Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method

Rigid and Deformable Image Registration for Radiation Therapy: A Self-Study Evaluation Guide for NRG Oncology Clinical Trial Participation

PURPOSE

The registration of multiple imaging studies to radiation therapy computed tomography simulation, including magnetic resonance imaging, positron emission tomography-computed tomography, etc. is a widely used strategy in radiation oncology treatment planning, and these registrations have valuable roles in image guidance, dose composition/accumulation, and treatment delivery adaptation. The NRG Oncology Medical Physics subcommittee formed a working group to investigate feasible workflows for a self-study credentialing process of image registration commissioning.

METHODS AND MATERIALS

The American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) report on the use of image registration and fusion algorithms in radiation therapy provides basic guidelines for quality assurance and quality control of the image registration algorithms and the overall clinical process. The report recommends a series of tests and the corresponding metrics that should be evaluated and reported during commissioning and routine quality assurance, as well as a set of recommendations for vendors. The NRG Oncology medical physics subcommittee working group found incompatibility of some digital phantoms with commercial systems. Thus, there is still a need to provide further recommendations in terms of compatible digital phantoms, clinical feasible workflow, and achievable thresholds, especially for future clinical trials involving deformable image registration algorithms. Nine institutions participated and evaluated 4 commonly used commercial imaging registration software and various versions in the field of radiation oncology.

RESULTS AND CONCLUSIONS

The NRG Oncology Working Group on image registration commissioning herein provides recommendations on the use of digital phantom/data sets and analytical software access for institutions and clinics to perform their own self-study evaluation of commercial imaging systems that might be employed for coregistration in radiation therapy treatment planning and image guidance procedures. Evaluation metrics and their corresponding values were given as guidelines to establish practical tolerances. Vendor compliance for image registration commissioning was evaluated, and recommendations were given for future development.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Will Airway Gene Therapy for Cystic Fibrosis Improve Lung Function? New Imaging Technologies Can Help Us Find Out

The promise of genetic therapies has turned into reality in recent years, with new first-line treatments for fatal diseases now available to patients. The development and testing of genetic therapies for respiratory diseases such as cystic fibrosis (CF) has also progressed. The addition of gene editing to the genetic agent toolbox, and its early success in other organ systems, suggests we will see rapid expansion of gene correction options for CF in the future. Although substantial progress has been made in creating techniques and genetic agents that can be highly effective for CF correction in vitro, physiologically relevant functional in vivo changes have been largely prevented by poor delivery efficiency within the lungs. Somewhat hidden from view, however, is the absence of reliable, accurate, detailed, and noninvasive outcome measures that can detect subtle disease and treatment effects in the lungs of humans or animal models. The ability to measure the fundamental function of the lung-ventilation, the effective transport of air throughout the lung-has been constrained by the available measurement technologies. Without sensitive measurement methods, it is difficult to quantify the effectiveness of genetic therapies for CF. The mainstays of lung health assessment are spirometry, which cannot provide adequate disease localization and is not sensitive enough to detect small early changes in disease; and computed tomography, which provides structural rather than functional information. Magnetic resonance imaging using hyperpolarized gases is increasingly useful for lung ventilation assessment, and it removes the radiation risk that accompanies X-ray methods. A new lung imaging technique, X-ray velocimetry, can now offer highly detailed regional lung ventilation information well suited to the diagnosis, treatment, and monitoring needs of CF lung disease, particularly after the application of genetic therapies. In this review, we discuss the options now available for imaging-based lung function measurement in the generation and use of genetic and other therapies for treating CF lung disease.

Real-time in vivo imaging of regional lung function in a mouse model of cystic fibrosis on a laboratory X-ray source

Most measures of lung health independently characterise either global lung function or regional lung structure. The ability to measure airflow and lung function regionally would provide a more specific and physiologically focused means by which to assess and track lung disease in both pre-clinical and clinical settings. One approach for achieving regional lung function measurement is via phase contrast X-ray imaging (PCXI), which has been shown to provide highly sensitive, high-resolution images of the lungs and airways in small animals. The detailed images provided by PCXI allow the application of four-dimensional X-ray velocimetry (4DxV) to track lung tissue motion and provide quantitative information on regional lung function. However, until recently synchrotron facilities were required to produce the highly coherent, high-flux X-rays that are required to achieve lung PCXI at a high enough frame rate to capture lung motion. This paper presents the first translation of 4DxV technology from a synchrotron facility into a laboratory setting by using a liquid-metal jet microfocus X-ray source. This source can provide the coherence required for PCXI and enough X-ray flux to image the dynamics of lung tissue motion during the respiratory cycle, which enables production of images compatible with 4DxV analysis. We demonstrate the measurements that can be captured in vivo in live mice using this technique, including regional airflow and tissue expansion. These measurements can inform physiological and biomedical research studies in small animals and assist in the development of new respiratory treatments.

A hybrid proton and hyperpolarized gas tagging MRI technique for lung respiratory motion imaging: a feasibility study

The aim of this work was to develop a novel hybrid 3D hyperpolarized (HP) gas tagging MRI (t-MRI) technique and to evaluate it for lung respiratory motion measurement with comparison to deformable image registrations (DIR) methods. Three healthy subjects underwent a hybrid MRI which combines 3D HP gas t-MRI with a low resolution (Low-R, 4.5 mm isotropic voxels) 3D proton MRI (p-MRI), plus a high resolution (High-R, 2.5 mm isotropic voxels) 3D p-MRI, during breath-holds at the end-of-inhalation (EOI) and the end-of-exhalation (EOE). Displacement vector field (DVF) of the lung motion was determined from the t-MRI images by tracking tagging grids and from the High-R p-MRI using three DIR methods (B-spline based method implemented by Velocity, Free Form Deformation by MIM, and B-spline by an open source software Elastix: denoted as A, B, and C, respectively), labeled as tDVF and dDVF, respectively. The tDVF from the HP gas t-MRI was used as ground-truth reference to evaluate performance of the three DIR methods. Differences in both magnitude and angle between the tDVF and dDVFs were analyzed. The mean lung motion of the three subjects was 37.3 mm, 8.9 mm and 12.9 mm, respectively. Relatively large discrepancies were observed between the tDVF and the dDVFs as compared to previously reported DIR errors. The mean  ±  standard deviation (SD) DVF magnitude difference was 8.3  ±  5.6 mm, 9.2  ±  4.5 mm, and 9.3  ±  6.1 mm, and the mean  ±  SD DVF angular difference was 29.1  ±  12.1°, 50.1  ±  28.6°, and 39.0  ±  6.3°, for the DIR Methods A, B, and C, respectively. These preliminary results showed that the hybrid HP gas t-MRI technique revealed different lung motion patterns as compared to the DIR methods. It may provide unique perspectives in developing and evaluating DIR of the lungs. Novelty and Significance We designed a MRI protocol that includes a novel hybrid MRI technique (3D HP gas t-MRI with a low resolution 3D p-MRI) plus a high resolution 3D p-MRI. We tested the novel hybrid MRI technique on three healthy subjects for measuring regional lung respiratory motion with comparison to deformable image registrations (DIR) methods, and observed relatively large discrepancies in lung motion between HP gas t-MRI and DIR methods.

