4D-CT Ventilation Research Articles

Robust quantification of CT-ventilation biomarker techniques and repeatability in a porcine model.

Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post-processing techniques. CT-ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT-ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remainingvariables. To characterize CT-ventilation biomarker repeatability and dependence on image acquisition and post-processing methodology in anesthetized and mechanically ventilatedpigs. Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four-dimensional CT (4DCT) and maximum inhale and exhale breath-hold CT (BH-CT) scans on five dates to generate CT-ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian-based post-processing techniques. measured local expansion between an image pair using either inhale and exhale BH-CT images or two 4DCT breathing phase images. measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra- and interday biomarker repeatability, image acquisition and post-processing technique dependence were quantitatively analyzed. Biomarkers showed strong agreement with voxel-wise Spearman correlation for intraday repeatability and for all other comparisons, including between image acquisition techniques. Intra- and interday repeatability were significantly different (p < 0.01). LER2 and LERN post-processing did not significantly affect intraday repeatability. 4DCT and BH-CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhumansubjects.

Quantifying robustness of CT-ventilation biomarkers to image noise.

Purpose: To quantify the impact of image noise on CT-based lung ventilation biomarkers calculated using Jacobian determinant techniques. Methods: Five mechanically ventilated swine were imaged on a multi-row CT scanner with acquisition parameters of 120kVp and 0.6mm slice thickness in static and 4-dimensional CT (4DCT) modes with respective pitches of 1 and 0.09. A range of tube current time product (mAs) values were used to vary image dose. On two dates, subjects received two 4DCTs: one with 10mAs/rotation (low-dose, high-noise) and one with CT simulation standard of care 100mAs/rotation (high-dose, low-noise). Additionally, 10 intermediate noise level breath-hold (BHCT) scans were acquired with inspiratory and expiratory lung volumes. Images were reconstructed with and without iterative reconstruction (IR) using 1mm slice thickness. The Jacobian determinant of an estimated transformation from a B-spline deformable image registration was used to create CT-ventilation biomarkers estimating lung tissue expansion. 24 CT-ventilation maps were generated per subject per scan date: four 4DCT ventilation maps (two noise levels each with and without IR) and 20 BHCT ventilation maps (10 noise levels each with and without IR). Biomarkers derived from reduced dose scans were registered to the reference full dose scan for comparison. Evaluation metrics were gamma pass rate (Γ) with 2mm distance-to-agreement and 6% intensity criterion, voxel-wise Spearman correlation (ρ) and Jacobian ratio coefficient of variation (CoV JR ). Results: Comparing biomarkers derived from low (CTDI vol = 6.07mGy) and high (CTDI vol = 60.7mGy) dose 4DCT scans, mean Γ, ρ and CoV JR values were 93% ± 3%, 0.88 ± 0.03 and 0.04 ± 0.009, respectively. With IR applied, those values were 93% ± 4%, 0.90 ± 0.04 and 0.03 ± 0.003. Similarly, comparisons between BHCT-based biomarkers with variable dose (CTDI vol = 1.35-7.95mGy) had mean Γ, ρ and CoV JR of 93% ± 4%, 0.97 ± 0.02 and 0.03 ± 0.006 without IR and 93% ± 4%, 0.97 ± 0.03 and 0.03 ± 0.007 with IR. Applying IR did not significantly change any metrics (p 0.05). Discussion: This work demonstrated that CT-ventilation, calculated using the Jacobian determinant of an estimated transformation from a B-spline deformable image registration, is invariant to Hounsfield Unit (HU) variation caused by image noise. This advantageous finding may be leveraged clinically with potential applications including dose reduction and/or acquiring repeated low-dose acquisitions for improved ventilation characterization.

Radiation pneumonitis prediction model with integrating multiple dose-function features on 4DCT ventilation images

