PerfTool: a software platform for investigating bolus-tracking perfusion imaging quantification strategies

AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet almost reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.

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

Purpose

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

Materials and Methods

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

Results

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

Conclusion

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

Cerebrovascular reactivity and white matter integrity

OBJECTIVE

To compare the diffusion and perfusion MRI metrics of normal-appearing white matter (NAWM) with and without impaired cerebrovascular reactivity (CVR).

METHODS

Seventy-five participants with moderate to severe leukoaraiosis underwent blood oxygen level-dependent CVR mapping using a 3T MRI system with precise carbon dioxide stimulus manipulation. Several MRI metrics were statistically compared between areas of NAWM with positive and negative CVR using one-way analysis of variance with Bonferroni correction for multiple comparisons.

RESULTS

Areas of NAWM with negative CVR showed a significant reduction in fractional anisotropy by a mean (SD) of 3.7% (2.4), cerebral blood flow by 22.1% (8.2), regional cerebral blood volume by 22.2% (7.0), and a significant increase in mean diffusivity by 3.9% (3.1) and time to maximum by 10.9% (13.2) (p < 0.01), compared to areas with positive CVR.

CONCLUSIONS

Impaired CVR is associated with subtle changes in the tissue integrity of NAWM, as evaluated using several quantitative diffusion and perfusion MRI metrics. These findings suggest that impaired CVR may contribute to the progression of white matter disease.

Vascular Dysfunction in Leukoaraiosis

BACKGROUND AND PURPOSE

The pathogenesis of leukoaraiosis has long been debated. This work addresses a less well-studied mechanism, cerebrovascular reactivity, which could play a leading role in the pathogenesis of this disease. Our aim was to evaluate blood flow dysregulation and its relation to leukoaraiosis.

MATERIALS AND METHODS

Cerebrovascular reactivity, the change in the blood oxygen level-dependent 3T MR imaging signal in response to a consistently applied step change in the arterial partial pressure of carbon dioxide, was measured in white matter hyperintensities and their contralateral spatially homologous normal-appearing white matter in 75 older subjects (age range, 50-91 years; 40 men) with leukoaraiosis. Additional quantitative evaluation of regions of leukoaraiosis was performed by using diffusion (n = 75), quantitative T2 (n = 54), and DSC perfusion MRI metrics (n = 25).

RESULTS

When we compared white matter hyperintensities with contralateral normal-appearing white matter, cerebrovascular reactivity was lower by a mean of 61.2% ± 22.6%, fractional anisotropy was lower by 44.9 % ± 6.9%, and CBF was lower by 10.9% ± 11.9%. T2 was higher by 61.7% ± 13.5%, mean diffusivity was higher by 59.0% ± 11.7%, time-to-maximum was higher by 44.4% ± 30.4%, and TTP was higher by 6.8% ± 5.8% (all P < .01). Cerebral blood volume was lower in white matter hyperintensities compared with contralateral normal-appearing white matter by 10.2% ± 15.0% (P = .03).

CONCLUSIONS

Not only were resting blood flow metrics abnormal in leukoaraiosis but there is also evidence of reduced cerebrovascular reactivity in these areas. Studies have shown that reduced cerebrovascular reactivity is more sensitive than resting blood flow parameters for assessing vascular insufficiency. Future work is needed to examine the sensitivity of resting-versus-dynamic blood flow measures for investigating the pathogenesis of leukoaraiosis.

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Background

Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology.

Results

Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters.

Conclusion

We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

ANTONIA perfusion and stroke. A software tool for the multi-purpose analysis of MR perfusion-weighted datasets and quantitative ischemic stroke assessment

OBJECTIVES

The objective of this work is to present the software tool ANTONIA, which has been developed to facilitate a quantitative analysis of perfusion-weighted MRI (PWI) datasets in general as well as the subsequent multi-parametric analysis of additional datasets for the specific purpose of acute ischemic stroke patient dataset evaluation.

METHODS

Three different methods for the analysis of DSC or DCE PWI datasets are currently implemented in ANTONIA, which can be case-specifically selected based on the study protocol. These methods comprise a curve fitting method as well as a deconvolution-based and deconvolution-free method integrating a previously defined arterial input function. The perfusion analysis is extended for the purpose of acute ischemic stroke analysis by additional methods that enable an automatic atlas-based selection of the arterial input function, an analysis of the perfusion-diffusion and DWI-FLAIR mismatch as well as segmentation-based volumetric analyses.

RESULTS

For reliability evaluation, the described software tool was used by two observers for quantitative analysis of 15 datasets from acute ischemic stroke patients to extract the acute lesion core volume, FLAIR ratio, perfusion-diffusion mismatch volume with manually as well as automatically selected arterial input functions, and follow-up lesion volume. The results of this evaluation revealed that the described software tool leads to highly reproducible results for all parameters if the automatic arterial input function selection method is used.

CONCLUSION

Due to the broad selection of processing methods that are available in the software tool, ANTONIA is especially helpful to support image-based perfusion and acute ischemic stroke research projects.

