Chitosan hydrogel to improve the efficacy of sclerotherapy for venous malformations: From preclinical experiment to clinical application
CT features of tension neck subcutaneous emphysema (tension pneumocollum)
Assessment of small bowel ischemia in mechanical small bowel obstruction: Diagnostic value of bowel wall iodine concentration using dual-energy CT
The purpose of this study was to determine whether dual-energy computed tomography (DECT), specifically by measuring bowel wall iodine concentration (BWIC), is superior to monoenergetic reconstructions (MR) for the diagnosis and staging of small bowel ischemia in patients with mechanical small bowel obstruction (SBO).
Improving 4D flow cardiac MRI analysis to accurately assess aortic regurgitation in patients with bicuspid aortic valves
Differentiating neoplastic from bland portal vein thrombus using dual-energy CT
Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty
The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE) in terms of image quality, lesion conspicuity, and lesion detection.
Ultra-high resolution spectral photon-counting CT outperforms dual layer CT for lung imaging: Results of a phantom study
The purpose of this study was to compare lung image quality obtained with ultra-high resolution (UHR) spectral photon-counting CT (SPCCT) with that of dual-layer CT (DLCT), at standard and low dose levels using an image quality phantom and an anthropomorphic lung phantom.
Generative AI smartphones: From entertainment to potentially serious risks in radiology
CT, MRI and contrast-enhanced ultrasound features of mucinous cystic neoplasm of the liver
Artificial intelligence for bone fracture detection: A promising tool but no substitute for human expertise
Diagnostic and prognostic value of MRI-based Node-RADS for the assessment of regional lymph node metastasis in renal cell carcinoma
The purpose of this study was to assess the capabilities of MRI-based Node Reporting and Data System (Node-RADS) in diagnosing regional lymph node metastasis (RLNM) and to estimate its prognostic significance in patients with renal cell carcinomas (RCCs).
Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram
The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).
Standard of care versus standard of care plus Ericksonian hypnosis for percutaneous liver biopsy: Results of a randomized control trial
The purpose of this study was to compare levels of pain and anxiety during percutaneous ultrasound-guided liver biopsy between patients receiving standard of care and those receiving standard of care plus the support of Ericksonian hypnosis.
Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography
The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).
Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study
The purpose of this study was to investigate the added value of artificial intelligence (AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography (CTA).
Artificial intelligence solutions for head and neck CT angiography: Ready for prime time?
Myocardial strain imaging: Advancing the diagnosis of cardiac amyloidosis with MRI
Comparison between artificial intelligence solution and radiologist for the detection of pelvic, hip and extremity fractures on radiographs in adult using CT as standard of reference
The purpose of this study was to compare the diagnostic performance of an artificial intelligence (AI) solution for the detection of fractures of pelvic, proximal femur or extremity fractures in adults with radiologist interpretation of radiographs, using standard dose CT examination as the standard of reference.
Photon-counting CT systems: A technical review of current clinical possibilities
In recent years, computed tomography (CT) has undergone a number of developments to improve radiological care. The most recent major innovation has been the development of photon-counting detectors. By comparison with the energy-integrating detectors traditionally used in CT, these detectors offer better dose efficiency, eliminate electronic noise, improve spatial resolution and have intrinsic spectral sensitivity. These detectors also allow the energy of each photon to be counted, thus improving the sampling of the X-ray spectrum in multiple energy bins, to better distinguish between photoelectric and Compton attenuation coefficients, resulting in better spectral images and specific color K-edge images. The purpose of this article was to make the reader more familiar with the basic principles and techniques of new photon-counting CT systems equipped with photon-counting detectors and also to describe the currently available devices that could be used in clinical practice.
Artificial intelligence in radiation therapy: An emerging revolution that will be driven by generative methodologies
Gadobenate dimeglumine-enhanced MRI: A surrogate marker of liver function recovery after auxiliary partial orthotopic liver transplantation
