Surface Mapping of Gastric Motor Functions Using MRI: A Comparative Study between Humans and Rats.

BACKGROUND: The stomach's ability to store, mix, propel, and empty its content requires highly coordinated motor functions. However, current diagnostic tools cannot simultaneously assess these motor processes. This study aimed to use magnetic resonance imaging (MRI) to map multifaceted gastric motor functions, including accommodation, tonic and peristaltic contractions, and emptying, through a single non-invasive experiment for both humans and rats. METHODS: Ten humans and ten Sprague-Dawley rats consumed MRI-visible semi-solid meals and underwent MRI scans. We used a surface model to analyze MRI data, capturing the deformation of the stomach wall upon ingestion or during digestion. We inferred muscle activity, mapped motor processes, parcellated the stomach into functional regions, and revealed cross-species distinctions. RESULTS: In humans, both the fundus and antrum distended post-meal, followed by sustained tonic contractions to regulate intragastric pressure. Peristaltic contractions initiate from the distal fundus, including three concurrent wavefronts oscillating at 3.3 cycles per minute (cpm) and traveling at 1.7 to 2.9 mm/s. These motor functions facilitate linear gastric emptying with a 61-min half-time. In contrast, rats exhibited peristalsis from the mid-corpus, showing two wavefronts oscillating at 5 cpm and traveling at 0.3 to 0.9 mm/s. For both species, motility features allowed functional parcellation of the stomach along a mid-corpus division. CONCLUSIONS: This study maps region- and species-specific gastric motor functions. We demonstrate the value of MRI with surface modeling in understanding gastric physiology and its potential to become a new standard for clinical and preclinical investigations of gastric disorders at both individual and group levels.

Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification.

Oscillating Steady-State Imaging (OSSI) is a recently developed fMRI acquisition method that can provide 2 to 3 times higher SNR than standard fMRI approaches. However, because the OSSI signal exhibits a nonlinear oscillation pattern, one must acquire and combine nc (e.g., 10) OSSI images to get an image that is free of oscillation for fMRI, and fully sampled acquisitions would compromise temporal resolution. To improve temporal resolution and accurately model the nonlinearity of OSSI signals, instead of using subspace models that are not well suited for the data, we build the MR physics for OSSI signal generation as a regularizer for the undersampled reconstruction. Our proposed physics-based manifold model turns the disadvantages of OSSI acquisition into advantages and enables joint reconstruction and quantification. OSSI manifold model (OSSIMM) outperforms subspace models and reconstructs high-resolution fMRI images with a factor of 12 acceleration and without spatial or temporal smoothing. Furthermore, OSSIMM can dynamically quantify important physics parameters, including R* 2 maps, with a temporal resolution of 150 ms.

Model-based reconstruction for looping-star MRI.

PURPOSE: The aim of this study was to develop a reconstruction method that more fully models the signals and reconstructs gradient echo (GRE) images without sacrificing the signal to noise ratio and spatial resolution, compared to conventional gridding and model-based image reconstruction method. METHODS: By modeling the trajectories for every spoke and simplifying the scenario to only echo-in and echo-out mixture, the approach explicitly models the overlapping echoes. After modeling the overlapping echoes with two system matrices, we use the conjugate gradient algorithm (CG-SENSE) with the nonuniform FFT (NUFFT) to optimize the image reconstruction cost function. RESULTS: The proposed method is demonstrated in phantoms and in-vivo volunteer experiments for three-dimensional, high-resolution T2*-weighted imaging and functional MRI tasks. Compared to the gridding method, the high resolution protocol exhibits improved spatial resolution and reduced signal loss as a result of less intra-voxel dephasing. The fMRI task shows that the proposed model-based method produced images with reduced artifacts and blurring as well as more stable and prominent time courses. CONCLUSION: The proposed model-based reconstruction results shows improved spatial resolution and reduced artifacts. The fMRI task shows improved time series and activation map due to the reduced overlapping echoes and under-sampling artifacts.

Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.

PURPOSE: Optimizing three-dimensional (3D) k-space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. METHODS: We built a differentiable simulation model to enable gradient-based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open-source toolbox. RESULTS: We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS-GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from "strong" to "mild." CONCLUSION: SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.

Magnetic Resonance Thermometry Targeting for Magnetic Resonance-Guided Histotripsy Treatments.

OBJECTIVE: The potential of transcranial magnetic resonance (MR)-guided histotripsy for brain applications has been described in prior in vivo studies in the swine brain through an excised human skull. The safety and accuracy of transcranial MR-guided histotripsy (tcMRgHt) rely on pre-treatment targeting guidance. In the work described here, we investigated the feasibility and accuracy of using ultrasound-induced low-temperature heating and MR thermometry for histotripsy pre-treatment targeting in ex vivo bovine brain. METHODS: A 15-element, 750-kHz MRI-compatible ultrasound transducer with modified drivers that can deliver both low-temperature heating and histotripsy acoustic pulses was used to treat seven bovine brain samples. The samples were first heated to an approximately 1.6°C temperature increase at the focus, and MR thermometry was used to localize the target. Once the targeting was confirmed, a histotripsy lesion was generated at the focus and visualized on post-histotripsy MR images. DISCUSSION: The accuracy of MR thermometry targeting was evaluated with the mean/standard deviation of the difference between the locus of peak heating identified by MR thermometry and the center of mass of the post-treatment histotripsy lesion, which was 0.59/0.31 mm and 1.31/0.93 mm in the transverse and longitudinal directions, respectively. CONCLUSION: This study determined that MR thermometry could provide reliable pre-treatment targeting for transcranial MR-guided histotripsy treatment.

Off-resonance artifact correction for MRI: A review.

In magnetic resonance imaging (MRI), inhomogeneity in the main magnetic field used for imaging, referred to as off-resonance, can lead to image artifacts ranging from mild to severe depending on the application. Off-resonance artifacts, such as signal loss, geometric distortions, and blurring, can compromise the clinical and scientific utility of MR images. In this review, we describe sources of off-resonance in MRI, how off-resonance affects images, and strategies to prevent and correct for off-resonance. Given recent advances and the great potential of low-field and/or portable MRI, we also highlight the advantages and challenges of imaging at low field with respect to off-resonance.

B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI.

Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and apply multi-scale optimization, which may help to avoid sub-optimal local minima. The algorithm includes an efficient non-Cartesian unrolled neural network-based reconstruction and an accurate approximation for backpropagation through the non-uniform fast Fourier transform (NUFFT) operator to accurately reconstruct and back-propagate multi-coil non-Cartesian data. Penalties on slew rate and gradient amplitude enforce hardware constraints. Sampling and reconstruction are trained jointly using large public datasets. To correct for possible eddy-current effects introduced by the curved trajectory, we use a pencil-beam trajectory mapping technique. In both simulations and in- vivo experiments, the learned trajectory demonstrates significantly improved image quality compared to previous model-based and learning-based trajectory optimization methods for 10× acceleration factors. Though trained with neural network-based reconstruction, the proposed trajectory also leads to improved image quality with compressed sensing-based reconstruction.

Simulated MRI Artifacts: Testing Machine Learning Failure Modes.

Objective. Seven types of MRI artifacts, including acquisition and preprocessing errors, were simulated to test a machine learning brain tumor segmentation model for potential failure modes. Introduction. Real-world medical deployments of machine learning algorithms are less common than the number of medical research papers using machine learning. Part of the gap between the performance of models in research and deployment comes from a lack of hard test cases in the data used to train a model. Methods. These failure modes were simulated for a pretrained brain tumor segmentation model that utilizes standard MRI and used to evaluate the performance of the model under duress. These simulated MRI artifacts consisted of motion, susceptibility induced signal loss, aliasing, field inhomogeneity, sequence mislabeling, sequence misalignment, and skull stripping failures. Results. The artifact with the largest effect was the simplest, sequence mislabeling, though motion, field inhomogeneity, and sequence misalignment also caused significant performance decreases. The model was most susceptible to artifacts affecting the FLAIR (fluid attenuation inversion recovery) sequence. Conclusion. Overall, these simulated artifacts could be used to test other brain MRI models, but this approach could be used across medical imaging applications.

