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Metz CT, Baka N, Kirisli H, Schaap M, Klein S, Neefjes LA, Mollet NR, Lelieveldt B, de Bruijne M, Niessen WJ, van Walsum T. Regression-based cardiac motion prediction from single-phase CTA. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1311-1325. [PMID: 22438512 DOI: 10.1109/tmi.2012.2190938] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.
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Affiliation(s)
- Coert T Metz
- Departments of Medical Informatics and Radiology, Erasmus MC-University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands.
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Fayad H, Pan T, Pradier O, Visvikis D. Patient specific respiratory motion modeling using a 3D patient's external surface. Med Phys 2012; 39:3386-95. [PMID: 22755719 PMCID: PMC4032399 DOI: 10.1118/1.4718578] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2011] [Revised: 04/30/2012] [Accepted: 05/01/2012] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal. METHODS Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient. RESULTS Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only. CONCLUSIONS The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.
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Affiliation(s)
- Hadi Fayad
- INSERM UMR1101, LaTIM, CHU Morvan, Brest F-29200, France.
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53
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Staub D, Docef A, Brock RS, Vaman C, Murphy MJ. 4D Cone-beam CT reconstruction using a motion model based on principal component analysis. Med Phys 2012; 38:6697-709. [PMID: 22149852 DOI: 10.1118/1.3662895] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. METHODS The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. RESULTS The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. CONCLUSIONS Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine.
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Affiliation(s)
- David Staub
- Department of Radiation Oncology, Virignia Commonwealth University, Richmond, Virginia 23298, USA.
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Buerger C, Clough RE, King AP, Schaeffter T, Prieto C. Nonrigid motion modeling of the liver from 3-D undersampled self-gated golden-radial phase encoded MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:805-815. [PMID: 22271830 DOI: 10.1109/tmi.2011.2181997] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Magnetic resonance imaging (MRI) has been commonly used for guiding and planning image guided interventions since it provides excellent soft tissue visualization of anatomy and allows motion modeling to predict the position of target tissues during the procedure. However, MRI-based motion modeling remains challenging due to the difficulty of acquiring multiple motion-free 3-D respiratory phases with adequate contrast and spatial resolution. Here, we propose a novel retrospective respiratory gating scheme from a 3-D undersampled high-resolution MRI acquisition combined with fast and robust image registrations to model the nonrigid deformation of the liver. The acquisition takes advantage of the recently introduced golden-radial phase encoding (G-RPE) trajectory. G-RPE is self-gated, i.e., the respiratory signal can be derived from the acquired data itself, and allows retrospective reconstructions of multiple respiratory phases at any arbitrary respiratory position. Nonrigid motion modeling is applied to predict the liver deformation of an average breathing cycle. The proposed approach was validated on 10 healthy volunteers. Motion model accuracy was assessed using similarity-, surface-, and landmark-based validation methods, demonstrating precise model predictions with an overall target registration error of TRE = 1.70 ± 0.94 mm which is within the range of the acquired resolution.
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Affiliation(s)
- C Buerger
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
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55
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Condino S, Ferrari V, Freschi C, Alberti A, Berchiolli R, Mosca F, Ferrari M. Electromagnetic navigation platform for endovascular surgery: how to develop sensorized catheters and guidewires. Int J Med Robot 2012; 8:300-10. [PMID: 22368145 DOI: 10.1002/rcs.1417] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/05/2011] [Indexed: 11/11/2022]
Abstract
BACKGROUND Endovascular procedures are nowadays limited by difficulties arising from the use of 2D images and are associated with dangerous X-ray exposure and the injection of nephrotoxic contrast medium. METHODS An electromagnetic navigator is proposed to guide endovascular procedures with reduced radiation dose and contrast medium injection. Five DOF electromagnetic sensors are calibrated and used to track in real time the positions and orientation of endovascular catheters and guidewires, while intraoperative 3D rotational angiography is used to acquire 3D models of patient anatomy. A preliminary prototype is developed to prove the feasibility of the system using an anthropomorphic phantom. RESULTS The spatial accuracy of the system was evaluated during 70 targeting trials obtaining an overall accuracy of 1.2 ± 0.3 mm; system usability was positively evaluated by three surgeons. CONCLUSIONS The strategy proposed to sensorize endovascular instruments paves the way for the development of surgical strategies with reduced radiation dose and contrast medium injection. Further in vitro, animal and clinical experiments are necessary for complete surgical validation.
