Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging.

Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Research Abstract Details 

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Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Abstract Text:

Mathews Jacob,Yoram Bresler,Vlad Toronov,Xiaofeng Zhang,Andrew Webb,

We introduce a new algorithm for the reconstruction of functional brain activations from near-infrared spectroscopic imaging (NIRSI) data. While NIRSI offers remarkable biochemical specificity, the attainable spatial resolution with this technique is rather limited, mainly due to the highly scattering nature of brain tissue and the low number of measurement channels. Our approach exploits the support-limited (spatially concentrated) nature of the activations to make the reconstruction problem well-posed. The new algorithm considers both the support and the function values of the activations as unknowns and estimates them from the data. The support of the activations is represented using a level-set scheme. We use a two-step alternating iterative scheme to solve for the activations. Since our approach uses the inherent nature of functional activations to make the problem well-posed, it provides reconstructions with better spatial resolution, fewer artifacts, and is more robust to noise than existing techniques. Numerical simulations and experimental data indicate a significant improvement in the quality (resolution and robustness to noise) over standard techniques such as truncated conjugate gradients (TCG) and simultaneous iterative reconstruction technique (SIRT) algorithms. Furthermore, results on experimental data obtained from simultaneous functional magnetic resonance imaging (fMRI) and optical measurements show much closer agreement of the optical reconstruction using the new approach with fMRI images than TCG and SIRT.

Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Publishing Authors By Initials

M Jacob,Y Bresler,V Toronov,X Zhang,A Webb,

For similar investigative techniques: chemistry, analytical: photometry: spectrophotometry: spectrophotometry, infrared research abstracts see: investigative techniques: chemistry, analytical: photometry: spectrophotometry: spectrophotometry, infrared research

PUBMED ID PMID:

MEDLINE DATE: 2006 Nov-Dec

Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Journal Published:

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Journal of biomedical optics

VOLUME: 11

Page Numbers: 064029

Journal Abbreviation:

ISSN: 1083-3668

DAY: 3

MONTH: 12

YEAR: 2007

Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 9605853

Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Keywords Mesh Terms:

KEYWORDS: Spectrophotometry, Infrared

MESH TERMS: methods

Chemical & Substance for Abstract: Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging. Information

Substance Name: Oxygen

Registry Number: 7782-44-7

Grant and Affiliation Information for Level-set algorithm for the reconstruction of functional activation in near-infrared spectroscopic imaging.

AFFILIATION: University of Illinois at Urbana-Champaign, Urbana, Illinois 61820, USA. mjacob@uiuc.edu

Country: United States

AGENCY: United States NIMH

GRANT: 5-R01-MH065429-2

ACRONYM: MH

MEDLINETA: J Biomed Opt

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