Detection of hypodense hepatic and renal lesions on abdominal CT reconstructed with deep learning image reconstruction technique in patients with large body habitus: A multi-reader study.

Deep learning image reconstruction improves visualization of arterial phase hyperenhancement and washout appearance on dual-energy CT for hepatocellular carcinoma: a non-inferiority study.

The DLIR-H/50-keV protocol demonstrates imaging performance approaching MRI standards, representing a promising reconstructive strategy for HCC assessment, particularly in clinical scenarios where MRI access is limited.

Ultra-low dose chest-abdomen-pelvis CT with deep-learning image reconstruction for cancer follow-up: Impact on image quality and lesion detection.

PURPOSE The purpose of this study was to compare the image quality and lesion detection between ultra-low dose (ULD) chest-abdomen-pelvis computed tomography (CAP-CT) reconstructed with a deep-learning image reconstruction (DLR) algorithm, and standard-dose CT (STD-CT) in cancer follow-up. MATERIALS AND METHODS A tot...

Photon-counting CT for hepato-bilio-pancreatic imaging: a qualitative head-to-head comparison with third-generation dual-source CT: preliminary results.

In this preliminary intra-patient comparison, photon-counting CT provided significantly superior image quality and anatomical detail in the hepato-bilio-pancreatic region compared to third-generation dual-source EID-CT.

Distinguishing enhancing from non-enhancing hyperdense adnexal lesions with spectral reconstructions at photon counting CT.

Attenuation change utilizing VUE images and iodine quantification can differentiate enhancing from nonenhancing hyperdense adnexal lesions at PCCT with excellent diagnostic performance.

Feasibility of reducing radiation dose and contrast dose while improving image quality in dual-energy CT pulmonary angiography : Use of low-energy virtual monochromatic images with deep learning image reconstruction algorithm.

Arthroscopy-Validated Diagnostic Performance of a Deep Learning Reconstruction Pipeline for Rapid 7-Minute Five-Sequence 3-T Knee MRI.

Clinical 7-minute five-sequence 3-T knee MRI with deep learning reconstruction provides good to excellent diagnostic performance for detecting arthroscopy-validated internal derangement of the knee with high diagnostic performance for arthroscopy-validated abnormalities.