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Title: SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration

Abstract

Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitude for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in uppermore » lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.« less

Authors:
;  [1];  [2]; ;  [3]; ; ;  [4]
  1. Department of Human Oncology, University of Wisconsin Hopspital and Clinics, Madison, WI (United States)
  2. Department of Medical Physics, University of Wisconsin, Madison, WI (United States)
  3. Department of Biomedical Engineering, The University of Iowa, Iowa City, IA (United States)
  4. Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA (United States)
Publication Date:
OSTI Identifier:
22494108
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; COMPUTERIZED TOMOGRAPHY; IMAGES; LUNGS; PATIENTS; RADIOTHERAPY; RESPIRATION

Citation Formats

Du, K, Bayouth, J, Patton, T, Reinhardt, J, Gerard, S, Christensen, G, Zhao, B, and Pan, Y. SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration. United States: N. p., 2015. Web. doi:10.1118/1.4924177.
Du, K, Bayouth, J, Patton, T, Reinhardt, J, Gerard, S, Christensen, G, Zhao, B, & Pan, Y. SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration. United States. doi:10.1118/1.4924177.
Du, K, Bayouth, J, Patton, T, Reinhardt, J, Gerard, S, Christensen, G, Zhao, B, and Pan, Y. Mon . "SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration". United States. doi:10.1118/1.4924177.
@article{osti_22494108,
title = {SU-E-J-90: Lobar-Level Lung Ventilation Analysis Using 4DCT and Deformable Image Registration},
author = {Du, K and Bayouth, J and Patton, T and Reinhardt, J and Gerard, S and Christensen, G and Zhao, B and Pan, Y},
abstractNote = {Purpose: To assess regional changes in human lung ventilation and mechanics using four-dimensional computed tomography (4DCT) and deformable image registration. This work extends our prior analysis of the entire lung to a lobe-based analysis. Methods: 4DCT images acquired from 20 patients prior to radiation therapy (RT) were used for this analysis. Jacobian ventilation and motion maps were computed from the displacement field after deformable image registration between the end of expiration breathing phase and the end of inspiration breathing phase. The lobes were manually segmented on the reference phase by a medical physicist expert. The voxel-by-voxel ventilation and motion magnitude for all subjects were grouped by lobes and plotted into cumulative voxel frequency curves respectively. In addition, to eliminate the effect of different breathing efforts across subjects, we applied the inter-subject equivalent lung volume (ELV) method on a subset of the cohort and reevaluated the lobar ventilation. Results: 95% of voxels in the lung are expanding during inspiration. However, some local regions of lung tissue show far more expansion than others. The greatest expansion with respiration occurs within the lower lobes; between exhale and inhale the median expansion in lower lobes is approximately 15%, while the median expansion in upper lobes is 10%. This appears to be driven by a subset of lung tissues within the lobe that have greater expansion; twice the number of voxels in the lower lobes (20%) expand by > 30% when compared to the upper lobes (10%). Conclusion: Lung ventilation and motion show significant difference on the lobar level. There are different lobar fractions of driving voxels that contribute to the major expansion of the lung. This work was supported by NIH grant CA166703.},
doi = {10.1118/1.4924177},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = {Mon Jun 15 00:00:00 EDT 2015},
month = {Mon Jun 15 00:00:00 EDT 2015}
}