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Affiliation(s)
- S Condino
- EndoCAS Center, Department of Oncology, Transplantation and New Technologies in Medicine, University of Pisa, Pisa, Italy.
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56
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Bauer S, Berkels B, Hornegger J, Rumpf M. Joint ToF Image Denoising and Registration with a CT Surface in Radiation Therapy. LECTURE NOTES IN COMPUTER SCIENCE 2012. [DOI: 10.1007/978-3-642-24785-9_9] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Wachinger C, Yigitsoy M, Rijkhorst EJ, Navab N. Manifold learning for image-based breathing gating in ultrasound and MRI. Med Image Anal 2011; 16:806-18. [PMID: 22226466 DOI: 10.1016/j.media.2011.11.008] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2011] [Revised: 11/27/2011] [Accepted: 11/28/2011] [Indexed: 11/24/2022]
Abstract
Respiratory motion is a challenging factor for image acquisition and image-guided procedures in the abdominal and thoracic region. In order to address the issues arising from respiratory motion, it is often necessary to detect the respiratory signal. In this article, we propose a novel, purely image-based retrospective respiratory gating method for ultrasound and MRI. Further, we apply this technique to acquire breathing-affected 4D ultrasound with a wobbler probe and, similarly, to create 4D MR with a slice stacking approach. We achieve the gating with Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional image space. Since Laplacian eigenmaps assign to each image frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. We perform the image-based gating on several 2D and 3D ultrasound datasets over time, and quantify its very good performance by comparing it to measurements from an external gating system. For MRI, we perform the manifold learning on several datasets for various orientations and positions. We achieve very high correlations by a comparison to an alternative gating with diaphragm tracking.
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Affiliation(s)
- Christian Wachinger
- Computer Aided Medical Procedures (CAMP), Technische Universität München, München, Germany.
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58
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Naini AS, Patel RV, Samani A. Measurement of Lung Hyperelastic Properties Using Inverse Finite Element Approach. IEEE Trans Biomed Eng 2011; 58:2852-9. [DOI: 10.1109/tbme.2011.2160637] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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59
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King AP, Buerger C, Tsoumpas C, Marsden PK, Schaeffter T. Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator. Med Image Anal 2011; 16:252-64. [PMID: 21959365 DOI: 10.1016/j.media.2011.08.003] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2011] [Revised: 08/18/2011] [Accepted: 08/22/2011] [Indexed: 10/17/2022]
Abstract
Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.
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Affiliation(s)
- A P King
- Division of Imaging Sciences and Biomedical Engineering, King's College, 4th Floor Lambeth Wing, St. Thomas' Hospital, London SE1 7EH, UK.
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60
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Wang Z, Lu X, Zhou G, Yan L, Zhang L, Zhu Y, Tian Y. Multiphase-computed tomography-based target volume definition in conventional fractionated radiotherapy of lung tumors: Dosimetric and reliable comparison with the technique using addition of generic margins. TUMORI JOURNAL 2011; 97:603-8. [DOI: 10.1177/030089161109700511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Aims and background The aim of the present study was to compare radiotherapeutic plans based on internal target volume determined by between multiphase computed tomography and addition of a generic margin in lung tumors and to evaluate the reliability of ITV determined by multiphase computed tomography during conventional fractionated radiotherapy. Methods and study design The radiotherapeutic plans based on internal target volume determined by between multiphase computed tomography and addition of a generic margin in 10 patients with lung tumors were applied. The difference of two planning target volumes (PTV) and irradiated dose and volume of normal lung tissue were compared. Weekly new targets were delineated on repeated computed tomography scans, and weekly dose coverage of clinical target volume under two different treatment plans was evaluated. Results For all patients, PTV3CT volume based on multiphase computed tomography was significantly smaller than that of PTVcon based on addition of a generic margin (t = 6.831, P <0.001). The volume receiving more than 20 Gy in Plan3CT and Plancon was 16.7 ± 5.2% and 20.0 ± 5.2% (t = 7.565, P <0.001), the volume receiving more than 5 Gy was 36.6 ± 7.2% and 42.7 ± 6.4% (t = 7.459, P <0.001), and mean lung dose was 1037.5 ± 275.0 cGy and 1246.8 ± 271.0 cGy (t = 8.078, P <0.001), respectively. Both Plan3CT and Planconprovided a satisfactory clinical target volume coverage weekly during conventional fractionated radiotherapy for 6–7 weeks, and the ratio of the volume receiving the prescription dose was 1.03 ± 0.02 and 1.04 ± 0.02, respectively. Conclusions The radiotherapeutic plan based on internal target volume determined by multiphase computed tomography can ensure weekly target coverage during conventional fractionated radiotherapy in lung tumors, and it is better than the plan based on the addition of generic internal target volume, which can effectively reduce normal lung tissue irradiation.
