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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.

Objectives The aim of this study was to assess the effect of a deep learning (DL)–based reconstruction algorithm on late gadolinium enhancement (LGE) image quality and to evaluate its influence on scar quantification. Methods Sixty patients (46 ± 17 years, 50% male) with suspected or known cardiomyopathy underwent CMR. Short-axis LGE images were reconstructed using the conventional reconstruction and a DL network (DLRecon) with tunable noise reduction (NR) levels from 0 to 100%. Image quality of standard LGE images and DLRecon images with 75% NR was scored using a 5-point scale (poor to excellent). In 30 patients with LGE, scar size was quantified using thresholding techniques with different standard deviations (SD) above remote myocardium, and using full width at half maximum (FWHM) technique in images with varying NR levels. Results DLRecon images were of higher quality than standard LGE images (subjective quality score 3.3 ± 0.5 vs. 3.6 ± 0.7, p p values p = 0.06). Conclusions LGE image quality improved significantly using a DL-based reconstruction algorithm. However, this algorithm has an important impact on scar quantification depending on which quantification technique is used. The FWHM method is preferred because of its independency of NR. Clinicians should be aware of this impact on scar quantification, as DL-based reconstruction algorithms are being used. Key Points • The image quality based on (subjective) visual assessment and image sharpness of late gadolinium enhancement images improved significantly using a deep learning–based reconstruction algorithm that aims to reconstruct high signal-to-noise images using a denoising technique. • Special care should be taken when scar size is quantified using thresholding techniques with different standard deviations above remote myocardium because of the large impact of these advanced image enhancement algorithms. • The full width at half maximum method is recommended to quantify scar size when deep learning algorithms based on noise reduction are used, as this method is the least sensitive to the level of noise and showed the best agreement with visual late gadolinium enhancement assessment.

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

The U-net is a deep-learning network model that has been used to solve a number of inverse problems. In this work, the concatenation of two-element U-nets, termed the W-net, operating in k-space (K) and image (I) domains, were evaluated for multi-channel magnetic resonance (MR) image reconstruction. The two-element network combinations were evaluated for the four possible image-k-space domain configurations: a) W-net II, b) W-net KK, c) W-net IK, and d) W-net KI. Selected four element (WW-nets) and six element (WWW-nets) networks were also examined. Two configurations of each network were compared: 1) each coil channel was processed independently, and 2) all channels were processed simultaneously. One hundred and eleven volumetric, T1-weighted, 12-channel coil k-space datasets were used in the experiments. Normalized root mean squared error, peak signal-to-noise ratio and visual information fidelity were used to assess the reconstructed images against the fully sampled reference images. Our results indicated that networks that operate solely in the image domain were better when independently processing individual channels of multi-channel data. Dual-domain methods were better when simultaneously reconstructing all channels of multi-channel data. In addition, the best cascade of U-nets performed better (p 0.01) than the previously published, state-of-the-art Deep Cascade and Hybrid Cascade models in three out of four experiments.

To compare interobserver agreement and image quality of 3D T2-weighted fast spin echo (T2w-FSE) L-spine MRI images processed with a deep learning reconstruction (DLRecon) against standard-of-care (SOC) reconstruction, as well as against 2D T2w-FSE images. The hypothesis was that DLRecon 3D T2w-FSE would afford improved image quality and similar interobserver agreement compared to both SOC 3D and 2D T2w-FSE.Under IRB approval, patients who underwent routine 3-T lumbar spine (L-spine) MRI from August 17 to September 17, 2020, with both isotropic 3D and 2D T2w-FSE sequences, were retrospectively included. A DLRecon algorithm, with denoising and sharpening properties was applied to SOC 3D k-space to generate 3D DLRecon images. Four musculoskeletal radiologists blinded to reconstruction status evaluated randomized images for motion artifact, image quality, central/foraminal stenosis, disc degeneration, annular fissure, disc herniation, and presence of facet joint cysts. Inter-rater agreement for each graded variable was evaluated using Conger's kappa (κ).Thirty-five patients (mean age 58 ± 19, 26 female) were evaluated. 3D DLRecon demonstrated statistically significant higher median image quality score (2.0/2) when compared to SOC 3D (1.0/2, p0.001), 2D axial (1.0/2, p0.001), and 2D sagittal sequences (1.0/2, p value0.001). κ ranges (and 95% CI) for foraminal stenosis were 0.55-0.76 (0.32-0.86) for 3D DLRecon, 0.56-0.73 (0.35-0.84) for SOC 3D, and 0.58-0.71 (0.33-0.84) for 2D. Mean κ (and 95% CI) for central stenosis at L4-5 were 0.98 (0.96-0.99), 0.97 (0.95-0.99), and 0.98 (0.96-0.99) for 3D DLRecon, 3D SOC and 2D, respectively.DLRecon 3D T2w-FSE L-spine MRI demonstrated higher image quality and similar interobserver agreement for graded variables of interest when compared to 3D SOC and 2D imaging.• 3D DLRecon T2w-FSE isotropic lumbar spine MRI provides improved image quality when compared to 2D MRI, with similar interobserver agreement for clinical evaluation of pathology. • 3D DLRecon images demonstrated better image quality score (2.0/2) when compared to standard-of-care (SOC) 3D (1.0/2), p value0.001; 2D axial (1.0/2), p value0.001; and 2D sagittal sequences (1.0/2), p value0.001. • Interobserver agreement for major variables of interest was similar among all sequences and reconstruction types. For foraminal stenosis, κ ranged from 0.55 to 0.76 (95% CI 0.32-0.86) for 3D DLRecon, 0.56-0.73 (95% CI 0.35-0.84) for standard-of-care (SOC) 3D, and 0.58-0.71 (95% CI 0.33-0.84) for 2D.

PURPOSE: To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets. MATERIALS AND METHODS: This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n = 22; mean age, 44 years ± 13 [standard deviation]; nine men) or shoulder (n = 32; mean age, 56 years ± 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence. Statistical analysis was performed with a nonparametric hypothesis comparing derived quantitative metrics from each reconstruction approach. In addition, inter- and intrarater agreement analysis was performed on the radiologists’ rankings. RESULTS: Both denoising settings of the DL reconstruction showed improved edge sharpness, rSNR, and rCNR relative to the conventional reconstructions. The reader rankings demonstrated strong agreement, with both DL reconstructions outperforming the conventional approach (Gwet agreement coefficient = 0.98). However, there was lower agreement between the readers on which DL reconstruction denoising setting produced higher-quality images (Gwet agreement coefficient = 0.31 for DL 50 and 0.35 for DL 75). CONCLUSION: The vendor-provided DL MRI reconstruction showed higher edge sharpness, rSNR, and rCNR in comparison with conventional methods; however, optimal levels of denoising may need to be further assessed. Keywords: MRI Reconstruction Method, Deep Learning, Image Analysis, Signal-to-Noise Ratio, MR-Imaging, Neural Networks, Hip, Shoulder, Physics, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021

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.

Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels. ispartof: NEUROIMAGE vol:191 pages:537-548 ispartof: location:United States status: published

Background Advances in sub-Nyquist-sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility. Purpose To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma. Materials and Methods Two methods for sub-Nyquist-sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M

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

PURPOSE To demonstrate the feasibility of integrating the magnetization transfer (MT) preparations required for inhomogeneous MT (ihMT) within an MPRAGE-style acquisition. Such a sequence allows for reduced power deposition and easy inclusion of other modules. METHODS An ihMT MPRAGE-style sequence (ihMTRAGE) was initially simulated to investigate acquisition of the 3D ihMT data sequentially, or in an interleaved manner. The ihMTRAGE sequence was implemented on a 3T clinical scanner to acquire ihMT data from the brain and spine. RESULTS Both simulations and in vivo data provided an ihMT signal that was significantly greater using a sequential ihMTRAGE acquisition, compared with an interleaved implementation. Comparison with a steady-state ihMT acquisition (defined as having one MT RF pulse between successive acquisition modules) demonstrated how ihMTRAGE allows for a reduction in average power deposition, or greater ihMT signal at equal average power deposition. Inclusion of a prospective motion-correction module did not significantly affect the ihMT signal obtained from regions of interest in the brain. The ihMTRAGE acquisition allowed combination with a spatial saturation module to reduce phase wrap artifacts in a cervical spinal cord acquisition. CONCLUSIONS Use of preparations necessary for ihMT experiments within an MPRAGE-style sequence provides a useful alternative for acquiring 3D ihMT data. Compared with our steady-state implementation, ihMTRAGE provided reduced power deposition, while allowing use of the maximum intensity from off-resonance RF pulses. The 3D ihMTRAGE acquisition allowed combination of other modules with the preparation necessary for ihMT experiments, specifically motion compensation and spatial saturation modules.

