Kaifang Du

Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the con figuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

Reproducibility of registration-based measures of lung tissue expansion

PURPOSE Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and 3D image registration can be used to locally estimate lung tissue expansion and contraction (regional lung volume change) by computing the determinant of the Jacobian matrix of the image registration deformation field. In this study, the authors examine the reproducibility of Jacobian-based measures of lung tissue expansion in two repeat 4DCT acquisitions of mechanically ventilated sheep and free-breathing humans. METHODS 4DCT image data from three white sheep and nine human subjects were used for this analysis. In each case, two 4DCT studies were acquired for each subject within a short time interval. The animal subjects were anesthetized and mechanically ventilated, while the humans were awake and spontaneously breathing based on respiratory pacing audio cues. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm and the Jacobian of the registration deformation field was used to measure regional lung expansion. The Jacobian map from the baseline data set was compared to the Jacobian map from the followup data by measuring the voxel-by-voxel Jacobian ratio. RESULTS In the animal subjects, the mean Jacobian ratio (baseline scan Jacobian divided by followup scan Jacobian, voxel-by-voxel) was 0.9984±0.021 (mean ± standard deviation, averaged over the entire lung region). The mean Jacobian ratio was 1.0224±0.058 in the human subjects. The reproducibility of the Jacobian values was found to be strongly dependent on the reproducibility of the subjects respiratory effort and breathing pattern. CONCLUSIONS Lung expansion, a surrogate for lung function, can be assessed using two or more respiratory-gated CT image acquisitions. The results show that good reproducibility can be obtained in anesthetized, mechanically ventilated animals, but variations in respiratory effort and breathing patterns reduce reproducibility in spontaneously-breathing humans. The global linear normalization can globally compensate for breathing effort differences, but a homogeneous scaling does not account for differences in regional lung expansion rates. Additional work is needed to develop compensation procedures or normalization schemes that can account for local variations in lung expansion during respiration.

Comparison of image registration based measures of regional lung ventilation from dynamic spiral CT with Xe‐CT

PURPOSE Regional lung volume change as a function of lung inflation serves as an index of parenchymal and airway status as well as an index of regional ventilation and can be used to detect pathologic changes over time. In this paper, the authors propose a new regional measure of lung mechanics-the specific air volume change by corrected Jacobian. The authors compare this new measure, along with two existing registration based measures of lung ventilation, to a regional ventilation measurement derived from xenon-CT (Xe-CT) imaging. METHODS 4DCT and Xe-CT datasets from four adult sheep 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. Approximately 200 annotated anatomical points are used as landmarks to evaluate registration accuracy. Three different registration based measures of regional lung mechanics are derived and compared: the specific air volume change calculated from the Jacobian (SAJ); the specific air volume change calculated by the corrected Jacobian (SACJ); and the specific air volume change by intensity change (SAI). The authors show that the commonly used SAI measure can be derived from the direct SAJ measure by using the air-tissue mixture model and assuming there is no tissue volume change between the end inspiration and end expiration datasets. All three ventilation measures are evaluated by comparing to Xe-CT estimates of regional ventilation. RESULTS After registration, the mean registration error is on the order of 1 mm. For cubical regions of interest (ROIs) in cubes with size 20 mm × 20 mm × 20 mm, the SAJ and SACJ measures show significantly higher correlation (linear regression, average r(2) = 0.75 and r(2) = 0.82) with the Xe-CT based measure of specific ventilation (sV) than the SAI measure. For ROIs in slabs along the ventral-dorsal vertical direction with size of 150 mm × 8 mm × 40 mm, the SAJ, SACJ, and SAI all show high correlation (linear regression, average r(2) = 0.88, r(2) = 0.92, and r(2) = 0.87) with the Xe-CT based sV without significant differences when comparing between the three methods. The authors demonstrate a linear relationship between the difference of specific air volume change and difference of tissue volume in all four animals (linear regression, average r(2) = 0.86). CONCLUSIONS Given a deformation field by an image registration algorithm, significant differences between the SAJ, SACJ, and SAI measures were found at a regional level compared to the Xe-CT sV in four sheep that were studied. The SACJ introduced here, provides better correlations with Xe-CT based sV than the SAJ and SAI measures, thus providing an improved surrogate for regional ventilation.