PurposeRadiation pneumonitis (RP) is dose-limiting toxicity for non-small-cell cancer (NSCLC). This study developed an RP prediction model by integrating dose-function features from computed four-dimensional computed tomography (4DCT) ventilation using the least absolute shrinkage and selection operator (LASSO). MethodsBetween 2013 and 2020, 126 NSCLC patients were included in this study who underwent a 4DCT scan to calculate ventilation images. We computed two sets of candidate dose-function features from (1) the percentage volume receiving > 20 Gy or the mean dose on the functioning zones determined with the lower cutoff percentile ventilation value, (2) the functioning zones determined with lower and upper cutoff percentile ventilation value using 4DCT ventilation images. An RP prediction model was developed by LASSO while simultaneously determining the regression coefficient and feature selection through fivefold cross-validation. ResultsWe found 39.3 % of our patients had a ≥ grade 2 RP. The mean area under the curve (AUC) values for the developed models using clinical, dose-volume, and dose-function features with a lower cutoff were 0.791, and the mean AUC values with lower and upper cutoffs were 0.814. The relative regression coefficient (RRC) on dose-function features with upper and lower cutoffs revealed a relative impact of dose to each functioning zone to RP. RRCs were 0.52 for the mean dose on the functioning zone, with top 20 % of all functioning zone was two times greater than that of 0.19 for these with 60 %–80 % and 0.17 with 40 %–60 % (P < 0.01). ConclusionsThe introduction of dose-function features computed from functioning zones with lower and upper cutoffs in a machine learning framework can improve RP prediction. The RRC given by LASSO using dose-function features allows for the quantification of the RP impact of dose on each functioning zones and having the potential to support treatment planning on functional image-guided radiotherapy.

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.

Different functional lung-sparing strategies and radiotherapy techniques for patients with esophageal cancer.

BackgroundIntegration of 4D-CT ventilation function images into esophageal cancer radiation treatment planning aimed to assess dosimetric differences between different functional lung (FL) protection strategies and radiotherapy techniques.MethodsA total of 15 patients with esophageal cancer who had 4D-CT scans were included. Lung ventilation function images based on Jacobian values were obtained by deformation image registration and ventilation imaging algorithm. Several different plans were designed for each patient: clinical treatment planning (non-sparing planning), the same beam distribution to FL-sparing planning, three fixed-beams FL-sparing intensity-modulated radiation therapy (IMRT) planning (5F-IMRT, 7F-IMRT, 9F-IMRT), and two FL-sparing volumetric modulated arc therapy (VMAT) planning [1F-VMAT (1-Arc), 2F-VMAT (2-Arc)]. The dosimetric parameters of the planning target volume (PTV) and organs at risk (OARs) were compared and focused on dosimetric differences in FL.ResultsThe FL-sparing planning compared with the non-sparing planning significantly decreased the FL-Dmean, V5-30 and Lungs-Dmean, V10-30 (Vx: volume of receiving ≥X Gy), although it slightly compromised PTV conformability and increased Heart-V40 (P< 0.05). The 5F-IMRT had the lowest PTV-conformability index (CI) but had a lower Lungs and Heart irradiation dose compared with those of the 7F-IMRT and 9F-IMRT (P< 0.05). The 2F-VMAT had higher PTV-homogeneity index (HI) and reduced irradiation dose to FL, Lungs, and Heart compared to those of the 1F-VMAT planning (P< 0.05). The 2F-VMAT had higher PTV conformability and homogeneity and decreased FL-Dmean, V5-20 and Lungs-Dmean, V5-10 but correspondingly increased spinal cord-Dmean compared with those of the 5F-IMRT planning (P< 0.05).ConclusionIn this study, 4D-CT ventilation function image-based FL-sparing planning for esophageal cancer can effectively reduce the dose of the FL. The 2F-VMAT planning is better than the 5F-IMRT planning in reducing the dose of FL.

Predicting radiation pneumonitis with fuzzy clustering neural network using 4DCT ventilation image based dosimetric parameters.

To develop a fuzzy clustering neural network to predict radiation-induced pneumonitis (RP) using four-dimensional computed tomography (4DCT) ventilation image (VI) based dosimetric parameters for thoracic cancer patients. The VI were retrospectively calculated from pre-treatment 4DCT data using a deformable image registration (DIR) and an improved VI algorithm. Similar to dose-volume histogram (DVH) of intensity modulated radiotherapy (IMRT), dose-function histogram (DFH) was derived from dose distribution and VI. Then, the dose-function metrics were calculated from DFH. For comparison, the dose-volume metrics were calculated from DVH. Correspondingly, two sets of feature vectors were formed from the dose-volume metrics and the dose-function metrics, respectively. For the feature vectors of each set, they were first pre-processed by principal component analysis (PCA) to reduce feature dimensions. Then, they were grouped to few clusters determined by fuzzy c-means (FCM) algorithm. Next, the neural network was trained to correlate the dosimetric parameters with RP based on the feature vectors of each cluster. Finally, the occurrence of RP was predicted by the neural network on the test data. Through PCA analysis, the top 5 principal components were selected. Their contribution is more than 98%, which is adequate to represent the original feature space of input data. Based on the clustering validity indexes, the optimal number of clusters is 4 and used for subsequent fuzzy clustering of the input data. After network training, the areas under the curve (AUC) of the prediction model is 0.77 using VI-based dosimetric parameters and 0.67 using structure-based dosimetric parameters. Compared to the structure-based dosimetric features, the VI-based dosimetric features are more relevant to lung function and presented higher prediction accuracy of RP. The fuzzy clustering neural network improved the prediction accuracy of RP compared to the conventional neural network. The combination of VI-based dose-function metrics and fuzzy clustering neural network provides an effective predictive model for assessing lung toxicity risk after radiotherapy.

