Multi-channel Magnetic Resonance Imaging Research Articles

Adaptable Dual-Tuned Optically Controlled On-Coil RF Power Amplifier for MRI.

An adaptable optically controlled RF power amplifier (RFPA) is presented for direct implementation on the Magnetic Resonance Imaging (MRI) transmit coil. Operation at 1H and multiple X-nuclei frequencies for 7T MRI was demonstrated through the automated tuning of an effective voltage-modulated inductor located in the gate driver circuit of the FET switches in the different amplification stages. Through this automated tuning the amplifier can be adapted from the control to operate at the selected 1H and X-nuclei frequency in a multinuclear MRI study. Bench and MRI data acquired with the adaptable dual-tuned on-coil RFPA is presented. This technology should allow a simpler, more efficient and versatile implementation of the multinuclear multichannel MRI hardware. Ultimately, to extend the research on MRI detectable nuclei that can provide new insights about healthy and diseased tissue.

M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research

Recently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of ~1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.

Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes

This retrospective and exploratory study investigated the efficiency of the 3T perfusion magnetic resonance imaging (MRI) at the classification of MS lesion subtypes. For the MS lesion subtype classification, firstly, it was necessary to segment all MS lesions. Therefore, a Bayesian classifier based on the adaptive mixture method was used to segment all lesions, and an artificial neural network (ANN) employed a multi-layer Perceptron as a subtype classifier. The Bayesian classifier accomplished the segmentation of lesions using Fluid Attenuated Inversion Recovery automatically, and the ANN part was used as a subtype classifier that worked based on extracted information from perfusion MRI (i.e. Mean Transit Time and Cerebral Blood Volume maps) along with the intensity information of the conventional multi-channel MRI in segmented lesions. Adding 3-Tesla perfusion MRI to the proposed model for the subtype classification led to an increment of about 7% and 13% in the sensitivity of acute and chronic lesion classifications, respectively. The sensitivity of T2 lesions did not meaningfully change. The overall accuracy of the classification for acute, chronic, and T2 lesion classifications was 96.1%, 90.5%, and 92.9%, respectively. The proposed architectures reached high sensitivity in discrimination between MS lesion subtypes when 3T perfusion MRIs were used.

PIC-GAN: A Parallel Imaging Coupled Generative Adversarial Network for Accelerated Multi-Channel MRI Reconstruction.

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

Noise Mapping and Removal in Complex-Valued Multi-Channel MRI via Optimal Shrinkage of Singular Values.

In magnetic resonance imaging (MRI), noise is a limiting factor for higher spatial resolution and a major cause of prolonged scan time, owing to the need for repeated scans. Improving the signal-to-noise ratio is therefore key to faster and higher-resolution MRI. Here we propose a method for mapping and reducing noise in MRI by leveraging the inherent redundancy in complex-valued multi-channel MRI data. Our method leverages a provably optimal strategy for shrinking the singular values of a data matrix, allowing it to outperform state-of-the-art methods such as Marchenko-Pastur PCA in noise reduction. Our method reduces the noise floor in brain diffusion MRI by 5-fold and remarkably improves the contrast of spiral lung 19F MRI. Our framework is fast and does not require training and hyper-parameter tuning, therefore providing a convenient means for improving SNR in MRI.

A Design Scheme for Data Transmission Module on Multi-Channel Magnetic Resonance Imaging Spectrometers

A Design Scheme for Data Transmission Module on Multi-Channel Magnetic Resonance Imaging Spectrometers

Deep learning-based approach for segmentation of glioma sub-regions in MRI

PurposeBrain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.Design/methodology/approachIn this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.FindingsThe proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.Practical implicationsThe model can be used by doctors to identify the exact location of the tumorous region.Originality/valueThe proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

Enhanced Deep-Learning-Based Magnetic Resonance Image Reconstruction by Leveraging Prior Subject-Specific Brain Imaging: Proof-of-Concept Using a Cohort of Presumed Normal Subjects

