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| ID | Type | Description | Link |
|---|---|---|---|
| SNCTP000006108 | Other Identifier | Basec Switzerland |
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MR pulse for whole brain optimal Deuterium (2H) Metabolic Imaging and EPSI (echo planar spectroscopic imaging) based SLOW-edited 1H-MRSI will be developed and optimized for use at an UHF scanner at 7 Tesla. The study has 4 phases.
Phase I: The 2H and 1H MRSI sequences are developed and optimized in vitro (phantoms)
Phase II: Sequences are applied in vivo in healthy volunteers and further optimized
Phase III: Optimal 2H 1H pulse sequences are applied in 4 cohorts of healthy volunteers, to study the effect of aging with whole brain 2H and 1H MRSI.
Phase IV: application of the sequences in 4 patient groups with different diseases: Alzheimer's diseases (AD) patients, diabetes mellitus type II (DM) patients, mild cognitive impaired (MCI) patients, and high grade carotid stenosis patients (HGCS).
The ultimate aim is to create for individual patient specific 3dimensional spatial resolved z-score maps (similar to FDG-PET) based on the healthy control data of phase III of the trial.
Introduction - Aging of the world's population is being increasingly recognized as a crucial societal challenge. Yet, tools to perform high-quality metabolic research on aging of the brain are very limited.
The proposed study will focus on methods to assess metabolic brain changes that occur during aging as well as in 10 patients with Alzheimer's disease (AD). Apart from AD, also 10 patients with minimal cognitive impairment (MCI), 10 patient with diabetes mellitus type 2 (DM), and 10 patients with high grade carotid stenosis will be examined.
The currently most prominent clinical method to study brain metabolism in vivo, is Positron Emission Tomography (PET) using 18F-fluorodeoxyglucose (FDG). A major drawback of this method is the ionizing radiation. A magnetic resonance spectroscopic imaging (MRSI) based method, called deuterium metabolic imaging (DMI) expands the MRSI capabilities offered by proton-based techniques and enables in vivo glucose metabolism imaging without ionizing radiation. A unique feature of DMI is that, unlike PET, it not only maps glucose uptake but also downstream products such as lactate, glutamate and glutamine thereby offering the possibility to detect metabolic disturbances associated with aging and neurodegeneration.
Due to the relatively low sensitivity of DMI, strong magnetic fields are required to increase the signal to noise ratio (SNR) and enable DMI. Recently the first commercially available Ultra High Field (UHF) 7T MR-scanner was approved for clinical use and is now available in Bern, making DMI accessible. The investigators' motivation is to provide non-invasive, radiation free, deuterium and proton based MRSI methods enabling metabolic studies of the brain and lay the foundation for long-term longitudinal observational studies of aging; something that can hardly be done with PET due to the radiation burden for healthy controls.
Objectives - The primary goal of the proposed project is to establish 3D spatially resolved deuterium (2H) and proton (1H) based MRSI methodology for studies of brain metabolism and apply this methodology in an in vivo feasibility study. To complement DMI, the investigators will establish UHF 3D-resolved spectral-edited 1H-MRSI mapping for glucose, gamma-Aminobutyric acid (GABA) and glutamate using the investigators' recently developed technique called SLOW.
The secondary goal is to create 3D spatially resolved reference atlas of metabolic information of the brain for healthy individuals.The atlas will allow spatially resolved analysis of metabolic information of individual patients having neurological disorders by comparing them to a normative data using z-score derived abnormality maps.
Hypotheses - (a.) 3D-MRSI based glucose/glutamate/lactate mapping using DMI facilitates spatially resolved quantitative comparisons between AD patients and healthy controls using z-score maps; (b.) 1H-SLOW-edited MRSI of glucose/GABA and glutamate facilitates spatially resolved quantitative comparisons between AD patients and healthy controls using z-score maps.
