Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging tool which allows the specific detection of metabolites that contain exchangeable amide, amine, and hydroxyl protons. Decades of development have progressed CEST imaging from an initial concept to a clinical imaging tool that is used to assess tumor metabolism. The first translation efforts involved brain imaging, but this has now progressed to imaging other body tissues. In this review, we summarize studies using CEST MRI to image a range of tumor types, including breast cancer, pelvic tumors, digestive tumors, and lung cancer. Approximately two thirds of the published studies involved breast or pelvic tumors which are sites that are less affected by body motion. Most studies conclude that CEST shows good potential for the differentiation of malignant from benign lesions with a number of reports now extending to compare different histological classifications along with the effects of anti-cancer treatments. Despite CEST being a unique ‘label-free’ approach with a higher sensitivity than MR spectroscopy, there are still some obstacles for implementing its clinical use. Future research is now focused on overcoming these challenges. Vigorous ongoing development and further clinical trials are expected to see CEST technology become more widely implemented as a mainstream imaging technology.

The growing need to understand the molecular mechanisms of diseases has prompted the revolution in molecular imaging techniques along with nanomedicine development. Conventional optical coherence tomography (OCT) is a low-cost in vivo imaging modality that provides unique high spatial and temporal resolution anatomic images but little molecular information. However, given the widespread adoption of OCT in research and clinical practice, its robust molecular imaging extensions are strongly desired to combine with anatomical images. A range of relevant approaches has been reported already. In this article, we review the recent advances of molecular contrast OCT imaging techniques, the corresponding contrast agents, especially the nanoparticle-based ones, and their applications. We also summarize the properties, design criteria, merit, and demerit of those contrast agents. In the end, the prospects and challenges for further research and development in this field are outlined.

The use of molecular imaging technologies for brain imaging can not only play an important supporting role in disease diagnosis and treatment but can also be used to deeply study brain functions. Recently, with the support of reporter gene technology, optical imaging has achieved a breakthrough in brain function studies at the molecular level. Reporter gene technology based on traditional clinical imaging modalities is also expanding. By benefiting from the deeper imaging depths and wider imaging ranges now possible, these methods have led to breakthroughs in preclinical and clinical research. This article focuses on the applications of magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and positron emission tomography (PET) reporter gene technologies for use in brain imaging. The tracking of cell therapies and gene therapies is the most successful and widely used application of these techniques. Meanwhile, breakthroughs have been achieved in the research and development of reporter genes and their imaging probe pairs with respect to brain function research. This paper introduces the imaging principles and classifications of the reporter gene technologies of these imaging modalities, lists the relevant brain imaging applications, reviews their characteristics, and discusses the opportunities and challenges faced by clinical imaging modalities based on reporter gene technology. The conclusion is provided in the last section.

Optical coherence tomography (OCT) has considerable application potential in noninvasive diagnosis and disease monitoring. Skin diseases, such as basal cell carcinoma (BCC), are destructive; hence, quantitative segmentation of the skin is very important for early diagnosis and treatment. Deep neural networks have been widely used in the boundary recognition and segmentation of diseased areas in medical images. Research on OCT skin segmentation and laser-induced skin damage segmentation based on deep neural networks is still in its infancy. Here, a segmentation and quantitative analysis pipeline of laser skin injury and skin stratification based on a deep neural network model is proposed. Based on the stratification of mouse skins, a laser injury model of mouse skins induced by lasers was constructed, and the multilayer structure and injury areas were accurately segmented by using a deep neural network method. First, the intact area of mouse skin and the damaged areas of different laser radiation doses are collected by the OCT system, and then the labels are manually labeled by experienced histologists. A variety of deep neural network models are used to realize the segmentation of skin layers and damaged areas on the skin dataset. In particular, the U-Net model based on a dual attention mechanism is used to realize the segmentation of the laser-damage structure, and the results are compared and analyzed. The segmentation results showed that the Dice coefficient of the mouse dermis layer and injury area reached more than 0.90, and the Dice coefficient of the fat layer and muscle layer reached more than 0.80. In the evaluation results, the average surface distance (ASSD) and Hausdorff distance (HD) indicated that the segmentation results are excellent, with a high overlap rate with the manually labeled area and a short edge distance. The results of this study have important application value for the quantitative analysis of laser-induced skin injury and the exploration of laser biological effects and have potential application value for the early noninvasive detection of diseases and the monitoring of postoperative recovery in the future.