Dynamic CT imaging of volumetric changes in pulmonary nodules correlates with physical measurements of stiffness

BACKGROUND AND PURPOSE

A major challenge in CT screening for lung cancer is limited specificity when distinguishing between malignant and non-malignant pulmonary nodules (PN). Malignant nodules have different mechanical properties and tissue characteristics ('stiffness') from non-malignant nodules. This study seeks to improve CT specificity by demonstrating in rats that measurements of volumetric ratios in PNs with varying composition can be determined by respiratory-gated dynamic CT imaging and that these ratios correlate with direct physical measurements of PN stiffness.

METHODS AND MATERIALS

Respiratory-gated MicroCT images acquired at extreme tidal volumes of 9 rats with PNs from talc, matrigel and A549 human lung carcinoma were analyzed and their volumetric ratios (δ) derived. PN stiffness was determined by measuring the Young's modulus using atomic force microscopy (AFM) for each nodule excised immediately after MicroCT imaging.

RESULTS

There was significant correlation (p=0.0002) between PN volumetric ratios determined by respiratory-gated CT imaging and the physical stiffness of the PNs determined from AFM measurements.

CONCLUSION

We demonstrated proof of concept that PN volume changes measured non-invasively correlate with direct physical measurements of stiffness. These results may translate clinically into a means of improving the specificity of CT screening for lung cancer and/or improving individual prognostic assessments based on lung tumor stiffness.

Lung motion estimation by robust point matching and spatiotemporal tracking for 4D CT

We propose a deformable registration approach to estimate patient-specific lung motion during free breathing for four-dimensional (4D) computed tomography (CT) based on point matching and tracking between images in different phases. First, a robust point matching (RPM) algorithm coarsely aligns the source phase image onto all other target phase images of 4D CT. Scale-invariant feature transform (SIFT) is introduced into the cost function in order to accelerate and stabilize the convergence of the point matching. Next, the temporal consistency of the estimated lung motion model is preserved by fitting the trajectories of the points in the respiratory phase using L₁ norm regularization. Then, the fitted positions of a point along the trajectory are used as the initial positions for the point tracking. Spatial mean-shift iteration is employed to track points in all phase images. The tracked positions in all phases are used to perform RPM again. These steps are repeated until the number of updated points is smaller than a given threshold σ. With this method, the correspondence between the source phase image and other target phase image is established more accurately. Trajectory fitting ensures the estimated trajectory does not fluctuate violently. We evaluated our method by using the public DIR-lab, POPI-model, CREATIS and COPDgene lung datasets. In the experimental results, the proposed method achieved satisfied accuracy for image registration. Our method also preserved the topology of the deformation fields well for image registration with large deformation.

The level of detail required in a deformable phantom to accurately perform quality assurance of deformable image registration

The primary purpose of the study was to determine how detailed deformable image registration (DIR) phantoms need to adequately simulate human anatomy and accurately assess the quality of DIR algorithms. In particular, how many distinct tissues are required in a phantom to simulate complex human anatomy? Pelvis and head-and-neck patient CT images were used for this study as virtual phantoms. Two data sets from each site were analyzed. The virtual phantoms were warped to create two pairs consisting of undeformed and deformed images. Otsu's method was employed to create additional segmented image pairs of n distinct soft tissue CT number ranges (fat, muscle, etc). A realistic noise image was added to each image. Deformations were applied in MIM Software (MIM) and Velocity deformable multi-pass (DMP) and compared with the known warping. Images with more simulated tissue levels exhibit more contrast, enabling more accurate results. Deformation error (magnitude of the vector difference between known and predicted deformation) was used as a metric to evaluate how many CT number gray levels are needed for a phantom to serve as a realistic patient proxy. Stabilization of the mean deformation error was reached by three soft tissue levels for Velocity DMP and MIM, though MIM exhibited a persisting difference in accuracy between the discrete images and the unprocessed image pair. A minimum detail of three levels allows a realistic patient proxy for use with Velocity and MIM deformation algorithms.

A Feasibility Study of Tumor Motion Estimate With Regional Deformable Registration Method for 4-Dimensional Radiation Therapy of Lung Cancer

This study aims to employ 4-dimensional computed tomography to quantify intrafractional tumor motion for patients with lung cancer to improve target localization in radiation therapy. A multistage regional deformable registration was implemented to calculate the excursion of gross tumor volume (GTV) during a breathing cycle. GTV was initially delineated on 0% phase of 4-dimensional computed tomography manually, and a subregion with 20 mm margin supplemented to GTV was generated with Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California). The structures, together with the 4-dimensional computed tomography set, were exported into an in-house software, with which a 3-stage B-spline deformable registration was carried out to map the subregion and warp GTV contour to other breathing phases. The center of mass of the GTV was computed using the contours, and the tumor motion was appraised as the excursion of the center of mass between 0% phase and other phases. Application of the algorithm to the 10 patients showed that clinically satisfactory outcomes were achievable with a spatial accuracy around 2 mm for GTV contour propagation between adjacent phases and 3 mm between opposite phases. The tumor excursion was determined in the vast range of 1 mm through 1.6 cm, depending on the tumor location and tumor size. Compared to the traditional whole image-based registration, the regional method was found computationally a factor of 5 more efficient. The proposed technique has demonstrated its capability in extracting thoracic tumor motion and should find its application in 4-dimensional radiation therapy in the future to maximally utilize the available spatial-temporal information.

Quantitative analysis of respiration-related movement for abdominal artery in multiphase hepatic CT

Objectives

Respiration-induced motion in the liver causes potential errors on the measurement of contrast medium in abdominal artery from multiphase hepatic CT scans. In this study, we investigated the use of hepatic CT images to quantitatively estimate the abdominal artery motion due to respiration by optical flow method.

Materials and Methods

A total of 132 consecutive patients were included in our patient cohort. We apply the optical flow method to compute the motion of the abdominal artery due to respiration.

Results

The minimum and maximum displacements of the abdominal artery motion were 0.02 and 30.87 mm by manual delineation, 0.03 and 40.75 mm calculated by optical flow method, respectively. Both high consistency and correlation between the present method and the physicians’ manual delineations were acquired with the regression equation of movement, y = 0.81x+0.25, r = 0.95, p<0.001.

Conclusion

We estimated the motion of abdominal artery due to respiration using the optical flow method in multiphase hepatic CT scans and the motion estimations were validated with the visualization of physicians. The quantitative analysis of respiration-related movement of abdominal artery could be used for motion correction in the measurement of contrast medium passing though abdominal artery in multiphase CT liver scans.

Motion freeze for respiration motion correction in PET/CT: a preliminary investigation with lung cancer patient data

PURPOSE

Respiratory motion presents significant challenges for accurate PET/CT. It often introduces apparent increase of lesion size, reduction of measured standardized uptake value (SUV), and the mismatch in PET/CT fusion images. In this study, we developed the motion freeze method to use 100% of the counts collected by recombining the counts acquired from all phases of gated PET data into a single 3D PET data, with correction of respiration by deformable image registration.

