A novel analytical approach to the prediction of respiratory diaphragm motion based on external torso volume change

Abstract

An analytical approach to predict respiratory diaphragm motion should have advantages over a correlation-based method, which cannot adapt to breathing pattern changes without re-calibration for a changing correlation and/or linear coefficient. To quantitatively calculate the diaphragm motion, a new expandable 'piston' respiratory (EPR) model was proposed and tested using 4DCT torso images of 14 patients. The EPR model allows two orthogonal lung motions (with a few volumetric constraints): (1) the lungs expand (DeltaV(EXP)) with the same anterior height variation as the thoracic surface, and (2) the lungs extend (DeltaV(EXT)) with the same inferior distance as the volumetrically equivalent 'piston' diaphragm. A volume conservation rule (VCR) established previously (Li et al 2009 Phys. Med. Biol. 54 1963-78) was applied to link the external torso volume change (TVC) to internal lung volume change (LVC) via lung air volume change (AVC). As the diaphragm moves inferiorly, the vacant space above the diaphragm inside the rib cage should be filled by lung tissue with a volume equal to DeltaV(EXT) (=LVC-DeltaV(EXP)), while the volume of non-lung tissues in the thoracic cavity should conserve. It was found that DeltaV(EXP) accounted for 3-24% of the LVC in these patients. The volumetric shape of the rib cage, characterized by the variation of cavity volume per slice over the piston motion range, deviated from a hollow cylinder by -1.1% to 6.0%, and correction was made iteratively if the variation is >3%. The predictions based on the LVC and TVC (with a conversion factor) were compared with measured diaphragm displacements (averaged from six pivot points), showing excellent agreements (0.2 +/- 0.7 mm and 0.2 +/- 1.2 mm, respectively), which are within clinically acceptable tolerance. Assuming motion synchronization between the piston and points of interest along the diaphragm, point motion was estimated but at higher uncertainty ( approximately 10% +/- 4%). This analytical approach provides a patient-independent technique to calculate the patient-specific diaphragm motion, using the anatomical and respiratory volumetric constraints.

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning

“…After all the respiratory phases are analyzed, the ADMT is obtained. This method is based on previous work, where six pivot points were averaged to represent the volumetrically-equivalent diaphragm position (Li et al 2009b). In this study, the ground truth is used for training the predictive model and making subsequent prediction.…”

“…The 3 points were the most inferior points at the anterior and posterior sides and the most-superior point at the diaphragm dome. The average diaphragm positions approximate the volume-equivalent position (Li et al 2009b). …”

Evaluation of mechanical accuracy for couch‐based tracking system (CBTS)

“…Further studies should be performed to analyze the correlation between the tumor motion and the respiration signal. Studies have addressed the prediction of tumor position using various algorithms and adaptive filters, such as the sequential forward floating search (SFFS) algorithm, (24) adaptive neural networks, (25) Kalman filters, (26) the expandable ‘piston’ respiratory (EPR) model, (27) the finite state automation (FSA) model, (28) and the multidimensional adaptive filter (MISO predictive model) (29) . A model involving a respiratory motion prediction algorithm for real‐time tracking would be useful.…”

Volumetric Image Registration of Multi-modality Images of CT, MRI and PET

“…This is achieved by proposing and validating a volume conservation hypothesis within torso (Li, et al, 2009a) and an expandable "piston" respiratory model during quiet respiration (Li, et al, 2009b). Further investigation is required to translate the diaphragm motion into the target motion away from the diaphragm.…”