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

SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data

PURPOSE

To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.

METHODS

The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC-MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data.

RESULTS

The proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods.

CONCLUSION

These results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground-truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning.

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases

BACKGROUND

Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) could be helpful to separate true disease progression from pseudo-progression in brain metastases when assessing the need for retreatment. However, the selection of arterial input functions (AIFs) is not standardized for analysis, limiting its use for this application.

PURPOSE

To compare population-based AIFs, AIFs specific to each patient, and AIFs specific to every visit in the longitudinal follow-up of brain metastases.

MATERIAL AND METHODS

Longitudinal data were collected from eight patients before treatment (6 of 8 patients) and after treatment (6-17 visits). Imaging was performed using a 1.5-T MRI system. Lesions were segmented by subtracting precontrast images from postcontrast images. Cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were computed, and Pearson's product moment correlation coefficients were calculated to evaluate similarity of DSC parameters dependent on various AIF choices across time. AIF shape characteristics were compared. Parameter differences between white matter (WM) and gray matter (GM) were obtained to determine which AIF choice maximizes tissue differentiation.

RESULTS

Although DSC parameters follow similar patterns in time, the various AIF selections cause large parameter variations with relative standard deviations of up to ±60%. AIFs sampled in one patient across sessions more similar in shape than AIFs sampled across patients. Estimates of rCBV based on scan-specific AIFs differentiated better between perfusion in WM and GM than patient-specific or population-based AIFs (P ≤ 0.02).

CONCLUSION

Results indicate that scan-specific AIFs are the best choice for DSC-MRI parameter estimations in the longitudinal follow-up of brain metastases.

Image-to-image generative adversarial networks for synthesizing perfusion parameter maps from DSC-MR images in cerebrovascular disease

Stroke is a major cause of death or disability. As imaging-based patient stratification improves acute stroke therapy, dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is of major interest in image brain perfusion. However, expert-level perfusion maps require a manual or semi-manual post-processing by a medical expert making the procedure time-consuming and less-standardized. Modern machine learning methods such as generative adversarial networks (GANs) have the potential to automate the perfusion map generation on an expert level without manual validation. We propose a modified pix2pix GAN with a temporal component (temp-pix2pix-GAN) that generates perfusion maps in an end-to-end fashion. We train our model on perfusion maps infused with expert knowledge to encode it into the GANs. The performance was trained and evaluated using the structural similarity index measure (SSIM) on two datasets including patients with acute stroke and the steno-occlusive disease. Our temp-pix2pix architecture showed high performance on the acute stroke dataset for all perfusion maps (mean SSIM 0.92-0.99) and good performance on data including patients with the steno-occlusive disease (mean SSIM 0.84-0.99). While clinical validation is still necessary for future studies, our results mark an important step toward automated expert-level perfusion maps and thus fast patient stratification.

Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging

BACKGROUND AND PURPOSE

Computed tomography perfusion imaging allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates using a deep learning approach.

METHODS

We trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]). The model was internally validated in a 5-fold cross-validation and externally in an independent dataset (CRISP study [CT Perfusion to Predict Response to Recanalization in Ischemic Stroke Project]). We calculated the mean absolute difference between the predictions of the deep learning model and the final infarct volume versus the mean absolute difference between computed tomography perfusion imaging processing by RAPID software (iSchemaView, Menlo Park, CA) and the final infarct volume. Next, we determined infarct growth rates for every patient.

RESULTS

We included 127 patients from the MR CLEAN (derivation) and 101 patients of the CRISP study (validation). The deep learning model improved final infarct volume prediction compared with the RAPID software in both the derivation, mean absolute difference 34.5 versus 52.4 mL, and validation cohort, 41.2 versus 52.4 mL (P<0.01). We obtained individual infarct growth rates enabling the estimation of final infarct volume based on time and grade of reperfusion.

CONCLUSIONS

We validated a deep learning-based method which improved final infarct volume estimations compared with classic computed tomography perfusion imaging processing. In addition, the deep learning model predicted individual infarct growth rates which could enable the introduction of tissue clocks during the management of acute stroke.

