Brain DiseasesMAGNETNCT05754476
Multi-center Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
An observational study of Brain Diseases, run by Beijing Tiantan Hospital at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Brain Diseases
- What is being tested?
- Low-dose GBCA levels
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 29 Mar 2019to 31 Dec 2024
- Phase
- Observational
- 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Patients with contraindications to MR examination.
- Patients with incomplete MRI data and obvious image artifacts.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Beijing Tiantan Hospital
Beijing, Beijing Municipality, 100053
Yaou Liu, PhD, yaouliu80@163.com
About this study
Official title: Multi-center and Prospective Cohort Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
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.
Read the detailed description
MRI with GBCA is an indispensable part of imaging exams for brain disease diagnosis. Generally, GBCA is safe, with a few mild side effects since GBCAs received FDA approval in 1989. There are numerous issues that challenge the current practice of widespread use of GBCA. GBCA can trigger nephrogenic systemic fibrosis(NSF) under particular circumstances, cause allergic reactions, may increase the risk of fetal death, and accumulate in the brain such as the dentate nucleus and globus pallidus. Efforts need to be made to reduce dose while still maintaining diagnostic capabilities. Artificial intelligence (AI) techniques have shown great potential in medical fields. Deep learning (DL), a branch of AI, has been applied to image segmentation, computer-aided diagnosis, and reduce GBCA dose.
This study intends to build a prospective brain MRI dataset including patients with suspected or known brain abnormalities to minimize the use of GBCA. Then train DL models to process pre-contrast images and/or low-dose T1 images to predict virtual contrast-enhanced T1 (vir-T1c) images, taking the full-dose images as the reference standard. Later quantitatively and qualitatively evaluating and comparing vir-T1c images from DL models about clinical diagnostic performance, focusing on lesion detection, diagnosis, and therapy, to explore a DL model universal, provide enhanced images faster and more convenient in clinical practice. To minimize the use of GBCA, we will:
- Use novel artificial intelligence (AI) methods with pre-contrast images including conventional (T1WI, T2WI, FLAIR, DWI/ADC), new physiological MRI techniques (ASL, APT-CEST, SWI/QSM) by adding physiological information from perfusion as well as metabolic and susceptibility imaging, and/or low-dose images (10% or 25%) to synthesize vir-T1c images;
- Quantify when (in which patients and at what follow-up times) GBCA can be omitted or minimized without influencing brain disease diagnosis and treatment evaluation for doctor raters and therefore patient prognosis.
This study does not limit manufacturers including 1.5T and 3.0T scanners， or kinds of GBCAs.
- Conditions
- Brain Diseases
- Being tested
- Low-dose GBCA levels
- Medical subject headings
- Brain Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Low-dose GBCA levelsOther
MRI examinations for patients at two dose levels: low-dose (10% or 25%）can be chosen.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Low-dose GBCA levels |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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.
Measured: after training and applying of the proposed deep learning model, an average of 15 months
Secondary outcomes (1)
clinical effects
To describe whether vir-T1c images combing other sequences affect diagnosis or treatment according to evaluation of neuroradiologist and neurologist from 1 to 5 scores.
Zero indicates enhancement error and can not be used. 1 indicates non-diagnostic. 2 indicates affecting diagnosis or treatment significantly. 3 indicates affecting diagnosis or treatment moderately. 4 indicates no affecting diagnosis or treatment almost. 5 indicates no affecting diagnosis or treatment completely.
Measured: after training and applying of the proposed deep learning model, an average of 18 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Mar 2023
- Study start29 Mar 2019
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Brain DiseasesStudies of the same condition, open and under way.
- More trials run by Beijing Tiantan HospitalThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Tiantan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yaou Liu, Beijing Tiantan Hospital
- Investigators
- Yaou Liu, PhD, Principal investigator, Study Principal Investigator
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05754476
- Sponsor's protocol ID
- KY-2021-184-04
- Acronym
- MAGNET
- Status last verified
- Mar 2023
- First submitted
- 17 Oct 2022
- Last updated
- 3 Mar 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Clinical and MR data can be shared.
