Bolus delay and dispersion in perfusion MRI: implications for tissue predictor models in stroke

Identifiability of spatiotemporal tissue perfusion models

Objective.Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.Approach.For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth.Main results.Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters.Significance.These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.

Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke

Predicting clinical outcomes after stroke, using magnetic resonance imaging (MRI) measures, remains a challenge. The purpose of this study was to investigate the prediction of long-term clinical outcomes after ischemic stroke using parametric response mapping (PRM) based on perfusion MRI data. Multiparametric perfusion MRI datasets from 30 patients with chronic ischemic stroke were acquired at four-time points ranging from V2 (6  weeks) to V5 (7  months) after stroke onset. All perfusion MR parameters were analyzed using the classic whole-lesion approach and voxel-based PRM at each time point. The imaging biomarkers from each acquired MRI metric that was predictive of both neurological and functional outcomes were prospectively investigated. For predicting clinical outcomes at V5, it was identified that PRM_Tmax-, PRM_rCBV-, and PRM_rCBV+ at V3 were superior to the mean values of the corresponding maps at V3. We identified correlations between the clinical prognosis after stroke and MRI parameters, emphasizing the superiority of the PRM over the whole-lesion approach for predicting long-term clinical outcomes. This indicates that complementary information for the predictive assessment of clinical outcomes can be obtained using PRM analysis. Moreover, new insights into the heterogeneity of stroke lesions revealed by PRM can help optimize the accurate stratification of patients with stroke and guide rehabilitation.

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.

Vessel wall magnetic resonance and arterial spin labelling imaging in the management of presumed inflammatory intracranial arterial vasculopathy

Optimal criteria for diagnosing and monitoring response to treatment for infectious and inflammatory medium–large vessel intracranial vasculitis presenting with stroke are lacking. We integrated intracranial vessel wall MRI with arterial spin labelling into our routine clinical stroke pathway to detect presumed inflammatory intracranial arterial vasculopathy, and monitor disease activity, in patients with clinical stroke syndromes. We used predefined standardized radiological criteria to define vessel wall enhancement, and all imaging findings were rated blinded to clinical details. Between 2017 and 2018, stroke or transient ischaemic attack patients were first screened in our vascular radiology meeting and followed up in a dedicated specialist stroke clinic if a diagnosis of medium–large inflammatory intracranial arterial vasculopathy was radiologically confirmed. Treatment was determined and monitored by a multi-disciplinary team. In this case series, 11 patients were managed in this period from the cohort of young stroke presenters (<55 years). The median age was 36 years (interquartile range: 33,50), of which 8 of 11 (73%) were female. Two of 11 (18%) had herpes virus infection confirmed by viral nucleic acid in the cerebrospinal fluid. We showed improvement in cerebral perfusion at 1 year using an arterial spin labelling sequence in patients taking immunosuppressive therapy for >4 weeks compared with those not receiving therapy [6 (100%) versus 2 (40%) P = 0.026]. Our findings demonstrate the potential utility of vessel wall magnetic resonance with arterial spin labelling imaging in detecting and monitoring medium–large inflammatory intracranial arterial vasculopathy activity for patients presenting with stroke symptoms, limiting the need to progress to brain biopsy. Further systematic studies in unselected populations of stroke patients are needed to confirm our findings and establish the prevalence of medium–large artery wall inflammation.

Recent Advances in Nanomaterials for Diagnosis, Treatments, and Neurorestoration in Ischemic Stroke

Stroke is a major public health issue, corresponding to the second cause of mortality and the first cause of severe disability. Ischemic stroke is the most common type of stroke, accounting for 87% of all strokes, where early detection and clinical intervention are well known to decrease its morbidity and mortality. However, the diagnosis of ischemic stroke has been limited to the late stages, and its therapeutic window is too narrow to provide rational and effective treatment. In addition, clinical thrombolytics suffer from a short half-life, inactivation, allergic reactions, and non-specific tissue targeting. Another problem is the limited ability of current neuroprotective agents to promote recovery of the ischemic brain tissue after stroke, which contributes to the progressive and irreversible nature of ischemic stroke and also the severity of the outcome. Fortunately, because of biomaterials’ inherent biochemical and biophysical properties, including biocompatibility, biodegradability, renewability, nontoxicity, long blood circulation time, and targeting ability. Utilization of them has been pursued as an innovative and promising strategy to tackle these challenges. In this review, special emphasis will be placed on the recent advances in the study of nanomaterials for the diagnosis and therapy of ischemic stroke. Meanwhile, nanomaterials provide much promise for neural tissue salvage and regeneration in brain ischemia, which is also highlighted.

Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI

Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOF_SSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test-retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOF_SSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOF_SSS derived semi-quantitative parameters was good to excellent (ICC = 0.717-0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOF_SSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.

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.

Robust arterial input function surrogate measurement from the superior sagittal sinus complex signal for fast dynamic contrast-enhanced MRI in the brain

PURPOSE

Accurately estimating the arterial input function for dynamic contrast-enhanced MRI is challenging. An arterial input function is typically determined from signal magnitude changes related to a contrast agent, often leading to underestimation of peak concentrations. Alternatively, signal phase recovers the accurate peak concentration for straight vessels but suffers from high noise. A recent method proposed to fit the signal in the complex plane by combining the advantages of the previous 2 methods. The purpose of this work is to refine this complex-based method to determine the venous output function (VOF), an arterial input function surrogate, from the superior sagittal sinus.

METHODS

We propose a state-of-the-art complex-based method that includes direct compensation for blood inflow and signal phase correction accounting for the curvature of the superior sagittal sinus, generally assumed collinear with B₀ . We compared the magnitude-, phase-, and complex-based VOF determination methods against various simulated biases as well as for 29 brain metastases patients.

RESULTS

Angulation of the superior sagittal sinus relative to B₀ varied widely within patients, and its effect on the signal phase caused an underestimation of peak concentrations of up to 65%. Correction significantly increased the VOF peak concentration for the phase- and complex-based VOFs in the cohort. The phase-based method recovered accurate peak concentrations but lacked precision in the tail of the VOF. Our complex-based VOF completely recovered the effect of inflow and resulted in a high-peak concentration with limited noise.

CONCLUSION

The new complex-based method resulted in high-quality VOF robust against superior sagittal sinus curvature and variations in patient positioning.

