CT

In a prospective study involving nearly 100 patients with Long Covid, 57 percent of patients had PET/MRI abnormalities and 90 percent of the cohort had abnormalities on dual-energy CT scans.

The dose length product (DLP) for the flash mode on a dual-source photon-counting CT system was less than a third of that for the spiral mode, according to a study of CTA findings for 1,000 patients presented at the Society of Cardiovascular Computed Tomography (SCCT) conference.

In a recent interview, Amir Ahmadi, M.D., discussed limitations of conventional diagnostic assessments for people with suspected coronary artery disease, and the emergence of AI-enabled plaque quantification to facilitate more timely detection and intervention.

Stay updated with the latest in radiology, including PET, MRI, and AI research, plus essential insights on mammography and cardiac imaging.

For patients who had coronary CTA for acute chest pain, emerging research found no significant association between plaque burden grades with CAD-RADS 2.0 and cardiac events.

For pediatric patients with congenital heart disease, new research showed that cardiac PCCT yielded comparable signal-to-noise and contrast-to-noise ratios to energy-integrating detector CT at over a 43 percent lower dose length product.

In a new study comparing standard resolution and ultra-high resolution modes for patients undergoing coronary CTA with photon-counting detector CT, researchers found that segment-level sensitivity and accuracy rates for diagnosing coronary artery stenosis were consistently > 89.6 percent.

Catch up on the top radiology content of the past week.

Preoperative use of contrast-enhanced cone-beam breast CT has over a 96 percent sensitivity for predicting nipple-areolar complex involvement in cases involving early-stage breast cancer, according to a new study.

Catch up on the top AI-related news and research in radiology over the past month.

Catch up on the top radiology content of the past week.

Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.

Catch up on the top radiology content of the past week.

The AI-powered Viz Subdural Plus reportedly provides automated measurements and labeling of subdural collections, including subdural hemorrhages (SDHs), based on non-contrast CT scans.

Researchers also noted a greater than 30 percent increase in treatment management changes resulting from the use of CT-based adjunctive AI to detect lung metastases in colorectal cancer patients.

Emphasizing increasing recognition of the capability of coronary computed tomography angiography (CCTA) for the evaluation of acute and stable chest pain, this author defuses common misperceptions and reviews key considerations for implementation of a CCTA program.

Emerging research suggests the use of computed tomography colonography (CTC) in colorectal cancer screening may provide significantly higher preventive benefit at lower costs in comparison to multitarget stool DNA testing.

Through AI assessment of chest CT scans, the SeleCT Screening may help identify candidates for bronchoscopic lung volume reduction to improve lung function.

Catch up on the top radiology content of the past week.

Catch up on the most-well viewed radiology content in May 2025.

In a review of 155 studies, researchers examined the capabilities of photon-counting computed tomography (PCCT) for enhanced accuracy, tissue characterization, artifact reduction and reduced radiation dosing across thoracic, abdominal, and cardiothoracic imaging applications.

Catch up on the top AI-related news and research in radiology over the past month.

Catch up on the top radiology content of the past week.

The Rapid CTP AI software offered 23 percent greater detection of medium vessel occlusions in comparison to the Viz CTP AI software, according to research presented at the European Stroke (Organization) Conference (ESOC).

In never-smokers, deep learning assessment of single baseline low-dose computed tomography (CT) scans demonstrated a 79 percent AUC for predicting lung cancer up to six years later, according to new research presented today at the American Thoracic Society (ATS) 2025 International Conference.