoperfusion and CBV index. In this review, we aim to familiarize the reader with the technical principles of CTP imaging, compare CTP maps generated by conventional and RAPID software, and discuss important thresholds for reperfusion and prognostic indices. Lastly, we discuss common pitfalls to help with the accurate interpretation of CTP imaging.

Perfusion Collateral Index versus Hypoperfusion Intensity Ratio in Assessment of Collaterals in Patients with Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Perfusion-based collateral indices such as the perfusion collateral index and the hypoperfusion intensity ratio have shown promise in the assessment of collaterals in patients with acute ischemic stroke. We aimed to compare the diagnostic performance of the perfusion collateral index and the hypoperfusion intensity ratio in collateral assessment compared with angiographic collaterals and outcome measures, including final infarct volume, infarct growth, and functional independence.

MATERIALS AND METHODS

Consecutive patients with acute ischemic stroke with anterior circulation proximal arterial occlusion who underwent endovascular thrombectomy and had pre- and posttreatment MRI were included. Using pretreatment MR perfusion, we calculated the perfusion collateral index and the hypoperfusion intensity ratio for each patient. The angiographic collaterals obtained from DSA were dichotomized to sufficient (American Society of Interventional and Therapeutic Neuroradiology [ASITN] scale 3-4) versus insufficient (ASITN scale 0-2). The association of collateral status determined by the perfusion collateral index and the hypoperfusion intensity ratio was assessed against angiographic collaterals and outcome measures.

RESULTS

A total of 98 patients met the inclusion criteria. Perfusion collateral index values were significantly higher in patients with sufficient angiographic collaterals (P < .001), while there was no significant (P = .46) difference in hypoperfusion intensity ratio values. Among patients with good (mRS 0-2) versus poor (mRS 3-6) functional outcome, the perfusion collateral index of ≥ 62 was present in 72% versus 31% (P = .003), while the hypoperfusion intensity ratio of ≤0.4 was present in 69% versus 56% (P = .52). The perfusion collateral index and the hypoperfusion intensity ratio were both significantly predictive of final infarct volume, but only the perfusion collateral index was significantly (P = .03) associated with infarct growth.

CONCLUSIONS

Results show that the perfusion collateral index outperforms the hypoperfusion intensity ratio in the assessment of collateral status, infarct growth, and determination of functional outcomes.

Computed tomography perfusion software pipelines to assess parameter maps and ischemic volumes: A comparative study

BACKGROUND AND PURPOSE

This study was dedicated to investigating the agreement of the calculated results of two CT perfusion (CTP) postprocessing software packages, including parameter maps and ischemic volume, focusing on the infarct core volume (ICV) and penumbra volume (PV).

METHODS

A retrospective collection of 235 patients with acute ischemic stroke who underwent CTP examination were enrolled. All images had been analyzed with two software pipelines, RAPID CTP and AccuCTP, and the comparative analysis was based on ICV and PV results calculated by both software packages. The agreement of parameter maps was evaluated by root mean square error and Bland-Altman analysis. The ICV and PV agreement was evaluated by intraclass correlation coefficient (ICC) and Bland-Altman analysis. The accuracy of ICV and PV based on multiple thresholds was also analyzed.

RESULTS

The ICV and PV of AccuCTP and RAPID CTP show excellent agreement. The relative differences of the parameter maps were all within 10% and the Bland-Altman analysis also showed a strong agreement. From ordinary least squares fitting results, both ICV and PV had a remarkably high goodness of fit (ICV, R2 = 0.975 [p<.001]; PV, R2 = 0.964 [p<.001]). For the ICC analysis, both had high ICC scores (ICV ICC 0.984, 95% CI [confidence interval] 0.973-0.989; PV ICC 0.955, 95% CI 0.947-0.964). Furthermore, multi-threshold analysis on the basis of ICV and PV also achieved reliable analytical accuracy.

CONCLUSIONS

The image analysis results of AccuCTP are in excellent agreement with RAPID CTP and can be used as an alternative analysis tool to RAPID CTP software in stroke clinical practice.

Cerebral hemodynamics in symptomatic anterior circulation intracranial stenosis measured by angiography-based quantitative flow ratio: association with CT perfusion

OBJECTIVES

Cerebral hemodynamics is important for the management of intracranial atherosclerotic stenosis (ICAS). This study aimed to determine the utility of angiography-based quantitative flow ratio (QFR) to reflect cerebral hemodynamics in symptomatic anterior circulation ICAS by evaluating its association with CT perfusion (CTP).

METHODS

Sixty-two patients with unilateral symptomatic stenosis in the intracranial internal carotid artery or middle cerebral artery who received percutaneous transluminal angioplasty (PTA) or PTA with stenting were included. Murray law-based QFR (μQFR) was computed from a single angiographic view. CTP parameters including cerebral blood flow, cerebral blood volume, mean transit time (MTT), and time to peak (TTP) were calculated, and relative values were obtained as the ratio between symptomatic and contralateral hemispheres. Relationships between μQFR and perfusion parameters, and between μQFR and perfusion response after intervention, were analyzed.

RESULTS

Thirty-eight patients had improved perfusion after treatment. μQFR was significantly correlated with relative values of TTP and MTT, with correlation coefficients of  -0.45 and  -0.26, respectively, on a per-patient basis, and  -0.72 and  -0.43, respectively, on a per-vessel basis (all p < 0.05). Sensitivity and specificity for μQFR to diagnose hypoperfusion at a cut-off value of 0.82 were 94.1% and 92.1%, respectively. Multivariate analysis revealed that μQFRpost (adjusted odds ratio [OR], 1.48; p = 0.002), collateral score (adjusted OR, 6.97; p = 0.01), and current smoking status (adjusted OR, 0.03; p = 0.01) were independently associated with perfusion improvement after treatment.

