title: Dynamic gamma knife radiosurgery.
abstract: Gamma knife has been the treatment of choice for various brain tumors and functional disorders. Current gamma knife radiosurgery is planned in a 'ball-packing' approach and delivered in a 'step-and-shoot' manner, i.e. it aims to 'pack' the different sized spherical high-dose volumes (called 'shots') into a tumor volume. We have developed a dynamic scheme for gamma knife radiosurgery based on the concept of 'dose-painting' to take advantage of the new robotic patient positioning system on the latest Gamma Knife C and Perfexion units. In our scheme, the spherical high dose volume created by the gamma knife unit will be viewed as a 3D spherical 'paintbrush', and treatment planning reduces to finding the best route of this 'paintbrush' to 'paint' a 3D tumor volume. Under our dose-painting concept, gamma knife radiosurgery becomes dynamic, where the patient moves continuously under the robotic positioning system. We have implemented a fully automatic dynamic gamma knife radiosurgery treatment planning system, where the inverse planning problem is solved as a traveling salesman problem combined with constrained least-square optimizations. We have also carried out experimental studies of dynamic gamma knife radiosurgery and showed the following. (1) Dynamic gamma knife radiosurgery is ideally suited for fully automatic inverse planning, where high quality radiosurgery plans can be obtained in minutes of computation. (2) Dynamic radiosurgery plans are more conformal than step-and-shoot plans and can maintain a steep dose gradient (around 13% per mm) between the target tumor volume and the surrounding critical structures. (3) It is possible to prescribe multiple isodose lines with dynamic gamma knife radiosurgery, so that the treatment can cover the periphery of the target volume while escalating the dose for high tumor burden regions. (4) With dynamic gamma knife radiosurgery, one can obtain a family of plans representing a tradeoff between the delivery time and the dose distributions, thus giving the clinician one more dimension of flexibility of choosing a plan based on the clinical situations.
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Shuang
authors.initials: S
authors.lastname: Luan
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Nathan
authors.initials: N
authors.lastname: Swanson
authors.affiliation: Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Radiation Oncology, University of California San Francisco, San Francisco, CA 94143, USA.
authors.forename: Lijun
authors.initials: L
authors.lastname: Ma
mesh_terms: D009068:Movement; D009369:Neoplasms / Q000503:physiopathology / Q000601:surgery; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D016634:Radiosurgery / Q000379:methods*; D013997:Time Factors