Computed tomography perfusion in patients of stroke with left ventricular assist device

Left ventricular assist devices (LVAD) are widely applied for patients with severe heart failure as a bridge to heart transplantation as well as destination therapy. Patients with implanted LVAD have an increased risk of cerebral thrombosis and computed tomographic perfusion (CTP) has the potential to be performed for early diagnosis and treatment of acute ischemic stroke (AIS), including interventional thrombectomy. Here, we report our series of CTP examination in patients having suspected AIS after LVAD implantation. We retrospectively investigated 33 contrast-enhanced CTPs from January 2017 to December 2018 which were performed in 12 cases of patients because of possible neurological findings leading to suspected AIS during LVAD circulatory support who did not have definite ischemic findings nor intracerebral hemorrhage on non-contrast computed tomography. AIS with perfusion disturbance area was diagnosed in 11 (33.3%) out of a total of 33 CTPs in 4 (33.3%) out of 12 patients. Endovascular thrombectomy (EVT) was successfully performed in this research study four times for three patients. CTP was able to detect and determine the indication for EVT without serious complications. CTP could potentially be the first-choice assessment for early diagnosis of AIS with recoverable ischemic penumbra in patients with LVAD implantation.

Iron Sucrose as MRI Contrast Agent in Ischemic Stroke Model

BACKGROUND

Despite the growing concern about the safety of gadolinium-based contrast agents (GBCAs), they are still the most commonly used. Ferumoxytol, as an off-label alternative MRI contrast agent, cannot be administered by a rapid bolus for dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI).

PURPOSE

To assess the feasibility of iron sucrose (IS) as a contrast agent for MR angiography (MRA) and DSC-PWI.

STUDY TYPE

Prospective animal model.

ANIMAL MODEL

Thirty-six normal rats (16 for MRA, 20 for biocompability tests) and 36 occlusion of the middle cerebral artery (MCAO) model rats.

FIELD STRENGTH/SEQUENCE

3.0T; head and neck angiography, using a fast spoiled gradient-recalled-echo (FSPGR) sequence and DSC-MRI using echo planar imaging(EPI) sequence.

ASSESSMENT

MRA was performed on normal rats to examine the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of different doses of IS. DSC-PWI was performed on MCAO rats at 0, 24, 48, and 72 hours postreperfusion to investigate the lesion detectability of IS. Arterial spin labeling (ASL) and DSC-PWI enhanced by GBCAs were conducted on MCAO rats as controls.

STATISTICAL TESTS

Kruskal-Wallis test was used to compare qualitative assessment. One-way analysis of variance (ANOVA) was used to compare the parametric data. Pearson's r values were evaluated between relative cerebral blood flow(rCBF)-ASL, rCBF-DSC_IS , and rCBF obtained from DSC-PWI enhanced by GBCA.

RESULTS

The mean SNR and CNR of the common carotid artery at doses of 10 mg Fe/kg of IS were comparable with the standard dose of GBCAs (SNR: 68.04 ± 12.55 vs. 67.72 ± 14.66; CNR: 23.78 ± 7.21vs. 21.63 ± 6.83). In MCAO rat models, rCBF and relative cerebral blood volume (rCBV) of ipsilateral striatum declined (0.72 ± 0.14, 0.86 ± 0.11) with prolonged relative mean transit time (rMTT) and relative time-to-peak (rTTP) (1.27 ± 0.24, 1.07 ± 0.03) following occlusion. Hyperperfusion was observed in all rats at 48 and 72 hours postreperfusion, in 4/6 rats at 24 hours postreperfusion for IS-mediated DSC-PWI.

DATA CONCLUSION

IS may be an effective contrast agent for both MRA and DSC-PWI in ischemic stroke models.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1 J. Magn. Reson. Imaging 2020;52:836-849.

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

OBJECTIVE

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

MATERIAL AND METHODS

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

RESULTS

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

CONCLUSION

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

Modeling glymphatic system of the brain using MRI

The glymphatic system is functional waste clearance path from the brain parenchyma through dynamic exchange of cerebrospinal fluid (CSF) with interstitial fluid (ISF). Impairment of glymphatic waste clearance is involved in the development of neurodegenerative conditions. Despite many recent studies investigating the glymphatic system, few studies have tried to use a mathematical model to describe this system, quantitatively. In this study, we aim to model the glymphatic system from the kinetics of Gd-DTPA tracer measured using MRI in order to: 1) map the glymphatic system path, 2) derive kinetic parameters of the glymphatic system, and 3) provide quantitative maps of the structure and function of this system. In the proposed model, the brain is clustered to similar regions with respect to the profile of contrast agent (CA) density measured by MRI. Then, each region is described as a two-compartment kinetic model 'derived from' or 'clears to' its neighbors with local input function. We thus fit our model to the local cerebral regions rather than to the averaged time signal curve (TSC) of the whole brain. The estimated parameters showed distinctive differences between diabetes mellitus (DM) and control rats. The results suggest that in a typical DM brain the CSF bulk speed in the para-vasculature network is low. In addition, the resulting maps indicate that there may be increased binding and decreased absorbing of large molecules in a diabetic compared with a non-diabetic brain. The important contribution of this work was to fit the model to the local regions rather than to the averaged time signal curve (TSC) of the whole brain. This enabled us to derive quantitative maps of the glymphatic system from MRI.

Absolute quantitative MR perfusion and comparison against stable-isotope microspheres

PURPOSE

This work sought to compare a quantitative T₁ bookend dynamic susceptibility contrast MRI based perfusion protocol for absolute cerebral blood flow (qCBF) against CBF measured by the stable-isotope neutron capture microsphere method, a recognized reference standard for measuring tissue blood flow, at normocapnia, hypercapnia, and in acute stroke.

METHODS

CBF was measured in anesthetized female canines by MRI and microspheres over 2 consecutive days for each case. On day 1, 5 canines were measured before and during a physiological challenge induced by carbogen inhalation; on day 2, 4 canines were measured following permanent occlusion of the middle cerebral artery. CBF and cerebrovascular reactivity measured by MRI and microsphere deposition were compared.

RESULTS

MRI correlated strongly with microspheres at the hemispheric level for CBF during normo- and hypercapnic states (r² = 0.96), for individual cerebrovascular reactivity (r² = 0.84), and for postocclusion CBF (r² = 0.82). Correction for the delay and dispersion of the contrast bolus resulted in a significant improvement in the correlation between MRI and microsphere deposition in the ischemic state (r² = 0.96). In all comparisons, moderate correlations were found at the regional level.

