Testing begins for MRI/PET diagnosis of neurodegenerative diseases

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The University Medical Center Hamburg-Eppendorf (UKE) and Royal Philips Electronics have developed a computer aided diagnosis (CAD) system for neurodegenerative diseases to support clinicians in diagnosing the onset and type of disease as early as possible. The new diagnostic technique, which has already proven its accuracy using historical image data and known patient outcomes, is about to undergo clinical evaluation at UKE.

The CAD system is a software package that automatically interprets PET brain scans of patients suspected of having a neurodegenerative disease that leads to dementia, and combines them with MRI scans for accurate differential diagnosis. The goal is earlier prescription of drugs that delay progression of the disease, particularly the most debilitating later stages. It will also provide pharmaceutical companies and clinicians with a valuable tool for the development and testing of new, potentially curative drugs for neurodegenerative diseases such as Alzheimer’s disease.

As world populations of older-age groups increases, dementia is widely expected to reach epidemic proportions unless effective treatments are found for it.

“Building on our expertise in multi-modal diagnostic imaging, we’ve combined functional and structural brain-scan information into a fully integrated and easy to use system for diagnosing the principal neurodegenerative diseases that cause dementia,” said Dr. Lothar Spies, head of the digital imaging department at Philips Research. “Ultimately, it will enable early treatment and highly personalized therapies.”

The software tool developed by Philips Research and UKE accurately overlays anatomical images of the brain obtained from MRI scans with PET scans that display brain activity – specifically the uptake of glucose that fuels brain activity. By using advanced image processing and computer learning techniques in combination with a database of reference brain-scans, the system then analyzes the images automatically and displays anomalous brain patterns in a concise way. Based on these patterns, it then suggests a diagnosis. As a result, the system will help less experienced doctors to achieve the same diagnostic accuracy as highly trained specialists.