Denoising of 3D magnetic resonance images using non-local PCA and Transform-Domain Filter

The Magnetic Resonance Imaging (MRI) technologyused in clinical diagnosis demands high Peak Signal-to-Noise ratio(PSNR) and improved resolution for accurate analysis and treatmentmonitoring. However, MRI data is often corrupted by random noisewhich degrades the quality of Magnetic Resonance (MR) images.Denoising is a paramount challenge as removing noise causesreduction in the fine details of MRI images. We have developed anovel algorithm which employs Principal Component Analysis(PCA) decomposition and Wiener filtering. We have proposed a twostage approach. In first stage, non-local PCA thresholding is appliedon noisy image and second stage uses Wiener filter over this filteredimage. Our algorithm is implemented using MATLAB andperformance is measured via PSNR. The proposed approach hasalso been compared with related state-of-art methods. Moreover, wepresent both qualitative and quantitative results which prove thatproposed algorithm gives superior denoising performance

Denoising magnetic resonance images using collaborative non-local means

Noise artifacts in magnetic resonance (MR) images increase the complexity of image processing workflows and decrease the reliability of inferences drawn from the images. It is thus often desirable to remove such artifacts beforehand for more robust and effective quantitative analysis. It is important to preserve the integrity of relevant image information while removing noise in MR images. A variety of approaches have been developed for this purpose, and the non-local means (NLM) filter has been shown to be able to achieve state-of-the-art denoising performance. For effective denoising, NLM relies heavily on the existence of repeating structural patterns, which however might not always be present within a single image. This is especially true when one considers the fact that the human brain is complex and contains a lot of unique structures. In this paper we propose to leverage the repeating structures from multiple images to collaboratively denoise an image. The underlying assumption is that it is more likely to find repeating structures from multiple scans than from a single scan. Specifically, to denoise a target image, multiple images, which may be acquired from different subjects, are spatially aligned to the target image, and an NLM-like block matching is performed on these aligned images with the target image as the reference. This will significantly increase the number of matching structures and thus boost the denoising performance. Experiments on both synthetic and real data show that the proposed approach, collaborative non-local means (CNLM), outperforms the classic NLM and yields results with markedly improved structural details

Improved Liver R2* Mapping by Averaging Decay Curves.

Liver R2* mapping is often degraded by the low signal-to-noise ratio (SNR) especially in the presence of severe iron. This study aims to improve liver R2* mapping at low SNRs by averaging decay curves before the process of curve-fitting. Independently filtering echo images by nonlocal means (NLM) demonstrated improved quality of R2* mapping, but may introduce new errors due to the nonlinear nature of the NLM filter, during which the averaging weights may vary with different image contents at multiple echo times. In addition, the image denoising effect of the NLM may decline when no sufficient similar patches are available. To overcome these drawbacks, we proposed to filter decay curves instead of images. In this novel scheme, decay curves were averaged in a local window, each with a weight assigned according to the curve-similarity measured by the distance between one of the neighboring curves and the targeted one. The proposed method was tested on simulated, phantom and patient data. The results demonstrate that the proposed method can provide more accurate R2* mapping compared with the NLM algorithm, and hence has the potential to improve diagnosis and therapy in patients with liver iron

Doctor of Philosophy

dissertationThe gold standard for evaluation of arterial disease using MR continues to be contrast-enhanced MR angiography (MRA) with gadolinium-based contrast agents (Gd-MRA). There has been a recent resurgence in interest in methods that do not rely on gadolinium for enhancement of blood vessels due to associations Gd-MRA has with nephrogenic systemic fibrosis (NSF) in patients with impaired renal function. The risk due to NSF has been shown to be minimized when selecting the appropriate contrast type and dose. Even though the risk of NSF has been shown to be minimized, demand for noncontrast MRA has continued to rise to reduce examination cost, and improve patient comfort and ability to repeat scans. Several methods have been proposed and used to perform angiography of the aorta and peripheral arteries without the use of gadolinium. These techniques have had limitations in transmit radiofrequency field (B1+) inhomogeneities, acquisition time, and specific hardware requirements, which have stunted the utility of noncontrast enhanced MRA. In this work feasibility of noncontrast (NC) MRA at 3T of the femoral arteries using dielectric padding, and using 3D radial stack of stars and compressed sensing to accelerate acquisitions in the abdomen and thorax were tested. Imaging was performed on 13 subjects in the pelvis and thighs using high permittivity padding, and 11 in the abdomen and 19 in the thorax using 3D radial stack of stars with tiny golden angle using gold standards or previously published techniques. Qualitative scores for each study were determined by radiologists who were blinded to acquisition type. Vessel conspicuity in the thigh and pelvis showed significant increase when high permittivity padding was used in the acquisition. No significant difference in image quality was observed in the abdomen and thorax when using undersampling, except for the descending aorta in thoracic imaging. All image quality scores were determined to be of diagnostic quality. In this work it is shown that NC-MRA can be improved through the use of high permittivity dielectric padding and acquisition time can be decreased through the use of 3D radial stack of stars acquisitions