Denoise ultra-low-field 3D magnetic resonance images using a joint signal-image domain filter.

Ultra-low-field magnetic resonance imaging (MRI) could suffer from heavy uncorrelated noise, and its removal could be a critical post-processing task. As a primary source of interference, Gaussian noise could corrupt the sampled MR signal (k-space data), especially at lower B0 field strength. For this reason, we consider both signal and image domains by proposing a new joint filter characterized by a Kalman filter with linear prediction and a nonlocal mean filter with higher-order singular value decomposition (HOSVD) for denoising 3D MR data. The Kalman filter first attenuates the noise in k-space, and then its reconstruction images are used to guide HOSVD denoising process with exploring self-similarity among 3D structures. The clearer prefiltered images could also generate improved HOSVD learned bases used to transform the noise corrupted patch groups in the original MR data. The flexibility of proposed method is also demonstrated by integrating other k-space filters into the algorithm scheme. Experimental data includes simulated MR images with the varying noise level and real MR images obtained from our 50 mT MRI scanner. The results reveal that our method has a better noise-removal ability and introduces lesser unexpected artifacts than other related MRI denoising approaches.

Use of 2.1 MHz MRI scanner for brain imaging and its preliminary results in stroke.

Cerebral stroke greatly contributes to death and disability rates in China and the whole world. Effective non-invasive imaging device for bedside monitoring of stroke is critically needed in clinically. This study developed a lightweight (350 kg) and low-footprint magnetic resonance imaging (MRI) system for brain imaging. Static magnetic field was built using an H-typed permanent magnet, which has 50.9 mT magnetic field strength (corresponding to 2.167 MHz proton Larmor frequency). Biplanar gradient coils were designed using the target field method based on dipole equivalent. Radio-frequency coils were optimized by particle swarm optimization. The 2 MHz MRI system was deployed in the Department of Neurology of hospital to test its performance in stroke imaging detection. Gradient recall echo and fast spin echo sequences were utilized to acquire T1- and T2-weighted MR images, respectively. Brain images of a healthy volunteer, a patient with hemorrhagic stroke, a patient of ischemic stroke, and a patient with ischemic stroke and images from 17-day long-term monitoring of hemorrhagic stroke were obtained with a 1.5 mm * 2.0 mm spatial resolution in plane, and a 10 mm thickness.

Optimized inside-out magnetic resonance probe for soil moisture measuring in situ.

We report a novel inside-out NMR (nuclear magnetic resonance) probe for the measurement of soil moisture. The probe consists of a dumbbell-shape magnet and an opposed-solenoid RF (radio frequency) coil. Optimization methods for the structure of the magnet and RF coil that maximize the SNR (signal-to-noise ratio) of NMR measurements are also described. The dumbbell-shape magnet consists of three cylindrical magnets in series whose magnetic field was calculated with analytic expression deduced by converting magnet to equivalent magnetization current on its cylindrical surface. Based on the analytic expression, a nonlinear optimization mathematical model was built to determine the optimal structure parameters automatically. The opposed-solenoid is a pair of reverse-connected solenoids and used as RF coil on inside-out NMR probe in this work. Its structure-parameter optimization was carried out based on FEM (finite element method) simulation, and UD (uniform design) was applied to increase the optimization efficiency. A prototype was designed and built consisting of a magnet with length of 100 mm and a diameter of 40 mm. An NMR-based soil moisture measuring experiment was conducted by this prototype, with NMR performed using the CPMG (Carr-Purcell-Meiboom-Gill) pulse sequence for soil sample in different moisture content. The T2 distribution spectrum reveals that there are two compartments of water in the soil sample: free water and bound water.