Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters.

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Multi-band MR fingerprinting (MRF) ASL imaging using artificial-neural-network trained with high-fidelity experimental data.

PURPOSE: We aim to leverage the power of deep-learning with high-fidelity training data to improve the reliability and processing speed of hemodynamic mapping with MR fingerprinting (MRF) arterial spin labeling (ASL). METHODS: A total of 15 healthy subjects were studied on a 3T MRI. Each subject underwent 10 runs of a multi-band multi-slice MRF-ASL sequence for a total scan time of approximately 40 min. MRF-ASL images were averaged across runs to yield a set of high-fidelity data. Training of a fully connected artificial neural network (ANN) was then performed using these data. The results from ANN were compared to those of dictionary matching (DM), ANN trained with single-run experimental data and with simulation data. Initial clinical performance of the technique was also demonstrated in a Moyamoya patient. RESULTS: The use of ANN reduced the processing time of MRF-ASL data to 3.6 s, compared to DM of 3 h 12 min. Parametric values obtained with ANN and DM were strongly correlated (R2 between 0.84 and 0.96). Results obtained from high-fidelity ANN were substantially more reliable compared to those from DM or single-run ANN. Voxel-wise coefficient of variation (CoV) of high-fidelity ANN, DM, and single-run ANN was 0.15 ± 0.08, 0.41 ± 0.20, 0.30 ± 0.16, respectively, for cerebral blood flow and 0.11 ± 0.06, 0.20 ± 0.19, 0.15 ± 0.10, respectively, for bolus arrival time. In vivo data trained ANN also outperformed ANN trained with simulation data. The superior performance afforded by ANN allowed more conspicuous depiction of hemodynamic abnormalities in Moyamoya patient. CONCLUSION: Deep-learning-based parametric reconstruction improves the reliability of MRF-ASL hemodynamic maps and reduces processing time.

Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer.

BACKGROUND: Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. METHODS: Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values < 0.05 were considered statistically significant. RESULTS: Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P < 0.001 and P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P < 0.001). CONCLUSIONS: Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

Improved sensitivity and temporal resolution in perfusion FMRI using velocity selective inversion ASL.

PURPOSE: This work aims to investigate the utility of velocity selective inversion pulses for perfusion weighted functional MRI. METHODS: Tracer kinetic properties of velocity selective inversion (VSI) pulses as an input function for an arterial spin labeling (ASL) experiment were characterized in a group of healthy participants. Numerical simulations were conducted to search for a robust set of timing parameters for FMRI time series acquisition with maximal signal to noise ratio efficiency. The performance of three VSI pulse sequences with different timing parameters was compared with a pseudocontinuous ASL sequence in a simple FMRI experiment conducted on healthy participants. RESULTS: The fit to the tracer kinetic model yielded arterial CBV of 1.24% ± 0.52% and 0.45 ± 0.11% and perfusion rates of 60.8 ± 32.3 and 34.4 ± 5.4 mL/min/100 g for gray and white matter, respectively. Bolus arrival times were estimated as 75.7 ± 21 ms and 349 ± 78 ms for gray and white matter, respectively. The FMRI experiments showed that VSI pulses yield comparable sensitivity to PCASL with similar timing parameters (TR = 4 s). However, VSI pulses could be used at a faster acquisition speed (TR = 3 s) and were more sensitive to neuronal activity than PCASL pulses, as evidenced by the 31% higher Z scores obtained on average in the active regions. CONCLUSION: VSI pulses can be very beneficial for perfusion weighted functional MRI because of their tracer kinetic characteristics, which allow a faster acquisition rate while maintaining an efficient labeling input function.

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

PURPOSE: To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver. METHODS: We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time. RESULTS: Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters. CONCLUSIONS: Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Multiparametric imaging of brain hemodynamics and function using gas-inhalation MRI.

