Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning.
To automatically estimate average diaphragm motion trajectory (ADMT) based on four-dimensional computed tomography (4DCT), facilitating clinical assessment of respiratory motion and motion variation and retrospective motion study. We have developed an effective motion extraction approach and a machine-learning-based algorithm to estimate the ADMT. Eleven patients with 22 sets of 4DCT images (4DCT1 at simulation and 4DCT2 at treatment) were studied. After automatically segmenting the lungs, the differential volume-per-slice (dVPS) curves of the left and right lungs were calculated as a function of slice number for each phase with respective to the full-exhalation. After 5-slice moving average was performed, the discrete cosine transform (DCT) was applied to analyze the dVPS curves in frequency domain. The dimensionality of the spectrum data was reduced by using several lowest frequency coefficients (fv) to account for most of the spectrum energy (Σfv2). Multiple linear regression (MLR) method was then applied to determine the weights of these frequencies by fitting the ground truth-the measured ADMT, which are represented by three pivot points of the diaphragm on each side. The 'leave-one-out' cross validation method was employed to analyze the statistical performance of the prediction results in three image sets: 4DCT1, 4DCT2, and 4DCT1 + 4DCT2. Seven lowest frequencies in DCT domain were found to be sufficient to approximate the patient dVPS curves (R= 91%-96% in MLR fitting). The mean error in the predicted ADMT using leave-one-out method was 0.3 ± 1.9 mm for the left-side diaphragm and 0.0 ± 1.4 mm for the right-side diaphragm. The prediction error is lower in 4DCT2 than 4DCT1, and is the lowest in 4DCT1 and 4DCT2 combined. This frequency-analysis-based machine learning technique was employed to predict the ADMT automatically with an acceptable error (0.2 ± 1.6 mm). This volumetric approach is not affected by the presence of the lung tumors, providing an automatic robust tool to evaluate diaphragm motion.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Guang
G
Li
Department of Computer Science, City College of New York, New York, USA.
Jie
J
Wei
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Hailiang
H
Huang
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Carl Philipp
CP
Gaebler
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Amy
A
Yuan
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Joseph O
JO
Deasy
4D computed tomography; machine learning; radiation therapy; respriatory motion; treatment planning