Background Arterial Spin Labeling (ASL) MRI can provide quantitative images that are sensitive to both time averaged blood flow and its temporal fluctuations. 3D image acquisitions for ASL are desirable because they are more readily compatible with background suppression to reduce noise, can reduce signal loss and distortion, and provide uniform flow sensitivity across the brain. However, single-shot 3D acquisition for maximal temporal resolution typically involves degradation of image quality through blurring or noise amplification by parallel imaging. Here, we report a new approach to accelerate a common stack of spirals 3D image acquisition by pseudo golden-angle rotation and compressed sensing reconstruction without any degradation of time averaged blood flow images. Methods 28 healthy volunteers were imaged at 3T with background-suppressed unbalanced pseudo-continuous ASL combined with a pseudo golden-angle Stack-of-Spirals 3D RARE readout. A fully-sampled perfusion-weighted volume was reconstructed by 3D non-uniform Fast Fourier Transform (nuFFT) followed by sum-of-squares combination of the 32 individual channels. Coil sensitivities were estimated followed by reconstruction of the 39 single-shot volumes using an L1-wavelet Compressed-Sensing reconstruction. Finally, brain connectivity analyses were performed in regions where BOLD signal suffers from low signal-to-noise ratio and susceptibility artifacts. Results Image quality, assessed with a non-reference 3D blurring metric, of full time averaged blood flow was comparable to a conventional interleaved acquisition. The temporal resolution provided by the acceleration enabled identification and quantification of resting-state networks even in inferior regions such as the amygdala and inferior frontal lobes, where susceptibility artifacts can degrade conventional resting-state fMRI acquisitions. Conclusion This approach can provide measures of blood flow modulations and resting-state networks for free within any research or clinical protocol employing ASL for resting blood flow.

The neurophysiological effects of pediatric concussion several years after injury remain inadequately characterized. The objective of this study was to determine if a history of concussion was associated with BOLD response differences during an n-back working memory task in youth.Observational, cross-sectional.Participants include 52 children and adolescents (M = 15.1 years, 95%CI = 14.4-15.8, range = 9-19) with past concussion (n = 33) or orthopedic injury (OI; n = 19). Mean time since injury was 2.5 years (95%CI = 2.0-3.0). Measures included postconcussion symptom ratings, neuropsychological testing, and blood-oxygen-dependent-level (BOLD) functional magnetic resonance imaging (fMRI) during an n-back working memory task.Groups did not differ on accuracy or speed during the three n-back conditions. They also did not differ in BOLD signal change for the 1- vs. 0-back or 2- vs. 0-back contrasts (controlling for task performance).This study does not support group differences in BOLD response during an n-back working memory task in youth who are on average 2.5 years post-concussion. The findings are encouraging from the perspective of understanding recovery after pediatric concussion.

