We developed and evaluated an automatic stent strut detection method in intravascular optical coherence tomography (IVOCT) pullback runs. Providing very high resolution images, IVOCT has been rapidly accepted as a coronary imaging modality for the optimization of the stenting procedure and its follow-up evaluation based on stent strut analysis. However, given the large number of struts visible in a pullback run, quantitative three-dimensional analysis is only feasible when the strut detection is performed automatically. The presented method first detects the candidate pixels using both a global intensity histogram and the intensity profile of each A-line. Gaussian smoothing is applied followed by specified Prewitt compass filters to detect the trailing shadow of each strut. Next, the candidate pixels are clustered using the shadow information. In the final step, several filters are applied to remove the false positives such as the guide wire. Our new method requires neither a priori knowledge of the strut status nor the lumen/vessel contours. In total, 10 IVOCT pullback runs from a 1-year follow-up study were used for validation purposes. 18,311 struts were divided into three strut status categories (malapposition, apposition or covered) and classified based on the image quality (high, medium or low). The inter-observer agreement is 95 %. The sensitivity was defined as the ratio of the number of true positives and the total number of struts in the expert defined result. The proposed approach demonstrated an average sensitivity of 94 %. For malapposed, apposed and covered stent struts, the sensitivity of the method is respectively 91, 93 and 94 %, which shows the robustness towards different situations. The presented method can detect struts automatically regardless of the strut status or the image quality, and thus can be used for quantitative measurement, 3D reconstruction and visualization of the stents in IVOCT pullback runs.

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.

Magnetic resonance imaging (MRI) is often used to diagnose diseases due to its high spatial, temporal and soft tissue resolution. Frequently, probes or contrast agents are used to enhance the contrast in MRI to improve diagnostic accuracy. With the development of molecular imaging techniques, molecular MRI can be used to obtain 3D anatomical structure, physiology, pathology, and other relevant information regarding the lesion, which can provide an important reference for the accurate diagnosis and treatment of the disease in the early stages. Among existing contrast agents, smart or activatable nanoprobes can respond to selective stimuli, such as proving the presence of acidic pH, active enzymes, or reducing environments. The recently developed environment-responsive or smart MRI nanoprobes can specifically target cells based on differences in the cellular environment and improve the contrast between diseased tissues and normal tissues. Here, we review the design and application of these environment-responsive MRI nanoprobes.