Diagnosis and treatment monitoring of cerebrovascular diseases routinely require hemodynamic imaging of the brain. Current methods either only provide part of the desired information or require the injection of multiple exogenous agents. In this study, we developed a multiparametric imaging scheme for the imaging of brain hemodynamics and function using gas-inhalation MRI. The proposed technique uses a single MRI scan to provide simultaneous measurements of baseline venous cerebral blood volume (vCBV), cerebrovascular reactivity (CVR), bolus arrival time (BAT), and resting-state functional connectivity (fcMRI). This was achieved with a novel, concomitant O2 and CO2 gas inhalation paradigm, rapid MRI image acquisition with a 9.3min BOLD sequence, and an advanced algorithm to extract multiple hemodynamic information from the same dataset. In healthy subjects, CVR and vCBV values were 0.23±0.03%/mmHg and 0.0056±0.0006%/mmHg, respectively, with a strong correlation (r=0.96 for CVR and r=0.91 for vCBV) with more conventional, separate acquisitions that take twice the scan time. In patients with Moyamoya syndrome, CVR in the stenosis-affected flow territories (typically anterior-cerebral-artery, ACA, and middle-cerebral-artery, MCA, territories) was significantly lower than that in posterior-cerebral-artery (PCA), which typically has minimal stenosis, flow territories (0.12±0.06%/mmHg vs. 0.21±0.05%/mmHg, p<0.001). BAT of the gas bolus was significantly longer (p=0.008) in ACA/MCA territories, compared to PCA, and the maps were consistent with the conventional contrast-enhanced CT perfusion method. FcMRI networks were robustly identified from the gas-inhalation MRI data after factoring out the influence of CO2 and O2 on the signal time course. The spatial correspondence between the gas-data-derived fcMRI maps and those using a separate, conventional fcMRI scan was excellent, showing a spatial correlation of 0.58±0.17 and 0.64±0.20 for default mode network and primary visual network, respectively. These findings suggest that advanced gas-inhalation MRI provides reliable measurements of multiple hemodynamic parameters within a clinically acceptable imaging time and is suitable for patient examinations.

Detectability of white matter cerebral blood flow using arterial spin labeling MRI in patients with sickle cell disease: Relevance of flow territory, bolus arrival time, and hematocrit.

Sickle cell disease (SCD) is the most common genetic blood disorder, characterized by red cell hemolysis, anemia, and corresponding increased compensatory cerebral blood flow (CBF). SCD patients are at high risk for cerebral infarcts and CBF quantification is likely critical to assess infarct risk. Infarcts primarily localize to white matter (WM), yet arterial spin labeling (ASL) MRI, the most common non-invasive CBF approach, has poor WM CBF sensitivity owing to low WM CBF and long WM bolus arrival time (BAT). We hypothesize that anemia, and associated cerebral hyperemia, in SCD leads to improved WM detection with ASL. We performed 3-Tesla multi-delay pulsed ASL in SCD (n = 35; age = 30.5 ± 8.3 years) and control (n = 15; age = 28.7 ± 4.5 years) participants and applied t-tests at each inversion time within different flow territories, and determined which regions were significantly above noise floor (criteria: one-sided p < 0.05). Total WM CBF-weighted signal was primarily detectable outside of borderzone regions in SCD (CBF = 17.7 [range = 12.9-25.0] mL/100 g/min), but was largely unphysiological in control (CBF = 8.1 [range = 7.6-9.9)] mL/100 g/min) participants. WM BAT was reduced in SCD versus control participants (ΔBAT = 37 [range = 46-70] ms) and BAT directly correlated with hematocrit (Spearman's-ρ = 0.62; p < 0.001). Findings support the feasibility of WM CBF quantification using ASL in SCD participants for appropriately parameterized protocols.

Early perfusion changes in multiple sclerosis patients as assessed by MRI using arterial spin labeling.

BACKGROUND: Gadolinium-perfusion magnetic resonance (MR) identifies gray matter abnormalities in early multiple sclerosis (MS), even in the absence of structural differences. These perfusion changes could be related to the cognitive disability of these patients, especially in the working memory. Arterial spin labeling (ASL) is a relatively recent perfusion technique that does not require intravenous contrast, making the technique especially attractive for clinical research. PURPOSE: To verify the perfusion alterations in early MS, even in the absence of cerebral volume changes. To introduce the ASL sequence as a suitable non-invasive method in the monitoring of these patients. MATERIAL AND METHODS: Nineteen healthy controls and 28 patients were included. The neuropsychological test EDSS and SDMT were evaluated. Cerebral blood flow and bolus arrival time were collected from the ASL study. Cerebral volume and cortical thickness were obtained from the volumetric T1 sequence. Spearman's correlation analyzed the correlation between EDSS and SDMT tests and perfusion data. Differences were considered significant at a level of P < 0.05. RESULTS: Reduction of the cerebral blood flow and an increase in the bolus arrival time were found in patients compared to controls. A negative correlation between EDSS and thalamus transit time, and between EDSS and cerebral blood flow in the frontal cortex, was found. CONCLUSION: ASL perfusion might detect changes in MS patients even in absent structural volumetric changes. More longitudinal studies are needed, but perfusion parameters could be biomarkers for monitoring these patients.