Sparing Functional Lung Using Optimized Volumetric Modulated Arc Therapy in Thoracic Radiation for Locally Advanced Non-Small Cell Lung Cancer

Chemoradiation (CRT) followed by maintenance durvalumab is the standard of care for locally advanced non-small cell lung cancer (NSCLC) but is limited by toxicity to normal lung tissue from radiotherapy and immunotherapy1. Many lung cancer patients have heterogeneous lung function as a result of the tumor and/or pre-existing co-morbidities2. We proposed Volumetric Modulated Arc Therapy (VMAT) could be used to spare the functional parts of the lung (FL) without exceeding organs at risk (OARs) constraints or compromising tumor coverage.Functional lung volumes were retrospectively contoured using 4D-CT ventilation maps for 32 patients with stage III NSCLC treated with CRT and maintenance durvalumab at a single institution from September 2017- October 2019. Functional lung volumes were created from 4D-CT data using methodology previously described3,4. Studies have shown 4D-CT ventilation maps generated from the changes in the density of lungs during the respiratory cycle correlate with pulmonary function tests and ventilation-perfusion imaging5. New VMAT plans were generated to spare the FL while also maintaining adequate planning target volume (PTV) coverage and without exceeding dose constraints to the OARs. The dosimetric values of the original plans were compared to the dosimetric values of the optimized plans using paired t-test or paired Wilcox rank-sum test as appropriate. Specifically, the PTV D95, heart mean, esophagus mean, spinal cord Dmax, as well as the FL mean, FL V5, FL V10, FL V20, and FL V30 were compared.Optimized plans had statically significant lower FL mean dose, FL V5, FL V10, FL V20, and FL V30 (all P values < 0.001). Numerically, the mean dose to the heart, mean dose to the esophagus, and spinal cord Dmax were lower in the optimized plan as compared to the original plan, though only the esophageal dose was statistically significant (P < 0.001). When comparing the PTV D95, the median for both plans were the same (60 Gy), though statistically lower (P = 0.032) in the optimized plan (range, 57-66.15) as compared to the original plan (range, 57-67.27). However, both plans provided adequate PTV coverage.We demonstrated the ability to optimize VMAT plans to avoid functional parts of the lung while maintaining adequate PTV coverage and meeting OAR constraints for locally advanced NSCLC. VMAT plans optimized to avoid functional lung can be created with minimal extra cost or change in treatment planning workflow as 4D-CT acquisition at time of simulation is standard practice, which is advantageous compared to other functional lung avoidance techniques, i.e., single photon emission computerized tomography (SPECT). Given already compromised lung function and risk of pulmonary toxicities from treatment, this work highlights a personalized approach to radiation treatment planning.

A\xa0novel lung-avoidance planning strategy based on 4DCT ventilation imaging and CT density characteristics for stage\xa0III non-small-cell lung cancer patients

