TY - GEN
T1 - Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study
AU - Ren, Ge
AU - Ho, Wai Yin
AU - Qin, Jing
AU - Cai, Jing
PY - 2019/10/10
Y1 - 2019/10/10
N2 - Functional avoidance radiation therapy for lung cancer patients aims to limit dose delivery to highly functional lung. However, the clinical functional imaging suffers from many shortcomings, including the need of exogenous contrasts, longer processing time, etc. In this study, we present a new approach to derive the lung functional images, using a deep convolutional neural network to learn and exploit the underlying functional information in the CT image and generate functional perfusion image. In this study, 99mTc MAA SPECT/CT scans of 30 lung cancer patients were retrospectively analyzed. The CNN model was trained using randomly selected dataset of 25 patients and tested using the remaining 5 subjects. Our study showed that it is feasible to derive perfusion images from CT image. Using the deep neural network with discrete labels, the main defect regions can be predicted. This technique holds the promise to provide lung function images for image guided functional lung avoidance radiation therapy.
AB - Functional avoidance radiation therapy for lung cancer patients aims to limit dose delivery to highly functional lung. However, the clinical functional imaging suffers from many shortcomings, including the need of exogenous contrasts, longer processing time, etc. In this study, we present a new approach to derive the lung functional images, using a deep convolutional neural network to learn and exploit the underlying functional information in the CT image and generate functional perfusion image. In this study, 99mTc MAA SPECT/CT scans of 30 lung cancer patients were retrospectively analyzed. The CNN model was trained using randomly selected dataset of 25 patients and tested using the remaining 5 subjects. Our study showed that it is feasible to derive perfusion images from CT image. Using the deep neural network with discrete labels, the main defect regions can be predicted. This technique holds the promise to provide lung function images for image guided functional lung avoidance radiation therapy.
KW - Deep learning
KW - Functional avoidance radiation therapy
KW - Perfusion imaging
UR - http://www.scopus.com/inward/record.url?scp=85075647444&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32486-5_13
DO - 10.1007/978-3-030-32486-5_13
M3 - Conference article published in proceeding or book
AN - SCOPUS:85075647444
SN - 9783030324858
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 102
EP - 109
BT - Artificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Nguyen, Dan
A2 - Jiang, Steve
A2 - Xing, Lei
PB - Springer
T2 - 1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -
