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

April 6, 2023 updated by: Julian Alexander Luetkens, University Hospital, Bonn
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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Prostate cancer has been among the most prevalent cancer types in men for years, being responsible for 7.8% of all newly diagnosed cases in 2020, holding the 2th place right after lung cancer. Early and non-invasive diagnostics was improved vastly by multiparametric MRI (mpMRI) of the prostate, detecting clinically significant prostate cancer and forming the baseline for guided biopsy of the prostate, while it can prevent unnecessary biopsies in patients with elevated prostate specific antigen, but no visible lesions. With an aging population fast, efficient and highly qualitative MRI scans are needed to satisfy this increasing demand. Deep learning image reconstruction has become increasingly important solving these tasks to produce highly qualitative images while drastically reducing acquisition time.

Standard acquisition protocols of prostate mpMRI include T2-weighted, diffusion-weighted and dynamically contrast-enhanced sequences to allow for the classification of prostatic lesions according to the Prostate Imaging Reporting & Data System (PI-RADS). While the assignment of the PI-RADS score in the peripheral zone of the prostate is mainly determined by the diffusion weighted imaging, the T2-weighted-sequences are mainly responsible for the assessment of the transitional zone. Furthermore, thorough assessment of the prostate necessitates acquisition of T2-weighted sequences in axial and sagittal planes, thus extending acquisition time of MRI protocols. Different approaches have been proposed to accelerate and improve the image acquisition, ranging from the implementation of shortened protocols to the improvement of diffusion weighted sequences or using compressed sensing for the reconstruction of non-Cartesian T2-weighted-sequences. Besides these methods that rely on traditional acquisition and reconstruction methods, deep learning (DL) image reconstruction has become increasingly important solving these tasks to produce highly qualitative images while drastically reducing acquisition time.

Despite that, reliable DL-methods for the process of image acquisition and reconstruction of prostate mpMRI itself are sparse. While first approaches for DL denoising has been established, effectively replacing the conventional wavelet function, the remainder of the iterative reconstruction cycle is unaffected and the impact on diagnostic performance of the PI-RADS score remains unclear. Recently developed super resolution deep learning networks are promising to overcome this limitation. First results for DL denoising in different applications, e.g. in musculoskeletal MRI already show good results, leading to significant acceleration of acquisition time while maintaining high image quality. However, the application of these denoising DL-networks in combination with more advanced super resolution networks in prostate mpMRI hasn't been evaluated yet.

In this prospective study, between August and November 2022, participants with suspicion for prostate cancer underwent prostate MRI with standard high-resolution Cartesian T2 (T2C) and non-Cartesian T2 (T2NC) sequences. Additionally, a low-resolution Cartesian T2 TSE (T2SR) with DL denoising and super resolution reconstruction was acquired. Artifacts, image sharpness, lesion conspicuity, capsule delineation, overall image quality and diagnostic confidence were rated on a 5-point-Likert-Scale with being non-diagnostic and 5 being excellent. Apparent signal-to-noise ratio (aSNR), contrast-to-noise ratio (aCNR) and edge rise distance (ERD) were calculated. Friedman test and One-way ANOVA were used for group comparisons. Regarding agreement of PI-RADS scores were compared with Cohen's Kappa.

Study Type

Interventional

Enrollment (Actual)

109

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Germany
    • NRW
      • Bonn, NRW, Germany, 53127
        • University Hospital Bonn

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Clinical suspicion of prostate cancer (PSA >4 ng/ml or suspicious digital rectal exam/transrectal ultrasound)

Exclusion Criteria:

  • General contraindications for MRI (cardiac pacemakers, neurostimulators, ferric metal) or gadolinium based contrast agents (GFR <30 ml/min/1.73 m2, prior severe allergic reactions)
  • Severe claustrophobia

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Deep learning based reconstruction of T2-TSE sequence
Participants undergo multiparametric MRI of the prostate with inclusion of standard cartesian T2-TSE and non-cartesian T2-weighted sequences, as well as a newly developed deep learning enhanced T2-TSE sequence. All patients in this study undergo the same imaging protocol.
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Qualitative assessment of image quality (Artifacts, image sharpness, lesion conspicuity, capsule delineation, overall image quality and diagnostic confidence)
Time Frame: 4 months
Artifacts, image sharpness, lesion conspicuity, capsule delineation, overall image quality and diagnostic confidence were rated on a 5-point-Likert-Scale with 1 being non-diagnostic and 5 being excellent. Friedman test was used for significance testing with p<0.05 considered as indicative of a significant difference.
4 months
Acquisition time
Time Frame: 4 months
Measurement of acquisition time of T2-weighted sequences
4 months
Degree of agreement on PI-RADS ratings
Time Frame: 4 months
To assess the PI-RADS score, all MRIs were read blinded by a radiologist with 11 years expertise at two different time points in random order. The MRI sequences for PI-RADS assessment included either T2NC (reference standard at our institution) or T2SR as the T2-weighted sequence in the reading protocol. The remainder of sequences were the same (axial T1-weighted TSE pre and post contrast administration, axial dynamically contrast enhanced T1, sagittal T2 TSE and axial diffusion weighted sequences with apparent diffusion coefficient map). Cohen's Kappa was used for correlation of readings with inclusion of either T2SR or T2NC.
4 months
Quantitative assessment of image quality (apparent signal-to-noise and contrast-to-noise ratio)
Time Frame: 4 months
Apparent signal-to-noise ratio (aSNR: signal intensity of peripheral zone/standard deviation of muscle) and contrast-to-noise ratio (aCNR: signal intensity of peripheral zone - signal intensity of muscle)/standard deviation of muscle) was calculated to quantify the image sharpness. One-way ANOVA was used for significance testing with p<0.05 considered as indicative of a significant difference.
4 months
Quantitative assessment of image quality (edge rise distance)
Time Frame: 4 months
Edge rise distance (ERD) was calculated to quantify the image sharpness. The ERD was determined as a measure of image sharpness. For this purpose, a line was drawn perpendicularly crossing the dorsal border of the prostate capsule. The edge rise distance was then determined as the distance (in mm) between the 10% and 90% signal intensity levels relative to the low and high signal intensity areas. One-way ANOVA was used for significance testing with p<0.05 considered as indicative of a significant difference.
4 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University Hospital, Bonn

Collaborators

Philips Healthcare

Investigators

  • Principal Investigator: Julian A Luetkens, PD Dr. med., University Hospital, Bonn

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

August 1, 2022

Primary Completion (Actual)

November 30, 2022

Study Completion (Actual)

November 30, 2022

Study Registration Dates

First Submitted

February 6, 2023

First Submitted That Met QC Criteria

April 6, 2023

First Posted (Actual)

April 19, 2023

Study Record Updates

Last Update Posted (Actual)

April 19, 2023

Last Update Submitted That Met QC Criteria

April 6, 2023

Last Verified

April 1, 2023

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SR.001

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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