ADC estimation of lesions in diffusion-weighted MR images: A maximum-likelihood approach

Abhinav K. Jha, Matthew A. Kupinski, Jeffrey J. Rodríguez, Renu M. Stephen, Alison T. Stopeck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In recent years, the apparent diffusion coefficient (ADC) of lesions obtained using diffusion-weighted magnetic resonance imaging (DWMRI) has emerged as a potentially novel non-invasive imaging bio-marker for prediction and monitoring of anti-cancer therapy response. However, the motion in visceral organs and different variances in DWMRI measurements at different magnetic diffusion gradient values can make ADC estimation a challenging task. We propose a maximum-likelihood method for ADC estimation of lesions in DWMRI. We show through simulations that our method outperforms the standard linear-least-squares and diffusion-map methods.

Original languageEnglish (US)
Title of host publication2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Proceedings
Pages209-212
Number of pages4
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010 - Austin, TX, United States
Duration: May 23 2010May 25 2010

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Other

Other2010 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2010
Country/TerritoryUnited States
CityAustin, TX
Period5/23/105/25/10

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'ADC estimation of lesions in diffusion-weighted MR images: A maximum-likelihood approach'. Together they form a unique fingerprint.

Cite this