Deep learning models have shown potential for reconstructing undersampled, multi-channel magnetic resonance (MR) image acquisitions. Recently proposed methods, however, have not leveraged information from prior subject-specific MR imaging sessions. Such data are often readily available through a picture archiving and communication system (PACS). We propose a flexible three-step method to incorporate this prior information into an enhanced deep-learning-based reconstruction process. The method consists of Step 1: an initial reconstruction; Step 2: registration of the previous scan to the initial reconstruction; and Step 3: an enhancement network. Training and testing used longitudinally acquired, three-dimensional, T1-weighted brain images acquired with different acquisition parameters. We tested our networks using data from $\mathbf {2808}$ images (obtained in 18 subjects) under four different acceleration factors ( $\mathbf {R=\lbrace 5,10,15,20\rbrace }$ ). Our enhanced reconstruction (Steps 1-3) produced higher-quality images: structural similarity and peak signal-to-noise ratio increased, and normalized root mean squared error decreased on average by $\mathbf {16.5\%}$ , $\mathbf {7.0\%}$ and $\mathbf {21.1\%}$ , respectively, compared to the non-enhanced reconstruction (Step 1 only) under the same network capacity as the enhanced reconstruction model. These differences were statistically significant ( $\boldsymbol{p , Wilcoxon signed-rank test). Further volumetric analysis performed on key brain regions (brain, white matter, gray matter and cortex) indicated that our enhanced images had better volume agreement with the fully sampled reference images compared to the non-enhanced images. Our enhanced images for $\mathbf {R=20}$ were comparable to the non-enhanced images for $\mathbf {R=10}$ demonstrating that our proposed method can use prior scan information to further accelerate MR examinations.

Three-Dimensional Block Matching Using Orthonormal Tree-Structured Haar Transform for Multichannel Images.

Multichannel images, i.e., images of the same object or scene taken in different spectral bands or with different imaging modalities/settings, are common in many applications. For example, multispectral images contain several wavelength bands and hence, have richer information than color images. Multichannel magnetic resonance imaging and multichannel computed tomography images are common in medical imaging diagnostics, and multimodal images are also routinely used in art investigation. All the methods for grayscale images can be applied to multichannel images by processing each channel/band separately. However, it requires vast computational time, especially for the task of searching for overlapping patches similar to a given query patch. To address this problem, we propose a three-dimensional orthonormal tree-structured Haar transform (3D-OTSHT) targeting fast full search equivalent for three-dimensional block matching in multichannel images. The use of a three-dimensional integral image significantly saves time to obtain the 3D-OTSHT coefficients. We demonstrate superior performance of the proposed block matching.

Memory-Efficient Learning for Large-Scale Computational Imaging

Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical physics-based reconstructions. Termed physics-based networks, they incorporate both the known physics of the system via its forward model, and the power of deep learning via data-driven training. However, for realistic large-scale physics-based networks, computing gradients via backpropagation is infeasible due to the memory limitations of graphics processing units. In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network's layers to enable physics-based learning for large-scale computational imaging systems. We demonstrate our method on a compressed sensing example, as well as two large-scale real-world systems: 3D multi-channel magnetic resonance imaging and super-resolution optical microscopy.

Development and preclinical testing of a novel biodegradable hydrogel vaginal packing technology for gynecologic high-dose-rate brachytherapy.

PurposeWe evaluated the performance of a novel hydrogel-based strategy developed for clinical use as vaginal packing using phantoms and cadavers, and to compare the hydrogel to gauze and balloon packing.Material and methodsThe biocompatible hydrogel is based on a thiol-Michael addition reaction, with delivery of reagents into the vaginal cavity using a custom-made system. Soft-cured cadavers were used for soft tissue-like mechanical properties. Two cadavers with intact uteri had magnetic resonance imaging (MRI) compatible with tandem and ovoids. For one cadaver, the temperature of the vaginal canal was measured before hydrogel application, during polymerization, and after hydrogel removal. The hydrogel packing and applicator was kept in a second cadaver, which was imaged using computed tomography (CT) and MRI. The hydrogel packing and imaging was repeated for an open multichannel MRI compatible, titanium-based vaginal cylinder placed in a post-hysterectomy cadaver.ResultsThe gel reaction occurred within 90 seconds, indicating polymerization at clinical quantities with a 5°C increase in vaginal temperature. CT and MRI imaging identified the hydrogel readily and showed a conformance to anatomy with few air pockets. The entire hydrogel packing was readily retrieved upon completion of imaging.ConclusionsThe novel strategy for polyethylene glycol (PEG)-based hydrogel intra-vaginal packing was able to rapidly polymerize in human cadavers with minimal heat production. Delivery was efficient and able to fill the contours of the vaginal cavity and displace tissue away from the applicator axis. The hydrogel has favorable imaging characteristics on CT and MRI, and shows a potential for clinical use, warranting additional studies for the use in humans.