Transcranial Magnetic Resonance-Guided Histotripsy for Brain Surgery: Pre-clinical Investigation.

Histotripsy has been previously applied to target various cranial locations in vitro through an excised human skull. Recently, a transcranial magnetic resonance (MR)-guided histotripsy (tcMRgHt) system was developed, enabling pre-clinical investigations of tcMRgHt for brain surgery. To determine the feasibility of in vivo transcranial histotripsy, tcMRgHt treatment was delivered to eight pigs using a 700-kHz, 128-element, MR-compatible phased-array transducer inside a 3-T magnetic resonance imaging (MRI) scanner. After craniotomy to open an acoustic window to the brain, histotripsy was applied through an excised human calvarium to target the inside of the pig brain based on pre-treatment MRI and fiducial markers. MR images were acquired pre-treatment, immediately post-treatment and 2-4 h post-treatment to evaluate the acute treatment outcome. Successful histotripsy ablation was observed in all pigs. The MR-evident lesions were well confined within the targeted volume, without evidence of excessive brain edema or hemorrhage outside of the target zone. Histology revealed tissue homogenization in the ablation zones with a sharp demarcation between destroyed and unaffected tissue, which correlated well with the radiographic treatment zones on MRI. These results are the first to support the in vivo feasibility of tcMRgHt in the pig brain, enabling further investigation of the use of tcMRgHt for brain surgery.

Joint Design of RF and Gradient Waveforms via Auto-differentiation for 3D Tailored Excitation in MRI.

This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially tailored saturation and inversion pulses. The joint design of both waveforms is characterized by the ODE Bloch equations, to which there is no known direct solution. Existing approaches therefore typically rely on simplified problem formulations based on, e.g., the small-tip approximation or constraining the gradient waveforms to particular shapes, and often apply only to specific objective functions for a narrow set of design goals (e.g., ignoring hardware constraints). This paper develops and exploits an auto-differentiable Bloch simulator to directly compute Jacobians of the (Bloch-simulated) excitation pattern with respect to RF and gradient waveforms. This approach is compatible with arbitrary sub-differentiable loss functions, and optimizes the RF and gradients directly without restricting the waveform shapes. For computational efficiency, we derive and implement explicit Bloch simulator Jacobians (approximately halving computation time and memory usage). To enforce hardware limits (peak RF, gradient, and slew rate), we use a change of variables that makes the 3D pulse design problem effectively unconstrained; we then optimize the resulting problem directly using the proposed auto-differentiation framework. We demonstrate our approach with two kinds of 3D excitation pulses that cannot be easily designed with conventional approaches: Outer-volume saturation (90° flip angle), and inner-volume inversion.

Transcranial MR-Guided Histotripsy System.

Histotripsy has been previously shown to treat a wide range of locations through excised human skulls in vitro. In this article, a transcranial magnetic resonance (MR)-guided histotripsy (tcMRgHt) system was developed, characterized, and tested in the in vivo pig brain through an excised human skull. A 700-kHz, 128-element MR-compatible phased-array ultrasound transducer with a focal depth of 15 cm was designed and fabricated in-house. Support structures were also constructed to facilitate transcranial treatment. The tcMRgHt array was acoustically characterized with a peak negative pressure up to 137 MPa in free field, 72 MPa through an excised human skull with aberration correction, and 48.4 MPa without aberration correction. The electronic focal steering range through the skull was 33.5 mm laterally and 50 mm axially, where a peak negative pressure above the 26-MPa cavitation intrinsic threshold can be achieved. The MR compatibility of the tcMRgHt system was assessed quantitatively using SNR, B0 field map, and B1 field map in a clinical 3T magnetic resonance imaging (MRI) scanner. Transcranial treatment using electronic focal steering was validated in red blood cell phantoms and in vivo pig brain through an excised human skull. In two pigs, targeted cerebral tissue was successfully treated through the human skull as confirmed by MRI. Excessive bleeding or edema was not observed in the peri-target zones by the time of pig euthanasia. These results demonstrated the feasibility of using this preclinical tcMRgHt system for in vivo transcranial treatment in a swine model.

