ARC Grant for Message Lab - BioMedical Imaging

September 2007: Message Lab was awarded an ARC Linkage Project entitled A high throughput Grid based environment for real time bio-medical imaging with funding of $570,000 over 2008-2010.

Since the advent of the microscope, the development and sophistication of imaging technologies have set the pace in life-sciences, and the ability to “distinguish” smaller and smaller structures continues to define the foundation for biological, bio-medical and bio-technological research. Emerging ultra-high resolution microscopes now allow us to monitor individual molecules in the context of a living cell. Already, off-the-shelf technologies provide real-time, non-invasive images of microscopic bio-medical phenomena such as the binding of anti-cancer drugs to individual tumour cells, or the trafficking of individually-marked metastasizing tumour cells in and out of blood vessels. However, we expect that both the spatial and temporal resolution of the instruments, and the associated data volumes and rates, will increase dramatically over the next 5 to 10 years. Moreover, our understanding of the underlying cell biology will demand increasingly sophisticated image processing and data mining techniques.

Currently, commercial microscopes are predominantly stand alone instruments, controlled by dedicated computer systems that can only provide limited storage and processing capabilities. Routinely, the experimenter interacts directly with the microscope, which captures and stores digital images on a local computer disk. The data is then transferred manually to various computer and storage devices for analysis, mining, archiving and visualization. Increasing data volumes are already beginning to create a bottleneck in which the processing times exceed the data acquisition times, significantly impeding real time image visualisation and interpretation. Modern image analysis techniques (such as volume rendering), and data mining software, require multi-processor computer systems for adequate performance and large data stores for file storage, but these require a range of ad-hoc and complicated methods involving meta-data capture, file transport, job submission and data archiving and visualization

To address this challenge we envisage the seamless integration of image capturing hardware and data analysis software into a wide area network of high performance computers, large storage devices and software systems, generating a ‘virtual’ multi-modal instrument. The required software has to meet the demands of advancing biomedical research, computational infrastructure and instrument development

In this research we aim to address the upcoming challenges in life-science imaging by advancing, leveraging, and merging, state of the art developments in microscope technologies and Grid computing.