Emre Kopanoglu

Analytic Expressions for the Ultimate Intrinsic Signal-to-Noise Ratio and Ultimate Intrinsic Specific Absorption Rate in MRI

The ultimate intrinsic signal‐to‐noise ratio is the highest possible signal‐to‐noise ratio, and the ultimate intrinsic specific absorption rate provides the lowest limit of the specific absorption rate for a given flip angle distribution. Analytic expressions for ultimate intrinsic signal‐to‐noise ratio and ultimate intrinsic specific absorption rate are obtained for arbitrary sample geometries. These expressions are valid when the distance between the point of interest and the sample surface is smaller than the wavelength, and the sample is homogeneous. The dependence on the sample permittivity, conductivity, temperature, size, and the static magnetic field strength is given in analytic form, which enables the easy evaluation of the change in signal‐to‐noise ratio and specific absorption rate when the sample is scaled in size or when any of its geometrical or electrical parameters is altered. Furthermore, it is shown that signal‐to‐noise ratio and specific absorption rate are independent of the permeability of the sample. As a practical case and a solution example, a uniform, circular cylindrically shaped sample is studied. Magn Reson Med, 2011.

Specific absorption rate reduction using nonlinear gradient fields

The specific absorption rate is used as one of the main safety parameters in magnetic resonance imaging. The performance of imaging sequences is frequently hampered by the limitations imposed on the specific absorption rate that increase in severity at higher field strengths. The most well‐known approach to reducing the specific absorption rate is presumably the variable rate selective excitation technique, which modifies the gradient waveforms in time. In this article, an alternative approach is introduced that uses gradient fields with nonlinear variations in space to reduce the specific absorption rate. The effect of such gradient fields on the relationship between the desired excitation profile and the corresponding radiofrequency pulse is shown. The feasibility of the method is demonstrated using three examples of radiofrequency pulse design. Finally, the proposed method is compared with and combined with the variable rate selective excitation technique. Magn Reson Med 70:537–546, 2013.

Radiofrequency pulse design using nonlinear gradient magnetic fields

An iterative k‐space trajectory and radiofrequency (RF) pulse design method is proposed for excitation using nonlinear gradient magnetic fields.

A Simple Analytical Expression for the Gradient Induced Potential on Active Implants During MRI

During magnetic resonance imaging, there is an interaction between the time-varying magnetic fields and the active implantable medical devices (AIMD). In this study, in order to express the nature of this interaction, simplified analytical expressions for the electric fields induced by time-varying magnetic fields are derived inside a homogeneous cylindrical volume. With these analytical expressions, the gradient induced potential on the electrodes of the AIMD can be approximately calculated if the position of the lead inside the body is known. By utilizing the fact that gradient coils produce linear magnetic field in a volume of interest, the simplified closed form electric field expressions are defined. Using these simplified expressions, the induced potential on an implant electrode has been computed approximately for various lead positions on a cylindrical phantom and verified by comparing with the measured potentials for these sample conditions. In addition, the validity of the method was tested with isolated frog leg stimulation experiments. As a result, these simplified expressions may help in assessing the gradient-induced stimulation risk to the patients with implants.

Accelerate data acquisition using Turbo Spin Echo and O-Space

Turbo Spin Echo (TSE) is a method of acquiring the echoes of a Cartesian readout in a T2-weighted echo train, resulting in a very fast imaging time. On the other hand, recent methods of spatial encoding with nonlinear magnetic fields have been studied by our group and others to reduce data acquisitions time, such as, PatLoc, O-Space, Null Space, 4D-RIO, etc. These efforts have focused on gradient echo imaging. In this paper, a novel method has been proposed to combine the benefits of both TSE acquisition and O-Space to accelerate the data acquisition for T2-weighted imaging. Because O-space acquires DC signal in at least some spatial location within each echo, modifications of the acquisition order and reconstruction method are required. We present simulations and experiments illustrating that the proposed method can further speed up data acquisition of an O-Space imaging sequence using spin echo trains.

A synthesis-based approach to compressive multi-contrast magnetic resonance imaging

In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using Alternating Direction Method of Multipliers (ADMM). We then compare the proposed algorithm to the state-of-the-art Compressive Sensing-MRI algorithms, and show that the proposed method results in better quality images in shorter computation time.

Fast recovery of compressed multi-contrast magnetic resonance images

In many settings, multiple Magnetic Resonance Imaging (MRI) scans are performed with different contrast characteristics at a single patient visit. Unfortunately, MRI data-acquisition is inherently slow creating a persistent need to accelerate scans. Multi-contrast reconstruction deals with the joint reconstruction of different contrasts simultaneously. Previous approaches suggest solving a regularized optimization problem using group sparsity and/or color total variation, using composite-splitting denoising and FISTA. Yet, there is significant room for improvement in existing methods regarding computation time, ease of parameter selection, and robustness in reconstructed image quality. Selection of sparsifying transformations is critical in applications of compressed sensing. Here we propose using non-convex p-norm group sparsity (with p < 1), and apply color total variation (CTV). Our method is readily applicable to magnitude images rather than each of the real and imaginary parts separately. We use the constrained form of the problem, which allows an easier choice of data-fidelity error-bound (based on noise power determined from a noise-only scan without any RF excitation). We solve the problem using an adaptation of Alternating Direction Method of Multipliers (ADMM), which provides faster convergence in terms of CPU-time. We demonstrated the effectiveness of the method on two MR image sets (numerical brain phantom images and SRI24 atlas data) in terms of CPU-time and image quality. We show that a non-convex group sparsity function that uses the p-norm instead of the convex counterpart accelerates convergence and improves the peak-Signal-to-Noise-Ratio (pSNR), especially for highly undersampled data.

O-space with high resolution readouts outperforms radial imaging

PURPOSE While O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts. THEORY AND METHODS A sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging. RESULTS Experimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image. CONCLUSIONS High resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging.

Compressed Multi-Contrast Magnetic Resonance Image reconstruction using Augmented Lagrangian Method

In this paper, a Multi-Channel/Multi-Contrast image reconstruction algorithm is proposed. The method, which is based on the Augmented Lagrangian Method uses joint convex objective functions to utilize the mutual information in the data from multiple channels to improve reconstruction quality. For this purpose, color total variation and group sparsity are used. To evaluate the performance of the method, the algorithm is compared in terms of convergence speed and image quality using Magnetic Resonance Imaging data to FCSA-MT [1], an alternative approach on reconstructing multi-contrast MRI data.