Non-parametric deconvolution using Bézier curves for quantification of cerebral perfusion in dynamic susceptibility contrast MRI

Objective

Deconvolution is an ill-posed inverse problem that tends to yield non-physiological residue functions R(t) in dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). In this study, the use of Bézier curves is proposed for obtaining physiologically reasonable residue functions in perfusion MRI.

Materials and methods

Cubic Bézier curves were employed, ensuring R(0) = 1, bounded-input, bounded-output stability and a non-negative monotonically decreasing solution, resulting in 5 parameters to be optimized. Bézier deconvolution (BzD), implemented in a Bayesian framework, was tested by simulation under realistic conditions, including effects of arterial delay and dispersion. BzD was also applied to DSC-MRI data from a healthy volunteer.

Results

Bézier deconvolution showed robustness to different underlying residue function shapes. Accurate perfusion estimates were observed, except for boxcar residue functions at low signal-to-noise ratio. BzD involving corrections for delay, dispersion, and delay with dispersion generally returned accurate results, except for some degree of cerebral blood flow (CBF) overestimation at low levels of each effect. Maps of mean transit time and delay were markedly different between BzD and block-circulant singular value decomposition (oSVD) deconvolution.

Discussion

A novel DSC-MRI deconvolution method based on Bézier curves was implemented and evaluated. BzD produced physiologically plausible impulse response, without spurious oscillations, with generally less CBF underestimation than oSVD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-021-00995-0.

Imaging selection for reperfusion therapy in acute ischemic stroke beyond the conventional time window

Originally, the efficacy of acute ischemic stroke treatment with thrombolysis or thrombectomy was only proven in narrow time windows of, respectively, 4.5 and 6 h after onset. Introducing imaging-based selection beyond non-contrast enhanced computed tomography has expanded the treatment window, focusing on presumed tissue status rather than solely on time after stroke onset. Different mismatch concepts have been adopted in clinical practice to select patients in the extended and unknown time window based on findings from randomized controlled trials. Since various concepts exist that can identify patients likely to benefit from reperfusion strategies, clinicians may wonder which imaging modality may be preferred in the emergency setting. In this review, we will discuss the different mismatch concepts and their practical implementation for patient selection for thrombolysis or thrombectomy, beyond the conventional time window.

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.

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

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

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.

Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke

The problem of final tissue outcome prediction of acute ischemic stroke is assessed from physically realistic simulated perfusion magnetic resonance images. Different types of simulations with a focus on the arterial input function are discussed. These simulated perfusion magnetic resonance images are fed to convolutional neural network to predict real patients. Performances close to the state-of-the-art performances are obtained with a patient specific approach. This approach consists in training a model only from simulated images tuned to the arterial input function of a tested real patient. This demonstrates the added value of physically realistic simulated images to predict the final infarct from perfusion.

Infarct Evolution in a Large Animal Model of Middle Cerebral Artery Occlusion

Mechanical thrombectomy for the treatment of ischemic stroke shows high rates of recanalization; however, some patients still have a poor clinical outcome. A proposed reason for this relates to the fact that the ischemic infarct growth differs significantly between patients. While some patients demonstrate rapid evolution of their infarct core (fast evolvers), others have substantial potentially salvageable penumbral tissue even hours after initial vessel occlusion (slow evolvers). We show that the dog middle cerebral artery occlusion model recapitulates this key aspect of human stroke rendering it a highly desirable model to develop novel multimodal treatments to improve clinical outcomes. Moreover, this model is well suited to develop novel image analysis techniques that allow for improved lesion evolution prediction; we provide proof-of-concept that MRI perfusion-based time-to-peak maps can be utilized to predict the rate of infarct growth as validated by apparent diffusion coefficient-derived lesion maps allowing reliable classification of dogs into fast versus slow evolvers enabling more robust study design for interventional research.

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.

