Fast and high resolution MRSI without aliasing using online SFAS and multiple regions of support
نویسندگان
چکیده
Introduction When localized magnetic resonance spectroscopic imaging (MRSI) is performed, unperfected signal saturation outside the region of interest (ROI) is typically encountered. This undesired signal can cause aliasing and signal contamination if fast high resolution MRSI is conducted by simply reducing FOV (i.e. Zoom MRSI). Alternatively, one can use the sequential forward array selection (SFAS) method to obtain the same resolution MRSI with the same or less data acquisition time required by the Zoom MRSI method [1]. In this work, we demonstrate that online SFAS can not only provide high resolution MRSI with less data acquisition time but also eliminate the aliasing unsaturated signal. Methods The ROI of the spectroscopic imaging slice was planned on a T1 weighted scout image (data matrix = 256x256, FOV 256cm). The MRSI was conducted by using a 3D localized adiabatic spin echo refocusing (LASER) sequence with two dimensions of phase encoding [2]. Water suppression was provided by an initial broadband semi-selective excitation pulse and frequency selective DANTE pulse applied to the water resonance. The data were acquired with a reduced number of nonuniform phase encodes using the online SFAS over an FOV of 256x256mm resulting in an acquisition time of 8 minutes (TR/TE of 2000/75 ms) and a nominal voxel size of 0.64cc (thk=1 cm). Free induction decay data were processed with 6 Hz line broadening, 50Hz convolution difference, and 1D FFT. The resulting spectra were processed by using projections onto convex sets reconstruction to generate MRSI [1]. Results
منابع مشابه
Fast spectroscopic imaging using online optimal sparse k-space acquisition and projections onto convex sets reconstruction.
Long acquisition times, low resolution, and voxel contamination are major difficulties in the application of magnetic resonance spectroscopic imaging (MRSI). To overcome these difficulties, an online-optimized acquisition of k-space, termed sequential forward array selection (SFAS), was developed to reduce acquisition time without sacrificing spatial resolution. A 2D proton MRSI region of inter...
متن کاملRobust Fuzzy Content Based Regularization Technique in Super Resolution Imaging
Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a n...
متن کاملUltra-high resolution brain metabolite mapping at 7 T by short-TR Hadamard-encoded FID-MRSI
MRSI in the brain at ≥7 T is a technique of great promise, but has been limited mainly by low B0/B1+-homogeneity, specific absorption rate restrictions, long measurement times, and low spatial resolution. To overcome these limitations, we propose an ultra-high resolution (UHR) MRSI sequence that provides a 128×128 matrix with a nominal voxel volume of 1.7×1.7×8mm3 in a comparatively short measu...
متن کاملFast MR Spectroscopic Imaging
MR spectroscopic imaging (MRSI) has become widely available as a research tool for mapping tissue metabolic status. Clinical research applications have been limited by intrinsically low sensitivity and by time-consuming phase encoding techniques that are used with conventional MRSI. The long encoding times limit volume coverage, introduce motion sensitivity and tax patient tolerance. The increa...
متن کاملAutomatic Interpretation of UltraCam Imagery by Combination of Support Vector Machine and Knowledge-based Systems
With the development of digital sensors, an increasing number of high-resolution images are available. Interpretation of these images is not possible manually, which necessitates seeking for practical, fast and automatic solutions to solve the environmental and location-based management problems. The land cover classification using high-resolution imagery is a difficult process because of the c...
متن کامل