Borrelli, Pasquale (2015) Denoising in magnetic resonance imaging: theory, algorithms and applications. [Tesi di dottorato]

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Item Type:	Tesi di dottorato
Resource language:	English
Title:	Denoising in magnetic resonance imaging: theory, algorithms and applications
Creators:	Creators Email Borrelli, Pasquale pasquale.borrelli@unina.it
Date:	31 March 2015
Number of Pages:	47
Institution:	Università degli Studi di Napoli Federico II
Department:	Scienze Biomediche Avanzate
Scuola di dottorato:	Scienze biomorfologiche e chirurgiche
Dottorato:	Imaging molecolare
Ciclo di dottorato:	27
Coordinatore del Corso di dottorato:	nome email Renda, Andrea renda@unina.it
Tutor:	nome email Palma, Giuseppe UNSPECIFIED
Date:	31 March 2015
Number of Pages:	47
Keywords:	denoising; magnetic resonance imaging; parallel imaging; susceptibility-weighted imaging; non-local mean; GPU
Settori scientifico-disciplinari del MIUR:	Area 02 - Scienze fisiche > FIS/07 - Fisica applicata (a beni culturali, ambientali, biologia e medicina) Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica Area 06 - Scienze mediche > MED/36 - Diagnostica per immagini e radioterapia
Date Deposited:	11 Apr 2015 10:46
Last Modified:	30 Apr 2016 01:00
URI:	http://www.fedoa.unina.it/id/eprint/10378
DOI:	10.6092/UNINA/FEDOA/10378

Collection description

In the context of medical image processing, denoising is widely considered as one of most fundamental postprocessing tasks. In this field, the non-local means (NLM) filter demonstrated to be a robust and performing approach respect to the previous state-of-art denoising methods. As the filtering strength must be tuned to obtain an optimized and customized restoring process, the estimation of image noise variance is an important issue. Althought in clinical practice noise estimation is performed on background (no signal area) of magnitude MR images, in case of parallel MR imaging (pMRI) techniques noise estimation from the image background produces biased results due to spatially varying noise distribution of the pMRI images. A novel NLM approach based on local noise estimation is introduced (hereafter indicated as SVN-NLM). As second task, since the susceptibility-weighted imaging (SWI) suffers from reduced SNR due to the high resolution required to obtain a proper contrast generation, a novel pipeline (Multicomponent-Imaginary-Real-SWI, hereafter MIR-SWI) to obtain susceptibility-weighted images with higher SNR and improved conspicuity is proposed. In this context, the application of a denoising filter is non-trivial as the distributions of magnitude and phase noise may introduce biases during image restoration. Taking advantage of the potential multispectral nature of MR images, the multicomponent approach of the MIR-SWI approach performs better than a component-by-component image restoration method. Finally, a new strategy to address the computational demand of the NLM filter is investigated. Due to high computational complexity of the NLM denoising filter, in literature several 2D NLM implementations on Graphic Processor Unit (GPU) architectures were proposed. Here a fully 3D NLM implementation on a multi-GPU architecture is presented and its high scalability is suggested.

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