CONCLUSION

In an experimental canine model with and without permanent occlusion of the middle cerebral artery, MRI-based qCBF yielded moderate to strong correlations for absolute quantitative CBF and cerebrovascular reactivity measurements during normocapnia and hypercapnia. Correction for delay and dispersion greatly improved the quantitation during occlusion of the middle cerebral artery, underscoring the importance for this correction under focal ischemic condition.

Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI

In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T₁, T₂ and T₂^∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T₁, T₂ and T₂^∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke

INTRODUCTION

Interpretation of the extent of perfusion deficits in stroke MRI is highly dependent on the method used for analyzing the perfusion-weighted signal intensity time-series after gadolinium injection. In this study, we introduce a new model-free standardized method of temporal similarity perfusion (TSP) mapping for perfusion deficit detection and test its ability and reliability in acute ischemia.

MATERIALS AND METHODS

Forty patients with an ischemic stroke or transient ischemic attack were included. Two blinded readers compared real-time generated interactive maps and automatically generated TSP maps to traditional TTP/MTT maps for presence of perfusion deficits. Lesion volumes were compared for volumetric inter-rater reliability, spatial concordance between perfusion deficits and healthy tissue and contrast-to-noise ratio (CNR).

RESULTS

Perfusion deficits were correctly detected in all patients with acute ischemia. Inter-rater reliability was higher for TSP when compared to TTP/MTT maps and there was a high similarity between the lesion volumes depicted on TSP and TTP/MTT (r(18) = 0.73). The Pearson's correlation between lesions calculated on TSP and traditional maps was high (r(18) = 0.73, p<0.0003), however the effective CNR was greater for TSP compared to TTP (352.3 vs 283.5, t(19) = 2.6, p<0.03.) and MTT (228.3, t(19) = 2.8, p<0.03).

DISCUSSION

TSP maps provide a reliable and robust model-free method for accurate perfusion deficit detection and improve lesion delineation compared to traditional methods. This simple method is also computationally faster and more easily automated than model-based methods. This method can potentially improve the speed and accuracy in perfusion deficit detection for acute stroke treatment and clinical trial inclusion decision-making.

Perfusion information extracted from resting state functional magnetic resonance imaging

It is widely known that blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) is an indirect measure for neuronal activations through neurovascular coupling. The BOLD signal is also influenced by many non-neuronal physiological fluctuations. In previous resting state (RS) fMRI studies, we have identified a moving systemic low frequency oscillation (sLFO) in BOLD signal and were able to track its passage through the brain. We hypothesized that this seemingly intrinsic signal moves with the blood, and therefore, its dynamic patterns represent cerebral blood flow. In this study, we tested this hypothesis by performing Dynamic Susceptibility Contrast (DSC) MRI scans (i.e. bolus tracking) following the RS scans on eight healthy subjects. The dynamic patterns of sLFO derived from RS data were compared with the bolus flow visually and quantitatively. We found that the flow of sLFO derived from RS fMRI does to a large extent represent the blood flow measured with DSC. The small differences, we hypothesize, are largely due to the difference between the methods in their sensitivity to different vessel types. We conclude that the flow of sLFO in RS visualized by our time delay method represents the blood flow in the capillaries and veins in the brain.

Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal Muscle

The purpose of this study was to assess the accuracy of fractional blood volume (v_b) estimates in low-perfused and low-vascularized tissue using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The results of different MRI methods were compared with histology to evaluate the accuracy of these methods under clinical conditions. v_b was estimated by DCE-MRI using a 3D gradient echo sequence with k-space undersampling in five muscle groups in the hind leg of 9 female pigs. Two gadolinium-based contrast agents (CA) were used: a rapidly extravasating, extracellular, gadolinium-based, low-molecular-weight contrast agent (LMCA, gadoterate meglumine) and an extracellular, gadolinium-based, albumin-binding, slowly extravasating blood pool contrast agent (BPCA, gadofosveset trisodium). LMCA data were evaluated using the extended Tofts model (ETM) and the two-compartment exchange model (2CXM). The images acquired with administration of the BPCA were used to evaluate the accuracy of v_b estimation with a bolus deconvolution technique (BD) and a method we call equilibrium MRI (EqMRI). The latter calculates the ratio of the magnitude of the relaxation rate change in the tissue curve at an approximate equilibrium state to the height of the same area of the arterial input function (AIF). Immunohistochemical staining with isolectin was used to label endothelium. A light microscope was used to estimate the fractional vascular area by relating the vascular region to the total tissue region (immunohistochemical vessel staining, IHVS). In addition, the percentage fraction of vascular volume was determined by multiplying the microvascular density (MVD) with the average estimated capillary lumen, π(d2)2, where d = 8μm is the assumed capillary diameter (microvascular density estimation, MVDE). Except for ETM values, highly significant correlations were found between most of the MRI methods investigated. In the cranial thigh, for example, the v_b medians (interquartile range, IQRs) of IHVS, MVDE, BD, EqMRI, 2CXM and ETM were v_b = 0.7(0.3)%, 1.1(0.4)%, 1.1(0.4)%, 1.4(0.3)%, 1.2(1.8)% and 0.1(0.2)%, respectively. Variances, expressed by the difference between third and first quartiles (IQR) were highest for the 2CXM for all muscle groups. High correlations between the values in four muscle groups—medial, cranial, lateral thigh and lower leg - estimated with MRI and histology were found between BD and EqMRI, MVDE and 2CXM and IHVS and ETM. Except for the ETM, no significant differences between the v_b medians of all MRI methods were revealed with the Wilcoxon rank sum test. The same holds for all muscle regions using the 2CXM and MVDE. Except for cranial thigh muscle, no significant difference was found between EqMRI and MVDE. And except for the cranial thigh and the lower leg muscle, there was also no significant difference between the v_b medians of BD and MVDE. Overall, there was good v_b agreement between histology and the BPCA MRI methods and the 2CXM LMCA approach with the exception of the ETM method. Although LMCA models have the advantage of providing excellent curve fits and can in principle determine more physiological parameters than BPCA methods, they yield more inaccurate results.

Quantitative measurement of cerebral blood volume using velocity-selective pulse trains

PURPOSE

To develop a non-contrast-enhanced MRI method for cerebral blood volume (CBV) mapping using velocity-selective (VS) pulse trains.

