Data-resolution based optimization of the data-collection strategy for near infrared diffuse optical tomography.
OBJECTIVE
To optimize the data-collection strategy for diffuse optical tomography and to obtain a set of independent measurements among the total measurements using the model based data-resolution matrix characteristics.


METHODS
The data-resolution matrix is computed based on the sensitivity matrix and the regularization scheme used in the reconstruction procedure by matching the predicted data with the actual one. The diagonal values of data-resolution matrix show the importance of a particular measurement and the magnitude of off-diagonal entries shows the dependence among measurements. Based on the closeness of diagonal value magnitude to off-diagonal entries, the independent measurements choice is made. The reconstruction results obtained using all measurements were compared to the ones obtained using only independent measurements in both numerical and experimental phantom cases. The traditional singular value analysis was also performed to compare the results obtained using the proposed method.


RESULTS
The results indicate that choosing only independent measurements based on data-resolution matrix characteristics for the image reconstruction does not compromise the reconstructed image quality significantly, in turn reduces the data-collection time associated with the procedure. When the same number of measurements (equivalent to independent ones) are chosen at random, the reconstruction results were having poor quality with major boundary artifacts. The number of independent measurements obtained using data-resolution matrix analysis is much higher compared to that obtained using the singular value analysis.


CONCLUSIONS
The data-resolution matrix analysis is able to provide the high level of optimization needed for effective data-collection in diffuse optical imaging. The analysis itself is independent of noise characteristics in the data, resulting in an universal framework to characterize and optimize a given data-collection strategy.
Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India.
Deepak
D
Karkala
Phaneendra K
PK
Yalavarthy
D000465:Algorithms; D000818:Animals; D016880:Anisotropy; D016477:Artifacts; D003625:Data Collection; D020342:Finite Element Analysis; D005780:Gelatin / Q000737:chemistry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D015233:Models, Statistical; D019047:Phantoms, Imaging; D012542:Scattering, Radiation; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D041622:Tomography, Optical / Q000379:methods*
D005780:Gelatin