Optical projection tomography reconstruction with few views using highly-generalizable deep learning at sinogram domain

Abstract

Optical projection tomography (OPT) reconstruction using a minimal number of measured views offers the potential to significantly reduce excitation dosage and greatly enhance temporal resolution in biomedical imaging. However, traditional algorithms for tomographic reconstruction exhibit severe quality degradation, e.g., presence of streak artifacts, when the number of views is reduced. In this study, we introduce a novel domain evaluation method which can evaluate the domain complexity, and thereby validate that the sinogram domain exhibits lower complexity as compared to the conventional spatial domain. Then we achieve robust deep-learning-based reconstruction with a feedback-based data initialization method at sinogram domain, which shows strong generalization ability that notably improves the overall performance for OPT image reconstruction. This learning-based approach, termed SinNet, enables 4-view OPT reconstructions of diverse biological samples showing robust generalization ability. It surpasses the conventional OPT reconstruction approaches in terms of peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, showing its potential for the augment of widely-used OPT techniques.

Advancements in fluorescence lifetime imaging microscopy Instrumentation: Towards high speed and 3D

“…It is noteworthy that FLIM tomography operates fundamentally as a frequency-domain scanning technique. In contrast to confocal or light-sheet FLIM, which requires continuous spatial sampling for recovering full spatial resolution, FLIM tomography can potentially selectively measure a subset of frequency components within the object’s 3D power spectrum [ 49 ]. This approach is especially effective when the sample exhibits sparsity in the spatial domain, allowing fewer measurements and faster processing speed while maintaining an optimal resolution.…”