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.

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.

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.

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.

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.

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.

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.

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.

TOPPE: A framework for rapid prototyping of MR pulse sequences.

PURPOSE: To introduce a framework for rapid prototyping of MR pulse sequences. METHODS: We propose a simple file format, called "TOPPE", for specifying all details of an MR imaging experiment, such as gradient and radiofrequency waveforms and the complete scan loop. In addition, we provide a TOPPE file "interpreter" for GE scanners, which is a binary executable that loads TOPPE files and executes the sequence on the scanner. We also provide MATLAB scripts for reading and writing TOPPE files and previewing the sequence prior to hardware execution. With this setup, the task of the pulse sequence programmer is reduced to creating TOPPE files, eliminating the need for hardware-specific programming. No sequence-specific compilation is necessary; the interpreter only needs to be compiled once (for every scanner software upgrade). We demonstrate TOPPE in three different applications: k-space mapping, non-Cartesian PRESTO whole-brain dynamic imaging, and myelin mapping in the brain using inhomogeneous magnetization transfer. RESULTS: We successfully implemented and executed the three example sequences. By simply changing the various TOPPE sequence files, a single binary executable (interpreter) was used to execute several different sequences. CONCLUSION: The TOPPE file format is a complete specification of an MR imaging experiment, based on arbitrary sequences of a (typically small) number of unique modules. Along with the GE interpreter, TOPPE comprises a modular and flexible platform for rapid prototyping of new pulse sequences. Magn Reson Med 79:3128-3134, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Design of spectral-spatial phase prewinding pulses and their use in small-tip fast recovery steady-state imaging.

PURPOSE: Spectrally selective "prewinding" radiofrequency pulses can counteract B0 inhomogeneity in steady-state sequences, but can only prephase a limited range of off-resonance. We propose spectral-spatial small-tip angle prewinding pulses that increase the off-resonance bandwidth that can be successfully prephased by incorporating spatially tailored excitation patterns. THEORY AND METHODS: We present a feasibility study to compare spectral and spectral-spatial prewinding pulses. These pulses add a prephasing term to the target magnetization pattern that aims to recover an assigned off-resonance bandwidth at the echo time. For spectral-spatial pulses, the design bandwidth is centered at the off-resonance frequency for each spatial location in a field map. We use these pulses in the small-tip fast recovery steady-state sequence, which is similar to balanced steady-state free precession. We investigate improvement of spectral-spatial pulses over spectral pulses using simulations and small-tip fast recovery scans of a gel phantom and human brain. RESULTS: In simulation, spectral-spatial pulses yielded lower normalized root mean squared excitation error than spectral pulses. For both experiments, the spectral-spatial pulse images are also qualitatively better (more uniform, less signal loss) than the spectral pulse images. CONCLUSION: Spectral-spatial prewinding pulses can prephase over a larger range of off-resonance than their purely spectral counterparts. Magn Reson Med 79:1377-1386, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A regularized, model-based approach to phase-based conductivity mapping using MRI.

PURPOSE: To develop a novel regularized, model-based approach to phase-based conductivity mapping that uses structural information to improve the accuracy of conductivity maps. THEORY AND METHODS: The inverse of the three-dimensional Laplacian operator is used to model the relationship between measured phase maps and the object conductivity in a penalized weighted least-squares optimization problem. Spatial masks based on structural information are incorporated into the problem to preserve data near boundaries. The proposed Inverse Laplacian method was compared against a restricted Gaussian filter in simulation, phantom, and human experiments. RESULTS: The Inverse Laplacian method resulted in lower reconstruction bias and error due to noise in simulations than the Gaussian filter. The Inverse Laplacian method also produced conductivity maps closer to the measured values in a phantom and with reduced noise in the human brain, as compared to the Gaussian filter. CONCLUSION: The Inverse Laplacian method calculates conductivity maps with less noise and more accurate values near boundaries. Improving the accuracy of conductivity maps is integral for advancing the applications of conductivity mapping. Magn Reson Med 78:2011-2021, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images.

One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T1 ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (Ktrans , vp and Kb ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

A parametric model of the brain vascular system for estimation of the arterial input function (AIF) at the tissue level.

