Transitioning from 0.5 to 0.9 mT: Protecting against inadvertent activation of magnet mode in active implants.

The "5 gauss line" is a phrase that is likely to be familiar to everyone working with MRI, but what is its significance, how was it defined, and what changes are currently in progress? This review explores the history of 5 gauss (0.5 mT) as a threshold for protecting against inadvertently putting cardiac pacemakers, implantable cardioverter defibrillators, and other active implantable medical devices into a "magnet mode." Additionally, it describes the background to the recent change of this threshold to 9 gauss (0.9 mT) in the International Standard IEC 60601-2-33 edition 4.0 that defines basic safety requirements for MRI. Practical implications of this change and some ongoing and emerging issues are also discussed.

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.

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.

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.

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.