CBCT-Guided Adaptive Radiotherapy

CBCT-based Synthetic CT

Related publications:

Lei Y, Tang X, Liu T, Jwoong J, Higgins K, Dhabaan A, Wang H, Press R, Curran W and Yang X*. "Learning‐based CBCT Correction Using Alternating Random Forest Based on Auto‐context Model", Medical Physics, 46(2), 601-6182018, 2019. (Selected and Featured in the journal cover and Editor’s Choice)

Harms J, Lei Y, Wang T, Zhang R, Zhou J, Tang X, Curran W, Liu T, and Yang X*. “Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography,” Medical Physics, 46(7), 3325-3337, 2019.

Liu Y, Lei Y, Wang T, Fu Y, Tang X, Curran W, Liu T, Patel P and Yang X*. “CBCT-based Synthetic CT Generation using Self-attention CycleGAN for Pancreatic Adaptive Radiotherapy,” Medical Physics, 47(6):2472-2483, 2020.

Wang T, Lei Y, Manohar N, Tian S, Jani A, Shu H, Higgins K, Dhabaan A, Patel P, Tang X, Liu T, Curran W, and Yang X*. “Dosimetric Study on Learning-Based Cone-Beam CT Correction in Adaptive Radiation Therapy," Medical Dosimetry, 19, 300342, 2019.

CBCT-based Synthetic MRI

We combined the superior bony structure information provided by CBCTs and the superior soft-tissue information provided by CBCT-based synthetic MRIs via our proposed deep learning networks to simultaneously segment both soft-tissue and bone organs required by prostate treatment planning.

Related publications:

Lei Y, Wang T, Tian S, Dong X, Jani A, Schuster D, Curran W, Patel P, Liu T and Yang X*. "Male Pelvic Multi-Organ Segmentation Aided by CBCT-based Synthetic MRI," Physics in Medicine and Biology, 65(3), 035013, 2020.

Fu Y, Lei Y, Fu Y, Wang T, Tian S, Patel P, Jani A, Curran W, Liu T and Yang X*. “Pelvic Multi-organ Segmentation on CBCT for Prostate Adaptive Radiotherapy," Medical Physics, 47(8):3415-3422, 2020.

Dong X, Lei Y, Liu Y, Wang T, Tian S, Jani A, Patel P, Shuster D, Curran W, Liu T and Yang X*. "Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network," Radiotherapy and Oncology, 141, 192-199 2019.

Lei Y, Dong X, Tian Z, Liu Y, Wang T, Tian S, Patel P, Jani A, Curran W, Mao H, Liu T and Yang X*. "CT Prostate Segmentation Based on Synthetic MRI-aided Deep Attention Fully Convolution Network," Medical Physics, 47(1), 530-540, 2020.

CBCT-based SPR Mapping

Compared to helical CTs, CBCT have less accurate Hounsfield Units and degrade image quality, limiting their potential use for proton dose calculation. A deep-learning-based approach is developed to accurately estimate relative stopping power (RSP) from to daily CBCTs, to enable future CBCT-guided online proton dose evaluation and adaptive proton planning.

Related publications:

Harms J, Lei Y, Wang T, Ghavidel B, Stokers W, McDonald M, Curran W, Liu T, Zhou J and Yang X*. “Cone-beam CT-derived relative stopping power map generation via deep learning for adaptive proton radiotherapy," Medical Physics, 47(9), 4416-4427, 2020.

Wang T, Harms J, Lei Y, Ghavidel B, Stokes W, Liu T, Curran W, McDonald M, Zhou J and Yang X*. "Deep Learning-Based Relative Stopping Power Mapping Generation with Cone-Beam CT in Proton Radiation Therapy," Proc. of SPIE, 11312, 113124L-30L, 2020.