Computed Tomography Ventilation Research Articles

Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database

A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods. The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]). Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively. Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.

Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.

The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

Airway ultrasound to detect subglottic secretion above endotracheal tube cuff

BackgroundSubglottic secretion had been proven as one of the causes of microaspiration and increased risk of ventilator-associated pneumonia (VAP). The role of ultrasound to detect subglottic secretion has not yet been established.PurposeThe purpose of this study is to determine the sensitivity and specificity of upper airway ultrasound (US) in the detection of subglottic secretions as compared to computed tomography (CT) scanning.Material and methodsA prospective observational study was carried out in adult trauma patients requiring mechanical ventilation and cervical CT scan. All patients had an endotracheal tube cuff-pressure maintained between 20 and 30 cm H2O. Airway US was performed at the bedside immediately before the patient was transferred to the CT scan suite. The sensitivity, specificity, and positive/negative predictive values (PPV, NPV) of the upper airway US detection of subglottic secretions were then calculated and compared with CT findings.ResultsFifty participants were consecutively included. Subglottic secretions were detected in 31 patients using upper airway US. The sensitivity and specificity of upper airway US in detecting subglottic secretion were 96.7% and 90%, respectively (PPV 93.5%, NPV 94.7%). 18 (58%) patients with subglottic secretions developed VAP during their ICU stay (p = 0.01). The area under the receiver operating curve (AUROC) was 0.977 (95% CI 0.936–1.00).ConclusionsUpper airway US is a useful tool for detecting subglottic secretions with high sensitivity and specificity.Clinical implicationsThis study shows:Upper airway US may aid in detecting subglottic secretions, which are linked to VAP.Detecting subglottic secretions at the bedside aids in determining the best frequency of subglottic aspiration to clean the subglottic trachea.Upper airway US may also aid in detecting the correct ETT position.Trial registration Clinicaltrials.gov. Clinicaltrials.gov identifier NCT04739878 Date of registration 2nd May 2021 URL of trial registry record https://clinicaltrials.gov/ct2/show/NCT04739878.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield . For the performance evaluation, the voxel- and region-wise differences between and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, and , and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the (0.33 ± 0.14, p < 0.05) and (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for (0.63 ± 0.07) was significantly higher than the corresponding values for the (0.43 ± 0.08, p < 0.05) and (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial

The primary objective of this prospective pilot trial was to assess the safety and feasibility of lung functional avoidance radiation therapy (RT) with 4-dimensional (4D) computed tomography (CT) ventilation imaging. Patients with primary lung cancer or metastatic disease to the lungs to receive conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible. Standard-of-care 4D-CT scans were used to generate ventilation images through image processing/analysis. Each patient required a standard intensity modulated RT plan and ventilation image guided functional avoidance plan. The primary endpoint was the safety of functional avoidance RT, defined as the rate of grade ≥3 adverse events (AEs) that occurred ≤12 months after treatment. Protocol treatment was considered safe if the rates of grade ≥3 pneumonitis and esophagitis were <13% and <21%, respectively for CFRT, and if the rate of any grade ≥3 AEs was <28% for SBRT. Feasibility of functional avoidance RT was assessed by comparison of dose metrics between the 2 plans using the Wilcoxon signed-rank test. Between May 2015 and November 2019, 34 patients with non-small cell lung cancer were enrolled, and 33 patients were evaluable (n=24 for CFRT; n=9 for SBRT). Median follow-up was 14.7 months. For CFRT, the rates of grade ≥3 pneumonitis and esophagitis were 4.2% (95% confidence interval, 0.1%-21.1%) and 12.5% (2.7%-32.4%). For SBRT, no patients developed grade ≥3 AEs. Compared with the standard plans, the functional avoidance plans significantly (P < .01) reduced the lung dose-function metrics without compromising target coverage or adherence to standard organs at risk constraints. This study, representing one of the first prospective investigations on lung functional avoidance RT, demonstrated that the 4D-CT ventilation image guided functional avoidance RT that significantly reduced dose to ventilated lung regions could be safely administered, adding to the growing body of evidence for its clinical utility.