METHODS

The new pulse sequence applied velocity-sensitive gradient waveforms in the VS label modules and velocity-compensated ones in the control scans. Sensitivities to the gradient imperfections (e.g., eddy currents) were evaluated through phantom studies. CBV quantification procedures based on simulated labeling efficiencies for arteriolar, capillary, and venular blood as a function of cutoff velocity (Vc) are presented. Experiments were conducted on healthy volunteers at 3T to examine the effects of unbalanced diffusion weighting, cerebrospinal (CSF) contamination and variation of Vc.

RESULTS

Phantom results of the used VS pulse trains demonstrated robustness to eddy currents. The mean CBV values of gray matter and white matter for the experiments using Vc = 3.5 mm/s and velocity-compensated control with CSF-nulling were 5.1 ± 0.6 mL/100 g and 2.4 ± 0.2 mL/100 g, respectively, which were 23% and 32% lower than results from the experiment with velocity-insensitive control, corresponding to 29% and 25% lower in averaged temporal signal-to-noise ratio values.

CONCLUSION

A novel technique using VS pulse trains was demonstrated for CBV mapping. The results were both qualitatively and quantitatively close to those from existing methods. Magn Reson Med 77:92-101, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

A Simplified Model for Intravoxel Incoherent Motion Perfusion Imaging of the Brain

BACKGROUND AND PURPOSE

Despite a recent resurgence, intravoxel incoherent motion MRI faces practical challenges, including limited SNR and demanding acquisition and postprocessing requirements. A simplified approach using linear fitting of a subset of higher b-values has seen success in other organ systems. We sought to validate this method for evaluation of brain pathology by comparing perfusion measurements using simplified linear fitting to conventional biexponential fitting.

MATERIALS AND METHODS

Forty-nine patients with gliomas and 17 with acute strokes underwent 3T MRI, including DWI with 16 b-values (range, 0-900 s/mm²). Conventional intravoxel incoherent motion was performed using nonlinear fitting of the standard biexponential equation. Simplified intravoxel incoherent motion was performed using linear fitting of the log-normalized signal curves for subsets of b-values >200 s/mm². Comparisons between ROIs (tumors, strokes, contralateral brain) and between models (biexponential and simplified linear) were performed by using 2-way ANOVA. The root mean square error and coefficient of determination (R²) were computed for the simplified model, with biexponential fitting as the reference standard.

RESULTS

Perfusion maps using simplified linear fitting were qualitatively similar to conventional biexponential fitting. The perfusion fraction was elevated in high-grade (n = 33) compared to low-grade (n = 16) gliomas and was reduced in strokes compared to the contralateral brain (P < .001 for both main effects). Decreasing the number of b-values used for linear fitting resulted in reduced accuracy (higher root mean square error and lower R²) compared with full biexponential fitting.

CONCLUSIONS

Intravoxel incoherent motion perfusion imaging of common brain pathology can be performed by using simplified linear fitting, with preservation of clinically relevant perfusion information.

Optimization of two-compartment-exchange-model analysis for dynamic contrast-enhanced mri incorporating bolus arrival time

PURPOSE

To optimize the analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) under the two-compartment-exchange-model (2CXM) and to incorporate voxelwise bolus-arrival-time (BAT).

MATERIALS AND METHODS

The accuracy of the pharmacokinetic (PK) parameters, extracted from 3T DCE-MRI using 2CXM, was tested under several conditions: eight algorithms for data estimation; correction for BAT; using model selection; different temporal resolution and scan duration. Comparisons were performed on simulated data. The best algorithm was applied to seven patients with brain tumors or following stroke. The extracted perfusion parameters were compared to those of dynamic susceptibility contrast MRI (DSC-MRI).

RESULTS

ACoPeD (AIF-corrected-perfusion-DCE-MRI), an analysis using a 2^nd derivative regularized-spline and incorporating BAT, achieved the most accurate estimation in simulated data, mean-relative-error: F_p , F, v_p , v_e : 24.8%, 41.7%, 26.4%, 27.2% vs. 76.5%, 190.8%, 78.8%, 82.39% of the direct four parameters estimation (one-sided two-sample t-test, P < 0.001). Correction for BAT increased the estimation accuracy of the PK parameters by more than 30% and provided a supertemporal resolution estimation of the BAT (higher than the acquired resolution, mean-absolute-error 0.2 sec). High temporal resolution (∼2 sec) is required to avoid biased estimation of PK parameters, and long scan duration (∼20 min) is important for reliable permeability but not for perfusion estimations, mean-error-reduction: E: ∼12%, v_e : ∼6%. Using ACoPeD, PK values from normal-appearing white matter, gray matter, and lesion were extracted from patients. Preliminary results showed significant voxelwise correlations to DSC-MRI, between flow values in a patient following stroke (r = 0.49, P < 0.001), and blood volume in a patient with a brain tumor (r = 0.62, P < 0.001).

CONCLUSION

This study proposes an optimized analysis method, ACoPeD, for tissue perfusion and permeability estimation using DCE-MRI, to be used in clinical settings.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:237-249.

Resting myocardial blood flow quantification using contrast-enhanced magnetic resonance imaging in the presence of stenosis: A computational fluid dynamics study

PURPOSE

The extent to which atherosclerotic plaques affect contrast agent (CA) transport in the coronary arteries and, hence, quantification of myocardial blood flow (MBF) using magnetic resonance imaging (MRI) is unclear. The purpose of this work was to evaluate the influence of plaque induced stenosis both on CA transport and on the accuracy of MBF quantification.

METHODS

Computational fluid dynamics simulations in a high-detailed realistic vascular model were employed to investigate CA bolus transport in the coronary arteries. The impact of atherosclerosis was analyzed by inserting various medium- to high-grade stenoses in the vascular model. The influence of stenosis morphology was examined by varying the stenosis shapes but keeping the area reduction constant. Errors due to CA bolus transport were analyzed using the tracer-kinetic model MMID4.

RESULTS

Dispersion of the CA bolus was found in all models and for all outlets, but with a varying magnitude. The impact of stenosis was complex: while high-grade stenoses amplified dispersion, mild stenoses reduced the effect. Morphology was found to have a marked influence on dispersion for a small number of outlets in the post-stenotic region. Despite this marked influence on the concentration-time curves, MBF errors were less affected by stenosis. In total, MBF was underestimated by -7.9% to -44.9%.

