Regularization functional semi-automated incorporation of anatomical prior information in image-guided fluorescence tomography.
The use of anatomical priors in fluorescence tomography is known to improve image quality and accuracy significantly. However, the use of prior information is often implemented by incorporating user segmented structural images into the optical reconstruction algorithm, a process requiring significant time and expertise. We propose an automated implementation which encodes the gray-scale prior image directly into the regularization term, eliminating the need for direct prior image segmentation, which is extendable to any spatially defined prior data. The proposed method is supported by in vivo studies.
Department of Physics and Astronomy, Dartmouth College, New Hampshire 03755, USA. Robert.W.Holt@Dartmouth.edu
Robert W
RW
Holt
Scott
S
Davis
Brian W
BW
Pogue
D001331:Automation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D013050:Spectrometry, Fluorescence; D014054:Tomography / Q000379:methods*