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

NCT05820113 | Completed

• 1 other identifier: SR.001
• Study Type: interventional
• Target: 109
• Locations: 1 country
• Sites: 1

Brief 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

Prostate Cancer; Multiparametric MRI

Outcome Measures

Primary Outcomes (5)

• 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.

  4 months

• Acquisition time

  Measurement of acquisition time of T2-weighted sequences

  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.

  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.

  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.

  4 months

Study Arms (1)

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.

Device: Deep learning based reconstruction of T2-TSE sequence

Interventions

Deep learning based reconstruction of T2-TSE sequence DEVICE

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.

Deep learning based reconstruction of T2-TSE sequence

Eligibility Criteria

• Age: 18 Years+
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• University Hospital, Bonn: lead
• Philips Healthcare: collaborator

Study Sites (1)

University Hospital Bonn

Bonn, North Rhine-Westphalia, 53127, Germany

Location

Related Publications (25)

• 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.

  PMID: 33538338 BACKGROUND

• Eklund 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.

  PMID: 34237810 BACKGROUND

• Hugosson 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.

  PMID: 36477032 BACKGROUND

• ACR, ESUR and AdMeTech Foundation. Prostate Imaging Reporting & Data System (PI-RADS). 2019. Version 2.1.

  BACKGROUND

• 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.

  PMID: 29077588 BACKGROUND

• Langbein 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.

  PMID: 34049334 BACKGROUND

• Bischoff 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.

  PMID: 36070533 BACKGROUND

• Hosny 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.

  PMID: 29777175 BACKGROUND

• Harder 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.

  PMID: 36497223 BACKGROUND

• Dong 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.

  PMID: 26761735 BACKGROUND

• 11. 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

• 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.

  PMID: 36318032 BACKGROUND

• Kim 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.

  PMID: 35776434 BACKGROUND

• Pezzotti N, de Weerdt E, Yousefi S, et al. Adaptive-CS-Net: FastMRI with Adaptive Intelligence. arxiv:1912.12259. 2019;(NeurIPS).

  BACKGROUND

• 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

  BACKGROUND

• Kim J, Lee JK, Lee KM. Accurate Image Super-Resolution Using Very Deep Convolutional Networks. arXiv:1511.04587

  BACKGROUND

• Chaudhari AS, Fang Z, Kogan F, Wood J, Stevens KJ, Gibbons EK, Lee JH, Gold GE, Hargreaves BA. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med. 2018 Nov;80(5):2139-2154. doi: 10.1002/mrm.27178. Epub 2018 Mar 26.

  PMID: 29582464 BACKGROUND

• Suzuki S, Machida H, Tanaka I, Ueno E. Measurement of vascular wall attenuation: comparison of CT angiography using model-based iterative reconstruction with standard filtered back-projection algorithm CT in vitro. Eur J Radiol. 2012 Nov;81(11):3348-53. doi: 10.1016/j.ejrad.2012.02.009. Epub 2012 Mar 19.

  PMID: 22436433 BACKGROUND

• Agarwal S, Singh O.P, Nagaria D. Analysis and Comparison of Wavelet Transforms for Denoising MRI Image. Biomed Pharmacol J 2017;10(2).

  BACKGROUND

• Gassenmaier S, Warm V, Nickel D, Weiland E, Herrmann J, Almansour H, Wessling D, Afat S. Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction. Cancers (Basel). 2023 Jan 18;15(3):578. doi: 10.3390/cancers15030578.

  PMID: 36765539 BACKGROUND

• Ueda T, Ohno Y, Yamamoto K, Murayama K, Ikedo M, Yui M, Hanamatsu S, Tanaka Y, Obama Y, Ikeda H, Toyama H. Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging. Radiology. 2022 May;303(2):373-381. doi: 10.1148/radiol.204097. Epub 2022 Feb 1.

  PMID: 35103536 BACKGROUND

• Johnson PM, Tong A, Donthireddy A, Melamud K, Petrocelli R, Smereka P, Qian K, Keerthivasan MB, Chandarana H, Knoll F. Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate. J Magn Reson Imaging. 2022 Jul;56(1):184-195. doi: 10.1002/jmri.28024. Epub 2021 Dec 7.

  PMID: 34877735 BACKGROUND

• Wright KL, Hamilton JI, Griswold MA, Gulani V, Seiberlich N. Non-Cartesian parallel imaging reconstruction. J Magn Reson Imaging. 2014 Nov;40(5):1022-40. doi: 10.1002/jmri.24521. Epub 2014 Jan 10.

  PMID: 24408499 BACKGROUND

• Zaitsev M, Maclaren J, Herbst M. Motion artifacts in MRI: A complex problem with many partial solutions. J Magn Reson Imaging. 2015 Oct;42(4):887-901. doi: 10.1002/jmri.24850. Epub 2015 Jan 28.

  PMID: 25630632 BACKGROUND

• Bischoff LM, Peeters JM, Weinhold L, Krausewitz P, Ellinger J, Katemann C, Isaak A, Weber OM, Kuetting D, Attenberger U, Pieper CC, Sprinkart AM, Luetkens JA. Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI. Radiology. 2023 Sep;308(3):e230427. doi: 10.1148/radiol.230427.

  PMID: 37750774 DERIVED

MeSH Terms

Conditions

Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Study Officials

• Julian A Luetkens, PD Dr. med.

  University Hospital, Bonn

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of Cardiovascular Imaging, Principal Investigator, Senior Physician

Study Record Dates

• First Submitted: February 6, 2023
• First Posted: April 19, 2023
• Study Start: August 1, 2022
• Primary Completion: November 30, 2022
• Study Completion: November 30, 2022
• Last Updated: April 19, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will not share

Locations

DE (1)