METLiT MRS AI Solution
Easier, Faster and Reliable
MRS AI Expert
METLiT MRS AI Solution
METLiT develops an automated MRS solution combining the conventional method with
deep learning-based quantification, effectively reducing MRS scan time and processing time.
With METLiT, MRS becomes a readily available imaging modality.
MRS AI Solution automatically analyzes Metabolite Information from MRS data for Diagnosis, Treatment Prognosis, or Medical Researches.
Even in the absence of MRS experts,
METLiT MRS Suite can effectively support your metabolite analysis.
01
Precise Analytic
Performance
Deep-learning based quantification enables powerful analysis of metabolites up to 17 even in a low-quality dataset.
02
Enhanced
Workflow
Fully automated analysis process operates with drag & drop;
Reducing MRS scan time and analytic time by 50% and 95%.
03
Seamless
Integration
Provided as a cloud-based SaaS solution, METLiT AI supports any standard MRI from GE, Philips, Siemens.
"More metabolites, More dimensions."
Conventional
Method
METLiT AI
Brain
5 Metabolites
17 Metabolites
(Including Glutamate, Glutamine, GABA, GSH)
Liver
Water to fat
ratio Only
Fat + Metabolites
(Major inhibitory neurotransmitter)
Reliability of GABA quantification results according to data quality change in the same MRS spectrum.
*Hyeonghun Lee and Hyeonjin Kim. "Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain." Magnetic resonance in medicine 82.1 (2019): 33-48.
*The product is in developing process. Contact METLiT if you have questions about the availability of the products in your area.
PRODUCTS & SERVICES
We collaborate with various institutions across
clinical research, clinical trials, and AI researches.
Pharmaceutical / Institution
-
MRS Study Design
-
Advanced MRS Analytic Service
-
Scrutinized Patient Selection
-
Detailed Patient Monitoring
Hospitals
-
Cloud-based SaaS MRS AI solution
-
Automated Processing & Quantification
-
Interface with Intuitive and Ease of Use
-
Metabolite information from up-to-date clinical research
