Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT.

PURPOSE: Computed tomography ventilation imaging derived from four-dimensional cone beam CT (CTVI4DCBCT ) can complement existing 4DCT-based methods (CTVI4DCT ) to track lung function changes over a course of lung cancer radiation therapy. However, the accuracy of CTVI4DCBCT needs to be assessed since anatomic 4DCBCT has demonstrably poor image quality and small field of view (FOV) compared to treatment planning 4DCT. We perform a direct comparison between short interval CTVI4DCBCT and CTVI4DCT pairs to understand the patient specific image quality factors affecting the intermodality CTVI reproducibility in the clinic. METHODS AND MATERIALS: We analysed 51 pairs of 4DCBCT and 4DCT scans acquired within 1 day of each other for nine lung cancer patients. To assess the impact of image quality, CTVIs were derived from 4DCBCT scans reconstructed using both standard Feldkamp-Davis-Kress backprojection (CTVIFDK4DCBCT) and an iterative McKinnon-Bates Simultaneous Algebraic Reconstruction Technique (CTVIMKBSART4DCBCT). Also, the influence of FOV was assessed by deriving CTVIs from 4DCT scans that were cropped to a similar FOV as the 4DCBCT scans (CTVIcrop4DCT), or uncropped (CTVIuncrop4DCT). All CTVIs were derived by performing deformable image registration (DIR) between the exhale and inhale phases and evaluating the Jacobian determinant of deformation. Reproducibility between corresponding CTVI4DCBCT and CTVI4DCT pairs was quantified using the voxel-wise Spearman rank correlation and the Dice similarity coefficient (DSC) for ventilation defect regions (identified as the lower quartile of ventilation values). Mann-Whitney U-tests were applied to determine statistical significance of each reconstruction and cropping condition. RESULTS: The (mean ± SD) Spearman correlation between CTVIFDK4DCBCT and CTVIuncrop4DCT was 0.60 ± 0.23 (range -0.03-0.88) and the DSC was 0.64 ± 0.12 (0.34-0.83). By comparison, correlations between CTVIMKBSART4DCBCT and CTVIuncrop4DCT showed a small but statistically significant improvement with = 0.64 ± 0.20 (range 0.06-0.90, P = 0.03) and DSC = 0.66 ± 0.13 (0.31-0.87, P = 0.02). Intermodal correlations were noted to decrease with an increasing fraction of lung truncation in 4DCBCT relative to 4DCT, albeit not significantly (Pearson correlation R = 0.58, P = 0.002). CONCLUSIONS: This study demonstrates that DIR based CTVIs derived from 4DCBCT can exhibit reasonable to good voxel-level agreement with CTVIs derived from 4DCT. These correlations outperform previous cross-modality comparisons between 4DCT-based ventilation and nuclear medicine. The use of 4DCBCT scans with iterative reconstruction and minimal lung truncation is recommended to ensure better reproducibility between 4DCBCT- and 4DCT-based CTVIs

Evaluating the accuracy of 4D-CT ventilation imaging: First comparison with Technegas SPECT ventilation.

PURPOSE: Computed tomography ventilation imaging (CTVI) is a highly accessible functional lung imaging modality that can unlock the potential for functional avoidance in lung cancer radiation therapy. Previous attempts to validate CTVI against clinical ventilation single-photon emission computed tomography (V-SPECT) have been hindered by radioaerosol clumping artifacts. This work builds on those studies by performing the first comparison of CTVI with 99m Tc-carbon ('Technegas'), a clinical V-SPECT modality featuring smaller radioaerosol particles with less clumping. METHODS: Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four-dimensional CT (4DCT) scans paired with Technegas V/Q-SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing-induced lung density changes (CTVIDIR-HU ), or breathing-induced lung volume changes (CTVIDIR-Jac ) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air-tissue densities (CTVIHU ) and did not involve DIR. Corresponding CTVI and V-SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer-selected anatomic landmarks. RESULTS: Interestingly, the overall best performing method (CTVIHU ) did not involve DIR. For nondefect regions, the CTVIHU , CTVIDIR-HU , and CTVIDIR-Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r-values were generally weak: 0.26 for CTVIHU , 0.18 for CTVIDIR-HU , and -0.02 for CTVIDIR-Jac . The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q-SPECT scans achieved good correlations with V-SPECT (mean r > 0.6), suggesting that the image quality of Technegas V-SPECT was not a limiting factor in this study. CONCLUSIONS: We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q-SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT

Imaging Biomarkers of Pulmonary Structure and Function

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airflow limitations resulting from airway obstruction and/or tissue destruction. The diagnosis and monitoring of these pulmonary diseases is primarily performed using spirometry, specifically the forced expiratory volume in one second (FEV1), which measures global airflow obstruction and provides no regional information of the different underlying disease pathologies. The limitations of spirometry and current therapies for lung disease patients have motivated the development of pulmonary imaging approaches, such as computed tomography (CT) and magnetic resonance imaging (MRI). Inhaled hyperpolarized noble gas MRI, specifically using helium-3 (3He) and xenon-129 (129Xe) gases, provides a way to quantify pulmonary ventilation by visualizing lung regions accessed by gas during a breath-hold, and alternatively, regions that are not accessed - coined “ventilation defects.” Despite the strong foundation and many advantages hyperpolarized 3He MRI has to offer research and patient care, clinical translation has been inhibited in part due to the cost and need for specialized equipment, including multinuclear-MR hardware and polarizers, and personnel. Accordingly, our objective was to develop and evaluate imaging biomarkers of pulmonary structure and function using MRI and CT without the use of exogenous contrast agents or specialized equipment. First, we developed and compared CT parametric response maps (PRM) with 3He MR ventilation images in measuring gas-trapping and emphysema in ex-smokers with and without COPD. We observed that in mild-moderate COPD, 3He MR ventilation abnormalities were related to PRM gas-trapping whereas in severe COPD, ventilation abnormalities correlated with both PRM gas-trapping and PRM emphysema. We then developed and compared pulmonary ventilation abnormalities derived from Fourier decomposition of free-breathing proton (1H) MRI (FDMRI) with 3He MRI in subjects with COPD and bronchiectasis. This work demonstrated that FDMRI and 3He MRI ventilation defects were strongly related in COPD, but not in bronchiectasis subjects. In COPD only, FDMRI ventilation defects were spatially related with 3He MRI ventilation defects and emphysema. Based on the FDMRI biomarkers developed in patients with COPD and bronchiectasis, we then evaluated ventilation heterogeneity in patients with severe asthma, both pre- and post-salbutamol as well as post-methacholine challenge, using FDMRI and 3He MRI. FDMRI free-breathing ventilation abnormalities were correlated with but under-estimated 3He MRI static ventilation defects. Finally, based on the previously developed free-breathing MRI approach, we developed a whole-lung free-breathing pulmonary 1H MRI technique to measure regional specific-ventilation and evaluated both asthmatics and healthy volunteers. These measurements not only provided similar information as specific-ventilation measured using plethysmography, but also information about regional ventilation defects that were correlated with 3He MRI ventilation abnormalities. These results demonstrated that whole-lung free-breathing 1H MRI biomarker of specific-ventilation may reflect ventilation heterogeneity and/or gas-trapping in asthma. These important findings indicate that imaging biomarkers of pulmonary structure and function using MRI and CT have the potential to regionally reveal the different pathologies in COPD and asthma without the use of exogenous contrast agents. The development and validation of these clinically meaningful imaging biomarkers are critically required to accelerate pulmonary imaging translation from the research workbench to being a part of the clinical workflow, with the overall goal to improve patient outcomes

What can computed tomography and magnetic resonance imaging tell us about ventilation?

This review provides a summary of pulmonary functional imaging approaches for determining pulmonary ventilation, with a specific focus on multi-detector x-ray computed tomography and magnetic resonance imaging (MRI). We provide the important functional definitions of pulmonary ventilation typically used in medicine and physiology and discuss the fact that some of the imaging literature describes gas distribution abnormalities in pulmonary disease that may or may not be related to the physiological definition or clinical interpretation of ventilation. We also review the current state-of-the-field in terms of the key physiological questions yet unanswered related to ventilation and gas distribution in lung disease. Current and emerging imaging research methods are described, including their strengths and the challenges that remain to translate these methods to more wide-spread research and clinical use. We also examine how computed tomography and MRI might be used in the future to gain more insight into gas distribution and ventilation abnormalities in pulmonary disease

