Prostate CancerNCT05820113
Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
A study testing Deep learning based reconstruction of T2-TSE sequence in people with Prostate Cancer and Multiparametric MRI, run by University Hospital, Bonn at 1 site in Germany.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostate Cancer, Multiparametric MRI
- What is being tested?
- Deep learning based reconstruction of T2-TSE sequence
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 109 participants
- Where
- 1 siteGermany
- Study dates
- 1 Aug 2022to 30 Nov 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Clinical suspicion of prostate cancer (PSA >4 ng/ml or suspicious digital rectal exam/transrectal ultrasound)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the 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.
- Conditions
- Prostate Cancer, Multiparametric MRI
- Being tested
- Deep learning based reconstruction of T2-TSE sequence
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning based reconstruction of T2-TSE sequenceDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Deep learning based reconstruction of T2-TSE sequence | Experimental | 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. Receives: Device: Deep learning based reconstruction of T2-TSE sequence |
What is being measured
The primary outcome is the main question the study is designed to answer.
Qualitative assessment of image quality (Artifacts, image sharpness, lesion conspicuity, capsule delineation, overall image quality and diagnostic confidence)
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.
Measured: 4 months
Acquisition time
Measurement of acquisition time of T2-weighted sequences
Measured: 4 months
Degree of agreement on PI-RADS ratings
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.
Measured: 4 months
Quantitative assessment of image quality (apparent signal-to-noise and contrast-to-noise ratio)
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.
Measured: 4 months
Quantitative assessment of image quality (edge rise distance)
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.
Measured: 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Apr 2023
- Study start1 Aug 2022
- Primary completion30 Nov 2022
- Study completion30 Nov 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (25)Papers the sponsor has linked to this study.
- Other trials for Prostate CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 109 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Bonn
- Sponsor type
- University, hospital or other
- Collaborators
- Philips Healthcare
- Responsible party
- Principal investigator, Julian Alexander Luetkens, University Hospital, Bonn
- Investigators
- Julian A Luetkens, PD Dr. med., Principal investigator, University Hospital, Bonn
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05820113
- Sponsor's protocol ID
- SR.001
- Status last verified
- Apr 2023
- First submitted
- 6 Feb 2023
- Last updated
- 19 Apr 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338Eklund M, Jaderling F, Discacciati A, Bergman M, Annerstedt M, Aly M, Glaessgen A, Carlsson S, Gronberg H, Nordstrom T; STHLM3 consortium. MRI-Targeted or Standard Biopsy in Prostate Cancer Screening. N Engl J Med. 2021 Sep 2;385(10):908-920. doi: 10.1056/NEJMoa2100852. Epub 2021 Jul 9.
PubMed 34237810Hugosson J, Mansson M, Wallstrom J, Axcrona U, Carlsson SV, Egevad L, Geterud K, Khatami A, Kohestani K, Pihl CG, Socratous A, Stranne J, Godtman RA, Hellstrom M; GOTEBORG-2 Trial Investigators. Prostate Cancer Screening with PSA and MRI Followed by Targeted Biopsy Only. N Engl J Med. 2022 Dec 8;387(23):2126-2137. doi: 10.1056/NEJMoa2209454.
PubMed 36477032ACR, ESUR and AdMeTech Foundation. Prostate Imaging Reporting & Data System (PI-RADS). 2019. Version 2.1.
Weiss J, Martirosian P, Notohamiprodjo M, Kaufmann S, Othman AE, Grosse U, Nikolaou K, Gatidis S. Implementation of a 5-Minute Magnetic Resonance Imaging Screening Protocol for Prostate Cancer in Men With Elevated Prostate-Specific Antigen Before Biopsy. Invest Radiol. 2018 Mar;53(3):186-190. doi: 10.1097/RLI.0000000000000427.
PubMed 29077588Langbein BJ, Szczepankiewicz F, Westin CF, Bay C, Maier SE, Kibel AS, Tempany CM, Fennessy FM. A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer. Invest Radiol. 2021 Dec 1;56(12):845-853. doi: 10.1097/RLI.0000000000000796.