Functional Lung Avoidance for Individualized Radiation Therapy: Results of a Double-Masked, Randomized Controlled Trial

To determine whether functional lung avoidance based on 3He magnetic resonance imaging (MRI) improves quality of life (QOL) for patients undergoing concurrent chemoradiotherapy (CCRT) for advanced non-small cell lung cancer. Patients with stage III non-small cell lung cancer (or oligometastatic disease treated with curative intent) undergoing CCRT with at least a 10 pack-year smoking history were eligible. Patients underwent pretreatment 3He MRI to measure lung ventilation and had 2 radiation therapy (RT) plans created before randomization: a standard plan, which did not make use of the 3He MRI, and an avoidance plan, preferentially sparing well-ventilated lung. All participants were masked to assignment except the physicist responsible for exporting the selected plan. The primary end point was patient-reported QOL measured at 3-months post-RT by the FACT-L lung cancer subscale (LCS); secondary end points included other QOL metrics, toxicity, and survival outcomes. Target accrual was 64. Twenty-seven patients were randomized before the trial was closed due to slower-than-expected accrual, with 11 randomized to the standard arm and 16 to the avoidance arm. Baseline patient characteristics were well-balanced. At 3 months post-RT, the mean ± SD LCS scores were 17.4 ± 2.8 versus 17.3 ± 6.1 for the standard and avoidance arms, respectively (P = .485). A clinically meaningful, prespecified decline of ≥3 points in the LCS score was observed in 50% (4/8) in the standard arm and 33% (4/12) in the avoidance arm (P = .648). Two patients in each arm developed grade ≥2 radiation pneumonitis, with no grade ≥4 toxicities. Although this trial did not reach full accrual, QOL scores were very similar between arms. Due to the scarcity of 3He MRI, other, more commonly available methods to measure functional lung, such as 4-dimensional computed tomography ventilation mapping, may be considered in the assessment of functional lung avoidance RT, and a larger, multicenter approach would be needed to accrue sufficient patients to test such approaches.

Contamination of CT scanner surfaces with SARS-CoV-2 and infective potential after examination of invasively ventilated, non-invasively ventilated and non-ventilated patients with positive throat swabs: prospective investigation using real-time reverse-transcription PCR and viral cell culture

BackgroundDuring the current severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic, computed tomography (CT) has become widely used in patients with suspected or known coronavirus disease 2019 (COVID-19). This prospective observational study in 28 invasively ventilated and 18 non-invasively ventilated patients with confirmed SARS-CoV-2 contamination aims at investigating SARS-CoV-2 contamination of CT scanner surfaces and its infectiousness.MethodsSwab sampling of the CT table and gantry before and after CT examinations was performed. Additionally, the CT ventilation system air grid was wiped off after each examination. Real-time reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 RNA (ribonucleic acid) and viral cell culture were performed in the virology core lab.ResultsAfter examination of non-invasively ventilated or non-ventilated patients, SARS-CoV-2 RNA was found in 11.1% (4/36) on patient near surfaces (CT table and gantry) and in 16.7% (3/18) on the CT air grid respectively after examination of invasively ventilated patients in 5.4% (3/56) on CT table and gantry and 7.1% (2/28) on the CT air grid. Surface contamination was more common in non-invasively ventilated or non-ventilated patients with a high viral load who were actively coughing. RT-PCR cycle threshold (Ct) was high (35.96–39.31) in all positive samples and no positive viral cell culture was found.ConclusionOur study suggests that CT scanner surface contamination with SARS-CoV-2 is considerable and more common after examination of non-invasively ventilated or non-ventilated patients compared to invasively ventilated patients. However, no viral cell culture positivity was found, hence the infectious potential seems low.

CT Ventilation Image-Guided Lung Functional Avoidance Radiotherapy: A Single-Arm Prospective Pilot Clinical Trial

