A customizable method (OPTIKS) for designing fast trajectory-constrained gradient waveforms with optimized time domain properties was developed. Given a specified multidimensional $k$ -space trajectory, the method optimizes traversal speed (and therefore timing) with position along the trajectory. OPTIKS facilitates optimization of objectives dependent on the time domain gradient waveform and the arc-length domain $k$ -space speed. OPTIKS is applied to design waveforms which limit peripheral nerve stimulation (PNS), minimize mechanical resonance excitation, and reduce acoustic noise. A variety of trajectory examples are presented including spirals, circular echo-planar-imaging, and rosettes. Design performance is evaluated based on duration, standardized PNS models, field measurements, gradient coil back-EMF measurements, and calibrated acoustic measurements. We show reductions in back-EMF of up to 94% and field oscillations up to 91.1%, acoustic noise decreases of up to 9.22 dB, and with efficient use of PNS models speed increases of up to 11.4%. The design method implementation is made available as an open source Python package through GitHub (https://github.com/mamccready/optiks).

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R =5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

It has been demonstrated that the training problem for a variety of (non) linear two-layer neural networks (such as two-layer perceptrons, convolutional networks, and self-attention) can be posed as equivalent convex optimization problems, with an induced regularizer which encourages low rank. However, this regularizer becomes prohibitively expensive to compute at moderate scales, impeding training convex neural networks. To this end, we propose applying the Burer-Monteiro factorization to convex neural networks, which for the first time enables a Burer-Monteiro perspective on neural networks with non-linearities. This factorization leads to an equivalent yet computationally tractable non-convex alternative with no spurious local minima. We develop a novel relative optimality bound of stationary points of the Burer-Monteiro factorization, providing verifiable conditions under which any stationary point is a global optimum. Further, for the first time, we show that linear self-attention with sufficiently many heads has no spurious local minima. Our experiments validate the novel relative optimality bound and the utility of the Burer-Monteiro factorization for scaling convex neural networks.

Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses additional challenges due to limited measurements. In this work, we propose a methodology of implicit Neural Representation learning with Prior embedding (NeRP) to reconstruct a computational image from sparsely sampled measurements. The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior and the physics of the sparsely sampled measurements to produce a representation of the unknown subject. No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements. In addition, we demonstrate that NeRP is a general methodology that generalizes to different imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). We also show that NeRP can robustly capture the subtle yet significant image changes required for assessing tumor progression.

The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (by as much as 26%) and more accurate phase profiles. Concretely, the SLR algorithm consists of two steps: (1) an invertible transform between frequency selective pulses and polynomial pairs that represent Cayley-Klein (CK) parameters and (2) the design of the CK polynomial pair to match the desired magnetization profiles. Because the CK polynomial pair is bi-linearly coupled, the original algorithm sequentially solves for each polynomial instead of jointly. This results in sub-optimal pulses. Instead, we leverage a convex relaxation technique, commonly used for low rank matrix recovery, to address the bi-linearity. Our numerical experiments show that the resulting pulses are almost always globally optimal in practice. For slice excitation, the proposed algorithm results in more accurate linear phase profiles. And in general the improved pulses have lower energy than the original SLR pulses.

Vision transformers using self-attention or its proposed alternatives have demonstrated promising results in many image related tasks. However, the underpinning inductive bias of attention is not well understood. To address this issue, this paper analyzes attention through the lens of convex duality. For the non-linear dot-product self-attention, and alternative mechanisms such as MLP-mixer and Fourier Neural Operator (FNO), we derive equivalent finite-dimensional convex problems that are interpretable and solvable to global optimality. The convex programs lead to block nuclear-norm regularization that promotes low rank in the latent feature and token dimensions. In particular, we show how self-attention networks implicitly clusters the tokens, based on their latent similarity. We conduct experiments for transferring a pre-trained transformer backbone for CIFAR-100 classification by fine-tuning a variety of convex attention heads. The results indicate the merits of the bias induced by attention compared with the existing MLP or linear heads.

