DECT in Cancer Radiotherapy

The "twin-beam" imaging scheme in dual-energy CT (DECT) is to simultaneously capture two CT images with different energy spectra, which can eliminate patient motion between two sets of CT images. Studies show that monochromatic images synthesized from DECT data have potential to reduce beam-hardening artifacts and, therefore, improve image quality and quantitative measurements. This study's aim is to investigate optimal monochromatic image for PTV and organ delineations in the head-and-neck radiotherapy treatment planning, and to develop "twin-beam" imaging protocol for head-and-neck radiotherapy.

Related publications:

Wang T, Ghbdel B, Beitler J, Tang X, Lei Y, Curran W, Liu T and Yang X*. “Optimal Virtual Monoenergetic Image in "TwinBeam" Dual-Energy CT for Organs-at-risk Delineation Based on Contrast-noise-ratio in Head-and-Neck Radiotherapy," Journal of Applied Clinical Medical Physics, 20(2):121-128, 2019.

Wang T, Lei Y, Roper J, Ghavidel B, Beitler JJ, McDonald M, Curran WJ, Liu T, Yang X*. “Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks.” Physics in Medicine and Biology, 66(6):065014, 2021.

Learning-based Relative Stopping Power Map from DECT

The accuracy of dual-energy CT (DECT)-derived parametric maps is directly affected by the level of photon noise and image artifacts. Such inconsistency degrades the accuracy of the physics-based mapping technique and affects subsequent processing for clinical applications. Deriving accurate relative stopping power (RSP) maps from Twin-beam DECT using current physical-based method is challenging due to the increased noise sensitivity caused by its limited energy spectra separation between the two energy levels. This work presents a learning-based method to predict RSP maps from Twin-beam DECT for head-and-neck cancer patients in proton radiation therapy.

Related publications:

Wang T, Lei Y, Harms J, Ghavidel B, Lin L, Beitler J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. "Learning-Based Stopping Power Mapping on Dual Energy CT for Proton Radiation Therapy," International Journal of Particle Therapy, 7(3):46-60, 2021.

SECT-based DECT

Dual-energy CT (DECT) has been shown to derive stopping power ratio (SPR) map with higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. However, DECT is not as widely implemented as SECT in proton radiation therapy simulation. We present a learning-based method to synthesize DECT images from SECT for proton radiation therapy.

Related publications:

Charyyev S, Wang T, Lei Y, Ghavidel B, Beitler J, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy,” arXiv preprint arXiv:2005.12908, 2020.