Lung functional avoidance radiotherapy (RT) that preferentially avoids irradiating functionally normal lung regions has the potential to reduce pulmonary toxicity after RT. The primary objective of this single-arm prospective pilot clinical trial (NCT02308709) was to assess the safety and feasibility of computed tomography (CT) ventilation image-guided functional avoidance RT. We herein report on the feasibility and dosimetric results. Patients with primary lung cancer or metastatic disease to the lungs to be treated with either conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible for this trial. All patients underwent 4-dimensional (4D) CT ventilation imaging during RT simulation based on a 4D CT scan, deformable image registration, and image analysis to quantify regional volume changes as surrogates for ventilation. Each patient required clinically-approved intensity-modulated RT plans for both standard anatomic RT and CT ventilation image-guided functional avoidance RT without compromising target coverage or adherence to constraints of organs at risk (OARs). The lung dose-function metrics were compared between the two plans using the paired two-tailed t test. The primary endpoint was the rate of grade ≥3 toxicities that occurred within 12 months after treatment, to be compared with predefined acceptable rates determined based on published toxicity data. Between May 2015 and November 2019, 34 patients were enrolled (25 patients with stage II-III NSCLC to be treated with CFRT with concurrent chemotherapy and 9 patients with stage I-II NSCLC to be treated with SBRT). One patient died before initiating RT. Of 33 evaluable patients, 32 patients received full dose (CFRT 60 Gy in 2 Gy daily fractions; SBRT 54 Gy in 3 fractions or 55 Gy in 5 fractions) and 1 patient chose to discontinue concurrent chemoradiotherapy due to grade 4 esophagitis. Compared with standard RT planning, functional avoidance RT planning significantly reduced the functional mean lung dose by 0.9 Gy (95% confidence interval [CI] 0.6-1.2 Gy; P < 0.01) (relative reduction 5.2%; 95% CI 3.5-6.8%) and the percentage of total lung function receiving ≥20 Gy by 4.2% (95% CI 3.0-5.4%; P < 0.01) (relative reduction 13.2%; 95% CI 10.5-15.9%) without compromising target coverage or adherence to constraints of OARs in patients treated with CFRT. In patients treated with SBRT, functional avoidance RT planning also significantly reduced the functional mean lung dose by 0.9 Gy (95% confidence interval [CI] 0.7-1.1 Gy; P < 0.01) (relative reduction 9.6%; 95% CI 6.6-12.7%). This pilot study demonstrates the feasibility of CT ventilation image-guided functional avoidance RT that significantly reduces the dose to functionally normal lung regions. Final toxicity results will be reported upon completion of the follow-up phase.

Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom.

We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VICBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VICBCT- and 4D-CT-based ventilation images (VICT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VICBCT. Correlations between the VICBCT and VICT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VICBCT with a gantry speed of 0.6deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ=0.05 and r=0.68). Thus, the r value of the Jacobian-based VICBCT was greater or equal to that of the HU-based VICBCT. In addition, the ventilation accuracy of VICBCT increased at low gantry speeds. Thus, the image quality of VICBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VICBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VICBCT imaging into the functional avoidance planning process.

Quantitative Multivolume Proton-Magnetic Resonance Imaging in Lung Transplant Recipients: Comparison With Computed Tomography and Spirometry

Acute and chronic graft rejection remains the major problem in clinical surveillance of lung-transplanted patients and early detection of complications is of capital importance to allow the optimal therapeutic option. The aim of this study was to investigate the role of quantitative non contrast-enhanced magnetic resonance imaging (MRI) as a non-ionizing imaging modality to assess ventilation impairment in patients who have undergone lung transplantation, in comparison with quantitative computed tomography (CT) and spirometry. Ten lung-transplanted patients (39 ±12 years, forced-expiratory volume in 1 second (FEV1) = 81 ± 27%, forced vital capacity (FVC) = 87 ± 27%) were acquired in breath-hold at full-expiration and full-inspiration with 1.5T MRI and CT. Maps of expiratory-inspiratory difference in MR signal-intensity and CT-density were computed to estimate regional ventilation. Based on expiratory, inspiratory, and expiratory-inspiratory difference values, each pixel was classified as healthy (H), low ventilation (LV), air trapping (AT), and consolidation (C) and the percent extent of each class was quantified. Overall, expiratory-inspiratory difference in MR signal-intensity correlated to CT-density (r = 0.64, p < 0.0001) and to FEV1 (ρ = 0.71, p = 0.02). The linear correlation between MRI and CT functional maps considering all the four classes is r = 0.93 (p < 0.0001). MRI percent volumes of H, AT, and C correlated to FEV1 %pred, with the highest correlation reported for AT (ρ = -0.82). Results demonstrated a good agreement between MRI and CT ventilation imaging and between the corresponding percent volumes of lung damage. Quantitative MRI may represent an accurate non-ionizing imaging technique for longitudinal monitoring of lung transplant recipients.

Robust HU-based CT ventilation from an integrated mass conservation formulation.

Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high‐intensity vessel segmentation, and a post‐processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel.PurposeWe introduce the novel mass‐conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post‐processing Gaussian smoothing.MethodsSubregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert‐determined landmark point pairs and inhale/exhale phases from 10 four‐dimensional CTs (4DCTs) available on http://www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation‐based ventilation method.ResultsThe ten Dir‐Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11).ConclusionWhile HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms.

Robust CT ventilation from the integral formulation of the Jacobian.

Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation‐based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation's Jacobian have been shown to be numerically unstable. As a result, transformation‐based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method.PurposeWe introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR‐recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit‐or‐miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that (a) respect the resolution of the digital grid and (b) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible.MethodsNumerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert‐determined landmark point pairs and inhale/exhale phases from 10 four‐dimensional computed tomographies (4DCTs) available on http://www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy.ResultsThe ten Dir‐Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For intermodality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method.ConclusionThe mathematical theory underpinning the IJF method allows for the generation of ventilation images that are (a) computed with respect to DIR spatial accuracy on the digital voxel grid and (b) based on DIR‐measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with respect to both DIR algorithm and CT acquisition method, in comparison to the standard finite difference approach.