This is what COPD looks like

Despite decades of research, and the growing healthcare and societal burden of chronic obstructive pulmonary disease (COPD), therapeutic COPD breakthroughs have not occurred. Sub-optimal COPD patient phenotyping, an incomplete understanding of COPD pathogenesis and a scarcity of sensitive tools that provide patient-relevant intermediate endpoints likely all play a role in the lack of new, efficacious COPD interventions. In other words, COPD patients are still diagnosed based on the presence of persistent airflow limitation measured using spirometry. Spirometry measurements reflect the global sum of all the different possible COPD pathologies and perhaps because of this, we lose sight of the different contributions of airway and parenchymal abnormalities. With recent advances in thoracic X-ray computed tomography (CT) and magnetic resonance imaging (MRI), lung structure and function abnormalities may be regionally identified and measured. These imaging endpoints may serve as biomarkers of COPD that can be used to better phenotype patients. Therefore, here we review novel CT and MRI measurements that help reveal COPD phenotypes and what COPD really \u27looks\u27 like, beyond spirometric indices. We discuss MR and CT imaging approaches for generating reproducible and sensitive measurements of COPD phenotypes related to pulmonary ventilation and perfusion as well as airway and parenchyma anatomical and morphological features. These measurements may provide a way to advance the development and testing of new COPD interventions and therapies

Evaluation of deformable image registration for improved 4D CT-derived ventilation for image guided radiotherapy

Recent treatment planning studies have demonstrated the use of physiologic images in radiation therapy treatment planning to identify regions for functional avoidance. This image-guided radiotherapy (IGRT) strategy may reduce the injury and/or functional loss following thoracic radiotherapy. 4D computed tomography (CT), developed for radiotherapy treatment planning, is a relatively new imaging technique that allows the acquisition of a time-varying sequence of 3D CT images of the patient\u27s lungs through the respiratory cycle. Guerrero et al. developed a method to calculate ventilation imaging from 4D CT, which is potentially better suited and more broadly available for IGRT than the current standard imaging methods. The key to extracting function information from 4D CT is the construction of a volumetric deformation field that accurately tracks the motion of the patient\u27s lungs during the respiratory cycle. The spatial accuracy of the displacement field directly impacts the ventilation images; higher spatial registration accuracy will result in less ventilation image artifacts and physiologic inaccuracies. Presently, a consistent methodology for spatial accuracy evaluation of the DIR transformation is lacking. Evaluation of the 4D CT-derived ventilation images will be performed to assess correlation with global measurements of lung ventilation, as well as regional correlation of the distribution of ventilation with the current clinical standard SPECT. This requires a novel framework for both the detailed assessment of an image registration algorithm\u27s performance characteristics as well as quality assurance for spatial accuracy assessment in routine application. Finally, we hypothesize that hypo-ventilated regions, identified on 4D CT ventilation images, will correlate with hypo-perfused regions in lung cancer patients who have obstructive lesions. A prospective imaging trial of patients with locally advanced non-small-cell lung cancer will allow this hypothesis to be tested. These advances are intended to contribute to the validation and clinical implementation of CT-based ventilation imaging in prospective clinical trials, in which the impact of this imaging method on patient outcomes may be tested

Inverse-Consistent Determination of Young\u27s Modulus of Human Lung

Human lung undergoes respiration-induced deformation due to sequential inhalation and exhalation. Accurate determination of lung deformation is crucial for tumor localization and targeted radiotherapy in patients with lung cancer. Numerical modeling of human lung dynamics based on underlying physics and physiology enables simulation and virtual visualization of lung deformation. Dynamical modeling is numerically complicated by the lack of information on lung elastic behavior, structural heterogeneity as well as boundary constrains. This study integrates physics-based modeling and image-based data acquisition to develop the patient-specific biomechanical model and consequently establish the first consistent Young\u27s modulus (YM) of human lung. This dissertation has four major components: (i) develop biomechanical model for computation of the flow and deformation characteristics that can utilize subject-specific, spatially-dependent lung material property; (ii) develop a fusion algorithm to integrate deformation results from a deformable image registration (DIR) and physics-based modeling using the theory of Tikhonov regularization; (iii) utilize fusion algorithm to establish unique and consistent patient specific Young\u27s modulus and; (iv) validate biomechanical model utilizing established patient-specific elastic property with imaging data. The simulation is performed on three dimensional lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of human subjects. The heterogeneous Young\u27s modulus is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The biomechanical model adequately predicts the spatio-temporal lung deformation, consistent with data obtained from imaging. The accuracy of the numerical solution is enhanced through fusion with the imaging data beyond the classical comparison of the two sets of data. Finally, the fused displacement results are used to establish unique and consistent patient-specific elastic property of the lung