BackgroundFunctional planning based merely on 4DCT ventilation imaging has limitations. In this study, we proposed a radiotherapy planning strategy based on 4DCT ventilation imaging and CT density characteristics.Materials and methodsFor 20 stage III non-small-cell lung cancer (NSCLC) patients, clinical plans and lung-avoidance plans were generated. Through deformable image registration (DIR) and quantitative image analysis, a 4DCT ventilation map was calculated. High-, medium-, and low-ventilation regions of the lung were defined based on the ventilation value. In addition, the total lung was also divided into high-, medium-, and low-density areas according to the HU threshold. The lung-avoidance plan aimed to reduce the dose to functional and high-density lungs while meeting standard target and critical structure constraints. Standard and dose–function metrics were compared between the clinical and lung-avoidance plans.ResultsLung avoidance plans led to significant reductions in high-function and high-density lung doses, without significantly increasing other organ at risk (OAR) doses, but at the expense of a significantly degraded homogeneity index (HI) and conformity index (CI; p < 0.05) of the planning target volume (PTV) and a slight increase in monitor units (MU) as well as in the number of segments (p > 0.05). Compared with the clinical plan, the mean lung dose (MLD) in the high-function and high-density areas was reduced by 0.59 Gy and 0.57 Gy, respectively.ConclusionA lung-avoidance plan based on 4DCT ventilation imaging and CT density characteristics is feasible and implementable, with potential clinical benefits. Clinical trials will be crucial to show the clinical relevance of this lung-avoidance planning strategy.

The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer

Functional lung avoidance during radiotherapy can help reduce pulmonary toxicity. This study assessed the potential impact of four-dimensional computed tomography (4DCT)-ventilation imaging-guided proton radiotherapy (PT) on stereotactic body radiotherapy (SBRT) by comparing it with three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT), which employ photon beams. Thirteen lung cancer patients who received SBRT with 3D-CRT were included in the study. 4DCT ventilation was calculated using the patients’ 4DCT data, deformable image registration, and a density-change-based algorithm. Three functional treatment plans sparing the functional lung regions were developed for each patient using 3D-CRT, VMAT, and PT. The prescribed doses and dose constraints were based on the Radiation Therapy Oncology Group 0618 protocol. We evaluated the region of interest (ROI) and functional map-based dose-function metrics for 4DCT ventilation and the irradiated dose. Using 3D-CRT, VMAT, and PT, the percentages of the functional lung regions that received ≥ 5 Gy (fV5) were 26.0%, 21.9%, and 10.7%, respectively; the fV10 were 14.4%, 11.4%, and 9.0%, respectively; and fV20 were 6.5%, 6.4%, and 6.6%, respectively, and the functional mean lung doses (fMLD) were 5.6 Gy, 5.2 Gy, and 3.8 Gy, respectively. These results indicated that PT resulted in a significant reduction in fMLD, fV5, and fV10, but not fV20. The use of PT reduced the radiation to highly functional lung regions compared with those for 3D-CRT and VMAT while meeting all dose constraints.

A comparison of two methods for segmentation of functional volumes in radiotherapy planning of lung cancer patients

Background In radiotherapy (RT) of lung cancer, dose to functional lung (FL) volumes segmented with two different methods (perfusion SPECT (Q-SPECT) and 4D-CT (4D) ventilation (V)) have been shown to correlate with the incidence of radiation pneumonitis (RP). This study aims to compare the FL volumes identified by both methods. Material and methods Thirty lung cancer patients had a 4D and Q-SPECT prior to treatment. Seventeen of these patients also had a ventilation SPECT (V-SPECT). FL sub-volumes were segmented automatically, using cut-off values. The volumes were compared in terms of overlap fraction (OF) relative to the minimal volume, and intersection fraction (IF) of the FL volume relative to the total lung volume (V Lung). Results Cut-off values suggested in literature for Q-SPECT and 4D-V resulted in volumes differing in size by a median 18% [6%;31%], and a median OF and IF of 0.48 [0.23;0.70] and 0.09 [0.02;0.25], respectively. Segmenting volumes of comparable size of about 1/3 of V Lung (FL-m(1/3), m = method) resulted in a median OF and IF of 0.43 [0.23;0.58] and 0.12 [0.06;0.19], respectively. Twenty-five patients (83%) had a reasonable overlap between FL-Q(1/3) and FL-4D-V(1/3) volumes, with OF values above 0.33. IF increased significantly (p = .036) compared to using fixed cut-off values. Similarly, volumes of comparable size of about 1/3 V Lung were produced for V-SPECT, and FL-Q(1/3), FL-V(1/3), and FL-4D-V(1/3) were compared. The overlaps and intersections of FL-V(1/3) with FL-Q(1/3) volumes were significantly (p<.001) larger than the corresponding overlaps and intersections of FL-Q(1/3) with FL-4D(1/3) and FL-V(1/3) with FL-4D(1/3). Conclusion The Q-SPECT and 4D-V methods do not segment entirely the same FL volumes. A reasonable overlap of the volumes along with the findings of other studies that both correlate to RP incidence, suggests that a combination of both volumes, e.g. using the IF, may be useful in RT treatment planning.

