Reference-based linear curve fitting for bolus arrival time estimation in 4D MRA and MR perfusion-weighted image sequences

Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

OBJECTIVES

To investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients.

METHODS

The study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board. The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores.

RESULTS

Penumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p < 0.001) and negatively with the ASPECTS (r =  - 0.43; p < 0.001). The CNN AIF estimated penumbra and core volume matching the patient symptoms, typically in patients with higher NIHSS (> 20) and lower ASPECT score (< 5). In group analysis, the median CBF < 20%, CBF < 30%, rCBF < 38%, Tmax > 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05).

CONCLUSIONS

With inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.

CRITICAL RELEVANCE STATEMENT

With CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.

Optimal Scaling Approaches for Perfusion MRI with Distorted Arterial Input Function (AIF) in Patients with Ischemic Stroke

Background: Diagnosis and timely treatment of ischemic stroke depends on the fast and accurate quantification of perfusion parameters. Arterial input function (AIF) describes contrast agent concentration over time as it enters the brain through the brain feeding artery. AIF is the central quantity required to estimate perfusion parameters. Inaccurate and distorted AIF, due to partial volume effects (PVE), would lead to inaccurate quantification of perfusion parameters. Methods: Fifteen patients suffering from stroke underwent perfusion MRI imaging at the Tri-Service General Hospital, Taipei. Various degrees of the PVE were induced on the AIF and subsequently corrected using rescaling methods. Results: Rescaled AIFs match the exact reference AIF curve either at peak height or at tail. Inaccurate estimation of CBF values estimated from non-rescaled AIFs increase with increasing PVE. Rescaling of the AIF using all three approaches resulted in reduced deviation of CBF values from the reference CBF values. In most cases, CBF map generated by rescaled AIF approaches show increased CBF and Tmax values on the slices in the left and right hemispheres. Conclusion: Rescaling AIF by VOF approach seems to be a robust and adaptable approach for correction of the PVE-affected multivoxel AIF. Utilizing an AIF scaling approach leads to more reasonable absolute perfusion parameter values, represented by the increased mean CBF/Tmax values and CBF/Tmax images.

Mathematical Models for Blood Flow Quantification in Dialysis Access Using Angiography: A Comparative Study

Blood flow rate in dialysis (vascular) access is the key parameter to examine patency and to evaluate the outcomes of various endovascular interve7ntions. While angiography is extensively used for dialysis access–salvage procedures, to date, there is no image-based blood flow measurement application commercially available in the angiography suite. We aim to calculate the blood flow rate in the dialysis access based on cine-angiographic and fluoroscopic image sequences. In this study, we discuss image-based methods to quantify access blood flow in a flow phantom model. Digital subtraction angiography (DSA) and fluoroscopy were used to acquire images at various sampling rates (DSA—3 and 6 frames/s, fluoroscopy—4 and 10 pulses/s). Flow rates were computed based on two bolus tracking algorithms, peak-to-peak and cross-correlation, and modeled with three curve-fitting functions, gamma variate, lagged normal, and polynomial, to correct errors with transit time measurement. Dye propagation distance and the cross-sectional area were calculated by analyzing the contrast enhancement in the vessel. The calculated flow rates were correlated versus an in-line flow sensor measurement. The cross-correlation algorithm with gamma-variate curve fitting had the best accuracy and least variability in both imaging modes. The absolute percent error (mean ± SEM) of flow quantification in the DSA mode at 6 frames/s was 21.4 ± 1.9%, and in the fluoroscopic mode at 10 pulses/s was 37.4 ± 3.6%. The radiation dose varied linearly with the sampling rate in both imaging modes and was substantially low to invoke any tissue reactions or stochastic effects. The cross-correlation algorithm and gamma-variate curve fitting for DSA acquisition at 6 frames/s had the best correlation with the flow sensor measurements. These findings will be helpful to develop a software-based vascular access flow measurement tool for the angiography suite and to optimize the imaging protocol amenable for computational flow applications.

Blood flow quantification in dialysis access using digital subtraction angiography: A retrospective study

BACKGROUND AND OBJECTIVE

Vascular access is the "lifeline" of end-stage renal disease patients, which is surgically constructed to remove blood-waste and return artificially filtered blood into circulation. The arteriovenous shunting causes an abrupt change in blood flow and results in increased fluidic stress, which predisposes to access stenosis and thrombosis. While access flow is crucial to evaluate interventional endpoint, application to measure flow using digital angiogram is not yet available. The goal of this study was to determine the feasibility of flow quantification in dialysis access using a software tool and to guide the design of an imaging protocol.