Reproducibility of intensity-based estimates of lung ventilation.

PURPOSE Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and image registration can be used to estimate the regional lung volume change by observing CT voxel density changes during inspiration or expiration. In this study, the authors examine the reproducibility of intensity-based estimates of lung tissue expansion and contraction in three mechanically ventilated sheep and ten spontaneously breathing humans. The intensity-based estimates are compared to the estimates of lung function derived from image registration deformation field. METHODS 4DCT data set was acquired for a cohort of spontaneously breathing humans and anesthetized and mechanically ventilated sheep. For each subject, two 4DCT scans were performed with a short time interval between acquisitions. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm. The CT density change in the registered image pair was used to compute intensity-based specific air volume change (SAC) and the intensity-based Jacobian (IJAC), while the transformation-based Jacobian (TJAC) was computed directly from the image registration deformation field. IJAC is introduced to make the intensity-based and transformation-based methods comparable since SAC and Jacobian may not be associated with the same physiological phenomenon and have different units. Scan-to-scan variations in respiratory effort were corrected using a global scaling factor for normalization. A gamma index metric was introduced to quantify voxel-by-voxel reproducibility considering both differences in ventilation and distance between matching voxels. The authors also tested how different CT prefiltering levels affected intensity-based ventilation reproducibility. RESULTS Higher reproducibility was found for anesthetized mechanically ventilated animals than for the humans for both the intensity-based (IJAC) and transformation-based (TJAC) ventilation estimates. The human IJAC maps had scan-to-scan correlation coefficients of 0.45 ± 0.14, a gamma pass rate 70 ± 8 without normalization and 75 ± 5 with normalization. The human TJAC maps had correlation coefficients 0.81 ± 0.10, a gamma pass rate 86 ± 11 without normalization and 93 ± 4 with normalization. The gamma pass rate and correlation coefficient of the IJAC maps gradually increased with increased smoothing, but were still much lower than those of the TJAC maps. CONCLUSIONS The transformation-based ventilation maps show better reproducibility than the intensity-based maps, especially in human subjects. Reproducibility was also found to depend on variations in respiratory effort; all techniques were better when applied to images from mechanically ventilated sheep compared to spontaneously breathing human subjects. Nevertheless, intensity-based techniques applied to mechanically ventilated sheep were less reproducible than the transformation-based applied to spontaneously breathing humans, suggesting the method used to determine ventilation maps is important. Prefiltering of the CT images may help to improve the reproducibility of the intensity-based ventilation estimates, but even with filtering the reproducibility of the intensity-based ventilation estimates is not as good as that of transformation-based ventilation estimates.PURPOSE Lung function depends on lung expansion and contraction during the respiratory cycle. Respiratory-gated CT imaging and image registration can be used to estimate the regional lung volume change by observing CT voxel density changes during inspiration or expiration. In this study, the authors examine the reproducibility of intensity-based estimates of lung tissue expansion and contraction in three mechanically ventilated sheep and ten spontaneously breathing humans. The intensity-based estimates are compared to the estimates of lung function derived from image registration deformation field. METHODS 4DCT data set was acquired for a cohort of spontaneously breathing humans and anesthetized and mechanically ventilated sheep. For each subject, two 4DCT scans were performed with a short time interval between acquisitions. From each 4DCT data set, an image pair consisting of a volume reconstructed near end inspiration and a volume reconstructed near end exhalation was selected. The end inspiration and end exhalation images were registered using a tissue volume preserving deformable registration algorithm. The CT density change in the registered image pair was used to compute intensity-based specific air volume change (SAC) and the intensity-based Jacobian (IJAC), while the transformation-based Jacobian (TJAC) was computed directly from the image registration deformation field. IJAC is introduced to make the intensity-based and transformation-based methods comparable since SAC and Jacobian may not be associated with the same physiological phenomenon and have different units. Scan-to-scan variations in respiratory effort were corrected using a global scaling factor for normalization. A gamma index metric was introduced to quantify voxel-by-voxel reproducibility considering both differences in ventilation and distance between matching voxels. The authors also tested how different CT prefiltering levels affected intensity-based ventilation reproducibility. RESULTS Higher reproducibility was found for anesthetized mechanically ventilated animals than for the humans for both the intensity-based (IJAC) and transformation-based (TJAC) ventilation estimates. The human IJAC maps had scan-to-scan correlation coefficients of 0.45 ± 0.14, a gamma pass rate 70 ± 8 without normalization and 75 ± 5 with normalization. The human TJAC maps had correlation coefficients 0.81 ± 0.10, a gamma pass rate 86 ± 11 without normalization and 93 ± 4 with normalization. The gamma pass rate and correlation coefficient of the IJAC maps gradually increased with increased smoothing, but were still much lower than those of the TJAC maps. CONCLUSIONS The transformation-based ventilation maps show better reproducibility than the intensity-based maps, especially in human subjects. Reproducibility was also found to depend on variations in respiratory effort; all techniques were better when applied to images from mechanically ventilated sheep compared to spontaneously breathing human subjects. Nevertheless, intensity-based techniques applied to mechanically ventilated sheep were less reproducible than the transformation-based applied to spontaneously breathing humans, suggesting the method used to determine ventilation maps is important. Prefiltering of the CT images may help to improve the reproducibility of the intensity-based ventilation estimates, but even with filtering the reproducibility of the intensity-based ventilation estimates is not as good as that of transformation-based ventilation estimates.

