We apply a new hardware and software platform called the Hamiltonian Engine for Radiotherapy Optimization (HERO) to the problem of Intensity-Modulated Radiation Therapy (IMRT) treatment planning. HERO solves large generalform binary optimization problems by decomposing them into sub-problems and approximating them using a quadratic pseudo-boolean function. Optimizing the resulting function becomes a quadratic unconstrained binary optimization (QUBO) problem, which has been widely studied and has numerous applications in various fields. A Quantum Annealer (QA) approach has been previously investigated to solve QUBO problems, including IMRT optimization. However, the QA can only accommodate a small number of variables and requires several hours to obtain optimized plans. HERO acts as an optimizer for QUBO problems, which not only addresses these shortcomings but also relies solely on conventional hardware design while operating at room temperature. We evaluate HERO on seven prostate IMRT cases with clinical objectives, each using approximately 6000 beamlets. Our method was compared to the commercial treatment planning software, Eclipse, for both time-to-solution and plan quality. HERO solves most cases in about 30 seconds, with significantly lower objective function scores than Eclipse. The results indicate that HERO is promising for radiation therapy optimization problems. Additionally, HERO has the potential to be applied to Volumetric-Modulated Arc Therapy (VMAT) and other complex types of treatment planning.
Gamma knife (GK) radiosurgery is a non-invasive treatment modality which allows single fraction delivery of focused radiation to one or more brain targets. Treatment planning mostly involves manual placement and shaping of shots to conform the prescribed dose to a surgical target. This process can be time consuming and labour intensive. An automated method is needed to determine the optimum combination of treatment parameters to decrease planning time and chance for operator-related error. Recent advancements in hardware platforms which employ parallel computational methods with stochastic optimization schemes are well suited to solving such combinatorial optimization problems efficiently. We present a method of generating optimized GK radiosurgery treatment plans using these techniques, which we name ROCKET (Radiosurgical Optimization Configuration Kit for Enhanced Treatments). Our approach consists of two phases in which shot isocenter positions are generated based on target geometry, followed by optimization of sector collimator parameters. Using this method, complex treatment plans can be generated, on average, in less than one minute, a substantial decrease relative to manual planning. Our results also demonstrate improved selectivity and treatment safety through decreased exposure to nearby organs-at-risk (OARs), compared to manual reference plans with matched coverage. Stochastic optimization is therefore shown to be a robust and efficient clinical tool for the automatic generation of GK radiosurgery treatment plans.
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