Browsing by Subject "motion modeling"

Purpose: To develop a generic procedure to make 5DCT from ECG 4DCTs and respiratory 4DCTs of cardiac RT patients. The 5DCT, whose dimension consists of 3D volume, cardiac cycle and respiratory cycle, will be used for quantitatively evaluating respiratory and cardiac motion of the heart, and supporting cardiac RT motion management, 5D dose calculation and dosimetry motion assessment. Methods: Images of ECG 4DCTs and respiratory 4DCTs for cardiac RT patients were obtained from the clinical system with IRB approval. For each patient, ten ECG 4DCT phases were registered using the groupwise deformable image registration algorithm GroupRegNet. The results were the template ECG CT representing the accurate average heart anatomy rather than an intensity-averaged CT, and the cardiac 4D DVF (deformation vector field). The ECG CT template and ten respiratory 4DCT phases were registered together using the 2nd groupwise registration to compute the 2nd respiratory 4D DVF. The computed DVFs from two groupwise registrations connected ECG 4DCTs to respiratory 4DCTs. A 10x10 cardiorespiratory 5DCT volume was generated by warping the ECG phases using composed DVFs. The final 5DCT phases were manually evaluated by visually the checking the respiratory and cardiac motion of the heart chambers. Results: The 5DCT generation procedure was implemented using Python and MATLAB, and was successfully applied to 4DCT images from five cardiac RT patients. The registration results were satisfactory based on visual evaluation. The quantitative evaluation and 5D dose calculation are planned for future work. Conclusion: A practical and effective procedure was developed to assess 5D motion of the heart and generate 5DCT phases from the clinical ECG 4DCTs and respiratory 4DCTs. The generated 5DCT could be used in dose calculation to assess the effect of 5D motion of the heart chambers on dosimetry for cardiac RT treatments.

Purpose: Direct measurement of regional lung ventilation is often more favorable than the calculated ones using deformable image registration (DIR), which has been found susceptible to many uncertainties. This study aims to investigate the possible implementation of a novel imaging technique, 3D Hyperpolarized (HP) gas tagging MRI, to evaluate the regional ventilation of lung calculated with DIR-based algorithms, and to model the respiratory motion of lung towards 4D digital phantom application, respectively.

Method: Three healthy volunteers involved in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton true fast imaging with steady state precession (TrueFISP) MRI (p-MRI) during two consecutive breath-holds at the inhalation and the exhalation, respectively.

A ground truth of displacement vector field (DVF) was obtained by tracking the location of each tagged grid between the exhalation and the inhalation t-MRI images. Meanwhile, p-MRI images were registered with two commercial DIR algorithms, Velocity AI and MIM, to generate the corresponding DVFs. The regional ventilations were calculated as the Jacobian of each DVF. The cross-correlation and the mutual information were computed between either the DIR-based and the ground truth ventilations for comparison.

Regional ventilation of lung, as a physiological property, should remain invariable during the image acquisition. Therefore, in order to develop a realistic model for the respiratory motion of lung, the optimization of the DIR-based DVF was performed by imposing a regularization on the regional ventilation of lung. The robustness of the proposed model was evaluated by comparing the optimized DVF to the original DIR-based DVF in cross-correlation and mutual information.

Results: Compared to the ground truth result of the 3D HP gas tagging MRI, the regional ventilation maps calculated using the commercial DIR algorithms varied considerably for all the subjects of different respiration amplitudes. The ventilation derived from Velocity AI was preferable for the better spatial homogeneity and the larger accuracy, given by the higher average cross correlation (0.328 v.s. 0.262) and average mutual information (0.528 v.s. 0.323).

The optimized model represents a more realistic lung motion with the average deviation from the ground truth DVF reduced from 6.4 mm to 5.2 mm (p<0.05, student t-test) compared to the original DIR-based DVF. Furthermore, the accuracy of regional ventilation was also improved with larger cross correlation (0.98 v.s. 0.37) and mutual information values (5.84 v.s. 0.45).

Conclusion:

The results suggest that direct measurement of lung motion using 3D hyperpolarized gas tagging MRI may have the potential to assess, validate, and improve the DIR-based application. The proposed 4D motion model of lung also holds great promises for the implementation in a digital phantom, which can be applied in versatile researches for 4D radiation therapy of lung cancer.