3D liver maps using magnetic resonance imaging

How High-Throughput Compute helps to develop alternative, non-invasive techniques to diagnose and monitor liver fibrosis

Chronic liver disease is never good news and in about 20 percent of the cases, the condition escalates to severe inflammation and fibrosis, a kind of scarring of the liver’s tissue.

When this happens, it is very important to follow the progression of the disease and monitor the onset of fibrosis to guarantee the best treatment. Current practice is to monitor fibrosis with biopsies: medical interventions where a physician removes a tiny sample of the liver for analysis. The trouble with biopsies is that they are an uncomfortable and invasive procedure with many risks for the patient.

So what could be the alternative?

Olivier Beuf, a physicist working in the medical imaging field for over twenty years, believes that 3D imaging of the liver is part of the solution. He teamed up with PhD student Benjamin Leporq, radiologist Frank Pilleul and a group of computer scientists to create a non-invasive imaging technique to replace biopsies as a method to diagnose and monitor liver fibrosis. The challenge was to develop an imaging method that can be used in everyday clinical examinations.

The team started by collecting adetailed 3D magnetic resonance images from the liver of six patients and one healthy person to serve as control. From these images, they extracted physiological information, which was adjusted to a mathematical model using nonlinear least-square fit to determine 3D liver maps.

To speed things up, Olivier used EGI’s High-Throughput Compute solutions through the biomed virtual organisation. The results were, on average, 126 times faster than with conventional computing and allowed “a reliable estimation of 3D perfusion parameter maps of the whole liver in a reasonable processing time – hours compared to weeks,” he says.

The results, published in the Journal of Medical Engineering, show that detailed 3D magnetic resonance mapping of blood flow parameters is achievable with HTC today.

Magnetic resonance imaging.
Source: wikicommons

References

B. Leporq et al. (2013). Enabling 3D-Liver Perfusion Mapping from MR-DCE Imaging Using Distributed Computing. Journal of Medical Engineering (full text)