Correction of MR kappa-space data corrupted by spike noise

A New Phased-Array Magnetic Resonance Imaging Receive-Only Coil for HBO2 Studies

The paper describes a new magnetic resonance imaging (MRI) phased-array receive-only (Rx) coil for studying decompression sickness and disorders of hyperbaricity, including nitrogen narcosis. Functional magnetic resonance imaging (fMRI) is noninvasive, is considered safe, and may allow studying the brain under hyperbaric conditions. All of the risks associated with simultaneous MRI and HBO2 therapy are described in detail, along with all of the mitigation strategies and regulatory testing. One of the most significant risks for this type of study is a fire in the hyperbaric chamber caused by the sparking of the MRI coils as a result of high-voltage RF arcs. RF pulses at 128 MHz elicit signals from human tissues, and RF sparking occurs commonly and is considered safe in normobaric conditions. We describe how we built a coil for HBO2-MRI studies by modifying an eight-channel phased-array MRI coil with all of the mitigation strategies discussed. The coil was fabricated and tested with a unique testing platform that simulated the worst-case RF field of a three-Tesla MRI in a Hyperlite hyperbaric chamber at 3 atm pressure. The coil was also tested in normobaric conditions for image quality in a 3 T scanner in volunteers and SNR measurement in phantoms. Further studies are necessary to characterize the coil safety in HBO2/MRI.

Leveraging redundancy in simultaneous multislice acquisitions to improve spike detection

PURPOSE

To improve the performance of low-level spike noise artifact detection for daily quality assurance protocols by taking advantage of redundancy in simultaneous multislice (SMS) acquisitions.

METHODS

Magnitude images were transformed into pseudo k-space images. Time series at each pseudo k-space point were detrended. A slice was determined to contain spiking artifact if it exceeded an intensity threshold and if all simultaneously acquired slices contained outliers.

RESULTS

A total of 401 112 slices were inspected. Of these, 42 showed a spike artifact, based on visual inspection of image data and k-space data. With an intensity threshold of 4.6 SDs over time for each pseudo k-space point, all slices containing artifact were correctly flagged, and only 30 slices were incorrectly flagged when using the SMS criterion. Without the SMS criterion, 12 908 slices were incorrectly flagged as containing artifact. Without the SMS criterion, sensitivity to artifact would have to be sacrificed to substantially reduce the number of incorrectly flagged slices.

CONCLUSION

This study demonstrates that the SMS criterion reduced the number of outliers reported to a manageable level while accurately identifying low-level spike artifacts. Successfully identifying low-level spikes allows early detection of hardware problems that can be fixed before the problem becomes debilitating and corrupts data. As part of a daily quality assurance protocol, the method prevents the need to retrospectively carry out time-intensive despiking and reanalysis of data.

Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction

Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

Improved MR-thermometry during hepatic microwave ablation by correcting for intermittent electromagnetic interference artifacts

MRI-guided microwave ablation (MWA) is a minimally invasive treatment for localized cancer. MR thermometry has been shown to be able to provide vital information for monitoring the procedure in real-time. However, MRI during active MWA can suffer from image quality degradation due to intermittent electromagnetic interference (EMI). A novel approach to correct for EMI-contaminated images is presented here to improve MR thermometry during clinical hepatic MWA. The method was applied to MR-thermometry images acquired during four MR-guided hepatic MWA treatments using a commercially available MRI-configured microwave generator system. During the treatments MR thermometry data acquisition was synchronized to respiratory cycle to minimize the impact of motion. EMI was detected and corrected using uncontaminated k-space data from nearby frames in k-space. Substantially improved temperature and thermal damage maps have been obtained and the treatment zone can be better visualized. Our ex vivo tissue sample study shows the correction introduced minimal errors to the temperature maps and thermal damage maps.

