Vol 49, No 1 (2018)

Tracing parameters of digestion process could help in setting more accurate diagnosis for patients with gastrointestinal diseases. For this purpose, we suggest a new type of food tracer. By soaking liquid perfluorocarbon in dry rodent food, each step of digestion process can be visualized on ¹⁹F-magnetic resonance (MR) images. Compared with liquid contrast agents, food is able to fill organs of gastrointestinal tract more tightly and yield properties of digestion process. However, rats, participating in such study, should be set on a water diet before experiment. ¹⁹F-MR images are obtained with volume scanning (3D) pulse sequence based on multiple spin echo methodic with minimal time intervals between echoes. Gastrointestinal ¹⁹F-magnetic resonance imaging (MRI) visualization is a harmless real-time tracking method which could be easily transferred into clinical practice. Moreover, it does not apply ionizing radiation, so in the combination with reference ¹H-MRI this method could become very useful in treatment process assessment.

A sharp variation of the near infrared photoluminescence intensity for spin-3/2 color centers in hexagonal (4H-, 6H-) and rhombic (15R-) SiC polytypes in the vicinity of level anticrossing (LAC) and cross-relaxation in an external magnetic field at room temperature are observed. This effect can be used for a purely all-optical sensing of the magnetic field with nanoscale spatial resolution. A distinctive feature of the LAC signal is a weak dependence on the magnetic field direction that allows monitoring of the LAC signals in the nonoriented systems, such as powder materials, without need to determine the nanocrystal orientation in the sensing measurements. Furthermore, an LAC-like signal is also observed for the spin color centers (NV centers) in diamond in low magnetic fields with only marginal dependence on the magnetic field direction. This effect is enabled to detect weak magnetic fields using nanodiamond samples in the form of disordered mixture. In addition, the optically detected magnetic resonance and LAC techniques are suggested to serve as a simple method to determine the local stress in nanodiamonds under ambient conditions.

Parallel imaging plays an important role to reduce data acquisition time in magnetic resonance imaging (MRI). Under-sampled non-Cartesian trajectories accelerate the MRI scan time, but the resulting images may have aliasing artifacts. To remove these artifacts, a variety of methods have been developed within the scope of parallel imaging in the recent past. In this paper, the use of Eigen-vector-based iterative Self-consistent Parallel Imaging Reconstruction Technique (ESPIRiT) along with self-calibrated GRAPPA operator gridding (self-calibrated GROG) on radial k-space data for accelerated MR image reconstruction is presented. The proposed method reconstructs the solution image from non-Cartesian k-space data in two steps: First, the acquired radial data is gridded using self-calibrated GROG and then ESPIRIT is applied on this gridded data to get the un-aliased image. The proposed method is tested on human head data and the short-axis cardiac radial data. The quality of the reconstructed images is evaluated using artifact power (AP), root-mean-square error (RMSE) and peak signal-to-noise ratio (PSNR) at different acceleration factors (AF). The results of the proposed method (GROG followed by ESPIRiT) are compared with GROG followed by pseudo-Cartesian GRAPPA reconstruction approach (conventionally used). The results show that the proposed method provides considerable improvement in the reconstructed images as compared to conventionally used pseudo-Cartesian GRAPPA with GROG, e.g., 87, 67 and 82% improvement in terms of AP for 1.5T, 3T human head and short-axis cardiac radial data, 63, 45 and 57% improvement in terms of RMSE for 1.5T, 3T human head and short-axis cardiac radial data, 11, 7 and 9% improvement in terms of PSNR for 1.5T, 3T human head and short-axis cardiac radial data, respectively, at AF = 4.

Recently, a number of magnetic resonance imaging protocols have been reported that seek to exploit the effect of dissolved oxygen (O₂, paramagnetic) on the longitudinal ¹H relaxation of tissue water, thus providing image contrast related to tissue oxygen content. However, tissue water relaxation is dependent on a number of mechanisms and this raises the issue of how best to model the relaxation data. This problem, the model selection problem, occurs in many branches of science and is optimally addressed by Bayesian probability theory. High signal-to-noise, densely sampled, longitudinal ¹H relaxation data were acquired from rat brain in vivo and from a cross-linked bovine serum albumin (xBSA) phantom, a sample that recapitulates the relaxation characteristics of tissue water in vivo. Bayesian-based model selection was applied to a cohort of five competing relaxation models: (1) monoexponential, (2) stretched-exponential, (3) biexponential, (4) Gaussian (normal) R₁-distribution, and (5) gamma R₁-distribution. Bayesian joint analysis of multiple replicate datasets revealed that water relaxation of both the xBSA phantom and in vivo rat brain was best described by a biexponential model, while xBSA relaxation datasets truncated to remove evidence of the fast relaxation component were best modeled as a stretched exponential. In all cases, estimated model parameters were compared to the commonly used monoexponential model. Reducing the sampling density of the relaxation data and adding Gaussian-distributed noise served to simulate cases in which the data are acquisition-time or signal-to-noise restricted, respectively. As expected, reducing either the number of data points or the signal-to-noise increases the uncertainty in estimated parameters and, ultimately, reduces support for more complex relaxation models.