Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction.

Objective: The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30.

Methods: Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high).

CT Esophagography for Evaluation of Esophageal Perforation.

Esophageal emergencies such as rupture or postoperative leak are uncommon but may be life threatening when they occur. Delay in their diagnosis and treatment may significantly increase morbidity and mortality. Causes of esophageal injury include iatrogenic (including esophagogastroduodenoscopy and stent placement), foreign body ingestion, blunt or penetrating trauma to the chest or abdomen, and forceful retching, also called Boerhaave syndrome.