CONCLUSIONS

The presented results reveal that local hemodynamics in the coronary vasculature appears to have a direct impact on CA bolus dispersion. Inclusion of atherosclerotic plaques resulted in a complex alteration of this effect, with both degree of area reduction and stenosis morphology affecting the amount of dispersion. This strong influence of vascular transport effects impairs the accuracy of MRI-based MBF quantification techniques and, potentially, other bolus-based perfusion measurement techniques like computed tomography perfusion imaging.

Validation of Perfusion Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using a Blood Pool Contrast Agent in Skeletal Swine Muscle

The purpose of our study was to validate perfusion quantification in a low-perfused tissue by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with shared k-space sampling using a blood pool contrast agent. Perfusion measurements were performed in a total of seven female pigs. An ultrasonic Doppler probe was attached to the right femoral artery to determine total flow in the hind leg musculature. The femoral artery was catheterized for continuous local administration of adenosine to increase blood flow up to four times the baseline level. Three different stable perfusion levels were induced. The MR protocol included a 3D gradient-echo sequence with a temporal resolution of approximately 1.5 seconds. Before each dynamic sequence, static MR images were acquired with flip angles of 5°, 10°, 20°, and 30°. Both static and dynamic images were used to generate relaxation rate and baseline magnetization maps with a flip angle method. 0.1 mL/kg body weight of blood pool contrast medium was injected via a central venous catheter at a flow rate of 5 mL/s. The right hind leg was segmented in 3D into medial, cranial, lateral, and pelvic thigh muscles, lower leg, bones, skin, and fat. The arterial input function (AIF) was measured in the aorta. Perfusion of the different anatomic regions was calculated using a one- and a two-compartment model with delay- and dispersion-corrected AIFs. The F-test for model comparison was used to decide whether to use the results of the one- or two-compartment model fit. Total flow was calculated by integrating volume-weighted perfusion values over the whole measured region. The resulting values of delay, dispersion, blood volume, mean transit time, and flow were all in physiologically and physically reasonable ranges. In 107 of 160 ROIs, the blood signal was separated, using a two-compartment model, into a capillary and an arteriolar signal contribution, decided by the F-test. Overall flow in hind leg muscles, as measured by the ultrasound probe, highly correlated with total flow determined by MRI, R = 0.89 and P = 10⁻⁷. Linear regression yielded a slope of 1.2 and a y-axis intercept of 259 mL/min. The mean total volume of the investigated muscle tissue corresponds to an offset perfusion of 4.7mL/(min ⋅ 100cm³). The DCE-MRI technique presented here uses a blood pool contrast medium in combination with a two-compartment tracer kinetic model and allows absolute quantification of low-perfused non-cerebral organs such as muscles.

Simulation of contrast agent transport in arteries with multilayer arterial wall: impact of arterial transmural transport on the bolus delay and dispersion

One assumption of DSC-MRI is that the injected contrast agent is kept totally intravascular and the arterial wall is impermeable to contrast agent. The assumption is unreal for such small contrast agent as Gd-DTPA can leak into the arterial wall. To investigate whether the unreal assumption is valid for the estimation of the delay and dispersion of the contrast agent bolus, we simulated flow and Gd-DTPA transport in a model with multilayer arterial wall and analyzed the bolus delay and dispersion qualified by mean vascular transit time (MVTT) and the variance of the vascular transport function. Factors that may affect Gd-DTPA transport hence the delay and dispersion were further investigated, such as integrity of endothelium and disturbed flow. The results revealed that arterial transmural transport would slightly affect MVTT and moderately increase the variance. In addition, although the integrity of endothelium can significantly affect the accumulation of contrast agent in the arterial wall, it had small effects on the bolus delay and dispersion. However, the disturbed flow would significantly increase both MVTT and the variance. In conclusion, arterial transmural transport may have a small effect on the bolus delay and dispersion when compared to the flow pattern in the artery.

Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques

Perfusion is a fundamental biological function that refers to the delivery of oxygen and nutrients to tissue by means of blood flow. Perfusion MRI is sensitive to microvasculature and has been applied in a wide variety of clinical applications, including the classification of tumors, identification of stroke regions, and characterization of other diseases. Perfusion MRI techniques are classified with or without using an exogenous contrast agent. Bolus methods, with injections of a contrast agent, provide better sensitivity with higher spatial resolution, and are therefore more widely used in clinical applications. However, arterial spin-labeling methods provide a unique opportunity to measure cerebral blood flow without requiring an exogenous contrast agent and have better accuracy for quantification. Importantly, MRI-based perfusion measurements are minimally invasive overall, and do not use any radiation and radioisotopes. In this review, we describe the principles and techniques of perfusion MRI. This review summarizes comprehensive updated knowledge on the physical principles and techniques of perfusion MRI.

Feasibility of Single-Input Tracer Kinetic Modeling with Continuous-Time Formalism in Liver 4-Phase Dynamic Contrast-Enhanced CT

The modeling of tracer kinetics with use of low-temporal-resolution data is of central importance for patient dose reduction in dynamic contrast-enhanced CT (DCE-CT) study. Tracer kinetic models of the liver vary according to the physiologic assumptions imposed on the model, and they can substantially differ in the ways how the input for blood supply and tissue compartments are modeled. In this study, single-input flow-limited (FL), Tofts-Kety (TK), extended TK (ETK), Hayton-Brady (HB), two compartment exchange (2CX), and adiabatic approximation to the tissue homogeneity (AATH) models were applied to the analysis of liver 4-phase DCE-CT data with fully continuous-time parameter formulation, including the bolus arrival time. The bolus arrival time for the 2CX and AATH models was described by modifying the vascular transport operator theory. Initial results indicate that single-input tracer kinetic modeling is feasible for distinguishing between hepatocellular carcinoma and normal liver parenchyma.

Modeling and correction of bolus dispersion effects in dynamic susceptibility contrast MRI

PURPOSE

Bolus dispersion in DSC-MRI can lead to errors in cerebral blood flow (CBF) estimation by up to 70% when using singular value decomposition analysis. However, it might be possible to correct for dispersion using two alternative methods: the vascular model (VM) and control point interpolation (CPI). Additionally, these approaches potentially provide a means to quantify the microvascular residue function.

METHODS

VM and CPI were extended to correct for dispersion by means of a vascular transport function. Simulations were performed at multiple dispersion levels and an in vivo analysis was performed on a healthy subject and two patients with carotid atherosclerotic disease.