CONCLUSIONS

μQFR was associated with CTP in patients with symptomatic anterior circulation ICAS and may be a potential marker for real-time hemodynamic evaluation during interventional procedures.

KEY POINTS

• Murray law-based QFR (μQFR) is associated with CT perfusion parameters in intracranial atherosclerotic stenosis and can differentiate hypoperfusion from normal perfusion. • Post-intervention μQFR, collateral score, and current smoking status are independent factors associated with improved perfusion after treatment.

Quantitative Analysis of Brain CT Perfusion in Healthy Beagle Dogs: A Pilot Study

Brain computed tomography (CT) perfusion is a technique that allows for the fast evaluation of cerebral hemodynamics. However, quantitative studies of brain CT perfusion in veterinary medicine are lacking. The purpose of this study was to investigate the normal range of perfusion determined via CT in brains of healthy dogs and to compare values between white matter and gray matter, differences in aging, and each hemisphere. Nine intact male beagle dogs were prospectively examined using dynamic CT scanning and post-processing for brain perfusion. Regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), mean transit time, and time to peak were calculated. Tissue ROIs were drawn in the gray matter and white matter of the frontal, temporal, parietal, and occipital lobes; caudate nucleus; thalamus; piriform lobe; hippocampus; and cerebellum. Significant differences were observed between the white matter regions and gray matter regions for rCBV and rCBF (p < 0.05). However, no significant differences were identified between hemispheres and between young and old groups in brain regions. The findings obtained in this study involving healthy beagle dogs might serve as a reference for regional CT perfusion values in specific brain regions. These results may aid in the characterization of various brain diseases in dogs.

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.

Comparison of Three Algorithms for Predicting Infarct Volume in Patients with Acute Ischemic Stroke by CT Perfusion Software: Bayesian, CSVD, and OSVD

This study aimed to compare the performance of the Bayesian probabilistic method, circular Singular Value Decomposition (cSVD), and oscillation index Singular Value Decomposition (oSVD) algorithms in Olea Sphere for predicting infarct volume in patients with acute ischemic stroke (AIS). Eighty-seven patients suffering from AIS with large vessel occlusion were divided into improvement and progression groups. The improvement group included patients with successful recanalization (TICI 2b-3) after thrombectomy or whose clinical symptoms improved after thrombolysis. The progression group consisted of patients whose clinical symptoms did not improve or even got worse. The infarct core volume from the Olea Sphere software was used as the predicted infarct volume (PIV) in the improvement group, whereas the hypoperfusion volume was used as the PIV in the progression group. We defined predicted difference (PD) as PIV minus final infarct volume (FIV) measured at follow-up imaging. Differences among the three algorithms were assessed by the Friedman test. Spearman correlation analysis was used to verify the correlation between PIV and FIV. In addition, we performed a subgroup analysis of the progression group based on collateral circulation status. The median [interquartile range (IQR)] of the PD and Spearman correlation coefficients (SCCs) between PIV and FIV for the improvement group (n = 22) were: Bayesian = [6.99 (-14.72, 18.99), 0.500]; oSVD = [-12.74 (-41.06, -3.46), 0.423]; cSVD = [-15.38 (-38.92, -4.68), 0.586]. For the progression group (n = 65), the median (IQR) of PD and SCCs were: Bayesian = [1.00 (-34.07, 49.37), 0.748]; oSVD = [-0.17 (-53.42, 29.73), 0.712]; cSVD = [66.55 (7.94, 106.32), 0.674]. The Bayesian algorithm in the Olea Sphere software predicted infarct volumes with better accuracy and stability than the other two algorithms in both the progression and improvement groups.

Correlation between pretreatment and follow-up infarct volume using CT perfusion imaging: the Bayesian versus singular value decomposition method

PURPOSE

Pretreatment ischemic core volume is conceptually equal to follow-up infarct volume (FIV) in patients with successful recanalization. However, there is sometimes an absolute volume difference (AD) between pretreatment core volume and FIV. The aim was to compare the AD values between the Bayesian and the singular value decomposition (SVD) methods with time from onset-to-imaging in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy.

METHODS

Consecutive AIS patients were included if they had the following: (1) anterior large vessel occlusion (internal carotid or middle cerebral artery); (2) within 24 h of onset; (3) pretreatment CT perfusion (CTP); (4) successful recanalization (mTICI ≥ 2b); and (5) 24-h diffusion-weighted imaging (DWI). FIV was measured on 24-h DWI. The AD value between FIV and the pretreatment core volume was calculated for Bayesian and SVD methods. Spearman's rank correlation coefficient (rho) was calculated as appropriate.

RESULTS

In the 47 patients enrolled (25 men; median age 78 years; median baseline National Institutes of Health Stroke Scale, 22), the median time from onset-to-imaging and onset-to-recanalization was 136 and 220 min, respectively. Shorter onset-to-imaging time was correlated with a larger AD value, and more trend was seen in the SVD method (rho =  - 0.28, p = 0.05) compared with the Bayesian method (rho =  - 0.08). A larger pretreatment core volume was correlated with a larger AD value, and this tendency was slightly stronger for the SVD (rho = 0.63, p < 0.01) than for the Bayesian (rho = 0.32, p = 0.03) method.