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Affiliation(s)
- Zheng Wang
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
- Department of Radiation Oncology, Changshu Affiliated Hospital of Soochow University, Suzhou, China
| | - Xueguan Lu
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
| | - Gang Zhou
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
| | - Liming Yan
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
| | - Liyuan Zhang
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
| | - Yaqun Zhu
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
| | - Ye Tian
- Department of Radiation Oncology, Second Affiliated Hospital of Soochow University, Suzhou
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61
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Li R, Lewis JH, Jia X, Zhao T, Liu W, Wuenschel S, Lamb J, Yang D, Low DA, Jiang SB. On a PCA-based lung motion model. Phys Med Biol 2011; 56:6009-30. [PMID: 21865624 DOI: 10.1088/0031-9155/56/18/015] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.
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Affiliation(s)
- Ruijiang Li
- Department of Radiation Oncology and Center for Advanced Radiotherapy Technologies, University of California San Diego, 3855 Health Sciences Dr, La Jolla, CA 92037-0843, USA
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62
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Fayad H, Pan T, Clement JF, Visvikis D. Technical note: Correlation of respiratory motion between external patient surface and internal anatomical landmarks. Med Phys 2011; 38:3157-64. [PMID: 21815390 DOI: 10.1118/1.3589131] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Current respiratory motion monitoring devices used for motion synchronization in medical imaging and radiotherapy provide either 1D respiratory signal over a specific region or 3D information based on few external or internal markers. On the other hand, newer technology may offer the potential to monitor the entire patient external surface in real time. The main objective of this study was to assess the motion correlation between such an external patient surface and internal anatomical landmarks motion. METHODS Four dimensional computed tomography (4D CT) volumes for ten patients were used in this study. Anatomical landmarks were manually selected in the thoracic region across the 4D CT datasets by two experts. The landmarks included normal structures as well as the tumor location. In addition, a distance map representing the entire external patient surface, which corresponds to surfaces acquired by a time of flight (ToF) camera or similar devices, was created by segmenting the skin of all 4D CT volumes using a thresholding algorithm. Finally, the correlation between the internal landmarks and external surface motion was evaluated for different regions (placement and size) throughout a patient's surface. RESULTS Significant variability was observed in the motion of the different parts of the external patient surface. The larger motion magnitude was consistently measured in the central regions of the abdominal and the thoracic areas for the different patient datasets considered. The highest correlation coefficients were observed between the motion of these external surface areas and internal landmarks such as the diaphragm and mediastinum structures as well as the tumor location landmarks (0.8 +/- 0.18 and 0.72 +/- 0.12 for the abdominal and the thoracic regions, respectively). Worse correlation was observed when one considered landmarks not significantly influenced by respiratory motion such as the apex and the sternum. CONCLUSIONS There were large differences in the motion correlation observed considering different regions of interest placed over a patients' external surface and internal anatomical landmarks. The positioning of current devices used for respiratory motion synchronization may reduce such correlation by averaging the motion over correlated and poorly correlated external regions. The potential of capturing in real-time the motion of the complete external patient surface as well as choosing the area of the surface that correlates best with the internal motion should allow reducing such variability and associated errors in both respiratory motion synchronization and subsequent motion modeling processes.
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Affiliation(s)
- Hadi Fayad
- INSERM U650, LaTIM, CHU Morvan, Brest F-29200, France.