RESULTS

Simulations showed that methods that could not address dispersion tended to underestimate CBF (ratio in CBF estimation, CBFratio = 0.57-0.77) in the presence of dispersion; whereas modified CPI showed the best performance at low-to-medium dispersion; CBFratio = 0.99 and 0.81, respectively. The in vivo data showed trends in CBF estimation and residue function that were consistent with the predictions from simulations.

CONCLUSION

In patients with atherosclerotic disease the estimated residue function showed considerable differences in the ipsilateral hemisphere. These differences could partly be attributed to dispersive effects arising from the stenosis when dispersion corrected CPI was used. It is thus beneficial to correct for dispersion in perfusion analysis using this method.

Arterial input function in perfusion MRI: a comprehensive review

Cerebral perfusion, also referred to as cerebral blood flow (CBF), is one of the most important parameters related to brain physiology and function. The technique of dynamic-susceptibility contrast (DSC) MRI is currently the most commonly used MRI method to measure perfusion. It relies on the intravenous injection of a contrast agent and the rapid measurement of the transient signal changes during the passage of the bolus through the brain. Central to quantification of CBF using this technique is the so-called arterial input function (AIF), which describes the contrast agent input to the tissue of interest. Due to its fundamental role, there has been a lot of progress in recent years regarding how and where to measure the AIF, how it influences DSC-MRI quantification, what artefacts one should avoid, and the design of automatic methods to measure the AIF. The AIF is also directly linked to most of the major sources of artefacts in CBF quantification, including partial volume effect, bolus delay and dispersion, peak truncation effects, contrast agent non-linearity, etc. While there have been a number of good review articles on DSC-MRI over the years, these are often comprehensive but, by necessity, with limited in-depth discussion of the various topics covered. This review article covers in greater depth the issues associated with the AIF and their implications for perfusion quantification using DSC-MRI.

The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI

Dynamic susceptibility contrast (DSC) MRI is the most commonly used MRI method to assess cerebral perfusion and other related haemodynamic parameters. Although the technique is well established and used routinely in clinical centres, there are still many problems that impede accurate perfusion quantification. In this review article, we present 39 steps which guide the reader through the theoretical principles, practical decisions, potential problems, current limitations and latest advances in DSC-MRI. The 39 steps span the collection, analysis and interpretation of DSC-MRI data, expounding issues and possibilities relating to the contrast agent, the acquisition of DSC-MRI data, data pre-processing, the contrast concentration-time course, the arterial input function, deconvolution, common perfusion parameters, post-processing possibilities, patient studies, absolute versus relative quantification and automated analysis methods.

Hemodynamic segmentation of brain perfusion images with delay and dispersion effects using an expectation-maximization algorithm

Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.

Computational fluid dynamics simulations of contrast agent bolus dispersion in a coronary bifurcation: impact on MRI-based quantification of myocardial perfusion

Contrast-enhanced first-pass magnetic resonance imaging (MRI) in combination with a tracer kinetic model, for example, MMID4, can be used to determine myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Typically, the arterial input function (AIF) required for this methodology is estimated from the left ventricle (LV). Dispersion of the contrast agent bolus might occur between the LV and the myocardial tissue. Negligence of bolus dispersion could cause an error in MBF determination. The aim of this study was to investigate the influence of bolus dispersion in a simplified coronary bifurcation geometry including one healthy and one stenotic branch on the quantification of MBF and MPR. Computational fluid dynamics (CFD) simulations were combined with MMID4. Different inlet boundary conditions describing pulsatile and constant flows for rest and hyperemia and differing outflow conditions have been investigated. In the bifurcation region, the increase of the dispersion was smaller than inside the straight vessels. A systematic underestimation of MBF values up to -16.1% for pulsatile flow and an overestimation of MPR up to 7.5% were found. It was shown that, under the conditions considered in this study, bolus dispersion can significantly influence the results of quantitative myocardial MR-perfusion measurements.

Validating a local Arterial Input Function method for improved perfusion quantification in stroke

In bolus-tracking perfusion magnetic resonance imaging (MRI), temporal dispersion of the contrast bolus due to stenosis or collateral supply presents a significant problem for accurate perfusion quantification in stroke. One means to reduce the associated perfusion errors is to deconvolve the bolus concentration time-course data with local Arterial Input Functions (AIFs) measured close to the capillary bed and downstream of the arterial abnormalities causing dispersion. Because the MRI voxel resolution precludes direct local AIF measurements, they must be extrapolated from the surrounding data. To date, there have been no published studies directly validating these local AIFs. We assess the effectiveness of local AIFs in reducing dispersion-induced perfusion error by measuring the residual dispersion remaining in the local AIF deconvolved perfusion maps. Two approaches to locating the local AIF voxels are assessed and compared with a global AIF deconvolution across 19 bolus-tracking data sets from patients with stroke. The local AIF methods reduced dispersion in the majority of data sets, suggesting more accurate perfusion quantification. Importantly, the validation inherently identifies potential areas for perfusion underestimation. This is valuable information for the identification of at-risk tissue and management of stroke patients.

Perfusion MRI using dynamic-susceptibility contrast MRI: quantification issues in patient studies

OBJECTIVES

Measurement of perfusion accurately, noninvasively, and with good spatial resolution offers the chance to characterize abnormal tissue in many clinical conditions. Dynamic-susceptibility contrast (DSC) MRI, also known as bolus-tracking MRI, is a dynamic MRI method to measure perfusion and other related hemodynamic parameters. This review article describes the principles involved in perfusion quantification using DSC-MRI as well as discusses the main issues affecting its quantification in patient studies.

CONCLUSIONS

It is shown that DSC-MRI is a very powerful technique that provides important information regarding cerebral hemodynamics. The relatively high contrast-to-noise ratio, fast acquisition, and wealth of information available have made DSC-MRI the most commonly used MRI technique for the rapid assessment of the brain hemodynamics in clinical investigations. While very important advances have been achieved in the last 2 decades, there are still some remaining limitations that users should be aware of to avoid misinterpretation of the findings and to make the most of the invaluable information provided by perfusion MRI.

Acute stroke thrombolysis: time to dispense with the clock and move to tissue-based decision making?