Methods - the investigators will (i.) adapt their UHF 1H-EPSI MRSI sequence for DMI; (ii.) optimize their 1H-SLOW-edited EPSI sequence aiming at whole brain measurement of GABA, glutamate and glucose editing, together with the metabolites N-acetyl-aspartate (NAA), choline, creatine, and aspartate; (iii.) extend their spectrIm-QMRS analytic tool to quantify and analyze 3D-2H-metabolic datasets, (iv.) compute all 3D-resolved 1H- and 2H-MRSI metabolic maps and co-register with high resolution 3D-anatomical images; (v.) develop methodology to generate metabolic atlas of normative data and perform z-score based comparisons using the atlas.
Significance - It is likely that the trend to higher field strength in MRI will continue making DMI increasingly available for research and clinical applications. UHF DMI and 1H-EPSI MRSI will provide a non-invasive way to quantify brain metabolism. DMI offers information on glucose metabolism, whereas 1H-SLOW on glucose, GABA- and glutamate-concentrations. The proposed approach to MRSI data analysis is fundamentally different from the one currently applied in clinical MRSI and would allow to detect and display even subtle variations from normative metabolic characteristic. If successful, UHF metabolic imaging would offer a radiation free modality, which could be repeatedly applied in young and healthy subjects to study aging. Importantly, the proposed methods will provide deeper insights into bioenergetics, specifically mitochondrial function, oxidative phosphorylation and use of alternative fuels for brain energy provision, information that FDG-PET cannot provide. Moreover, comparative analyses utilizing normative datasets would facilitate studies of the broad spectrum of disorders with impaired brain bioenergetics for example neurodegeneration, neuroinflammation but also diseases not specific to the central nervous systems like obesity and diabetes, all having a high socio-economic impact.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Application of novel MRSI pulse sequences to healthy persons | Active Comparator | Optimized MR-pulse sequences are applied to three healthy person groups after glucose solution ingestion to obtain healthy control 3D metabolic reference data. |
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| Application of novel MRSI pulse sequences to four patient groups | Experimental | Optimized MR-pulse sequences are applied to four groups of 10 patients after glucose solution ingestion followed by patient level comparison of patient 3D metabolic data to healthy control data by z-score mapping. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Application of new pulse sequence package SIGNATURES2023 to healthy controls | Device | One or more novel or further optimized non CE-marked pulse MRSI sequence is/are applied to 100 healthy subjects to determined reference metabolite maps of the whole brain |
| Measure | Description | Time Frame |
|---|---|---|
| 3D spatial resolved Deuterium metabolite imaging (DMI) | Coregistered deuterium metabolite distributions (glucose, glutamate) is measured in a young healthy control group of 20 person (<40 years) is obtained, as well as coregistered deuterium metabolic images of metabolite distribution in a elder healthy control group of 20 person (>40 years) is obtained. | 36 months |
| 3D spatial resolved SLOW-EPSI MRSI metabolite mapping | Coregistered neuro-metabolite distributions (e.g. GABA, glutamate) distribution in a young healthy control group of 20 persons (age < 40 years) are measured, as well as coregistered neuro-metabolite distributions (e.g. GABA, glutamate, ..) are measured in healthy control group of 20 persons (age > 40 years). | 36 months |
| 3D spatial resolved 3D DMI and 3D SLOW-EPSI MRSI mapping in Alzheimer's disease (AD) patient group | Coregistered DMI (glucose, glutamate) maps and neuro-metabolite images (e.g. GABA, glutamate, ..) distribution in an Alzheimer's disease (AD) patient group of 10 persons are obtained. | 48 months |
| 3D spatial resolved 3D DMI and 3D SLOW-EPSI MRSI mapping in Mild Cognitive Impairment (MCI) patient group | Coregistered DMI (glucose, glutamate) maps and neuro-metabolite images (e.g. GABA, glutamate, ..) distribution in a Mild Cognitive Impairment (MCI) patient group of 10 persons are obtained. | 48 months |