Cerebral Hemodynamic and White Matter Changes of Type 2 Diabetes Revealed by Multi-TI Arterial Spin Labeling and Double Inversion Recovery Sequence.

Diabetes has been reported to affect the microvasculature and lead to cerebral small vessel disease (SVD). Past studies using arterial spin labeling (ASL) at single post-labeling delay reported reduced cerebral blood flow (CBF) in patients with type 2 diabetes. The purpose of this study was to characterize cerebral hemodynamic changes of type 2 diabetes using a multi-inversion-time 3D GRASE pulsed ASL (PASL) sequence to simultaneously measure CBF and bolus arrival time (BAT). Thirty-six patients with type 2 diabetes (43-71 years, 17 male) and 36 gender- and age-matched control subjects underwent MRI scans at 3 T. Mean CBF/BAT values were computed for gray and white matter (GM and WM) of each subject, while a voxel-wise analysis was performed for comparison of regional CBF and BAT between the two groups. In addition, white matter hyperintensities (WMHs) were detected by a double inversion recovery (DIR) sequence with relatively high sensitivity and spatial resolution. Mean CBF of the WM, but not GM, of the diabetes group was significantly lower than that of the control group (p < 0.0001). Regional CBF decreases were detected in the left middle occipital gyrus (p = 0.0075), but failed to reach significance after correction of partial volume effects. BAT increases were observed in the right calcarine fissure (p < 0.0001), left middle occipital gyrus (p < 0.0001), and right middle occipital gyrus (p = 0.0011). Within the group of diabetic patients, BAT in the right middle occipital gyrus was positively correlated with the disease duration (r = 0.501, p = 0.002), BAT in the left middle occipital gyrus was negatively correlated with the binocular visual acuity (r = -0.408, p = 0.014). Diabetic patients also had more WMHs than the control group (p = 0.0039). Significant differences in CBF, BAT, and more WMHs were observed in patients with diabetes, which may be related to impaired vision and risk of SVD of type 2 diabetes.

DUSTER: dynamic contrast enhance up-sampled temporal resolution analysis method.

Dynamic contrast enhanced (DCE) MRI using Tofts' model for estimating vascular permeability is widely accepted, yet inter-tissue differences in bolus arrival time (BAT) are generally ignored. In this work we propose a method, incorporating the BAT in the analysis, demonstrating its applicability and advantages in healthy subjects and patients. A method for DCE Up Sampled TEmporal Resolution (DUSTER) analysis is proposed which includes: baseline T1 map using DESPOT1 analyzed with flip angle (FA) correction; preprocessing; raw-signal-to-T1-to-concentration time curves (CTC) conversion; automatic arterial input function (AIF) extraction at temporal super-resolution; model fitting with model selection while incorporating BAT in the pharmacokinetic (PK) model, and fits contrast agent CTC while using exhaustive search in the BAT dimension in super-resolution. The method was applied to simulated data and to human data from 17 healthy subjects, six patients with glioblastoma, and two patients following stroke. BAT values were compared to time-to-peak (TTP) values extracted from dynamic susceptibility contrast imaging. Results show that the method improved the AIF estimation and allowed extraction of the BAT with a resolution of 0.8 s. In simulations, lower mean relative errors were detected for all PK parameters extracted using DUSTER compared to analysis without BAT correction (vp:5% vs. 20%, Ktrans: 9% vs. 24% and Kep: 8% vs. 17%, respectively), and BAT estimates demonstrated high correlations (r = 0.94, p < 1e− 10) with true values. In real data, high correlations between BAT values were detected when extracted from data acquired with high temporal resolution (2 s) and sub-sampled standard resolution data (6 s) (mean r = 0.85,p < 1e− 10). BAT and TTP values were significantly correlated in the different brain regions in healthy subjects (mean r = 0.72,p = < 1e− 3), as were voxel-wise comparisons in patients (mean r = 0.89, p < 1e− 10). In conclusion, incorporating BAT in DCE analysis improves estimation accuracy for the AIF and the PK parameters while providing an additional clinically important parameter.