METHODS

Six patients with diagnosis of lung cancer confirmed by oncologists were recruited. PET/CT scans were performed with Discovery STE system. The 4D PET/CT with the Varian real-time position management for respiratory motion tracking was followed by a clinical 3D PET/CT scan procedure in the static mode. Motion freeze applies the deformation matrices calculated by optical flow method to generate a single 3D effective PET image using the data from all the 4D PET phases.

RESULTS

The increase in SUV and decrease in tumor size with motion freeze for all lesions compared to the results from 3D and 4D was observed in the preliminary data of lung cancer patients. In addition, motion freeze substantially reduced tumor mismatch between the CT image and the corresponding PET images.

CONCLUSION

Motion freeze integrating 100% of the PET counts has the potential to eliminate the influences induced by respiratory motion in PET data.

Respiratory motion reduction in PET/CT using abdominal compression for lung cancer patients

Purpose

Respiratory motion causes substantial artifacts in reconstructed PET images when using helical CT as the attenuation map in PET/CT imaging. In this study, we aimed to reduce the respiratory artifacts in PET/CT images of patients with lung tumors using an abdominal compression device.

Methods

Twelve patients with lung cancer located in the middle or lower lobe of the lung were recruited. The patients were injected with 370 MBq of ¹⁸F-FDG. During PET, the patients assumed two bed positions for 1.5 min/bed. After conducting free-breathing imaging, we obtained images of the patients with abdominal compression by applying the same setup used in the free-breathing scan. The differences in the standardized uptake value (SUV)_max, SUV_mean, tumor volume, and the centroid of the tumors between PET and various CT schemes were measured.

Results

The SUV_max and SUV_mean derived from PET/CT imaging using an abdominal compression device increased for all the lesions, compared with those obtained using the conventional approach. The percentage increases were 18.1% ±14% and 17% ±16.8% for SUV_max and SUV_mean, respectively. PET/CT imaging combined with abdominal compression generally reduced the tumor mismatch between CT and the corresponding attenuation corrected PET images, with an average decrease of 1.9±1.7 mm over all the cases.

Conclusions

PET/CT imaging combined with abdominal compression reduces respiratory artifacts and PET/CT misregistration, and enhances quantitative SUV in tumor. Abdominal compression is easy to set up and is an effective method used in PET/CT imaging for clinical oncology, especially in the thoracic region.

Evaluation of image registration spatial accuracy using a Bayesian hierarchical model

To evaluate the utility of automated deformable image registration (DIR) algorithms, it is necessary to evaluate both the registration accuracy of the DIR algorithm itself, as well as the registration accuracy of the human readers from whom the "gold standard" is obtained. We propose a Bayesian hierarchical model to evaluate the spatial accuracy of human readers and automatic DIR methods based on multiple image registration data generated by human readers and automatic DIR methods. To fully account for the locations of landmarks in all images, we treat the true locations of landmarks as latent variables and impose a hierarchical structure on the magnitude of registration errors observed across image pairs. DIR registration errors are modeled using Gaussian processes with reference prior densities on prior parameters that determine the associated covariance matrices. We develop a Gibbs sampling algorithm to efficiently fit our models to high-dimensional data, and apply the proposed method to analyze an image dataset obtained from a 4D thoracic CT study.

Dependence of ventilation image derived from 4D CT on deformable image registration and ventilation algorithms

Ventilation imaging using 4D CT is a convenient and low-cost functional imaging methodology which might be of value in radiotherapy treatment planning to spare functional lung volumes. Deformable image registration (DIR) is needed to calculate ventilation imaging from 4D CT. This study investigates the dependence of calculated ventilation on DIR methods and ventilation algorithms. DIR of the normal end expiration and normal end inspiration phases of the 4D CT images was used to correlate the voxels between the two respiratory phases. Three different DIR algorithms, optical flow (OF), diffeomorphic demons (DD), and diffeomorphic morphons (DM) were retrospectively applied to ten esophagus and ten lung cancer cases with 4D CT image sets that encompassed the entire lung volume. The three ventilation extraction methods were used based on either the Jacobian, the change in volume of the voxel, or directly calculated from Hounsfield units. The ventilation calculation algorithms used are the Jacobian, ΔV, and HU method. They were compared using the Dice similarity coefficient (DSC) index and Bland-Altman plots. Dependence of ventilation images on the DIR was greater for the ΔV and the Jacobian methods than for the HU method. The DSC index for 20% of low-ventilation volume for ΔV was 0.33 ± 0.03 (1 SD) between OF and DM, 0.44 ± 0.05 between OF and DD, and 0.51 ± 0.04 between DM and DD. The similarity comparisons for Jacobian were 0.32 ± 0.03, 0.44 ± 0.05, and 0.51 ± 0.04, respectively, and for HU they were 0.53 ± 0.03, 0.56 ± 0.03, and 0.76 ± 0.04, respectively. Dependence of extracted ventilation on the ventilation algorithm used showed good agreement between the ΔV and Jacobian methods, but differed significantly for the HU method. DSC index for using OF as DIR was 0.86 ± 0.01 between ΔV and Jacobian, 0.28 ± 0.04 between ΔV and HU, and 0.28 ± 0.04 between Jacobian and HU, respectively. When using DM or DD as DIR, similar values were obtained when comparing the different ventilation calculation methods. The similarity values for the 20% high-ventilation volume were close to those found for the 20% low-ventilation volume. The results obtained with DSC index were confirmed when using the Bland-Altman plots for comparing the ventilation images. Our data suggest that ventilation calculated from 4D CT depends on the DIR algorithm employed. Similarities between ΔV and Jacobian are higher than between ΔV and HU, and Jacobian and HU.

Reducing redundancy in wireless capsule endoscopy videos

We eliminate similar frames from a wireless capsule endoscopy video of the human intestines to maximize spatial coverage and minimize the redundancy in images. We combine an intensity correction method with a method based an optical flow and features to detect and reduce near-duplicate images acquired during the repetitive backward and forward egomotions due to peristalsis. In experiments, this technique reduced duplicate image of 52.3% from images of the small intestine.

Deformation effect on SUVmax changes in thoracic tumors using 4-D PET/CT scan

Respiratory motion blurs the standardized uptake value (SUV) and leads to a further signal reduction and changes in the SUV maxima. 4D PET can provide accurate tumor localization as a function of the respiratory phase in PET/CT imaging. We investigated thoracic tumor motion by respiratory 4D CT and assessed its deformation effect on the SUV changes in 4D PET imaging using clinical patient data. Twelve radiation oncology patients with thoracic cancer, including five lung cancer patients and seven esophageal cancer patients, were recruited to the present study. The 4D CT and PET image sets were acquired and reconstructed for 10 respiratory phases across the whole respiratory cycle. The optical flow method was applied to the 4D CT data to calculate the maximum displacements of the tumor motion in respiration. Our results show that increased tumor motion has a significant degree of association with the SUVmax loss for lung cancer. The results also show that the SUVmax loss has a higher correlation with tumors located at lower lobe of lung or at lower regions of esophagus.