Boosted Tree Model Reforms Multimodal Magnetic Resonance Imaging Infarct Prediction in Acute Stroke

BACKGROUND AND PURPOSE

Stroke imaging is pivotal for diagnosis and stratification of patients with acute ischemic stroke to treatment. The potential of combining multimodal information into reliable estimates of outcome learning calls for robust machine learning techniques with high flexibility and accuracy. We applied the novel extreme gradient boosting algorithm for multimodal magnetic resonance imaging-based infarct prediction.

METHODS

In a retrospective analysis of 195 patients with acute ischemic stroke, fluid-attenuated inversion recovery, diffusion-weighted imaging, and 10 perfusion parameters were derived from acute magnetic resonance imaging scans. They were integrated to predict final infarct as seen on follow-up T2-fluid-attenuated inversion recovery using the extreme gradient boosting and compared with a standard generalized linear model approach using cross-validation. Submodels for recanalization and persistent occlusion were calculated and were used to identify the important imaging markers. Performance in infarct prediction was analyzed with receiver operating characteristics. Resulting areas under the curve and accuracy rates were compared using Wilcoxon signed-rank test.

RESULTS

The extreme gradient boosting model demonstrated significantly higher performance in infarct prediction compared with generalized linear model in both cross-validation approaches: 5-folds (P<10e-16) and leave-one-out (P<0.015). The imaging parameters time-to-peak, mean transit time, time-to-maximum, and diffusion-weighted imaging were indicated as most valuable for infarct prediction by the systematic algorithm rating. Notably, the performance improvement was higher with 5-folds cross-validation approach than leave-one-out.

CONCLUSIONS

We demonstrate extreme gradient boosting as a state-of-the-art method for clinically applicable multimodal magnetic resonance imaging infarct prediction in acute ischemic stroke. Our findings emphasize the role of perfusion parameters as important biomarkers for infarct prediction. The effect of cross-validation techniques on performance indicates that the intrapatient variability is expressed in nonlinear dynamics of the imaging modalities.

A Comparison of Relative Time to Peak and Tmax for Mismatch-Based Patient Selection

BACKGROUND AND PURPOSE

The perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI) mismatch profile is used to select patients for endovascular treatment. A PWI map of Tmax is commonly used to identify tissue with critical hypoperfusion. A time to peak (TTP) map reflects similar hemodynamic properties with the added benefit that it does not require arterial input function (AIF) selection and deconvolution. We aimed to determine if TTP could substitute Tmax for mismatch categorization.

METHODS

Imaging data of the DEFUSE 2 trial were reprocessed to generate relative TTP (rTTP) maps. We identified the rTTP threshold that yielded lesion volumes comparable to Tmax > 6 s and assessed the effect of reperfusion according to mismatch status, determined based on Tmax and rTTP volumes.

RESULTS

Among 102 included cases, the Tmax > 6 s lesion volumes corresponded most closely with rTTP > 4.5 s lesion volumes: median absolute difference 6.9 mL (IQR: 2.3-13.0). There was 94% agreement in mismatch classification between Tmax and rTTP-based criteria. When mismatch was assessed by Tmax criteria, the odds ratio (OR) for favorable clinical response associated with reperfusion was 7.4 (95% CI 2.3-24.1) in patients with mismatch vs. 0.4 (95% CI 0.1-2.6) in patients without mismatch. When mismatch was assessed with rTTP criteria, these ORs were 7.2 (95% CI 2.3-22.2) and 0.3 (95% CI 0.1-2.2), respectively.

CONCLUSION

rTTP yields lesion volumes that are comparable to Tmax and reliably identifies the PWI/DWI mismatch profile. Since rTTP is void of the problems associated with AIF selection, it is a suitable substitute for Tmax that could improve the robustness and reproducibility of mismatch classification in acute stroke.

Advanced MRI Measures of Cerebral Perfusion and Their Clinical Applications

Cerebral blood flow measurement by magnetic resonance imaging perfusion (MRP) techniques is broadly applied to patients with acute ischemic stroke, vasospasm following aneurysmal subarachnoid hemorrhage, chronic arterial steno-occlusive disease, cervical atherosclerotic disease, and primary brain neoplasms. MRP may be performed using an exogenous tracer, most commonly gadolinium-based intravenous contrast, or an endogenous tracer, such as arterial spin labeling (ASL) or intravoxel incoherent motion (IVIM). Here, we review the technical basis of commonly performed MRP techniques, the interpretation of MRP imaging maps, and how MRP provides valuable clinical information in the triage of patients with cerebral disease.

