Development of A High Throughput Image-Guided IMRT System forPreclinical Research

Project Summary/Abstract Preclinical radiobiology experiments on small animals are crucial to test the safety and efficacy before human clinical trials. However, limited by currently available technologies, preclinical animal studies substantially differ from state-of-the-art human treatments in dose conformity. Consequently, the animal studies poorly mimic the

radiobiological, radioimmunological, and toxicity environment of human therapies. The disparity adversely affects our ability to meaningfully test hypotheses that are intended for human translation. With decades of advancement, human radiotherapy has achieved high targeting accuracy and dose conformality based on

technological breakthroughs, including intensity-modulated radiotherapy (IMRT), which is unavailable for mouse experiments. A practical device and algorithm to modulate the x-ray intensity for the scale of small animals is the first step to bridge the gap. With the support of an NIH R21 grant, we engineered a novel small animal IMRT

dose modulator termed sparse orthogonal collimator (SOC). Equally important as the hardware, we created the enabling mathematical tools to deliver SOC IMRT plans with higher achievable resolution than a theoretically miniaturized MLC-based IMRT. We commissioned and tested prototypical SOCs to deliver highly modulated

doses in silico and on phantoms. Nonetheless, there are still large gaps between an intensity modulation device and a small animal IMRT system suitable for broad adoption and impact. The required time, resources, and training to create sophisticated SOC-IMRT plans are incompatible with preclinical settings. Furthermore, without

automation, the existing image-guided small animal IMRT treatment is prohibitively slow for treating live animals under anesthesia. Lastly, the current manual method to switch between imaging and therapy modes results in intractable uncertainties in dose delivery. We propose to fill these gaps using automation, robotics, and system

optimization. We propose the following specific aims. Specific Aim 1 (SA1). Automated organ segmentation for mice using deep learning neural networks. Specific Aim 2 (SA2). Development of a fully functional, automated, and efficient IMRT system. Specific Aim 3 (SA3). Development and validation of a robotic Multi Mouse Automated

Treatment Environment (Multi-MATE) for automated imaging and treatment. Besides dosimetry, we will quantify the time performance, which is critical to small animal IMRT system. As a result, in addition to improving the hardware accuracy and reliability, the proposed project will provide a fully automated planning and delivery

system, thus removing the last barriers towards the broad adoption of small animal IMRT. The success of the proposed project will help existing research to achieve the full potential for human translation and enable future hypotheses testing where accurate complex dose distribution is critical.