Miha Fuderer

Parallel magnetic resonance imaging

Parallel imaging has been the single biggest innovation in magnetic resonance imaging in the last decade. The use of multiple receiver coils to augment the time consuming Fourier encoding has reduced acquisition times significantly. This increase in speed comes at a time when other approaches to acquisition time reduction were reaching engineering and human limits. A brief summary of spatial encoding in MRI is followed by an introduction to the problem parallel imaging is designed to solve. There are a large number of parallel reconstruction algorithms; this article reviews a cross-section, SENSE, SMASH, g-SMASH and GRAPPA, selected to demonstrate the different approaches. Theoretical (the g-factor) and practical (coil design) limits to acquisition speed are reviewed. The practical implementation of parallel imaging is also discussed, in particular coil calibration. How to recognize potential failure modes and their associated artefacts are shown. Well-established applications including angiography, cardiac imaging and applications using echo planar imaging are reviewed and we discuss what makes a good application for parallel imaging. Finally, active research areas where parallel imaging is being used to improve data quality by repairing artefacted images are also reviewed.

Susceptibility artifacts in 2DFT spin-echo and gradient-echo imaging: The cylinder model revisited

Susceptibility-induced geometry and intensity distortions are a familiar observation in MR imaging. In the past few years several attempts have been made to aid in the understanding of susceptibility artifacts by means of simulation studies. Although these studies, which were mostly carried out with simple test objects, have produced some qualitative insight into chi-artifacts, the results lacked precision in describing finer details. In this paper we show the discrepancy between theory and experiment in previous work to be the result of an inadequate theoretical approach. In most studies so far, delta B0 effects are taken into account in the frequency domain, that is, after Fourier transformation of the data. In our view the simulation should follow the actual sequence of events in an imaging experiment and deal with the effect of error fields in the time domain (k-space) already. The correctness of this view is demonstrated here by comparing the results of time and frequency domain simulation against experimental observation for a coaxial cylinder phantom, a widely used model in this type of work. Having established the superiority of the time domain simulation, we demonstrate its use in predicting chi-artifacts under various experimental conditions, for example, in spin-echo and gradient-echo imaging with a reduced number of phase-encoding steps.

The information content of MR images

The theoretical information content, defined by C.E. Shannon (1948), is proposed as an objective measure of MR (magnetic resonance) image quality. This measure takes into account the contrast-to-noise ratio (CNR), scan resolution, and field of view. It is used to derive an optimum in the tradeoff problem between image resolution and CNR, and as a criterion to assess the usefulness of high-resolution (512(2)) MR images. The result tells that for a given total acquisition time, an optimum value of the resolution can be found. This optimum is very broad. To apply Shannons theory on information constant to MR images, a model for the spatial spectral power density of these images is required. Such a model has been derived from experimental observations of ordinary MR images, as well as from theoretical considerations.

Magnetic resonance imaging method and device for reducing image errors in a magnetic resonance image

In magnetic resonance imaging various error sources lead to deterioration of the image quality. One class of errors is formed by errors which vary only slowly over the time required to sample a data line but vary substantially over the time required to acquire data for the complete magnetic resonance image. These error sources are, for example external magnetic fields, motion due to respiration, drift in amplifiers or drift phenomena in permanent magnets due to temperature influences. It is proposed to utilize mutually intersecting data lines in the Fourier domain so as to estimate these error sources and to use these estimates to correct the data sets obtained before execution of image reconstruction.

Method and arrangement for determining a corrected MRI surface coil image

In MRI (Magnetic Resonance Imaging), the signal-to-noise ratio with nuclear magnetic resonance signals received by a surface coil from an excited body is considerably larger than with corresponding signals received by a body coil in the relevant region. The sensitivity pattern of a surface coil is very non-uniform, however. By correction of a reconstructed image from MR signals received by a surface coil by a corresponding image from MR signals received by a body coil, in which event the surface coil has an arbitrary shape and occupies an arbitrary position with respect to the body, the non-uniformity in the surface coil image is brought back to the uniformity of the body coil image.