To enable wireless MRI receive arrays, per-channel power consumption must be reduced by a significant factor. To address this, a low-power SiGe alternative to industry standard MRI pre-amplifier blocks has been proposed and its impact on imaging performance evaluated in a benchtop environment. The SiGe amplifier reduces power consumption 28x, but exhibits increased non-linearity and reduced dynamic range relative to industry standard amplifiers. This distorts the images, causing reduced contrast and a blurring of fine features. In conjunction with the amplifier, a semi-blind calibration and compensation framework has been proposed to remove artifacts caused by this non-linearity. Requiring the knowledge of the calibration signal bandwidth, the associated peak transmit powers, and the distorted baseband signals, a second non-linearity is constructed that when cascaded with the receive chain produces a linear response. This method was evaluated for both knee and phantom image datasets of peak input power -20dBm with a -40dBm peak input power image as reference. In the benchtop environment, industry standard amplifiers produced input normalized RMSEs of 0.0199 and 0.0310 for phantom and knee datasets, respectively. The low-power SiGe amplifier resulted in RMSEs of 0.0869 and 0.1130 which were reduced to 0.0158 and 0.0168 following compensation, for phantom and knee images respectively. The ability to effectively compensate for this reduced dynamic range encourages further investigation of low-power SiGe amplifiers for power limited MRI receive arrays.

Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-transparent nature of deep learning (DL) leaves substantial uncertainty about the accuracy of DL reconstruction. With this in mind, this study aims to quantify the uncertainty in image recovery with DL models. To this end, we first leverage variational autoencoders (VAEs) to develop a probabilistic reconstruction scheme that maps out (low-quality) short scans with aliasing artifacts to the diagnostic-quality ones. The VAE encodes the acquisition uncertainty in a latent code and naturally offers a posterior of the image from which one can generate pixel variance maps using Monte-Carlo sampling. Accurately predicting risk requires knowledge of the bias as well, for which we leverage Stein's Unbiased Risk Estimator (SURE) as a proxy for mean-squared-error (MSE). A range of empirical experiments is performed for Knee MRI reconstruction under different training losses (adversarial and pixel-wise) and unrolled recurrent network architectures. Our key observations indicate that: 1) adversarial losses introduce more uncertainty; and 2) recurrent unrolled nets reduce the prediction uncertainty and risk.

Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network - a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

Multi-echo saturation recovery sequence can provide redundant information to synthesize multi-contrast magnetic resonance imaging. Traditional synthesis methods, such as GE's MAGiC platform, employ a model-fitting approach to generate parameter-weighted contrasts. However, models' over-simplification, as well as imperfections in the acquisition, can lead to undesirable reconstruction artifacts, especially in T2-FLAIR contrast. To improve the image quality, in this study, a multi-task deep learning model is developed to synthesize multi-contrast neuroimaging jointly using both signal relaxation relationships and spatial information. Compared with previous deep learning-based synthesis, the correlation between different destination contrast is utilized to enhance reconstruction quality. To improve model generalizability and evaluate clinical significance, the proposed model was trained and tested on a large multi-center dataset, including healthy subjects and patients with pathology. Results from both quantitative comparison and clinical reader study demonstrate that the multi-task formulation leads to more efficient and accurate contrast synthesis than previous methods.

The attenuation coefficient is a relevant biomarker for many diagnostic medical applications. Recently, the Depth-Resolved Confocal (DRC) technique was developed to automatically estimate the attenuation coefficients from Optical Coherence Tomography (OCT) data with pixel-level resolution. However, DRC requires that the confocal function parameters (i.e., focal plane location and apparent Rayleigh range) be known a priori. In this paper, we present the autoConfocal algorithm: a simple, automatic method for estimating those parameters directly from OCT imagery when the focal plane is within the sample. We present autoConfocal+DRC results on phantom data, ex-vivo biological tissue data, and in-vivo clinical data.

Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ1/ℓ2cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the ℓ1/ℓ2cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high-quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which are two orders of magnitude faster than the current state-of-the-art CS-MRI schemes.

