QMENTA-research

Simplify and reduce the costs of running your neurology and neuroimaging research studies

Execute your innovative research and preclinical studies, collect and manage your data, apply and develop biomarker tools, and seamlessly collaborate with your multi-center team, all in a single, easy-to-use cloud software solution.

 

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Purpose-built by and supported by our neuroimaging experts, our solutions flexibly support your research with simple yet highly customizable functionality meeting the distinct needs of your project and team.

80%

Time savings by automated advanced image analysis (1)

75%

Cost savings in imaging collection and storage (2)

Neurology research doesn't have time or money to waste, and when its comes to neuroimaging data and analysis, the highest levels of quality, interoperability and collaboration is key.

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Costs

Many research facilities face significant budgetary constraints and lack access to state-of-the-art image processing techniques and scalable local infrastructures with high computing power for their research projects.

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Time

Researchers are often forced to spend weeks on manual data preparation and management, and conduct image analysis using local tools and infrastructure that are rarely user-friendly, scaleable, or  aid in reproducability of results.

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Collaboration

Sharing data, methods, results and insights are the key to the advancement of research, however few investigators have the technical means to effectively and securely  collaborate with their disparate project members.

QMENTA's expert data management and advanced analytics enable your team to focus their time and expertise on their critical research outcomes

Easily Validate Your Own Advanced Biomarkers or Use Our Library of Neurological Disease AI Biomarker Tools

QMENTA Neuro Research Suite users get access to the newest & most widely used neuroimaging AI Biomarker Tools to provide quantitative and longitudinal evidence for projects as well as other developer tools. Scalable and convenient use of the algorithms saves valuable time, accelerating the research workflow. Select neurology analytical tools from a wide range of neurological diseases in our searchable interface or use your own. Leverage our neuroimaging library of 10M+ scans for reference or to train new AI alogrithms. 

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AI-powered Data Uploader

  • Simply and easily upload and store your neuroimaging and associated data.
  • Work with a full range of data types and formats (imaging, clinical data, metadata). 
  • Easily upload with browser "drag and drop", batch processing PACS Nexus, Python API capabilities.
  • Eliminate manual & time-consuming data and image aggregation work,  with automated modality recognition, classification, de-identification.

Automated Visual Reports

  • Easily review and share analysis results with a visual and concise tabular exportable PDF report.
  • Reports include image slices at different orientations (coronal, sagittal, etc.), clearly marked lesions or damage, and tables compiling the main quantitative outputs including  volumes of different regions, number of lesions or longitudinal changes.
  • Simply export quantification results in CSV format.

Custom-made Research Setup

  • Optimize your study setup with customizable workflows.
  • Enhance global  collaboration with customizable access & location settings and easily share the highest quality data.
  • Receive neuroimaging expert support on all aspects of your research projects or studies.

SDK Software Development Kit

  • QMENTA’s SDK, a special environment for neuroscience research users, allows experts to easily develop, test, and run their algorithms in QMENTA’s scalable, secure, and compliant cloud environment, eliminating the need for expensive local computing resources.
  • Share, benchmark and validate algorithms while increasing visibility and IP transfer.

Interested to know more?

Get in touch with our team for a demo meeting or sign up and try it for free


1. Comparing hours spent by radiologist vs. tool compute time
2. QMENTA calculation compared to cost of local computational resources