Validation of three deformable image registration algorithms for the thorax

Deformable image registration (DIR) has been proposed for lung ventilation calculation using 4D CT. Spatial accuracy of DIR can be evaluated using expert landmark correspondences. Additionally, image differences between the deformed and the target images give a degree of accuracy of DIR algorithms for the same image modality registration. DIR of the normal end-expiration (50%), end-inspiration (0%), midexpiration (30%), and midinspiration image (70%) phases of the 4D CT images was used to correlate the voxels between the respiratory phases. Three DIR algorithms, optical flow (OF), diffeomorphic morphons (DM), and diffeomorphic demons (DD) were validated using a 4D thorax model, consisting of a 4D CT image dataset, along with associated landmarks delineated by a radiologist. Image differences between the deformed and the target images were used to evaluate the degree of registration accuracy of the three DIR algorithms. In the validation of the DIR algorithms, the average target registration error (TRE) for normal end-expiration-to-end-inspiration registration with one standard deviation (SD) for the DIR algorithms was 1.6 ± 0.9 mm (maximum 3.1 mm) for OF, 1.4 ± 0.6 mm (maximum 3.3 mm) for DM, and 1.4 ± 0.7 mm (maximum 3.3 mm) for DD, indicating registration errors were within two voxels. As a reference, the median value of TRE between 0 and 50% phases with rigid registration only was 5.0 mm with one SD of 2.5 mm and the maximum value of 12.0 mm. For the OF algorithm, 81% of voxels were within a difference of 50 HU, and 93% of the voxels were within 100 HU. For the DM algorithm, 69% of voxels were within 50 HU, and 87% within 100 HU. For the DD algorithm, 71% of the voxels were within 50 HU, and 87% within a difference of 100 HU. These data suggest that the three DIR methods perform accurate registrations in the thorax region. The mean TRE for all three DIR methods was less than two voxels suggesting that the registration performed by all methods are equally accurate in the thorax.

IMRT treatment plans and functional planning with functional lung imaging from 4D-CT for thoracic cancer patients

Background and purpose

Currently, the inhomogeneity of the pulmonary function is not considered when treatment plans are generated in thoracic cancer radiotherapy. This study evaluates the dose of treatment plans on highly-functional volumes and performs functional treatment planning by incorporation of ventilation data from 4D-CT.

Materials and methods

Eleven patients were included in this retrospective study. Ventilation was calculated using 4D-CT. Two treatment plans were generated for each case, the first one without the incorporation of the ventilation and the second with it. The dose of the first plans was overlapped with the ventilation and analyzed. Highly-functional regions were avoided in the second treatment plans.

Results

For small targets in the first plans (PTV < 400 cc, 6 cases), all V5, V20 and the mean lung dose values for the highly-functional regions were lower than that of the total lung. For large targets, two out of five cases had higher V5 and V20 values for the highly-functional regions. All the second plans were within constraints.

Conclusion

Radiation treatments affect functional lung more seriously in large tumor cases. With compromise of dose to other critical organs, functional treatment planning to reduce dose in highly-functional lung volumes can be achieved

Advances in 4D radiation therapy for managing respiration: part II - 4D treatment planning

The development of 4D CT imaging technology made possible the creation of patient models that are reflective of respiration-induced anatomical changes by adding a temporal dimension to the conventional 3D, spatial-only, patient description. This had opened a new venue for treatment planning and radiation delivery, aimed at creating a comprehensive 4D radiation therapy process for moving targets. Unlike other breathing motion compensation strategies (e.g. breath-hold and gating techniques), 4D radiotherapy assumes treatment delivery over the entire respiratory cycle - an added bonus for both patient comfort and treatment time efficiency. The time-dependent positional and volumetric information holds the promise for optimal, highly conformal, radiotherapy for targets experiencing movements caused by respiration, with potentially elevated dose prescriptions and therefore higher cure rates, while avoiding the uninvolved nearby structures. In this paper, the current state of the 4D treatment planning is reviewed, from theory to the established practical routine. While the fundamental principles of 4D radiotherapy are well defined, the development of a complete, robust and clinically feasible process still remains a challenge, imposed by limitations in the available treatment planning and radiation delivery systems.

Tracking the motion trajectories of junction structures in 4D CT images of the lung

Respiratory motion poses a major challenge in lung radiotherapy. Based on 4D CT images, a variety of intensity-based deformable registration techniques have been proposed to study the pulmonary motion. However, the accuracy achievable with these approaches can be sub-optimal because the deformation is defined globally in space. Therefore, the accuracy of the alignment of local structures may be compromised. In this work, we propose a novel method to detect a large collection of natural junction structures in the lung and use them as the reliable markers to track the lung motion. Specifically, detection of the junction centers and sizes is achieved by analysis of local shape profiles on one segmented image. To track the temporal trajectory of a junction, the image intensities within a small region of interest surrounding the center are selected as its signature. Under the assumption of the cyclic motion, we describe the trajectory by a closed B-spline curve and search for the control points by maximizing a metric of combined correlation coefficients. Local extrema are suppressed by improving the initial conditions using random walks from pair-wise optimizations. Several descriptors are introduced to analyze the motion trajectories. Our method was applied to 13 real 4D CT images. More than 700 junctions in each case are detected with an average positive predictive value of greater than 90%. The average tracking error between automated and manual tracking is sub-voxel and smaller than the published results using the same set of data.

Deformable image registration with inclusion of autodetected homologous tissue features

A novel deformable registration algorithm is proposed in the application of radiation therapy. The algorithm starts with autodetection of a number of points with distinct tissue features. The feature points are then matched by using the scale invariance features transform (SIFT) method. The associated feature point pairs are served as landmarks for the subsequent thin plate spline (TPS) interpolation. Several registration experiments using both digital phantom and clinical data demonstrate the accuracy and efficiency of the method. For the 3D phantom case, markers with error less than 2 mm are over 85% of total test markers, and it takes only 2-3 minutes for 3D feature points association. The proposed method provides a clinically practical solution and should be valuable for various image-guided radiation therapy (IGRT) applications.

Quantifying the interfractional displacement of the gastroesophageal junction during radiation therapy for esophageal cancer

PURPOSE

Accounting for interfractional changes in tumor location improves the accuracy of radiation treatment delivery. The purpose of this study was to quantify the interfractional displacement of the gastroesophageal junction (GEJ) based on standard treatment setup in patients with esophageal cancer undergoing radiation therapy.

METHODS AND MATERIALS

Free-breathing four-dimensional computed tomography (4D-CT) datasets were acquired weekly from 22 patients during treatment for esophageal adenocarcinoma. Scans were registered to baseline (simulation) 4D-CT scans by using bony landmarks. The distance between the center of the GEJ contour on the simulation scan and the mean location of GEJ centers on subsequent scans was used to assess changes in GEJ location between fractions; displacement was also correlated with clinical and respiratory variables.