Comparison of perfusion- and diffusion-weighted imaging parameters in brain tumor studies processed using different software platforms

RATIONALE AND OBJECTIVES

To compare quantitative imaging parameter measures from diffusion- and perfusion-weighted imaging magnetic resonance imaging (MRI) sequences in subjects with brain tumors that have been processed with different software platforms.

MATERIALS AND METHODS

Scans from 20 subjects with primary brain tumors were selected from the Comprehensive Neuro-oncology Data Repository at Washington University School of Medicine (WUSM) and the Swedish Neuroscience Institute. MR images were coregistered, and each subject's data set was processed by three software packages: 1) vendor-specific scanner software, 2) research software developed at WUSM, and 3) a commercially available, Food and Drug Administration-approved, processing platform (Nordic Ice). Regions of interest (ROIs) were chosen within the brain tumor and normal nontumor tissue. The results obtained using these methods were compared.

RESULTS

For diffusion parameters, including mean diffusivity and fractional anisotropy, concordance was high when comparing different processing methods. For perfusion-imaging parameters, a significant variance in cerebral blood volume, cerebral blood flow, and mean transit time (MTT) values was seen when comparing the same raw data processed using different software platforms. Correlation was better with larger ROIs (radii ≥ 5 mm). Greatest variance was observed in MTT.

CONCLUSIONS

Diffusion parameter values were consistent across different software processing platforms. Perfusion parameter values were more variable and were influenced by the software used. Variation in the MTT was especially large suggesting that MTT estimation may be unreliable in tumor tissues using current MRI perfusion methods.

UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine

To develop a generic Open Source MRI perfusion analysis tool for quantitative parameter mapping to be used in a clinical workflow and methods for quality management of perfusion data. We implemented a classic, pixel-by-pixel deconvolution approach to quantify T1-weighted contrast-enhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in. It features parallel computing capabilities and an automated reporting scheme for quality management. Furthermore, by our implementation design, it could be easily extendable to other perfusion algorithms. Obtained results are saved as DICOM objects and directly added to the patient study. The plug-in was evaluated on ten MR perfusion data sets of the prostate and a calibration data set by comparing obtained parametric maps (plasma flow, volume of distribution, and mean transit time) to a widely used reference implementation in IDL. For all data, parametric maps could be calculated and the plug-in worked correctly and stable. On average, a deviation of 0.032 ± 0.02 ml/100 ml/min for the plasma flow, 0.004 ± 0.0007 ml/100 ml for the volume of distribution, and 0.037 ± 0.03 s for the mean transit time between our implementation and a reference implementation was observed. By using computer hardware with eight CPU cores, calculation time could be reduced by a factor of 2.5. We developed successfully an Open Source OsiriX plug-in for T1-DCE-MRI perfusion analysis in a routine quality managed clinical environment. Using model-free deconvolution, it allows for perfusion analysis in various clinical applications. By our plug-in, information about measured physiological processes can be obtained and transferred into clinical practice.

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.

Deconvolution with simple extrapolation for improved cerebral blood flow measurement in dynamic susceptibility contrast magnetic resonance imaging during acute ischemic stroke

Magnetic resonance (MR) perfusion imaging is a clinical technique for measuring brain blood flow parameters during stroke and other ischemic events. Ischemia in brain tissue can be difficult to accurately measure or visualize when using MR-derived cerebral blood flow (CBF) maps. The deconvolution techniques used to estimate flow can introduce a mean transit time-dependent bias following application of noise stabilization techniques. The underestimation of the CBF values, greatest in normal tissues, causes a decrease in the image contrast observed in CBF maps between normally perfused and ischemic tissues; resulting in ischemic areas becoming less conspicuous. Through application of the proposed simple extrapolation technique, CBF biases are reduced when missing high-frequency signal components in the MR data removed during deconvolution noise stabilization are restored. The extrapolation approach was compared with other methods and showed a statistically significant increase in image contrast in CBF maps between normal and ischemic tissues for white matter (P<.05) and performed better than most other methods for gray matter. Receiver operator characteristic curve analysis demonstrated that extrapolated CBF maps better-detected penumbral regions. Extrapolated CBF maps provided more accurate CBF estimates in simulations, suggesting that the approach may provide a better prediction of outcome in the absence of treatment.

