Computational challenges for image-guided radiation therapy: framework and current research.
It is arguable that the imaging and delivery hardware necessary for delivering real-time adaptive image-guided radiotherapy is available on high-end linear accelerators. Robust and computationally efficient software is the limiting factor in achieving highly accurate and precise radiotherapy to the constantly changing anatomy of a cancer patient. The limitations are not caused by the availability of algorithms but rather issues of reliability, integration, and calculation time. However, each of the software components is an active area of research and development at academic and commercial centers. This article describes the software solutions in 4 broad areas: deformable image registration, adaptive replanning, real-time image guidance, and dose calculation and accumulation. Given the pace of technological advancement, the integration of these software solutions to develop real-time adaptive image-guided radiotherapy and the associated challenges they bring will be implemented to varying degrees by all major manufacturers over the coming years.
Xing
Lei
L
Department of Radiation Oncology, Stanford University, Stanford, CA 94305-5304, USA.
Siebers
Jeffrey
J
Keall
Paul
P
D000465:Algorithms; D003952:Diagnostic Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009369:Neoplasms / Q000532:radiotherapy*; D011878:Radiotherapy / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software