News

The medical imaging industry contributes billions of dollars to state economies, according to three new reports published June 12 by the Medical Imaging & Technology Alliance (MITA). The findings, experts say, provide more reason to permanently repeal the medical device tax.

Researchers created and validated a machine learning model using features taken from baseline, laboratory, electrocardiography (ECG), echocardiography and cardiovascular resonance (CMR) imaging data.

Until now, the $168 million facility was producing limited amounts of Mo-99, a radioisotope used in approximately 85% of all Australian nuclear medicine procedures such as SPECT scans.

Fractional flow reserve derived from CT (FFR-CT) is a superior predictor of long-term outcomes of heart disease compared to traditional coronary CT angiography (CCTA), according to a new study published in Radiology.

Combining MRI and ultrasound (US) detected up to 33% more cancers than standard biopsy methods alone, according to new research published in JAMA Surgery.

A new three-dimensional (3D) tissue imaging technique can help scientists noninvasively study cells and may lead to improved treatments for a variety of diseases, according to research published in eLife.

“Search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake,” said co-senior author Kristen Yeom, MD, associate professor of radiology at Stanford University.

Aidoc announced Tuesday, June 11, that it has received FDA clearance for its AI-based solution for triaging cervical spine fractures.

A new deep learning approach lowered radiation exposure from CT imaging while producing higher quality scans compared to traditional iterative reconstruction techniques, according to research published in Nature Machine Intelligence.

Open-access (OA) publishing in radiology and nuclear medicine has slowed in recent years, and authors of a new study believe radiology—as a whole—needs to be more supportive in offering free access to the field’s latest research.

Deep convolutional neural networks (DCNNs) can better classify chest x-rays when trained on augmented datasets, according to a new study published in Clinical Radiology.

A team of German researchers has found a new class of radiopharmaceuticals capable of identifying 28 types of malignant tumors, imaging them with high uptake and image contrast.