Imaging the developing human external and internal urogenital organs with light sheet fluorescence microscopy.

Technological advances in three-dimensional (3D) reconstruction techniques have previously enabled paradigm shifts in our understanding of human embryonic and fetal development. Light sheet fluorescence microscopy (LSFM) is a recently-developed technique that uses thin planes of light to optically section whole-mount cleared and immunolabeled biologic specimens. The advent of commercially-available light sheet microscopes has facilitated a new generation of research into protein localization and tissue dynamics at extremely high resolution. Our group has applied LSFM to study developing human fetal external genitalia, internal genitalia and kidneys. This review describes LSFM and presents our group's technique for preparing, clearing, immunostaining and imaging human fetal urogenital specimens. We then present light sheet images and videos of each element of the developing human urogenital system. To the extent of our knowledge, the work conducted by our laboratory represents the first description of a method for performing LSFM on the full human urogenital system during the embryonic and fetal periods

Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging

Compressed sensing in fluorescence microscopy.

Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy

Multi-Beam Scan Analysis with a Clinical LINAC for High Resolution Cherenkov-Excited Molecular Luminescence Imaging in Tissue.

Cherenkov-excited luminescence scanned imaging (CELSI) is achieved with external beam radiotherapy to map out molecular luminescence intensity or lifetime in tissue. Just as in fluorescence microscopy, the choice of excitation geometry can affect the imaging time, spatial resolution and contrast recovered. In this study, the use of spatially patterned illumination was systematically studied comparing scan shapes, starting with line scan and block patterns and increasing from single beams to multiple parallel beams and then to clinically used treatment plans for radiation therapy. The image recovery was improved by a spatial-temporal modulation-demodulation method, which used the ability to capture simultaneous images of the excitation Cherenkov beam shape to deconvolve the CELSI images. Experimental studies used the multi-leaf collimator on a clinical linear accelerator (LINAC) to create the scanning patterns, and image resolution and contrast recovery were tested at different depths of tissue phantom material. As hypothesized, the smallest illumination squares achieved optimal resolution, but at the cost of lower signal and slower imaging time. Having larger excitation blocks provided superior signal but at the cost of increased radiation dose and lower resolution. Increasing the scan beams to multiple block patterns improved the performance in terms of image fidelity, lower radiation dose and faster acquisition. The spatial resolution was mostly dependent upon pixel area with an optimized side length near 38mm and a beam scan pitch of P = 0.33, and the achievable imaging depth was increased from 14mm to 18mm with sufficient resolving power for 1mm sized test objects. As a proof-of-concept, in-vivo tumor mouse imaging was performed to show 3D rendering and quantification of tissue pO2 with values of 5.6mmHg in a tumor and 77mmHg in normal tissue