A Fully Integrated Dual-Channel On-Coil CMOS Receiver for Array Coils in 1.5-10.5 T MRI.

Magnetic resonance imaging (MRI) is among the most important medical imaging modalities. Coil arrays and receivers with high channel counts (16 and more) have to be deployed to obtain the image quality and acquisition speed required by modern clinical protocols. In this paper, we report the theoretical analysis, the system-level design, and the circuit implementation of the first receiver IC (RXIC) for clinical MRI fully integrated in a modern CMOS technology. The dual-channel RXIC sits directly on the sensor coil, thus eliminating any RF cable otherwise required to transport the information out of the magnetic field. The first stage LNA was implemented using a noise-canceling architecture providing a highly reflective input used to decouple the individual channels of the array. Digitization is performed directly on-chip at base-band by means of a delta-sigma modulator, allowing the subsequent optical transmission of data. The presented receiver, implemented in a CMOS technology, is compatible with MRI scanners up to . It reaches sub- noise figure for MRI units and features a dynamic range up to at a power consumption below per channel, with an area occupation of . Mounted on a small-sized printed circuit board (PCB), the receiver IC has been employed in a commercial MRI scanner to acquire in-vivo images matching the quality of traditional systems, demonstrating the first step toward multichannel wearable MRI array coils.

A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis.

IntroductionLongitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using features from image intensities, subtraction values, and deformation fields (DF).MethodsOne year apart multi-channel brain MRI scans were obtained for 60 patients, 36 of them with new T2-w lesions. Images from both temporal points were preprocessed and co-registered. Afterwards, they were registered using multi-resolution affine registration, allowing their subtraction. In particular, the DFs between both images were computed with the Demons non-rigid registration algorithm. Afterwards, a logistic regression model was trained with features from image intensities, subtraction values, and DF operators. We evaluated the performance of the model following a leave-one-out cross-validation scheme.ResultsIn terms of detection, we obtained a mean Dice similarity coefficient of 0.77 with a true-positive rate of 74.30% and a false-positive detection rate of 11.86%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.56. The performance of our model was significantly higher than state-of-the-art methods.ConclusionsThe performance of the proposed method shows the benefits of using DF operators as features to train a supervised learning model. Compared to other methods, the proposed model decreases the number of false-positives while increasing the number of true-positives, which is relevant for clinical settings.

On the estimation of the worst-case implant-induced RF-heating in multi-channel MRI

The increasing use of multiple radiofrequency (RF) transmit channels in magnetic resonance imaging (MRI) systems makes it necessary to rigorously assess the risk of RF-induced heating. This risk is especially aggravated with inclusions of medical implants within the body. The worst-case RF-heating scenario is achieved when the local tissue deposition in the at-risk region (generally in the vicinity of the implant electrodes) reaches its maximum value while MRI exposure is compliant with predefined general specific absorption rate (SAR) limits or power requirements. This work first reviews the common approach to estimate the worst-case RF-induced heating in multi-channel MRI environment, based on the maximization of the ratio of two Hermitian forms by solving a generalized eigenvalue problem. It is then shown that the common approach is not rigorous and may lead to an underestimation of the worst-case RF-heating scenario when there is a large number of RF transmit channels and there exist multiple SAR or power constraints to be satisfied. Finally, this work derives a rigorous SAR-based formulation to estimate a preferable worst-case scenario, which is solved by casting a semidefinite programming relaxation of this original non-convex problem, whose solution closely approximates the true worst-case including all SAR constraints. Numerical results for 2, 4, 8, 16, and 32 RF channels in a 3T-MRI volume coil for a patient with a deep-brain stimulator under a head imaging exposure are provided as illustrative examples.

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features.

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.

