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Purpose: To develop a deep‐learning model that leverages the spatial and temporal information from dynamic contrast‐enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling. Methods: Patients with glioblastoma multiforme were scanned post‐resection approximately every 2 months using a high spatial and temporal resolution DCE MR imaging sequence (≈5 s and ≈2 cm3). A region over the transverse sinus was manually drawn in the dynamic T1‐weighted images to provide a ground truth VF. The manual regions and their resulting VF curves were used to train a deep‐learning model based on a 3D U‐net architecture. The model concurrently utilized the spatial and temporal information in DCE MR images to predict the VF. In order to analyze the contribution of the spatial and temporal terms, different weighted combinations were examined. The manual and deep‐learning predicted regions and VF curves were compared. Results: Forty‐three patients were enrolled in this study and 155 DCE MR scans were processed. The 3D U‐net was trained using a loss function that combined the spatial and temporal information with different weightings. The best VF curves were obtained when both spatial and temporal information were considered. The predicted VF curve was similar to the manual ground truth VF curves. Conclusion: The use of spatial and temporal information improved VF curve prediction relative to when only the spatial information is used. The method generalized well for unseen data and can be used to automatically estimate a VF curve suitable for quantitative PK modeling. This method allows for a more efficient clinical pipeline and may improve automation of permeability mapping. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high‐resolution whole‐brain dynamic contrast‐enhanced–magnetic resonance imaging (DCE‐MRI). Methods: Variable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high‐resolution whole‐brain DCE‐MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model‐based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise‐mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high‐grade gliomas. Results: T1‐mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was <25 ms in white matter (WM) and <11 ms in brain tumor (BT). T1 standard deviation (SD) was <119.5 ms in WM (coefficient of variation [COV] ~11.0%) and <253.2 ms in BT (COV ~12.7%). In the noisy DRO, T1 bias was <50 ms in WM and <30 ms in BT. For R ≤ 10, T1 SD was <107.1 ms in WM (COV ~9.9%) and <240.9 ms in BT (COV ~12.1%). In the healthy subject, T1 bias was <30 ms for R ≤ 16. At R = 4, T1 SD was 171.4 ms (COV ~13.0%). In the prospective brain tumor study, T1 values were consistent with literature values in WM and BT. Conclusion: High‐resolution whole‐brain VFA T1 mapping is feasible with sparse sampling, supporting its use for quantitative DCE‐MRI. [ABSTRACT FROM AUTHOR]

In vivo quantification of glutamate (Glu) and γ‐aminobutyric acid (GABA) using MRS is often achieved using two separate sequences: a short‐echo point resolved spectroscopy (PRESS) acquisition for Glu and a Mescher‐Garwood PRESS (MEGA‐PRESS) acquisition for GABA. The purpose of this study was to examine the agreement of Glu and Glx (the combined signal of glutamate + glutamine) quantified from two different GABA‐edited MEGA‐PRESS acquisitions (GABA plus macromolecules, GABA+, TE = 68 ms, and macromolecule suppressed, MMSup, TE = 80 ms) with Glu and Glx quantified from a short‐echo PRESS (PRESS‐35, TE = 35 ms) acquisition. Fifteen healthy male volunteers underwent a single scan session, in which data were acquired using the three acquisitions (GABA+, MMSup and PRESS‐35) in both the sensorimotor and anterior cingulate cortices using a voxel size of 3 × 3 × 3 cm3. Glx and Glu were quantified from the MEGA‐PRESS data using both the OFF sub‐spectra and the difference (DIFF) spectra. Agreement was assessed using correlation analyses, Bland–Altman plots and intraclass correlation coefficients. Glx quantified from the OFF sub‐spectra from both the GABA+ and MMSup acquisitions showed poor agreement with PRESS‐35 in both brain regions. In the sensorimotor cortex, Glu quantified from the OFF sub‐spectra of GABA+ showed moderate agreement with PRESS‐35 data, but this finding was not replicated in the anterior cingulate cortex. Glx and Glu quantified using the DIFF spectra of either MEGA‐PRESS sequence were in poor agreement with the PRESS‐35 data in both brain regions. In conclusion, Glx and Glu measured from MEGA‐PRESS data generally showed poor agreement with Glx and Glu measured using PRESS‐35. [ABSTRACT FROM AUTHOR]

Background: Arterial spin labeling (ASL) can be confounded by varying arterial transit times (ATT) across the brain and with disease. Hadamard encoding schemes can be applied to 3D pseudocontinuous ASL (pCASL) to acquire ASL data with multiple postlabeling delays (PLDs) to estimate ATT and then correct cerebral blood flow (CBF).Purpose: To assess the longitudinal reproducibility of 3D pCASL with Hadamard-encoded multiple PLDs.Study Type: Prospective, longitudinal.Population: Fifty-two healthy, right-handed male subjects who underwent imaging at four timepoints over 45 days.Field Strength/sequence: A Hadamard-encoded 3D pCASL sequence was acquired at 3.0T with seven PLDs from 1.0-3.7 sec.Assessment: ATT and corrected CBF (cCBF) were computed. Conventional uncorrected CBF (unCBF) was also estimated. Within- and between-subject coefficient of variation (wCV and bCV, respectively) and intraclass correlation coefficient (ICC) were evaluated across four time intervals: 7, 14, 30, and 45 days, in gray matter and 17 independent regions of interest (ROIs). A power analysis was also conducted.Statistical Tests: A repeated-measures analysis of variance (ANOVA) was used to compare ATT, cCBF, and unCBF across the four scan sessions. A paired two-sample t-test was used to compare cCBF and unCBF. Pearson's correlation was used to examine the relationship between the cCBF and unCBF difference and ATT. Power calculations were completed using both the cCBF and unCBF variances.Results: ATT showed the lowest wCV and bCV (3.3-4.4% and 6.0-6.3%, respectively) compared to both cCBF (10.5-11.7% and 20.6-22.2%, respectively) and unCBF (12.0-13.6% and 22.7-23.7%, respectively). wCV and bCV were lower for cCBF vs. unCBF. A significant difference between cCBF and unCBF was found in most regions (P = 5.5 × 10-5 -3.8 × 10-4 in gray matter) that was highly correlated with ATT (R2 = 0.79-0.86). A power analysis yielded acceptable power at feasible sample sizes using cCBF.Data Conclusion: ATT and ATT-corrected CBF were longitudinally stable, indicating that ATT and CBF changes can be reliably evaluated with Hadamard-encoded 3D pCASL with multiple PLDs.Level Of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1846-1853. [ABSTRACT FROM AUTHOR]

Purpose: To apply tracer kinetic models as temporal constraints during reconstruction of under‐sampled brain tumor dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI). Methods: A library of concentration vs time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under‐sampling experiments are performed on a brain tumor DCE digital reference object (DRO), and 12 brain tumor in‐vivo 3T datasets. The performances of the proposed under‐sampled reconstruction scheme and an existing compressed sensing‐based temporal finite‐difference (tFD) under‐sampled reconstruction were compared against the fully sampled inverse Fourier Transform‐based reconstruction. Results: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts‐Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO‐based experiments showed good fidelity in recovery of kinetic maps from 20‐fold under‐sampled data. In‐vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at under‐sampled reduction factors >= 20. Conclusions: Tracer kinetic models can be applied as temporal constraints during brain tumor DCE‐MRI reconstruction. The proposed under‐sampled scheme resulted in model parameter estimates less biased with respect to conventional fully sampled DCE MRI reconstructions and parameter estimation. The approach is flexible, can use nonlinear kinetic models, and does not require tuning of regularization parameters. [ABSTRACT FROM AUTHOR]