Browsing by Author "Li, Xinyi"

Purpose: To develop an automatic collimator setting optimization algorithm to improve dosimetric quality of pancreas Volumetric Modulated Arc Therapy (VMAT) plans for Stereotactic Body Radiation Therapy (SBRT).

Methods: Fifty-five pancreas SBRT cases were retrospectively studied. Different from the conventional practice of initializing collimator settings manually, the proposed algorithm simultaneously optimizes the collimator angles and jaw positions which are customized to the patient geometry. This algorithm includes two key steps: an iterative optimization algorithm via simulated annealing that generates a set of collimator settings candidates, and a scoring system that choose the final collimator settings based on organs-at-risk (OARs) sparing criteria and dose prescription. The scoring system penalizes 3 factors: 1) jaw opening ratio on Y direction to X direction; 2) unmodulated MLC area within the jaw aperture in a dynamic MLC sequence; 3) OAR shielding capability by MLC with MLC aperture control constraints. For validation, the other 16 pancreas SBRT cases were analyzed. Two dual-arc plans were generated for each validation case, an optimized plan (Planopt) and a conventional plan (Planconv). Each plan was generated by a same set of auxiliary planning structures and dose-volume-histogram (DVH) constraints in inverse optimization. Dosimetric results were analyzed and compared. All results were tested by Wilcoxon signed-rank tests.

Results: Both plan groups had no statistical differences in target dose coverage V95% (p=0.84) and Root Conformity Index (p=0.30). Mean doses of OARs were improved or comparable. In comparison with Planconv, Planopt reduced maximum dose (D0.03cc) to stomach (-49.5cGy, p=0.03), duodenum (-63.5cGy, p<0.01), and bowel (-62.5cGy, p=0.01). Planopt also showed lower modulation complexity score (p=0.02), which implies its higher modulation complexity of the dynamic MLC sequence.

Conclusions: The proposed collimator settings optimization algorithm successfully improved dosimetric performance for dual-arc VMAT plans in pancreas SBRT. The proposed algorithm was demonstrated with great clinical feasibility and readiness.

Artificial intelligence (AI) has been rapidly developing in various fields, featuring automation in complex tasks with superior efficiency. This feature meets the urgent need for the automation of resource-intensive tasks in clinics. In radiation oncology, AI has been investigated for almost every process in patient management and treatment. Among these, radiotherapy treatment planning is one of the most time-consuming and labor-intensive processes. This dissertation work focuses on AI-based planning agents for intensity-modulated radiation therapy (IMRT) for various treatment sites. Fluence map prediction for prostate simultaneous integrated boost (SIB) or Stereotactic Body Radiotherapy (SBRT) cases was selected for a feasibility study. Prostate cases have one of the most consistent anatomic geometries and dosimetric constraints among all treatment sites. The developed prostate AI planning agent employed a customized convolutional neuro network (CNN), Dense-Res Hybrid Network (DRHN). DRHN was trained to predict optimal fluence maps from patient anatomic information. The proposed method avoids the time-consuming inverse planning process and thus could make fluence map predictions in seconds and generate IMRT plans in a few minutes. The resulting AI plan quality met institutional clinical guidelines. This preliminary study demonstrated the feasibility of the proposed AI strategy in automatic treatment planning and provided a solid foundation for the following studies. As a step forward, a more sophisticated AI agent for oropharyngeal cases was developed based on the prostate AI agent. This AI agent had the following two upgrades to adapt to the much more complex geometry in head-and-neck (H&N) treatment site: 1) conditional generative adversarial networks (cGAN) training architecture; 2) the generator, PyraNet, was a customized CNN network with more complicated network structure design in the shape of pyramids. This H&N AI agent demonstrated encouraging plan quality, especially that organs-at-risk (OAR) dosimetric outcomes achieved expectations. A graphical user interface (GUI) was developed and commissioned to make this AI tool available for clinical implementation. In summary, a DL-based fluence map prediction was developed for prostate and H&N cases. The H&N AI agent was implemented for clinical use, and more related research and applications are around the corner.