TU-F-17A-01: BEST IN PHYSICS (JOINT IMAGING-THERAPY) – An Automatic Toolkit for Efficient and Robust Analysis of 4D Respiratory Motion




To provide an automatic image analysis toolkit to process thoracic 4-dimensional computed tomography (4DCT) and extract patient-specific motion information to facilitate investigational or clinical use of 4DCT.


We developed an automatic toolkit in MATLAB to overcome the extra workload from the time dimension in 4DCT. This toolkit employs image/signal processing, computer vision, and machine learning methods to visualize, segment, register, and characterize lung 4DCT automatically or interactively. A fully-automated 3D lung segmentation algorithm was designed and 4D lung segmentation was achieved in batch mode. Voxel counting was used to calculate volume variations of the torso, lung and its air component, and local volume changes at the diaphragm and chest wall to characterize breathing pattern. Segmented lung volumes in 12 patients are compared with those from a treatment planning system (TPS). Voxel conversion was introduced from CT# to other physical parameters, such as gravity-induced pressure, to create a secondary 4D image. A demon algorithm was applied in deformable image registration and motion trajectories were extracted automatically. Calculated motion parameters were plotted with various templates. Machine learning algorithms, such as Naive Bayes and random forests, were implemented to study respiratory motion. This toolkit is complementary to and will be integrated with the Computational Environment for Radiotherapy Research (CERR).


The automatic 4D image/data processing toolkit provides a platform for analysis of 4D images and datasets. It processes 4D data automatically in batch mode and provides interactive visual verification for manual adjustments. The discrepancy in lung volume calculation between this and the TPS is <±2% and the time saving is by 1–2 orders of magnitude.


A framework of 4D toolkit has been developed to analyze thoracic 4DCT automatically or interactively, facilitating both investigational and clinical use. More software tools are under development for automatic analysis of other 4D imaging modalities.

This research is in part supported by NIH (U54CA137788/132378) and City Seed FY13(Grant No. 93348-14 01). AY would like to thank MSKCC summer medical student research program supported by National Cancer Institute and hosted by Department of Medical Physics at MSKCC.