Real-time diffusion-perfusion mismatch analysis in acute stroke

Diffusion-perfusion mismatch can be used to identify acute stroke patients that could benefit from reperfusion therapies. Early assessment of the mismatch facilitates necessary diagnosis and treatment decisions in acute stroke. We developed the RApid processing of PerfusIon and Diffusion (RAPID) for unsupervised, fully automated processing of perfusion and diffusion data for the purpose of expedited routine clinical assessment. The RAPID system computes quantitative perfusion maps (cerebral blood volume, CBV; cerebral blood flow, CBF; mean transit time, MTT; and the time until the residue function reaches its peak, T(max)) using deconvolution of tissue and arterial signals. Diffusion-weighted imaging/perfusion-weighted imaging (DWI/PWI) mismatch is automatically determined using infarct core segmentation of ADC maps and perfusion deficits segmented from T(max) maps. The performance of RAPID was evaluated on 63 acute stroke cases, in which diffusion and perfusion lesion volumes were outlined by both a human reader and the RAPID system. The correlation of outlined lesion volumes obtained from both methods was r(2) = 0.99 for DWI and r(2) = 0.96 for PWI. For mismatch identification, RAPID showed 100% sensitivity and 91% specificity. The mismatch information is made available on the hospital's PACS within 5-7 min. Results indicate that the automated system is sufficiently accurate and fast enough to be used for routine care as well as in clinical trials.

Evolution of hyperacute stroke over 6 hours using serial MR perfusion and diffusion maps

PURPOSE

To develop an appropriate method to evaluate the time-course of diffusion and perfusion changes in a clinically relevant animal model of ischemic stroke and to examine lesion progression on MR images. An exploration of acute stroke infarct expansion was performed in this study by using a new methodology for developing time-to-infarct maps based on the time at which each voxel becomes infarcted. This enabled definition of homogeneous regions from the heterogeneous stroke infarct.

MATERIALS AND METHODS

Time-to-infarct maps were developed based on apparent diffusion coefficient (ADC) changes. These maps were validated and then applied to blood flow and time-to-peak maps to examine perfusion changes.

RESULTS

ADC stroke infarct showed different evolution patterns depending on the time at which that region of tissue infarcted. Applying the time-to-infarct maps to the perfusion maps showed localized perfusion evolution characteristics. In some regions, perfusion was immediately affected and showed little change over the experiment; however, in some regions perfusion changes were more dynamic.

CONCLUSION

Results were consistent with the diffusion-perfusion mismatch hypothesis. In addition, characteristics of collateral recruitment were identified, which has interesting stroke pathophysiology and treatment implications.

3-Tesla versus 1.5-Tesla Magnetic Resonance Diffusion and Perfusion Imaging in Hyperacute Ischemic Stroke

BACKGROUND

Clinical 3-tesla magnetic resonance imaging systems are becoming widespread. No studies have examined differences between 1.5-tesla and 3-tesla imaging for the assessment of hyperacute ischemic stroke (<6 h from symptom onset). Our objective was to compare 1.5-tesla and 3-tesla diffusion and perfusion imaging for hyperacute stroke using optimized protocols.

METHODS

Three patients or their surrogate provided informed consent. Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) was performed sequentially at 1.5 T and 3 T. DWI, apparent diffusion coefficient (ADC) maps and relative time-to-peak (TTP) maps were registered and assessed. DWI contrast-to-noise ratio (CNR) and ADC contrast were measured and compared. The infarct lesion volume (ILV) and thresholded ischemic volume (TIV) were estimated on the ADC and TTP maps, respectively, with the penumbral volume being defined as the difference between these volumes.

RESULTS

Qualitatively, the 3-tesla TTP images exhibited greater feature detail. Quantitatively, the DWI CNR and ILV were similar at both field strengths, the ADC contrast was greater at 3 T and the TIV and penumbral volumes were much smaller at 3 T.

CONCLUSIONS

Overall, the 3-tesla diffusion and perfusion images were at least as good and in some ways superior to the 1.5-tesla images for assessing hyperacute stroke. The TTP maps showed greater feature detail at 3 T. The ischemic and penumbra volumes were much greater at 1.5 T, indicating a potential difference in the diagnostic utility of the PWI-DWI mismatch between field strengths.

Improved dynamic susceptibility contrast (DSC)-MR perfusion estimates by motion correction

PURPOSE

To investigate the effect of patient motion on quantitative cerebral blood flow (CBF) maps in ischemic stroke patients and to evaluate the efficacy of a motion-correction scheme.

MATERIALS AND METHODS

Perfusion data from 25 ischemic stroke patients were selected for analysis. Two motion profiles were applied to a digital anthropomorphic brain phantom to estimate accuracy. CBF images were generated for motion-corrupted and motion-corrected data. To correct for motion, rigid-body registration was performed. The realignment parameters and mean CBF in regions of interest were recorded.

RESULTS

All patient data with motion exhibited visibly reduced intervolume misalignment after motion correction. Improved flow delineation between different tissues and a more clearly defined ischemic lesion (IL) were achieved in the motion-corrected CBF. A significant difference occurred in the IL (P < 0.05) for patients with severe motion with an average difference between corrupted and corrected data of 4.8 mL/minute/100 g. The phantom data supported the patient results with better CBF accuracy after motion correction and high registration accuracy (<1 mm translational and <1 degrees rotational error).

CONCLUSION

Motion degrades flow differentiation between adjacent tissues in CBF maps and can cause ischemic severity to be underestimated. A registration motion correction scheme improves dynamic susceptibility contrast (DSC)-MR perfusion estimates.