PubMed 34049334Bischoff LM, Katemann C, Isaak A, Mesropyan N, Wichtmann B, Kravchenko D, Endler C, Kuetting D, Pieper CC, Ellinger J, Weber O, Attenberger U, Luetkens JA. T2 Turbo Spin Echo With Compressed Sensing and Propeller Acquisition (Sampling k-Space by Utilizing Rotating Blades) for Fast and Motion Robust Prostate MRI: Comparison With Conventional Acquisition. Invest Radiol. 2023 Mar 1;58(3):209-215. doi: 10.1097/RLI.0000000000000923. Epub 2022 Sep 2.
PubMed 36070533Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.
PubMed 29777175Harder FN, Weiss K, Amiel T, Peeters JM, Tauber R, Ziegelmayer S, Burian E, Makowski MR, Sauter AP, Gschwend JE, Karampinos DC, Braren RF. Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer. Cancers (Basel). 2022 Nov 22;14(23):5741. doi: 10.3390/cancers14235741.
PubMed 36497223Dong C, Loy CC, He K, Tang X. Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell. 2016 Feb;38(2):295-307. doi: 10.1109/TPAMI.2015.2439281.
PubMed 2676173511. Li Y, Sixou B, Peyrin F. A Review of the Deep Learning Methods for Medical Images Super Resolution Problems. IRBM, Volume 42, Issue 2, April 2021, Pages 120-133. doi: 10.1016/j.irbm.2020.08.004
Almansour H, Herrmann J, Gassenmaier S, Afat S, Jacoby J, Koerzdoerfer G, Nickel D, Mostapha M, Nadar M, Othman AE. Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability. Radiology. 2023 Mar;306(3):e212922. doi: 10.1148/radiol.212922. Epub 2022 Nov 1.
PubMed 36318032Kim M, Lee SM, Park C, Lee D, Kim KS, Jeong HS, Kim S, Choi MH, Nickel D. Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI. Invest Radiol. 2022 Dec 1;57(12):826-833. doi: 10.1097/RLI.0000000000000900. Epub 2022 Jul 1.
PubMed 35776434Pezzotti N, de Weerdt E, Yousefi S, et al. Adaptive-CS-Net: FastMRI with Adaptive Intelligence. arxiv:1912.12259. 2019;(NeurIPS).
Zhang J and Ghanem B. ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1828-1837
Study at a glance
From the registry record, last updated by the sponsor on 19 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Super Resolution Reconstruction for Fast and Motion Robust T2-weighted Prostate MRI
- ClinicalTrials.gov ID
- NCT05820113
- Acronym / sponsor ID
- SR.001
- Conditions
- Prostate Cancer, Multiparametric MRI
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 109 participants (actual)
- Lead sponsor
- University Hospital, BonnUniversity, hospital or other
- Collaborators
- Philips Healthcare
- Study start
- 1 Aug 2022 (actual)
- Primary completion
- 30 Nov 2022 (actual)
- Study completion
- 30 Nov 2022 (actual)
- First posted
- 19 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Deep learning based reconstruction of T2-TSE sequence | Experimental | Device: Deep learning based reconstruction of T2-TSE sequenceParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Clinical suspicion of prostate cancer (PSA >4 ng/ml or suspicious digital rectal exam/transrectal ultrasound)
Key exclusion criteria (2 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Deep learning based reconstruction of T2-TSE sequenceA 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. | Device | Deep learning based reconstruction of T2-TSE sequence |
Primary outcome
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.
Acquisition time
Time frame: 4 months
Measurement of acquisition time of T2-weighted sequences
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.
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.
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Apr 2023.
- Background: PubMed 33538338Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
- Background: PubMed 34237810Eklund M, Jaderling F, Discacciati A, Bergman M, Annerstedt M, Aly M, Glaessgen A, Carlsson S, Gronberg H, Nordstrom T; STHLM3 consortium. MRI-Targeted or Standard Biopsy in Prostate Cancer Screening. N Engl J Med. 2021 Sep 2;385(10):908-920. doi: 10.1056/NEJMoa2100852. Epub 2021 Jul 9.
