Imaging
Imaging

Generative AI improves a wireless vision system that sees through obstructions

With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.

AI algorithm enables tracking of vital white matter pathways

Opening a new window on the brainstem, a new tool reliably and finely resolves distinct nerve bundles in live diffusion MRI scans, revealing signs of injury or disease.

SMART launches new Wearable Imaging for Transforming Elderly Care research group

WITEC is working to develop the first wearable ultrasound imaging system to monitor chronic conditions in real-time, with the goal of enabling earlier detection and timely intervention.

A “scientific sandbox” lets researchers explore the evolution of vision systems

The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.

Deep-learning model predicts how fruit flies form, cell by cell

The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.

Checking the quality of materials just got easier with a new AI tool

Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.

New AI system could accelerate clinical research

By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.

Machine-learning tool gives doctors a more detailed 3D picture of fetal health

MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.

Making AI models more trustworthy for high-stakes settings

A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.

New method assesses and improves the reliability of radiologists’ diagnostic reports

The framework helps clinicians choose phrases that more accurately reflect the likelihood that certain conditions are present in X-rays.