Recommendations for Imaging Patients With Cardiac Implantable Electronic Devices (CIEDs).

Historically, the presence of cardiac implantable electronic devices (CIEDs), including pacemakers and implantable cardioverter defibrillators (ICDs), was widely considered an absolute contraindication to magnetic resonance imaging (MRI). The recent development of CIEDs with MR Conditional labeling, as well as encouraging results from retrospective studies and a prospective trial on the safety of MRI performed in patients with CIEDs without MR Conditional labeling, have led to a reevaluation of this practice. The purpose of this report is to provide a concise summary of recent developments, including practical guidelines that an institution could adopt for radiologists who choose to image patients with CIEDs that do not have MR Conditional labeling. This report was written on behalf of and approved by the International Society for Magnetic Resonance in Medicine (ISMRM) Safety Committee. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 1.

Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.

LEVEL OF EVIDENCE: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom.

The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi-institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application-specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48-61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Electromagnetic computation and modeling in MRI.

Electromagnetic (EM) computational modeling is used extensively during the development of a Magnetic Resonance Imaging (MRI) scanner, its installation, and use. MRI, which relies on interactions between nuclear magnetic moments and the applied magnetic fields, uses a range of EM tools to optimize all of the magnetic fields required to produce the image. The main field magnet is designed to exacting specifications but challenges in manufacturing, installation, and use require additional tools to maintain target operational performance. The gradient magnetic fields, which provide the primary signal localization mechanism, are designed under another set of complex design trade-offs which include conflicting imaging performance specifications and patient physiology. Gradients are largely impervious to external influences, but are also used to enhance main field operational performance. The radiofrequency (RF) magnetic fields, which are used to elicit the signals fundamental to the MR image, are a challenge to optimize for a host of reasons that include patient safety, image quality, cost optimization, and secondary signal localization capabilities. This review outlines these issues and the EM modeling used to optimize MRI system performance.

Guidelines for documentation and consent for nonclinical, nonresearch MRI in human subjects.

Magnetic resonance imaging (MRI) of human subjects is widely performed for clinical and research purposes. Clinical MRI requires a physician order, while research MRI typically requires an approved protocol from a local Institutional Review Board, as well as informed consent. However, there are several circumstances in which it is appropriate to perform MRI in human subjects, that constitute neither clinical nor research activities. Examples include clinical protocol development, training and teaching, and quality assurance testing. We refer to such activities as nonclinical, nonresearch MRI. The purpose of this document is to provide principles and guidelines for appropriate and safe use of MRI in human subjects for nonclinical, nonresearch purposes. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:36-41.

A simple method for estimating the noise level in a signal region of an MR image.

PURPOSE: A simple extension to the NEMA MS-1 "difference of neighboring pixels" SNR method is presented, which can accurately determine the noise level within a signal region over a wide range of noise levels, image nonuniformities, and artifact levels, as demonstrated by simple simulations and experimental phantom images. METHODS: The new method computes difference of neighboring pixels in the read, phase, and diagonal directions. The variance of these three sets of pixel differences appear to contain the simple sum of the underlying variance of noise and any additional component unique to the read and phase directions, respectively, while the diagonal set of pixel differences contains all three components. By solving a set of three equations with three unknowns, it is possible to extract the components and isolate the desired noise variance term. RESULTS: Simulations show that the technique produces good results even when various artifact mechanisms present singly or jointly. Experimental results also demonstrate the technique works well but, depending on the severity of the artifact, cannot be guaranteed to always produce accurate results. CONCLUSIONS: Simulations and experimental results show the method to be accurate and robust. This method is applicable to multichannel receiver images, but not parallel reconstructed images.

A new single acquisition, two-image difference method for determining MR image SNR.