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

BACKGROUND AND PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke

PURPOSE

The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge-preserving spatial regularization for the deconvolution of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke.

THEORY AND METHODS

Ischemic tissues are not randomly distributed in the brain but form a spatially organized entity. The addition of a spatial regularization term allows to take into account this spatial organization contrarily to the sole temporal regularization approach which processes each voxel independently. The robustness of the spatial regularization in relation to shape variability, hemodynamic variability in tissues, noise in the magnetic resonance imaging apparatus, and uncertainty on the arterial input function selected for the deconvolution is addressed via an original in silico validation approach.

RESULTS

The deconvolution algorithm proved robust to the different sources of variability, outperforming temporal Tikhonov regularization in most realistic conditions considered. The limiting factor is the proper estimation of the arterial input function.

CONCLUSION

This study quantified the robustness of a spatio-temporal approach for dynamic susceptibility contrast-magnetic resonance imaging deconvolution via a new simulator. This simulator, now accessible online, is of wide applicability for the validation of any deconvolution algorithm. Magn Reson Med 78:1981-1990, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation

Relative cerebral blood volume (rCBV) is a magnetic resonance imaging biomarker that is used to differentiate progression from pseudoprogression in patients with glioblastoma multiforme, the most common primary brain tumor. However, calculated rCBV depends considerably on the software used. Automating all steps required for rCBV calculation is important, as user interaction can lead to increased variability and possible inaccuracies in clinical decision-making. Here, we present an automated tool for computing rCBV from dynamic susceptibility contrast-magnetic resonance imaging that includes leakage correction. The entrance and exit bolus time points are automatically calculated using wavelet-based detection. The proposed tool is compared with 3 Food and Drug Administration-approved software packages, 1 automatic and 2 requiring user interaction, on a data set of 43 patients. We also evaluate manual and automated white matter (WM) selection for normalization of the cerebral blood volume maps. Our system showed good agreement with 2 of the 3 software packages. The intraclass correlation coefficient for all comparisons between the same software operated by different people was >0.880, except for FuncTool when operated by user 1 versus user 2. Little variability in agreement between software tools was observed when using different WM selection techniques. Our algorithm for automatic rCBV calculation with leakage correction and automated WM selection agrees well with 2 out of the 3 FDA-approved software packages.

Optimising MR perfusion imaging: comparison of different software-based approaches in acute ischaemic stroke

OBJECTIVES

Perfusion imaging (PI) is susceptible to confounding factors such as motion artefacts as well as delay and dispersion (D/D). We evaluate the influence of different post-processing algorithms on hypoperfusion assessment in PI analysis software packages to improve the clinical accuracy of stroke PI.

METHODS

Fifty patients with acute ischaemic stroke underwent MRI imaging in the first 24 h after onset. Diverging approaches to motion and D/D correction were applied. The calculated MTT and CBF perfusion maps were assessed by volumetry of lesions and tested for agreement with a standard approach and with the final lesion volume (FLV) on day 6 in patients with persisting vessel occlusion.

RESULTS

MTT map lesion volumes were significantly smaller throughout the software packages with correction of motion and D/D when compared to the commonly used approach with no correction (p = 0.001-0.022). Volumes on CBF maps did not differ significantly (p = 0.207-0.925). All packages with advanced post-processing algorithms showed a high level of agreement with FLV (ICC = 0.704-0.879).

CONCLUSIONS

Correction of D/D had a significant influence on estimated lesion volumes and leads to significantly smaller lesion volumes on MTT maps. This may improve patient selection.

KEY POINTS

• Assessment on hypoperfusion using advanced post-processing with correction for motion and D/D. • CBF appears to be more robust regarding differences in post-processing. • Tissue at risk is estimated more accurately by correcting software algorithms. • Advanced post-processing algorithms show a higher agreement with the final lesion volume.