Browsing by Subject "Ventilation"

The ability to image ventilation and perfusion enables pulmonary researchers to study functional metrics of gas exchange on a regional basis. There is a huge interest in applying imaging methods to study the large number of genetic models of pulmonary diseases available in small animals. Existing techniques to image ventilation and perfusion are often associated with low spatial resolution and ionizing radiation. Magnetic Resonance Imaging (MRI) has been demonstrated successfully for ventilation and perfusion studies in humans. Translating these techniques in small animals remains challenging. This work addresses the ventilation and perfusion imaging in small animals using MRI.

Qualitative ventilation imaging in rats and mice is possible and has been demonstrated using MRI, however perfusion imaging remains a challenge. In humans and large animals perfusion can be assessed using dynamic contrast-enhanced (DCE) MRI with a single bolus injection of a gadolinium (Gd)-based contrast agent. But the method developed for the clinic cannot be translated directly to image the rat due to the combined requirements of higher spatial and temporal resolution. This work describes a novel image acquisition technique staggered over multiple, repeatable bolus injections of contrast agent using an automated microinjector, synchronized with image acquisition to achieve dynamic first-pass contrast enhancement in the rat lung. This allows dynamic first-pass imaging that can be used to quantify pulmonary perfusion. Further improvements are made in the spatial and temporal resolution by combining the multiple injection acquisition method with Interleaved Radial Imaging and 'Sliding window-keyhole' reconstruction (IRIS). The results demonstrate a simultaneous increase in spatial resolution (<200>um) and temporal resolution (<200>ms) over previous methods, with a limited loss in signal-to-noise-ratio.

While is it possible to create high resolution images of ventilation in rats using hyperpolarized ³He, extracting meaningful quantitative information indicative of changes in ventilation is difficult. In this work, we also present a signal calibration technique used to normalize the signal of ³He to volume of ³He which can then be used to extract quantitative information of changes in ventilation via normalized difference maps. Combining the techniques for quantitative ventilation and quantitative perfusion we perform studies of change in ventilation/perfusion (V/Q) before and after airway obstruction in rats. The technique is sensitive in detecting statistically significant differences in the heterogeneity of the distribution of V/Q ratio.

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.