Purpose To develop a breath-hold acquisition for regional mapping of ventilation and the fractions of hyperpolarized xenon-129 (Xe129) dissolved in tissue (lung parenchyma and plasma) and red blood cells (RBCs), and to perform an exploratory study to characterize data obtained in human subjects. Materials and Methods A three-dimensional, multi-echo, radial-trajectory pulse sequence was developed to obtain ventilation (gaseous Xe129), tissue and RBC images in healthy subjects, smokers and asthmatics. Signal ratios (total dissolved Xe129 to gas, tissue-to-gas, RBC-to-gas and RBC-to-tissue) were calculated from the images for quantitative comparison. Results Healthy subjects demonstrated generally uniform values within coronal slices, and a gradient in values along the anterior-to-posterior direction. In contrast, images and associated ratio maps in smokers and asthmatics were generally heterogeneous and exhibited values mostly lower than those in healthy subjects. Whole-lung values of total dissolved Xe129 to gas, tissue-to-gas, and RBC-to-gas ratios in healthy subjects were significantly larger than those in diseased subjects. Conclusion Regional maps of tissue and RBC fractions of dissolved Xe129 were obtained from a short breath-hold acquisition, well tolerated by healthy volunteers and subjects with obstructive lung disease. Marked differences were observed in spatial distributions and overall amounts of Xe129 dissolved in tissue and RBCs among healthy subjects, smokers and asthmatics.
Magnetic-resonance spectroscopy and imaging using hyperpolarized xenon-129 show great potential for evaluation of the most important function of the human lung -- gas exchange. In particular, Chemical Shift Saturation Recovery (CSSR) xenon-129 spectroscopy provides important physiological information for the lung as a whole by characterizing the dynamic process of gas exchange, while dissolved-phase xenon-129 imaging captures the time-averaged regional distribution of gas uptake by lung tissue and blood. Herein, we present recent advances in assessing lung function using CSSR spectroscopy and dissolved-phase imaging in a total of 45 subjects (23 healthy, 13 chronic obstructive pulmonary disease (COPD) and 9 asthma). From CSSR acquisitions, the COPD subjects showed red blood cell to tissue/plasma (RBC-to-TP) ratios below the average for the healthy subjects (p<0.001), but significantly higher septal wall thicknesses, as compared with the healthy subjects (p<0.005); the RBC-to-TP ratios for the asthmatics fell outside 2 standard deviations (either higher or lower) from the mean of the healthy subjects although there was no statistically significant difference for the average ratio of the study group as a whole. Similarly, from the 3D DP imaging acquisitions, we found all the ratios (TP-to-GP, RBC-to-GP, RBC-to-TP) measured in the COPD subjects were lower than those from the healthy subjects (p<0.05 for all ratios), while these ratios in the asthmatics differed considerably between subjects. Despite having been performed at different lung inflation levels, the RBC-to-TP ratios measured by CSSR and 3D DP imaging were fairly consistent with each other, with a mean difference of 0.037 (ratios from 3D DP imaging larger). In ten subjects the RBC-to-GP ratios obtained from the 3D DP imaging acquisitions were also highly correlated with their DLCO/Va ratios measured by pulmonary function testing (R=0.91).
Purpose Automatic segmentation of organs‐at‐risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation applications but there are still challenges in dealing with large 3D images for optimal results. The purpose of this study is to develop a novel DCNN method for thoracic OARs segmentation using cropped 3D images. Methods To segment the five organs (left and right lungs, heart, esophagus and spinal cord) from the thoracic CT scans, preprocessing to unify the voxel spacing and intensity was first performed, a 3D U‐Net was then trained on resampled thoracic images to localize each organ, then the original images were cropped to only contain one organ and served as the input to each individual organ segmentation network. The segmentation maps were then merged to get the final results. The network structures were optimized for each step, as well as the training and testing strategies. A novel testing augmentation with multiple iterations of image cropping was used. The networks were trained on 36 thoracic CT scans with expert annotations provided by the organizers of the 2017 AAPM Thoracic Auto‐segmentation Challenge and tested on the challenge testing dataset as well as a private dataset. Results The proposed method earned second place in the live phase of the challenge and first place in the subsequent ongoing phase using a newly developed testing augmentation approach. It showed superior‐than‐human performance on average in terms of Dice scores (spinal cord: 0.893 ± 0.044, right lung: 0.972 ± 0.021, left lung: 0.979 ± 0.008, heart: 0.925 ± 0.015, esophagus: 0.726 ± 0.094), mean surface distance (spinal cord: 0.662 ± 0.248 mm, right lung: 0.933 ± 0.574 mm, left lung: 0.586 ± 0.285 mm, heart: 2.297 ± 0.492 mm, esophagus: 2.341 ± 2.380 mm) and 95% Hausdorff distance (spinal cord: 1.893 ± 0.627 mm, right lung: 3.958 ± 2.845 mm, left lung: 2.103 ± 0.938 mm, heart: 6.570 ± 1.501 mm, esophagus: 8.714 ± 10.588 mm). It also achieved good performance in the private dataset and reduced the editing time to 7.5 min per patient following automatic segmentation. Conclusions The proposed DCNN method demonstrated good performance in automatic OAR segmentation from thoracic CT scans. It has the potential for eventual clinical adoption of deep learning in radiation treatment planning due to improved accuracy and reduced cost for OAR segmentation.
The proposed framework yields accurate automated quantification in near real time. CNNs drastically reduce processing time after offline model construction and demonstrate significant future potential for facilitating quantitative analysis of functional lung MRI.
Rationale and Objectives: Hyperpolarized xenon-129 magnetic resonance (MR) provides sensitive tools that may detect early stages of lung disease in smokers before it has progressed to chronic obstructive pulmonary disease (COPD) apparent to conventional spirometric measures. We hypothesized that the functional alveolar wall thickness as assessed by hyperpolarized xenon-129 MR spectroscopy would be elevated in clinically healthy smokers before xenon MR diffusion measurements would indicate emphysematous tissue destruction. Materials and Methods:Using hyperpolarized xenon-129 MR we measured the functional septal wall thickness and apparent diffusion coefficient of the gas phase in 16 subjects with smoking-related COPD, 9 clinically healthy current or former smokers, and 10 healthy never smokers. All subjects were age-matched and characterized by conventional pulmonary function tests. 11 data sets from younger healthy never smokers were added to determine the age dependence of the septal wall thickness measurements. Results:In healthy never smokers the septal wall thickness increased by 0.04 µm per year of age. The healthy smoker cohort exhibited normal pulmonary function test measures that did not significantly differ from the never-smoker cohort. The age-corrected septal wall thickness correlated well with diffusion capacity for carbon monoxide (R 2 = 0.56) and showed a highly significant difference between healthy subjects and COPD patients (8.8 µm versus 12.3 µm; p < 0.001), but was the only measure that actually discriminated healthy subjects from healthy smokers (8.8 µm versus 10.6 µm; p < 0.006).
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