Across-vendor standardization of semi-LASER for single-voxel MRS at 3T.

The semi-adiabatic localization by adiabatic selective refocusing (sLASER) sequence provides single-shot full intensity signal with clean localization and minimal chemical shift displacement error and was recommended by the international MRS Consensus Group as the preferred localization sequence at high- and ultra-high fields. Across-vendor standardization of the sLASER sequence at 3 tesla has been challenging due to the B1 requirements of the adiabatic inversion pulses and maximum B1 limitations on some platforms. The aims of this study were to design a short-echo sLASER sequence that can be executed within a B1 limit of 15 µT by taking advantage of gradient-modulated RF pulses, to implement it on three major platforms and to evaluate the between-vendor reproducibility of its perfomance with phantoms and in vivo. In addition, voxel-based first and second order B0 shimming and voxel-based B1 adjustments of RF pulses were implemented on all platforms. Amongst the gradient-modulated pulses considered (GOIA, FOCI and BASSI), GOIA-WURST was identified as the optimal refocusing pulse that provides good voxel selection within a maximum B1 of 15 µT based on localization efficiency, contamination error and ripple artifacts of the inversion profile. An sLASER sequence (30 ms echo time) that incorporates VAPOR water suppression and 3D outer volume suppression was implemented with identical parameters (RF pulse type and duration, spoiler gradients and inter-pulse delays) on GE, Philips and Siemens and generated identical spectra on the GE 'Braino' phantom between vendors. High-quality spectra were consistently obtained in multiple regions (cerebellar white matter, hippocampus, pons, posterior cingulate cortex and putamen) in the human brain across vendors (5 subjects scanned per vendor per region; mean signal-to-noise ratio > 33; mean water linewidth between 6.5 Hz to 11.4 Hz). The harmonized sLASER protocol is expected to produce high reproducibility of MRS across sites thereby allowing large multi-site studies with clinical cohorts.

A retrospective physiological noise correction method for oscillating steady-state imaging.

PURPOSE: Oscillating steady-state imaging (OSSI) is an SNR-efficient steady-state sequence with T2∗ sensitivity suitable for FMRI. Due to the frequency sensitivity of the signal, respiration- and drift-induced field changes can create unwanted signal fluctuations. This study aims to address this issue by developing retrospective signal correction methods that utilize OSSI signal properties to denoise task-based OSSI FMRI experiments. METHODS: A retrospective denoising approach was developed that leverages the unique signal properties of OSSI to perform denoising without a manually specified noise region of interest and works with both voxel timecourses (oscillating steady-state correction [OSSCOR]) or FID timecourses (F-OSSCOR). Simulations were performed to estimate the number of principal components optimal for denoising. In vivo experiments at 3 T field strength were conducted to compare the performance of proposed methods against a standard principal component analysis-based method, measured using mean t score within an region of interest, number of activations, and mean temporal SNR. RESULTS: Correction using OSSCOR was significantly better than the standard method in all metrics. Correction using F-OSSCOR was not significantly different from the standard method using an equal number of principal components. Increasing the number of OSSCOR principal components decreased activation strength and increased the number of suspected false positives. However, increasing the number of principal components in F-OSSCOR increased activation strength with little to no increase in false activation. CONCLUSION: Both OSSCOR and F-OSSCOR substantially reduce physiological noise components and increase temporal SNR, improving the functional results of task-based OSSI functional experiments. F-OSSCOR demonstrates a proof of concept utilization of coil-localized FID signal information for physiological noise correction.