Electromagnetic Near-Field Inhomogeneity Reduction for Image Acquisition Optimization in High-Resolution Multi-Channel Magnetic Resonance Imaging (MRI) Systems

This paper is a study of the inhomogeneity reduction for near-field acquisition in high-resolution magnetic resonance imaging (MRI) systems. The acquisition homogeneity in MRI imaging modality is an open issue concerning the optimal MRI image generation in terms of the RF signal acquisition. The acquisition inhomogeneity is related to the radiation patterns of the receiving antennas and its location in the MRI system, among other relevant aspects. The acquisition inhomogeneity is translated into two main effects: pattern ripples at the outer cylindrical rings and radial inhomogeneity when comparing the center value (maximum) with the rest of the pattern. To overcome these effects, two strategies are proposed. In the first one, it is proposed to progressively vary the antenna location in the azimuthal array distribution. In the second one, it is proposed to progressively vary the antenna amplitude and phase feeding in the array distribution. To compute a figure of merit of the pattern radial uniformity and the ripples, two metrics are defined in this paper. It is proved that both the progressive modification in the location at each array ring and the variation of the feeding phase of each array ring reduce the pattern ripples and radial inhomogeneity. Optimal values for either the angular rotation or the feeding phase values can be calculated, depending on the particular dimensions of the cylinder that conforms the region of interest.

Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model.

Sparsity-promoting regularizers can enable stable recovery of highly undersampled magnetic resonance imaging (MRI), promising to improve the clinical utility of challenging applications. However, lengthy computation time limits the clinical use of these methods, especially for dynamic MRI with its large corpus of spatiotemporal data. Here, we present a holistic framework that utilizes the balanced sparse model for compressive sensing and parallel computing to reduce the computation time of cardiac MRI recovery methods. We propose a fast, iterative soft-thresholding method to solve the resulting ℓ1-regularized least squares problem. In addition, our approach utilizes a parallel computing environment that is fully integrated with the MRI acquisition software. The methodology is applied to two formulations of the multichannel MRI problem: image-based recovery and k-space-based recovery. Using measured MRI data, we show that, for a 224 × 144 image series with 48 frames, the proposed k-space-based approach achieves a mean reconstruction time of 2.35 min, a 24-fold improvement compared a reconstruction time of 55.5 min for the nonlinear conjugate gradient method, and the proposed image-based approach achieves a mean reconstruction time of 13.8 s. Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Aggregated motion estimation for real-time MRI reconstruction

In real-time MRI serial images are generally reconstructed from highly undersampled datasets as the iterative solutions of an inverse problem. While practical realizations based on regularized nonlinear inversion (NLINV) have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal fidelity of the estimated reconstructions. The proposed method for real-time image reconstruction integrates the deformations between nearby frames into the data consistency term of the inverse problem. The aggregated motion estimation (AME) is not required to be affine or rigid and does not need additional measurements. Moreover, it handles multi-channel MRI data by simultaneously determining the image and its coil sensitivity profiles in a nonlinear formulation which also adapts to non-Cartesian (e.g., radial) sampling schemes. The new method was evaluated for real-time MRI studies using highly undersampled radial gradient-echo sequences. AME reconstructions for a motion phantom with controlled speed as well as for measurements of human heart and tongue movements demonstrate improved temporal fidelity and reduced residual undersampling artifacts when compared with NLINV reconstructions without motion estimation. Nonlinear inverse reconstructions with aggregated motion estimation offer improved image quality and temporal acuity for visualizing rapid dynamic processes by real-time MRI.

Medusa: a scalable MR console using USB.

Magnetic resonance imaging (MRI) pulse sequence consoles typically employ closed proprietary hardware, software, and interfaces, making difficult any adaptation for innovative experimental technology. Yet MRI systems research is trending to higher channel count receivers, transmitters, gradient/shims, and unique interfaces for interventional applications. Customized console designs are now feasible for researchers with modern electronic components, but high data rates, synchronization, scalability, and cost present important challenges. Implementing large multichannel MR systems with efficiency and flexibility requires a scalable modular architecture. With Medusa, we propose an open system architecture using the universal serial bus (USB) for scalability, combined with distributed processing and buffering to address the high data rates and strict synchronization required by multichannel MRI. Medusa uses a modular design concept based on digital synthesizer, receiver, and gradient blocks, in conjunction with fast programmable logic for sampling and synchronization. Medusa is a form of synthetic instrument, being reconfigurable for a variety of medical/scientific instrumentation needs. The Medusa distributed architecture, scalability, and data bandwidth limits are presented, and its flexibility is demonstrated in a variety of novel MRI applications.