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information. Learning a good inverse mapping from training data faces severe challenges, including: (i) scarcity of training data; (ii) need for plausible reconstructions that are physically feasible; (iii) need for fast reconstruction, especially in real-time applications. We develop a successful system solving all these challenges, using as basic architecture the repetitive application of alternating proximal and data fidelity constraints. We learn a proximal map that works well with real images based on residual networks with recurrent blocks. Extensive experiments are carried out under different settings: (a) reconstructing abdominal MRI of pediatric patients from highly undersampled k-space data and (b) super-resolving natural face images. Our key findings include: 1. a recurrent ResNet with a single residual block (10-fold repetition) yields an effective proximal which accurately reveals MR image details. 2. Our architecture significantly outperforms conventional non-recurrent deep ResNets by 2dB SNR; it is also trained much more rapidly. 3. It outperforms state-of-the-art compressed-sensing Wavelet-based methods by 4dB SNR, with 100x speedups in reconstruction time.

Patients who have implanted medical devices with long conductive leads are often restricted from receiving MRI scans due to the danger of RF-induced heating near the lead tips. Phantom studies have shown that this heating varies significantly on a case-by-case basis, indicating that many patients with implanted devices can receive clinically useful MRI scans without harm. However, the difficulty of predicting RF-induced lead tip heating prior to scanning prevents numerous implant recipients from being scanned. Here, we demonstrate that thermo-acoustic ultrasound (TAUS) has the potential to be utilized for a pre-scan procedure assessing the risk of RF-induced lead tip heating in MRI. A system was developed to detect TAUS signals by four different TAUS acquisition methods. We then integrated this system with an MRI scanner and detected a peak in RF power absorption near the tip of a model lead when transmitting from the scanner's body coil. We also developed and experimentally validated simulations to characterize the thermo-acoustic signal generated near lead tips. These results indicate that TAUS is a promising method for assessing RF implant safety, and with further development, a TAUS pre-scan could allow many more patients to have access to MRI scans of significant clinical value.

This paper demonstrates a robust diffusion-weighted single-shot fast spin echo (SS-FSE) sequence in the presence of significant off-resonance, which includes a variable-density acquisition and a self-calibrated reconstruction as improvements. A non-Carr-Purcell-Meiboom-Gill (nCPMG) SS-FSE acquisition stabilizes both the main and parasitic echo families for each echo. This preserves both the in-phase and quadrature components of the magnetization throughout the echo train. However, nCPMG SS-FSE also promotes aliasing of the quadrature component, which complicates reconstruction. A new acquisition and reconstruction approach is presented here, where the field-of-view is effectively doubled, but a partial k-space and variable density sampling is used to improve scan efficiency. The technique is presented in phantom scans to validate SNR and robustness against rapidly varying object phase. In vivo healthy volunteer examples and the clinical cases are demonstrated in abdominal imaging. This new approach provides comparable SNR to previous nCPMG acquisition techniques as well as providing more uniform apparent diffusion coefficient maps in phantom scans. In vivo scans suggest that this method is more robust against motion than previous approaches. The proposed reconstruction is an improvement to the nCPMG sequence as it is auto-calibrating and is justified to accurately treat the signal model for the nCPMG SS-FSE sequence.

A millimeter (mm) wave radio is presented in this work to support wireless MRI data transmission. High path loss and availability of wide bandwidth make mm-waves an ideal candidate for short range, high data rata communication required for wireless MRI. The proposed system uses a custom designed integrated chip (IC) mm-wave radio with 60 GHz as radio frequency carrier. In this work, we assess performance in a 1.5 T MRI field, with the addition of optical links between the console room and magnet. The system uses ON-OFF keying (OOK) modulation for data transmission and supports data rates from 200 Mb/s to 2.5 Gb/s for distances up-to 65 cm. The presence of highly directional, linearly polarized, on-chip dipole antennas on the mm-wave radio along with the time division multiplexing (TDM) circuitry allows multiple wireless links to be created simultaneously with minimal inter-channel interference. This leads to a highly scalable solution for wireless MRI.

