SBIR Phase I: Cloud-based Automated Dose Accumulation for Online Adaptive Radiotherapy

Segana, Llc., Orlando FL

Investigators

Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of novel real-time cloud-based treatment guidance software for radiation cancer treatment (adaptive radiotherapy), which will improve radiation treatment efficacy, patient's quality of life, and ultimately reduce patient re-hospitalizations. Radiotherapy is the most common approach for treating cancer. The radiotherapy treatment procedures happen for multiple days (5-35 days) during which the radiation treatment efficacy may be lowered because of changes in the patient anatomy. This project provides the clinician the ability to obtain information regarding the radiation dose to be delivered to both the tumor as well as the surrounding normal organs before the treatment is delivered. The successful outcome of this project will allow radiation oncologists to plan and deliver advanced treatments where the radiation dose delivered can be quickly modified to suit the current tumor location and motion, to only encompass the tumor and effectively spare normal tissues. The project outcome will also enable physicians at remote clinics to receive guidance from experts to perform online adaptive radiotherapy, enabling a new era of radiotherapy treatment world-wide. The cloud-based framework will work with most image-guided radiotherapy equipment. This Small Business Innovation Research (SBIR) Phase I project will enable development of technology to effectively perform quality assurance tests to determine a more accurate radiation dose and reduce the number of fractions in the treatment of many cancerous tumors. The soft tissue surrounding the cancer varies in its physiological behavior, which alters the treatment efficacy when not accounted for. While a conventional hypothesis would dictate that better tumor targeting during radiotherapy would yield an improved treatment response, patient survival statistics dictate otherwise, indicating that accurate treatment guidance has not been facilitated to date. The research will focus on developing a cloud-based framework of computed tomography and magnetic resonance image processing for fast deformable image registration and radiation dose accumulation estimation, with an automated methodology for quantifying the deformable image registration performance. The technical results may facilitate an automated framework for accurately tracking a patient's anatomy, computing the accumulated dose delivered and reporting dosimetric endpoints for critical structures in near real-time, which will be vital for enabling online adaptive radiotherapy.