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63
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Correlation between external and internal respiratory motion: a validation study. Int J Comput Assist Radiol Surg 2011; 7:483-92. [DOI: 10.1007/s11548-011-0653-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2011] [Accepted: 08/08/2011] [Indexed: 12/12/2022]
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64
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Buerger C, Schaeffter T, King AP. Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation. Med Image Anal 2011; 15:551-64. [DOI: 10.1016/j.media.2011.02.009] [Citation(s) in RCA: 71] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2010] [Revised: 01/10/2011] [Accepted: 02/23/2011] [Indexed: 11/28/2022]
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65
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Chin E, Otto K. Investigation of a novel algorithm for true 4D-VMAT planning with comparison to tracked, gated and static delivery. Med Phys 2011; 38:2698-707. [DOI: 10.1118/1.3578608] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Chandler A, Wei W, Herron DH, Anderson EF, Johnson VE, Ng CS. Semiautomated motion correction of tumors in lung CT-perfusion studies. Acad Radiol 2011; 18:286-93. [PMID: 21295733 DOI: 10.1016/j.acra.2010.10.008] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2010] [Revised: 10/18/2010] [Accepted: 10/20/2010] [Indexed: 11/18/2022]
Abstract
RATIONALE AND OBJECTIVES To compare the relative performance of one-dimensional (1D) manual, rigid-translational, and nonrigid registration techniques to correct misalignment of lung tumor anatomy acquired from computed tomography perfusion (CTp) datasets. MATERIALS AND METHODS Twenty-five datasets in patients with lung tumors who had undergone a CTp protocol were evaluated. Each dataset consisted of one reference CT image from an initial cine slab and six subsequent breathhold helical volumes (16-row multi-detector CT), acquired during intravenous contrast administration. Each helical volume was registered to the reference image using two semiautomated intensity-based registration methods (rigid-translational and nonrigid), and 1D manual registration (the only registration method available in the relevant application software). The performance of each technique to align tumor regions was assessed quantitatively (percent overlap and distance of center of mass), and by a visual validation study (using a 5-point scale). The registration methods were statistically compared using linear mixed and ordinal probit regression models. RESULTS Quantitatively, tumor alignment with the nonrigid method compared to rigid-translation was borderline significant, which in turn was significantly better than the 1D manual method: average (± SD) percent overlap, 91.8 ± 2.3%, 87.7 ± 5.5%, and 77.6 ± 5.9%, respectively; and average (± SD) DCOM, 0.41 ± 0.16 mm, 1.08 ± 1.13 mm, and 2.99 ± 2.93 mm, respectively (all P < .0001). Visual validation confirmed these findings. CONCLUSION Semiautomated registration methods achieved superior alignment of lung tumors compared to the 1D manual method. This will hopefully translate into more reliable CTp analyses.
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Affiliation(s)
- Adam Chandler
- Department of Imaging Physics, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
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67
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Ehrhardt J, Werner R, Schmidt-Richberg A, Handels H. Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2011; 30:251-265. [PMID: 20876013 DOI: 10.1109/tmi.2010.2076299] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.
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Affiliation(s)
- Jan Ehrhardt
- Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany.
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68
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Sadeghi Naini A, Pierce G, Lee TY, Patel RV, Samani A. CT image construction of a totally deflated lung using deformable model extrapolation. Med Phys 2011; 38:872-83. [DOI: 10.1118/1.3531985] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Naini AS, Ting-Yim Lee, Patel RV, Samani A. Estimation of Lung's Air Volume and Its Variations Throughout Respiratory CT Image Sequences. IEEE Trans Biomed Eng 2011; 58:152-8. [DOI: 10.1109/tbme.2010.2086457] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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71
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Vandemeulebroucke J, Rit S, Kybic J, Clarysse P, Sarrut D. Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs. Med Phys 2010; 38:166-78. [DOI: 10.1118/1.3523619] [Citation(s) in RCA: 107] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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McClelland JR, Hughes S, Modat M, Qureshi A, Ahmad S, Landau DB, Ourselin S, Hawkes DJ. Inter-fraction variations in respiratory motion models. Phys Med Biol 2010; 56:251-72. [DOI: 10.1088/0031-9155/56/1/015] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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73
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Al-Mayah A, Moseley J, Velec M, Hunter S, Brock K. Deformable image registration of heterogeneous human lung incorporating the bronchial tree. Med Phys 2010; 37:4560-71. [PMID: 20964173 DOI: 10.1118/1.3471020] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To investigate the effect of the bronchial tree on the accuracy of biomechanical-based deformable image registration of human lungs. METHODS Three dimensional finite element models have been developed using four dimensional computed tomography image data of ten lung cancer patients. Each model is built of a body, left and right lungs, tumor, and bronchial trees. Triangular shell elements are used for the bronchial trees while tetrahedral elements are used for other components. Hyperelastic material properties based on experimental investigation on human lungs are used for the lung parenchyma. Different material properties are assigned for the bronchial tree using five values for the modulus of elasticity of 0.01, 0.12, 0.5, 10, and 18 MPa. Lungs are modeled to slide inside chest cavities by applying frictionless contact surfaces between each lung and corresponding chest cavity. The accuracy of the models is examined using an average of 40 bronchial bifurcation points identified on inhale and exhale images. Relative accuracy is evaluated by comparing the displacement of all nodes within the lungs as well as the dosimetric difference at the exhale position predicted by the model. RESULTS There is no significant effect of bronchial tree on the model accuracy based on the bifurcation points analysis. However, on the local level, using an average of 38 000 nodes, there is a maximum difference of 8.5 mm in the deformation of the bronchial trees, as the modulus of elasticity of the bronchial trees increases from 0.01 to 18 MPa; however, more than 96% of nodes are within a 2.5 mm difference in each direction. The average dose difference at the predicted exhale position is less than 35 cGy between the models. CONCLUSIONS The bronchial tree has little effect on the global deformation and the accuracy of deformable image registration of lungs. Hence, the homogenous model is a reasonable assumption. Since there are some local deformation differences between nodes as the material properties of the bronchial tree change that may affect the accuracy of dosimetric results, heterogeneity may be required for a smaller scale modeling of lungs.