Publications
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.
PubMed 34688602Luo 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.
PubMed 33735394Gong 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.
PubMed 29437269
Study at a glance
From the registry record, last updated by the sponsor on 3 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multi-center and Prospective Cohort Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
- ClinicalTrials.gov ID
- NCT05754476
- Acronym / sponsor ID
- MAGNET / KY-2021-184-04
- Conditions
- Brain Diseases
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Beijing Tiantan HospitalUniversity, hospital or other
- Study start
- 29 Mar 2019 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 3 Mar 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.
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.
Detailed description
MRI with GBCA is an indispensable part of imaging exams for brain disease diagnosis. Generally, GBCA is safe, with a few mild side effects since GBCAs received FDA approval in 1989. There are numerous issues that challenge the current practice of widespread use of GBCA. GBCA can trigger nephrogenic systemic fibrosis(NSF) under particular circumstances, cause allergic reactions, may increase the risk of fetal death, and accumulate in the brain such as the dentate nucleus and globus pallidus. Efforts need to be made to reduce dose while still maintaining diagnostic capabilities. Artificial intelligence (AI) techniques have shown great potential in medical fields. Deep learning (DL), a branch of AI, has been applied to image segmentation, computer-aided diagnosis, and reduce GBCA dose.
This study intends to build a prospective brain MRI dataset including patients with suspected or known brain abnormalities to minimize the use of GBCA. Then train DL models to process pre-contrast images and/or low-dose T1 images to predict virtual contrast-enhanced T1 (vir-T1c) images, taking the full-dose images as the reference standard. Later quantitatively and qualitatively evaluating and comparing vir-T1c images from DL models about clinical diagnostic performance, focusing on lesion detection, diagnosis, and therapy, to explore a DL model universal, provide enhanced images faster and more convenient in clinical practice. To minimize the use of GBCA, we will:
- Use novel artificial intelligence (AI) methods with pre-contrast images including conventional (T1WI, T2WI, FLAIR, DWI/ADC), new physiological MRI techniques (ASL, APT-CEST, SWI/QSM) by adding physiological information from perfusion as well as metabolic and susceptibility imaging, and/or low-dose images (10% or 25%) to synthesize vir-T1c images;
- Quantify when (in which patients and at what follow-up times) GBCA can be omitted or minimized without influencing brain disease diagnosis and treatment evaluation for doctor raters and therefore patient prognosis.
This study does not limit manufacturers including 1.5T and 3.0T scanners， or kinds of GBCAs.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Brain Diseases | – | Other: Low-dose GBCA levelsThis 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (2 listed)
- Patients with contraindications to MR examination.
- Patients with incomplete MRI data and obvious image artifacts.
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 |
|---|---|---|
| Low-dose GBCA levelsMRI examinations for patients at two dose levels: low-dose (10% or 25%）can be chosen. | Other | Brain Diseases |
Primary outcome
quantitative metrics
Time frame: after training and applying of the proposed deep learning model, an average of 1 year
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).
qualitative assessments
Time frame: after training and applying of the proposed deep learning model, an average of 15 months
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.
1 secondary outcome
clinical effects
Time frame: after training and applying of the proposed deep learning model, an average of 18 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Mar 2023.
- Background: PubMed 34688602Jayachandran 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.
- Background: PubMed 33735394Luo 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.
- Background: PubMed 29437269Gong 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.
- Individual participant dataSharing planned: Yes. Clinical and MR data can be shared.
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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.
Yaou Liu, PhD principal investigator
Study Principal Investigator
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Brain Diseases, run by Beijing Tiantan Hospital at 1 site in China.
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 2 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: status not confirmed, as of the sponsor's last update on 3 Mar 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yaou Liu, PhD (+86 1059975396, yaouliu80@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
quantitative metrics (time frame: after training and applying of the proposed deep learning model, an average of 1 year). The study lists 2 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 NCT05754476, which also lists 3 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 3 Mar 2023; AzTrials last refreshed its copy on 2 Nov 2026.