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PurposeCT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities.MethodsThe VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient rS , and Dice similarity coefficients evaluated for low function lung ( DSClow ) and high function lung ( DSChigh ).ResultsA total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The rS and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for rS , 0.52 (0.36-0.67) for DSClow , and 0.45 (0.28-0.62) for DSChigh . All other algorithms exhibited at least one negative rS value, and/or one DSC value less than 0.5.ConclusionsThe VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

Purpose: Radiation therapy (RT) for lung cancer is commonly limited to subtherapeutic doses due to unintended toxicity to normal lung tissue. Reducing the frequency of occurrence and magnitude of normal lung function loss may benefit from treatment plans that incorporate the regional lung and radiation dose information. In this article, the authors propose a method that quantitatively measures the regional changes in lung tissue function following a course of radiation therapy by using 4DCT and image registration techniques. Methods: 4DCT data sets before and after RT from two subjects are used in this study. Nonlinear 3D image registration is applied to register an image acquired near end inspiration to an image acquired near end expiration to estimate the pulmonary function. The Jacobian of the image registration transformation, indicating local lung expansion or contraction, serves as an index of regional pulmonary function. Approximately 120 annotated vascular bifurcation points are used as landmarks to evaluate registration accuracy. The authors compare regional pulmonary function before and after RT to the planned radiation dose at different locations of the lung. Results: In all registration pairs, the average landmark distances after registration are on the order of 1 mm. The pulmonary function change as indicated by the Jacobian change ranges from -0.15 to 0.1 in the contralateral lung and -0.22 to 0.23 in the ipsilateral lung for subject A, and ranges from -0.4 to 0.39 in the contralateral lung and -0.25 to 0.5 in the ipsilateral lung for subject B. Both of the subjects show larger range of the increase in the pulmonary function in the ipsilateral lung than the contralateral lung. For lung tissue regions receiving a radiation dose larger than 24 Gy, a decrease in pulmonary function was observed. For regions receiving a radiation dose smaller than 24 Gy, either an increase or a decrease in pulmonary function was observed. The relationship between the pulmonary function change and the radiation dose varies at different locations. Conclusions: With the use of 4DCT and image registration techniques, the pulmonary function prior to and following a course of radiation therapy can be measured. In the preliminary application of this approach for two subjects, changes in pulmonary function were observed with a weak correlation between the dose and pulmonary function change. In certain sections of the lung, detected locally compromised pulmonary function may have resulted from radiation injury. [ABSTRACT FROM AUTHOR]

Tracking lung tissues during the respiratory cycle has been a challenging task for diagnostic CT and CT-guided radiotherapy. We propose an intensity- and landmark-based image registration algorithm to perform image registration and warping of 3D pulmonary CT image data sets, based on consistency constraints and matching corresponding airway branchpoints. In this paper, we demonstrate the effectivenss and accuracy of this algorithm in tracking lung tissues by both animal and human data sets. In the animal study, the result showed a tracking accuracy of 1.9 mm between 50% functional residual capacity (FRC) and 85% total lung capacity (TLC) for 12 metal seeds implanted in the lungs of a breathing sheep under precise volume control using a pulmonary ventilator. Visual inspection of the human subject results revealed the algorithm’s potential not only in matching the global shapes, but also in registering the internal structures (e.g., oblique lobe fissures, pulmonary artery branches, etc.). These results suggest that our algorithm has significant potential for warping and tracking lung tissue deformation with applications in diagnostic CT, CT-guided radiotherapy treatment planning, and therapeutic effect evaluation. [ABSTRACT FROM AUTHOR]