High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.

The goals of fMRI acquisition include high spatial and temporal resolutions with a high signal to noise ratio (SNR). Oscillating Steady-State Imaging (OSSI) is a new fMRI acquisition method that provides large oscillating signals with the potential for high SNR, but does so at the expense of spatial and temporal resolutions. The unique oscillation pattern of OSSI images makes it well suited for high-dimensional modeling. We propose a patch-tensor low-rank model to exploit the local spatial-temporal low-rankness of OSSI images. We also develop a practical sparse sampling scheme with improved sampling incoherence for OSSI. With an alternating direction method of multipliers (ADMM) based algorithm, we improve OSSI spatial and temporal resolutions with a factor of 12 acquisition acceleration and 1.3 mm isotropic spatial resolution in prospectively undersampled experiments. The proposed model yields high temporal SNR with more activation than other low-rank methods. Compared to the standard grad- ient echo (GRE) imaging with the same spatial-temporal resolution, 3D OSSI tensor model reconstruction demonstrates 2 times higher temporal SNR with 2 times more functional activation.

Oscillating steady-state imaging (OSSI): A novel method for functional MRI.

PURPOSE: Signal-to-noise ratio (SNR) is crucial for high-resolution fMRI; however, current methods for SNR improvement are limited. A new approach, called oscillating steady-state imaging (OSSI), produces a signal that is large and T2∗ -weighted, and is demonstrated to produce improved SNR compared to gradient echo (GRE) imaging with matched effective TE and spatial-temporal acquisition characteristics for high-resolution fMRI. METHODS: Quadratic phase sequences were combined with balanced gradients to produce a large, oscillating steady-state signal. The quadratic phase progression was periodic over short intervals such as 10 TRs, inducing a frequency-dependent phase dispersal. Images over one period were combined to produce a single image with effectively T2∗ -weighting. The OSSI parameters were explored through simulation and phantom data, and 2D and 3D human fMRI data were collected using OSSI and GRE imaging. RESULTS: Phantom and human OSSI data showed highly reproducible signal oscillations with greater signal strength than GRE. Compared to single slice GRE with matched effective TE and spatial-temporal resolution, OSSI yielded more activation in the visual cortex by a factor of 1.84 and an improvement in temporal SNR by a factor of 1.83. Voxelwise percentage change comparisons between OSSI and GRE demonstrate a similar T2∗ -weighted contrast mechanism with additional T2' -weighting of about 15 ms immediately after the RF pulse. CONCLUSIONS: OSSI is a new acquisition method that exploits a large, oscillating signal that is T2∗ -weighted and suitable for fMRI. The steady-state signal from balanced gradients creates higher signal strength than single slice GRE at varying TEs, enabling greater volumes of functional activity and higher SNR for high-resolution fMRI.

A GRAPPA algorithm for arbitrary 2D/3D non-Cartesian sampling trajectories with rapid calibration.

PURPOSE: GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non-Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non-Cartesian imaging. METHODS: We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k-space location with a distinct constellation, that is, local sampling pattern. We calibrate these generalized kernels using the Fourier transform phase shift property applied to fully gridded or separately acquired Cartesian Autocalibration signal (ACS) data. To handle the resulting large number of different kernels, we introduce a fast calibration algorithm based on nonuniform FFT (NUFFT) and adoption of circulant ACS boundary conditions. We applied our method to retrospectively under-sampled rotated stack-of-stars/spirals in vivo datasets, and to a prospectively under-sampled rotated stack-of-spirals functional MRI acquisition with a finger-tapping task. RESULTS: We reconstructed all datasets without performing any trajectory-specific manual adaptation of the method. For the retrospectively under-sampled experiments, our method achieved image quality (i.e., error and g-factor maps) comparable to conjugate gradient SENSE (cg-SENSE) and SPIRiT. Functional activation maps obtained from our method were in good agreement with those obtained using cg-SENSE, but required a shorter total reconstruction time (for the whole time-series): 3 minutes (proposed) vs 15 minutes (cg-SENSE). CONCLUSIONS: This paper introduces a general 3D non-Cartesian GRAPPA that is fast enough for practical use on today's computers. It is a direct generalization of original GRAPPA to non-Cartesian scenarios. The method should be particularly useful in dynamic imaging where a large number of frames are reconstructed from a single set of ACS data.

