Last update:

Radiology & Imaging news

Neuroscience

Novel PET tracer detects synaptic changes in spinal cord and brain after spinal cord injury

A new PET tracer can provide insights into how spinal cord injuries affect not only the spinal cord, but also the brain, according to new research published in The Journal of Nuclear Medicine. By identifying synapse loss, ...

Alzheimer's disease & dementia

Portable light-based brain monitor shows promise for dementia diagnosis

Early and accurate diagnosis of dementia remains a major challenge. Standard approaches such as MRI and PET scans can provide valuable information about brain structure and function, but they are expensive, not always accessible, ...

Radiology & Imaging

First 'perovskite camera' can see inside the human body

Physicians rely on nuclear medicine scans, like SPECT scans, to watch the heart pump, track blood flow and detect diseases hidden deep inside the body. But today's scanners depend on expensive detectors that are difficult ...

Radiology & Imaging

Researchers advocate for separate roles between AI and humans

Renowned physician-scientist Eric J. Topol, M.D., and Harvard artificial intelligence (AI) expert Pranav Rajpurkar, Ph.D., advocate for a clear separation of the roles between AI systems and radiologists in an editorial published ...

Radiology & Imaging

AI medical imaging technology that cuts radiation by 99%

Researchers at The Hong Kong University of Science and Technology (HKUST) have developed a groundbreaking AI technology that reconstructs precise 3D bones and organs models from minimal X-ray images, slashing patients' radiation ...

Radiology & Imaging

New deep learning model enhances handheld 3D medical imaging

Ultrasound (US) imaging is a widely employed diagnostic tool used for real-time imaging of various organs and tissues using ultrasonic sound waves. The waves are sent into the body, and images are created based on how the ...

Oncology & Cancer

AI tool accurately detects tumor location on breast MRI

An AI model trained to detect abnormalities on breast MR images accurately depicted tumor locations and outperformed benchmark models when tested in three different groups, according to a study published in Radiology.