The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer.

Functional lung avoidance during radiotherapy can help reduce pulmonary toxicity. This study assessed the potential impact of four-dimensional computed tomography (4DCT)-ventilation imaging-guided proton radiotherapy (PT) on stereotactic body radiotherapy (SBRT) by comparing it with three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT), which employ photon beams. Thirteen lung cancer patients who received SBRT with 3D-CRT were included in the study. 4DCT ventilation was calculated using the patients' 4DCT data, deformable image registration, and a density-change-based algorithm. Three functional treatment plans sparing the functional lung regions were developed for each patient using 3D-CRT, VMAT, and PT. The prescribed doses and dose constraints were based on the Radiation Therapy Oncology Group 0618 protocol. We evaluated the region of interest (ROI) and functional map-based dose-function metrics for 4DCT ventilation and the irradiated dose. Using 3D-CRT, VMAT, and PT, the percentages of the functional lung regions that received ≥ 5Gy (fV5) were 26.0%, 21.9%, and 10.7%, respectively; the fV10 were 14.4%, 11.4%, and 9.0%, respectively; and fV20 were 6.5%, 6.4%, and 6.6%, respectively, and the functional mean lung doses (fMLD) were 5.6Gy, 5.2Gy, and 3.8Gy, respectively. These results indicated that PT resulted in a significant reduction in fMLD, fV5, and fV10, but not fV20. The use of PT reduced the radiation to highly functional lung regions compared with those for 3D-CRT and VMAT while meeting all dose constraints.

4DCT Ventilation Map Construction Using Biomechanics-base Image Registration and Enhanced Air Segmentation.

Current lung radiation therapy (RT) treatment planning algorithms used in most centers assume homogeneous lung function. However, co-existing pulmonary dysfunctions present in many non-small cell lung cancer (NSCLC) patients, particularly smokers, cause regional variations in both perfusion and ventilation, leading to inhomogeneous lung function. An adaptive RT treatment planning that deliberately avoids highly functional lung regions can potentially reduce pulmonary toxicity and morbidity. The ventilation component of lung function can be measured using a variety of techniques. Recently, 4DCT ventilation imaging has emerged as a cost-effective and accessible method. Current 4DCT ventilation calculation methods, including the intensity-based and Jacobian models, suffer from inaccurate estimations of air volume distribution and unreliability of intensity-based image registration algorithms. In this study, we propose a novel method that utilizes a biomechanical model-based registration along with an accurate air segmentation algorithm to calculate 4DCT ventilation maps. The results show a successful development of ventilation maps using the proposed method.

4D CT Ventilation Predicts for Changes in Pulmonary Perfusion after Radiation Treatment for Lung Cancer

4D CT Ventilation Predicts for Changes in Pulmonary Perfusion after Radiation Treatment for Lung Cancer

Dosimetric feasibility of 4DCT-ventilation imaging guided proton therapy for locally advanced non-small-cell lung cancer

BackgroundThe principle aim of this study is to incorporate 4DCT ventilation imaging into functional treatment planning that preserves high-functioning lung with both double scattering and scanning beam techniques in proton therapy.MethodsEight patients with locally advanced non-small-cell lung cancer were included in this study. Deformable image registration was performed for each patient on their planning 4DCTs and the resultant displacement vector field with Jacobian analysis was used to identify the high-, medium- and low-functional lung regions. Five plans were designed for each patient: a regular photon IMRT vs. anatomic proton plans without consideration of functional ventilation information using double scattering proton therapy (DSPT) and intensity modulated proton therapy (IMPT) vs. functional proton plans with avoidance of high-functional lung using both DSPT and IMPT. Dosimetric parameters were compared in terms of tumor coverage, plan heterogeneity, and avoidance of normal tissues.ResultsOur results showed that both DSPT and IMPT plans gave superior dose advantage to photon IMRTs in sparing low dose regions of the total lung in terms of V5 (volume receiving 5Gy). The functional DSPT only showed marginal benefit in sparing high-functioning lung in terms of V5 or V20 (volume receiving 20Gy) compared to anatomical plans. Yet, the functional planning in IMPT delivery, can further reduce the low dose in high-functioning lung without degrading the PTV dosimetric coverages, compared to anatomical proton planning. Although the doses to some critical organs might increase during functional planning, the necessary constraints were all met.ConclusionsIncorporating 4DCT ventilation imaging into functional proton therapy is feasible. The functional proton plans, in intensity modulated proton delivery, are effective to further preserve high-functioning lung regions without degrading the PTV coverage.

