Comparative Assessment of Inverse versus Convolution Treatment Planning Algorithms used in Gamma Knife

Abstract

Background: This study aims to assess the inverse gamma knife algorithm compared to the convolution planning algorithms for a brain tumor in patients treated with the Gamma Knife Iconâ. Materials and Methods: Sixty patients with benign or malignant brain tumors treated with a gamma knife Icon. Cone Beam computerized Scan (CBCT) is used to scan the brain. The patient had a 3.0 Tesla MRI (Philips Achieva model) to get a brain anatomical view. A patient's mask was installed. Neurosurgeons outline the tumor and plan the prescribed dosage. The medical physicist used collimator size, beam angle, radiation weighting dosage, and grid size to optimize the target dose and decrease Organ at Risk (OAR) exposure. First, convolution used, then advanced inverse. The neurosurgeon approves a better patient plan based on tumor and surrounding tissue dosage and evaluation parameters: coverage, selectivity, gradient index (GI), and Paddick conformance index (PCI). Results: The dosage administered to the tumor indicates that the inverse planning method is better than the convolution planning technique. The organs at risk (OAR) engaged in this investigation are the left and optic nerve, brain stem, and pituitary gland. The maximal dosage of the left and right optic nerves reveals no significant variation between the inverse and convolution techniques. While the minimum and mean doses of left and right optic nerves are exposed to radiation much greater in convolution than the inverse planning. The inverse method's maximum and mean brain stem doses were substantially greater than the convolution algorithm, but the lowest dosage was not significantly different. Inverse planning protects the pituitary better than convolution. The convolution algorithm is superior to the inverse algorithm for producing a high selectivity index, the Paddick conformity index (PCI). The inverse algorithms had a higher selectivity, GI, and PCI than the convolution. The convolution shows significantly better coverage and less treatment time. Conclusion: The gamma inverse planning algorithm may be optimum for treatment planning cancers with intact vital tissue such as optic nerve, brain stem, or pituitary glands, whereas the convolution method is preferred for tumors with an appropriate distance from other brain-sensitive structures. It may help cover targets effectively without irradiating OARs.