Browsing by Subject "4DCT"

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: Lung ventilation imaging offers guidance for functional avoidance during radiation therapy. Ventilation imaging modalities such as radioactive aerosols PET and SPECT, and hyperpolarized gas MRI, are not widely available at many institutions. In contrast, 4DCT images are part of standard treatment planning for lung malignancies and contain characteristics that reflect changes in the air content of the lungs due to ventilation. The purpose of this work was to develop a voxel-based delta radiomic feature extraction process using 4DCT images to quantify lung ventilation.

Materials and Methods: Twenty-five patients from the VAMPIRE dataset were used in this study with 4DCT/Galligas 4DPET images. For each patient, end-of-exhalation (EOE) and end-of-inhalation (EOI) phase CT images were both registered to the average phase CT using a contour-based deformable image registration algorithm. Next, 62 radiomic features were extracted spatially throughout the lungs using a sliding-window technique. The resulting tensor images were extracted to create 62 delta radiomic feature maps. Delta feature maps were compared with corresponding Galligas PET images by calculating Spearman correlation, mutual information. Delta feature distributions in clinical defect and non-defect lung regions were compared. The effect of sliding window kernel size was characterized to investigate its impact on correlation with Galligas PET.

Results: The best agreement between delta feature maps and Galligas PET images using a 5x5x5cm3 kernel was obtained by first-order energy, which demonstrates a mean Spearman correlation of r(s)=0.45±0.16. Other highly correlated filtered images were of features designed to capture high gray level intensities. Correlations with Galligas PET were found to increase and then saturate with increasing kernel size.

Conclusion: We have developed a promising method to quantify lung ventilation using voxel-based delta radiomics extracted from thoracic 4DCT. The results were comparable with a HU-based CT ventilation imaging (CTVI) method. Voxel-based radiomics is a potentially useful technique that can be used to generate synthetic ventilation images from standard-of-care image data.