Abstract
For proper modelling of signal and noise in MR data requires proper interpretation and analysis of data, the different approaches with this degradation due to random fluctuations in the MR data, probabilistic modeling is power solution, which needs correctness in the computation of noise is challenging task and various stastical approaches can be utilized. After modelling the noise it can be integrated to denoising pipeline, in this research work, the recognition of noise only pixels and the evaluation of standard deviation of noise using median, mean or other optimal sample quantiles are combined in to single frame work for noise assement and uses fixed point iterative procedure to obtain standard deviation of noise. We tested the effectiveness of the algorithm to the MR clinical and synthetic data base.
Highlights
For proper modelling of signal and noise in MR data requires proper interpretation and analysis of data, the different approaches with this degradation due to random fluctuations in the MR data, probabilistic modeling is power solution, which needscorrectnessin thecomputation of noise is challenging task and various stastical approaches can be utilized
Earlier methods can be separated into two methods for the computation of noise, first method involves the manually selected region of interest (ROI), in the second method entire volumetric data or image is considered for estimation without human interpretation
The problem is facing for the current automatic estimation method is separation of original signal from noise effected signals and other in homogeneity artifacts4, 5 the proposed work of two authors is by aggregate the values of all pixels from an entire pixel data set in to one dimensional array and estimate the standard deviation of noise from the histogram of one dimensional array.in this research work, introduce simpler method for noise assement in MR image to eliminate the drawback of two authors
Summary
The data set obtained from the DIPY (diffusion imaging in python) and JSS hospital Mysore Karnataka, India Data set 1. STANFORD_HARDI (High resolution Diffusion Weighted imaging dataset (N=4)) Uses phased array coil system and SOS reconstruction (without parallel imaging). TAIWAN_NTU_DSI (Diffusion Spectrum imaging) Dataset (N=1) uses phased array coil system and SENSE reconstruction. (With parallel imaging) Data set 2 Philips 3.0Tscanner: MRI T1 weighted axial Brain image having pathology acquisition parameters are TR=5.3sec, TE=20ms, slice thickness=3.5mm, Resolution of 512x512. Parallel Image Reconstruction: SENSE Philips 3.0Tscanner: MRI T1 weighted sagittal Brain image with acquisition parameters are TR=5.3sec, TE=20ms, slice thickness=3.5mm, Resolution of 512x512. Parallel Image Reconstruction: SENSE Diffusion Imaging in Python (Dipy) is a free and open source software project tool where image processing library tools are available for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments
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