Multi-center Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)

NCT05754476 | Unknown DMC

• 1 other identifier: KY-2021-184-04
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief Summary

MAGNET is a multi-center and prospective study to minimize Gadolinium-based Contrast Agent (GBCA) combining novel artificial intelligence (AI) methods with pre-contrast images and/or low-dose images to synthesize virtual contrast-enhanced T1 (vir-T1c) images, based on a large clinical and MRI database and subsequently validated for its clinical value. MRI examinations for patients included T1-weighted images (T1WI) before and after contrast agent administration and at two dose levels: low-dose (10% or 25%) and full-dose (100%), T2-weighted images (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging sequences (DWI) and the computed apparent diffusion coefficient (ADC), all either acquired three dimensional \[3D\] or two dimensional \[2D\]). The standard dose of intravenous gadolinium contrast agent was 0.1mmol/kg(body weight) by manual injection or automatic injection with a high-pressure syringe at a flow rate of 4mL/s.The sequence parameters used for the 3DT1WI scans must be consistent, and the standard for intravenous injection of gadolinium contrast agent is 0.1mmol/kg (body weight), administered either manually or automatically with a high-pressure syringe at a rate of 4mL/s. Additionally, arterial spin labeling (ASL), amide-proton transfer chemical exchange saturation transfer (APT-CEST), susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) can be acquired at the same time if the conditions permit.

Conditions

Brain Diseases

Outcome Measures

Primary Outcomes (2)

• quantitative metrics

  To quantitatively describe the discrepancies between the vir-T1c and the full-dose images by measuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The PSNR measures the voxelwise difference and the PSNR range is between -1 and 1. The SSIM compares nonlocal structural similarity and the minimum value of PSNR is 0. The metrics will be reported in separate(e.g.,SSIM, 0.90; PSNR,42 in vir-T1c, SSIM, 0.94; PSNR,45 in full-dose images).

  after training and applying of the proposed deep learning model, an average of 1 year

• qualitative assessments

  To qualitatively describe discrepancies between the vir-T1c and full-dose images by evaluating enhancement degree, homogeneity, and pattern. Firstly, zero indicates no intracranial or non-enhancing lesion. For enhancement degree, 1 indicates mild enhancement, 2 indicates moderate enhancement, and 3 indicates clear enhancement. For enhancement homogeneity, 1 indicates heterogeneous enhancement, 2 indicates mildly heterogeneous enhancement, and 3 indicates homogeneous enhancement. For enhancement pattern, 1 indicates mass enhancement（proportion enhancement more than 50%）, 2 indicates nodular enhancement (proportion enhancement less than or equal to 50%）, 3 indicates ring enhancement, 4 indicates linear enhancement, and 5 indicates other enhancement.

  after training and applying of the proposed deep learning model, an average of 15 months

Secondary Outcomes (1)

• clinical effects

  after training and applying of the proposed deep learning model, an average of 18 month

Study Arms (1)

Brain Diseases

This study does not limit the kinds of brain diseases. The cohort includes patients with suspected or known brain diseases including tumors, vascular disorder, inflammatory disease, neurodegenerative diseases and trauma, follow-up, routine brain, and others requiring MRI exams with GBCAs.

Other: Low-dose GBCA levels

Interventions

Low-dose GBCA levels OTHER

MRI examinations for patients at two dose levels: low-dose (10% or 25%）can be chosen.

Brain Diseases

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

To reflect the daily practices, this study includes all patients with suspected or known brain diseases requiring MRI exams with GBCAs at the beginning of the study.

You may qualify if:

• Patients with suspected or known brain diseases including tumors, vascular disorders, inflammatory diseases, neurodegenerative diseases and trauma, follow-up, routine brain, and others requiring MRI exams with GBCAs.
• Informed written consent obtained from the patient, and/or patient's parent(s), and/or legal representative.

You may not qualify if:

• Patients with contraindications to MR examination.
• Patients with incomplete MRI data and obvious image artifacts.

Sponsors & Collaborators

• Beijing Tiantan Hospital: lead

Study Sites (1)

Beijing Tiantan Hospital

Beijing, Beijing Municipality, 100053, China

RECRUITING

Related Publications (3)

• Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pfluger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health. 2021 Dec;3(12):e784-e794. doi: 10.1016/S2589-7500(21)00205-3. Epub 2021 Oct 20.

  PMID: 34688602 BACKGROUND

• Luo H, Zhang T, Gong NJ, Tamir J, Venkata SP, Xu C, Duan Y, Zhou T, Zhou F, Zaharchuk G, Xue J, Liu Y. Deep learning-based methods may minimize GBCA dosage in brain MRI. Eur Radiol. 2021 Sep;31(9):6419-6428. doi: 10.1007/s00330-021-07848-3. Epub 2021 Mar 18.

  PMID: 33735394 BACKGROUND

• Gong E, Pauly JM, Wintermark M, Zaharchuk G. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J Magn Reson Imaging. 2018 Aug;48(2):330-340. doi: 10.1002/jmri.25970. Epub 2018 Feb 13.

  PMID: 29437269 BACKGROUND

MeSH Terms

Conditions

Brain Diseases

Condition Hierarchy (Ancestors)

Central Nervous System Diseases; Nervous System Diseases

Study Officials

• Yaou Liu, PhD

  Study Principal Investigator

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Yaou Liu, PhD

CONTACT

+86 1059975396; yaouliu80@163.com

Siyao Xu, Postgraduate

CONTACT

+86 17780540030; xusiyao97@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: October 17, 2022
• First Posted: March 3, 2023
• Study Start: March 29, 2019
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2024
• Last Updated: March 3, 2023

Record last verified: 2023-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: Within 5 years after the end of the trial.
• Access Criteria: Neurologist and radiologist who submitting an application to Prof. Liu.

Locations

CN (1)