Background The hardware and software differences between MR vendors and individual sites influence the quantification of MR spectroscopy data. An analysis of a large data set may help to better understand sources of the total variance in quantified metabolite levels. Purpose To compare multisite quantitative brain MR spectroscopy data acquired in healthy participants at 26 sites by using the vendor-supplied single-voxel point-resolved spectroscopy (PRESS) sequence. Materials and Methods An MR spectroscopy protocol to acquire short-echo-time PRESS data from the midparietal region of the brain was disseminated to 26 research sites operating 3.0-T MR scanners from three different vendors. In this prospective study, healthy participants were scanned between July 2016 and December 2017. Data were analyzed by using software with simulated basis sets customized for each vendor implementation. The proportion of total variance attributed to vendor-, site-, and participant-related effects was estimated by using a linear mixed-effects model. P values were derived through parametric bootstrapping of the linear mixed-effects models (denoted Pboot). Results In total, 296 participants (mean age, 26 years ± 4.6; 155 women and 141 men) were scanned. Good-quality data were recorded from all sites, as evidenced by a consistent linewidth of N-acetylaspartate (range, 4.4-5.0 Hz), signal-to-noise ratio (range, 174-289), and low Cramér-Rao lower bounds (≤5%) for all of the major metabolites. Among the major metabolites, no vendor effects were found for levels of myo-inositol (Pboot > .90), N-acetylaspartate and N-acetylaspartylglutamate (Pboot = .13), or glutamate and glutamine (Pboot = .11). Among the smaller resonances, no vendor effects were found for ascorbate (Pboot = .08), aspartate (Pboot > .90), glutathione (Pboot > .90), or lactate (Pboot = .28). Conclusion Multisite multivendor single-voxel MR spectroscopy studies performed at 3.0 T can yield results that are coherent across vendors, provided that vendor differences in pulse sequence implementation are accounted for in data analysis. However, the site-related effects on variability were more profound and suggest the need for further standardization of spectroscopic protocols. © RSNA, 2020 Online supplemental material is available for this article. ispartof: RADIOLOGY vol:295 issue:1 pages:171-180 ispartof: location:United States status: published

The long-term effects of concussion in youth remain poorly understood. The objective of this study was to determine the association between history of concussion and cerebral blood flow (CBF) in youth.A total of 53 children and adolescents with a history of concussion (n = 37) or orthopaedic injury (OI; n = 16) were considered. Measures included pseudo-continuous arterial spin labelling magnetic resonance imaging to quantify CBF, post-concussion symptoms, psychological symptoms, and cognitive testing.Participants (mean age: 14.4 years, 95% CI = 13.8-15.4, range = 8-19) were on average 2.7 years (95% CI = 2.2-3.1) post-injury. Youth with a history of concussion had higher parent-reported physical, cognitive, anxiety, and depression symptoms than children with OI, but the groups did not differ on self-reported symptoms (post-concussive or psychological) or cognitive testing. Global CBF did not differ between groups. Regional CBF analyses suggested that youth with a history of concussion had hypoperfusion in posterior and inferior regions and hyperperfusion in anterior/frontal/temporal regions as compared to those with OI. However, neither global nor regional CBF were significantly associated with demographics, pre-injury functioning, number of concussions, time since injury, post-concussive symptoms, psychological symptoms, or cognitive abilities.Youth with a history of concussion demonstrate differences in regional CBF (not global CBF), but without clear clinical expression.

Background Deep learning–based methods have been used to denoise magnetic resonance imaging. Purpose The purpose of this study was to evaluate a deep learning reconstruction (DL Recon) in cardiovascular black-blood T2-weighted images and compare with intensity filtered images. Material and Methods Forty-five DL Recon images were compared with intensity filtered and the original images. For quantitative image analysis, the signal to noise ratio (SNR) of the septum, contrast ratio (CR) of the septum to lumen, and sharpness of the endocardial border were calculated in each image. For qualitative image quality assessment, a 4-point subjective scale was assigned to each image (1 = poor, 2 = fair, 3 = good, 4 = excellent). Results The SNR and CR were significantly higher in the DL Recon images than in the intensity filtered and the original images ( p < .05 in each). Sharpness of the endocardial border was significantly higher in the DL Recon and intensity filtered images than in the original images ( p < .05 in each). The image quality of the DL Recon images was significantly better than that of intensity filtered and original images ( p < .001 in each). Conclusions DL Recon reduced image noise while improving image contrast and sharpness in the cardiovascular black-blood T2-weight sequence.

