Computed Tomography Ventilation Imaging 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.

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.

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with 81m Kr-gas SPECT ventilation imaging.

PurposeThis study aimed to evaluate the accuracy of deep learning (DL)‐based computed tomography (CT) ventilation imaging (CTVI).MethodsA total of 71 cases that underwent single‐photon emission CT 81mKr‐gas ventilation (SPECT V) and CT imaging were included. Sixty cases were assigned to the training and validation sets, and the remaining 11 cases were assigned to the test set. To directly transform three‐dimensional (3D) CT (free‐breathing CT) images to SPECT V images, a DL‐based model was implemented based on the U‐Net architecture. The input and output data were 3DCT‐ and SPECT V‐masked, respectively, except for whole‐lung volumes. These data were rearranged in voxel size, registered rigidly, cropped, and normalized in preprocessing. In addition to a standard estimation method (i.e., without dropout during the estimation process), a Monte Carlo dropout (MCD) method (i.e., with dropout during the estimation process) was used to calculate prediction uncertainty. To evaluate the two models’ (CTVIMCD U‐Net, CTVIU‐Net) performance, we used fivefold cross‐validation for the training and validation sets. To test the final model performances for both approaches, we applied the test set to each trained model and averaged the test prediction results from the five trained models to acquire the mean test result (bagging) for each approach. For the MCD method, the models were predicted repeatedly (sample size = 200), and the average and standard deviation (SD) maps were calculated in each voxel from the predicted results: The average maps were defined as test prediction results in each fold. As an evaluation index, the voxel‐wise Spearman rank correlation coefficient (Spearman r s) and Dice similarity coefficient (DSC) were calculated. The DSC was calculated for three functional regions (high, moderate, and low) separated by an almost equal volume. The coefficient of variation was defined as prediction uncertainty, and these average values were calculated within three functional regions. The Wilcoxon signed‐rank test was used to test for a significant difference between the two DL‐based approaches.ResultsThe average indexes with one SD (1SD) between CTVIMCD U‐Net and SPECT V were 0.76 ± 0.06, 0.69 ± 0.07, 0.51 ± 0.06, and 0.75 ± 0.04 for Spearman r s, DSChigh, DSCmoderate, and DSClow, respectively. The average indexes with 1SD between CTVIU‐Net and SPECT V were 0.72 ± 0.05, 0.66 ± 0.04, 0.48 ± 0.04, and 0.74 ± 0.06 for Spearman r s, DSChigh, DSCmoderate, and DSClow, respectively. These indexes between CTVIMCD U‐Net and CTVIU‐Net showed no significance difference (Spearman r s, p = 0.175; DSChigh, p = 0.123; DSCmoderate, p = 0.278; DSClow, p = 0.520). The average coefficient of variations with 1SD were 0.27 ± 0.00, 0.27 ± 0.01, and 0.36 ± 0.03 for the high‐, moderate‐, and low‐functional regions, respectively, and the low‐functional region showed a tendency to exhibit larger uncertainties than the others.ConclusionWe evaluated DL‐based framework for estimating lung‐functional ventilation images only from CT images. The results indicated that the DL‐based approach could potentially be used for lung‐ventilation estimation.

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.

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.

Estimating lung ventilation directly from 4D CT Hounsfield unit values.

Computed tomography ventilation imaging (CTVI) aims to visualize air-volume changes in the lung by quantifying respiratory motion in 4DCT using deformable image registration (DIR). A problem is that DIR-based CTVI is sensitive both to 4DCT image artifacts and DIR parameters, hindering clinical validation of the technique. To address this, the authors present a streamlined CTVI approach that estimates blood-gas exchange in terms of time-averaged 4DCT Hounsfield unit (HU) values without relying on DIR. The purpose of this study is to quantify the accuracy of the HU-based CTVI method using high-resolution (68)Ga positron emission tomography ("Galligas PET") scans in lung cancer patients. The authors analyzed Galligas 4D-PET/CT scans acquired for 25 lung cancer patients at up to three imaging timepoints during lung cancer radiation therapy. For each 4DCT scan, the authors produced three types of CTVIs: (i) the new method (CTV IHU¯), which takes the 4D time-averaged product of regional air and tissue densities at each voxel, and compared this to DIR-based estimates of (ii) breathing-induced density changes (CTV IDIR-HU), and (iii) breathing-induced volume changes (CTV IDIR-Jac) between the exhale/inhale phase images. The authors quantified the accuracy of CTV IHU¯, CTV IDIR-HU and CTV IDIR-Jac versus Galligas PET in terms of voxel-wise Spearman correlation (r) and the separation of mean voxel values between clinically defined defect/nondefect regions. Averaged over 62 scans, CTV IHU¯ showed better accuracy than CTV IDIR-HU and CTV IDIR-Jac in terms of Spearman correlation with Galligas PET, with (mean ± SD) r values of (0.50 ± 0.17), (0.42 ± 0.20), and (0.19 ± 0.23), respectively. A two-sample Kolmogorov-Smirnov test indicates that CTV IHU¯ shows statistically significant separation of mean ventilation values between clinical defect/nondefect regions. Qualitatively, CTV IHU¯ appears concordant with Galligas PET for emphysema related defects, but differences arise in tumor-obstructed regions (where aeration is overestimated due to motion blur) and for other abnormal morphology (e.g., fluid-filled or peritumoral lung with HU ≳ - 600) where the assumptions of the HU model may break down. The HU-based CTVI method can improve voxel-wise correlations with Galligas PET compared to DIR-based methods and may be a useful approximation for voxels with HU values in the range (-1000, - 600). With further clinical verification, HU-based CTVI could provide a straightforward and reproducible means to estimate lung ventilation using free-breathing 4DCT.

The first patient treatment of computed tomography ventilation functional image-guided radiotherapy for lung cancer

Background and purposeRadiotherapy that selectively avoids irradiating highly-functional lung regions may reduce pulmonary toxicity. We report on the first clinical implementation and patient treatment of lung functional image-guided radiotherapy using an emerging technology, computed tomography (CT) ventilation imaging. Material and methodsA protocol was developed to investigate the safety and feasibility of CT ventilation functional image-guided radiotherapy. CT ventilation imaging is based on (1) deformable image registration of four-dimensional (4D) CT images, and (2) quantitative image analysis for regional volume change, a surrogate for ventilation. CT ventilation functional image-guided radiotherapy plans were designed to minimize specific lung dose–function metrics, including functional V20 (fV20), while maintaining target coverage and meeting standard constraints to other critical organs. ResultsCT ventilation functional image-guided treatment planning reduced the lung fV20 by 5% compared to an anatomic image-guided plan for an enrolled patient with stage IIIB non-small cell lung cancer. Although the doses to several other critical organs increased, the necessary constraints were all met. ConclusionsAn emerging technology, CT ventilation imaging has been translated into the clinic and used in functional image-guided radiotherapy for the first time. This milestone represents an important first step toward hypothetically reduced pulmonary toxicity in lung cancer radiotherapy.

Krypton for computed tomography lung ventilation imaging: preliminary animal data.

The objective of this study was to assess the feasibility and safety of krypton ventilation imaging with intraindividual comparison to xenon ventilation computed tomography (CT). In a first step, attenuation of different concentrations of xenon and krypton was analyzed in a phantom setting. Thereafter, 7 male New Zealand white rabbits (4.4-6.0 kg) were included in an animal study. After orotracheal intubation, an unenhanced CT scan was obtained in end-inspiratory breath-hold. Thereafter, xenon- (30%) and krypton-enhanced (70%) ventilation CT was performed in random order. After a 2-minute wash-in of gas A, CT imaging was performed. After a 45-minute wash-out period and another 2-minute wash-in of gas B, another CT scan was performed using the same scan protocol. Heart rate and oxygen saturation were measured. Unenhanced and krypton or xenon data were registered and subtracted using a nonrigid image registration tool. Enhancement was quantified and statistically analyzed. One animal had to be excluded from data analysis owing to problems during intubation. The CT scans in the remaining 6 animals were completed without complications. There were no relevant differences in oxygen saturation or heart rate between the scans. Xenon resulted in a mean increase of enhancement of 35.3 ± 5.5 HU, whereas krypton achieved a mean increase of 21.9 ± 1.8 HU in enhancement (P = 0.0055). The use of krypton for lung ventilation imaging appears to be feasible and safe. Despite the use of a markedly higher concentration of krypton, enhancement is significantly worse when compared with xenon CT ventilation imaging, but sufficiently high for CT ventilation imaging studies.