METHODS

173 digital subtraction angiographic (DSA) images were retrospectively analyzed to evaluate access flow in a custom-programming environment. Four bolus transit time algorithms and a distance calculation method were assessed for flow computation. Gamma variate function was applied to remove secondary flow and intensity outliers in the bolus time-intensity curves and evaluated for enhancement in computational accuracy. The percent deviations of flow rates computed from dilution of iodinated radio-contrast material were compared with in situ catheter-based flow measurement.

RESULTS

Among the implemented bolus transit time algorithms, quantification error (mean ± standard error) of cross-correlation algorithm without and with gamma variate curve fitting was 35 ± 1% and 22 ± 1%, respectively. All other algorithms had quantification error >27%. The bias and limits of agreement of the cross-correlation algorithm with gamma variate curve fit was -94 ml/min and [-353, 165] mL/min, respectively.

CONCLUSIONS

The cross-correlation algorithm with gamma variate curve fit had the best accuracy and reproducibility for image-based blood flow computation. To further enhance accuracy, images may need to be acquired with a dedicated injection protocol with predetermined parameters such as the duration, rate and mode of bolus injection, and the acquisition frame rate.

Segmentation-Based Blood Flow Parameter Refinement in Cerebrovascular Structures Using 4-D Arterial Spin Labeling MRA

OBJECTIVE

Cerebrovascular diseases are one of the main global causes of death and disability in the adult population. The preferred imaging modality for the diagnostic routine is digital subtraction angiography, an invasive modality. Time-resolved three-dimensional arterial spin labeling magnetic resonance angiography (4D ASL MRA) is an alternative non-invasive modality, which captures morphological and blood flow data of the cerebrovascular system, with high spatial and temporal resolution. This work proposes advanced medical image processing methods that extract the anatomical and hemodynamic information contained in 4D ASL MRA datasets.

METHODS

A previously published segmentation method, which uses blood flow data to improve its accuracy, is extended to estimate blood flow parameters by fitting a mathematical model to the measured vascular signal. The estimated values are then refined using regression techniques within the cerebrovascular segmentation. The proposed method was evaluated using fifteen 4D ASL MRA phantoms, with ground-truth morphological and hemodynamic data, fifteen 4D ASL MRA datasets acquired from healthy volunteers, and two 4D ASL MRA datasets from patients with a stenosis.

RESULTS

The proposed method reached an average Dice similarity coefficient of 0.957 and 0.938 in the phantom and real dataset segmentation evaluations, respectively. The estimated blood flow parameter values are more similar to the ground-truth values after the refinement step, when using phantoms. A qualitative analysis showed that the refined blood flow estimation is more realistic compared to the raw hemodynamic parameters.

CONCLUSION

The proposed method can provide accurate segmentations and blood flow parameter estimations in the cerebrovascular system using 4D ASL MRA datasets.

SIGNIFICANCE

The information obtained with the proposed method can help clinicians and researchers to study the cerebrovascular system non-invasively.

Cerebral Hemodynamics in Patients with Hemolytic Uremic Syndrome Assessed by Susceptibility Weighted Imaging and Four-Dimensional Non-Contrast MR Angiography

Background and Purpose

Conventional magnetic resonance imaging (MRI) of patients with hemolytic uremic syndrome (HUS) and neurological symptoms performed during an epidemic outbreak of Escherichia coli O104:H4 in Northern Europe has previously shown pathological changes in only approximately 50% of patients. In contrast, susceptibility-weighted imaging (SWI) revealed a loss of venous contrast in a large number of patients. We hypothesized that this observation may be due to an increase in cerebral blood flow (CBF) and aimed to identify a plausible cause.

Materials and Methods

Baseline 1.5T MRI scans of 36 patients (female, 26; male, 10; mean age, 38.2±19.3 years) were evaluated. Venous contrast was rated on standard SWI minimum intensity projections. A prototype four-dimensional (time resolved) magnetic resonance angiography (4D MRA) assessed cerebral hemodynamics by global time-to-peak (TTP), as a surrogate marker for CBF. Clinical parameters studied were hemoglobin, hematocrit, creatinine, urea levels, blood pressure, heart rate, and end-tidal CO₂.