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Affiliation(s)
- Adil Al-Mayah
- Radiation Medicine Program, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.
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Shackleford JA, Kandasamy N, Sharp GC. On developing B-spline registration algorithms for multi-core processors. Phys Med Biol 2010; 55:6329-51. [DOI: 10.1088/0031-9155/55/21/001] [Citation(s) in RCA: 115] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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75
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Eom J, Shi C, Xu XG, De S. Modeling respiratory motion for cancer radiation therapy based on patient-specific 4DCT data. ACTA ACUST UNITED AC 2010; 12:348-55. [PMID: 20426131 DOI: 10.1007/978-3-642-04271-3_43] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Prediction of respiratory motion has the potential to substantially improve cancer radiation therapy. A nonlinear finite element (FE) model of respiratory motion during full breathing cycle has been developed based on patient specific pressure-volume relationship and 4D Computed Tomography (CT) data. For geometric modeling of lungs and ribcage we have constructed intermediate CAD surface which avoids multiple geometric smoothing procedures. For physiologically relevant respiratory motion modeling we have used pressure-volume (PV) relationship to apply pressure loading on the surface of the model. A hyperelastic soft tissue model, developed from experimental observations, has been used. Additionally, pleural sliding has been considered which results in accurate deformations in the superior-inferior (SI) direction. The finite element model has been validated using 51 landmarks from the CT data. The average differences in position is seen to be 0.07 cm (SD = 0.20 cm), 0.07 cm (0.15 cm), and 0.22 cm (0.18 cm) in the left-right, anterior-posterior, and superior-inferior directions, respectively.
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Affiliation(s)
- Jaesung Eom
- Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
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76
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Kuo HC, Mah D, Chuang KS, Wu A, Hong L, Yaparpalvi R, Spierer M, Kalnicki S. A method incorporating 4DCT data for evaluating the dosimetric effects of respiratory motion in single-arc IMAT. Phys Med Biol 2010; 55:3479-97. [PMID: 20508324 DOI: 10.1088/0031-9155/55/12/014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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77
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Moreno A, Chambon S, P. Santhanam A, P. Rolland J, Angelini E, Bloch I. Combining a breathing model and tumor-specific rigidity constraints for registration of CT-PET thoracic data. ACTA ACUST UNITED AC 2010; 13:281-98. [DOI: 10.3109/10929080802431980] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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78
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Al-Mayah A, Moseley J, Velec M, Brock KK. Sliding characteristic and material compressibility of human lung: parametric study and verification. Med Phys 2010; 36:4625-33. [PMID: 19928094 DOI: 10.1118/1.3218761] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To find and verify the optimum sliding characteristics and material compressibility that provide the minimum error in deformable image registration of the lungs. METHODS A deformable image registration study has been conducted on a total of 16 lung cancer patients. Patient specific three dimensional finite element models have been developed to model left and right lungs, chest (body), and tumor based on 4D CT images. Contact surfaces have been applied to lung-chest cavity interfaces. Experimental test data are used to model nonlinear material properties of lungs. A parametric study is carried out on seven patients, 20 conditions for each, to investigate the sliding behavior and the tissue compressibility of lungs. Three values of coefficient of friction of 0, 0.1, and 0.2 are investigated to model lubrication and sliding restriction on the lung-chest cavity interface. The effect of material compressibility of lungs is studied using Poisson's ratios of 0.35, 0.4, 0.45, and 0.499. The model accuracy is examined by calculating the difference between the image-based displacement of bronchial bifurcation points identified in the lung images and the calculated corresponding model-based displacement. Furthermore, additional bifurcation points around the tumor and its center of mass are used to examine the effect of the mentioned parameters on the tumor localization. RESULTS The frictionless contact model with 0.4 Poisson's ratio provides the smallest residual errors of 1.1 +/- 0.9, 1.5 +/- 1.3, and 2.1 +/- 1.6 mm in the LR, AP, and SI directions, respectively. Similarly, this optimum model provides the most accurate location of the tumor with residual errors of 1.0 +/- 0.6, 0.9 +/- 0.7, and 1.4 +/- 1.0 mm in all three directions. The accuracy of this model is verified on an additional nine patients with average errors of 0.8 +/- 0.7, 1.3 +/- 1.1, and 1.7 +/- 1.6 mm in the LR, AP, and SI directions, respectively. CONCLUSIONS The optimum biomechanical model with the smallest registration error is when frictionless contact model and 0.4 Poisson's ratio are applied. The overall accuracies of all bifurcation points in all 16 patients including tumor points are 1.0 +/- 0.7, 1.2 +/- 1.0, and 1.7 +/- 1.4 mm in the LR, AP, and SI directions, respectively.
