CT

Four Medicare administrative contractors (MACs) will provide coverage of AI-enabled coronary plaque analysis and quantitative coronary tomography assessment of coronary computed tomography angiography (CCTA) for Medicare beneficiaries starting in November 2024.

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Researchers found that ultra-high resolution photon-counting significantly enhanced visualization of small vessels and facilitated improved reduction of blooming artifacts for head and neck computed tomography angiography (CTA) scans.

While staffing shortages in radiology continue to persist after the COVID-19 pandemic, current and emerging innovations powered by artificial intelligence (AI) may help facilities navigate these challenges and mitigate rising costs of health care.

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Through analysis of coronary computed tomography angiography (CCTA) images, the PlaqueIQ software provides quantification and classification of atherosclerosis, a common cause of myocardial infarction (MI) and ischemic stroke.

In a multicenter study of over 1,100 patients with COVID-19, pleural effusion was detected on CT scans in nearly a third of patients, who also had significantly higher ICU admission and 30-day mortality rates.

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Intestinal ultrasound demonstrated an 88.2 percent agreement with computed tomography enterography in characterizing creeping fat in patients with Crohn’s disease, according to new research.

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

Irrespective of differences with radiation dosing and tube voltage, obtaining CT-derived fat fraction from photon-counting detector CT provides consistently comparative results to MRI-derived proton density fat fraction assessment, according to new research.

For patients who had neuroradiological trauma CT scans, researchers noted no significant visual differences between deep learning denoising at 25% mAs and iterative reconstruction at 100% mAs.

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The CINA-CSpine AI software reportedly demonstrated a 90.3 percent sensitivity rate for cervical spine fractures in validation testing on more than 300 non-contrast CT scans.

Emphasizing multi-structure segmentation and feature extraction from chest CT scans, an emerging AI model demonstrated an approximately 70 percent AUC for predicting significant incidental extrapulmonary findings as well as two-year and 10-year all-cause mortality.

Radiomics models offered a pooled AUC of 86 percent for differentiating between ruptured and unruptured intracranial aneurysms, according to a recently published meta-analysis.

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Two ultralow-dose photon counting CT protocols yielded satisfactory quality for diagnostic assessment in over 95 percent of patients who had post-transplant lung deformities, according to new research.

Review the case and test your knowledge to make the correct diagnosis.

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A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.

In a study involving 396 confirmed neuroendocrine tumor liver metastases, researchers found that Gd-EOB-DTPA-enhanced MRI offered 19 percent higher sensitivity than polyenergetic CT.

Adjunctive AI showed no difference in accuracy than unassisted radiologists for intracranial hemorrhage (ICH) detection and had a slightly longer mean report turnaround time for ICH-positive cases, according to newly published prospective research.

Catch up on the most-well viewed radiology content in August 2024.

Catch up on the latest research in neuroradiology over the past month.