4D CT lung ventilation images are affected by the 4D CT sorting method.

Four-dimensional (4D) computed tomography (CT) ventilation imaging is a novel promising technique for lung functional imaging. The current standard 4D CT technique using phase-based sorting frequently results in artifacts, which may deteriorate the accuracy of ventilation imaging. The purpose of this study was to quantify the variability of 4D CT ventilation imaging due to 4D CT sorting. 4D CT image sets from nine lung cancer patients were each sorted by the phase-based method and anatomic similarity-based method, designed to reduce artifacts, with corresponding ventilation images created for each method. Artifacts in the resulting 4D CT images were quantified with the artifact score which was defined based on the difference between the normalized cross correlation for CT slices within a CT data segment and that for CT slices bordering the interface between adjacent CT data segments. The ventilation variation was quantified using voxel-based Spearman rank correlation coefficients for all lung voxels, and Dice similarity coefficients (DSC) for the spatial overlap of low-functional lung volumes. Furthermore, the correlations with matching single-photon emission CT (SPECT) ventilation images (assumed ground truth) were evaluated for three patients to investigate which sorting method provides higher physiologic accuracy. Anatomic similarity-based sorting reduced 4D CT artifacts compared to phase-based sorting (artifact score, 0.45 ± 0.14 vs 0.58 ± 0.24, p = 0.10 at peak-exhale; 0.63 ± 0.19 vs 0.71 ± 0.31, p = 0.25 at peak-inhale). The voxel-based correlation between the two ventilation images was 0.69 ± 0.26 on average, ranging from 0.03 to 0.85. The DSC was 0.71 ± 0.13 on average. Anatomic similarity-based sorting yielded significantly fewer lung voxels with paradoxical negative ventilation values than phase-based sorting (5.0 ± 2.6% vs 9.7 ± 8.4%, p = 0.05), and improved the correlation with SPECT ventilation regionally. The variability of 4D CT ventilation imaging due to 4D CT sorting was moderate overall and substantial in some cases, suggesting that 4D CT artifacts are an important source of variations in 4D CT ventilation imaging. Reduction of 4D CT artifacts provided more physiologically convincing and accurate ventilation estimates. Further studies are needed to confirm this result.

Evaluation of various deformable image registration algorithms for thoracic images

We evaluated the accuracy of one commercially available and three publicly available deformable image registration (DIR) algorithms for thoracic four-dimensional (4D) computed tomography (CT) images. Five patients with esophagus cancer were studied. Datasets of the five patients were provided by DIR-lab (dir-lab.com) and consisted of thoracic 4D CT images and a coordinate list of anatomical landmarks that had been manually identified. Expert landmark correspondence was used for evaluating DIR spatial accuracy. First, the manually measured displacement vector field (mDVF) was obtained from the coordinate list of anatomical landmarks. Then the automatically calculated displacement vector field (aDVF) was calculated by using the following four DIR algorithms: B-spine implemented in Velocity AI (Velocity Medical, Atlanta, GA, USA), free-form deformation (FFD), Horn–Schunk optical flow (OF) and Demons in DIRART of MATLAB software. Registration error is defined as the difference between mDVF and aDVF. The mean 3D registration errors were 2.7 ± 0.8 mm for B-spline, 3.6 ± 1.0 mm for FFD, 2.4 ± 0.9 mm for OF and 2.4 ± 1.2 mm for Demons. The results showed that reasonable accuracy was achieved in B-spline, OF and Demons, and that these algorithms have the potential to be used for 4D dose calculation, automatic image segmentation and 4D CT ventilation imaging in patients with thoracic cancer. However, for all algorithms, the accuracy might be improved by using the optimized parameter setting. Furthermore, for B-spline in Velocity AI, the 3D registration error was small with displacements of less than ∼10 mm, indicating that this software may be useful in this range of displacements.