RESULTS

The mean absolute random error was 1.69 mm (range, 0.11-4.11 mm) in the lateral direction, 1.87 mm (range, 0.51-4.09 mm) in the anterior-posterior (AP) direction, and 3.09 mm (range, 0.99-6.16 mm) in the superior-inferior (SI) direction. The mean absolute systemic GEJ displacement between fractions was 2.88 mm lateral (≥ 5 mm in 14%), mostly leftward; 2.90 mm (≥ 5 mm in 14%) AP, mostly anterior; and 6.77 mm (≥ 1 cm in 18%) SI, mostly inferior. Variations in tidal volume and diaphragmatic excursion during treatment correlated strongly with systematic SI GEJ displacement (r = 0.964, p < 0.0001; and r = 0.944, p < 0.0001, respectively) and moderately with systematic AP GEJ displacement (r = 0.678, p = 0.0005; r = 0.758, p < 0.0001, respectively). Systematic displacement in the inferior direction resulted in higher-than-intended doses (≥ 60 Gy) to the GEJ, with increased hot-spot to the adjacent stomach and lung base.

CONCLUSION

We found large (>1-cm) interfractional displacements in the GEJ in the SI (especially inferior) direction that was not accounted for when skeletal alignment alone was used for patient positioning. Because systematic displacement in the SI direction had dosimetric impact and correlated with tidal volume, better accounting for depth of breathing is needed to reduce interfractional variability.

Techniques for respiration-induced artifacts reductions in thoracic PET/CT

The advent of positron emission tomography/computed tomography (PET/CT) provides fusion of both anatomical and functional information. CT-based attenuation correction replaced (68)Ge-based attenuation correction for shortening acquisition time, improving image quality and quantitative accuracy. However, due to the temporal difference of PET and CT, mis-registration and motion artifacts are observed in the attenuation-corrected images mainly due to the respiratory motion. Reducing the spatial mismatch of the PET and CT reconstructed image remains a challenge. This review provides an introduction to various respiratory image artifacts reduction techniques especially for thoracic lesions, including breathing instruction based methods, CT protocol based methods and 4-dimensional PET/CT. The advantages and drawbacks of different methods are also discussed.

Attenuation correction of PET images with interpolated average CT for thoracic tumors

To reduce positron emission tomography (PET) and computed tomography (CT) misalignments and standardized uptake value (SUV) errors, cine average CT (CACT) has been proposed to replace helical CT (HCT) for attenuation correction (AC). A new method using interpolated average CT (IACT) for AC is introduced to further reduce radiation dose with similar image quality. Six patients were recruited in this study. The end-inspiration and -expiration phases from cine CT were used as the two original phases. Deformable image registration was used to generate the interpolated phases. The IACT was calculated by averaging the original and interpolated phases. The PET images were then reconstructed with AC using CACT, HCT and IACT, respectively. Their misalignments were compared by visual assessment, mutual information, correlation coefficient and SUV. The doses from different CT maps were analyzed. The misalignments were reduced for CACT and IACT as compared to HCT. The maximum SUV difference between the use of IACT and CACT was ∼3%, and it was ∼20% between the use of HCT and CACT. The estimated dose for IACT was 0.38 mSv. The radiation dose using IACT could be reduced by 85% compared to the use of CACT. IACT is a good low-dose approximation of CACT for AC.

Multi-scale optical flow including normalized mutual information for planar deformable lung motion estimation from 4D CT

A novel energy function for computing planar optical flow from X-ray CT images was presented and reported in detail in [1]. The technique combines four terms: brightness constancy, gradient constancy, continuity equation based on mass conservation, and discontinuity-preserving spatio-temporal smoothness. Both qualitative and quantitative evaluation of the proposed method demonstrated that the method results in significantly better angular errors than previous well-known techniques for optical flow estimation. A multi-scale approach to motion field computation based on this framework is presented in this paper. The proposed approach significantly speeds up the calculations, realizing computational savings. Additionally, an approach to determination of optimum values of scalar weights in the energy function is herein proposed. Normalized mutual information measured between the first image warped with the estimated motion and the second image is used to determine the optimum value for weighting parameters.

Analysis of regional mechanics in canine lung injury using forced oscillations and 3D image registration

Acute lung injury is characterized by heterogeneity of regional mechanical properties, which is thought to be correlated with disease severity. The feasibility of using respiratory input impedance (Z_rs) and computed tomographic (CT) image registration for assessing parenchymal mechanical heterogeneity was evaluated. In six dogs, measurements of Z_rs before and after oleic acid injury at various distending pressures were obtained, followed by whole lung CT scans. Each Z_rs spectrum was fit with a model incorporating variable distributions of regional compliances. CT image pairs at different inflation pressures were matched using an image registration algorithm, from which distributions of regional compliances from the resulting anatomic deformation fields were computed. Under baseline conditions, average model compliance decreased with increasing inflation pressure, reflecting parenchymal stiffening. After lung injury, these average compliances decreased at each pressure, indicating derecruitment, alveolar flooding, or alterations in intrinsic tissue elastance. However, average compliance did not change as inflation pressure increased, consistent with simultaneous recruitment and strain stiffening. Image registration revealed peaked distributions of regional compliances, and that small portions of the lung might undergo relative compression during inflation. The authors conclude that assessments of lung function using Z_rs combined with the structural alterations inferred from image registration provide unique but complementary information on the mechanical derangements associated with lung injury.

A GPU-based framework for modeling real-time 3D lung tumor conformal dosimetry with subject-specific lung tumor motion

In this paper, we present a graphics processing unit (GPU)-based simulation framework to calculate the delivered dose to a 3D moving lung tumor and its surrounding normal tissues, which are undergoing subject-specific lung deformations. The GPU-based simulation framework models the motion of the 3D volumetric lung tumor and its surrounding tissues, simulates the dose delivery using the dose extracted from a treatment plan using Pinnacle Treatment Planning System, Phillips, for one of the 3DCTs of the 4DCT and predicts the amount and location of radiation doses deposited inside the lung. The 4DCT lung datasets were registered with each other using a modified optical flow algorithm. The motion of the tumor and the motion of the surrounding tissues were simulated by measuring the changes in lung volume during the radiotherapy treatment using spirometry. The real-time dose delivered to the tumor for each beam is generated by summing the dose delivered to the target volume at each increase in lung volume during the beam delivery time period. The simulation results showed the real-time capability of the framework at 20 discrete tumor motion steps per breath, which is higher than the number of 4DCT steps (approximately 12) reconstructed during multiple breathing cycles.

Four-dimensional measurement of the displacement of internal fiducial markers during 320-multislice computed tomography scanning of thoracic esophageal cancer

PURPOSE

To investigate the three-dimensional movement of internal fiducial markers placed near esophageal cancers using 320-multislice CT.

METHODS AND MATERIALS

This study examined 22 metal markers in the esophageal wall near the primary tumors of 12 patients treated with external-beam photon radiotherapy. Motion assessment was analyzed in 41 respiratory phases during 20 s of cine CT in the radiotherapy position.