This study aimed to evaluate the usefulness of transit time corrected cerebral blood flow (CBF) maps based on multi-phase arterial spin labeling MR perfusion imaging (ASL-MRP). The Institutional Review Board of our hospital approved this retrospective study. Written informed consent was waived. Conventional and multi-phase ASL-MRPs and dynamic susceptibility contrast MR perfusion imaging (DSC-MRP) were acquired for 108 consecutive patients. Vascular territory-based volumes of interest were applied to CBF and time to peak (TTP) maps obtained from DSC-MRP and CBF maps obtained from conventional and multi-phase ASL-MRPs. The concordances between normalized CBF (nCBF) from DSC-MRP and nCBF from conventional and transition time corrected CBF maps from multi-phase ASL-MRP were evaluated using Bland-Altman analysis. In addition, the dependence of difference between nCBF (ΔnCBF) values obtained from DSC-MRP and conventional ASL-MRP (or multi-phase ASL-MRP) on TTP obtained from DSC-MRP was also analyzed using regression analysis. The values of nCBFs from conventional and multi-phase ASL-MRPs had lower values than nCBF based on DSC-MRP (mean differences, 0.08 and 0.07, respectively). The values of ΔnCBF were dependent on TTP values from conventional ASL-MRP technique (F = 5.5679, P = 0.0384). No dependency of ΔnCBF on TTP values from multi-phase ASL-MRP technique was revealed (F = 0.1433, P > 0.05). The use of transit time corrected CBF maps based on multi-phase ASL-MRP technique can overcome the effect of delayed transit time on perfusion maps based on conventional ASL-MRP.

Purpose To develop and evaluate a model-based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast-enhanced MRI (DCE-MRI) data. Methods The proposed method poses the tracer-kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down-sampled brain tumor DCE-MRI datasets. We also demonstrate application to 30-fold prospectively undersampled brain tumor DCE-MRI. Results In DRO studies with up to 60-fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third-party TK solver. In retrospective undersampling studies, this method provided patient-specific AIF with normalized root mean-squared-error (normalized by the 90th percentile value) less than 8% at up to 100-fold undersampling. In the 30-fold undersampled prospective study, the proposed method provided high-resolution whole-brain TK maps and patient-specific AIF. Conclusion The proposed model-based DCE-MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient-specific AIF. TK maps and patient-specific AIF with high fidelity can be reconstructed at up to 100-fold undersampling in k,t-space. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Intraspinal microstimulation (ISMS) is currently under investigation for its ability to restore function following spinal cord injury and aid in addressing basic investigations of the spinal cord in feline and murine (rat) models. In this report we describe the procedures for fabricating and implanting intraspinal microwires, with special emphasis on the rat model. We also report our results on targeting success and long-term stability and functionality of the implants. Early targeting with implants fabricated based on general “average” dimensions of the spinal cord was approximately 50% successful in reaching the proper targets within the ventral grey matter in cats. Improvements in insertion technique and the use of multiple contact electrodes have raised the targeting success to 100%. Furthermore, the manufacturing of ISMS arrays has been improved by the use of magnetic resonance imaging to create subject-specific implants for cats and track the location of the arrays post-implant. In the rat, our procedures have produced desirable targeting of all recovered microwires. We speculate this is due to the different targeting parameters and the shorter depth of insertion in the rat spinal cord. Although there is a heightened mechanical mismatch between the $30~\mu \mathrm {m}$ -diameter microwires and the small rat spinal cord, chronic implantation and stimulation produce limited histological damage and do not compromise function. Furthermore, despite the increased difficulties of implanting into the smaller rat spinal cord, ISMS is effective in activating spinal cord networks in the lumbosacral enlargement in a manner that is safe, stable and reproducible.

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.

