Jason Ford

Rapid diagnosis of Alzheimer's

News

VTT Technical Research Centre of Finland has developed a method for analysing MR images in the space of a few minutes when diagnosing Alzheimer’s disease.

The accuracy of the analysis is comparable to manual measurements made by skilled professionals, which are currently considered the most reliable method for diagnosing Alzheimer’s disease. The accurate and rapid analysis method is claimed to be well suited for clinical use.

Early detection of Alzheimer’s disease requires that the patient displays some other symptom or sign of Alzheimer’s disease in addition to memory problems. Such other symptoms include atrophy, namely the loss of brain cells, visible in MR images.

One of the first areas of the brain where atrophy can be detected is the hippocampus - the part of the brain that plays a major role in learning and memory. With VTT’s new computational method, the volume of the hippocampus can be accurately calculated automatically.

Currently, diagnosis of Alzheimer’s disease often makes use of visual assessment of MR images. Manual determination of brain structures in this way is a difficult task for the physician and the repeatability of results is typically poor. This has led to a high demand for objective methods. Earlier automatic systems for calculating the volume of the hippocampus are not in general clinical use because of deficiencies in speed and reliability.

Using VTT’s method, the assessment of MR images is said to take three minutes. With the fastest currently available automatic MR image-assessment methods, the assessment takes 15-20 minutes. However, VTT claims that it is not uncommon for assessments to last for several hours.

The method is part of a system that is currently being developed under the EU Predict AD project to help diagnose Alzheimer’s disease. The system will be completed in 2011. The aim of the project is to develop objective methods that are sufficiently accurate, reliable and fast for clinical use but do not require large investments in equipment.

Other organisations involved in developing the method include GE Healthcare and Imperial College London. The method is currently being tested to guarantee its operational reliability.