In this paper, we introduce a novel model of the brain vascular system, which is developed based on laws of fluid dynamics and vascular morphology. This model is used to address dispersion and delay of the arterial input function (AIF) at different levels of the vascular structure and to estimate the local AIF in DCE images. We developed a method based on the simplex algorithm and Akaike information criterion to estimate the likelihood of the contrast agent concentration signal sampled in DCE images belonging to different layers of the vascular tree or being a combination of different signal levels from different nodes of this structure. To evaluate this method, we tested the method on simulated local AIF signals at different levels of this structure. Even down to a signal to noise ratio of 5.5 our method was able to accurately detect the branching level of the simulated signals. When two signals with the same power level were combined, our method was able to separate the base signals of the composite AIF at the 50% threshold. We applied this method to dynamic contrast enhanced computed tomography (DCE-CT) data, and using the parameters estimated by our method we created an arrival time map of the brain. Our model corrected AIF can be used for solving the pharmacokinetic equations for more accurate estimation of vascular permeability parameters in DCE imaging studies.

Coil compression in simultaneous multislice functional MRI with concentric ring slice-GRAPPA and SENSE.

PURPOSE: Simultaneous multislice (SMS) imaging is a useful way to accelerate functional magnetic resonance imaging (fMRI). As acceleration becomes more aggressive, an increasingly larger number of receive coils are required to separate the slices, which significantly increases the computational burden. We propose a coil compression method that works with concentric ring non-Cartesian SMS imaging and should work with Cartesian SMS as well. We evaluate the method on fMRI scans of several subjects and compare it to standard coil compression methods. METHODS: The proposed method uses a slice-separation k-space kernel to simultaneously compress coil data into a set of virtual coils. Five subjects were scanned using both non-SMS fMRI and SMS fMRI with three simultaneous slices. The SMS fMRI scans were processed using the proposed method, along with other conventional methods. Code is available at https://github.com/alcu/sms. RESULTS: The proposed method maintained functional activation with a fewer number of virtual coils than standard SMS coil compression methods. Compression of non-SMS fMRI maintained activation with a slightly lower number of virtual coils than the proposed method, but does not have the acceleration advantages of SMS fMRI. CONCLUSION: The proposed method is a practical way to compress and reconstruct concentric ring SMS data and improves the preservation of functional activation over standard coil compression methods in fMRI. Magn Reson Med 76:1196-1209, 2016. © 2015 Wiley Periodicals, Inc.

Improved spoiling efficiency in dynamic RF-spoiled imaging by ghost phase modulation and temporal filtering.

PURPOSE: Radiofrequency-spoiled steady-state sequences offer rapid data acquisition with T1- or T2*-weighting. The spoiler gradients in these sequences must be large enough to suppress ghost artifacts, and are chosen empirically. However, certain factors such as the need to minimize gradient first moments or acoustic noise can limit the spoiler size and, hence, the ability to suppress ghosts. We present an acquisition and preprocessing strategy for improved spoiling efficiency in conventional and echo-shifted dynamic radiofrequency-spoiled 3D imaging. THEORY AND METHODS: By requiring each time-frame in a dynamic imaging sequence to contain a particular (restricted) number of total radiofrequency shots, the ghost signal can be made to alternate in sign every other frame. The ghost is then suppressed by Fourier transforming along the temporal dimension, and removing the Nyquist frequency in preprocessing (similar to UNFOLD). The method works for both Cartesian and non-Cartesian imaging. RESULTS: We demonstrate improved ghost suppression with the proposed approach, for both conventional and echo-shifted spoiled gradient echo imaging in stationary phantoms and in vivo. Cartesian echo-shifted spoiled gradient echo imaging produces two ghosts shifted in opposite directions, both of which are suppressed with our method. CONCLUSION: For a given spoiler gradient area, the proposed approach substantially suppresses the ghost signal in both conventional and echo-shifted dynamic radiofrequency-spoiled imaging. Magn Reson Med 75:2388-2393, 2016. © 2015 Wiley Periodicals, Inc.

Rapid inner-volume imaging in the steady-state with 3D selective excitation and small-tip fast recovery imaging.

