Feb 22, 2017
9 a.m.
Green Hall, Room 0120
Shuangyue Zhang, PhD Candidate, will present.
Abstract: Proton beam therapy uses a beam of protons to irradiate cancerous tissues. One of the main
advantages of proton therapy is that protons deposit the maximum energy at the end of the beam path, which is
known as the Bragg peak. The position of the Bragg peak is determined by proton stopping power (SP) of
material along the beam path. So the accuracy of SP estimation is crucial for accurate dose calculation and
geometric targeting in proton therapy planning. In current clinical practice, patient-specific SP information is
obtained from single-energy computed-tomography (SECT) images using the stoichiometric calibration
method. However, SECT methods may introduce large intrinsic uncertainties into estimation results. Compared
with SECT, dual-energy CT (DECT) has shown the potential to achieve more accurate SP estimation. We are
developing a method to reduce the uncertainty in stopping power estimation by a novel combination of a linear,
separable basis vector model (BVM) for material composition and a statistical, model-based DECT image
reconstruction algorithm. Unlike post-processing DECT methods that reconstruct the two CT images separately
using conventional methods, we model both photon attenuation coefficients and proton stopping powers of
unknown materials by a simple combination of those of two reference materials. We use a model-based image
reconstruction algorithm to estimate the model parameters, and then use these parameters to estimate the proton
stopping power. To our knowledge, our method is the first application of an integrated statistical image
reconstruction algorithm that operates on two DECT sinograms simultaneously to energy-uncompensated
sinograms extracted from a commercial scanner for mapping proton stopping power. Our method is evaluated
by simulation and experimental phantom studies and shows the potential to estimate proton stopping power
with high accuracy and lower variance.
Thesis advisor:
Dr. Joseph O’Sullivan