CONCLUSIONS

The Bayesian method might be more correlated with FIV than the SVD method in patients with a large ischemic lesion immediately after stroke onset, but not perfect.

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.

Value of CT Perfusion for Collateral Status Assessment in Patients with Acute Ischemic Stroke

Good collateral status in acute ischemic stroke patients is an important indicator for good outcomes. Perfusion imaging potentially allows for the simultaneous assessment of local perfusion and collateral status. We combined multiple CTP parameters to evaluate a CTP-based collateral score. We included 85 patients with a baseline CTP and single-phase CTA images from the MR CLEAN Registry. We evaluated patients' CTP parameters, including relative CBVs and tissue volumes with several time-to-maximum ranges, to be candidates for a CTP-based collateral score. The score candidate with the strongest association with CTA-based collateral score and a 90-day mRS was included for further analyses. We assessed the association of the CTP-based collateral score with the functional outcome (mRS 0-2) by analyzing three regression models: baseline prognostic factors (model 1), model 1 including the CTA-based collateral score (model 2), and model 1 including the CTP-based collateral score (model 3). The model performance was evaluated using C-statistic. Among the CTP-based collateral score candidates, relative CBVs with a time-to-maximum of 6-10 s showed a significant association with CTA-based collateral scores (p = 0.02) and mRS (p = 0.05) and was therefore selected for further analysis. Model 3 most accurately predicted favorable outcomes (C-statistic = 0.86, 95% CI: 0.77-0.94) although differences between regression models were not statistically significant. We introduced a CTP-based collateral score, which is significantly associated with functional outcome and may serve as an alternative collateral measure in settings where MR imaging is not feasible.

Clinical Imaging of the Penumbra in Ischemic Stroke: From the Concept to the Era of Mechanical Thrombectomy

The ischemic penumbra is defined as the severely hypoperfused, functionally impaired, at-risk but not yet infarcted tissue that will be progressively recruited into the infarct core. Early reperfusion aims to save the ischemic penumbra by preventing infarct core expansion and is the mainstay of acute ischemic stroke therapy. Intravenous thrombolysis and mechanical thrombectomy for selected patients with large vessel occlusion has been shown to improve functional outcome. Given the varying speed of infarct core progression among individuals, a therapeutic window tailored to each patient has recently been proposed. Recent studies have demonstrated that reperfusion therapies are beneficial in patients with a persistent ischemic penumbra, beyond conventional time windows. As a result, mapping the penumbra has become crucial in emergency settings for guiding personalized therapy. The penumbra was first characterized as an area with a reduced cerebral blood flow, increased oxygen extraction fraction and preserved cerebral metabolic rate of oxygen using positron emission tomography (PET) with radiolabeled O₂. Because this imaging method is not feasible in an acute clinical setting, the magnetic resonance imaging (MRI) mismatch between perfusion-weighted imaging and diffusion-weighted imaging, as well as computed tomography perfusion have been proposed as surrogate markers to identify the penumbra in acute ischemic stroke patients. Transversal studies comparing PET and MRI or using longitudinal assessment of a limited sample of patients have been used to define perfusion thresholds. However, in the era of mechanical thrombectomy, these thresholds are debatable. Using various MRI methods, the original penumbra definition has recently gained a significant interest. The aim of this review is to provide an overview of the evolution of the ischemic penumbra imaging methods, including their respective strengths and limitations, as well as to map the current intellectual structure of the field using bibliometric analysis and explore future directions.

CT Perfusion collateral index in assessment of collaterals in acute ischemic stroke with delayed presentation: Comparison to single phase CTA

BACKGROUND & PURPOSE

Perfusion collateral index (PCI) has been recently defined as a promising measure of collateral status. We sought to compare collateral status assessed via CT-PCI in comparison to single-phase CTA and their relationship to outcome measures including final infarction volume, final recanalization status and functional outcome in ELVO patients.

METHODS

ELVO patients with anterior circulation large vessel occlusion who had baseline CTA and CT perfusion and underwent endovascular treatment were included. Collateral status was assessed on CTA. PCI from CT perfusion was calculated in each patient and an optimal threshold to separate good vs insufficient collaterals was identified using DSA as reference. The collateral status determined by CTA and PCI were assessed against 3 measured outcomes: 1) final infarction volume; 2) final recanalization status defined by TICI scores; 3) functional outcome measured by 90-day mRS.

RESULTS

A total of 53 patients met inclusion criteria. Excellent recanalization defined by TICI ≥2C was achieved in 36 (68%) patients and 23 patients (43%) had good functional outcome (mRS ≤2). While having good collaterals on both CTA and CTP-PCI was associated with significantly (p<0.05) smaller final infarction volume, only good collaterals status determined by CTP-PCI was associated with achieving excellent recanalization (p = 0.001) and good functional outcome (p = 0.003).

CONCLUSION

CTP-based PCI outperforms CTA collateral scores in determination of excellent recanalization and good functional outcome and may be a promising imaging marker of collateral status in patients with delayed presentation of AIS.