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.

Improving perfusion quantification in arterial spin labeling for delayed arrival times by using optimized acquisition schemes.

OBJECTIVE: The improvement in Arterial Spin Labeling (ASL) perfusion quantification, especially for delayed bolus arrival times (BAT), with an acquisition redistribution scheme mitigating the T1 decay of the label in multi-TI ASL measurements is investigated. A multi inflow time (TI) 3D-GRASE sequence is presented which adapts the distribution of acquisitions accordingly, by keeping the scan time constant. MATERIAL AND METHODS: The MR sequence increases the number of averages at long TIs and decreases their number at short TIs and thus compensating the T1 decay of the label. The improvement of perfusion quantification is evaluated in simulations as well as in-vivo in healthy volunteers and patients with prolonged BATs due to age or steno-occlusive disease. RESULTS: The improvement in perfusion quantification depends on BAT. At healthy BATs the differences are small, but become larger for longer BATs typically found in certain diseases. The relative error of perfusion is improved up to 30% at BATs>1500ms in comparison to the standard acquisition scheme. CONCLUSION: This adapted acquisition scheme improves the perfusion measurement in comparison to standard multi-TI ASL implementations. It provides relevant benefit in clinical conditions that cause prolonged BATs and is therefore of high clinical relevance for neuroimaging of steno-occlusive diseases.

Angiographic analysis for phantom simulations of endovascular aneurysm treatments with a new fully retrievable asymmetric flow diverter.

Digital Subtraction Angiography (DSA) is the main diagnostic tool for intracranial aneurysms (IA) flow-diverter (FD) assisted treatment. Based on qualitative contrast flow evaluation, interventionists decide on subsequent steps. We developed a novel fully Retrievable Asymmetric Flow-Diverter (RAFD) which allows controlled deployment, repositioning and detachment achieve optimal flow diversion. The device has a small low porosity or solid region which is placed such that it would achieve maximum aneurysmal in-jet flow deflection with minimum impairment to adjacent vessels. We tested the new RAFD using a flow-loop with an idealized and a patient specific IA phantom in carotid-relevant physiological conditions. We positioned the deflection region at three locations: distally, center and proximally to the aneurysm orifice and analyzed aneurysm dome flow using DSA derived maps for mean transit time (MTT) and bolus arrival times (BAT). Comparison between treated and untreated (control) maps quantified the RAFD positioning effect. Average MTT, related to contrast presence in the aneurysm dome increased, indicating flow decoupling between the aneurysm and parent artery. Maximum effect was observed in the center and proximal position (~75%) of aneurysm models depending on their geometry. BAT maps, correlated well with inflow jet direction and magnitude. Reduction and jet dispersion as high as about 50% was observed for various treatments. We demonstrated the use of DSA data to guide the placement of the RAFD and showed that optimum flow diversion within the aneurysm dome is feasible. This could lead to more effective and a safer IA treatment using FDs.

The precision of DCE-MRI using the tissue homogeneity model with continuous formulation of the perfusion parameters.

The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than~20dB. The application of the new analysis model on a real DCE-MRI data set is also presented.

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.

Parameter estimation in arterial spin labeling MRI: Comparing the four phase model and the buxton model with fourier transform.

This paper presents a comparison between two algorithms that analyze and extract brain perfusion parameters from pulsed arterial spin labeling (ASL) MRI images. One algorithm is based on a Four Phase Single Capillary Stepwise (FPSCS) model, which divides the time course of the signal difference between the control and labeled images into four phases. The other algorithm utilizes the Buxton model and Fourier transformation (FTB). Both algorithms were implemented on MATLAB to extract the bolus arrival time (BAT) and the cerebral blood flow (CBF). In-vivo brain MRI images acquired at 4T from health volunteers were used in the comparison. Results indicated that the FTB algorithm had similar estimations of the BAT and CBF compared to the FPSCS model when the time signals are sufficiently sampled, but the former had faster processing speed while the FPSCS method provides additional information.