The Resolution Matrix in Tomographic Multiplexing: Optimization of Inter-Parameter Cross-Talk, Relative Quantitation,...

Area of Science:

• Biomedical Optics
• Fluorescence Imaging
• Computational Biology

Background:

• Accurate quantification of multiple fluorophores in biological tissues is crucial for understanding complex biological processes.
• Existing inversion methods for fluorescence lifetime tomography face challenges in quantitation and spatial localization, particularly in diffuse media.
• A robust framework is needed to compare and improve these inversion techniques for multiplexed fluorescence lifetime measurements.

Purpose of the Study:

• To compare the performance of three inversion methods—asymptotic time domain (ATD), direct time domain (DTD), and cross-talk constrained time domain (CCTD)—for tomographic fluorescence lifetime multiplexing.
• To evaluate these methods using Monte Carlo simulations and experimental measurements in tissue-mimicking phantoms.
• To provide a theoretical basis for the observed performance differences and derive an efficient estimator for CCTD.

Main Methods:

• Development of a resolution matrix-based Bayesian framework for comparing inversion algorithms.
• Implementation and comparison of ATD, DTD, and CCTD inversion methods.
• Validation through Monte Carlo simulations and time-domain fluorescence measurements using tissue phantoms.

Main Results:

• ATD demonstrated high accuracy in relative quantitation and spatial localization of two fluorophores (up to 1:4.25 concentration ratio) in an 18-mm thick turbid medium.
• DTD exhibited significant errors in both quantitation and localization.
• CCTD improved quantitation accuracy over DTD and spatial resolution over ATD, with a rigorous theoretical derivation provided.
• A formula for rapid computation of the DTD inverse operator for large-scale tomography was derived.

Conclusions:

• ATD and CCTD inversion methods significantly outperform DTD for accurate estimation of multiple, overlapping fluorophores in diffuse media.
• Time domain fluorescence tomography, especially with zero cross-talk estimators like CCTD, is a powerful tool for quantifying multiple fluorescently labeled biological processes.
• The presented Bayesian framework is applicable to general multiparameter inverse problems for quantitative estimation.