  • Purpose: Deformable Image Registration (DIR) is gaining wider clinical acceptance in radiation oncology. The aim of this work is to characterise a DIR algorithm on publically available 4DCT lung images, such that comparison can be performed against other algorithms. We propose an evaluation method of registration accuracy that takes into account the initial misregistration of the datasets. Methods: The “DIR Validation dataset” ( http://www.creatis.insa-lyon.fr/rio/dir{sub v}alidation{sub d}ata ) provides benchmark data for evaluating 3D CT registration algorithms. It consists of six 4DCT lung datasets (1x1x2mm resolution) with 100 landmarks identified on the end-exhalation and end-inhalation phases. Images were registered to end-inhalationmore » using proprietary form of optical flow in commercial software (Mirada RTx, Mirada Medical, UK). Target registration error was measured before and after DIR, referred to as Initial Registration Error (IRE) and Final Registration Error (FRE). Results: The mean FRE over all landmarks was 1.37±1.81mm. FRE increased with IRE. Mean FRE of 0.86, 0.86, 1.53, 3.38, 4.45, 7.58mm was observed for IRE in the ranges 0–5, 5–10, 10–15, 15–20, 20–25, >25 mm. Higher FRE was observed at the inferior lung, where IRE was greater. Out-of-plane motion contributed more to IRE, and therefore to FRE. Maximum FRE of 20.6mm was observed for IRE of 32.1mm, located at the posterior of the middle lobe for dataset 2. Sub-voxel registration accuracy was achieved for up to 10mm IRE, and increased linearly at 0.3mm FRE/mm IRE thereafter. Conclusion: Publicly available clinical datasets enable algorithms to be compared objectively between publications. However, only reporting average TRE after registration can be misleading as the ability of an algorithm to correct for displacements varies with the IRE or position within the patient. Consequently, algorithms should be characterized using the entire range of initial displacements. For the algorithm assessed, clinically acceptable error within one voxel was achieved for IRE of up to 15mm. TK, AL, and MG are employees of Mirada Medical.« less
  • Purpose: To allow a reliable deformable image registration (DIR) method for dose calculation in radiation therapy. This work proposes a performance assessment of a morphological segmentation algorithm that generates a deformation field from lung surface displacements with 4DCT datasets. Methods: From the 4DCT scans of 15 selected patients, the deep exhale phase of the breathing cycle is identified as the reference scan. Varian TPS EclipseTM is used to draw lung contours, which are given as input to the morphological segmentation algorithm. Voxelized contours are smoothed by a Gaussian filter and then transformed into a surface mesh representation. Such mesh ismore » adapted by rigid and elastic deformations to match each subsequent lung volumes. The segmentation efficiency is assessed by comparing the segmented lung contour and the TPS contour considering two volume metrics, defined as Volumetric Overlap Error (VOE) [%] and Relative Volume Difference (RVD) [%] and three surface metrics, defined as Average Symmetric Surface Distance (ASSD) [mm], Root Mean Square Symmetric Surface Distance (RMSSD) [mm] and Maximum Symmetric Surface Distance (MSSD) [mm]. Then, the surface deformation between two breathing phases is determined by the displacement of corresponding vertices in each deformed surface. The lung surface deformation is linearly propagated in the lung volume to generate 3D deformation fields for each breathing phase. Results: The metrics were averaged over the 15 patients and calculated with the same segmentation parameters. The volume metrics obtained are a VOE of 5.2% and a RVD of 2.6%. The surface metrics computed are an ASSD of 0.5 mm, a RMSSD of 0.8 mm and a MSSD of 6.9 mm. Conclusion: This study shows that the morphological segmentation algorithm can provide an automatic method to capture an organ motion from 4DCT scans and translate it into a volume deformation grid needed by DIR method for dose distribution combination.« less
  • Purpose: For four-dimensional (4D) planning and adaptive radiotherapy, deformable image registration (DIR) is needed and the accuracy is essential. We evaluated the accuracy of one free-downloadable DIR software library package (NiftyReg) and one commercial DIR software (MIM) in lung SBRT cancer patients. Methods: A rigid and non-rigid registrations were implemented to our in-house software. The non-rigid registration algorithm of the NiftyReg and MIM was based on the free-form deformation. The accuracy of the two software was evaluated when contoured structures to peak-inhale and peak-exhale 4DCT image data sets were measured using the dice similarity coefficient (DSC). The evaluation was performedmore » in 20 lung SBRT patients. Results: In our visual evaluation, the eighteen cases show good agreement between the deformed structures for the peak-inhale phase and the peak-exhale phase structures (more than 0.8 DSC value). In