Real-Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging.

Estimating a time-varying signal, such as head motion from magnetic resonance imaging data, becomes particularly challenging in the face of other temporal dynamics such as functional activation. This paper describes a new Kalman filter-like framework that includes a sparse residual term in the measurement model. This additional term allows the extended Kalman filter to generate real-time motion estimates suitable for prospective motion correction when such dynamics occur. An iterative augmented Lagrangian algorithm similar to the alterating direction method of multipliers implements the update step for this Kalman filter. This paper evaluates the accuracy and convergence rate of this iterative method for small and large motion in terms of its sensitivity to parameter selection. The included experiment on a simulated functional magnetic resonance imaging acquisition demonstrates that the resulting method improves the maximum Youden's J index of the time series analysis by 2-3% versus retrospective motion correction, while the sensitivity index increases from 4.3 to 5.4 when combining prospective and retrospective correction.

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.

Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling.

In this study, the acquisition of ASL data and quantification of multiple hemodynamic parameters was explored using a Magnetic Resonance Fingerprinting (MRF) approach. A pseudo-continuous ASL labeling scheme was used with pseudo-randomized timings to acquire the MRF ASL data in a 2.5 min acquisition. A large dictionary of MRF ASL signals was generated by combining a wide range of physical and hemodynamic properties with the pseudo-random MRF ASL sequence and a two-compartment model. The acquired signals were matched to the dictionary to provide simultaneous quantification of cerebral blood flow, tissue time-to-peak, cerebral blood volume, arterial time-to-peak, B1, and T1. A study in seven healthy volunteers resulted in the following values across the population in grey matter (mean ±â€¯standard deviation): cerebral blood flow of 69.1 ±â€¯6.1 ml/min/100 g, arterial time-to-peak of 1.5 ±â€¯0.1 s, tissue time-to-peak of 1.5 ±â€¯0.1 s, T1 of 1634 ms, cerebral blood volume of 0.0048 ±â€¯0.0005. The CBF measurements were compared to standard pCASL CBF estimates using a one-compartment model, and a Bland-Altman analysis showed good agreement with a minor bias. Repeatability was tested in five volunteers in the same exam session, and no statistical difference was seen. In addition to this validation, the MRF ASL acquisition's sensitivity to the physical and physiological parameters of interest was studied numerically.

Fast Spatial Resolution Analysis of Quadratic Penalized Least-Squares Image Reconstruction With Separate Real and Imaginary Roughness Penalty: Application to fMRI.

Penalized least-squares iterative image reconstruction algorithms used for spatial resolution-limited imaging, such as functional magnetic resonance imaging (fMRI), commonly use a quadratic roughness penalty to regularize the reconstructed images. When used for complex-valued images, the conventional roughness penalty regularizes the real and imaginary parts equally. However, these imaging methods sometimes benefit from separate penalties for each part. The spatial smoothness from the roughness penalty on the reconstructed image is dictated by the regularization parameter(s). One method to set the parameter to a desired smoothness level is to evaluate the full width at half maximum of the reconstruction method's local impulse response. Previous work has shown that when using the conventional quadratic roughness penalty, one can approximate the local impulse response using an FFT-based calculation. However, that acceleration method cannot be applied directly for separate real and imaginary regularization. This paper proposes a fast and stable calculation for this case that also uses FFT-based calculations to approximate the local impulse responses of the real and imaginary parts. This approach is demonstrated with a quadratic image reconstruction of fMRI data that uses separate roughness penalties for the real and imaginary parts.