Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field.

Computer-assisted oral and maxillofacial surgery (OMS) has been rapidly evolving since the last decade. State-of-the-art surgical navigation in OMS still suffers from bulky tracking sensors, troublesome image registration procedures, patient movement, loss of depth perception in visual guidance, and low navigation accuracy. We present an augmented reality navigation system with automatic marker-free image registration using 3-D image overlay and stereo tracking for dental surgery. A customized stereo camera is designed to track both the patient and instrument. Image registration is performed by patient tracking and real-time 3-D contour matching, without requiring any fiducial and reference markers. Real-time autostereoscopic 3-D imaging is implemented with the help of a consumer-level graphics processing unit. The resulting 3-D image of the patient's anatomy is overlaid on the surgical site by a half-silvered mirror using image registration and IP-camera registration to guide the surgeon by exposing hidden critical structures. The 3-D image of the surgical instrument is also overlaid over the real one for an augmented display. The 3-D images present both stereo and motion parallax from which depth perception can be obtained. Experiments were performed to evaluate various aspects of the system; the overall image overlay error of the proposed system was 0.71 mm.

A 3-D augmented reality navigation system using autostereoscopic images was developed for MRI-guided surgery. The 3-D images are created by employing an animated autostereoscopic image, integral videography (IV), which provides geometrically accurate 3-D spatial images and reproduces motion parallax without using any supplementary eyeglasses or tracking devices. The spatially projected 3-D images are superimposed onto the surgical area and viewed via a half-slivered mirror. A fast and accurate spatial image registration method was developed for intraoperative IV image-guided therapy. Preliminary experiments showed that the total system error in patient-to-image registration was 0.90 ± 0.21 mm, and the procedure time for guiding a needle toward a target was shortened by 75%. An animal experiment was also conducted to evaluate the performance of the system. The feasibility studies showed that augmented reality of the image overlay system could increase the surgical instrument placement accuracy and reduce the procedure time as a result of intuitive 3-D viewing.

This article describes a worm-like soft robot capable of operating in complicated tubular environments, such as the complex pipeline with different diameters, water, oil, and gas environments, or the clinical application in natural orifice transluminal endoscopic surgery. The robot is completely soft and robust, and consists of one multidegree of freedom (DoF) extension module and two clampers for locomotion and steering. The multi-DoF extension module is able to adjust the heading direction in the three-dimensional space. The clamper has a basic expansion module structure and detachable sucking module structure. The combined clamping principle for sticking to the inner wall can be reconfigurable to adapt the tubes with multiple tubular scales and super elastic materials. For fabrication of the mechanical structure, a low-cost and time-efficient method is proposed in this article. Based on our proposed robot, a series of phantom and application experiments are performed. The results demonstrate that the soft robot can freely bend and elongate with the entire soft body, and pass through tubes with changing diameters or branches, dry tubes, liquid environments, hard surfaces, and even soft deformable tubes. It has the ability to remove a load of >10 times its own weight. In addition, an additional visualization unit, biopsy, and electromagnetic sensor are mounted on the robot tip for the real-time image inspection, manipulation, and robot tracking. The proposed worm-like soft robot is compact, flexible-actuated, and sufficiently safe, as well as extensible. Its ability to move in the complex unstructured environment shows a great potential for search and medical applications.