Probing Changes in Lung Physiology in COPD Using CT, Perfusion MRI, and Hyperpolarized Xenon-129 MRI

Chronic obstructive pulmonary disease (COPD) is highly heterogeneous and not well understood. Hyperpolarized xenon-129 (Xe129) magnetic resonance imaging (MRI) provides a unique way to assess important lung functions such as gas uptake. In this pilot study, we exploited multiple imaging modalities, including computed tomography (CT), gadolinium-enhanced perfusion MRI, and Xe129 MRI, to perform a detailed investigation of changes in lung morphology and functions in COPD. Utility and strengths of Xe129 MRI in assessing COPD were also evaluated against the other imaging modalities. Four COPD patients and four age-matched normal subjects participated in this study. Lung tissue density measured by CT, perfusion measures from gadolinium-enhanced MRI, and ventilation and gas uptake measures from Xe129 MRI were calculated for individual lung lobes to assess regional changes in lung morphology and function, and to investigate correlations among the different imaging modalities. No significant differences were found for all measures among the five lobes in either the COPD or age-matched normal group. Strong correlations (R > 0.5 or < -0.5, p < 0.001) were found between ventilation and perfusion measures. Also gas uptake by blood as measured by Xe129 MRI showed strong correlations with CT tissue density and ventilation measures (R > 0.5 or < -0.5, p < 0.001) and moderate to strong correlations with perfusion measures (R > 0.4 or < -0.5, p < 0.01). Four distinctive patterns of functional abnormalities were found in patients with COPD. Xe129 MRI has high potential to uniquely identify multiple changes in lung physiology in COPD using a single breath-hold acquisition.

Spatial Comparison of CT-Based Surrogates of Lung Ventilation With Hyperpolarized Helium-3 and Xenon-129 Gas MRI in Patients Undergoing Radiation Therapy

To develop and apply an image acquisition and analysis strategy for spatial comparison of computed tomography (CT)-ventilation images with hyperpolarized gas magnetic resonance imaging (MRI). Eleven lung cancer patients underwent xenon-129 (129Xe) and helium-3 (3He) ventilation MRI and coregistered proton (1H) anatomic MRI. Expiratory and inspiratory breath-hold CTs were used for deformable image registration and calculation of 3 CT-ventilation metrics: Hounsfield unit (CTHU), Jacobian (CTJac), and specific gas volume change (CTSGV). Inspiration CT and hyperpolarized gas ventilation MRI were registered via same-breath anatomic 1H-MRI. Voxel-wise Spearman correlation coefficients were calculated between each CT-ventilation image and its corresponding 3He-/129Xe-MRI, and for the mean values in regions of interest (ROIs) ranging from fine to coarse in-plane dimensions of 5×5, 10×10, 15×15, and 20×20, located within the lungs as defined by the same-breath 1H-MRI lung mask. Correlation of 3He and 129Xe-MRI was also assessed. Spatial correlation of CT-ventilation against 3He/129Xe-MRI increased with ROI size. For example, for CTHU, mean±SD Spearman coefficients were 0.37±0.19/0.33±0.17 at the voxel-level and 0.52±0.20/0.51±0.18 for 20×20 ROIs, respectively. Correlations were stronger for CTHU than for CTJac or CTSGV. Correlation of 3He with 129Xe-MRI was consistently higher than either gas against CT-ventilation maps over all ROIs (P<.05). No significant differences were observed between CT-ventilation versus 3He-MRI and CT-ventilation versus 129Xe-MRI. Comparison of ventilation-related measures from CT and registered hyperpolarized gas MRI is feasible at a voxel level using a dedicated acquisition and analysis protocol. Moderate correlation between CT-ventilation and MRI exists at a regional level. Correlation between MRI and CT is significantly less than that between 3He and 129Xe-MRI, suggesting that CT-ventilation surrogate measures may not be measuring lung ventilation alone.