Evaluation of the ΔV 4D CT ventilation calculation method using in vivo xenon CT ventilation data and comparison to other methods

Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon-enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published methods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44±0.13 with a range between 0.29 and 0.61 between the XeCT and DLV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55±0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV-4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution. PACS number(s): 87.57.N-, 87.57.nj, 87.57.Q-, 87.85.Pq.Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon‐enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published methods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44±0.13 with a range between 0.29 and 0.61 between the XeCT and DLV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55±0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV‐4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution. PACS number(s): 87.57.N‐, 87.57.nj, 87.57.Q‐, 87.85.Pq

Registration-based measurement of regional expiration volume ratio using dynamic 4DCT imaging

Lung function depends on mechanical lung expansion and contraction during the respiratory cycle. Recently developed dynamic 4D CT imaging and 3D image registration can be used to analyze regional lung function, which are significant for lung disease diagnosis, treatment and lung ventilation change during radiation therapy. 4D CT images of the lung can be reconstructed at any respiratory phase point based on the breathing trace signal during image acquisition. In this paper, we propose an image registration-based technique for assessing regional pulmonary function during specified time interval and estimating regional expiration volume ratio using 4D CT. Data from four anesthetized mechanically-ventilated sheep and one human patient undergoing Radiation Therapy were analyzed. Sheep lung images were divided into 30 slabs in the ventral-dorsal direction with equal lung height to study the ventilation variation. We found that the mean values of expiration volume ratio across slabs demonstrate a similar pattern and lung tissue near dorsal part contracts more in the first expiratory phase. The reproducibility of regional lung function of one human patient was compared between end-expiration-to-end-inspiration pair and the phase pair spanning the first expiratory interval, in which the latter pair shows 30% higher reproducibility. The accuracy of image registration is assessed by 200 semi-automatically annotated lung landmarks. Sheep data show landmark error on the level of 0.8 mm after registration.