SS-FSE is a fast technique that does not suffer from off-resonance distortions to the degree that EPI does. Unlike EPI, SS-FSE is ill-suited to diffusion weighted imaging (DWI) due to the Carr-Purcell-Meiboom-Geill (CPMG) condition. Non-CPMG phase cycling does accommodate SS-FSE and DWI but places constraints on reconstruction, which are resolved here through parallel imaging. Additionally, improved echo stability can be achieved by using short duration and highly selective DIVERSE radiofrequency pulses. Here, signal-to-noise ratio (SNR) comparisons between EPI and nCPMG SS-FSE acquisitions and reconstruction techniques give similar values. Diffusion imaging with nCPMG SS-FSE gives similar SNR to an EPI acquisition, though apparent diffusion coefficient values are higher than seen with EPI. In vivo images have good image quality with little distortion. This method has the ability to capture distortion-free DWI images near areas of significant off-resonance as well as preserve adequate SNR. Parallel imaging and DIVERSE refocusing RF pulses allow shorter ETL compared to previous implementations and thus reduces phase encode direction blur and SAR accumulation.

Q-spoiling is the process of decoupling an MRI receive coil to protect the equipment and patient. Conventionally, Q-spoiling is performed using a PIN diode switch that draws significant current. In this work, a Q-spoiling technique using a depletion-mode Gallium Nitride HEMT device was developed for coil detuning at both 1.5 T and 3 T MRI. The circuits with conventional PIN diode Q-spoiling and the GaN HEMT device were implemented on surface coils. SNR was measured and compared for all surfaces coils. At both 1.5 T and 3 T, comparable SNR was achieved for all coils with the proposed technique and conventional Q-spoiling. The GaN HEMT device has significantly reduced the required power for Q-spoiling. The GaN HEMT device also provides useful safety features by detuning the coil when unpowered.

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

We present a method for automated, depth-resolved extraction of the attenuation coefficient from Optical Coherence Tomography (OCT) data. In contrast to previous automated, depth-resolved methods, the Depth-Resolved Confocal (DRC) technique derives an invertible mapping between the measured OCT intensity data and the attenuation coefficient while considering the confocal function and sensitivity fall-off, which are critical to ensure accurate measurements of the attenuation coefficient in practical settings (e.g., clinical endoscopy). We also show that further improvement of the estimated attenuation coefficient is possible by formulating image denoising as a convex optimization problem that we term Intensity Weighted Horizontal Total Variation (iwhTV). The performance and accuracy of DRC alone and DRC+iwhTV are validated with simulated data, optical phantoms, and ex-vivo porcine tissue. Our results suggest that implementation of DRC+iwhTV represents a novel way to improve OCT contrast for better tissue characterization through quantitative imaging.

Stimulated-echoes in MR can be used to provide high sensitivity to motion and flow, creating diffusion and perfusion weighting as well as T(1) contrast, but conventional approaches inherently suffer from a 50% signal loss. The super stimulated-echo, which uses a specialized radio-frequency (RF) pulse train, has been proposed in order to improve the signal while preserving motion and T(1) sensitivity. This paper presents a novel and straightforward method for designing the super stimulated-echo pulse train using inversion pulse design techniques. This method can also create adiabatic designs with an improved response to RF transmit field variations. The scheme was validated in phantom experiments and shown in vivo to improve signal-to-noise ratio (SNR). We have applied a super stimulated-echo to metabolic MRI with hyperpolarized (13)C-labeled molecules. For spectroscopic imaging of hyperpolarized agents, several repetition times are required but only a single stimulated-echo encoding is feasible, which can lead to unwanted motion blurring. To address this, a super stimulated-echo preparation scheme was used in which the diffusion weighting is terminated prior to the acquisition, and we observed a SNR increases of 60% in phantoms and 49% in vivo over a conventional stimulated-echo. Experiments following injection of hyperpolarized [1-(13)C] -pyruvate in murine transgenic cancer models have shown improved delineation for tumors since signals from metabolites within tumor tissues are retained while those from the vasculature are suppressed by the diffusion preparation scheme.

Radio-frequency ablation (RFA) is an effective minimally invasive treatment for tumors. One primary source of difficulty is monitoring and controlling the ablation region. Currently, RFA is performed at 460 kHz, for which magnetic resonance imaging (MRI) could play a role given its capability for temperature monitoring and tumor visualization. If instead the ablation were to be performed at the MRI Larmor frequency, then the MR capability for B(1) field mapping could be used to directly visualize the radio-frequency (RF) fields created by the ablation currents. Visualizing the RF fields may enable better control of the ablation currents, enabling better control of lesion shape and size and improving repeatability. We demonstrate the feasibility of performing RFAs at 64 MHz and show preliminary results from imaging the RF fields from the ablation. The post-ablation RF fields show an increase in current density in the ablated region, consistent with an increase in conductivity of the ablated tissue.