Applications of artificial intelligence for DWI and PWI data processing in acute ischemic stroke: Current practices and future directions

Multimodal Magnetic Resonance Imaging (MRI) techniques of Perfusion-Weighted Imaging (PWI) and Diffusion-Weighted Imaging (DWI) data are integral parts of the diagnostic workup in the acute stroke setting. The visual interpretation of PWI/DWI data is the most likely procedure to triage Acute Ischemic Stroke (AIS) patients who will access reperfusion therapy, especially in those exceeding 6 h of stroke onset. In fact, this process defines two classes of tissue: the ischemic core, which is presumed to be irreversibly damaged, visualized on DWI data and the penumbra which is the reversibly injured brain tissue around the ischemic tissue, visualized on PWI data. AIS patients with a large ischemic penumbra and limited infarction core have a high probability of benefiting from endovascular treatment. However, it is a tedious and time-consuming procedure. Consequently, it is subject to high inter- and intra-observer variability. Thus, the assessment of the potential risks and benefits of endovascular treatment is uncertain. Fast, accurate and automatic post-processing of PWI and DWI data is important for clinical diagnosis and is necessary to help the decision making for therapy. Therefore, an automated procedure that identifies stroke slices, stroke hemisphere, segments stroke regions in DWI, and measures hypoperfused tissue in PWI enhances considerably the reproducibility and the accuracy of stroke assessment. In this work, we draw an overview of several applications of Artificial Intelligence (AI) for the automation processing and their potential contributions in clinical practices. We compare the current approaches among each other's with respect to some key requirements.

Spatio-temporal multi-task network cascade for accurate assessment of cardiac CT perfusion

The assessment of myocardial perfusion has become increasingly important in the early diagnosis of coronary artery disease. Currently, the process of perfusion assessment is time-consuming and subjective. Although automated methods by threshold processing have been proposed, they cannot obtain an accurate perfusion assessment. Thus, there is a great clinical demand to obtain a rapid and accurate assessment of myocardial perfusion through a standard procedure using an automated algorithm. In this work, we present a spatio-temporal multi-task network cascade (ST-MNC) to provide an accurate and robust assessment of myocardial perfusion. The proposed network captures patch-based spatio-temporal representations for each pixel through a spatio-temporal encoder-decoder network. Then the multi-task network cascade uses spatio-temporal representations as shared features to predict various perfusion parameters and myocardial ischemic regions. Extensive experiments on CT images of 232 subjects demonstrate ST-MNC could produce a good approximation for perfusion parameters and an accurate classification for ischemic regions. These results show that our proposed method can provide a fast and accurate assessment of myocardial perfusion.

Emerging role of artificial intelligence in stroke imaging

Introduction: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefore, clinicians must regularly learn new skill, stay up to date with the literature and integrate advances into daily practice. The application of artificial intelligence (AI) to assist clinical decision making could diminish inter-rater variation in routine clinical practice and accelerate the mining of vital data that could expand recognition of patients with stroke, forecast of treatment responses and patient outcomes.Areas covered: In this review, the authors provide an up-to-date review of AI in stroke, analyzing the latest papers on this subject. These have been divided in two main groups: stroke diagnosis and outcome prediction.Expert opinion: The highest value of AI is its capability to merge, select and condense a large amount of clinical and imaging features of a single patient and to associate these with fitted models that have gone through robust assessment and optimization with large cohorts of data to support clinical decision making.

Automated Prediction of Ischemic Brain Tissue Fate from Multiphase Computed Tomographic Angiography in Patients with Acute Ischemic Stroke Using Machine Learning

Background and Purpose

Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict tissue perfusion and infarction from mCTA source images.

Methods

284 patients with AIS were included from the Precise and Rapid assessment of collaterals using multi-phase CTA in the triage of patients with acute ischemic stroke for Intra-artery Therapy (Prove-IT) study. All patients had non-contrast computed tomography, mCTA, and computed tomographic perfusion (CTP) at baseline and follow-up magnetic resonance imaging/non-contrast-enhanced computed tomography. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict a pre-defined Tmax thresholded perfusion abnormality, core and penumbra on CTP. The remaining 144 patient images were used to test the ML models. The predicted perfusion, core and penumbra lesions from ML models were compared to CTP perfusion lesion and to follow-up infarct using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient.

Results

Mean difference between the mCTA predicted perfusion volume and CTP perfusion volume was 4.6 mL (limit of agreement [LoA], –53 to 62.1 mL; P=0.56; CCC 0.63 [95% confidence interval [CI], 0.53 to 0.71; P<0.01], ICC 0.68 [95% CI, 0.58 to 0.78; P<0.001]). Mean difference between the mCTA predicted infarct and follow-up infarct in the 100 patients with acute reperfusion (modified thrombolysis in cerebral infarction [mTICI] 2b/2c/3) was 21.7 mL, while it was 3.4 mL in the 44 patients not achieving reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 (95% CI, 0.15 to 0.55; P<0.01) and ICC was 0.42 (95% CI, 0.18 to 0.50; P<0.01); in non-reperfused subjects CCC was 0.52 (95% CI, 0.20 to 0.60; P<0.001) and ICC was 0.60 (95% CI, 0.37 to 0.76; P<0.001). No difference was observed between the mCTA and CTP predicted infarct volume in the test cohort (P=0.67).

Conclusions

A ML based mCTA model is able to predict brain tissue perfusion abnormality and follow-up infarction, comparable to CTP.

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance

PURPOSE

Surveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem).

METHODS

Study participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists' classifications.

RESULTS

SVM classification based on combined perfusion and structural features outperformed radiologists' classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists' classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists).

CONCLUSION

Our results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001).

