Nanobodies are the recombinant variable domains of heavy-chain-only antibodies, with many unique properties such as small size, excellent solubility, superior stability, quick clearance from blood, and deep tissue penetration. As a result, nanobodies have become a promising tool for the diagnosis and therapy of diseases. As imaging tracers, nanobodies allow an early acquisition of high-quality images, provide a comprehensive evaluation of the disease, and subsequently enable a personalized precision therapy. As therapeutic agents, nanobodies enable a targeted therapy by lesion-specific delivery of drugs and effector domains, thereby improving the specificity and efficacy of the therapy. Up to date, a wide variety of nanobodies have been developed for a broad range of molecular targets and have played a significant role in patients with a broad spectrum of diseases. In this review, we aim to outline the current state-of-the-art research on the nanobodies for medical applications and then discuss the challenges and strategies for their further clinical translation.

Positron emission tomography (PET) is an advanced medical imaging technique widely used in various clinical applications, such as tumor detection and neurologic disorders. Reducing the radiotracer dose is desirable in PET imaging because it decreases the patient’s radiation exposure. However, reducing the dose can also increase noise, affecting the image quality. Therefore, an advanced image reconstruction algorithm based on low-dose PET data is needed to improve the quality of the reconstructed image. In this study, we propose the use of a direct PET image reconstruction network (DPIR-Net) using an improved Wasserstein generative adversarial network (WGAN) framework to enhance image quality. This study provides two main findings. First, our network uses sinogram data as input and outputs high-quality PET images direct, resulting in shorter reconstruction times compared with traditional model-based reconstruction networks. Second, we combine perceptual loss, mean square error, and the Wasserstein distance as the loss function, which effectively solves the problem of excessive smoothness and loss of detailed information in traditional network image reconstruction. We performed a comparative study using maximum-likelihood expectation-maximization (MLEM) with a post-Gaussian filter, a total variation (TV)-norm regularization, a nonlocal means (NLM) denoising method, a neural network denoising method, a traditional deep learning PET reconstruction network, and our proposed DPIR-Net method and evaluated the proposed method using both human and mouse data. The mouse data were obtained from a small-animal PET prototype system developed by our laboratory. The quantitative and qualitative results show that our proposed method outperformed the conventional methods.