Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction

By H. Dehghani, Matthew Eames, Phaneendra K. Yalavarthy, S. Davis, Subhadra Srinivasan, Colin Carpenter, Brian W. Pogue, Keith D. Paulsen. Communications in Numerical Methods in Engineering, 2008

785 citations recorded by the source corpus.

SUMMARY Diffuse optical tomography, also known as near infrared tomography, has been under investigation, for non-invasive functional imaging of tissue, specifically for the detection and characterization of breast cancer or other soft tissue lesions. Much work has been carried out for accurate modeling and image reconstruction from clinical data. NIRFAST, a modeling and image reconstruction package has been developed, which is capable of single wavelength and multi-wavelength optical or functional imaging from measured data. The theory behind the modeling techniques as well as the image reconstruction algorithms is presented here, and 2D and 3D examples are presented to demonstrate its capabilities. The results show that 3D modeling can be combined with measured data from multiple wavelengths to reconstruct chromophore concentrations within the tissue. Additionally it is possible to recover scattering spectra, resulting from the dominant Mie-type scatter present in tissue. Overall, this paper gives a comprehensive over view of the modeling techniques used in diffuse optical tomographic imaging, in the context of NIRFAST software package.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9594115700571eb73148114f38f283a34f8b2112

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Diffuse Optics for Tissue Monitoring and Tomography (2010, methodology): “Many researchers employ numerical methods to solve these and other problems; in such cases the partialflux boundary condition is often directly incorporated into the numerical codes rather than the extrapolated-zero boundary condition (for example, see [117]).”
A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology (2014, methodology): “Dehghani et al. (2009b) simulated using NIRFAST high density arrays of NIRI.”
The NIRS Brain AnalyzIR Toolbox (2018, methodology): “Parts of the toolbox interface to fNIRS forward models (models of the light propagation through tissue/brain) require separate download of the software such as NIRFAST (Near-Infrared light transport in tissue and image reconstruction) [21,22], Matlab/Octave-based mesh generation toolbox (Iso2mesh) [”