the evaluation of the DSC in-house software, averaged DSC values of GTV and lung, heart, spinal cord, stomach and body were 0.862 and 0.979, 0.932, 0.974, 0.860, 0.998, respectively. As the Resultof the registration using the MIM program in the two cases which had less than 0.7 DSC value when analyzed using the in-house software, the DSC value were improved to 0.8. The CT images in a case with low DSC value shows the tumor was surrounded by the structure with the similar CT values, which were the chest wall or the diaphragm. Conclusion: Not only a free-downloadable DIR software but also a commercial software may provide unexpected results and there is a possibility that the results would make us misjudge the treatment planning. Therefore, we recommend that a commissioning test of any DIR software should be performed before clinical use and we should understand the characteristics of the software.« less
  • Purpose: Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Methods: Four lung cancer patients previously treated with conventionally fractionated radiotherapy that exhibited notable tumor shrinkage during treatment were retrospectively evaluated. Exhale breathhold CT scans were obtained at treatment planning (PCT) and following three weeks (W3CT) of treatment. For each patient, the PCT was registered to the W3CT using Morfeus, a biomechanicalmore » model-based deformable registration algorithm, consisting of boundary conditions on the lungs and incorporating a sliding interface between the lung and chest wall. To model the complex response of the lung, an extension to Morfeus has been developed: (i) The vessel tree was segmented by thresholding a vesselness image based on the Hessian matrix’s eigenvalues and the centerline was extracted; (ii) A 3D shape context method was used to find correspondences between the trees of the two images; (ii) Correspondences were used as additional boundary conditions (Morfeus+vBC). An expert independently identified corresponding landmarks well distributed in the lung to compute Target Registration Errors (TRE). Results: The TRE within 15mm of the tumor boundaries (on average 11 landmarks) is: 6.1±1.8, 4.6±1.1 and 3.8±2.3 mm after rigid registration, Morfeus and Morfeus+vBC, respectively. The TRE in the rest of the lung (on average 13 landmarks) is: 6.4±3.9, 4.7±2.2 and 3.6±1.9 mm, which is on the order of the 2mm isotropic dose grid vector (3.5mm). Conclusion: The addition of boundary conditions on the vessels improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies. This work was funded in part by NIH 2P01CA059827-16.« less
  • Purpose: The purpose of this study was to quantify the lobar lung function using the novel PET Galligas ([68Ga]-carbon nanoparticle) ventilation imaging and the investigational CT ventilation imaging in lung cancer patients pre-treatment. Methods: We present results on our first three lung cancer patients (2 male, mean age 78 years) as part of an ongoing ethics approved study. For each patient a PET Galligas ventilation (PET-V) image and a pair of breath hold CT images (end-exhale and end-inhale tidal volumes) were acquired using a Siemens Biograph PET CT. CT-ventilation (CT-V) images were created from the pair of CT images usingmore » deformable image registration (DIR) algorithms and the Hounsfield Unit (HU) ventilation metric. A comparison of ventilation quantification from each modality was done on the lobar level and the voxel level. A Bland-Altman plot was used to assess the difference in mean percentage contribution of each lobe to the total lung function between the two modalities. For each patient, a voxel-wise Spearmans correlation was calculated for the whole lungs between the two modalities. Results: The Bland-Altman plot demonstrated strong agreement between PET-V and CT-V for assessment of lobar function (r=0.99, p<0.001; range mean difference: −5.5 to 3.0). The correlation between PET-V and CT-V at the voxel level was moderate(r=0.60, p<0.001). Conclusion: This preliminary study on the three patients data sets demonstrated strong agreement between PET and CT ventilation imaging for the assessment of pre-treatment lung function at the lobar level. Agreement was only moderate at the level of voxel correlations. These results indicate that CT ventilation imaging has potential for assessing pre-treatment lobar lung function in lung cancer patients.« less