Imaging Biomarkers
The integration of medical imaging into the clinical Radiation Oncology workflow is rapidly advancing. Computed tomography (CT), as well as a gamut of other imaging modalities — namely, magnetic resonance imaging (MRI) and positron emission tomography (PET) — are now mainstay in Radiation Oncology Departments. Advancement of imaging methods and their application in Radiation Oncology have led to better delineation of targets, such as highly metabolic regions in PET images, the ability to change patient’s treatment plan day-to-day using onboard imaging, and to follow patients’ post-treatment to evaluate treatment response. Our work focuses on incorporating multimodal imaging, such as dynamic contrast enhanced (DCE) CT and intravoxel incoherent motion (IVIM) MRI, to better predict treatment response. Additionally, we are focusing on leveraging advanced image processing techniques, such as deep learning and machine learning, to extract functional information from anatomical images to help guide radiotherapy as well as to improve delineation and tracking of difficult to see targets during image guided interventions. In collaboration with Radiology at UCSF, we are developing lung MR imaging protocols, such as 4DMRI, to extract functional information from free-breathing proton MRI.
Parametric Response Mapping
Deep Learning Ventilation MRI
Dual Energy AI Target Tracking
Motion Management Systems
Paramount to the implementation of gating or breath-hold motion management in radiotherapy is the detection of respiratory signals using either internal or external probes to track and monitor respiratory motion. Furthermore, to improve the reproducibility of these methods, audiovisual feedback systems previously developed have shown to improve lung tumor position reproducibility and volume consistency. Unfortunately, most clinical systems are sophisticated, complex, and costly, which impedes the widespread use of these systems, especially in locations where staff and resources are limited. Accordingly, our effort is to develop simple-to-use, easy-to-implement low-cost alternatives to commercially available products with the potential to facilitate the translation of respiratory gated techniques to centers that currently do not have access to respiratory motion management systems, namely in lower-middle income countries (LMICs). Our current projects involve developing applications on smartphones with LiDAR capabilities for surface guided radiation therapy (SGRT) and using artificial intelligence to better assist in predicting motion patterns to reduce latency between measuring motion and radiation delivery.
iOS SGRT Application
Quality Assurance Phantoms
OneIso Phantom
Calypso Phantom
With the advancement of technology in Radiation Oncology, there is a need for quality assurance (QA) advancement. QA programs need not only to be developed in parallel to ensure safe delivery of radiation to patients but must also ensure that emerging technologies reach their fullest potential. Due to the speed in which novel treatment methods are translating into the clinic, fast prototyping of QA phantoms is becoming desirable, if not a necessity. 3D printing offers a wide variety of materials and printing methods to help facilitate the manufacturing of custom QA phantoms to meet the needs in the clinic. In collaboration with other UCSF Radiation Oncology Faculty, our group has access to multiple 3D printing platforms to quickly test and prototype QA phantoms and patient specific devices for clinical applications. Current research projects include testing and refining a novel 3D printed QA phantom, in collaboration with Stanford University, to assist in facilitating the implementation of a frameless single-isocenter, multitarget cranial SRS program, as well as developing devices to evaluate novel surface guided radiation therapy imaging systems. We have previously investigated combining 3D printing with robotics to provide a programmable motion model used for evaluating motion management systems.
