Characterization of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy system.

PURPOSE: To characterize the performance of the onboard imaging unit for the first clinical magnetic resonance image guided radiation therapy (MR-IGRT) system. METHODS: The imaging performance characterization included four components: ACR (the American College of Radiology) phantom test, spatial integrity, coil signal to noise ratio (SNR) and uniformity, and magnetic field homogeneity. The ACR phantom test was performed in accordance with the ACR phantom test guidance. The spatial integrity test was evaluated using a 40.8 × 40.8 × 40.8 cm(3) spatial integrity phantom. MR and computed tomography (CT) images of the phantom were acquired and coregistered. Objects were identified around the surfaces of 20 and 35 cm diameters of spherical volume (DSVs) on both the MR and CT images. Geometric distortion was quantified using deviation in object location between the MR and CT images. The coil SNR test was performed according to the national electrical manufacturers association (NEMA) standards MS-1 and MS-9. The magnetic field homogeneity test was measured using field camera and spectral peak methods. RESULTS: For the ACR tests, the slice position error was less than 0.10 cm, the slice thickness error was less than 0.05 cm, the resolved high-contrast spatial resolution was 0.09 cm, the resolved low-contrast spokes were more than 25, the image intensity uniformity was above 93%, and the percentage ghosting was less than 0.22%. All were within the ACR recommended specifications. The maximum geometric distortions within the 20 and 35 cm DSVs were 0.10 and 0.18 cm for high spatial resolution three-dimensional images and 0.08 and 0.20 cm for high temporal resolution two dimensional cine images based on the distance-to-phantom-center method. The average SNR was 12.0 for the body coil, 42.9 for the combined torso coil, and 44.0 for the combined head and neck coil. Magnetic field homogeneities at gantry angles of 0°, 30°, 60°, 90°, and 120° were 23.55, 20.43, 18.76, 19.11, and 22.22 ppm, respectively, using the field camera method over the 45 cm DSV. CONCLUSIONS: The onboard imaging unit of the first commercial MR-IGRT system meets ACR, NEMA, and vendor specifications.

Chimpanzee (Pan troglodytes) precentral corticospinal system asymmetry and handedness: a diffusion magnetic resonance imaging study.

BACKGROUND: Most humans are right handed, and most humans exhibit left-right asymmetries of the precentral corticospinal system. Recent studies indicate that chimpanzees also show a population-level right-handed bias, although it is less strong than in humans. METHODOLOGY/PRINCIPAL FINDINGS: We used in vivo diffusion-weighted and T1-weighted magnetic resonance imaging (MRI) to study the relationship between the corticospinal tract (CST) and handedness in 36 adult female chimpanzees. Chimpanzees exhibited a hemispheric bias in fractional anisotropy (FA, left>right) and mean diffusivity (MD, right>left) of the CST, and the left CST was centered more posteriorly than the right. Handedness correlated with central sulcus depth, but not with FA or MD. CONCLUSIONS/SIGNIFICANCE: These anatomical results are qualitatively similar to those reported in humans, despite the differences in handedness. The existence of a left>right FA, right>left MD bias in the corticospinal tract that does not correlate with handedness, a result also reported in some human studies, suggests that at least some of the structural asymmetries of the corticospinal system are not exclusively related to laterality of hand preference.

Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging.

k-space-based reconstruction in parallel imaging depends on the reconstruction kernel setting, including its support. An optimal choice of the kernel depends on the calibration data, coil geometry and signal-to-noise ratio, as well as the criterion used. In this work, data consistency, imposed by the shift invariance requirement of the kernel, is introduced as a goodness measure of k-space-based reconstruction in parallel imaging and demonstrated. Data consistency error (DCE) is calculated as the sum of squared difference between the acquired signals and their estimates obtained based on the interpolation of the estimated missing data. A resemblance between DCE and the mean square error in the reconstructed image was found, demonstrating DCE's potential as a metric for comparing or choosing reconstructions. When used for selecting the kernel support for generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction and the set of frames for calibration as well as the kernel support in temporal GRAPPA reconstruction, DCE led to improved images over existing methods. Data consistency error is efficient to evaluate, robust for selecting reconstruction parameters and suitable for characterizing and optimizing k-space-based reconstruction in parallel imaging.

Single-shot multiecho parallel echo-planar imaging (EPI) for diffusion tensor imaging (DTI) with improved signal-to-noise ratio (SNR) and reduced distortion.

In this work, a multiecho parallel echo-planar imaging (EPI) acquisition strategy is introduced as a way to improve the acquisition efficiency in parallel diffusion tensor imaging (DTI). With the use of an appropriate echo combination strategy, the sequence can provide signal-to-noise ratio (SNR) enhancement while maintaining the advantages of parallel EPI. Simulations and in vivo experiments demonstrate that a weighted summation of multiecho images provides a significant gain in SNR over the first echo image. It is experimentally demonstrated that this SNR gain can be utilized to reduce the number of measurements often required to ensure adequate SNR for accurate DTI measures. Furthermore, the multiple echoes can be used to derive a T(2) map, providing additional information that might be useful in some applications.

Cross-validation-based kernel support selection for improved GRAPPA reconstruction.

The extended version of the generalized autocalibrating partially parallel acquisition (GRAPPA) technique incorporates multiple lines and multiple columns of measured k-space data to estimate missing data. For a given accelerated dataset, the selection of the measured data points for fitting a missing datum (i.e., the kernel support) that provides optimal reconstruction depends on coil array configuration, noise level in the acquired data, imaging configuration, and number and position of autocalibrating signal lines. In this work, cross-validation is used to select the kernel support that best balances the conflicting demands of fit accuracy and stability in GRAPPA reconstruction. The result is an optimized tradeoff between artifacts and noise. As demonstrated with experimental data, the method improves image reconstruction with GRAPPA. Because the method is simple and applied in postprocessing, it can be used with GRAPPA routinely.