Currently, imaging is predominantly used to exclude patients for thrombolysis, rather than identify patients most likely to benefit. This means that patients are being selected for treatment without reference to tissue pathophysiology. Imaging of specific stroke pathophysiology may be the key to selecting patients most likely to benefit from thrombolysis, and could revolutionize acute stroke assessment and treatment. The technology is available to identify the acute infarct core and possibly the penumbra, via magnetic resonance diffusion-weighted imaging, and both magnetic resonance- and computed tomography-perfusion imaging techniques. However, these modalities require fine tuning before they can be reliably implemented in a routine clinical setting.

Quantitative myocardial perfusion magnetic resonance imaging: the impact of pulsatile flow on contrast agent bolus dispersion

Myocardial blood flow (MBF) can be quantified using T1-weighted first-pass magnetic resonance imaging (MRI) in combination with a tracer-kinetic model, like MMID4. This procedure requires the knowledge of an arterial input function which is usually estimated from the left ventricle (LV). Dispersion of the contrast agent bolus may occur between the LV and the tissue of interest. The aim of this study was to investigate the dispersion under conditions of physiological pulsatile blood flow, and to simulate its effect on MBF quantification. The dispersion was simulated in coronary arteries using a computational fluid dynamics (CFD) approach. Simulations were accomplished on straight vessels with stenosis of different degrees and shapes. The results show that dispersion is more pronounced under resting conditions than during hyperemia. Stenosis leads to a reduction of dispersion. In consequence, dispersion results in a systematic MBF underestimation between -0.4% and -9.3%. The relative MBF error depends not only on the dispersion but also on the actual MBF itself. Since MBF under rest is more underestimated than under stress, myocardial perfusion reserve is overestimated between 0.1% and 4.5%. Considering other sources of errors in myocardial perfusion MRI, systematic errors of MBF by bolus dispersion are relatively small.

Phenylephrine-modulated cardiopulmonary blood flow measured with use of X-ray digital subtraction angiography

INTRODUCTION

Cardiopulmonary blood flow is an important indicator of organ function. Limitations in measuring blood flow in live rodents suggest that rapid physiological changes may be overlooked. For instance, relative measurements limit imaging to whole organs or large sections without adequately visualizing vasculature. Additionally, current methods use small samples and invasive techniques that often require killing animals, limiting sampling speed, or both. A recently developed high spatial- and temporal-resolution X-ray digital subtraction angiography (DSA) system visualizes vasculature and measures blood flow in rodents. This study was the first to use this system to measure changes in cardiopulmonary blood flow in rats after administering the vasoconstrictor phenylephrine.

METHODS

Cardiopulmonary blood flow and vascular anatomy were assessed in 11 rats before, during, and after recovery from phenylephrine. After acquiring DSA images at 12 time points, a calibrated non-parametric deconvolution technique using singular value decomposition (SVD) was applied to calculate quantitative aortic blood flow in absolute metrics (mL/min). Trans-pulmonary transit time was calculated as the time interval between maximum signal enhancement in the pulmonary trunk and aorta. Pulmonary blood volume was calculated based on the central volume principle. Statistical analysis compared differences in trans-pulmonary blood volume and pressure, and aortic diameter using paired t-tests on baseline, peak, and late-recovery time points.

RESULTS

Phenylephrine had dramatic qualitative and quantitative effects on vascular anatomy and blood flow. Major vessels distended significantly (aorta, ~1.2-times baseline) and mean arterial blood pressure increased ~2 times. Pulmonary blood volume, flow, pressure, and aortic diameter were not significantly different between baseline and late recovery, but differences were significant between baseline and peak, as well as peak and recovery time points.

DISCUSSION

The DSA system with calibrated SVD technique acquired blood flow measurements every 30s with a high level of regional specificity, thus providing a new option for in vivo functional assessment in small animals.

Toward local arterial input functions in dynamic contrast-enhanced MRI

PURPOSE

To present a method for estimating the local arterial input function (AIF) within a dynamic contrast-enhanced MRI scan, based on the alternating minimization with model (AMM) method.

MATERIALS AND METHODS

This method clusters a subset of data into representative curves, which are then input to the AMM algorithm to return a parameterized AIF and pharmacokinetic parameters. Computer simulations are used to investigate the accuracy with which the AMM is able to estimate the true AIF as a function of the input tissue curves.

RESULTS

Simulations show that a power law relates uncertainty in kinetic parameters and SNR and heterogeneity of the input. Kinetic parameters calculated with the measured AIF are significantly different from those calculated with either a global (P < 0.005) or a local input function (P = 0.0). The use of local AIFs instead of measured AIFs yield mean lesion-averaged parameter changes: K(trans): +24% [+15%, +70%], k(ep): +13% [-36%, +300%]. Globally estimated input functions yield mean lesion-averaged changes: K(trans): +9% [-38%, +65%], k(ep): +13% [-100%, +400%]. The observed improvement in fit quality with local AIFs was found to be significant when additional free parameters were accounted for using the Akaike information criterion.

CONCLUSION

Local AIFs result in significantly different kinetic parameter values. The statistically significant improvement in fit quality suggests that changes in parameter estimates using local AIFs reflect differences in underlying tissue physiology.

Quantitative cerebral blood flow in dynamic susceptibility contrast MRI using total cerebral flow from phase contrast magnetic resonance angiography

Dynamic susceptibility contrast magnetic resonance imaging during bolus injection of gadolinium contrast agent is commonly used to investigate cerebral hemodynamics. The large majority of clinical applications of dynamic susceptibility contrast magnetic resonance imaging to date have reported relative cerebral blood flow values because of dependence of the result on the accuracy of determining the arterial input function, the robustness of the singular value decomposition algorithm, and others. We propose a calibration approach that directly measures the total (i.e., whole brain) cerebral blood flow in individual subjects using phase contrast magnetic resonance angiography. The method was applied to data from 11 patients with intracranial pathology. The sum of squares variance about the mean (uncorrected: white matter = 105.6, gray matter = 472.2; corrected: white matter = 34.1, gray matter = 99.8) after correction was significantly lower for white matter (P = 0.045) and for gray matter (P = 0.011). However, the mean gray and white matter cerebral blood flow in the contralateral hemisphere were not significantly altered by the correction. The proposed phase contrast magnetic resonance angiography calibration technique appears to be one of the most direct correction schemes available for dynamic susceptibility contrast magnetic resonance imaging cerebral blood flow values and can be performed rapidly, requiring only a few minutes of additional scan time.

