4/2/2023 0 Comments Apodization inmr![]() After presenting our main approximation, which allows the implementation of a constrained Gauss–Seidel (GS +) algorithm, three methods, based on deconvolution or iterative techniques, are tested on phantom measurements and numerical simulations. ![]() To start, we will introduce the PSF and mathematical relation between the observed image and the true one. Our goal is then to implement simple and robust post-processing FT methods to deal with the contamination induced by PSF without broadening image spatial structure. However, the implementation of new acquisition sequences are limited on clinical systems. A hybrid acquisition and reconstruction technique was also introduced where the high k-space data were acquired with echo-planar SI while the low k-space was sampled with phase-encoded SI. Alternatively, spectral analysis methods such as maximum entropy associated with Fourier analysis have been used to improve spatial resolution of truncated data samples. Modelling image reconstruction methods, using generalized series, and extrapolation, have been also presented. ![]() Such methods, based on hybrid griding and convolution back-projection reconstruction using non-equidistant k-space polar sampling, or wavelet-encoded method have been tested. Other image reconstruction methods have been applied to reduce PSF contamination. Also, a selective FT method, consisting of broadening gradient range, , can improve the shape of the sensitive region. Compared to cartesian k-space sampling, spherical and weighted k-space sampling acquisitions can be used to reduce Gibbs ringings along the two phase-encoding directions. However, the main inconvenience is that spatial structures are significantly broadened, losing spatial resolution.ĭifferent techniques of k-sampling schemes for MRSI acquisition has been also studied to improve voxel sensitivity. ![]() The use of these windows produces a drastic decrease of ripples amplitudes. Dolph–Chebycheff window is considered to be optimal, although both Hamming and Kaiser windows can be easily implemented and provide almost as good results. Suitable functions are Cosine, Hanning, Hamming, and Kaiser windows. To lower ripple effect, a suitable weighting window function h(k) is conventionally applied before processing k-space signal with FT. This convolution induces spatial contamination, which has to be kept in mind for precise quantification of metabolite concentrations in proton ( 1H) MSR. MR spectroscopic images result from the convolution of the undistorted or true image with FT of the rectangular function Π(k), called the point spread function (PSF). The FT reconstruction of truncated signal introduces the so-called Gibbs-ringings the origin of which is due to truncation of the original signal by a rectangular function inducing spatial contamination. Spatial information in MRSI is considerably affected by the reconstruction with Fourier transform (FT) of low-resolution k-space. Magnetic resonance spectroscopic imaging (MRSI) is limited by a low signal to noise ratio, so a compromise between spatial resolution and examination time is needed in clinical application. More specifically, the regions with sharp variations, defined in the following as spatial structures, should be recovered with minimum broadening and/or contamination. The main purpose of image technique is to better display spatial information. This post-processing method can provide a contrast enhancement of clinical spectroscopic images without changes in acquisition time. A significant decrease of contamination without broadening the spatial resolution was obtained with GS+ method compared to a conventional apodization. The linear property of contamination was validated on a point sample phantom. Convergence and noise dependence studies of the GS algorithm have been done. A Gauss-Seidel (GS) algorithm is used for iterative techniques with and without a non-negative constraint (GS+). In order to reduce spatial contamination, three methods, applied after Fourier transform image reconstruction, based on deconvolution or iterative techniques are tested to decrease Point Spread Function contamination. The reconstruction of truncated signal introduces a Point Spread Function that considerably affects the spatial resolution. Magnetic resonance spectroscopic imaging is limited by a low signal-to-noise ratio, so a compromise between spatial resolution and examination time is needed in clinical application.
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