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Affiliation(s)
- A Al-Mayah
- Radiation Medicine Program, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada.
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79
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Online 4-D CT Estimation for Patient-Specific Respiratory Motion Based on Real-Time Breathing Signals. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2010 2010; 13:392-9. [DOI: 10.1007/978-3-642-15711-0_49] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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80
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Prediction Framework for Statistical Respiratory Motion Modeling. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2010 2010; 13:327-34. [DOI: 10.1007/978-3-642-15711-0_41] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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81
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Rijkhorst EJ, Heanes D, Odille F, Hawkes D, Barratt D. Simulating Dynamic Ultrasound Using MR-derived Motion Models to Assess Respiratory Synchronisation for Image-Guided Liver Interventions. INFORMATION PROCESSING IN COMPUTER-ASSISTED INTERVENTIONS 2010. [DOI: 10.1007/978-3-642-13711-2_11] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
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82
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Radiofrequency ablation of lung tumors in swine assisted by a navigation device with preprocedural volumetric planning. J Vasc Interv Radiol 2009; 21:122-9. [PMID: 19939704 DOI: 10.1016/j.jvir.2009.09.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2008] [Revised: 07/31/2009] [Accepted: 09/15/2009] [Indexed: 10/20/2022] Open
Abstract
PURPOSE To develop an image guidance system that incorporates volumetric planning of spherical ablations and electromagnetic tracking of radiofrequency (RF) electrodes during insertion. MATERIALS AND METHODS Simulated tumors were created in three live swine by percutaneously injecting agar nodules into the lung. A treatment plan was devised for each tumor with optimization software to solve the planning problem. The desired output was the minimum number of overlapping ablation spheres necessary to ablate each tumor and the margin. The insertion plan was executed with use of the electromagnetic tracking system that guided the insertion of the probe into precomputed locations. After a 72-hour survival period, animals were killed and histopathologic sections of the tissue were examined for cell viability and burn pattern analysis. RESULTS A planning algorithm to spherically cover the tumors and the margin was computed. Electromagnetic tracking allowed successful insertion of the instrument, and impedance roll-off was reached in all ablations. Depending on their size, the tumors and the tumor margins were successfully covered with two to four ablation spheres. The image registration error was 1.0 mm +/- 0.64. The overall error of probe insertion was 9.4 mm +/- 3.0 (N = 8). Analysis of histopathologic sections confirmed successful ablations of the tissue. CONCLUSIONS Computer-assisted RF ablation planning and electromagnetically tracked probe insertion were successful in three swine, validating the feasibility of electromagnetic tracking-assisted tumor targeting. Image misregistration caused by respiratory motion and tissue deformation contributed to the overall error of probe insertion.