Changes in Regional Ventilation During Treatment and Dosimetric Advantages of CT Ventilation Image Guided Radiation Therapy for Locally Advanced Lung Cancer

Lung functional image guided radiation therapy (RT) that avoids irradiating highly functional regions has potential to reduce pulmonary toxicity following RT. Tumor regression during RT is common, leading to recovery of lung function. We hypothesized that computed tomography (CT) ventilation image-guided treatment planning reduces the functional lung dose compared to standard anatomic image-guided planning in 2 different scenarios with or without plan adaptation. CT scans were acquired before RT and during RT at 2 time points (16-20Gy and 30-34Gy) for 14 patients with locally advanced lung cancer. Ventilation images were calculated by deformable image registration of four-dimensional CT image data sets and image analysis. We created 4 treatment plans at each time point for each patient: functional adapted, anatomic adapted, functional unadapted, and anatomic unadapted plans. Adaptation was performed at 2 time points. Deformable image registration was used for accumulating dose and calculating a composite of dose-weighted ventilation used to quantify the lung accumulated dose-function metrics. The functional plans were compared with the anatomic plans for each scenario separately to investigate the hypothesis at a significance level of 0.05. Tumor volume was significantly reduced by 20% after 16 to 20Gy(P=.02) and by 32% after 30 to 34Gy(P<.01) on average. In both scenarios, the lung accumulated dose-function metrics were significantly lower in the functional plans than in the anatomic plans without compromising target volume coverage and adherence to constraints to critical structures. For example, functional planning significantly reduced the functional mean lung dose by 5.0% (P<.01) compared to anatomic planning in the adapted scenario and by 3.6% (P=.03) in the unadapted scenario. This study demonstrated significant reductions in the accumulated dose to the functional lung with CT ventilation image-guided planning compared to anatomic image-guided planning for patients showing tumor regression and changes in regional ventilation during RT.

Evaluation of Lung Toxicity Risk with Computed Tomography Ventilation Functional Image for Lung Stereotactic Body Radiation Therapy and Three-Dimensional Conformal Radiation Therapy

Ventilated regions using 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated clinical correlations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. Pre-treatment 4D-CT data were used to compute ventilation images for 40 lung cancer patients. Ventilation images were calculated from 4D-CT data using a DIR and Jacobian-based algorithm. We normalized each ventilation map by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5, and V20 in highly and poorly ventilated regions) were calculated. To test whether the ventilation-based dosimetric parameters can be used for prediction of radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from receiver operating characteristic analysis. For Mean Dose, poorly ventilated lung regions in the 0–30% observed the highest AUC value (0.809) (95% confidence interval (CI), 0.663 to 0.955). For V20, poorly ventilated lung regions in the 0–20% had the highest AUC value (0.774)(95% CI, 0.598 to 0.915). For V5, poorly ventilated lung regions in the 0–30% observed the highest AUC value (0.843)(95% CI, 0.732 to 0.954). These results showed that the highest AUC values for Mean Dose, V20, and V5 were obseved in poorly ventilated regions (0-20%, 0-30%). Our results showed that poorly ventilated lung regions had higher AUC values than highly ventilated regions, suggesting that 4D-CT ventilation-based functional planning may reduce the lung toxicity risk after radiation therapy.

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

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. 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. 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>3.6mm 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. 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.

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

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. 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. 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). 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 Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy

Four-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials.There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP). Seventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics. The most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20Gy (AUC, 0.70). Probabilities of grade 3+RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20Gy) to the functional subvolumes of 26.8% and 9.3%, respectively.Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20Gy.All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55). A full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+RP provides valuable data to inform the functional-guided radiation therapy process.

The numerical stability of transformation-based CT ventilation

Computed tomography (CT)-derived ventilation imaging utilizes deformable image registration (DIR) to recover respiratory-induced tissue volume changes from inhale/exhale 4DCT phases. While current strategies for validating CT ventilation rely on analyzing its correlation with existing functional imaging modalities, the numerical stability of the CT ventilation calculation has not been characterized.PurposeThe purpose of this study is to examine how small changes in the DIR displacement field can affect the calculation of transformation-based CT ventilation.MethodsFirst, we derive a mathematical theorem, which states that the change in ventilation metric induced by a perturbation to single displacement vector is bounded by the perturbation magnitude. Second, we introduce a novel Jacobian constrained optimization method for computing user-defined CT ventilation images.ResultsUsing the Jacobian constrained method, we demonstrate that for the same inhale/exhale CT pair, it is possible to compute two DIR transformations that have similar spatial accuracies, but generate ventilation images with significantly different physical characteristics. In particular, we compute a CT ventilation image that perfectly correlates with a single-photon emission CT perfusion scan.ConclusionThe analysis and experiments indicate that while transformation-based CT ventilation is a promising modality, small changes in the DIR displacement field can result in large relative changes in the ventilation image. As such, approaches for improving the reproducibility of CT ventilation are still needed.