Fluorescence Molecular Tomography for In Vivo Imaging of Glioblastoma Xenografts

Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions

A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.

Depth-recognizable time-domain fluorescence molecular tomography in reflective geometry

Conventional fluorescence molecular tomography (FMT) reconstruction requires photons penetrating the whole object, which limits its applications to small animals. However, by utilizing reflective photons, fluorescence distribution near the surface could be reconstructed regardless of the object size, which may extend the applications of FMT to surgical navigation and so on. Therefore, time-domain reflective fluorescence molecular tomography (TD-rFMT) is proposed in this paper. The system excites and detects the emission light from the same angle within a field of view of 5 cm. Because the detected intensities of targets depend strongly on the depth, the reconstruction of targets in deep regions would be evidently affected. Therefore, a fluorescence yield reconstruction method with depth regularization and a weighted separation reconstruction strategy for lifetime are developed to enhance the performance for deep targets. Through simulations and phantom experiments, TD-rFMT is proved capable of reconstructing fluorescence distribution within a 2.5-cm depth with accurate reconstructed yield, lifetime, and target position(s).

Monoclonal antibody-based molecular imaging strategies and theranostic opportunities

Molecular imaging modalities hold great potential as less invasive techniques for diagnosis and management of various diseases. Molecular imaging combines imaging agents with targeting moieties to specifically image diseased sites in the body. Monoclonal antibodies (mAbs) have become increasingly popular as novel therapeutics against a variety of diseases due to their specificity, affinity and serum stability. Because of the same properties, mAbs are also exploited in molecular imaging to target imaging agents such as radionuclides to the cell of interest in vivo. Many studies investigated the use of mAb-targeted imaging for a variety of purposes, for instance to monitor disease progression and to predict response to a specific therapeutic agent. Herein, we highlighted the application of mAb-targeted imaging in three different types of pathologies: autoimmune diseases, oncology and cardiovascular diseases. We also described the potential of molecular imaging strategies in theranostics and precision medicine. Due to the nearly infinite repertoire of mAbs, molecular imaging can change the future of modern medicine by revolutionizing diagnostics and response prediction in practically any disease.