Results

SWI venous contrast was abnormally low in 33 of 36 patients. TTP ranged from 3.7 to 10.2 frames (mean, 7.9 ± 1.4). Hemoglobin at the time of MRI (n = 35) was decreased in all patients (range, 5.0 to 12.6 g/dL; mean, 8.2 ± 1.4); hematocrit (n = 33) was abnormally low in all but a single patient (range, 14.3 to 37.2%; mean, 23.7 ± 4.2). Creatinine was abnormally high in 30 of 36 patients (83%) (range, 0.8 to 9.7; mean, 3.7 ± 2.2). SWI venous contrast correlated significantly with hemoglobin (r = 0.52, P = 0.0015), hematocrit (r = 0.65, P < 0.001), and TTP (r = 0.35, P = 0.036). No correlation of SWI with blood pressure, heart rate, end-tidal CO₂, creatinine, and urea level was observed. Findings suggest that the loss of venous contrast is related to an increase in CBF secondary to severe anemia related to HUS. SWI contrast of patients with pathological conventional MRI findings was significantly lower compared to patients with normal MRI (mean SWI score, 1.41 and 2.05, respectively; P = 0.04). In patients with abnormal conventional MRI, mean TTP (7.45), mean hemoglobin (7.65), and mean hematocrit (22.0) were lower compared to patients with normal conventional MRI scans (mean TTP = 8.28, mean hemoglobin = 8.63, mean hematocrit = 25.23).

Conclusion

In contrast to conventional MRI, almost all patients showed pathological changes in cerebral hemodynamics assessed by SWI and 4D MRA. Loss of venous contrast on SWI is most likely the result of an increase in CBF and may be related to the acute onset of anemia. Future studies will be needed to assess a possible therapeutic effect of blood transfusions in patients with HUS and neurological symptoms.

Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging

Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

Acute stroke imaging: feasibility and value of MR angiography with high spatial and temporal resolution for vessel assessment and perfusion analysis in patients with wake-up stroke

RATIONALE AND OBJECTIVES

Magnetic resonance (MR) imaging (MRI) provides information that can be used to estimate the symptom onset in patients with wake-up stroke (WUS). Time-resolved MR angiography (MRA) is the fastest available MR sequence technique for vessel assessment, and the different phases acquired can provide information about cerebral perfusion. The aim of this study was to evaluate the diagnostic performance of time-resolved MRA both for the assessment of vessel morphology and for the feasibility of perfusion.

MATERIALS AND METHODS

Nineteen patients with WUS were included. Image quality and vessel pathologies were evaluated and correlated to time-of-flight-MRA (n = 14), computed tomography-angiography (n = 4), sonography (n = 12), and conventional angiography (n = 6). The temporal delay of signal enhancement in all pixels of the time-resolved MRA measurement after contrast injection was evaluated and compared to dynamic susceptibility contrast-enhanced (DSC) perfusion imaging (n = 13).

RESULTS

Time-resolved MRA resulted in the diagnosis of large vessel disease in 14 of 19 patients, involving the internal carotids (n = 4), the vertebral arteries (n = 3), and the circle of Willis (n = 10). All severe vascular pathologies which influence patients' acute stroke therapy were obtained by time-resolved MRA. Overestimation of stenoses in two of 14 patients resulted in sensitivity and specificity of 100% and 71%, respectively. Time-to-peak (TTP) estimations were hampered by movement artifacts in four patients (31%). Compared to DSC, the area of TTP delay was comparable in size and localization without relevant overestimation or underestimation.

CONCLUSIONS

Time-resolved MRA is a valuable technique in patients with WUS with high sensitivity and high negative predictive value. Cerebral perfusion estimation can be performed in selected cases for therapy decision but can be hampered by patient movement.

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 10 TTP and Tmax estimation techniques for MR perfusion-diffusion mismatch quantification in acute stroke

BACKGROUND AND PURPOSE

The mismatch between lesions identified in perfusion- and diffusion-weighted MR imaging is typically used to identify tissue at risk of infarction in acute stroke. The purpose of this study was to analyze the variability of mismatch volumes resulting from different time-to-peak or time-to-maximum estimation techniques used for hypoperfused tissue definition.

MATERIALS AND METHODS

Data of 50 patients with middle cerebral artery stroke and intracranial vessel occlusion imaged within 6 hours of symptom onset were analyzed. Therefore, 10 different TTP/Tmax techniques and delay thresholds between +2 and +12 seconds were used for calculation of perfusion lesions. Diffusion lesions were semiautomatically segmented and used for mismatch quantification after registration.

RESULTS

Mean volumetric differences up to 40 and 100 mL in individual patients were found between the mismatch volumes calculated by the 10 TTP/Tmax estimation techniques for typically used delay thresholds. The application of typical criteria for the identification of patients with a clinically relevant mismatch volume resulted in different mismatch classifications in ≤24% of all cases, depending on the TTP/Tmax estimation method used.

CONCLUSIONS

High variations of tissue-at-risk volumes have to be expected when using different TTP/Tmax estimation techniques. An adaption of different techniques by using correction formulas may enable more comparable study results until a standard has been established by agreement.

Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations

PURPOSE

Exact knowledge about the nidus of an arteriovenous malformation (AVM) and the connected vessels is often required for image-based research projects and optimal therapy planning. The aim of this work is to present and evaluate a computer-aided nidus segmentation technique and subsequent angiographic characterization of the connected vessels that can be visualized in 3D.

METHODS

The proposed method was developed and evaluated based on 15 datasets of patients with an AVM. Each dataset consists of a high-resolution 3D and a 4D magnetic resonance angiography (MRA) image sequence. After automatic cerebrovascular segmentation from the 3D MRA dataset, a voxel-wise support vector machine classification based on four extracted features is performed to generate a new parameter map. The nidus is represented by positive values in this parameter map and can be extracted using volume growing. Finally, the nidus segmentation is dilated and used for an automatic identification of feeding arteries and draining veins by integrating hemodynamic information from the 4D MRA datasets.

RESULTS

A quantitative comparison of the computer-aided AVM nidus segmentation results to manual gold-standard segmentations by two observers revealed a mean Dice coefficient of 0.835, which is comparable to the inter-observer agreement for which a mean Dice coefficient of 0.830 was determined. The angiographic characterization was visually rated feasible for all patients.

CONCLUSION

The presented computer-aided method enables a reproducible and fast extraction of the AVM nidus as well as an automatic angiographic characterization of the connected vessels, which can be used to support image-based research projects and therapy planning of AVMs.

Persistent hemodynamic changes in ruptured brain arteriovenous malformations

BACKGROUND AND PURPOSE

Hemodynamic properties of brain arteriovenous malformations (AVMs) with risk factors for a future hemorrhage are essentially unknown. We hypothesized that AVMs with anatomic properties, which are associated with an increased rupture risk, exhibit different hemodynamic characteristics than those without these properties.

METHODS

Seventy-two consecutive patients with AVMs diagnosed by conventional angiography underwent MRI examination, including time-resolved 3-dimensional MR angiography. Signal-intensity curves derived from the time-resolved 3-dimensional MR angiography datasets were used to calculate relative blood flow transit times through the AVM nidus based on the time-to-peak parameter. For identification of characteristics associated with altered transit times, a multiple normal regression model was fitted with stepwise selection of the following regressors: intracranial hemorrhage, deep nidus location, infratentorial location, deep drainage, associated aneurysm, nidus size, draining venous stenosis, and number of draining veins.

RESULTS

A previous intracranial hemorrhage is the only characteristic that was associated with a significant alteration of the relative transit time, leading to an increase of 2.4 seconds (95% CI, 1.2-3.6 seconds;, P<0.001) without adjustment and 2.1 seconds (95% CI, 0.6-3.6 seconds; P=0.007) with adjustment for all other regressors considered. The association was independent of the bleeding age.

CONCLUSIONS

Hemodynamic parameters do not seem useful for risk assessment of an AVM-related hemorrhage because only a previous AVM rupture leads to a significant and permanent alteration of the hemodynamic situation.

Intranidal signal distribution in post-contrast time-of-flight MRA is associated with rupture risk factors in arteriovenous malformations

PURPOSE

To evaluate if arteriovenous malformations (AVMs) that are associated with a high rupture risk (HRR) are represented by different intranidal Time-of-Flight (TOF) magnetic resonance angiography intensity distributions compared to those with presumably low rupture risk (LRR).

METHODS

Fifty post-contrast TOF datasets of patients with an AVM were analyzed in this study. The patients were classified to the HRR group in case of a deep location, presence of exclusive deep venous drainage, previous hemorrhagic event or a combination thereof. For each TOF dataset, the AVM nidus was semi-automatically delineated and used for histogram extraction. Each histogram was analyzed by calculating the skewness, kurtosis, mean and median intensity and full-width-half-maximum. Statistical analysis was performed using parameter-wise two-sided t-tests of the parameters between the two groups.

RESULTS

Based on morphological analysis, 21 patients were classified to the HRR and 29 patients to the LRR group. Statistical analysis revealed that TOF intensity distributions of HRR AVMs exhibit a significant higher skewness (p=0.0005) parameter compared to LRR AVMs. Contrary to these findings, no significant differences were found for the other parameters evaluated.

CONCLUSION

Intranidal flow heterogeneity, for example, caused by turbulent flow conditions, may play an important role for risk of a hemorrhage. An analysis of post-contrast TOF intensities within the nidus of an AVM may offer simple and valuable information for clinical risk estimation of AVMs and needs to be tested prospectively.