RESULTS

Motion in the cranial-caudal (CC) direction showed a strong correlation (R(2) > 0.4) with the respiratory curve in most markers (73%). The average absolute amplitude of the marker movement was 1.5 ± 1.6 mm, 1.6 ± 1.7 mm, and 3.3 ± 3.3 mm in the left-right (LR), anterior-posterior (AP), and CC directions, respectively. The average marker displacements in the CC direction between peak exhalation and inhalation for the 22 clips were 1.1 mm (maximum, 5.5 mm), 3.0 mm (14.5 mm), and 5.1 mm (16.3 mm) for the upper, middle, and lower thoracic esophagus, respectively.

CONCLUSIONS

Motion in primary esophagus tumor was evaluated with 320-multislice CT. According to this study, 4.3 mm CC, 1.5 mm AP, and 2.0 mm LR in the upper, 7.4 mm CC, 3.0 mm AP, and 2.4 mm LR in the middle, and 13.8 mm CC, 6.6 mm AP, and 6.8 mm LR in the lower thoracic esophagus provided coverage of tumor motion in 95% of the cases in our study population.

Esophageal motion during radiotherapy: quantification and margin implications

The purpose was to evaluate interfraction and intrafraction esophageal motion in the right-left (RL) and anterior-posterior (AP) directions using computed tomography (CT) in esophageal cancer patients. Eight patients underwent CT simulation and CT-on-rails imaging before and after radiotherapy. Interfraction displacement was defined as differences between pretreatment and simulation images. Intrafraction displacement was defined as differences between pretreatment and posttreatment images. Images were fused using bone registries, adjusted to the carina. The mean, average of the absolute, and range of esophageal motion were calculated in the RL and AP directions, above and below the carina. Thirty-one CT image sets were obtained. The incidence of esophageal interfraction motion > or =5 mm was 24% and > or =10 mm was 3%; intrafraction motion > or =5 mm was 13% and > or =10 mm was 4%. The average RL motion was 1.8 +/- 5.1 mm, favoring leftward movement, and the average AP motion was 0.6 +/- 4.8 mm, favoring posterior movement. Average absolute motion was 4.2 mm or less in the RL and AP directions. Motion was greatest in the RL direction above the carina. Coverage of 95% of esophageal mobility requires 12 mm left, 8 mm right, 10 mm posterior, and 9 mm anterior margins. In all directions, the average of the absolute interfraction and intrafraction displacement was 4.2 mm or less. These results support a 12 mm left, 8 mm right, 10 mm posterior, and 9 mm anterior margin for internal target volume (ITV) and can guide margins for future intensity modulated radiation therapy (IMRT) trials to account for organ motion and set up error in three-dimensional planning.

Assessment of a commercially available automatic deformable registration system

In recent years, a number of approaches have been applied to the problem of deformable registration validation. However, the challenge of assessing a commercial deformable registration system - in particular, an automatic registration system in which the deformable transformation is not readily accessible - has not been addressed. Using a collection of novel and established methods, we have developed a comprehensive, four-component protocol for the validation of automatic deformable image registration systems over a range of IGRT applications. The protocol, which was applied to the Reveal-MVS system, initially consists of a phantom study for determination of the system's general tendencies, while relative comparison of different registration settings is achieved through postregistration similarity measure evaluation. Synthetic transformations and contour-based metrics are used for absolute verification of the system's intra-modality and inter-modality capabilities, respectively. Results suggest that the commercial system is more apt to account for global deformations than local variations when performing deform-able image registration. Although the protocol was used to assess the capabilities of the Reveal-MVS system, it can readily be applied to other commercial systems. The protocol is by no means static or definitive, and can be further expanded to investigate other potential deformable registration applications.

Four-dimensional dosimetry validation and study in lung radiotherapy using deformable image registration and Monte Carlo techniques

Thoracic cancer treatment presents dosimetric difficulties due to respiratory motion and lung inhomogeneity. Monte Carlo and deformable image registration techniques have been proposed to be used in four-dimensional (4D) dose calculations to overcome the difficulties. This study validates the 4D Monte Carlo dosimetry with measurement, compares 4D dosimetry of different tumor sizes and tumor motion ranges, and demonstrates differences of dose-volume histograms (DVH) with the number of respiratory phases that are included in 4D dosimetry. BEAMnrc was used in dose calculations while an optical flow algorithm was used in deformable image registration and dose mapping. Calculated and measured doses of a moving phantom agreed within 3% at the center of the moving gross tumor volumes (GTV). 4D CT image sets of lung cancer cases were used in the analysis of 4D dosimetry. For a small tumor (12.5 cm³) with motion range of 1.5 cm, reduced tumor volume coverage was observed in the 4D dose with a beam margin of 1 cm. For large tumors and tumors with small motion range (around 1 cm), the 4D dosimetry did not differ appreciably from the static plans. The dose-volume histogram (DVH) analysis shows that the inclusion of only extreme respiratory phases in 4D dosimetry is a reasonable approximation of all-phase inclusion for lung cancer cases similar to the ones studied, which reduces the calculation in 4D dosimetry.

Analysis of deformable image registration accuracy using computational modeling

Computer aided modeling of anatomic deformation, allowing various techniques and protocols in radiation therapy to be systematically verified and studied, has become increasingly attractive. In this study the potential issues in deformable image registration (DIR) were analyzed based on two numerical phantoms: One, a synthesized, low intensity gradient prostate image, and the other a lung patient's CT image data set. Each phantom was modeled with region-specific material parameters with its deformation solved using a finite element method. The resultant displacements were used to construct a benchmark to quantify the displacement errors of the Demons and B-Spline-based registrations. The results show that the accuracy of these registration algorithms depends on the chosen parameters, the selection of which is closely associated with the intensity gradients of the underlying images. For the Demons algorithm, both single resolution (SR) and multiresolution (MR) registrations required approximately 300 iterations to reach an accuracy of 1.4 mm mean error in the lung patient's CT image (and 0.7 mm mean error averaged in the lung only). For the low gradient prostate phantom, these algorithms (both SR and MR) required at least 1600 iterations to reduce their mean errors to 2 mm. For the B-Spline algorithms, best performance (mean errors of 1.9 mm for SR and 1.6 mm for MR, respectively) on the low gradient prostate was achieved using five grid nodes in each direction. Adding more grid nodes resulted in larger errors. For the lung patient's CT data set, the B-Spline registrations required ten grid nodes in each direction for highest accuracy (1.4 mm for SR and 1.5 mm for MR). The numbers of iterations or grid nodes required for optimal registrations depended on the intensity gradients of the underlying images. In summary, the performance of the Demons and B-Spline registrations have been quantitatively evaluated using numerical phantoms. The results show that parameter selection for optimal accuracy is closely related to the intensity gradients of the underlying images. Also, the result that the DIR algorithms produce much lower errors in heterogeneous lung regions relative to homogeneous (low intensity gradient) regions, suggests that feature-based evaluation of deformable image registration accuracy must be viewed cautiously.