Direct Comparison of Bayesian and Fermi Deconvolution Approaches for Myocardial Blood Flow Quantification: In silico and Clinical Validations

Cardiac magnetic resonance myocardial perfusion imaging can detect coronary artery disease and is an alternative to single-photon emission computed tomography or positron emission tomography. However, the complex, non-linear MR signal and the lack of robust quantification of myocardial blood flow have hindered its widespread clinical application thus far. Recently, a new Bayesian approach was developed for brain imaging and evaluation of perfusion indexes (Kudo et al., 2014). In addition to providing accurate perfusion measurements, this probabilistic approach appears more robust than previous approaches, particularly due to its insensitivity to bolus arrival delays. We assessed the performance of this approach against a well-known and commonly deployed model-independent method based on the Fermi function for cardiac magnetic resonance myocardial perfusion imaging. The methods were first evaluated for accuracy and precision using a digital phantom to test them against the ground truth; next, they were applied in a group of coronary artery disease patients. The Bayesian method can be considered an appropriate model-independent method with which to estimate myocardial blood flow and delays. The digital phantom comprised a set of synthetic time-concentration curve combinations generated with a 2-compartment exchange model and a realistic combination of perfusion indexes, arterial input dynamics, noise and delays collected from the clinical dataset. The myocardial blood flow values estimated with the two methods showed an excellent correlation coefficient (r² > 0.9) under all noise and delay conditions. The Bayesian approach showed excellent robustness to bolus arrival delays, with a similar performance to Fermi modeling when delays were considered. Delays were better estimated with the Bayesian approach than with Fermi modeling. An in vivo analysis of coronary artery disease patients revealed that the Bayesian approach had an excellent ability to distinguish between abnormal and normal myocardium. The Bayesian approach was able to discriminate not only flows but also delays with increased sensitivity by offering a clearly enlarged range of distribution for the physiologic parameters.

Multiparametric MRI for early identification of therapeutic response in recurrent glioblastoma treated with immune checkpoint inhibitors

BACKGROUND

Physiologic changes quantified by diffusion and perfusion MRI have shown utility in predicting treatment response in glioblastoma (GBM) patients treated with cytotoxic therapies. We aimed to investigate whether quantitative changes in diffusion and perfusion after treatment by immune checkpoint inhibitors (ICIs) would determine 6-month progression-free survival (PFS6) in patients with recurrent GBM.

METHODS

Inclusion criteria for this retrospective study were: (i) diagnosis of recurrent GBM treated with ICIs and (ii) availability of diffusion and perfusion in pre and post ICI MRI (iii) at ≥6 months follow-up from treatment. After co-registration, mean values of the relative apparent diffusion coefficient (rADC), Ktrans (volume transfer constant), Ve (extravascular extracellular space volume) and Vp (plasma volume), and relative cerebral blood volume (rCBV) were calculated from a volume-of-interest of the enhancing tumor. Final assignment of stable/improved versus progressive disease was determined on 6-month follow-up using modified Response Assessment in Neuro-Oncology criteria.

RESULTS

Out of 19 patients who met inclusion criteria and follow-up (mean ± SD: 7.8 ± 1.4 mo), 12 were determined to have tumor progression, while 7 had treatment response after 6 months of ICI treatment. Only interval change of rADC was suggestive of treatment response. Patients with treatment response (6/7: 86%) had interval increased rADC, while 11/12 (92%) with tumor progression had decreased rADC (P = 0.001). Interval change in rCBV, Ktrans, Vp, and Ve were not indicative of treatment response within 6 months.

CONCLUSIONS

In patients with recurrent GBM, interval change in rADC is promising in assessing treatment response versus progression within the first 6 months following ICI treatment.

KEY POINTS

• In recurrent GBM treated with ICIs, interval change in rADC suggests early treatment response.• Interval change in rADC can be used as an imaging biomarker to determine PFS6.• Interval change in MR perfusion and permeability measures do not suggest ICI treatment response.

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.

Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA)

CTP (Computed Tomography Perfusion) is widely used in clinical practice for the evaluation of cerebrovascular disorders. However, CTP involves high radiation dose (≥~200mGy) as the X-ray source remains continuously on during the passage of contrast media. The purpose of this study is to present a low dose CTP technique termed K-space Weighted Image Average (KWIA) using a novel projection view-shared averaging algorithm with reduced tube current. KWIA takes advantage of k-space signal property that the image contrast is primarily determined by the k-space center with low spatial frequencies and oversampled projections. KWIA divides each 2D Fourier transform (FT) or k-space CTP data into multiple rings. The outer rings are averaged with neighboring time frames to achieve adequate signal-to-noise ratio (SNR), while the center region of k-space remains unchanged to preserve high temporal resolution. Reduced dose sinogram data were simulated by adding Poisson distributed noise with zero mean on digital phantom and clinical CTP scans. A physical CTP phantom study was also performed with different X-ray tube currents. The sinogram data with simulated and real low doses were then reconstructed with KWIA, and compared with those reconstructed by standard filtered back projection (FBP) and simultaneous algebraic reconstruction with regularization of total variation (SART-TV). Evaluation of image quality and perfusion metrics using parameters including SNR, CNR (contrast-to-noise ratio), AUC (area-under-the-curve), and CBF (cerebral blood flow) demonstrated that KWIA is able to preserve the image quality, spatial and temporal resolution, as well as the accuracy of perfusion quantification of CTP scans with considerable (50-75%) dose-savings.

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.

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

BACKGROUND

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

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.

