Project: MR Brain Image Quantification in Dementia
Project goal:_x000D__x000D_The aim of this project is to develop and evaluate software for the automated analysis of MR brain imaging data to (1) support the (early) detection and differential diagnosis of dementia such as Alzheimer's disease (AD), and (2) to quantify the effect of treatment strategies. This software has market applications both in the field of computer-aided diagnosis in the clinic and in supporting large clinical trials by providing outcome measures and means to perform subject selection._x000D__x000D_Background:_x000D__x000D_Dementia constitutes a major burden on society, both in monetary costs and the suffering of patients and their relatives. AD, the most common form of dementia, is one of the most devastating healthcare problems faced by western society. The ideal treatment will mostly likely require intervention in a pre-symptomatic stage, to prevent cognitive decline. In order to achieve this ideal treatment, it is first crucial to identify people at risk, those having the underlying specific pathology of the type of dementia for which treatment is available. Secondly, following disease detection and diagnosis, there are many candidate drugs that could transform treatment, but tools are needed to evaluate their efficacy on patients. _x000D__x000D_The proposed research project addresses both areas by developing a common methodology base for the analysis of MR brain imaging data. Recent research has shown that imaging techniques are the most promising of the available technologies to address both issues. Large prospective neuroimaging studies, both in selected patient groups and unselected populations, have provided insight into changes over time in health and disease. These studies have shown that the brain of patients with neurological diseases such as AD undergoes changes many years before the development of the first clinical symptoms. Brain changes that take place occur at the molecular (amyloid deposition, neurofibrillary tangles), cellular (hypometabolic changes due to loss of synapses), tissue (connectivity re-arrangement in the gray and white matter), and organ level (gray and white matter atrophy). In addition, brain vascular pathologies, such as white matter lesions and microbleeds have been associated with the presence and stage of disease. _x000D__x000D_Magnetic Resonance Imaging (MRI) is the most promising imaging technique to monitor brain changes in the preclinical and early stages of neurodegenerative disease, and among the most promising for monitoring treatment effects. This is due to its low cost, non-invasiveness, and its versatility. Structural MRI can be used for assessing global and regional atrophy, and assessment of volume and shape of the hippocampus, a structure affected early in the course of AD. In addition, dedicated MR imaging sequences have been developed to visualize white matter lesions, microbleeds and enlarged Virchow Robin spaces. Diffusion tensor MRI has been used to study axonal and myelin integrity in the white matter, which represent subtle tissue degeneration. Recently, quantitative measures derived from structural MRI have been established as key biomarkers for diagnosis of AD; in 2011, the NINCDS-ADRDA criteria for diagnosis of AD, originally published in1984, was updated for the first time, and one of the major changes was the incorporation of biomarkers from positron emission tomography (PET), cerebrospinal fluid (CSF), and structural MRI._x000D__x000D_However, the widespread use of structural MRI for disease detection, diagnosis and therapy evaluation purposes, requires the availability of efficient, accurate, robust, and reproducible software which can extract relevant quantitative imaging parameters (so called "quantitative imaging biomarkers" (QIBs)) from the data. In this project, software that meets the requirements for the robust and accurate extraction of QIBs relevant for the (early) detection and differential diagnosis of dementia, and for the evaluation of the effects of treatment, will be developed and evaluated. In addition, most QIBs are currently studied in isolation. In this project we will investigate the potential of a combination of QIBs. _x000D__x000D_The utility, accuracy, and usability of the software will be evaluated in collaboration with a clinical P. The software will then be introduced into the market in two manners: _x000D__x000D_1) The software will be delivered to the CO medical imaging companies (business-to-business model) for incorporation into diagnostic workstations or PACS (Picture Archiving and Communication System)-based solutions. Quantib, the lead applicant, already has a contract with a CO medical imaging company to deliver such software._x000D_2) The standardized quantitative analysis will be delivered as a service. The other SME involved in the project, Biomediq, already has experience in the introduction of QIBs in clinical trials conducted by the pharmaceutical industry and has contracts with one of the major clinical research orgnizations (CROs) performing clinical trials.
|
Acronym
|
BrainIQ
(Reference Number: 8234)
|
|
Duration
|
01/01/2014 - 31/12/2016
|
|
Project Topic
|
Software is developed and evaluated for the automated quantitative analysis of MR brain images, to support early and differential diagnosis of dementia, and to monitor the effect of treatment strategies. This software will find applications in the diagnostic workstation and clinical trial market.
|
|
Network
|
Eurostars
|
|
Call
|
Eurostars Cut-Off 10
|
Project partner