Tissue-Volume Preserving Deformable Image Registration for 4DCT Pulmonary Images

We propose a 4D (three spatial dimensions plus time) tissue-volume preserving non-rigid image registration algorithm for pulmonary 4D computed tomography (4DCT) data sets to provide relevant information for radiation therapy and estimate pulmonary ventilation. The sum of squared tissue volume difference (SSTVD) similarity cost takes into account the CT intensity changes of spatially corresponding voxels, which is caused by the variations of fraction of tissue within voxels throughout the respiratory cycle. The proposed 4D SSTVD registration scheme considers the entire dynamic 4D data set simultaneously, using both spatial and temporal information. We employed a uniform 4D cubic B-spline parametrization of the transform and a temporally extended linear elasticity regularization of deformation field to ensure temporal smoothness and thus biological plausibility of estimated deformation. We used a multiresolution multi-grid registration framework with limitedmemory Broyden Fletcher Goldfarb Shanno (L-BFGS) optimization procedure for rapid convergence and limited memory consumption. We conducted experiments using synthetic 2D+t images and clinical 4DCT pulmonary data sets and evaluated accuracy and temporal smoothness of the proposed method via manually annotated landmarks.

Intensity-Based Registration for Lung Motion Estimation

Image registration plays an important role within pulmonary image analysis. The task of registration is to find the spatial mapping that brings two images into alignment. Registration algorithms designed for matching 4D lung scans or two 3D scans acquired at different inflation levels can catch the temporal changes in position and shape of the region of interest. Accurate registration is critical to post-analysis of lung mechanics and motion estimation. In this chapter, we discuss lung-specific adaptations of intensity-based registration methods for 3D/4D lung images and review approaches for assessing registration accuracy. Then we introduce methods for estimating tissue motion and studying lung mechanics. Finally, we discuss methods for assessing and quantifying specific volume change, specific ventilation, strain/ stretch information and lobar sliding.

SU‐E‐J‐192: Static Breath‐Hold MRI Based Measurement of Change in Pulmonary Function Following a Course of Radiation Therapy

PURPOSE Radiation Therapy (RT) induced pulmonary function change may depend on the location, underlying function of that lung prior to radiations, radiation dose/fractionation and other factors. We propose to evaluate the radiation induced pulmonary function change using static breath-hold MRI scans with vascular information and 3D deformable image registration which can provide pulmonary function relative to RT dose on a regional basis. METHODS A MRI scan pair near the end of inhale and near the end of exhale with breath hold were acquired for one lung cancer patient before RT and 6 months after RT. The patient was treated with SBRT with 55 Gy to PTVs in the right and the left lung respectively. B-spline based vesselness preserving image registration algorithm was applied to register the MRI pair for the calculation of local lung expansion as a measurement of regional pulmonary function (PF). The PF maps before RT and after RT were then mapped to the planning CT using the same algorithm tuned for MRI-CT registration. The pulmonary function change was calculated via the PF ratio between two MRI pairs. RESULTS Strong spatial correlation was found between the irradiated lung region and the region with greatly decreased PF. Based on dose and PFC distribution, no strong determinant factor was found for PF lost in the left lung while the right lung shows that all the lung tissue receiving dose larger than 28 Gy will have a decreased PF. CONCLUSIONS We demonstrated a method that uses static breath-hold MRI based lung imaging to evaluate radiation induced pulmonary function change which can be applied to study the dose and the pulmonary function change in a regional basis. This work is supported by NIH grant support 1R21CA144063.

Tracking regional tissue volume and function change in lung using image registration

We have previously demonstrated the 24-hour redistribution and reabsorption of bronchoalveolar lavage (BAL) fluid delivered to the lung during a bronchoscopic procedure in normal volunteers. In this work we utilize image-matching procedures to correlate fluid redistribution and reabsorption to changes in regional lung function. Lung CT datasets from six human subjects were used in this study. Each subject was scanned at four time points before and after BAL procedure. Image registration was performed to align images at different time points and different inflation levels. The resulting dense displacement fields were utilized to track tissue volume changes and reveal deformation patterns of local parenchymal tissue quantitatively. The registration accuracy was assessed by measuring landmark matching errors, which were on the order of 1 mm. The results show that quantitative-assessed fluid volume agreed well with bronchoscopist-reported unretrieved BAL volume in the whole lungs (squared linear correlation coefficient was 0.81). The average difference of lung tissue volume at baseline and after 24 hours was around 2%, which indicates that BAL fluid in the lungs was almost absorbed after 24 hours. Regional lung-function changes correlated with the presence of BAL fluid, and regional function returned to baseline as the fluid was reabsorbed.