QMENTA Partners With Vanderbilt University Medical Center On MEMENTO Challange

Contributes Unique Medical Imaging Platform to Transform Brain Research

 

Barcelona, Spain. 31 March 2020. For immediate release. QMENTA, a leading innovator in cloud-based, AI solutions for medical imaging, today announced it is partnering with Vanderbilt University Medical Center (VUMC) on the MEMENTO Challenge, an investigation into White Matter Reconstruction and Diffusion MRI. In the partnership, QMENTA is providing its AI-powered, cloud-based platform to store and manage all imaging data and to facilitate analyses, thus allowing research participants from around the globe to focus on developing insights and innovative solutions, not on managing data. 

QMENTA’s partnership with VUMC follows last year’s successful IronTRACT Challenge partnership with Harvard University, which involved 12 research teams from around the world competing to develop the technique of tractography to provide an objective assessment of the accuracy of brain pathways as reconstructed with diffusion MRI tractography, by direct comparison to chemical tracing in the same brain. 

Currently, numerous barriers inhibit the research community’s ability to utilize the complex and richly detailed Medical Images needed in the fight against brain disease. The data collection and management process is highly fragmented, manual, and tedious, which not only requires researchers to spend the majority of their time on data administration but also limits the sharing of images and the associated collaboration between institutions across geographies and time zones. As a result, brain research is slower and more cumbersome than it needs to be.

“In this challenge, QMENTA’s platform allows us to bring groups from around the world together to confront an important brain research topic – how to better understand the relationship between the MRI signal and the underlying tissue microstructure. QMENTA facilitates the investigation into how algorithms perform on standardized data and provides an easy to use interface with leaderboards and real-time results to get everyone on the same page in identifying and tackling relevant problems and challenges in the community.”  Bennet Landman, Associate Professor of Electrical Engineering at Vanderbilt, said.

The Challenge is focused on White Matter Reconstruction and is split into 3 sub-challenges. Derived from the growing interest in Diffusion MRI, the objective of the challenge is to validate the growing number of techniques that have been recently developed to explore the microstructure of White Matter. Pathological changes in tissue microstructure are important targets for measurement to better understand and monitor neurological diseases such as Alzheimer’s, Parkinson’s and others. 

Pablo Villoslada, QMENTA Chief Medical Officer, said:

“The QMENTA platform’s capabilities to accelerate and streamline the upload, integration, visualization, and analysis of medical imaging data empower participants to focus on the challenge and the creation of innovative techniques rather than manual data processing.  The power of these tools to enable researchers to not only extract greater insights but also save tremendous time in the process will be extremely valuable for the development of new imaging solutions in medical care and research in the near future. We are extremely proud to be able to contribute to the fight against brain disease and to the advancement of the Medical Imaging industry.” 

 

About QMENTA

QMENTA transforms the speed, accuracy, scalability, and cost-effectiveness of diagnostic neuroimaging to accelerate the conquest of brain diseases.  

Founded in 2013, the company provides a best-in-class, cloud-based data management platform that houses its proprietary AI-powered biomarker algorithms as well as a marketplace of partnered algorithms.  The platform streamlines the highly fragmented and tedious process of managing, harmonizing, and sharing complex neuroimaging data.  The broad array of algorithms provide objective, highly precise, and reproducible quantification of a variety of pathologies that today’s subjective image reading approaches cannot match.  QMENTA Trials was developed for Biopharmaceutical companies to accelerate the development of new therapies for neurological diseases by streamlining the use of neuroimages in clinical trials.   QMENTA Labs enables universities and global research consortia to easily standardize, curate, and analyze massive levels of neuroimaging data in one place.  Together, QMENTA’s integrated solutions empower researchers and clinicians to harness complex neuroimaging data with certainty and speed, leading to faster and more efficient drug development and better-informed patient diagnosis and treatment decisions. 

Headquartered in Boston USA with an office in Barcelona, QMENTA consists of an international team of neuroimaging, software, and deep learning experts.

 

About Vanderbilt University Medical Center

Vanderbilt University Medical Center (VUMC) is one of the nation’s leading academic medical centers and is the largest comprehensive health system in Tennessee. Its core missions are the delivery of patient care, performing biomedical research and training future leaders in healthcare. VUMC is the recipient of top accolades by the National Academies, the Magnet Recognition Program, the Leapfrog Group, and has been named a Top Hospital by Truven Health Analytics 14 times. In 2018, U.S. News & World Report named VUMC to the ‘Honor Roll’ of the nation’s top 20 hospitals with 10 nationally-ranked adult specialty programs. In 2018, U.S. News also named the Monroe Carell Jr. Children’s Hospital at Vanderbilt among the nation’s ‘Best Children’s Hospitals’ with 10 out of 10 pediatric specialty programs nationally ranked. In 2018, VUMC experienced more than 2.2 million patient encounters and provided more than $613 million in charity care and other community benefits to persons in Middle Tennessee. For more information and the latest news follow VUMC on Facebook, LinkedIn, Twitter, and in the VUMC Reporter.

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