Using Geometric information analysis and processing.
Dr. Iyengar's research group is currently developing a computational framework to model the respiratory motion of the tumor and surrounding organs, which could guide the automatic offline planning and intelligent online management of lung radiotherapy.
Jointly with FIU, LSU and UT Southeastern Medical Center.
[Iyengar, Li, Sawant et al. “Toward More Precise Radiotherapy Treatment of Lung Tumors,” IEEE Computer, Vol. 45, Issue 1, pp. 59-65, 2012]
Computational Medicine Application
Modeling Respiratory Motion of Lung Tumor
- Most solid tumors should receive radiation treatment: the radiation beam should hit the tumor without damaging nearby organs and tissues
- Tumors could be moving and deforming during respiratory cycles, it is desirable to model their trajectories and deformations accurately
Our goal: a computational framework to model the movement and deformation of the tumor and surrounding structures
- The temporal lung data are reconstructed from 3D CT and MRI scans
- Offline preprocessing stage: we process the scanned sequential medical images, and build a 4D parametric lung deformation model that approximates the respiratory cycles
- Online radiology treatment: the parametric model is used to predict the position and shape of the tumor; this will guide the shape and targeting direction of the radiation beam
Our Computational Framework
Offline Modeling and Planning
- Extract and reconstruct the geometric model from 3D CT scanning
- Compute volumetric mapping to match 3D volumes; interpolate the mapped temporal data to construct the deforming 4D (spatial + temporal) model
- Refining the 4D model (constructed from spatially dense but temporally sparse CT images) using the (spatially sparse but temporally dense) MRI images
Online Prediction and Delivery
- Synchronize (match) the 4D model with the online X-ray image taken when the patient is on the table
- Optimize the shape, position and targeting direction of the radiation beam