Researchers developed an AI-based system which has has a broad ability to identify even subtle differences between medical images taken at different times, and to predict related outcome measures.
Using a prototype field cycling imager (FCI) scanner to examine breast cancer tissue, scientists distinguish tumour material from healthy tissue with more accuracy than current MRI methods.
Science, sustainability, and society – these are the three cornerstones of this year’s European Congress of Radiology (ECR) in Vienna, Austria. Following the motto “Planet Radiology”, the congress focuses on the environmental impact of diagnostic imaging while exploring ways to advance equitable global healthcare access. Congress President Prof. Andrea Rockall provides a preview on the…
Pancreatic cystic lesions – indicating an increased risk of pancreatic cancer – are an occasional incidental finding in routine computed tomography (CT) abdominal imaging. New research suggests that the superior image quality of photon-counting CT (PCCT) can help detect more of these lesions. At the RSNA annual meeting, an expert outlined the benefits and limitations of the imaging technique…
By detecting nerve connections in the injured spinal cord, a newly developed radiotracer could help diagnose injuries more precisely, monitor recovery, and evaluate therapy effectiveness.
A new AI-enhanced technique makes breast cancer tissue “glow” on MRI scans. This not only improves breast cancer detection but also can help treat it more effectively, the researchers say.
Nuclear cardiology is a specialised field in nuclear medicine that evaluates the heart function to help diagnosing conditions such as coronary artery disease and to assess treatment efficacy, whilst minimising invasive procedures like biopsies. More recently, clinicians have increasingly opted for nuclear imaging over endomyocardial biopsy to diagnose cardiac amyloidosis, allowing for earlier…
The shimmering blue wings of a Morpho butterfly are not just beautiful to look at - they can also help make cancer diagnosis faster, more accurate and more accessible, new research finds.
Medical imaging has come a long way in the past four decades: Advances have been made in the digitization of images, but also towards more gender equality in a once male-dominated field. We talked with Katherine P. Andriole, Ph.D., a leading expert and one of the first women to enter the field, about the evolution of medical imaging informatics, experiences she has had, and her advice to those…
A new open-source automated MRI segmentation tool is setting new standards, developers say. It adapts to any new dataset with minimal user intervention, outperforming similar tools.
The Diagnostica e Terapia Centro Aktis in Marano di Napoli, Italy, has expanded its diagnostic department with the addition of three imaging systems from United Imaging.
A new AI tool creates super-resolution images showing the inner structures of bones in great detail. This can be used to better determine risk for fracture in elderly patients with osteoporosis.
To improve breathing monitoring during radiotherapy, researchers have developed a millimeter-wave sensor capable of non-invasively visualizing respiratory movement during X-ray and CT examinations.
Using 3D imaging and deep learning AI, researchers have developed a new way to accurately assess body fat and muscle distribution, which are crucial for understanding health risks.
New AI-enabled systems, intelligent software, and imaging cloud services for improved radiology workflows: Royal Philips announces its solutions that will be showcased at ECR 2025 in Vienna.
Two new radiotracers outperform the current FDA-approved tracer for detecting Alzheimer's disease, showing higher binding to affected tissue and greater selectivity for identifying tau tangles.
Coronary computed tomography angiography (CCTA) 3D imaging of the heart greatly contributes to preventing heart attacks, compared to standard tests, a new study finds.
A new deep learning model shows promise in detecting and segmenting lung tumors. The findings of the study could have important implications for lung cancer treatment.
Researchers have developed an artificial intelligence (AI) model to detect the spread of metastatic brain cancer using MRI scans, offering insights into patients’ cancer without aggressive surgery.
