Optimizing 4D Cone Beam Computed Tomography Acquisition by Varying the Gantry Velocity and Projection Time Interval

Four Dimensional Cone Beam Computed Tomography (4DCBCT) is an emerging clinical image guidance strategy for tumour sites affected by respiratory motion. In current generation 4DCBCT techniques, both the gantry rotation speed and imaging frequency are constant and independent of the patient's breathing which can lead to projection clustering. We present a Mixed Integer Quadratic Programming (MIQP) model for Respiratory Motion Guided-4DCBCT (RMG-4DCBCT) which regulates the gantry velocity and projection time interval, in response to the patient's respiratory signal, so that a full set of evenly spaced projections can be taken in a number of phase, or displacement, bins during the respiratory cycle. In each respiratory bin, an image can be reconstructed from the projections to give a 4D view of the patient's anatomy so that the motion of the lungs, and tumour, can be observed during the breathing cycle. A solution to the full MIQP model in a practical amount of time, 10 seconds, is not possible with the leading commercial MIQP solvers, so a heuristic method is presented. Using parameter settings typically used on current generation 4DCBCT systems (4 minute image acquisition, 1200 projections, 10 respiratory bins) and a patient with a four second breathing period, we show that the root mean square (RMS) of the angular separation between projections with displacement binning is 2:7 degrees using existing constant gantry speed systems and 0:6degrees using RMG-4DCBCT. For phase based binning the RMS is 2:7degrees using constant gantry speed systems and 2:5 degrees using RMG-4DCBCT. The optimization algorithm presented is a critical step on the path to developing a system for RMG-4DCBCT

Metaheuristic Algorithm for Constrained Optimization in Radiation Therapy Treatment Planning: Design and Performance Comparison

Radiation Therapy (RT) plays a pivotal role in the treatment of cancer, offering the potential to effectively target and eliminate tumour cells while minimizing harm to surrounding healthy tissues. However, the success of RT heavily depends on meticulous treatment planning that ensures the optimal balance between delivering a sufficiently high dose to the tumour and sparing nearby critical organs. This critical process demands a multidisciplinary approach that combines medical expertise, advanced imaging techniques, and computational tools. Optimization techniques have emerged as indispensable tools in refining RT planning, enabling the precise adjustment of radiation beam arrangements and intensities to achieve treatment objectives while adhering to strict dose constraints. This study delves into the realm of constrained optimization within RT Treatment Planning, employing metaheuristic algorithms to enhance the efficacy of this process. The research focuses on the design and performance comparison of three prominent optimization techniques: Bat Search Optimization (BSO), Bacterial Foraging Algorithm (BFA), and Artificial Bee Colony (ABC). Through systematic evaluation, it is observed that BFA exhibits superior execution time and convergence capabilities in comparison to the other algorithms. This research underscores the crucial importance of RT planning and highlights the imperative need for optimization methodologies to achieve optimal treatment outcomes.Comment: 7 pages, 7 figure