Learning a Unified Control Policy for Safe Falling

Being able to fall safely is a necessary motor skill for humanoids performing highly dynamic tasks, such as running and jumping. We propose a new method to learn a policy that minimizes the maximal impulse during the fall. The optimization solves for both a discrete contact planning problem and a continuous optimal control problem. Once trained, the policy can compute the optimal next contacting body part (e.g. left foot, right foot, or hands), contact location and timing, and the required joint actuation. We represent the policy as a mixture of actor-critic neural network, which consists of n control policies and the corresponding value functions. Each pair of actor-critic is associated with one of the n possible contacting body parts. During execution, the policy corresponding to the highest value function will be executed while the associated body part will be the next contact with the ground. With this mixture of actor-critic architecture, the discrete contact sequence planning is solved through the selection of the best critics while the continuous control problem is solved by the optimization of actors. We show that our policy can achieve comparable, sometimes even higher, rewards than a recursive search of the action space using dynamic programming, while enjoying 50 to 400 times of speed gain during online execution

DEVELOPMENT AND CLINICAL VALIDATION OF KNOWLEDGE-BASED PLANNING MODELS FOR STEREOTACTIC BODY RADIOTHERAPY OF EARLY-STAGE NON-SMALL-CELL LUNG CANCER PATIENTS

Lung stereotactic body radiotherapy (SBRT) is a viable alternative to surgical intervention for the treatment of early-stage non-small-cell lung cancer (NSCLC) patients. This therapy achieves strong local control rates by delivering ultra-high, conformal radioablative doses in typically one to five fractions. Historically, lung SBRT plans are manually generated using 3D conformal radiation therapy, dynamic conformal arcs (DCA), intensity-modulated radiation therapy, and more recently via volumetric modulated arc therapy (VMAT) on a C-arm linear accelerator (linac). Manually planned VMAT is an advanced technique to deliver high-quality lung SBRT due to its dosimetric capabilities and utilization of flattening-filter free beams to improve patient compliance. However, there are limitations in manual treatment planning as the final plan quality heavily depends on a planner’s skill and available planning time. This could subject the plan quality to inter-planner variability from a single institution with multiple planners. Generally, the standard lung SBRT patient ‘simulation-to-treatment’ time is 7 working days. This delays clinic workflow and degrades the quality of treatment by eliminating adaptive re-planning capabilities. There is an ongoing effort to automate treatment planning by creating a model library of previously treated, high-quality plans and using it to prospectively generate new plans termed model-based knowledge-based planning (KBP). KBP aims to mitigate the previously mentioned limitations of manual planning and improve clinic workflow. As part of this dissertation, lung SBRT KBP models were created using a commercially available KBP engine that was trained using non-coplanar VMAT lung SBRT plans with the final dose reported from an advanced Acuros-based algorithm. The dissertation begins with the development of a robust and adaptable lung SBRT KBP model for early-stage, centrally-located NSCLC tumors that is fully compliant with Radiation Therapy Oncology Group (RTOG)-0813 protocol’s requirements. This new model provided similar or better plan quality to clinical plans, however it significantly increased total monitor units and plan complexity. This prompted the development and validation of an automated KBP routine for SBRT of peripheral lung tumors via DCA-based VMAT per RTOG-0618 criteria. This planning routine helped incorporate a historical DCA-based treatment planning approach with a VMAT optimization automated KBP engine that helps reduce plan complexity. For both central and peripheral lung lesions, the validated models are able to generate high-quality, standardized plans in under 30 min with minimal planner effort compared to an estimated 129 ± 34 min of a dedicated SBRT planner’s time. In practice, planners are expected to meticulously work on multiple plans at once, significantly increasing manual planning time. Thus, these KBP models will shorten the ‘simulation-to-treatment’ time down to as few as 3 working days, reduce inter-planer variability and improve patient safety. This will help standardize clinics and enable offline adaptive re-planning of lung SBRT treatment to account for physiological changes errors resulting from improper patient set-up. Lastly, this dissertation sought to further expand these KBP models to support delivering lung SBRT treatments on a new O-ring linac that was recently introduced to support underserved areas and fast patient throughput. Despite learning from a C-arm modality training dataset, these KBP models helped the O-ring linac to become a viable treatment modality for lung SBRT by providing an excellent plan quality similar to a C-arm linac in under 30 min. These KBP models will facilitate the easy transfer of patients across these diverse modalities and will provide a solution to unintended treatment course disruption due to lengthy machine downtime. Moreover, they will relieve the burden on a single machine in a high-volume lung SBRT clinic. Further adaptation and validation of these KBP models for large lung tumors (\u3e 5 cm) with multi-level dosing scheme and synchronous multi-lesion lung SBRT is ongoing

