Magnetic resonance imaging

Spatially dependent filtering for removing phase distortions at the cortical surface

Recent advances in high field magnetic resonance technology have increased the interest in the phase of the complex data. Processed phase images are derived from the phase signal by removing the bias field and phase wraps from the initial data. However, the usefulness of this data has been hindered by artifacts at the brain/non-brain surface, particularly in cortical regions. A method is proposed that efficiently removes surface artifacts by performing Gaussian filtering with spatially varying parameters of unwrapped or complex filtered phase images. The proposed method is shown to produce improved images, revealing underlying structure and detail that are otherwise obscured by surface artifacts in images produced by traditional phase processing methods.

Infinite impulse response GRAPPA for parallel MR image reconstruction

DOI: 10.1002/mrm.22197

Accelerated parallel MRI has advantage in imaging speed, and its image quality has been improved continuously in recent years. This paper introduces a two-dimensional infinite impulse response model of inverse filter to replace the finite impulse response model currently used in generalized autocalibrating partially parallel acquisitions class image reconstruction methods. The infinite impulse response model better characterizes the correlation of k-space data points and better approximates the perfect inversion of parallel imaging process, resulting in a novel generalized image reconstruction method for accelerated parallel MRI. This k-space-based reconstruction method includes the conventional generalized autocalibrating partially parallel acquisitions class methods as special cases and has a new infinite impulse response data estimation mechanism for effective improvement of image quality. The experiments on in vivo MRI data show that the proposed method significantly reduces reconstruction errors compared with the conventional two-dimensional generalized autocalibrating partially parallel acquisitions method, particularly at the high acceleration rates.

An optimised framework for reconstructing and processing MR phase images

DOI: 10.1016/j.neuroimage.2009.09.071

Phase contrast imaging holds great potential for in vivo biodistribution studies of paramagnetic molecules and materials. However, in vivo quantification of iron storage and other paramagnetic materials requires improvements in reconstruction and processing of MR complex images. To achieve this, we have developed a framework including (i) an optimal coil sensitivity smoothing filter for phase imaging determined at the maximal signal to noise ratio, (ii) a phase optimised and a complex image optimised reconstruction approach, and (iii) a magnitude and phase correlation test criterion to determine the low pass filter parameter for background phase removal. The method has been evaluated using 3T and 7T MRI data containing cortical regions, the basal ganglia including the caudate, and the midbrain including the substantia nigra. The optimised reconstruction improves phase image contrast and noise suppression compared with conventional reconstruction approaches, and the correlation test criterion provides an objective method for separation of the local phase signal from the background phase measurements. Phase values of several brain regions of interest have been calculated, including gray matter (-1.23 Hz at 7T and -0.55 Hz at 3T), caudate (-3.8 Hz at 7T), and the substantia nigra (-6.2 Hz at 7T).