Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion.

Objective: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (QCT) measures.

Methods: Patients with idiopathic pulmonary fibrosis (IPF) who underwent unenhanced chest CTs with vendor A and a pulmonary function test (PFT) were retrospectively evaluated. As deep-learning based ILD quantification software was mainly developed using vendor B CT, style-converted images from vendor A to B style were generated using RouteGAN.

Infrared thermal modulation endoscopy for label-free tumor detection.

In optical imaging of solid tumors, signal contrasts derived from inherent tissue temperature differences have been employed to distinguish tumor masses from surrounding tissue. Moreover, with the advancement of active infrared imaging, dynamic thermal characteristics in response to exogenous thermal modulation (heating and cooling) have been proposed as novel measures of tumor assessment. Contrast factors such as the average rate of temperature changes and thermal recovery time constants have been investigated through an active thermal modulation imaging approach, yielding promising tumor characterization results in a xenograft mouse model.

FPANet: Frequency-based video demoiréing using frame-level post alignment.

Moiré patterns, created by the interference between overlapping grid patterns in the pixel space, degrade the visual quality of images and videos. Therefore, removing such patterns (demoiréing) is crucial, yet remains a challenge due to their complexities in sizes and distortions. Conventional methods mainly tackle this task by only exploiting the spatial domain of the input images, limiting their capabilities in removing large-scale moiré patterns.

Proximity to Explosive Synchronization Determines Network Collapse and Recovery Trajectories in Neural and Economic Crises.

When complex systems move away from criticality-a balance between order and chaos-they are no longer optimized. Furthermore, when criticality is lost too quickly, or recovery is delayed, system damage can result. However, the mechanism for these abnormally fast or slow critical transitions remains unknown.