Completed

NCT Number: 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

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

Completed

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Key information

Conditions

Bone Scan

Age range

18 year–99 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Nuclear Medicine Department

Vandœuvre-lès-Nancy, 54511, France

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients who had a whole-body thee dimensions bone scan for rheumatological or oncological indications.

Exclusion criteria

Patients opposed to the use of their data

Treatment and study plan

Artificial intelligence algorithm

Other

to apply an artificial intelligence algorithm to treat the imaging

Primary outcomes

To compare imaging treated by the intelligence artificial algorithm with imaging treated with the traditionnal filter artificial algorithm

Time frame: one day

Quantification value on imaging measured with intelligence artificial algorithm compared with quantification value measured with conventional filter

Sponsors and collaborators

Lead sponsor

Central Hospital, Nancy, France