RESUMO
Tumor cryoablation is a clinical procedure where supercooled probes are used to destroy cancerous lesions. Cryoablation is a safe and effective palliative treatment for skeletal metastases, providing immediate and long term pain relief, increasing mobility and improving quality of life. Ideally, lesions are encompassed by an ice ball and frozen to a sufficiently low temperature to ensure cell death. "Lethal ice" is the term used to describe regions within the ice ball where cell death occurs. Failure to achieve lethal ice in all portions of a lesion may explain the high recurrence rate currently observed. Tracking growth of lethal ice is critical to success of percutaneous ablations, however, no practical methods currently exist for non-invasive temperature monitoring. Physicians lack planning tools which provide accurate estimation of the ice formation. Simulation of ice formation, while possible, is computationally demanding and too time consuming to be of clinical utility. We developed the computational framework for the simulation, acceleration strategies for multicore Intel x86 and IBM Cell architectures, and performed preliminary validation of the simulation. Our results demonstrate that the streaming SIMD implementation has better performance and scalability. Both accelerated and non-accelerated algorithms demonstrate good agreement between simulation and manually identified ice ball boundaries in phantom and patient images. Our results show promise for the development of novel cryoablation planning tools with real-time monitoring capability for clinical use.
