Share Email Print

Proceedings Paper

Interaction between noise suppression and inhomogeneity correction in MRI
Author(s): Albert Montillo; Jayaram K. Udupa; Leon Axel; Dimitri N. Metaxas
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

While cardiovascular disease is the leading cause of death in most developed countries, SPAMM-MRI can reduce morbidity by facilitating patient diagnosis. An image analysis method with a high degree of automation is essential for clinical adoption of SPAMM-MRI. The degree of this automation is dependent on the amount of thermal noise and surface coil-induced intensity inhomogeneity that can be removed from the images. An ideal noise suppression algorithm removes thermal noise yet retains or enhances the strength of the edges of salient structures. In this paper, we quantitatively compare and rank several noise suppression algorithms in images from both normal and diseased subjects using measures of the residual noise and edge strength and the statistical significance levels and confidence intervals of these measures. We also investigate the interrelationship between inhomogeneity correction and noise suppression algorithms and compare the effect of the ordering of these algorithms. The variance of thermal noise does not tend to change with position, however, inhomogeneity correction increases noise variance in deep thoracic regions. We quantify the degree to which an inhomogeneity estimate can improve noise suppression and how well noise suppression can facilitate the identification of homogeneous tissue regions and thereby, assist in inhomogeneity correction.

Paper Details

Date Published: 15 May 2003
PDF: 12 pages
Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.483555
Show Author Affiliations
Albert Montillo, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Leon Axel, New York Univ. (United States)
Dimitri N. Metaxas, Rutgers Univ. (United States)

Published in SPIE Proceedings Vol. 5032:
Medical Imaging 2003: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

© SPIE. Terms of Use
Back to Top