Related Experiment Video
Updated: May 1, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses
Published on: March 30, 2015
Agreement between Routine-Dose and Lower-Dose CT with and without Deep Learning-based Denoising for Active
Jens Borgbjerg1, Bendik Stensby Breen1, Cathrine Helgestad Kristiansen1
1Department of Radiology, Akershus University Hospital, Sykehusveien 25, 1478 Lorenskog, Norway.
Routine-dose CT scans for small renal masses (SRMs) can be reduced by 75% without compromising assessment accuracy. Deep learning-based denoising (DLD) further enables sub-millisievert radiation dose CT scans for evaluating SRMs during active surveillance.
Area of Science:
- Radiology
- Medical Imaging
- Oncology
Background:
- Active surveillance is crucial for managing small renal masses (SRMs).
- Evaluating SRMs requires high-quality contrast-enhanced CT scans.
- Reducing radiation dose is a key goal in medical imaging.
Purpose of the Study:
- To assess the agreement between routine-dose (RD) and lower-dose (LD) contrast-enhanced CT scans for evaluating SRMs.
- To evaluate the impact of deep learning-based denoising (DLD) on LD CT image quality and diagnostic accuracy.
- To determine the feasibility of dose reduction in CT for SRMs during active surveillance.
Main Methods:
- Retrospective analysis of CT scans from patients with SRMs under active surveillance.
- Generation of LD CT images simulating 75% (LD75) and 90% (LD90) dose reductions from RD images.
- Application of DLD to LD images (LD75-DLD, LD90-DLD).
- Independent evaluation of 350 CT scans by nine radiologists across five datasets for tumor size, collecting system proximity, and shape irregularity.
- Assessment of interobserver reproducibility and agreement using LOAM and Gwet AC2 coefficients.
- Subjective and quantitative image quality assessments.
Main Results:
- LD75 CT showed agreement with RD scans for SRM diameter assessment (LOAM ±2.4 mm vs. ±2.2 mm).
- A 90% dose reduction compromised reproducibility (LOAM ±3.0 mm), but LD90-DLD preserved it (LOAM ±2.4 mm).
- Observer agreement for tumor nearness to collecting system and shape irregularity was comparable across all image sets (P ≥ .35 for TN, P ≥ .02 for TSI).
- DLD effectively restored image quality at reduced doses; LD75-DLD demonstrated superior image quality, lower noise, and improved contrast-to-noise ratio compared to RD (P < .001).
Conclusions:
- A 75% radiation dose reduction is feasible for SRM assessment in active surveillance using CT with conventional iterative reconstruction.
- Deep learning-based denoising (DLD) enables sub-millisievert dose CT scans for evaluating SRMs.
- DLD effectively maintains diagnostic performance and image quality at significantly reduced radiation doses.
More Related Videos
09:49A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
Published on: April 24, 2020
07:53Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
Published on: October 13, 2023
Related Concept Videos
Difference from Background: Limit of Detection
The LOD indicates the presence or absence...
Tandem Mass Spectrometry