| 3D DMI and 3D SLOW-EPSI MRSI mapping in a Diabetes Mellitus (DM) Type II patient group | Coregistered DMI (glucose, glutamate) maps and neuro-metabolite images (e.g. GABA, glutamate, ..) distribution in a Diabetes Mellitus (DM) Type II patient group of 10 persons is obtained | 48 months |
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General Inclusion Criteria (applicable to all groups):
Disease-Specific Inclusion Criteria:
I. Type 2 Diabetes Patients group (PG-IV-2H-DM):
II. High-Grade Carotid Stenosis Patient Group (PG-IV-1H-HGCS):
III. Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Patients (PG-IV-2H-AD/MCI):
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Johannes Slotboom, PhD | Contact | +41316327469 | johannes.slotboom@insel.ch | |
| Piotr Radojewski, MD | Contact | +41316641467 | piotr.radojewski@insel.ch |
| Name | Affiliation | Role |
|---|---|---|
| Johannes Slotboom, PhD | University Hospital / Inselspital /University Bern / 3010 Bern / Switzerland | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Translational Imaging Center / Sitem | Bern | 3010 | Switzerland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35344608 | Background | Weng G, Radojewski P, Sheriff S, Kiefer C, Schucht P, Wiest R, Maudsley AA, Slotboom J. SLOW: A novel spectral editing method for whole-brain MRSI at ultra high magnetic field. Magn Reson Med. 2022 Jul;88(1):53-70. doi: 10.1002/mrm.29220. Epub 2022 Mar 28. | |
| 36875625 | Background | Weng G, Ermis E, Maragkou T, Krcek R, Reinhardt P, Zubak I, Schucht P, Wiest R, Slotboom J, Radojewski P. Accurate prediction of isocitrate dehydrogenase -mutation status of gliomas using SLOW-editing magnetic resonance spectroscopic imaging at 7 T MR. Neurooncol Adv. 2023 Jan 3;5(1):vdad001. doi: 10.1093/noajnl/vdad001. eCollection 2023 Jan-Dec. |
| Label | URL |
|---|---|
| Website where a description of a MRSI processing tool is described and can be downloaded | View source |
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One raw 1H-MRSI dataset for one patient is about 25Gb large, and direct sharing will be difficult.
Therefore; the data will be shared upon request.
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2H-MRSI and 1H-MRSI pulse sequences will be developed and protocols optimized in vitro.
Suitable MR-protocols will be further optimized in vivo.
These DMI-MRSI protocols will be applied after 1H/2H-glucose supplementation:
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| Application of new pulse sequence package SIGNATURES2023 to 4 groups of patients (pilot) | Device | One or more novel or further optimized non CE-marked pulse MRSI sequence is/are applied to 4 time 10 patients with AD, MCI, DM and HGCS to determine metabolite maps of the whole brain. |
|
| 3D DMI and 3D SLOW-EPSI MRSI mapping in a High Grade Carotid Stenosis (HGCS) patient group | Coregistered DMI (glucose, glutamate) maps and neuro-metabolite images (e.g. GABA, glutamate, ..) distribution in a Diabetes Mellitus (DM) Type II patient group of 10 persons is obtained | 48 months |
| 38184158 | Background | Weng G, Slotboom J, Schucht P, Ermis E, Wiest R, Kloppel S, Peter J, Zubak I, Radojewski P. Simultaneous multi-region detection of GABA+ and Glx using 3D spatially resolved SLOW-editing and EPSI-readout at 7T. Neuroimage. 2024 Feb 1;286:120511. doi: 10.1016/j.neuroimage.2024.120511. Epub 2024 Jan 5. |
| 37518942 | Background | Rakic M, Turco F, Weng G, Maes F, Sima DM, Slotboom J. Deep learning pipeline for quality filtering of MRSI spectra. NMR Biomed. 2024 Jul;37(7):e5012. doi: 10.1002/nbm.5012. Epub 2023 Jul 30. |
| ID | Term |
|---|---|
| D001928 | Brain Diseases, Metabolic |
| D016893 | Carotid Stenosis |
| D000544 | Alzheimer Disease |
| D003924 | Diabetes Mellitus, Type 2 |
| D060825 | Cognitive Dysfunction |
| D001927 | Brain Diseases |
| ID | Term |
|---|---|
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D002340 | Carotid Artery Diseases |
| D002561 | Cerebrovascular Disorders |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D003704 | Dementia |
| D024801 | Tauopathies |
| D019636 | Neurodegenerative Diseases |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D004700 | Endocrine System Diseases |
| D003072 | Cognition Disorders |
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