Classification of cerebral arteriovenous malformations and intranidal flow patterns by color-encoded 4D-hybrid-MRA

BACKGROUND AND PURPOSE

4D MRA has been evolving as a noninvasive supplement for DSA. The purpose of this study was to evaluate the feasibility of a newly developed blood flow visualization technique for the classification of cerebral AVMs. We hypothesized that 4D-hMRA allows detection of different flow patterns within the nidus as well as differentiation of feeders and draining veins and has very good agreement with DSA regarding the Spetzler-Martin grade.

MATERIALS AND METHODS

Thirty-one consecutive patients with AVMs were evaluated by using 4D-hMRA and DSA by 2 blinded raters. Rating criteria included Spetzler-Martin score and other morphologic variables together with a new scale for 3 intranidal flow patterns (homogeneous = 1, unidirectional = 2, heterogeneous = 3).

RESULTS

The Spetzler-Martin grades were rated different from DSA in 5 cases by rater 1 and in 3 cases by rater 2 with an excellent interrater reliability of κ = 0.96 (4/31, 1 by size and 3 by drainage). Each reader missed 5 feeders on 4D-hMRA. Draining veins were distinguished in the temporal course in 7 on DSA but in 8 and 12 on 4D-hybrid-MRA (raters 1 and 2 respectively), with κ = 0.79. A type 1 intranidal flow pattern was recognizable in 9 (30%) patients; type 2, in 19 (60%); and type 3, in 3 (10%).

CONCLUSIONS

4D-hMRA allows reliable Spetzler-Martin grading and detection of brain arteriovenous malformation feeding arteries and draining veins, with the drawback that for small vessels DSA is still needed. Draining veins might even be detected with higher sensitivity than on DSA. Discrimination of different intranidal flow patterns is possible, but their relevance for hemorrhage risk assessment and therapy planning requires further study.

Analysis of the influence of 4D MR angiography temporal resolution on time-to-peak estimation error for different cerebral vessel structures

BACKGROUND AND PURPOSE

Time-resolved MRA imaging is a promising technique for blood flow evaluation in case of cerebrovascular malformations. Unfortunately, 4D MRA imaging is a trade-off between spatial and temporal resolution. The aim of this study was to investigate the influence of temporal resolution on the error associated with TTP estimation from indicator dilution curves derived from different vascular structures.

MATERIALS AND METHODS

Monte Carlo simulation was performed to compute indicator dilution curves with known criterion standard TTP at temporal resolutions between 0.1 and 5 seconds. TTPs were estimated directly and by using 4 hemodynamic models for each curve and were compared with criterion standard TTP. Furthermore, clinical evaluation was performed by using 226 indicator dilution curves from different vessel structures obtained from clinical datasets. The temporal resolution was artificially decreased, and TTPs were estimated and compared with those obtained at the original temporal resolutions. The results of the clinical evaluations were further stratified for different vessel structures.

RESULTS

The results of both evaluations show that the TTP estimation error increases exponentially when one lowers the temporal resolution. TTP estimation by using hemodynamic model curves leads to lower estimation errors compared with direct estimation. A temporal resolution of 1.5 seconds for arteries and 2.5 seconds for venous and arteriovenous malformation vessel structures appears to be reasonable to achieve TTP estimations adequate for clinical application.

CONCLUSIONS

Different vessel structures require different temporal resolutions to enable comparable TTP estimation errors, which should be considered for achieving a case-optimal temporal and spatial resolution.

4D blood flow visualization fusing 3D and 4D MRA image sequences

PURPOSE

To present and evaluate the feasibility of a novel automatic method for generating 4D blood flow visualizations fusing high spatial resolution 3D and time-resolved (4D) magnetic resonance angiography (MRA) datasets.

MATERIALS AND METHODS

In a first step, the cerebrovascular system is segmented in the 3D MRA dataset and a surface model is computed. The hemodynamic information is extracted from the 4D MRA dataset and transferred to the surface model using rigid registration where it can be visualized color-coded or dynamically over time. The presented method was evaluated using software phantoms and 20 clinical datasets from patients with an arteriovenous malformation. Clinical evaluation was performed by comparison of Spetzler-Martin scores determined from the 4D blood flow visualizations and corresponding digital subtraction angiographies.

RESULTS

The performed software phantom validation showed that the presented method is capable of producing reliable visualization results for vessels with a minimum diameter of 2 mm for which a mean temporal error of 0.27 seconds was achieved. The clinical evaluation based on 20 datasets comparing the 4D visualization to DSA images revealed an excellent interrater reliability.

CONCLUSION

The presented method enables an improved combined representation of blood flow and anatomy while reducing the time needed for clinical rating.