Arterial spin labeling (ASL) MRI can provide seizure onset zone (SOZ) localizing information in up to 80% of patients. Clinical implementation of this technique is limited by the need to obtain two scans per patient: a postictal scan that is subtracted from an interictal scan. We aimed to determine whether it is possible to limit the number of ASL scans to one per patient by comparing patient postictal ASL scans to baseline scans of 100 healthy controls. Eighteen patients aged 20-55 years underwent ASL MRI

Objectives We attempted to demonstrate the clinical applicability and utility of a three-dimensional multidelay arterial spin labeling magnetic resonance imaging technique in pediatric neuroimaging through a series of case studies. Methods Whole-brain three-dimensional multidelay arterial spin labeling data were acquired in five pediatric patients with different neurological conditions using 3 mm to 4 mm slices and a scan time of six to seven minutes. Results Three-dimensional multidelay arterial spin labeling provided complementary diagnostic information via quantitative cerebral blood flow and arterial transit time maps. Conclusions Three-dimensional multidelay arterial spin labeling sequence provides simultaneous quantification of cerebral blood flow and arterial transit time and is feasible for pediatric patients.

Adequate cerebral blood flow (CBF) is essential to proper brain development and function. Detailed characterization of CBF developmental trajectories will lead to better understanding of the development of cognitive, motor, and sensory functions, as well as behaviour in children. Previous studies have shown CBF increases during infancy and decreases during adolescence; however, the trajectories during childhood, and in particular the timing of peak CBF, remain unclear. Here, we used arterial spin labeling to map age-related changes of CBF across a large longitudinal sample that included 279 scans on 96 participants (46 girls and 50 boys) aged 2–7 years. CBF maps were analyzed using hierarchical linear regression for every voxel inside the grey matter mask, controlling for multiple comparisons. The results revealed a significant positive linear association between CBF and age in distributed brain regions including prefrontal, temporal, parietal, and occipital cortex, and in the cerebellum. There were no differences in developmental trajectories between males and females. Our findings show that CBF continues to increase until the age of 7 years, likely supporting ongoing improvements in behaviour, cognition, motor, and sensory functions in early childhood.

Adequate cerebral blood flow (CBF) is essential to proper brain development and function. Detailed characterization of CBF developmental trajectories will lead to better understanding of the development of cognitive, motor, and sensory functions, as well as behaviour in children. Previous studies have shown CBF increases during infancy and decreases during adolescence; however, the trajectories during childhood, and in particular the timing of peak CBF, remain unclear. Here, we used arterial spin labeling to map age-related changes of CBF across a large longitudinal sample that included 279 scans on 96 participants (46 girls and 50 boys) aged 2-7 years. CBF maps were analyzed using hierarchical linear regression for every voxel inside the grey matter mask, controlling for multiple comparisons. The results revealed a significant positive linear association between CBF and age in distributed brain regions including prefrontal, temporal, parietal, and occipital cortex, and in the cerebellum. There were no differences in developmental trajectories between males and females. Our findings suggest that CBF continues to increase at least until the age of 7 years, likely supporting the ongoing improvements in behaviour, cognition, motor, and sensory functions of the children in early childhood.Highlights*We mapped cerebral blood flow development longitudinally in 96 typically developing children aged 2-7 years*Cerebral blood flow increased over the full age range*No significant differences between males and females were foundConflicting of InterestR.M.L. is an employee of GE.

Background Smaller hippocampal volumes, as assessed by magnetic resonance imaging (MRI), and proton magnetic resonance spectroscopy ( 1 H-MRS) indexed alterations in brain metabolites have been identified in adults with major depressive disorder (MDD). Our group has found similar effects in MDD youth. However, this has not been studied in youth with treatment resistant MDD (TRD), nor has the interaction between regional N-acetyl-aspartate and volume deficits. N-acetyl-aspartate is an amino acid in the synthesis pathway of glutamate, and serves a marker of neuronal viability/number. Methods Fifteen typically developing youth (16–22 years of age; 7 males, 8 females) and eighteen youth with TRD (14–22 years of age; 8 males, 10 females) underwent 1 H-MRS and MRI on a 3 T scanner. A short echo PRESS protocol was used with voxels in the right and left hippocampi (6 mL each). Hippocampal volume was evaluated using FreeSurfer. Results Compared with the typically developing group, youth with TRD had lower concentrations of N-acetyl-aspartate in the left hippocampus (p=0.004), and a trend for smaller left hippocampal volume (p=0.067). In TRD subjects, hippocampal N-acetyl-aspartate was inversely correlated with left (r=−0.68, p=0.003) but not right hippocampal volume. Right hippocampal glutamate+glutamine was greater in TRD youth compared to typically developing controls (p=0.007). Conclusions These results suggest a neurochemical and structural deficit in the hippocampi of youth with TRD. These findings fit with the role of N-acetyl-aspartate in glutamate neurotransmission and the effect of glutamate on brain morphology.

