Exploring the Neural Organoid in High Definition: Physics-Inspired High-Throughout Super-Resolution 3D Image Reconstruction

Organoids serve as a versatile platform for biomedical research, including drug screening, disease progression, cancer, developmental, and mechanobiology studies. However, precise 3D modeling of organoids remains a formidable challenge due to the complexity of tissue architecture, resolution limitations of confocal microscopy, and the time and labor-intensive process of acquiring data to achieve peak results. In this paper, we propose a novel strategy named LayerLink to enhance the 3D structure of Neural Organoids' TUJ1 fluorescently labeled nerve fibers using neighboring layers of stacked 3D image. Inspired by the Beer-Lamber Beer-Lambert law, we link each vertical layer to its neighboring layers through a blending process where the weights of each layer are from a generalized normal distribution, forming the input for a super-resolution diffusion model to reconstruct the entire volume. When data is limited, our reconstructed layers achieve an 11.02% improvement over the conventional deep learning method with a peak signal-to-noise ratio of 22.46. Notably, the reconstructed nerve fibers and fascicles in the vertical sections exhibit remarkable continuity. This precise modeling algorithm shows great promise for high-resolution monitoring of organoids and tissues exhibiting continuous fine structures. Furthermore, it holds the potential for advancing our understanding of cell-to-tissue-to-organ interactions and advancing 3D tissue bioprinting techniques in the future.