TY - JOUR
T1 - A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images
AU - Wu, Jian
AU - Mazur, Thomas R.
AU - Ruan, Su
AU - Lian, Chunfeng
AU - Daniel, Nalini
AU - Lashmett, Hilary
AU - Ochoa, Laura
AU - Zoberi, Imran
AU - Anastasio, Mark A.
AU - Gach, H. Michael
AU - Mutic, Sasa
AU - Thomas, Maria
AU - Li, Hua
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/7
Y1 - 2018/7
N2 - Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges. The proposed heart motion tracking method makes use of a heart shape model that characterizes the statistical variations in heart shapes present in a training data set. This heart shape model was established by training a three-layered deep Boltzmann machine (DBM) in order to characterize both local and global heart shape variations. During the tracking phase, a distance regularized level-set evolution (DRLSE) method was applied to delineate the heart contour on each frame of a cine MRI image sequence. The trained shape model was embedded into the DRLSE method as a shape prior term to constrain an evolutional shape to reach the desired heart boundary. Frame-by-frame heart motion tracking was achieved by iteratively mapping the obtained heart contour for each frame to the next frame as a reliable initialization, and performing a level-set evolution. The performance of the proposed motion tracking method was demonstrated using thirty-eight coronal cine MRI image sequences.
AB - Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges. The proposed heart motion tracking method makes use of a heart shape model that characterizes the statistical variations in heart shapes present in a training data set. This heart shape model was established by training a three-layered deep Boltzmann machine (DBM) in order to characterize both local and global heart shape variations. During the tracking phase, a distance regularized level-set evolution (DRLSE) method was applied to delineate the heart contour on each frame of a cine MRI image sequence. The trained shape model was embedded into the DRLSE method as a shape prior term to constrain an evolutional shape to reach the desired heart boundary. Frame-by-frame heart motion tracking was achieved by iteratively mapping the obtained heart contour for each frame to the next frame as a reliable initialization, and performing a level-set evolution. The performance of the proposed motion tracking method was demonstrated using thirty-eight coronal cine MRI image sequences.
KW - Deep Boltzmann machine
KW - Distance regularized level-set evolution
KW - Generative shape model
KW - Heart motion tracking
KW - MRI-guided radiation therapy
UR - http://www.scopus.com/inward/record.url?scp=85045707967&partnerID=8YFLogxK
U2 - 10.1016/j.media.2018.03.015
DO - 10.1016/j.media.2018.03.015
M3 - Article
C2 - 29679848
AN - SCOPUS:85045707967
SN - 1361-8415
VL - 47
SP - 68
EP - 80
JO - Medical Image Analysis
JF - Medical Image Analysis
ER -