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.

High-quality magnetic resonance imaging (MRI) requires precise control of the transmit radio-frequency (RF) field. In parallel excitation applications such as transmit SENSE, high RF power linearity is essential to cancel aliased excitations. In widely-employed class AB power amplifiers, gain compression, cross-over distortion, memory effects, and thermal drift all distort the RF field modulation and can degrade image quality. Cartesian feedback (CF) linearization can mitigate these effects in MRI, if the quadrature mismatch and dc offset imperfections inherent in the architecture can be minimized. In this paper, we present a modified Cartesian feedback technique called "frequency-offset Cartesian feedback" (FOCF) that significantly reduces these problems. In the FOCF architecture, the feedback control is performed at a low intermediate frequency rather than dc, so that quadrature ghosts and dc errors are shifted outside the control bandwidth. FOCF linearization is demonstrated with a variety of typical MRI pulses. Simulation of the magnetization obtained with the Bloch equation demonstrates that high-fidelity RF reproduction can be obtained even with inexpensive class AB amplifiers. Finally, the enhanced RF fidelity of FOCF over CF is demonstrated with actual images obtained in a 1.5 T MRI system.

Partially parallel imaging with localized sensitivities is a fast parallel image reconstruction method for both Cartesian and non-Cartesian trajectories, but suffers from aliasing artifacts when there are deviations from the assumption of perfect localization. Such reconstructions would normally crop the individual coil images to remove the artifacts prior to combination. However, the sampling densities in variable-density k-space trajectories support different field-of-views for separate regions in k -space. In fact, the higher sampling density of low frequencies can be used to reconstruct a bigger field-of-view without introducing aliasing artifacts and the resulting image signal-to-noise ratio (SNR) can be improved. A novel, fast variable-density parallel imaging method is presented, which reconstructs different field-of-views from separate frequencies according to the local sampling density in k-space. Aliasing-suppressed images can be produced with high SNR-efficiency without the need for accurate estimation of coil sensitivities and complex or iterative computations.

The currents induced in long conductors such as guidewires by the radio-frequency (RF) field in magnetic resonance imaging (MRI) are responsible for potentially dangerous heating of surrounding media, such as tissue. This paper presents an optically coupled system with the potential to quantitatively measure the RF currents induced on these conductors. The system uses a self shielded toroid transducer and active circuitry to modulate a high speed light-emitting-diode transmitter. Plastic fiber guides the light to a photodiode receiver and transimpedance amplifier. System validation included a series of experiments with bare wires that compared wire tip heating by fluoroptic thermometers with the RF current sensor response. Validations were performed on a custom whole body 64 MHz birdcage test platform and on a 1.5 T MRI scanner. With this system, a variety of phenomena were demonstrated including cable trap current attenuation, lossy dielectric Q-spoiling and even transverse electromagnetic wave node patterns. This system should find applications in studies of MRI RF safety for interventional devices such as pacemaker leads, and guidewires. In particular, variations of this device could potentially act as a realtime safety monitor during MRI guided interventions.

Time series of in vivo magnetic resonance images exhibit high levels of temporal correlation. Higher temporal resolution reconstructions are obtained by acquiring data at a fraction of the Nyquist rate and resolving the resulting aliasing using the correlation information. The dynamic imaging experiment is modeled as a linear dynamical system. A Kalman filter based unaliasing reconstruction is described for accelerated dynamic magnetic resonance imaging (MRI). The algorithm handles arbitrary readout trajectories naturally. The reconstruction is causal and very fast, making it applicable to real-time imaging. In vivo results are presented for cardiac MRI of healthy volunteers.

A practical acceleration algorithm for real-time magnetic resonance imaging (MRI) is presented. Neither separate training scans nor embedded training samples are used. The Kalman filter based algorithm provides a fast and causal reconstruction of dynamic MRI acquisitions with arbitrary readout trajectories. The algorithm is tested against abrupt changes in the imaging conditions and offline reconstructions of in vivo cardiac MRI experiments are presented.

A fast and simple algorithm for designing time-optimal waveforms is presented. The algorithm accepts a given arbitrary multidimensional k-space trajectory as the input and outputs the time-optimal gradient waveform that traverses k-space along that path in minimum time. The algorithm is noniterative, and its run time is independent of the complexity of the curve, i.e., the number of switches between slew-rate limited acceleration, slew-rate limited deceleration, and gradient amplitude limited regions. The key in the method is that the gradient amplitude is designed as a function of arc length along the k-space trajectory, rather than as a function of time. Several trajectory design examples are presented.

Prepolarized magnetic resonance imaging (PMRI) uses two pulsed electromagnets to achieve high-field image quality with the benefits of low-field data acquisition. The principal challenge with all resistive MRI systems is the implementation of a highly precise magnet current supply. The noise current through the magnet is fundamentally limited by the current transducer used to provide feedback and the voltage reference used to generate the demand signal. Field instability in the main field magnet can both corrupt the received data and degrade the robustness of Carr¿Purcell¿Meiboom¿Gill (CPMG) echo trains, which are paramount to efficient imaging in PMRI. In this work, we present the magnet control system that achieved sufficient field stability for PMRI at $0.5/0.13$ T, identify the dominant sources of noise in the control system, examine the imaging artifacts that can occur if the field stability is insufficient, and identify how the design can be improved for better field stability, should it be required for future implementations of PMRI.

The purpose of this work was to present and evaluate a new method for directly designing T2-selective preparation pulses. Using a modified Shinnar-Le-Roux (SLR) transform, the design of T2-selective pulses becomes equivalent to designing a pair of polynomials one of which represents the longitudinal magnetization and the other the transverse magnetization. The polynomials enable one to directly analyze the various tradeoffs involved in the design. To evaluate the new method, a short-T2-selective magnetization preparation pulse was designed. Following the preparation pulse, a 2D Fourier transform (2DFT) multislice gradient echo sequence was used for imaging. For verification Bloch equation simulations were performed along with both in vivo and phantom scans. Phantom scans showed good signal suppression of long-T2 species. This is supported by good long-T2 signal suppression seen on the in vivo images. Simulations indicate that the pulse is robust to +/-150 Hz B0 inhomogeneities and +/-10% B1 inhomogeneities.

Reconstruction of magnetic resonance images from data not falling on a Cartesian grid is a Fourier inversion problem typically solved using convolution interpolation, also known as gridding. Gridding is simple and robust and has parameters, the grid oversampling ratio and the kernel width, that can be used to trade accuracy for computational memory and time reductions. We have found that significant reductions in computation memory and time can be obtained while maintaining high accuracy by using a minimal oversampling ratio, from 1.125 to 1.375, instead of the typically employed grid oversampling ratio of two. When using a minimal oversampling ratio, appropriate design of the convolution kernel is important for maintaining high accuracy. We derive a simple equation for choosing the optimal Kaiser-Bessel convolution kernel for a given oversampling ratio and kernel width. As well, we evaluate the effect of presampling the kernel, a common technique used to reduce the computation time, and find that using linear interpolation between samples adds negligible error with far less samples than is necessary with nearest-neighbor interpolation. We also develop a new method for choosing the optimal presampled kernel. Using a minimal oversampling ratio and presampled kernel, we are able to perform a three-dimensional (3-D) reconstruction in one-eighth the time and requiring one-third the computer memory versus using an oversampling ratio of two and a Kaiser-Bessel convolution kernel, while maintaining the same level of accuracy.

A graphical user interface (GUI) has been developed which enables interactive feedback and control to the real-time diminishing variance algorithm (DVA). This interactivity allows the user to set scan parameters, view scan statistics, and view image updates during the course of the scan. In addition, the DVA has been extended to simultaneously reduce motion artifacts in three dimensions using three orthogonal navigators. Preliminary in vivo studies indicate that these improvements to the standard DVA allow for significantly improved consistency and robustness in eliminating respiratory motion artifacts from MR images, particularly when imaging the coronary arteries.