Patient setup error and day-to-day esophageal motion error analyzed by cone-beam computed tomography in radiation therapy

Little has been reported on the errors of setup and daily organ motion that occur during radiation therapy (RT) for esophageal cancer. The purpose of this paper was to determine the margins of esophageal motion during RT.

METHODS AND MATERIALS

The shift of the esophagus was analyzed in 20 consecutive patients treated with RT for esophageal cancer from November 2007. CT images for RT planning were used as the primary image series. Computed tomography (CT) images were acquired using an Elekta Synergy System, equipped with a kilovoltage-based cone-beam CT (CBCT) unit. The subsequent CBCT image series used for daily RT setup were compared with the primary image series to analyze esophageal motion. CBCT was performed before treatment sessions a total of 10 times in each patient twice a week. The outer esophageal wall was contoured on the CBCT images of all 200 sets.

RESULTS

In the 200 sets of CBCT images, the mean (absolute) +/- standard deviation (SD) of setup errors were 2 +/- 2 mm (max, 8 mm) in the lateral direction, 4 +/- 3 mm (max, 11 mm) in the longitudinal direction, and 4 +/- 3 mm (max, 13 mm) in the vertical direction. Additionally, the mean +/- SD values of daily esophageal motion comparing the CBCT with RT planning CT were 5 +/- 3 mm (max, 15 mm) in the lateral direction and 5 +/- 3 mm (max, 15 mm) in the vertical direction.

CONCLUSIONS

Our data support the use of target margins (between the clinical target volume and planning target volume) of 9 mm for day-to-day esophageal motion and 8 mm for patient setup in all directions, respectively.

Four-dimensional deformable image registration using trajectory modeling

A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy.

Development of a deformable lung phantom for the evaluation of deformable registration

A deformable lung phantom was developed to simulate patient breathing motion and to evaluate a deformable image registration algorithm. The phantom consisted of an acryl cylinder filled with water and a latex balloon located in the inner space of the cylinder. A silicon membrane was attached to the inferior end of the phantom. The silicon membrane was designed to simulate a real lung diaphragm and to reduce motor workload. This specific design was able to reduce the use of metal, which may prevent infrared sensing of the real position management (RPM) gating system for four‐dimensional (4D) CT image acquisition. Verification of intensity based three‐dimensional (3D) demons deformable registration was based on the peak exhale and peak inhale breathing phases. The registration differences ranged from 0.60 mm to 1.11 mm and accuracy was determined according to inner target deformation. The phantom was able to simulate features and deformation of a real human lung and has the potential for wide application for 4D radiation treatment planning.

PACS number: 87.57.Gg

Mass preserving nonrigid registration of CT lung images using cubic B-spline

The authors propose a nonrigid image registration approach to align two computed-tomography (CT)-derived lung datasets acquired during breath-holds at two inspiratory levels when the image distortion between the two volumes is large. The goal is to derive a three-dimensional warping function that can be used in association with computational fluid dynamics studies. In contrast to the sum of squared intensity difference (SSD), a new similarity criterion, the sum of squared tissue volume difference (SSTVD), is introduced to take into account changes in reconstructed Hounsfield units (scaled attenuation coefficient, HU) with inflation. This new criterion aims to minimize the local tissue volume difference within the lungs between matched regions, thus preserving the tissue mass of the lungs if the tissue density is assumed to be relatively constant. The local tissue volume difference is contributed by two factors: Change in the regional volume due to the deformation and change in the fractional tissue content in a region due to inflation. The change in the regional volume is calculated from the Jacobian value derived from the warping function and the change in the fractional tissue content is estimated from reconstructed HU based on quantitative CT measures. A composite of multilevel B-spline is adopted to deform images and a sufficient condition is imposed to ensure a one-to-one mapping even for a registration pair with large volume difference. Parameters of the transformation model are optimized by a limited-memory quasi-Newton minimization approach in a multiresolution framework. To evaluate the effectiveness of the new similarity measure, the authors performed registrations for six lung volume pairs. Over 100 annotated landmarks located at vessel bifurcations were generated using a semiautomatic system. The results show that the SSTVD method yields smaller average landmark errors than the SSD method across all six registration pairs.

Red blood cell velocity measurements of complete capillary in finger nail-fold using optical flow estimation

A new approach for the measurement of the red blood cell (RBC) velocity from capillary video by using optical flow estimation has been developed. An image registration function based on mutual information was used for stabilizing images in order to cope with slight finger movement during video acquisition. After image alignment, a skeleton extraction algorithm implemented by thinning was followed which enabled tracking blood flow entirely in arteriolar and venular limbs, and the curved segment as well. Optical flow and cross-correlation approaches were applied individually for velocity estimation of twelve microcirculation videos acquired independently from three healthy volunteers. The RBC velocity of 12 vessels at three given measurement sites (arteriolar, curve and venular sites) in a 45-second period of occlusion-release condition of vessel were examined. There were four stages of flow conditions: resting (T(1)), pre-occlusion (T(2)), post-occlusion (T(3)) and release (T(4)). The results from both approaches revealed that the velocity difference among the three sites were not significant. The pattern of distribution of RBC velocity was also reported. The correlation coefficient (r) of the velocity calculated using optical flow and cross-correlation in four stages of blood flow conditions and the overall correlation were: 1-window: r(T1)=0.68, r(T2)=0.67, r(T3)=0.92, r(T4)=0.88 and r(All)=0.79; 2-window: r(T1)=0.84, r(T2)=0.88, r(T3)=0.87, r(T4)=0.93 and r(All)=0.88. The averaged velocity results showed no significant differences between optical flow and 2-window cross-correlation in all flow conditions. Optical flow estimation is not only independent to the direction of flow, but also able to calculate the intensity displacement of all pixels. The proposed velocity measurement system has been shown to provide complete velocity information for the whole vessel limb which demonstrates the advantage of measuring blood flow at the level of microcirculation more accurately.

Patient-specific finite element modeling of respiratory lung motion using 4D CT image data

Development and optimization of methods for adequately accounting for respiratory motion in radiation therapy of thoracic tumors require detailed knowledge of respiratory dynamics and its impact on corresponding dose distributions. Thus, computer aided modeling and simulation of respiratory motion have become increasingly important. In this article a biophysical approach for modeling respiratory lung motion is described: Major aspects of the process of lung ventilation are formulated as a contact problem of elasticity theory which is solved by finite element methods; lung tissue is assumed to be isotropic, homogeneous, and linearly elastic. A main focus of the article is to assess the impact of biomechanical parameters (values of elastic constants) on the modeling process and to evaluate modeling accuracy. Patient-specific models are generated based on 4D CT data of 12 lung tumor patients. Simulated motion patterns of inner lung landmarks are compared with corresponding motion patterns observed in the 4D CT data. Mean absolute differences between model-based predicted landmark motion and corresponding breathing-induced landmark displacements as observed in the CT data sets are in the order of 3 mm (end expiration to end inspiration) and 2 mm (end expiration to midrespiration). Modeling accuracy decreases with increasing tumor size both locally (landmarks close to tumor) and globally (landmarks in other parts of the lung). The impact of the values of the elastic constants appears to be small. Outcomes show that the modeling approach is an adequate strategy in predicting lung dynamics due to lung ventilation. Nevertheless, the decreased prediction quality in cases of large tumors demands further study of the influence of lung tumors on global and local lung elasticity properties.