Team-based learning for first year engineering students

Although it was originally developed for a business school environment to promote the benefits of small-group teaching in a large group setting, the method of the team-based learning (TBL) has recently been increasingly used within medical education. On the other hand, the reports on its implementation in engineering and science education are much scarcer. The aim of this work is to discuss the experience, evaluation and lessons learned from the implementation of the TBL within a Year 1 engineering module—Process Engineering Fundamentals, enrolling 115 students, and the TBL method was introduced for the first time. To evaluate the acquired knowledge and perception of TBL, a students’ performance analysis and questionnaire were completed on two occasions. It was observed that the TBL approach improved student learning, enhanced their integration and sharing of knowledge in class, supporting the implementation of this method in engineering disciplines

Solubility of mixtures containing soybean oil, ionic liquid and methanol

This paper presents data on mutual solubility of the binary (soybean oil + ionic liquid) and ternary (soybean oil + methanol + ionic liquid) systems, where ionic liquid stands for 1-butyl-3-methylimidazolium thiocyanate [C4MIM][SCN] or 1-butyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide [C4MIM][NTf2] or 1-butyl-3-methylimidazolium dicyanamide [C4MIM][DCA] or 1-butyl-3-methylimidazolium hexafluorophosphate [C4MIM][PF6] or 1-butyl-3-methyl imida zolium hydrogensulfate [C4MIM] [HSO4] or 1-decyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C10MIM][NTf2] or methyltrioctylammonium bis(trifluoromethylsulfonyl)imide [ALIQUAT][NTf2] or methyltrioctylammonium chloride [ALIQUAT][Cl]. Solubilities were determined by the cloud point titration method in the temperature range of 298 K to 343 K. Obtained results suggest that imidazolium based ionic liquids exhibit lower solubility in soybean oil than ionic liquids with the aliquat cation. Thus, aliquat based ionic liquids are good candidate to be used as co-solvents for biphasic (methanol + soybean oil) mixture

A Novel and Clinically Useful Dynamic Conformal Arc (DCA)-Based VMAT Planning Technique for Lung SBRT

PURPOSE: Volumetric modulated arc therapy (VMAT) is gaining popularity for stereotactic treatment of lung lesions for medically inoperable patients. Due to multiple beamlets in delivery of highly modulated VMAT plans, there are dose delivery uncertainties associated with small-field dosimetry error and interplay effects with small lesions. We describe and compare a clinically useful dynamic conformal arc (DCA)-based VMAT (d-VMAT) technique for lung SBRT using flattening filter free (FFF) beams to minimize these effects. MATERIALS AND METHODS: Ten solitary early-stage I-II non-small-cell lung cancer (NSCLC) patients were treated with a single dose of 30 Gy using 3-6 non-coplanar VMAT arcs (clinical VMAT) with 6X-FFF beams in our clinic. These clinically treated plans were re-optimized using a novel d-VMAT planning technique. For comparison, d-VMAT plans were recalculated using DCA with user-controlled field aperture shape before VMAT optimization. Identical beam geometry, dose calculation algorithm, grid size, and planning objectives were used. The clinical VMAT and d-VMAT plans were compared via RTOG-0915 protocol compliances for conformity, gradient indices, and dose to organs at risk (OAR). Additionally, treatment delivery efficiency and accuracy were recorded. RESULTS: All plans met RTOG-0915 requirements. Comparing with clinical VMAT, d-VMAT plans gave similar target coverage with better target conformity, tighter radiosurgical dose distribution with lower gradient indices, and dose to OAR. Lower total number of monitor units and small beam modulation factor reduced beam-on time by 1.75 min (P \u3c 0.001), on average (maximum up to 2.52 min). Beam delivery accuracy was improved by 2%, on average (P \u3c 0.05) and maximum up to 6% in some cases for d-VMAT plans. CONCLUSION: This simple d-VMAT technique provided excellent plan quality, reduced intermediate dose-spillage, and dose to OAR while providing faster treatment delivery by significantly reducing beam-on time. This novel treatment planning approach will improve patient compliance along with potentially reducing intrafraction motion error. Moreover, with less MLC modulation through the target, d-VMAT could potentially minimize small-field dosimetry errors and MLC interplay effects. If available, d-VMAT planning approach is recommended for future clinical lung SBRT plan optimization