Free-space fluorescence tomography with adaptive sampling based on anatomical information from microCT

Area of Science:

• Biomedical Optics
• Medical Imaging
• Fluorescence Tomography

Background:

• Image reconstruction in fluorescence tomography is challenging due to inhomogeneous optical properties and irregular surfaces in small animals.
• Accurate forward modeling and photodetector dynamic range limitations hinder current free-space reconstruction methods.
• Existing limitations can be mitigated using advanced signal processing techniques.

Purpose of the Study:

• To present a novel data processing strategy for free-space panoramic fluorescence diffuse optical tomography (PDT).
• To improve image reconstruction by adaptively selecting optical sampling points using co-registered microCT data.
• To address challenges in forward modeling and signal reliability in in vivo small animal studies.

Main Methods:

• Developed a free-space panoramic fluorescence diffuse optical tomography system integrated with microCT data.
• Implemented an adaptive sampling strategy to select optimal sampling points from 2-D fluorescent CCD images.
• Utilized 3-D anatomical information from microCT to exclude problematic data points (e.g., poor signal, skin artifacts).
• Employed parallel Monte Carlo software for sensitivity function computation in image reconstruction.

Main Results:

• The adaptive sampling strategy successfully excluded unreliable data points, enhancing reconstruction quality.
• Parallel Monte Carlo software provided favorable sensitivity functions for image reconstruction.
• Experimental results on a mouse cadaver demonstrated improved reconstruction accuracy compared to previous finite element methods.

Conclusions:

• The developed adaptive sampling strategy and parallel Monte Carlo software significantly improve image reconstruction in free-space panoramic fluorescence diffuse optical tomography.
• This approach effectively overcomes limitations posed by inhomogeneous optical properties and signal unreliability in small animal imaging.
• The integration of microCT data is crucial for refining sampling and enhancing the accuracy of fluorescence tomography.