Evaluation of Rapid T2-weighted and DWI MR Sequences Reconstructed by Deep Learning for Prostate Imaging
NCT ID: NCT06094322
Last Updated: 2025-12-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
34 participants
INTERVENTIONAL
2022-07-28
2025-12-01
Brief Summary
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Detection and characterization of malignant lesion is important to address appropriate patient care pathway. The purpose of this project is to evaluate novel deep learning (DL) T2-weighted TSE (T2DL) and Diffusion (DWIDL) sequences for prostate MR exam and investigate its impact on diagnostic, examination time, image quality, and PI-RADS classification compared to standard T2-weighted TSE (T2S) and standard Diffusion (DWIS) sequences.
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Detailed Description
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Conditions
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Study Design
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NA
SINGLE_GROUP
DIAGNOSTIC
NONE
Interventions
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MR prostate exam
Subjects will lie in supine position. The systematic use of a headset will reduce the acoustic noise inherent to the machine. We are going to carry out the standard MR prostate protocol which patients usually benefit from in clinical routine. This protocol consists of morphological sequences (T2 weighting with spin echo readout), Diffusion MR and dynamic contrast-enhanced sequences. We will then perform an additional faster enhanced T2-weighting SE and DWI sequences combined with Deep Learning reconstruction
Eligibility Criteria
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Inclusion Criteria
* Healthy subject without history of hepatic disease
* Patient addressed for an prostate MRI
* Ability to give consent
Exclusion Criteria
* major obesity (\>140 kg),
* Patient under guardianship or curators
* Age \< 18 years,
* Women,
* History of prostatectomy or irradiation of the prostate
* any contraindication to MRI exam
18 Years
ALL
Yes
Sponsors
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Centre Hospitalier Universitaire, Amiens
OTHER
Responsible Party
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Locations
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Centre Hospitalier Universitaire d'Amiens
Amiens, Picardie, France
Countries
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Other Identifiers
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PI2022_843_0108
Identifier Type: -
Identifier Source: org_study_id
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