Speeding up the preparation of high precision radiotherapy treatment

The challenge of delivering high precision radiotherapy

Radiotherapy is an essential aspect of cancer treatment, responsible for destroying the cancer in 40% of those patients who are cured. It makes a greater contribution to overall survival than chemotherapy. This project will build upon STFC-funded research to deliver a high performance, service-based computing solution, ready for use with existing treatment platforms, and validate this solution in partnership with a leading technology supplier, Siemens Healthcare.

Whether treatment is implemented with X-rays or particle beams, a common characteristic is increased precision in dose delivery. The ability to target only the cancerous tissue has immediate benefits in terms of both survival and quality of life.

High precision radiotherapy treatment comes at a cost, in terms of manual, labour-intensive treatment planning. Before a patient can be treated, a radiation oncologist has to analyse the results of imaging with multiple modalities - CT scans, X-rays, MRI scans and PET images - delineate the tumour, and all adjacent anatomical structures before the planning physicist can calculate the optimal selection of beam angles and intensities. They must take account also of the movement of tumours and organs during radiotherapy. Furthermore, as tumour volume and position will change over time, this analysis would ideally be repeated for each radiotherapy session

To translate the research successes in novel radiotherapy into routine, medical practice, we need to provide computing support for the treatment planning process, building upon recent advances in image analysis - also funded by STFC - and the progress made in metadata-driven data management and integration.

The scale of the computational problem

In multimodal imaging, the data throughput for treatment planning objects is modest: a typical image data set for one patient receiving high precision skull base radiotherapy requires 300Mb of storage space; if the patient is undergoing daily imaging to verify the correct position of the tumour target, the verification image data for one patient is 2.3Gb. For a facility treating 600 patients per year with high precision image-guided radiotherapy, this would generate a data set of 1.4Tb per year for each institution. In order to perform a complete multiple timepoint image registration for a dataset of this size in near real time requires 16 Teraflops of processing power (approximately 100 times the power of a standard PC workstation). To undertake this kind of processing in a clinical environment - and the refinement and optimization of the algorithms involved - will require the application of techniques developed for Grid computing. In due course, these algorithms will be implemented on the newer generation of multi-core workstations and GPGPU.

Work programme and deliverables

We will produce working open source software tools to be used by radiation oncologists and therapy radiographers, to complement existing radiotherapy treatment planning and delivery tools. Our key points for software development:

• - Establish a clinical community
• - Re-factor effective data mining and warehousing tools
• - Develop grid-based solutions to address computation and data sharing issues.
• - Industrial valorisation of the project ab-initio
• - PLM & Software Development Plan

The project team

University of Cambridge: Andy Parker, Neil Burnet, Raj Jena, Mark Hayes & Michael Simmons

University of Oxford: Jim Davies, Steve Harris, Ken Peach and Charles Crichton