We present VolMoVis, a method for dynamic tomographic reconstruction that supports real-time volume generation and volumetric motion visualization from 2D projections. Visualizing the motion of 3D anatomical structures, such as organs and tumors, is critical for computer-aided interventions. However, conventional 4D volumetric reconstruction methods typically produce a limited set of volumes at discrete phases, suffering from low temporal resolution. Moreover, it often requires extensive segmentation of 3D structures or regions for visualizing volumetric data, making it challenging to segment and visualize dynamic volumes in real-time. To address these challenges, VolMoVis framework employs a continuous implicit neural representation that decomposes the dynamic volumetric data into a static reference volume and a continuous deformation field. This decomposition, along with an efficient deformation network, enables our framework to achieve real-time volume generation and volumetric visualization of continuous anatomical motions. We evaluate VolMoVis on both 4D digital phantoms and real patient datasets, demonstrating its effectiveness for accurate anatomical reconstruction and motion tracking. Furthermore, we highlight its capabilities in real-time simultaneous volume generation and tumor segmentation for visualizing dynamic volumes and 4D tumor tracking, showcasing its potential in image-guided radiation therapy.

Lower limb wearable robotics can assist human walking during rehabilitation or help people who suffer from neuromuscular disease. Many advanced control algorithms have been developed for lower-limb wearable robots. They often rely on timely and accurate detection of the gait phases in each gait cycle. This paper presents a novel design of an instrumented shoe, which is wireless, low-cost, and has high-resolution for plantar pressure distribution sensing capability. In the system design, a voltage feedback non-scanning electrode method was used to eliminate the crosstalk effect in the sensing arrays. A peak heuristic algorithm based on a pressure sensing signal is developed to detect the sub-phases during stance. The results show that compared to a commercial system and video reference, there is a marginal difference in the duration of the four gait stages detected by the instrumented shoe. This indicates that this instrument shoe, together with the gait phase detection algorithm, can help provide detailed gait information, which can be further used to aid in the design of an advanced control algorithm for the lower limb wearable robots.

Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We presentCOVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis.COVID-viewincorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist usingCOVID-viewand provides the attention heatmap as an explainable DL for the model output. We designed and evaluatedCOVID-viewthrough suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases.

UNLABELLED: Chromothripsis is a single catastrophic event that can lead to massive genomic rearrangements confined to one or a few chromosomes. It provides an alternative paradigm in cancer development and changes the conventional view that cancer develops in a stepwise progression. The mechanisms underlying chromothripsis and their specific impact on tumorigenesis are still poorly understood, and further examination of a large number of identified chromothripsis samples is needed. Unfortunately, this data are difficult to access, as they are scattered across multiple publications, come in different formats and descriptions, or are hidden in figures and supplementary materials. To improve access to this data and promote meta-analysis, we developed ChromothripsisDB, a manually curated database containing a unified description of all published chromothripsis cases and relevant genomic aberrations. Currently, 423 chromothripsis samples representing 107 research articles are included in our database. ChromothripsisDB represents an extraordinary resource for mining the existing knowledge of chromothripsis, and will facilitate the identification of mechanisms involved in this phenomenon. AVAILABILITY AND IMPLEMENTATION: ChromothripsisDB is freely available at http://cgma.scu.edu.cn/ChromothripsisDB CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.