No abstract

The use of self-calibrating techniques in parallel magnetic resonance imaging eliminates the need for coil sensitivity calibration scans and avoids potential mismatches between calibration scans and subsequent accelerated acquisitions (e.g., as a result of patient motion). Most examples of self-calibrating Cartesian parallel imaging techniques have required the use of modified k-space trajectories that are densely sampled at the center and more sparsely sampled in the periphery. However, spiral and radial trajectories offer inherent self-calibrating characteristics because of their densely sampled center. At no additional cost in acquisition time and with no modification in scanning protocols, in vivo coil sensitivity maps may be extracted from the densely sampled central region of k-space. This work demonstrates the feasibility of self-calibrated spiral and radial parallel imaging using a previously described iterative non-Cartesian sensitivity encoding algorithm. The accuracy of coil sensitivity estimates is a major determinant of the quality of parallel magnetic resonance image reconstructions. While the level of tolerance for sensitivity miscalibration differs from technique to technique, any significant discrepancy in coil sensitivity references introduces systematic errors in reconstructed images while degrading overall image quality. Self-calibrating parallel imaging techniques that employ variable-density k-space acquisition schemes have been demonstrated (1-4). Selfcalibration eliminates the need for an external sensitivity reference, making parallel image reconstructions less susceptible to miscalibration and image degradation resulting from bulk patient motion.Self-calibrating parallel image reconstructions have been commonly implemented for rectilinear (or Cartesian) trajectories. Phase-encoded lines in the outer k-space region are omitted at a chosen outer reduction factor, whereas those in the central k-space region remain densely sampled. The densely sampled central lines are used for calibration, either through fitting directly in k-space (1-3) or through Fourier transformation to generate low-resolution in vivo sensitivity maps (4). The omitted phase-encoded lines are then reconstructed by a parallel imaging technique of choice.It has been noted that non-Cartesian trajectories such as spiral and radial trajectories are logical candidates for self-calibration due to their characteristic oversampled kspace center (4). Even for accelerated acquisitions, the center of k-space will generally be sampled with sufficient density to enable the creation of reliable low-resolution maps of component coil sensitivities without aliasing artifacts. These low-resolution maps can then provide the coil sensitivity references required for reconstruction of the outer k-space signal data.However, non-Cartesian k-space trajectories pose greater challenges for parallel image reconstruction than their Cartesian counterparts due to the memory requirements and computational demand of inverting large matrices. Recent work...

A positron emission tomography (PET) system or ‘insert’ has been constructed for placement and operation in the bore of a small animal magnetic resonance imaging (MRI) scanner to allow simultaneous MR and PET imaging. The insert contains electronics, components with a variety of magnetic properties, and large continuous sheets of metal— all characteristics of an object that should, by conventional wisdom, never be placed in the bore of an MR scanner, especially near the imaging volume. There are a variety of ways the two systems might be expected to interact that could negatively impact the performance of either or both. In this article, the interaction mechanisms, particularly the impacts of the PET insert and shielding on MR imaging, are defined and explored. Additionally, some of the difficulties in quantifying errors introduced into the MR images as a result of the presence of the PET components are demonstrated. Several different approaches are used to characterize image artifacts and determine optimal placement of the shielding. Data are also presented that suggest ways the shielding could be modified to reduce errors and enable placement closer to the isocenter of the magnet.

A technique is described that uses compressed sensing and parallel imaging to reconstruct R2*-corrected water and fat images from accelerated datasets. Acceleration factors as high as 7.0 are shown with excellent image quality. These high acceleration factors enable water-fat separation with higher resolution or greater anatomical coverage in breath-hold applications.

Measuring signal‐to‐noise ratio (SNR) for parallel MRI reconstructions is difficult due to spatially dependent noise amplification. Existing approaches for measuring parallel MRI SNR are limited because they are not applicable to all reconstructions, require significant computation time, or rely on repeated image acquisitions. A new SNR estimation approach is proposed, a hybrid of the repeated image acquisitions method detailed in the National Electrical Manufacturers Association (NEMA) standard and the Monte Carlo based pseudo‐multiple replica method, in which the difference between images reconstructed from the unaltered acquired data and that same data reconstructed after the addition of calibrated pseudo‐noise is used to estimate the noise in the parallel MRI image reconstruction. This new noise estimation method can be used to rapidly compute the pixel‐wise SNR of the image generated from any parallel MRI reconstruction of a single acquisition. SNR maps calculated with the new method are validated against existing SNR calculation techniques. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.