Purpose The purpose of this work was to develop and evaluate a T1-weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. Theory and Methods The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R = 20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R = 30 undersampled whole-brain DCE-MRI data sets. Results In the retrospective study, the proposed method performed statistically better than indirect method at R ≥ 80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. Conclusion Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566–1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

To develop and evaluate a novel 3D Cartesian sampling scheme which is well suited for time-resolved 3D MRI using parallel imaging and compressed sensing.The proposed sampling scheme, termed GOlden-angle CArtesian Randomized Time-resolved (GOCART) 3D MRI, is based on golden angle (GA) Cartesian sampling, with random sampling of the ky-kz phase encode locations along each Cartesian radial spoke. This method was evaluated in conjunction with constrained reconstruction of retrospectively and prospectively undersampled in-vivo dynamic contrast enhanced (DCE) MRI data and simulated phantom data.In in-vivo retrospective studies and phantom simulations, images reconstructed from phase encodes defined by GOCART were equal to or superior to those with Poisson disc or GA sampling schemes. Typical GOCART sampling tables were generated in100ms. GOCART has also been successfully utilized prospectively to produce clinically valuable whole-brain DCE-MRI images.GOCART is a practical and efficient sampling scheme for time-resolved 3D MRI. It shows great potential for highly accelerated DCE-MRI and is well suited to modern reconstruction methods such as parallel imaging and compressed sensing.

Background: Deep gray matter (DGM) is affected in relapsing–remitting multiple sclerosis (RRMS) and may be studied using short-term longitudinal MRI. Objective: To investigate two-year changes in spin-echo transverse relaxation rate (R2) and atrophy in DGM, and its relationship with disease severity in RRMS patients. Methods: Twenty six RRMS patients and 26 matched controls were imaged at 4.7 T. Multiecho spin-echo R2 maps and atrophy measurements were obtained in DGM at baseline and two-year follow-up. Differences between MRI measures and correlations to disease severity were examined. Results: After two years, mean R2 values in the globus pallidus and pulvinar increased by ~4% ( p2 showed significant correlation to disease severity in the globus pallidus, pulvinar, substantia nigra, and thalamus. Multiple regression of the two-year R2 difference using these four DGM structures as variables, yielded high correlation with disease severity ( r=0.83, p2 showed significant correlation only for the globus pallidus in multiple sclerosis (MS) ( pConclusions: Two-year difference R2 measurements in DGM correlate to disease severity in MS. R2 mapping and atrophy measurements over two years can be used to identify changes in DGM in MS.

Aim Structural, functional, and metabolic changes in the dorsolateral prefrontal cortex (DLPFC) are implicated in the pathogenesis of major depressive disorder (MDD). We used proton magnetic resonance spectroscopy ((1) H-MRS) to examine the metabolite choline (glycerophosphocholine plus phosphocholine), which is used as an index of membrane integrity in the left DLPFC, in adolescents and young adults with MDD who were treatment-resistant and had a positive family history compared to healthy controls. Differences in the choline resonance indicate an imbalance between synthesis and degradation activity of neuronal and glia membrane phospholipids. Methods Seventeen adolescents with MDD and 11 healthy controls underwent (1) H-MRS. A short echo point-resolved spectroscopy (echo time = 30 ms, repetition time = 2000 ms) protocol was used with a voxel (4.5cm(3) , 128 averages) placed within the left DLPFC. Results There were significantly increased choline (P = 0.04) and creatine concentrations (P = 0.005) in the left DLPFC of the MDD group compared to controls. In MDD participants, choline concentration correlated with scores on the Beck Depression Inventory (r = 0.41, P = 0.03). Conclusion Increased left DLPFC choline and creatine levels in depressed adolescents may be biomarkers for the disorder. The increased choline levels may indicate abnormalities in neuronal membrane integrity, and the increased creatine could be reflective of altered energy demands and metabolism.