CT ventilation imaging derived from breath hold CT exhibits good regional accuracy with Galligas PET

Background and purposeCT ventilation imaging (CTVI) derived from four dimensional CT (4DCT) has shown only moderate spatial accuracy in humans due to 4DCT image artefacts. Here we assess the accuracy of an improved CTVI using high quality exhale/inhale breath-hold CT (BHCT). Materials and methodsEighteen lung cancer patients underwent exhale/inhale BHCT, 4DCT and Galligas PET ventilation scans in a single imaging session. For each BHCT and 4DCT scan, we performed deformable image registration (DIR) between the inhale and exhale phase images to quantify ventilation using three published metrics: (i) breathing induced lung density change, CTVIDIR-HU (ii) breathing induced volume change CTVIDIR-Jac and (iii) the regional air-tissue product, CTVIHU Spatial accuracy was reported as the voxel-wise Spearman correlation r between CTVI and Galligas PET. ResultsFor BHCT-based CTVIs (N = 16), the CTVIDIR-HU, CTVIDIR-Jac and CTVIHU methods yielded mean (range) r values of 0.67 (0.52–0.87), 0.57 (0.18–0.77) and 0.49 (0.14–0.75) respectively. By comparison the 4DCT-based CTVIs (n = 14) had values of 0.32 (−0.04 to 0.51), 0.16 (−0.31 to 44) and 0.49 (0.20–0.77) respectively. ConclusionsHigh quality CT imaging is a key requirement for accurate CT ventilation imaging. The use of exhale/inhale BHCT can improve the accuracy of CTVI for human subjects.

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.

Regional Lung Function Profiles of Stage I and III Lung Cancer Patients: An Evaluation for Functional Avoidance Radiation Therapy

The development of clinical trials is underway to use 4-dimensional computed tomography (4DCT) ventilation imaging to preferentially spare functional lung in patients undergoing radiation therapy. The purpose of this work was to generate data to aide with clinical trial design by retrospectively characterizing dosimetric and functional profiles for patients with different stages of lung cancer. A total of 118 lung cancer patients (36% stage I and 64% stage III) from 2 institutions were used for the study. A 4DCT-ventilation map was calculated using the patient's 4DCT imaging, deformable image registration, and a density-change-based algorithm. To assess each patient's spatial ventilation profile both quantitative and qualitative metrics were developed, including an observer-based defect observation and metrics based on the ventilation in each lung third. For each patient we used the clinical doses to calculate functionally weighted mean lung doses and metrics that assessed the interplay between the spatial location of the dose and high-functioning lung. Both qualitative and quantitative metrics revealed a significant difference in functional profiles between the 2 stage groups (P<.01). We determined that 65% of stage III and 28% of stage I patients had ventilation defects. Average functionally weighted mean lung dose was 19.6Gy and 5.4Gy for stage III and I patients, respectively, with both groups containing patients with large spatial overlap between dose and high-function regions. Our 118-patient retrospective study found that 65% of stage III patients have regionally variant ventilation profiles that are suitable for functional avoidance. Our results suggest that regardless of disease stage, it is possible to have unique spatial interplay between dose and high-functional lung, highlighting the importance of evaluating the function of each patient and developing a personalized functional avoidance treatment approach.

Evaluation of four-dimensional computed tomography (4D-CT)-based pulmonary ventilation: The high correlation between 4D-CT ventilation and 81mKr-planar images was found

PurposeTo evaluate four-dimensional computed tomography (4D-CT)-derived pulmonary ventilation by comparing with 81mKr-gas ventilation (VRI). We also proposed two methods to improve the functional accuracy of 4D-CT ventilation images and evaluated these methods. Methods and materialsEleven lung cancer patients with 4D-CT and VRI were analyzed. Hounsfield unit-based (VHU) and a Jacobian-based (VJac) 4D-CT ventilation images were calculated. They were evaluated by voxel-by-voxel spearman’s rank correlation coefficient (r) between 4D-CT ventilation and VRI images. After applying an averaging ventilation method and a slope calculating method, correlations were also calculated. Results4D-CT ventilation showed the high correlation to VRI (r=0.875 with VHU). An averaging method brought significantly higher (p=0.012) correlations to nuclear medicine images with VHU. The improvement was not significant (p=0.619) with VJac. Slope calculating method improved the correlation with VHU and slightly worsened the correlation with VJac. ConclusionsThe averaging method we proposed might be useful to improve 4D-CT ventilation images. We found good agreement between 4D-CT ventilation and nuclear medicine ventilation, indicating the high physiologic accuracy of 4D-CT ventilation.