Dynamic susceptibility contrast MRI with localized arterial input functions

Compared to gold-standard measurements of cerebral perfusion with positron emission tomography using H(2)[(15)O] tracers, measurements with dynamic susceptibility contrast MR are more accessible, less expensive, and less invasive. However, existing methods for analyzing and interpreting data from dynamic susceptibility contrast MR have characteristic disadvantages that include sensitivity to incorrectly modeled delay and dispersion in a single, global arterial input function. We describe a model of tissue microcirculation derived from tracer kinetics that estimates for each voxel a unique, localized arterial input function. Parameters of the model were estimated using Bayesian probability theory and Markov-chain Monte Carlo, circumventing difficulties arising from numerical deconvolution. Applying the new method to imaging studies from a cohort of 14 patients with chronic, atherosclerotic, occlusive disease showed strong correlations between perfusion measured by dynamic susceptibility contrast MR with localized arterial input function and perfusion measured by quantitative positron emission tomography with H(2)[(15)O]. Regression to positron emission tomography measurements enabled conversion of dynamic susceptibility contrast MR to a physiologic scale. Regression analysis for localized arterial input function gave estimates of a scaling factor for quantitation that described perfusion accurately in patients with substantial variability in hemodynamic impairment.

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results

Accurate quantification of pharmacokinetic model parameters in tracer kinetic imaging experiments requires correspondingly accurate determination of the arterial input function (AIF). Despite significant effort expended on methods of directly measuring patient-specific AIFs in modalities as diverse as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), dynamic positron emission tomography (PET), and perfusion computed tomography (CT), fundamental and technical difficulties have made consistent and reliable achievement of that goal elusive. Here, we validate a new algorithm for AIF determination, the Monte Carlo blind estimation (MCBE) method (which is described in detail and characterized by extensive simulations in a companion paper), by comparing AIFs measured in DCE-MRI studies of eight brain tumor patients with results of blind estimation. Blind AIFs calculated with the MCBE method using a pool of concentration-time curves from a region of normal brain tissue were found to be quite similar to the measured AIFs, with statistically significant decreases in fit residuals observed in six of eight patients. Biases between the blind and measured pharmacokinetic parameters were the dominant source of error. Averaged over all eight patients, the mean biases were +7% in K(trans), 0% in k(ep), -11% in v(p) and +10% in v(e). Corresponding uncertainties (median absolute deviation from the best fit line) were 0.0043 min(-1) in K(trans), 0.0491 min(-1) in k(ep), 0.29% in v(p) and 0.45% in v(e). The use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (-23% in K(trans), -22% in k(ep), -63% in v(p)), with the bias in v(e) unchanged, and led to larger uncertainties in all four parameters (0.0083 min(-1) in K(trans), 0.1038 min(-1) in k(ep), 0.31% in v(p) and 0.95% in v(e)). When blind AIFs were calculated from a region of tumor tissue, statistically significant decreases in fit residuals were observed in all eight patients despite larger deviations of these blind AIFs from the measured AIFs. The observed decrease in root-mean-square fit residuals between the normal brain and tumor tissue blind AIFs suggests that the local blood supply in tumors is measurably different from that in normal brain tissue and that the proposed method is able to discriminate between the two. We have shown the feasibility of applying the MCBE algorithm to DCE-MRI data acquired in brain, finding generally good agreement with measured AIFs and decreased biases and uncertainties relative to the use of a population-averaged AIF. These results demonstrate that the MCBE algorithm is a useful alternative to direct AIF measurement in cases where acquisition of high-quality arterial input function data is difficult or impossible.

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: I. Simulations

Widespread adoption of quantitative pharmacokinetic modeling methods in conjunction with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has led to increased recognition of the importance of obtaining accurate patient-specific arterial input function (AIF) measurements. Ideally, DCE-MRI studies use an AIF directly measured in an artery local to the tissue of interest, along with measured tissue concentration curves, to quantitatively determine pharmacokinetic parameters. However, the numerous technical and practical difficulties associated with AIF measurement have made the use of population-averaged AIF data a popular, if sub-optimal, alternative to AIF measurement. In this work, we present and characterize a new algorithm for determining the AIF solely from the measured tissue concentration curves. This Monte Carlo blind estimation (MCBE) algorithm estimates the AIF from the subsets of D concentration-time curves drawn from a larger pool of M candidate curves via nonlinear optimization, doing so for multiple (Q) subsets and statistically averaging these repeated estimates. The MCBE algorithm can be viewed as a generalization of previously published methods that employ clustering of concentration-time curves and only estimate the AIF once. Extensive computer simulations were performed over physiologically and experimentally realistic ranges of imaging and tissue parameters, and the impact of choosing different values of D and Q was investigated. We found the algorithm to be robust, computationally efficient and capable of accurately estimating the AIF even for relatively high noise levels, long sampling intervals and low diversity of tissue curves. With the incorporation of bootstrapping initialization, we further demonstrated the ability to blindly estimate AIFs that deviate substantially in shape from the population-averaged initial guess. Pharmacokinetic parameter estimates for K(trans), k(ep), v(p) and v(e) all showed relative biases and uncertainties of less than 10% for measurements having a temporal sampling rate of 4 s and a concentration measurement noise level of sigma = 0.04 mM. A companion paper discusses the application of the MCBE algorithm to DCE-MRI data acquired in eight patients with malignant brain tumors.

Determination of the venous output function from MR signal phase: feasibility for quantitative DCE-MRI in human brain

For dynamic contrast-enhanced MRI studies in the human brain, it is useful to measure the venous output function (VOF). The purpose of this work was to explore the feasibility of measuring the VOF using the MR signal phase (in absolute units of gadolinium concentration) in the superior sagittal sinus. Phantom experiments were performed to validate the technique for different superior sagittal sinus angles (theta = 0-48 degrees relative to the main magnetic field), different curvatures (straight or radius = 45 mm), and different spatial resolutions (2.2-5.5 mm, to study partial-volume effects). Additionally, the technique was tested on three patients. The phantom experimental results (echo time = 5.5 ms, theta <or= 21 degrees) agreed with theoretical predictions to within 10%. For the patient studies, the measured VOFs had reasonable amplitude and shape characteristics and the patients' superior sagittal sinus angles (<15 degrees) and curvatures (radii approximately 40 mm) were within the range explored with phantoms. Our results suggest that partial-volume contributions to the VOF will be <5% and that the VOF can be evaluated in vivo to within 10% error. In conclusion, it is highly feasible to use MR signal phase to measure the VOF in the superior sagittal sinus for human dynamic contrast-enhanced MRI.