Detection of Urinary Tract Stones on Ultra-low Dose Abdominopelvic CT Imaging With Deep-learning Image Reconstruction Algorithm
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Urolithiasis
- Sponsor
- Centre Hospitalier Universitaire, Amiens
- Enrollment
- 62
- Locations
- 1
- Primary Endpoint
- Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones
- Status
- Active, not recruiting
- Last Updated
- 3 years ago
Overview
Brief Summary
Urolithiasis has an increasing incidence and prevalence worldwide, and some patients may have multiple recurrences. Because these stone-related episodes may lead to multiple diagnostic examinations requiring ionizing radiation, urolithiasis is a natural target for dose reduction efforts. Abdominopelvic low dose CT, which has the highest sensitivity and specificity among available imaging modalities, is the most appropriate diagnostic exam for this pathology. The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in urolithiasis patients.
Investigators
Eligibility Criteria
Inclusion Criteria
- •Age ≥ 18 years old,
- •Patient referred for abdominopelvic CT to confirm urolithiasis or for follow-up,
- •Affiliation to a social security program,
- •Ability of the subject to understand and express opposition
Exclusion Criteria
- •Age \<18 years old,
- •Person under guardianship or curators,
- •Pregnant woman,
- •Any contraindications to CT
Outcomes
Primary Outcomes
Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones
Time Frame: day 1
Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones. Patients who were referred to the department for abdominopelvic CT exam for urolithiasis diagnostic or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULD, \<1 mSv) with deep learning-based reconstruction (DLIR).