PURPOSE: Develop a method for rapid three-dimensional inner-volume (IV), or reduced field-of-view, steady-state imaging. METHODS: Tailored radiofrequency pulses for exciting a three-dimensional IV were designed using a recently proposed algorithm and used in three different sequences: spoiled gradient echo, balanced steady-state free precession, and "small-tip fast recovery" (STFR) which uses a "tip-up" RF pulse after the readout to fast recover spins to the longitudinal axis. The inner- and outer-volume (OV) steady-state signals were analyzed. To demonstrate the potential utility of the proposed method, segmented stack-of-spirals reduced field-of-view images in a volunteer were acquired. RESULTS: For a given three-dimensional IV excitation pulse, STFR can achieve higher IV/OV signal ratio compared with spoiled gradient echo and balanced steady-state free precession. For spoiled gradient echo and balanced steady-state free precession, this ratio is significantly lower than that produced by a single IV excitation. For STFR, this ratio exceeds that produced by a single IV excitation, due to partial OV saturation produced by the nonspatially selective tip-up pulse. Reduced FOV STFR stack-of-spirals imaging with 2-fold under-sampling in both x-y and z is demonstrated. CONCLUSION: STFR provides an effective mechanism for OV suppression in steady-state IV imaging. The recently proposed joint pulse design method can be used in the STFR sequence to achieve fast reduced field-of-view imaging. Magn Reson Med 76:1217-1223, 2016. © 2015 Wiley Periodicals, Inc.

Balanced SSFP-like steady-state imaging using small-tip fast recovery with a spectral prewinding pulse.

PURPOSE: Small-tip fast recovery (STFR) imaging has been proposed recently as a potential alternative to balanced steady-state free precession (bSSFP). STFR relies on a tailored "tip-up" radio-frequency pulse to achieve comparable signal level as bSSFP, but with reduced banding artifacts and transient oscillations, and is compatible with magnetization-preparation pulses. Previous STFR implementations used two-dimensional or three-dimensional pulses spatially tailored to the accumulated phase calculated from a B0 field map, making the steady-state STFR signal contain some T2* weighting. Here, we propose to replace the spatially tailored pulse with a recently introduced spectrally selective "pre-winding" pulse that is precomputed to a target frequency range. The proposed "spectral-STFR" sequence produces T2/T1-weighted images similar to bSSFP, but with reduced banding and potentially other benefits. THEORY AND METHODS: We investigated the steady-state signal properties of spectral-STFR using simulations, and phantom and human volunteer experiments. RESULTS: Our simulation and experimental results showed that the spectral-STFR sequence has similar signal level and tissue contrast as bSSFP, but has a wider passband and more consistent banding profiles across different tissues (e.g., less hyperintense signal at band edges for low flip angles). Care is needed in designing the spectral radio-frequency pulse to ensure that the small tip angle approximation holds during radio-frequency transmission. CONCLUSION: Spectral-STFR has similar tissue contrast as bSSFP but a wider passband and more consistent cerebrospinal fluid/brain tissue contrast across the passband. The spectral-STFR sequence is a potential alternative to bSSFP in some applications. Compared to a spatially tailored STFR sequence, spectral-STFR can be precomputed, is easier to implement in practice, and potentially has more uniform image contrast and minimal T2* weighting.

Decreased Fronto-Limbic Activation and Disrupted Semantic-Cued List Learning in Major Depressive Disorder.

OBJECTIVES: Individuals with major depressive disorder (MDD) demonstrate poorer learning and memory skills relative to never-depressed comparisons (NDC). Previous studies report decreased volume and disrupted function of frontal lobes and hippocampi in MDD during memory challenge. However, it has been difficult to dissociate contributions of short-term memory and executive functioning to memory difficulties from those that might be attributable to long-term memory deficits. METHODS: Adult males (MDD, n=19; NDC, n=22) and females (MDD, n=23; NDC, n=19) performed the Semantic List Learning Task (SLLT) during functional magnetic resonance imaging. The SLLT Encoding condition consists of 15 lists, each containing 14 words. After each list, a Distractor condition occurs, followed by cued Silent Rehearsal instructions. Post-scan recall and recognition were collected. Groups were compared using block (Encoding-Silent Rehearsal) and event-related (Words Recalled) models. RESULTS: MDD displayed lower recall relative to NDC. NDC displayed greater activation in several temporal, frontal, and parietal regions, for both Encoding-Silent Rehearsal and the Words Recalled analyses. Groups also differed in activation patterns in regions of the Papez circuit in planned analyses. The majority of activation differences were not related to performance, presence of medications, presence of comorbid anxiety disorder, or decreased gray matter volume in MDD. CONCLUSIONS: Adults with MDD exhibit memory difficulties during a task designed to reduce the contribution of individual variability from short-term memory and executive functioning processes, parallel with decreased activation in memory and executive functioning circuits. Ecologically valid long-term memory tasks are imperative for uncovering neural correlates of memory performance deficits in adults with MDD.