Deformable image registration for radiation therapy planning: Algorithms and applications

Approximately 60% of cancer patients are treated with external beam radiotherapy at some point during disease management. Despite the extended time frame of fractionated therapy (4-6 weeks), radiation therapy planning is carried out based on information that is currently limited to a single 3D anatomical computed tomography scan at the onset of treatment. This concept may result in severe treatment uncertainties, including the irradiation of risk organs and reduced tumor coverage. Repeat 3D single or multi-modality imaging acquired at various time intervals during and after a radiation course provides the opportunity to increase treatment accuracy and precision by optimizing treatment in response to anatomical changes; to improve target delineation through modality-specific complementary tumor representations, and to assess treatment response. Integration of multiple imaging sources into a single patient model requires compensation of geometric differences while maintaining modality-specific differences in information content. Deformable image registration aims to reduce such uncertainties by estimating the spatial relationship between the volume elements of corresponding structures across image data. This paper reviews the algorithmic components of deformation algorithms, and their application to treatment sites with evident geometric changes, including mono- and multi-modal image registration for cancer of the head and neck, lung, liver, and prostate.