Analysis of Long-Term 4-Dimensional Computed Tomography Regional Ventilation After Radiation Therapy

To determine whether regional ventilation, as measured using 4-dimensional computed tomography (4D-CT), declines after radiation therapy (RT). We analyzed pretreatment 4D-CT scans associated with 2 RT courses. We quantified regional pulmonary function over equivalent dose in 2 Gy (EQD2α/β=3) intervals of 0 to 5 Gy, 5 to 20 Gy, 20 to 40 Gy, and >40 Gy using percentile-normalized intensity-based (VentInt) and Jacobian-based (VentJac) ventilation metrics. We modeled the impact of dose on mean ventilation (Vent¯) and regional tidal volume (rTV: tidal volume [TV] within a dose interval normalized to total lung TV). We also identified clinical and dosimetric factors that affected regional ventilation changes (ΔVent¯ and ΔrTV) after RT for the >20 Gy dose interval. After RT, Vent¯Int exhibited statistically significant dose-dependent declines within the 20 to 40 Gy (-5.0%; P=.03) and >40 Gy (-6.8%; P<.01) intervals. Vent¯Jac exhibited a declining trend after RT only for the >40 Gy interval (-4.6%; P=.07). Factors associated with ΔVent¯Int for the >20 Gy dose interval included airway stenosis progression (P=.03) and gross tumor volume (P=.09). Both rTVInt and rTVJac were associated with small (<2%) but significant declines after RT for 20 to 40 Gy and >40 Gy intervals. Factors associated with declining rTVInt (P<.05) for the >20 Gy dose interval included airway stenosis progression, greater V20 (volume of lung receiving >20 Gy), and smaller fraction of emphysema in V20. The association between the absence of chronic obstructive pulmonary disease and declining rTV trended toward significance (P=.09). Regional ventilation, as measured using 4D-CT, demonstrates a dose-dependent decline after RT. Our results support the use of 4D-CT ventilation imaging for monitoring regional pulmonary function change after RT.

Clinical Validation of 4-Dimensional Computed Tomography Ventilation With Pulmonary Function Test Data

A new form of functional imaging has been proposed in the form of 4-dimensional computed tomography (4DCT) ventilation. Because 4DCTs are acquired as part of routine care for lung cancer patients, calculating ventilation maps from 4DCTs provides spatial lung function information without added dosimetric or monetary cost to the patient. Before 4DCT-ventilation is implemented it needs to be clinically validated. Pulmonary function tests (PFTs) provide a clinically established way of evaluating lung function. The purpose of our work was to perform a clinical validation by comparing 4DCT-ventilation metrics with PFT data. Ninety-eight lung cancer patients with pretreatment 4DCT and PFT data were included in the study. Pulmonary function test metrics used to diagnose obstructive lung disease were recorded: forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity. Four-dimensional CT data sets and spatial registration were used to compute 4DCT-ventilation images using a density change-based and a Jacobian-based model. The ventilation maps were reduced to single metrics intended to reflect the degree of ventilation obstruction. Specifically, we computed the coefficient of variation (SD/mean), ventilation V20 (volume of lung ≤20% ventilation), and correlated the ventilation metrics with PFT data. Regression analysis was used to determine whether 4DCT ventilation data could predict for normal versus abnormal lung function using PFT thresholds. Correlation coefficients comparing 4DCT-ventilation with PFT data ranged from 0.63 to 0.72, with the best agreement between FEV1 and coefficient of variation. Four-dimensional CT ventilation metrics were able to significantly delineate between clinically normal versus abnormal PFT results. Validation of 4DCT ventilation with clinically relevant metrics is essential. We demonstrate good global agreement between PFTs and 4DCT-ventilation, indicating that 4DCT-ventilation provides a reliable assessment of lung function. Four-dimensional CT ventilation enables exciting opportunities to assess lung function and create functional avoidance radiation therapy plans. The present work provides supporting evidence for the integration of 4DCT-ventilation into clinical trials.