Optimal image guided adaptive radiotherapy requires a 4D representation of the patients anatomy, that allows the position of tumor and normal tissue voxels to be tracked through the processes of biological imaging, planning and simulation, delivery of brachytherapy, and administration of each IMRT fraction. The scientific objective of this project is to investigate novel methods of nonrigid image registration for constructing and validating such representations of the patient's anatomy as it changes during the treatment process. The practical goal is to create a suite of image processing resources that will enable the routine application of image-guided adaptive radiotherapy techniques in the clinic.
In specific aim 1, we will investigate contour-driven deformable registration methods for mapping high-dose brachytherapy (HDR) dose distributions in the pelvis to IMRT dose distributions, and for registering biological images to external beam planning images, including development of a novel surface matching algorithm that accounts for contouring uncertainties. To efficiently map information from planning CT images to onboard CT images, acquired prior to administering each daily fraction, we will develop fast parametric image deformation algorithms that do not require manually contoured landmarks.
In Specific Aim 2. we will investigate novel methods for reconstructing CT images from incomplete projection data by matching deformation models to sequences of planar image projections, thereby integrating image reconstruction and deformable registration into a single process. This will be used to develop 4D anatomic representations of patient respiration with improved temporal resolution and to estimate intrafraction anatomic deformation from higher temporal resolution sequences of 2D images. Finally, in Specific Aim 3, novel methods for estimating the uncertainty and error of deformable image registration will be developed.
|Shieh, Chun-Chien; Caillet, Vincent; Dunbar, Michelle et al. (2017) A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy. Phys Med Biol 62:3065-3080|
|Hugo, Geoffrey D; Weiss, Elisabeth; Sleeman, William C et al. (2017) A longitudinal four-dimensional computed tomography and cone beam computed tomography dataset for image-guided radiation therapy research in lung cancer. Med Phys 44:762-771|
|Shieh, Chun-Chien; Keall, Paul J; Kuncic, Zdenka et al. (2015) Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy. Phys Med Biol 60:9437-54|
|Jan, Nuzhat; Hugo, Geoffrey D; Mukhopadhyay, Nitai et al. (2015) Respiratory motion variability of primary tumors and lymph nodes during radiotherapy of locally advanced non-small-cell lung cancers. Radiat Oncol 10:133|
|Shieh, Chun-Chien; Kipritidis, John; O'Brien, Ricky T et al. (2015) Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR). Phys Med Biol 60:841-68|
|Kipritidis, John; Hugo, Geoffrey; Weiss, Elisabeth et al. (2015) Measuring interfraction and intrafraction lung function changes during radiation therapy using four-dimensional cone beam CT ventilation imaging. Med Phys 42:1255-67|
|Xu, Huijun; Gordon, J James; Siebers, Jeffrey V (2015) Coverage-based treatment planning to accommodate delineation uncertainties in prostate cancer treatment. Med Phys 42:5435-43|
|Watkins, W Tyler; Moore, Joseph A; Gordon, James et al. (2014) Multiple anatomy optimization of accumulated dose. Med Phys 41:111705|
|Xu, Huijun; Vile, Douglas J; Sharma, Manju et al. (2014) Coverage-based treatment planning to accommodate deformable organ variations in prostate cancer treatment. Med Phys 41:101705|
|Shieh, Chun-Chien; Kipritidis, John; O'Brien, Ricky T et al. (2014) Image quality in thoracic 4D cone-beam CT: a sensitivity analysis of respiratory signal, binning method, reconstruction algorithm, and projection angular spacing. Med Phys 41:041912|
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