Automated CT perfusion imaging for acute ischemic stroke: Pearls and pitfalls for real-world use

Recent positive trials have thrust acute cerebral perfusion imaging into the routine evaluation of acute ischemic stroke. Updated guidelines state that in patients with anterior circulation large vessel occlusions presenting beyond 6 hours from time last known well, advanced imaging selection including perfusion-based selection is necessary. Centers that receive patients with acute stroke must now have the capability to perform and interpret CT or magnetic resonance perfusion imaging or provide rapid transfer to centers with the capability of selecting patients for a highly impactful endovascular therapy, particularly in delayed time windows. Many stroke centers are quickly incorporating the use of automated perfusion processing software to interpret perfusion raw data. As CT perfusion (CTP) is being assimilated in real-world clinical practice, it is essential to understand the basics of perfusion acquisition, quantification, and interpretation. It is equally important to recognize the common technical and clinical diagnostic challenges of automated CTP including ischemic core and penumbral misclassifications that could result in underestimation or overestimation of the core and penumbra volumes. This review highlights the pitfalls of automated CTP along with practical pearls to address the common challenges. This is particularly tailored to aid the acute stroke clinician who must interpret automated perfusion studies in an emergency setting to make time-dependent treatment decisions for patients with acute ischemic stroke.

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.

Cerebral blood flow changes after radiation therapy identifies pseudoprogression in diffuse intrinsic pontine gliomas

Background

The interval between progression and death in diffuse intrinsic pontine glioma (DIPG) is usually <6 months. However, reports of longer patient survival following radiotherapy, in the presence of radiological signs of progression, suggest that these cases may be comparable to pseudoprogression observed in adult glioblastoma. Our aim was to identify such cases and compare their multimodal MRI features with those of patients who did not present the same evolution.

Methods

Multimodal MRIs of 43 children treated for DIPG were retrospectively selected at 4 timepoints: baseline, after radiotherapy, during true progression, and at the last visit. The patients were divided into 2 groups depending on whether they presented conventional MRI changes that mimicked progression. The apparent diffusion coefficient, arterial spin labeling cerebral blood flow (ASL-CBF), and dynamic susceptibility contrast perfusion relative cerebral blood volume (DSCrCBV) and flow (DSCrCBF) values were recorded for each tumor voxel, avoiding necrotic areas.

Results

After radiotherapy, 19 patients (44%) showed radiological signs that mimicked progression: 16 survived >6 months following so-called pseudoprogression, with a median of 8.9 months and a maximum of 35.6 months. All 43 patients exhibited increased blood volume and flow after radiotherapy, but the 90th percentile of those with signs of pseudoprogression had a greater increase of ASL-CBF (P < 0.001). Survival between the 2 groups did not differ significantly. During true progression, DSCrCBF and DSCrCBV values increased only in patients who had not experienced pseudoprogression.

Conclusions

Pseudoprogression is a frequent phenomenon in DIPG patients. This condition needs to be recognized before considering treatment discontinuation. In this study, the larger increase of the ASL-CBF ratio after radiotherapy accurately distinguished pseudoprogression from true progression.

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.

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.

Computer-aided imaging analysis in acute ischemic stroke - background and clinical applications

Tools for medical image analysis have been developed to reduce the time needed to detect abnormalities and to provide more accurate results. Particularly, tools based on artificial intelligence and machine learning techniques have led to significant improvements in medical imaging interpretation in the last decade. Automatic evaluation of acute ischemic stroke in medical imaging is one of the fields that witnessed a major development. Commercially available products so far aim to identify (and quantify) the ischemic core, the ischemic penumbra, the site of arterial occlusion and the collateral flow but they are not (yet) intended as standalone diagnostic tools. Their use can be complementary; they are intended to support physicians' interpretation of medical images and hence standardise selection of patients for acute treatment. This review provides an introduction into the field of computer-aided diagnosis and focuses on the automatic analysis of non-contrast-enhanced computed tomography, computed tomography angiography and perfusion imaging. Future studies are necessary that allow the evaluation and comparison of different imaging strategies and post-processing algorithms during the diagnosis process in patients with suspected acute ischemic stroke; which may further facilitate the standardisation of treatment and stroke management.

Computed Tomography Perfusion Measurements in Renal Lesions Obtained by Bayesian Estimation, Advanced Singular-Value Decomposition Deconvolution, Maximum Slope, and Patlak Models: Intermodel Agreement and Diagnostic Accuracy of Tumor Classification

OBJECTIVES

The aims of this study were to evaluate the agreement of computed tomography (CT)-perfusion parameter values of the normal renal cortex and various renal tumors, which were obtained by different mathematical models, and to evaluate their diagnostic accuracy.

MATERIALS AND METHODS

Perfusion imaging was performed prospectively in 35 patients to analyze 144 regions of interest of the normal renal cortex and 144 regions of interest of renal tumors, including 21 clear-cell renal cell carcinomas (RCC), 6 papillary RCCs, 5 oncocytomas, 1 chromophobe RCC, 1 angiomyolipoma with minimal fat, and 1 tubulocystic RCC. Identical source data were postprocessed and analyzed on 2 commercial software applications with the following implemented mathematical models: maximum slope, Patlak plot, standard singular-value decomposition (SVD), block-circulant SVD, oscillation-limited block-circulant SVD, and Bayesian estimation technique. Results for blood flow (BF), blood volume (BV), and mean transit time (MTT) were recorded. Agreement and correlation between pairs of models and perfusion parameters were assessed. Diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Significant differences and poor agreement of BF, BV, and MTT values were noted for most of model comparisons in both the normal renal cortex and different renal tumors. The correlations between most model pairs and perfusion parameters ranged between good and perfect (Spearman ρ = 0.79-1.00), except for BV values obtained by Patlak method (ρ = 0.61-0.72). All mathematical models computed BF and BV values, which differed significantly between clear cell RCCs, papillary RCCs, and oncocytomas, which introduces them as useful diagnostic tests to differentiate between different histologic subgroups (areas under ROC curve, 0.83-0.99). The diagnostic accuracy to discriminate between clear-cell RCCs and the renal cortex was the lowest based on the Patlak plot model (area under ROC curve, 0.76); BF and BV values obtained by other algorithms did not differ significantly in their diagnostic accuracy.

