NCT06782438

Evaluation of an Artificial Intelligence Algorithm Reducing Noise on Fast Whole-body Bone Tomoscintigraphy Acquisitions Recorded by a 360 Degree Cadmium-Zinc-Tellurid Camera

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 20 (estimated)
• Sponsor: Central Hospital, Nancy, France · Academic / Other
• Sex: All
• Age: 18 Years – 99 Years
• Healthy volunteers: Not accepted

Summary

Recently, artificial intelligence algorithms reducing noise by deep learning have been developed with application to SPECT and PET images. Many studies have reported the possibility of reducing the recording time in bone scintigraphy by applying artificial intelligence algorithms reducing noise

Detailed description

Only two studies compared images denoised by a Deep Learning algorithm to those denoised by conventional filters (Gaussian and median filters). The first study was conducted only on patients, without phantom analysis and without taking into account the size of the lesions. The second study included an analysis on phantom and patients, but with application to planar images rather than to SPECT images that are increasingly used today The hypothesis of our study conducted on phantom and patients is that an artificial intelligence algorithm reducing noise could replace the conventional filters usually used in bone SPECT for the denoising of scintigraphic images.

Conditions

• Bone Scan

Interventions

Type	Name	Description
OTHER	artificial intelligence algorithm	to apply an artificial intelligence algorithm to treat the imaging

Timeline

• Start date: 2025-02-27
• Primary completion: 2025-03-15
• Completion: 2025-03-30
• First posted: 2025-01-17
• Last updated: 2025-03-04

Locations

1 site across 1 country: France