November 20th 2024
While a large retrospective study found that interstitial lung abnormalities (ILAs) were evident on 1.7 percent of computed tomography (CT) scans, researchers found that 43.9 percent of ILAs, including fibrotic ILAs, were not reported.
Can Polyenergetic Reconstruction Help Resolve Streak Artifacts in Photon Counting CT?
July 22nd 2024New research looking at photon-counting computed tomography (PCCT) demonstrated significantly reduced variation and tracheal air density attenuation with polyenergetic reconstruction in contrast to monoenergetic reconstruction on chest CT.
Systematic Review: PET/MRI May be More Advantageous than PET/CT in Cancer Imaging
July 18th 2024While PET/MRI and PET/CT had comparable sensitivity for patient-level regional nodal metastases and lesion-level recurrence, the authors of a systematic review noted that PET/MRI had significantly higher accuracy in breast cancer and colorectal cancer staging.
FDA Clears Enhanced Mobile CT System with High-Resolution Photon-Counting Technology
July 15th 2024Photon-counting CT-optimized features with the OmniTom Elite system include 30 cm field of view scanning, continuous spiral scanning, and an ultra-high-resolution capability of 0.141 mm resolution.
Can a Dual-Energy CT Model Bolster Breast Cancer Detection?
July 12th 2024Incorporating age, lesion shape and effective atomic number in the venous phase of dual-energy CT (DECT), an emerging model demonstrated a 79.1 percent AUC for differentiating between malignant and benign breast lesions.
A Victory for Radiology: New CMS Proposal Would Provide Coverage of CT Colonography in 2025
July 12th 2024In newly issued proposals addressing changes to coverage for Medicare services in 2025, the Centers for Medicare and Medicaid Services (CMS) announced its intent to provide coverage of computed tomography colonography (CTC) for Medicare beneficiaries in 2025.
Can Photon-Counting CT Provide Superior Lung Perfusion Imaging Over Dual-Energy CT?
July 8th 2024Photon-counting CT enables enhanced fissure visualization and a lower degree of cardiac motion artifacts for lung perfusion imaging at a significantly reduced scan time in contrast to dual-energy CT, according to new research findings.
Adjunctive AI Leads to 16 Percent Increase in CT Sensitivity for Incidental Pulmonary Embolism
June 20th 2024Artificial intelligence facilitated a 96.2 percent sensitivity rate for incidental pulmonary embolism (IPE) on contrast-enhanced CT chest or abdomen exams, according to new prospective research involving over 4,300 patients.
Nanox Adds AI Applications to Teleradiology Platform for CT Second Opinions
Published: June 7th 2024 | Updated: June 7th 2024Facilitating additional consultation on chest and abdominal CT scans, the Second Opinions teleradiology platform now features FDA-cleared AI tools for cardiac, bone and liver assessments.
Large CT Study Shows Benefits of AI in Predicting CV Risks in Patients Without Obstructive CAD
June 3rd 2024An AI algorithm that incorporates scoring of coronary inflammation based on coronary CT angiography (CCTA) may enhance long-term cardiovascular risk stratification beyond conventional risk factor and imaging assessments, even in patients without obstructive CAD.
CT-Based AI Model May Enhance Prediction of Lung Cancer Recurrence
May 30th 2024An AI model that includes extracted radiomic features from CT scans more than doubled the sensitivity rate for preoperative prediction of lung cancer recurrence in comparison to traditional TNM staging, according to study findings to be presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.
Qure.ai to Debut Multimodality AI Platform for Lung Cancer Imaging at ASCO 2024
May 29th 2024In addition to detecting missed lung nodules on X-rays, the AI-powered Qure.ai lung cancer continuum platform reportedly automates lung nodule measurement on CT scans and facilitates multimodality reporting.
Can Deep Learning Models Improve CT Differentiation of Small Solid Pulmonary Nodules?
May 29th 2024One deep learning model had a 72.4 percent accuracy rate for differentiating between benign and malignant solid pulmonary nodules on non-contrast CT while another deep learning model demonstrated an 87.1 percent AUC for differentiating benign and inflammatory findings.