Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones.

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and/or ureteral stones were included.

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).

Implementation of Institutional Size-Specific Diagnostic Reference Levels for CT Angiography.

Rationale And Objectives: To generate institutional size-specific diagnostic reference levels (DRLs) for computed tomography angiography (CTA) examinations and assess the potential for dose optimization compared to size-independent DRLs.

Materials And Methods: CTA examinations of the aorta, the pulmonary arteries and of the pelvis/lower extremity performed between January 2016 and January 2017 were included in our retrospective study. Water equivalent diameter (Dw) was automatically calculated for each patient.

Potential of a machine-learning model for dose optimization in CT quality assurance.

Objectives: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study.

Methods: Three thousand one hundred ninety-nine CT chest examinations were used for training and testing of the feed-forward, single hidden layer neural network (January 2016-December 2017, 60% male, 62 ± 15 years, 80/20 split). The model was optimized and trained to predict the volumetric computed tomography dose index (CTDI) based on scan patient metrics (scanner, study description, protocol, patient age, sex, and water-equivalent diameter (D)).

Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

Purpose: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma.

Materials And Methods: We retrospectively included consecutive malignant melanoma patients with a chest CT between 01/2015 and 01/2016. Machine learning based CAD software was used to reconstruct additional vessel-suppressed axial images.

Tailoring CT Dose to Patient Size: Implementation of the Updated 2017 ACR Size-specific Diagnostic Reference Levels.

Rationale And Objectives: To use an automatic computed tomography (CT) dose monitoring system to analyze the institutional chest and abdominopelvic CT dose data as regards the updated 2017 American College of Radiology (ACR) diagnostic reference levels (DRLs) based on water-equivalent diameter (Dw) and size-specific dose estimates (SSDE) to detect patient-size subgroups in which CT dose can be optimized.

Materials And Methods: All chest CT examinations performed between July 2016 and April 2017 with and without contrast material, CT of the pulmonary arteries, and abdominopelvic CT with and without contrast material were included in this retrospective study. Dw and SSDE were automatically calculated for all scans using a previously validated in-house developed Matlab software and stored into our CT dose monitoring system.

ACCURACY OF SIZE-SPECIFIC DOSE ESTIMATE CALCULATION FROM CENTER SLICE IN COMPUTED TOMOGRAPHY.

To evaluate the accuracy of size-specific dose estimate (SSDE) calculation from center slice with water-equivalent diameter (Dw) and effective diameter (Deff). A total of 1812 CT exams (1583 adult and 229 pediatric) were included in this retrospective study. Dw and Deff were automatically calculated for all slices of each scan.