SENSE with improved tolerance to inaccuracies in coil sensitivity maps

In this work, an extension of the Cartesian sensitivity encoding (SENSE) parallel imaging framework is proposed. In the well‐known SENSE solution, the overdetermined reconstruction inversion problem is optimized to get the highest signal‐to‐noise ratio in the image. In this extension, the probability of artifacts due to incorrect knowledge of the receiver coil sensitivities is also taken into account. This is realized by assuming an uncertainty in measured receiver coil sensitivities to enable weighting of residual artifact level and signal‐to‐noise ratio in the inversion problem. This inversion problem can still be solved by a least‐squares optimization without the need of any complex iterative scheme. Results in abdominal imaging show that artifact levels can be substantially reduced, at the cost of a signal‐to‐noise ratio penalty. The size of the signal‐to‐noise ratio penalty depends on the assumed inaccuracy of the coil sensitivities, sensitivity encoding acceleration factor, and coil configuration. Magn Reson Med, 2013.

Method and apparatus for magnetic resonance imaging by radially scanning through frequency space and employing a correction image derived from an edge image

Nuclear or electron spin magnetic resonance method using multiple radial scans through frequency-space. The problem of insufficient sample density at the higher image frequencies is solved by deriving an edge image, determining a correction image therefrom by using a priori knowledge about the pixel value distribution and using the correction image after Fourier transformation to frequency-space.

The relative impact of ghosting and noise on the perceived quality of MR images

Magnetic resonance (MR) imaging is vulnerable to a variety of artifacts, which potentially degrade the perceived quality of MR images and, consequently, may cause inefficient and/or inaccurate diagnosis. In general, these artifacts can be classified as structured or unstructured depending on the correlation of the artifact with the original content. In addition, the artifact can be white or colored depending on the flatness of the frequency spectrum of the artifact. In current MR imaging applications, design choices allow one type of artifact to be traded off with another type of artifact. Hence, to support these design choices, the relative impact of structured versus unstructured or colored versus white artifacts on perceived image quality needs to be known. To this end, we conducted two subjective experiments. Clinical application specialists rated the quality of MR images, distorted with different types of artifacts at various levels of degradation. The results demonstrate that unstructured artifacts deteriorate quality less than structured artifacts, while colored artifacts preserve quality better than white artifacts.

Studying the relative impact of ghosting and noise on the perceived quality of MR images

In current magnetic resonance (MR) imaging systems, design choices are confronted with a trade-off between structured (i.e. artifacts) and unstructured noise. The impact of both types of noise on perceived image quality, however, is so far unknown, while this knowledge would be highly beneficial for further improvement of MR imaging systems. In this paper, we investigate how ghosting artifacts (i.e. structured noise) and random noise, applied at the same energy level in the distortion, affect the perceived quality of MR images. To this end, a perception experiment is conducted with human observers rating the quality of a set of images, distorted with various levels of ghosting and noise. To also understand the influence of professional expertise on the image quality assessment task, two groups of observers with different levels of medical imaging experience participated in the experiment: one group contained fifteen clinical scientists or application specialists, and the other group contained eighteen naïve observers. Experimental results indicate that experts and naïve observers differently assess the quality of MR images degraded with ghosting/noise. Naïve observers consistently rate images degraded with ghosting higher than images degraded with noise, independent of the energy level of the distortion, and of the image content. For experts, the relative impact of ghosting and noise on perceived quality tends to depend on the energy level of the distortion and on the image content, but overall the energy of the distortion is a promising metric to predict perceived image quality.

Bayesian Estimation of MR Images from Incomplete Raw Data

This work concerns reduction of the MRI scan time through optimal sampling. We derive optimal sample positions from Cramer-Rao theory. These positions are nonuniformly distributed, which hampers Fourier transformation to the image domain. With the aid of Bayesian formalism we estimate an image that satisfies prior knowledge while its inverse Fourier transform is compatible with the acquired samples. The new technique is applied successfully to a real-world MRI scan of a human brain.