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83
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Brock KK. Results of a multi-institution deformable registration accuracy study (MIDRAS). Int J Radiat Oncol Biol Phys 2009; 76:583-96. [PMID: 19910137 DOI: 10.1016/j.ijrobp.2009.06.031] [Citation(s) in RCA: 282] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2009] [Revised: 06/01/2009] [Accepted: 06/03/2009] [Indexed: 11/30/2022]
Abstract
PURPOSE To assess the accuracy, reproducibility, and computational performance of deformable image registration algorithms under development at multiple institutions on common datasets. METHODS AND MATERIALS Datasets from a lung patient (four-dimensional computed tomography [4D-CT]), a liver patient (4D-CT and magnetic resonance imaging [MRI] at exhale), and a prostate patient (repeat MRI) were obtained. Radiation oncologists localized anatomic structures for accuracy assessment. Algorithm accuracy was determined by comparing the computer-predicted displacement at each bifurcation point with the displacement computed from the oncologists' annotations. Thirty-seven academic institutions and medical device manufacturers with published evidence of active deformable image registration capabilities were invited to participate. RESULTS Twenty-seven groups agreed to participate; 6 did not return results. Sixteen completed the liver 4D-CT, 12 the lung 4D-CT, 3 the prostate MRI, and 3 the liver MRI-CT. The range of average absolute error for the lung 4D-CT was 0.6-1.2 mm (left-right [LR]), 0.5-1.8 mm (anterior-posterior [AP]), and 0.7-2.0 mm (superior-inferior [SI]); the liver 4D-CT was 0.8-1.5 mm (LR), 1.0-5.2 mm (AP), and 1.0-5.9 mm (SI); the liver MRI-CT was 1.1-2.6 mm (LR), 2.0-5.0 mm (AP), and 2.2-2.6 mm (SI); and the repeat prostate MRI prostate datasets was 0.5-6.2 mm (LR), 3.1-3.7 mm (AP), and 0.4-2.0 mm (SI). CONCLUSIONS An infrastructure was developed to assess multi-institution deformable registration accuracy. The results indicate large discrepancies in reported shifts, although the majority of deformable registration algorithms performed at an accuracy equivalent to the voxel size, promising to improve treatment planning, delivery, and assessment.
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Affiliation(s)
- Kristy K Brock
- Princess Margaret Hospital, University Health Network, Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
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84
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White MJ, Hawkes DJ, Melbourne A, Collins DJ, Coolens C, Hawkins M, Leach MO, Atkinson D. Motion artifact correction in free-breathing abdominal MRI using overlapping partial samples to recover image deformations. Magn Reson Med 2009; 62:440-9. [PMID: 19449437 DOI: 10.1002/mrm.22017] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
This article presents a method to reconstruct liver MRI data acquired continuously during free breathing, without any external sensor or navigator measurements. When the deformations associated with k-space data are known, generalized matrix inversion reconstruction has been shown to be effective in reducing the ghosting and blurring artifacts of motion. This article describes a novel method to obtain these nonrigid deformations. A breathing model is built from a fast dynamic series: low spatial resolution images are registered and their deformations parameterized by overall superior-inferior displacement. The correct deformation for each subset of the subsequent imaging data is then found by comparing a few lines of k-space with the equivalent lines from a deformed reference image while varying the deformation over the model parameter. This procedure is known as image deformation recovery using overlapping partial samples (iDROPS). Simulations using 10 rapid dynamic studies from volunteers showed the average error in iDROPS-derived deformations within the liver to be 1.43 mm. A further four volunteers were imaged at higher spatial resolution. The complete reconstruction process using data from throughout several breathing cycles was shown to reduce blurring and ghosting in the liver. Retrospective respiratory gating was also demonstrated using the iDROPS parameterization.
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Affiliation(s)
- M J White
- Centre for Medical Image Computing, University College London, London, United Kingdom.