Reproducibility of Four-dimensional Computed Tomography-based Lung Ventilation Imaging

A novel ventilation imaging method based on four-dimensional (4D) computed tomography (CT) has been applied to the field of radiation oncology. Understanding its reproducibility is a prerequisite for clinical applications. The purpose of this study was to quantify the reproducibility of 4D CT ventilation imaging over different days and the same session. Two ventilation images were created from repeat 4D CT scans acquired over the average time frames of 15 days for 6lung cancer patients and 5 minutes for another 6 patients. The reproducibility was quantified using the voxel-based Spearman rank correlation coefficients for all lung voxels and Dice similarity coefficients (DSC) for the spatial overlap of segmented high-, moderate-, and low-functional lung volumes. Furthermore, the relationship between the variation in abdominal motion range as a measure of the depth of breathing and variation in ventilation was evaluated using linear regression. The voxel-based correlation between the two ventilation images was moderate on average (0.50 ± 0.15). The DSCs were also moderate for the high- (0.60 ± 0.08), moderate- (0.46 ± 0.06), and low-functional lung (0.58 ± 0.09). No patients demonstrated strong correlations. The relationship between the motion range variation and ventilation variation was found to be moderate and significant. We investigated the reproducibility of 4D CT ventilation imaging over the time frames of 15 days and 5 minutes and found that it was only moderately reproducible. Respiratory variation during 4D CT scans was found to deteriorate the reproducibility. Improvement of 4D CT imaging is necessary to increase the reproducibility of 4D CT ventilation imaging.

Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions

A pulmonary ventilation imaging technique based on four-dimensional (4D) computed tomography (CT) has advantages over existing techniques. However, physiologically accurate 4D-CT ventilation imaging has not been achieved in patients. The purpose of this study was to evaluate 4D-CT ventilation imaging by correlating ventilation with emphysema. Emphysematous lung regions are less ventilated and can be used as surrogates for low ventilation. We tested the hypothesis: 4D-CT ventilation in emphysematous lung regions is significantly lower than in non-emphysematous regions. Four-dimensional CT ventilation images were created for 12 patients with emphysematous lung regions as observed on CT, using a total of four combinations of two deformable image registration (DIR) algorithms: surface-based (DIRsur) and volumetric (DIRvol), and two metrics: Hounsfield unit (HU) change (VHU) and Jacobian determinant of deformation (VJac), yielding four ventilation image sets per patient. Emphysematous lung regions were detected by density masking. We tested our hypothesis using the one-tailed t-test. Visually, different DIR algorithms and metrics yielded spatially variant 4D-CT ventilation images. The mean ventilation values in emphysematous lung regions were consistently lower than in non-emphysematous regions for all the combinations of DIR algorithms and metrics. VHU resulted in statistically significant differences for both DIRsur (0.14 ± 0.14 versus 0.29 ± 0.16, p = 0.01) and DIRvol (0.13 ± 0.13 versus 0.27 ± 0.15, p < 0.01). However, VJac resulted in non-significant differences for both DIRsur (0.15 ± 0.07 versus 0.17 ± 0.08, p = 0.20) and DIRvol (0.17 ± 0.08 versus 0.19 ± 0.09, p = 0.30). This study demonstrated the strong correlation between the HU-based 4D-CT ventilation and emphysema, which indicates the potential for HU-based 4D-CT ventilation imaging to achieve high physiologic accuracy. A further study is needed to confirm these results.