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Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI

NCT05820113 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 109

Last updated 2023-04-19

No results posted yet for this study

Summary

The aim of this study was therefore to investigate a new unrolled DL super resolution reconstruction of an initially low-resolution Cartesian T2 turbo spin echo sequence (T2 TSE) and compare it qualitatively and quantitatively to standard high-resolution Cartesian and non-Cartesian T2 TSE sequences in the setting of prostate mpMRI with particular interest in image sharpness, conspicuity of lesions and acquisition time. Furthermore, the investigators assessed the agreement of assigned PI-RADS scores between deep learning super resolution and standard sequences.

Conditions

Interventions

DEVICE

Deep learning based reconstruction of T2-TSE sequence

A newly developed deep-learning based reconstruction of a primarily low-resolved T2-TSE sequence is included in the imaging protocol for evaluation of prostate cancer.

Sponsors & Collaborators

  • Philips Healthcare

    collaborator INDUSTRY

  • University Hospital, Bonn

    lead OTHER

Principal Investigators

  • Julian A Luetkens, PD Dr. med. · University Hospital, Bonn

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
18 Years
Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-08-01
Primary Completion
2022-11-30
Completion
2022-11-30

Countries

  • Germany

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05820113 on ClinicalTrials.gov