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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 noninvasive experiment for both humans and rats. Ten humans and 10 Sprague-Dawley rats consumed MRI-visible semisolid meals and underwent MRI scans. We used a surface model to analyze MRI data, capturing the deformation of the stomach wall on ingestion or during digestion. We inferred muscle activity, mapped motor processes, parcellated the stomach into functional regions, and revealed cross-species distinctions. In humans, both the fundus and antrum distended postmeal, followed by sustained tonic contractions to regulate intragastric pressure. Peristaltic contractions initiated from the distal fundus, including three concurrent wavefronts oscillating at 3.3 cycles/min and traveling at 1.7 to 2.9 mm/s. These motor functions facilitated linear gastric emptying with a 61-min half-time. In contrast, rats exhibited peristalsis from the midcorpus, showing two wavefronts oscillating at 5.0 cycles/min and traveling at 0.4 to 0.9 mm/s. For both species, motility features allowed functional parcellation of the stomach along a midcorpus division. 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. NEW & NOTEWORTHY: A novel MRI technique can visualize how the stomach accommodates, mixes, and propels food for digestion in humans and animals alike. Digital models of gastric MRI reveal the functional maps, organization, and distinction of the stomach across individuals and species. This technique holds the unique potential to advance basic and clinical studies of functional gastric disorders. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In fast magnetic resonance (MR) imaging with long readout times, such as echo-planar imaging (EPI) and spiral scans, it is important to correct for the effects of field inhomogeneity to reduce image distortion and blurring. Such corrections require an accurate field map, a map of the off-resonance frequency at each voxel. Standard field map estimation methods yield noisy field maps, particularly in image regions with low spin density. This paper describes regularized methods for field map estimation from two or more MR scans having different echo times. These methods exploit the fact that field maps are generally smooth functions. The methods use algorithms that decrease monotonically a regularized least-squares cost function, even though the problem is highly nonlinear. Results show that the proposed regularized methods significantly improve the quality of field map estimates relative to conventional unregularized methods. Index Terms--Magnetic field inhomogeneity, susceptibility, penalized-likelihood estimation, phase unwrapping, regularization.

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) is conventionally done by reconstructing [T.sup.*.sub.2]-weighted images. However, since the images are unitless they are nonquantifiable in terms of important physiological parameters. An alternative approach is to reconstruct [R.sup.*.sub.2] maps which are quantifiable and have comparable BOLD contrast as [T.sup.*.sub.2]-weighted images. However, conventional [R.sup.*.sub.2] mapping involves long readouts and ignores relaxation during readout. Another problem with fMRI imaging is temporal drift/fluctuations in off-resonance. Conventionally, a field map is collected at the start of the fMRI study to correct for off-resonance, ignoring any temporal changes. Here, we propose a new fast regularized iterative algorithm that jointly reconstructs [R.sup.*.sub.2] and field maps for all time frames in fMRI data. To accelerate the algorithm we linearize the MR signal model, enabling the use of fast regularized iterative reconstruction methods. The regularizer was designed to account for the different resolution properties of both [R.sup.*.sub.2] and field maps and provide uniform spatial resolution. For fMRI data with the same temporal frame rate as data collected for [R.sup.*.sub.2]-weighted imaging the resulting [R.sup.*.sub.2] maps performed comparably to [T.sup.*.sub.2]-weighted images in activation detection while also correcting for spatially global and local temporal changes in off-resonance. Index Terms--Field map, functional magnetic resonance imaging (fMRI), joint reconstruction, linear approximation, magnetic field drift correction, physiological noise correction, [R.sup.*.sub.2].

Magnetic resonance (MR) compatibility of biomedical implants and devices represents a challenge for designers and potential risks for users. This paper addresses these problems and presents the first MR-compatible multichannel silicon chronic microelectrode system, used for recording and electrical stimulation of the central nervous system for animal models. A standard chronic assembly, from the Center for Neural Communication Technology at the University of Michigan, was tested on a 2 Tesla magnet to detect forces, heating, and image distortions, and modified to minimize or eliminate susceptibility artifacts, tissue damage, and electrode displacement, maintaining good image quality and safety to the animals. Multiple commercial connectors were tested for MR compatibility and several options for the reference electrode were also tested to minimize image artifacts and provide a stable biocompatible reference for short-and long-term neural recordings. Different holding screws were tested to anchor the microelectrode assembly on the top of the skull. The final selection of this part was based on MR-compatibility, biocompatibility, durability, and mechanical and chemical stability. The required adaptor to interconnect the MR-compatible microelectrode with standard data acquisition systems was also designed and fabricated. The final design is fully MR-compatible and has been successfully tested on guinea pigs. Index Terms--Biomedical implants, magnetic resonance compatibility, magnetic resonance imaging, magnetic susceptibility, multichannel silicon microelectrodes.

A faster iterative approach to field-correlated magnetic resonance (MR) image reconstruction based on the conjugate gradient (CG) algorithm and certain Toeplitz matrices is proposed. Simulation results show that the proposed CG-Toeplitz approach produces equivalent image quality as the CG-NUFFT (nonuniform fast Fourier transform) method with significantly reduced computation time.

A new image reconstruction method to correct for the effects of magnetic field inhomogeneity in non-Cartesian sampled magnetic resonance imaging (MRI) is proposed. The conjugate phase reconstruction method, which corrects for phase accumulation due to applied gradients and magnetic field inhomogeneity, has been commonly used for this case. This can lead to incomplete correction, in part, due to the presence of gradients in the field inhomogeneity function. Based on local distortions to the k-space trajectory from these gradients, a spatially variant sample density compensation function is introduced as part of the conjugate phase reconstruction. This method was applied to both simulated and experimental spiral imaging data and shown to produce more accurate image reconstructions. Two approaches for fast implementation that allow the use of fast Fourier transforms are also described. The proposed method is shown to produce fast and accurate image reconstructions for spiral sampled MRI. Index Terms--Image reconstruction, magnetic field inhomogeneity, magnetic resonance imaging, spiral imaging.

In magnetic resonance imaging, magnetic field inhomogeneities cause distortions in images that are reconstructed by conventional fast Fourier trasform (FFT) methods. Several noniterative image reconstruction methods are used currently to compensate for field inhomogeneities, but these methods assume that the field map that characterizes the off-resonance frequencies is spatially smooth. Recently, iterative methods have been proposed that can circumvent this assumption and provide improved compensation for off-resonance effects. However, straightforward implementations of such iterative methods suffer from inconveniently long computation times. This paper describes a tool for accelerating iterative reconstruction of field-corrected MR images: a novel time-segmented approximation to the MR signal equation. We use a min-max formulation to derive the temporal interpolator. Speedups of around 60 were achieved by combining this temporal interpolator with a nonuniform fast Fourier transform with normalized root mean squared approximation errors of 0.07%. The proposed method provides fast, accurate, field-corrected image reconstruction even when the field map is not smooth. Index Terms--Field inhomogeneity correction, image reconstruction, iterative methods, magnetic resonance imaging, temporal interpolation, time segmentation.