A new method for computing the signal-to-noise ratio (SNR) of magnetic resonance images is presented. The proposed method is a "difference of images" based technique where two images are produced from one acquisition in which the readout direction field of view (FOV) and matrix size are doubled compared to the phase encode direction. Two "normal" unaliased FOV images are produced by splitting (undersampling) the even versus odd data points in the read direction into two separate raw data sets. After image reconstruction, conventional difference of images SNR computations are applied. [Signal defined as mean within signal producing region of interest (ROI) in one image, noise defined as standard deviation of the difference between the two images using the same signal ROI position and size, divided by sqrt(2) to account for the subtraction process.] This method combines the desirable minimal acquisition time of a single image acquisition technique and the superior noise quantification characteristics of the difference of images methodology. The proposed method is more robust against system drift than existing SNR difference of images methods because the two images are effectively acquired nearly simultaneously in time. This method is compatible with phased array coils and is useful for parallel image reconstruction analysis because it is very stable. This method produces results that can be made equivalent to, and compared with, other existing SNR methods with simple known scale factors, assuming the image noise follows theoretical expectations.

Regularized sensitivity encoding (SENSE) reconstruction using Bregman iterations.

In parallel imaging, the signal-to-noise ratio (SNR) of sensitivity encoding (SENSE) reconstruction is usually degraded by the ill-conditioning problem, which becomes especially serious at large acceleration factors. Existing regularization methods have been shown to alleviate the problem. However, they usually suffer from image artifacts at high acceleration factors due to the large data inconsistency resulting from heavy regularization. In this paper, we propose Bregman iteration for SENSE regularization. Unlike the existing regularization methods where the regularization function is fixed, the method adaptively updates the regularization function using the Bregman distance at different iterations, such that the iteration gradually removes the aliasing artifacts and recovers fine structures before the noise finally comes back. With a discrepancy principle as the stopping criterion, our results demonstrate that the reconstructed image using Bregman iteration preserves both sharp edges lost in Tikhonov regularization and fines structures missed in total variation (TV) regularization, while reducing more noise and aliasing artifacts.

A statistical approach to SENSE regularization with arbitrary k-space trajectories.

SENSE reconstruction suffers from an ill-conditioning problem, which increasingly lowers the signal-to-noise ratio (SNR) as the reduction factor increases. Ill-conditioning also degrades the convergence behavior of iterative conjugate gradient reconstructions for arbitrary trajectories. Regularization techniques are often used to alleviate the ill-conditioning problem. Based on maximum a posteriori statistical estimation with a Huber Markov random field prior, this study presents a new method for adaptive regularization using the image and noise statistics. The adaptive Huber regularization addresses the blurry edges in Tikhonov regularization and the blocky effects in total variation (TV) regularization. Phantom and in vivo experiments demonstrate improved image quality and convergence speed over both the unregularized conjugate gradient method and Tikhonov regularization method, at no increase in total computation time.

MR systems for MRI-guided interventions.

The field of MR imaging has grown from diagnosis via morphologic imaging to more sophisticated diagnosis via both physiologic and morphologic imaging and finally to the guidance and control of interventions. A wide variety of interventional procedures from open brain surgeries to noninvasive focused ultrasound ablations have been guided with MR and the differences between diagnostic and interventional MR imaging systems have motivated the creation of a new field within MR. This review discusses the various systems that research groups and vendors have designed to meet the requirements of interventional MR and suggest possible solutions to those requirements that have not yet been met. The common requirements created by MR imaging guidance of interventional procedures are reviewed and different imaging system designs will be independently considered. The motivation and history of the different designs are discussed and the ability of the designs to satisfy the requirements is analyzed.

Measuring magnetic fields generated by DC currents in receive-only coils.

DC decoupling currents applied to receive-only coils during radiofrequency transmission can create stray magnetic fields capable of changing the resonant frequency of nearby nuclei. It is difficult to measure these fields with conventional field-mapping techniques because the fields are not present when the signal is acquired. The stray fields can be measured empirically with cardiac tags.

Characteristics and quality assurance of a dedicated open 0.23 T MRI for radiation therapy simulation.

A commercially available open MRI unit is under routine use for radiation therapy simulation. The effects of a gradient distortion correction (GDC) program used to post process the images were assessed by comparison with the known geometry of a phantom. The GDC reduced the magnitude of the distortions at the periphery of the axial images from 12 mm to 2 mm horizontally along the central axis and distortions exceeding 20 mm were reduced to as little as 2 mm at the image periphery. Coronal and sagittal scans produced similar results. Coalescing these data into distortion as a function of radial distance, we found that for radial distances of <10 cm, the distortion after GDC was <2 mm and for radial distances up to 20 cm, the distortion was <5 mm. The dosimetric errors resulting from homogeneous dose calculations with this level of distortion of the external contour is <2%. A set of triangulation lasers has been added to establish a virtual isocenter for convenient setup and marking of patients and phantoms. Repeated measurements of geometric phantoms over several months showed variations in position between the virtual isocenter and the magnetic isocenter were constrained to <2 mm. Additionally, the interscan variations of 12 randomly selected points in space defined by a rectangular grid phantom was found to be within the intraobserver error of approximately 1 mm in the coronal, sagittal, and transverse planes. Thus, the open MRI has sufficient geometric accuracy for most radiation therapy planning and is temporally stable.