MR Performance in the Presence of a Radio Frequency-Penetrable Positron Emission Tomography (PET) Insert for Simultaneous PET/MRI

Despite the great promise of integrated positron emission tomography (PET)/magnetic resonance (MR) imaging to add molecular information to anatomical and functional MR, its potential impact in medicine is diminished by a very high cost, limiting its dissemination. An RF-penetrable PET ring that can be inserted into any existing MR system has been developed to address this issue. Employing optical signal transmission along with battery power enables the PET ring insert to electrically float with respect to the MR system. Then, inter-modular gaps of the PET ring allow the RF transmit field from the standard built-in body coil to penetrate into the PET fields-of-view (FOV) with some attenuation that can be compensated for. MR performance, including RF noise, magnetic susceptibility, RF penetrability through and $B_{1}$ uniformity within the PET insert, and MR image quality, were analyzed with and without the PET ring present. The simulated and experimentally measured RF field attenuation factors with the PET ring present were -2.7 and -3.2 dB, respectively. The magnetic susceptibility effect (0.063 ppm) and noise emitted from the PET ring in the MR receive channel were insignificant. $B_{1}$ homogeneity of a spherical agar phantom within the PET ring FOV dropped by 8.4% and MR image SNR was reduced by 3.5 and 4.3 dB with the PET present for gradient-recalled echo and fast-spin echo, respectively. This paper demonstrates, for the first time, an RF-penetrable PET insert comprising a full ring of operating detectors that achieves simultaneous PET/MR using the standard built-in body coil as the RF transmitter.

Using the robust principal component analysis algorithm to remove RF spike artifacts from MR images

Brief bursts of RF noise during MR data acquisition (“k‐space spikes”) cause disruptive image artifacts, manifesting as stripes overlaid on the image. RF noise is often related to hardware problems, including vibrations during gradient‐heavy sequences, such as diffusion‐weighted imaging. In this study, we present an application of the Robust Principal Component Analysis (RPCA) algorithm to remove spike noise from k‐space. Methods: Corrupted k‐space matrices were decomposed into their low‐rank and sparse components using the RPCA algorithm, such that spikes were contained within the sparse component and artifact‐free k‐space data remained in the low‐rank component. Automated center refilling was applied to keep the peaked central cluster of k‐space from misclassification in the sparse component. Results: This algorithm was demonstrated to effectively remove k‐space spikes from four data types under conditions generating spikes: (i) mouse heart T₁ mapping, (ii) mouse heart cine imaging, (iii) human kidney diffusion tensor imaging (DTI) data, and (iv) human brain DTI data. Myocardial T₁ values changed by 86.1 ± 171 ms following despiking, and fractional anisotropy values were recovered following despiking of DTI data. Conclusion: The RPCA despiking algorithm will be a valuable postprocessing method for retrospectively removing stripe artifacts without affecting the underlying signal of interest. Magn Reson Med 75:2517–2525, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

POCS-enhanced correction of motion artifacts in parallel MRI

A new method for correction of MRI motion artifacts induced by corrupted k-space data, acquired by multiple receiver coils such as phased arrays, is presented. In our approach, a projections onto convex sets (POCS)-based method for reconstruction of sensitivity encoded MRI data (POCSENSE) is employed to identify corrupted k-space samples. After the erroneous data are discarded from the dataset, the artifact-free images are restored from the remaining data using coil sensitivity profiles. The error detection and data restoration are based on informational redundancy of phased-array data and may be applied to full and reduced datasets. An important advantage of the new POCS-based method is that, in addition to multicoil data redundancy, it can use a priori known properties about the imaged object for improved MR image artifact correction. The use of such information was shown to improve significantly k-space error detection and image artifact correction. The method was validated on data corrupted by simulated and real motion such as head motion and pulsatile flow.

Robust correction of spike noise: application to diffusion tensor imaging

Echo-planar imaging (EPI) -based diffusion tensor imaging (DTI) is particularly prone to spike noise. However, existing spike noise correction methods are impractical for corrupted DTI data because the methods correct the complex MRI signal, which is not usually stored on clinical MRI systems. The present work describes a novel Outlier Detection De-spiking technique (ODD) that consists of three steps: detection, localization, and correction. Using automated outlier detection schemes, ODD exploits the data redundancy available in DTI data sets that are acquired with a minimum of six different diffusion-weighted images (DWIs) with similar signal and noise properties. A mathematical formulation, describing the effects of spike noise on magnitude images, yields appropriate measures for an outlier detection scheme used for spike detection while a normalization-dependent outlier detection scheme is used for spike localization. ODD performs accurately on diverse DTI data sets corrupted by spike noise and can be used for automated control of DTI data quality. ODD can also be extended to other MRI applications with data redundancy, such as dynamic imaging and functional MRI.

Fast, iterative image reconstruction for MRI in the presence of field inhomogeneities

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.

Current awareness in NMR in biomedicine

In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.