Adapting liver motion models using a navigator channel technique

Deformable registration can improve the accuracy of tumor targeting; however for online applications, efficiency as well as accuracy is important. A navigator channel technique has been developed to combine a biomechanical model-based deformable registration algorithm with a population motion model and patient specific motion information to perform fast deformable registration for application in image-guided radiation therapy. A respiratory population-based liver motion model was generated from breath-hold CT data sets of ten patients using a finite element model as a framework. The population model provides a biomechanical reference template of the average liver motions, which were found to be (absolute mean +/-SD) 0.12 +/- 0.10, 0.84 +/- 0.13, and 1.24 +/- 0.18 cm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The population motion model was then adapted to the specific liver motion of 13 patients based on their exhale and inhale CT images. The patient motion was calculated using a navigator channel (a narrow region of interest window) on liver boundaries in the images. The absolute average accuracy of the navigator channel to predict the 1D SI and AP motions of the liver was less than 0.11, which is less than the out-of-plane image voxel size, 0.25 cm. This 1D information was then used to adapt the 4D population motion model in the SI and AP directions to predict the patient specific liver motion. The absolute average residual error of the navigator channel technique to adapt the population motion to the patients' specific motion was verified using three verification methods: (1) vessel bifurcation, (2) tumor center of mass, and (3) MORFEUS deformable algorithm. All three verification methods showed statistically similar results where the technique's accuracy was approximately on the order of the voxel image sizes. This method has potential applications in online assessment of motion at the time of treatment to improve image-guided radiotherapy and monitoring of intrafraction motion.

Generation of composite dose and biological effective dose (BED) over multiple treatment modalities and multistage planning using deformable image registration

Currently there are no commercially available tools to generate composite plans across different treatment modalities and/or different planning image sets. Without a composite plan, it may be difficult to perform a meaningful dosimetric evaluation of the overall treatment course. In this paper, we introduce a method to generate composite biological effective dose (BED) plans over multiple radiotherapy treatment modalities and/or multistage plans, using deformable image registration. Two cases were used to demonstrate the method. Case I was prostate cancer treated with intensity-modulated radiation therapy (IMRT) and a permanent seed implant. Case II involved lung cancer treated with two treatment plans generated on two separate computed tomography image sets. Thin-plate spline or optical flow methods were used as appropriate to generate deformation matrices. The deformation matrices were then applied to the dose matrices and the resulting physical doses were converted to BED and added to yield the composite plan. Cell proliferation and sublethal repair were considered in the BED calculations. The difference in BED between normal tissues and tumor volumes was accounted for by using different BED models, alpha/beta values, and cell potential doubling times. The method to generate composite BED plans presented in this paper provides information not available with the traditional simple dose summation or physical dose summation. With the understanding of limitations and uncertainties of the algorithms involved, it may be valuable for the overall treatment plan evaluation.

A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets

Expert landmark correspondences are widely reported for evaluating deformable image registration (DIR) spatial accuracy. In this report, we present a framework for objective evaluation of DIR spatial accuracy using large sets of expert-determined landmark point pairs. Large samples (>1100) of pulmonary landmark point pairs were manually generated for five cases. Estimates of inter- and intra-observer variation were determined from repeated registration. Comparative evaluation of DIR spatial accuracy was performed for two algorithms, a gradient-based optical flow algorithm and a landmark-based moving least-squares algorithm. The uncertainty of spatial error estimates was found to be inversely proportional to the square root of the number of landmark point pairs and directly proportional to the standard deviation of the spatial errors. Using the statistical properties of this data, we performed sample size calculations to estimate the average spatial accuracy of each algorithm with 95% confidence intervals within a 0.5 mm range. For the optical flow and moving least-squares algorithms, the required sample sizes were 1050 and 36, respectively. Comparative evaluation based on fewer than the required validation landmarks results in misrepresentation of the relative spatial accuracy. This study demonstrates that landmark pairs can be used to assess DIR spatial accuracy within a narrow uncertainty range.

Potential dosimetric benefits of four-dimensional radiation treatment planning

PURPOSE

To determine the extent of dosimetric differences between conventional three-dimensional (3D) dose calculations and four-dimensional (4D) dose calculations based on deformation of organ models.

METHODS AND MATERIALS

Four-dimensional dose calculations were retrospectively performed on computed tomography data sets for 15 patients with Stage III non-small-cell lung cancer, using a model-based deformable registration algorithm on a research version of a commercial radiation treatment planning system. Target volume coverage and doses to critical structures calculated using the 4D methodology were compared with those calculated using conventional 3D methodology.

RESULTS

For 11 of 15 patients, clinical target volume coverage was comparable in the 3D and 4D calculations, whereas for 7 of 15 patients, planning target volume coverage was comparable. For the other patients, the 4D calculation indicated a difference in target volume dose sufficiently great to warrant replanning. No correlations could be established between differences in 3D and 4D calculations and gross tumor volume size or extent of motion. Negligible differences were observed between 3D and 4D dose-volume relationships for normal anatomic structures.

CONCLUSIONS

Use of 4D dose calculations, when possible, helps ensure that target volumes will not be underirradiated when respiratory motion may affect the dose distribution.

Objective assessment of deformable image registration in radiotherapy: a multi-institution study

The looming potential of deformable alignment tools to play an integral role in adaptive radiotherapy suggests a need for objective assessment of these complex algorithms. Previous studies in this area are based on the ability of alignment to reproduce analytically generated deformations applied to sample image data, or use of contours or bifurcations as ground truth for evaluation of alignment accuracy. In this study, a deformable phantom was embedded with 48 small plastic markers, placed in regions varying from high contrast to roughly uniform regional intensity, and small to large regional discontinuities in movement. CT volumes of this phantom were acquired at different deformation states. After manual localization of marker coordinates, images were edited to remove the markers. The resulting image volumes were sent to five collaborating institutions, each of which has developed previously published deformable alignment tools routinely in use. Alignments were done, and applied to the list of reference coordinates at the inhale state. The transformed coordinates were compared to the actual marker locations at exhale. A total of eight alignment techniques were tested from the six institutions. All algorithms performed generally well, as compared to previous publications. Average errors in predicted location ranged from 1.5 to 3.9 mm, depending on technique. No algorithm was uniformly accurate across all regions of the phantom, with maximum errors ranging from 5.1 to 15.4 mm. Larger errors were seen in regions near significant shape changes, as well as areas with uniform contrast but large local motion discontinuity. Although reasonable accuracy was achieved overall, the variation of error in different regions suggests caution in globally accepting the results from deformable alignment.