Open low-field magnetic resonance imaging in radiation therapy treatment planning.

PURPOSE: To evaluate the possibilities of an open low-field magnetic resonance imaging (MRI) scanner in external beam radiotherapy treatment (RT) planning. METHODS AND MATERIALS: A custom-made flat tabletop was constructed for the open MR, which was compatible with standard therapy positioning devices. To assess and correct image distortion in low-field MRI, a custom-made phantom was constructed and a software algorithm was developed. A total of 243 patients (43 patients with non-small-cell lung cancer, 155 patients with prostate cancer, and 45 patients with brain tumors) received low-field MR imaging in addition to computed tomographic (CT) planning imaging between January 1998 and September 2001 before the start of the irradiation. RESULTS: Open low-field MRI provided adequate images for RT planning in nearly 95% of the examined patients. The mean and the maximal distortions 15 cm around the isocenter were reduced from 2.5 mm to 0.9 mm and from 6.1 mm to 2.1 mm respectively. The MRI-assisted planning led to better discrimination of tumor extent in two-thirds of the patients and to an optimization in lung cancer RT planning in one-third of the patients. In prostate cancer planning, low-field MRI resulted in significant reduction (40%) of organ volume and clinical target volume (CTV) compared with CT and to a reduction of the mean percentage of rectal dose of 15%. In brain tumors, low-field MR image quality was superior compared with CT in 39/45 patients for planning purposes. CONCLUSIONS: The data presented here show that low-field MRI is feasible in RT treatment planning when image correction regarding system-induced distortions is performed and by selecting MR imaging protocol parameters with the emphasis on adequate images for RT planning.

MRI simulation: effect of gradient distortions on three-dimensional prostate cancer plans.

PURPOSE: To quantify the dosimetric consequences of external patient contour distortions produced on low-field and high-field MRIs for external beam radiation of prostate cancer. METHODS AND MATERIALS: A linearity phantom consisting of a grid filled with contrast material was scanned on a spiral CT, a 0.23 T open MRI, and a 1.5 T closed bore system. Subsequently, 12 patients with prostate cancer were scanned on CT and the open MRI. A gradient distortion correction (GDC) program was used to postprocess the MRI images. Eight of the patients were also scanned on the 1.5 T MRI with integrated GDC correction. All data sets were fused according to their bony landmarks using a chamfer-matching algorithm. The prostate volume was contoured on an MRI image, irrespective of the apparent prostate location in those sets. Thus, the same target volume was planned and used for calculating the anterior-posterior (AP) and lateral separations. The number of monitor units required for treatment using a four-field conformal technique was compared. Because there are also setup variations in patient outer contours, two different CT scans from 20 different patients were fused, and the differences in AP and lateral separations were measured to obtain an estimate of the mean interfractional separation variation. RESULTS: All AP separations measured on MRI were statistically indistinguishable from those on CT within the interfractional separation variations. The mean differences between CT and low-field MRI and CT and high-field MRI lateral separations were 1.6 cm and 0.7 cm, respectively, and were statistically significantly different from zero. However, after the GDC was applied to the low-field images, the difference became 0.4 +/- 0.4 mm (mean +/- standard deviation), which was statistically insignificant from the CT-to-CT variations. The mean variations in the lateral separations from the low-field images with GDC would result in a dosimetric difference of <1%, assuming an equally weighted four-field 18-MV technique for patient separations up to approximately 40 cm. CONCLUSIONS: For patients with lateral separations <40 cm, a homogeneous calculation simulated using a 1.5 T MRI or a 0.23 T MRI with a gradient distortion correction will yield a monitor unit calculation indistinguishable from that generated using CT simulation.