De-noising of Real-time Dynamic Magnetic Resonance Images by the Combined Application of Karhunen-Loeve Transform (KLT) and Wavelet Filtering

A hybrid filtering method called Karhunen Loeve Transform-Wavelet (KW) filtering is presented to de-noise dynamic cardiac magnetic resonance images that simultaneously takes advantage of the intrinsic spatial and temporal redundancies of real-time cardiac cine. This new image filtering technique combines two well-established methods: temporal Karhunen-Loeve transform (KLT) and spatial adaptive wavelet filtering. KW filtering has four steps: 1. Apply KLT along the temporal direction, generating a series of “eigenimages”. Because of the high temporal correlations, most of the energy is concentrated into a few eigenimages. 2. Marcenko-Pastur (MP) law is used to identify and discard the noise-only eigenimages; 3. 2-D spatial wavelet filter with adaptive threshold is applied to each eigenimage. An adaptive threshold is used to define the wavelet filter strength for each of the eigenimages based on the noise variance and standard deviation of the signal, resulting in stronger filtering of the eigenimages that primarily contain noise. 4. Apply the inverse KLT to the filtered eigenimages to generate a new series of cine images with reduced image noise. KW filter was compared with 2 other filters – Spatial Wavelet filter and Temporal KLT filter in terms of SNR gain and edge sharpness. For four volunteer data acquired using rate 5 acceleration, KW filter showed an SNR gain of 98%. For a matched value of SNR gain between KW filter, Wavelet filter and KLT filter, KW filter preserved 93.83% of original image sharpness while Wavelet filter and KLT filter preserved 82.23% and 88.05% respectively.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor dc:publisher

The Ohio State University

Year dc:date

2013

Author dc:creator

• Palaniappan, Prashanth

Contributors dc:contributor

• Simonetti, Orlando P.

• Electrical Engineering
• Noise reduction
• SNR
• Image Processing
• MRI

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=osu1357269157

OAI identifier oai:identifier

oai:etd.ohiolink.edu:osu1357269157