Frequency-domain Diffuse Optical Tomography Implemented with Edge-preserving Regularization

2010 ◽  
Author(s):  
Liang-Yu Chen ◽  
Min-Cheng Pan ◽  
Min-Chun Pan
Keyword(s):  
Frequency Domain ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Edge Preserving

scholarly journals Frequency Shifting Model for Diffuse Optical Tomography

2021 ◽  
Author(s):  
Huseyin Ozgur Kazanci
Keyword(s):  
Inverse Problem ◽  
Frequency Domain ◽  
Continuous Wave ◽  
Model Problem ◽  
Optical Tomography ◽  
Diffuse Optical Tomography ◽  
Solution Space ◽  
Forward Model ◽  
Problem Solution ◽  
Frequency Shifting

Abstract Diffuse Optical Tomography (DOT) imaging technique has been interesting research field for researchers since it has uncertainties in the solution space. DOT modality is unsolved scientific problem. Inverse problem solution and image reconstruction has never been in its best quality. Reconstructed images have low spatial resolution. Scattering nature of diffusive light is the obscuring effect for DOT modality. DOT has 3 functional sub-branches which of these are Continuous Wave (CW), Time-Resolved (TR), and Frequency-Domain (FD). In this work, one new approach to Frequency Domain Diffuse Optical Tomography (FDDOT) biomedical optic imaging modality is presented to the readers. Frequency Shifting data were added to the forward model problem which basically has source-detector couplings and number of imaging voxels. 100 MHz center core light modulation frequency was selected. 169 source-detector matches were used on back-reflected imaging geometry. Absorption coefficient ma was selected 0.1 cm− 1. Scattering coefficient µs was selected 100 cm− 1. 1 micrometer x, y, z cartesian grid coordinates were used in each direction for imaging tissue-like simulation media. The total of 100 frequency shift was added to the forward model problem which has 5 Hz frequency step. 2 inclusion objects were embedded inside the imaging simulation phantom. 2 inclusion images were successfully reconstructed with the low contrast to noise ratio (CNR) error and position error (PE). Frequency shifting technique is first applied for FDDOT here. This technique has increased the total number of equations in the forward model problem; hence it is helping to solve the inverse problem. In this work, the positive effect of using multi frequency methodology was observed. Differentiation of 2 embedded inclusions was successfully completed and illustrated in this work.

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