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Proceedings Paper

Empirical factors affecting reconstruction of image-guided near infrared spectroscopy
Author(s): Michael A. Mastanduno; Shudong Jiang; Roberta DiFlorio-Alexander; Brian W. Pogue; Keith D. Paulsen
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Paper Abstract

Empirical factors in image reconstruction that can have a dramatic effect on final image quantification in image guided optical spectroscopy using MRI are examined. We present a model for image segmentation that finds that tumor region quantification is influenced by defined size of that region, and we conclude that the most effective segmentation is done using a ktrans map to define the tumor region along with guidance from a radiologist experienced in breast MRI. Data calibration also introduces another empirical factor and we weigh advantages and disadvantages of different methods of calibrating data based on imaging geometry and volume. Finally, we investigate the effects of a modified Levenburg- Marquardt reconstruction that uses a regularization parameter to make the reconstruction less ill-posed and limit the effects of image noise. We find that the regularization parameter must fall within a certain range to adequately smooth the image but if it is too large then image contrast is lost. Understanding and minimizing these factors' contribution will be increasingly important as MRI/NIRS becomes closer to clinical use to ensure that the best possible images are always produced to aid in prognostic clinical decisions.

Paper Details

Date Published: 10 February 2011
PDF: 9 pages
Proc. SPIE 7892, Multimodal Biomedical Imaging VI, 789204 (10 February 2011); doi: 10.1117/12.877161
Show Author Affiliations
Michael A. Mastanduno, Dartmouth College (United States)
Shudong Jiang, Dartmouth College (United States)
Roberta DiFlorio-Alexander, Dartmouth Medical School (United States)
Brian W. Pogue, Dartmouth College (United States)
Keith D. Paulsen, Dartmouth College (United States)
Dartmouth Medical School (United States)


Published in SPIE Proceedings Vol. 7892:
Multimodal Biomedical Imaging VI
Fred S. Azar; Xavier Intes, Editor(s)

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