Purpose: To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints. Methods: Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 braintumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm3, FOV 22 × 22 × 4.2 cm3, and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm3, and broader coverage 22 × 22 × 19 cm3. Temporal resolution was 5 s for both protocols. Time-resolvedimages and blood–brain barrier permeability maps were qualitatively evaluated by two radiologists. Results: The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans. Conclusions: The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.

Objective To construct a clinical diagnostic biomarker using state-of-the-art microstructural MRI in the motor cortex of people with amyotrophic lateral sclerosis (ALS). Methods Clinical and MRI data were obtained from 21 ALS patients (aged 54 ± 14 years, 33% female) and 63 age- and gender-matched controls (aged 48 ± 18 years, 43% female). MRI was acquired at 3T and included T1-weighted scan (for volumetrics), arterial spin labelling (for cerebral blood flow), susceptibility-weighted angiography (for iron deposition) and multiband diffusion kurtosis imaging (for tissue microstructure). Group differences in imaging measures in the motor cortex were tested by general linear model and relationships to clinical variables by linear regression. Results The ALS group had mild-to-moderate impairment (disease duration: 1.8 ± 0.8 years; ALS functional rating scale 40.2 ± 6.0; forced vital capacity 83% ± 22%). No age or gender differences were present between groups. We found significant group differences in diffusion kurtosis metrics (apparent, mean, radial and axial kurtosis: p, Highlights • A biomarker based on diffusion kurtosis imaging achieved an accuracy of 83%. • Kurtosis-based measures were more abnormal in ALS than tensor-based measures. • Motor cortex in the symptomatic hemisphere was smaller and had greater iron concentration. • There was a 1 mL volume loss per year in ALS motor cortex.

BACKGROUND AND PURPOSE:Multidelay arterial spin-labeling is a promising emerging method in clinical practice. The effect of imaging parameters in multidelay arterial spin-labeling on estimated cerebral blood flow measurements remains unknown. We directly compared 3-delay versus 7-delay sequences, assessing the difference in the estimated transit time and blood flow. MATERIALS AND METHODS:This study included 87 cognitively healthy controls (78.7 3.8 years of age; 49 women). We assessed delay and transit time-uncorrected and transit time-corrected CBF maps. Data analysis included voxelwise permutation-based between-sequence comparisons of 3-delay versus 7-delay, within-sequence comparison of transit time-uncorrected versus transit time-corrected maps, and average CBF calculations in regions that have been shown to differ. RESULTS:The 7-delay sequence estimated a higher CBF value than the 3-delay for the transit time-uncorrected and transit time-corrected maps in regions corresponding to the watershed areas (transit time-uncorrected = 27.62 +/- 12.23 versus 24.58 +/- 11.70 mL/min/100 g, Cohen's d = 0.25; transit time-corrected = 33.48 +/- 14.92 versus 30.16 +/- 14.32 mL/min/100 g, Cohen's d = 0.23). In the peripheral regions of the brain, the estimated delay was found to be longer for the 3-delay sequence (1.52408 +/- 0.25236 seconds versus 1.47755 +/- 0.24242 seconds, Cohen's d = 0.19), while the inverse was found in the center of the brain (1.39388 +/- 0.22056 seconds versus 1.42565 +/- 0.21872 seconds, Cohen's d = 0.14). Moreover, 7-delay had lower hemispheric asymmetry. CONCLUSIONS:The results of this study support the necessity of standardizing acquisition parameters in multidelay arterial spin-labeling and identifying basic parameters as a confounding factor in CBF quantification studies. Our findings conclude that multidelay arterial spin-labeling sequences with a high number of delays estimate higher CBF values than those with a lower number of delays.