- Background: PubMed 36477032Hugosson J, Mansson M, Wallstrom J, Axcrona U, Carlsson SV, Egevad L, Geterud K, Khatami A, Kohestani K, Pihl CG, Socratous A, Stranne J, Godtman RA, Hellstrom M; GOTEBORG-2 Trial Investigators. Prostate Cancer Screening with PSA and MRI Followed by Targeted Biopsy Only. N Engl J Med. 2022 Dec 8;387(23):2126-2137. doi: 10.1056/NEJMoa2209454.
- BackgroundACR, ESUR and AdMeTech Foundation. Prostate Imaging Reporting & Data System (PI-RADS). 2019. Version 2.1.
- Background: PubMed 29077588Weiss J, Martirosian P, Notohamiprodjo M, Kaufmann S, Othman AE, Grosse U, Nikolaou K, Gatidis S. Implementation of a 5-Minute Magnetic Resonance Imaging Screening Protocol for Prostate Cancer in Men With Elevated Prostate-Specific Antigen Before Biopsy. Invest Radiol. 2018 Mar;53(3):186-190. doi: 10.1097/RLI.0000000000000427.
- Background: PubMed 34049334Langbein BJ, Szczepankiewicz F, Westin CF, Bay C, Maier SE, Kibel AS, Tempany CM, Fennessy FM. A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer. Invest Radiol. 2021 Dec 1;56(12):845-853. doi: 10.1097/RLI.0000000000000796.
- Background: PubMed 36070533Bischoff LM, Katemann C, Isaak A, Mesropyan N, Wichtmann B, Kravchenko D, Endler C, Kuetting D, Pieper CC, Ellinger J, Weber O, Attenberger U, Luetkens JA. T2 Turbo Spin Echo With Compressed Sensing and Propeller Acquisition (Sampling k-Space by Utilizing Rotating Blades) for Fast and Motion Robust Prostate MRI: Comparison With Conventional Acquisition. Invest Radiol. 2023 Mar 1;58(3):209-215. doi: 10.1097/RLI.0000000000000923. Epub 2022 Sep 2.
- Background: PubMed 29777175Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500-510. doi: 10.1038/s41568-018-0016-5.
- Background: PubMed 36497223Harder FN, Weiss K, Amiel T, Peeters JM, Tauber R, Ziegelmayer S, Burian E, Makowski MR, Sauter AP, Gschwend JE, Karampinos DC, Braren RF. Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer. Cancers (Basel). 2022 Nov 22;14(23):5741. doi: 10.3390/cancers14235741.
- Background: PubMed 26761735Dong C, Loy CC, He K, Tang X. Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell. 2016 Feb;38(2):295-307. doi: 10.1109/TPAMI.2015.2439281.
- Background11. Li Y, Sixou B, Peyrin F. A Review of the Deep Learning Methods for Medical Images Super Resolution Problems. IRBM, Volume 42, Issue 2, April 2021, Pages 120-133. doi: 10.1016/j.irbm.2020.08.004
- Background: PubMed 36318032Almansour H, Herrmann J, Gassenmaier S, Afat S, Jacoby J, Koerzdoerfer G, Nickel D, Mostapha M, Nadar M, Othman AE. Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability. Radiology. 2023 Mar;306(3):e212922. doi: 10.1148/radiol.212922. Epub 2022 Nov 1.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Julian A Luetkens, PD Dr. med. principal investigator
University Hospital, Bonn
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Deep learning based reconstruction of T2-TSE sequence in people with Prostate Cancer and Multiparametric MRI, run by University Hospital, Bonn at 1 site in Germany.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Germany. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Qualitative assessment of image quality (Artifacts, image sharpness, lesion conspicuity, capsule delineation, overall image quality and diagnostic confidence) (time frame: 4 months). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05820113, which also lists 25 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Apr 2023; AzTrials last refreshed its copy on 30 Oct 2026.