Assessment of task-based image quality for abdominal CT protocols linked with national diagnostic reference levels.

Objectives: To assess task-based image quality for two abdominal protocols on various CT scanners. To establish a relationship between diagnostic reference levels (DRLs) and task-based image quality.

Methods: A protocol for the detection of focal liver lesions was used to scan an anthropomorphic abdominal phantom containing 8- and 5-mm low-contrast (20 HU) spheres at five CTDI levels (4, 8, 12, 16, and 20 mGy) on 12 CTs.

ANNUAL EXPOSURE OF THE SWISS POPULATION FROM MEDICAL IMAGING IN 2018.

Nationwide surveys on radiation dose to the population from medical imaging are recommended in order to follow trends in population exposure. The goal of the 2018 survey was to investigate the current exposure. The invoice coding information was collected in five university hospitals and large clinics.

Characterisation and mapping of scattered radiation fields in interventional radiology theatres.

We used the Timepix3 hybrid pixel detector technology in order to determine the exposure of medical personnel to ionizing radiation in an interventional radiology room. We measured the energy spectra of the scattered radiation generated by the patient during X-ray image-guided interventional procedures. We performed measurements at different positions and heights within the theatre.

Search of low-contrast liver lesions in abdominal CT: the importance of scrolling behavior.

Visual search using volumetric images is becoming the standard in medical imaging. However, we do not fully understand how eye movement strategies mediate diagnostic performance. A recent study on computed tomography (CT) images showed that the search strategies of radiologists could be classified based on saccade amplitudes and cross-quadrant eye movements [eye movement index (EMI)] into two categories: drillers and scanners.

Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.

Purpose: We aimed to thoroughly characterize image quality of a novel deep learning image reconstruction (DLIR), and investigate its potential for dose reduction in abdominal CT in comparison with filtered back-projection (FBP) and a partial model-based iterative reconstruction (ASiR-V).

Methods: We scanned a phantom at three dose levels: regular (7 mGy), low (3 mGy) and ultra-low (1 mGy). Images were reconstructed using DLIR (low, medium and high levels) and ASiR-V (0% = FBP, 50% and 100%).