Breaking the noise floor: A framework for correcting the noise-induced bias in noisy magnitude MR signals Cheng Guan Koay, Evren Özarslan, Peter J. Basser STBB / NICHD, National Institutes of Health, Bethesda, MD, United States MR signals are complex numbers where the real and imaginary components are independently Gaussian distributed [1]. The phase of the complex MRI signal is highly sensitiv...

Least Squares (LS) and its weighted version are standard techniques to estimate the Diffusion Tensor (DT) from Diffusion Weighted Images (DWI). They require to linearize the problem by computing the logarithm of the DWI. For the single-coil Rician noise model it has been shown that this model does not introduce a significant bias, but for multiple array coils and parallel imaging, the noise can...

Unlike Gaussian noise, Rician noise filtering is more challenging, since this type of noise exists in Functional Magnetic Resonance Imaging (fMRI) data which makes the analysis of fMRI data very difficult for experimental and clinical purposes. To cope with the situation, normally (smoothing) de-noising is done before the analysis of the data using conventional methods like Gaussian filtering a...

Conventional estimation methods applied to Rician distributed data, (such as magnitude magnetic resonance data) yield biased results. In our work, it is shown where the bias appears. Furthermore, a novel estimation technique, based on Maximum Likelihood estimation, is developed for optimal estimation of signal as well as noise from Rician distributed data. It is shown that the proposed method i...

This paper discusses the assumption of Gaussian noise in the blood-oxygenation-dependent (BOLD) contrast for functional MRI (fMRI). In principle, magnitudes in MRI images follow a Rice distribution. We start by reviewing differences between Rician and Gaussian noise. An analytic expression is derived for the null (resting-state) distribution of the difference between two Rician distributed imag...

We derive the information theoretic limit to storage capacity in volume holographic optical memories for the limiting cases of dominant intensity noise (Gaussian noise) and dominant field noise (Rician noise). These capacity bounds are compared with the performance achievable using simple Reed-Solomon error-correcting codes.

Rician noise pollutes Magnetic Resonance Imaging (MRI) image and makes later work worse. In allusion to remove noise while lessen the loss of details as low as possible, this paper proposed an filter algorithm which comprehensive utilize Genetic Algorithm (GA), PDE and TV, based on 4th order Partial Differential Equations (PDE) and Total Variation (TV) theory. First, it calculates the Total Var...

BACKGROUND Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. METHODS Considering the characteristics of both Rician noise and the NLM filter, this stud...

Magnetic Resonance Imaging (MRI) established itself as a key imaging modality in diagnosis and treatment of brain tumors. Automatic segmentation of tumors becomes a tedious task due to complex anatomical brain structure. In addition, presence of noise degrades the quality of MRI scans. MRI images are usually corrupted by Rician noise which would mislead the image analysis algorithms and results...

In medical images, noise suppression is a delicate and difficult task. A trade off between noise reduction and the preservation of actual image features is a challenging task. Post acquisition denoising of magnetic resonance (MR) images is of importance for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It has been shown that the noise in MR magnit...