CONCLUSIONS

Quantitative perfusion parameters obtained from different mathematical models cannot be used interchangeably. Based on BF and BV estimates, all models are a useful tool in the differential diagnosis of kidney tumors, with the Patlak plot model yielding a significantly lower diagnostic accuracy.

A Quantitative Comparison of Clinically Employed Parameters in the Assessment of Acute Cerebral Ischemia Using Dynamic Susceptibility Contrast Magnetic Resonance Imaging

Purpose: Perfusion magnetic resonance imaging (P-MRI) is part of the mismatch concept employed for therapy decisions in acute ischemic stroke. Using dynamic susceptibility contrast (DSC) MRI the time-to-maximum (Tmax) parameter is quite popular, but its inconsistently defined computation, arterial input function (AIF) selection, and the applied deconvolution method may introduce bias into the assessment. Alternatively, parameter free methods, namely, standardized time-to-peak (stdTTP), z_f-score, and standardized-z_f (stdZ) are also available, offering consistent calculation procedures without the need of an AIF or deconvolution.

Methods: Tmax was compared to stdTTP, z_f-, and stdZ to evaluate robustness of infarct volume estimation in 66 patients, using data from two different sites and MR systems (i.e., 1.5T vs. 3T; short TR (= 689 ms) vs. medium TR (= 1,390 ms); bolus dose 0.1 or 0.2 ml/kgBW, respectively).

Results: Quality factors (QF) for Tmax were 0.54 ± 0.18 (sensitivity), 0.90 ± 0.06 (specificity), and 0.87 ± 0.05 (accuracy). Though not significantly different, best specificity (0.93 ± 0.05) and accuracy (0.90 ± 0.04) were found for stdTTP with a sensitivity of 0.56 ± 0.17. Other tested parameters performed not significantly worse than Tmax and stdTTP, but absolute values of QFs were slightly lower, except for z_f showing the highest sensitivity (0.72 ± 0.16). Accordingly, in ROC-analysis testing the parameter performance to predict the final infarct volume, stdTTP and z_f showed the best performance. The odds for stdTTP to obtain the best prediction of the final infarct size, was 6.42 times higher compared to all other parameters (odds-ratio test; p = 2.2^*10–16).

Conclusion: Based on our results, we suggest to reanalyze data from large cohort studies using the parameters presented here, particularly stdTTP and zf-score, to further increase consistency of perfusion assessment in acute ischemic stroke.

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.

A Machine Learning Approach to Perfusion Imaging With Dynamic Susceptibility Contrast MR

Background: Dynamic susceptibility contrast (DSC) MR perfusion is a frequently-used technique for neurovascular imaging. The progress of a bolus of contrast agent through the tissue of the brain is imaged via a series of T2*-weighted MRI scans. Clinically relevant parameters such as blood flow and Tmax can be calculated by deconvolving the contrast-time curves with the bolus shape (arterial input function). In acute stroke, for instance, these parameters may help distinguish between the likely salvageable tissue and irreversibly damaged infarct core. Deconvolution typically relies on singular value decomposition (SVD): however, studies have shown that these algorithms are very sensitive to noise and artifacts present in the image and therefore may introduce distortions that influence the estimated output parameters. Methods: In this work, we present a machine learning approach to the estimation of perfusion parameters in DSC-MRI. Various machine learning models using as input the raw MR source data were trained to reproduce the output of an FDA approved commercial implementation of the SVD deconvolution algorithm. Experiments were conducted to determine the effect of training set size, optimal patch size, and the effect of using different machine-learning models for regression. Results: Model performance increased with training set size, but after 5,000 samples (voxels) this effect was minimal. Models inferring perfusion maps from a 5 by 5 voxel patch outperformed models able to use the information in a single voxel, but larger patches led to worse performance. Random Forest models produced had the lowest root mean squared error, with neural networks performing second best: however, a phantom study revealed that the random forest was highly susceptible to noise levels, while the neural network was more robust. Conclusion: The machine learning-based approach produces estimates of the perfusion parameters invariant to the noise and artifacts that commonly occur as part of MR acquisition. As a result, better robustness to noise is obtained, when evaluated against the FDA approved software on acute stroke patients and simulated phantom data.

Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

One-compartment models are widely used to quantify hemodynamic parameters such as perfusion, blood volume and mean transit time. These parameters are routinely used for clinical diagnosis and monitoring of disease development and are thus of high relevance. However, it is known that common estimation techniques are discretization dependent and values can be erroneous. In this paper we present a new model that enables systematic quantification of discretization errors. Specifically, we introduce a continuous flow model for tracer propagation within the capillary tissue, used to evaluate state-of-the-art one-compartment models. We demonstrate that one-compartment models are capable of recovering perfusion accurately when applied to only one compartment, i.e. the whole region of interest. However, substantial overestimation of perfusion occurs when applied to fractions of a compartment. We further provide values of the estimated overestimation for various discretization levels, and also show that overestimation can be observed in real-life applications. Common practice of using compartment models for fractions of tissue violates model assumptions and careful interpretation is needed when using the computed values for diagnosis and treatment planning.

Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI

OBJECTIVE

Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurrence has recently provided encouraging results, the prediction of the severity and territory of the HT could bring valuable insights that are beyond current methods.

METHODS

This study tackles these issues and aims to predict the spatial occurrence of HT in AIS from perfusion-weighted magnetic resonance imaging (PWI) combined with diffusion weighted imaging. In all, 165 patients were included in this study and analyzed retrospectively from a cohort of AIS patients treated with reperfusion therapy in a single stroke center.

RESULTS

Machine learning models are compared within our framework; support vector machines, linear regression, decision trees, neural networks, and kernel spectral regression were applied to the dataset. Kernel spectral regression performed best with an accuracy of $\text{83.7} \pm \text{2.6}\%$.

CONCLUSION

The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics.

SIGNIFICANCE

Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.

MR Perfusion to Determine the Status of Collaterals in Patients with Acute Ischemic Stroke: A Look Beyond Time Maps

BACKGROUND AND PURPOSE

Patients with acute stroke with robust collateral flow have better clinical outcomes and may benefit from endovascular treatment throughout an extended time window. Using a multiparametric approach, we aimed to identify MR perfusion parameters that can represent the extent of collaterals, approximating DSA.

MATERIALS AND METHODS

Patients with anterior circulation proximal arterial occlusion who had baseline MR perfusion and DSA were evaluated. The volume of arterial tissue delay (ATD) at thresholds of 2-6 seconds (ATD^{2-6 seconds}) and >6 seconds (ATD^{>6 seconds}) in addition to corresponding values of normalized CBV and CBF was calculated using VOI analysis. The association of MR perfusion parameters and the status of collaterals on DSA were assessed by multivariate analyses. Receiver operating characteristic analysis was performed.

RESULTS

Of 108 patients reviewed, 39 met our inclusion criteria. On DSA, 22/39 (56%) patients had good collaterals. Patients with good collaterals had significantly smaller baseline and final infarct volumes, smaller volumes of severe hypoperfusion (ATD^{>6 seconds}), larger volumes of moderate hypoperfusion (ATD^{2-6 seconds}), and higher relative CBF and relative CBV values than patients with insufficient collaterals. Combining the 2 parameters into a Perfusion Collateral Index (volume of ATD^{2-6 seconds} × relative CBV^{2-6 seconds}) yielded the highest accuracy for predicting collateral status: At a threshold of 61.7, this index identified 15/17 (88%) patients with insufficient collaterals and 22/22 (100%) patients with good collaterals, for an overall accuracy of 94.1%.

CONCLUSIONS

The Perfusion Collateral Index can predict the baseline collateral status with 94% diagnostic accuracy compared with DSA.

Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization

Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.

Multiparametric MRI for Differentiation of Radiation Necrosis From Recurrent Tumor in Patients With Treated Glioblastoma

OBJECTIVE

Differentiation of radiation necrosis (RN) from recurrent tumor (RT) in treated patients with glioblastoma remains a diagnostic challenge. The purpose of this study is to evaluate the diagnostic performance of multiparametric MRI in distinguishing RN from RT in patients with glioblastoma, with the use of a combination of MR perfusion and diffusion parameters.

MATERIALS AND METHODS

Patients with glioblastoma who had a new enhancing mass develop after completing standard treatment were retrospectively evaluated. Apparent diffusion coefficient (ADC), volume transfer constant (K^trans), and relative cerebral blood volume (rCBV) values were calculated from the MR images on which the enhancing lesions first appeared. Repeated measure of analysis, logistic regression, and ROC analysis were performed.

RESULTS

Of a total of 70 patients evaluated, 46 (34 with RT and 12 with RN) met our inclusion criteria. Patients with RT had significantly higher mean rCBV (p < 0.001) and K^trans (p = 0.006) values and lower ADC values (p = 0.004), compared with patients with RN. The overall diagnostic accuracy was 85.8% for rCBV, 75.5% for K^trans, and 71.3% for ADC values. The logistic regression model showed a significant contribution of rCBV (p = 0.024) and K^trans (p = 0.040) as independent imaging classifiers for differentiation of RT from RN. Combined use of rCBV and K^trans at threshold values of 2.2 and 0.08 min^-1, respectively, improved the overall diagnostic accuracy to 92.8%.

CONCLUSION

In patients with treated glioblastoma, rCBV outperforms ADC and K^trans as a single imaging classifier to predict recurrent tumor versus radiation necrosis; however, the combination of rCBV and K^trans may be used to improve overall diagnostic accuracy.

[Sinogram restoration for low-dose cerebral perfusion CT images]

In clinical cerebral perfusion CT examination, repeated scanning the region of interest in the cine mode increases the radiation dose of the patients, while decreasing the radiation dose by lowering the scanning current results in poor image quality and affects the clinical diagnosis. We propose a penalized weighted least-square (PWLS) method for recovering the projection data to improve the quality of low-dose cerebral perfusion CT imaged. This method incorporates the statistical distribution characteristics of brain perfusion CT projection data and uses the statistical properties of the projection data for modeling. The PWLS method was used to recover the data, and the Gauss-Seidel (GS) method was employed for iterative solving. Adaptive weighting is introduced between the original projection data and the projection data after PWLS restoration. The experimental results on the clinical data demonstrated that the PWLS-based sinogram restoration method improved noise reduction and artifact suppression as compared with the conventional noise reduction methods, and better retained the edges and details to generate better cerebral perfusion maps.