Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography.

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)].

Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.

Development of size-specific institutional diagnostic reference levels for computed tomography protocols in neck imaging.

Purpose: To develop size-specific institutional diagnostic reference levels (DRLs) for computed tomography (CT) protocols used in neck CT imaging (cervical spine CT, cervical CT angiography (CTA) and cervical staging CT) and to compare institutional to national DRLs.

Materials And Methods: Cervical CT examinations (spine, n = 609; CTA, n = 505 and staging CT, n = 184) performed between 01/2016 and 06/2017 were included in this retrospective study. For each region and examination, the volumetric CT dose index (CTDI) and dose-length product (DLP) were determined and binned into size bins according to patient water-equivalent diameter (d).