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85
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Li R, Lewis JH, Cerviño LI, Jiang SB. 4D CT sorting based on patient internal anatomy. Phys Med Biol 2009; 54:4821-33. [DOI: 10.1088/0031-9155/54/15/012] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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86
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Ehler ED, Bzdusek K, Tomé WA. A Method to Automate the Segmentation of the GTV and ITV for Lung Tumors. Med Dosim 2009; 34:145-53. [PMID: 19410144 DOI: 10.1016/j.meddos.2008.08.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2008] [Revised: 08/13/2008] [Accepted: 08/21/2008] [Indexed: 11/20/2022]
Affiliation(s)
- Eric D Ehler
- Department of Medical Physics, University of Wisconsin, Madison, WI 53792, USA
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87
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Hughes S, McClelland J, Tarte S, Lawrence D, Ahmad S, Hawkes D, Landau D. Assessment of two novel ventilatory surrogates for use in the delivery of gated/tracked radiotherapy for non-small cell lung cancer. Radiother Oncol 2009; 91:336-41. [PMID: 19395076 DOI: 10.1016/j.radonc.2009.03.016] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2008] [Revised: 03/08/2009] [Accepted: 03/12/2009] [Indexed: 11/24/2022]
Abstract
BACKGROUND In selected patients with NSCLC the therapeutic index of radical radiotherapy can be improved with gating/tracking technology. Both techniques require real-time information on target location. This is often derived from a surrogate ventilatory signal. We assessed the correlation of two novel surrogate ventilatory signals with a spirometer-derived signal. The novel signals were obtained using the VisionRT stereoscopic camera system. The VisionRT-Tracked-Point (VRT-TP) signal was derived from tracking a point located midway between the umbilicus and xiphisternum. The VisionRT-Surface-Derived-Volume (VRT-SDV) signal was derived from 3D body surface imaging of the torso. Both have potential advantages over the current surrogate signals. METHODS Eleven subjects with NSCLC were recruited. Each was positioned as for radiotherapy treatment, and then instructed to breathe in five different modes: normal, abdominal, thoracic, deep and shallow breathing. Synchronous ventilatory signals were recorded for later analysis. The signals were analysed for correlation across all modes of breathing, and phase shifts. The VRT-SDV was also assessed for its ability to determine the mode of breathing. RESULTS Both novel respiratory signals showed good correlation (r>0.80) with spirometry in 9 of 11 subjects. For all subjects the correlation with spirometry was better for the VRT-SDV signal than for the VRT-TP signal. Only one subject displayed a phase shift between the VisionRT-derived signals and spirometry. The VRT-SDV signal could also differentiate between different modes of breathing. Unlike the spirometer-derived signal, neither VisionRT-derived signal was subject to drift. CONCLUSION Both the VRT-TP and VRT-SDV signals have potential applications in ventilatory-gated and tracked radiotherapy. They can also be used as a signal for sorting 4DCT images, and to drive 4DCT single- and multiple-parameter motion models.
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Affiliation(s)
- Simon Hughes
- Division of Imaging Sciences, King's College London, London, UK.
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88
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Carnes G, Gaede S, Yu E, Van Dyk J, Battista J, Lee TY. A fully automated non-external marker 4D-CT sorting algorithm using a serial cine scanning protocol. Phys Med Biol 2009; 54:2049-66. [DOI: 10.1088/0031-9155/54/7/013] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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89
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Kashani R, Hub M, Balter JM, Kessler ML, Dong L, Zhang L, Xing L, Xie Y, Hawkes D, Schnabel JA, McClelland J, Joshi S, Chen Q, Lu W. Objective assessment of deformable image registration in radiotherapy: a multi-institution study. Med Phys 2009; 35:5944-53. [PMID: 19175149 DOI: 10.1118/1.3013563] [Citation(s) in RCA: 116] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
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.
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Affiliation(s)
- Rojano Kashani
- Department of Radiation Oncology, University of Michigan, Ann Arbor Michigan 48109-0010, USA.
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Respiratory Motion Estimation from Cone-Beam Projections Using a Prior Model. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2009 2009; 12:365-72. [DOI: 10.1007/978-3-642-04271-3_45] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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91
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Murphy K, van Ginneken B, Pluim JPW, Klein S, Staring M. Semi-automatic reference standard construction for quantitative evaluation of lung CT registration. ACTA ACUST UNITED AC 2008; 11:1006-13. [PMID: 18982703 DOI: 10.1007/978-3-540-85990-1_121] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
An algorithm is presented for the efficient semi-automatic construction of a detailed reference standard for registration in thoracic CT. A well-distributed set of 100 landmarks is detected fully automatically in one scan of a pair to be registered. Using a custom-designed interface, observers locate corresponding anatomic locations in the second scan. The manual annotations are used to learn the relationship between the scans and after approximately twenty manual marks the remaining points are matched automatically. Inter-observer differences demonstrate the accuracy of the matching and the applicability of the reference standard is demonstrated on two different sets of registration results over 19 CT scan pairs.
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Affiliation(s)
- K Murphy
- University Medical Center, Utrecht, The Netherlands
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92
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Yang D, Lu W, Low DA, Deasy JO, Hope AJ, El Naqa I. 4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling. Med Phys 2008; 35:4577-90. [PMID: 18975704 PMCID: PMC2673589 DOI: 10.1118/1.2977828] [Citation(s) in RCA: 82] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2008] [Revised: 08/13/2008] [Accepted: 08/13/2008] [Indexed: 11/07/2022] Open
Abstract
Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.
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Affiliation(s)
- Deshan Yang
- Department of Radiation Oncology, School of Medicine, Washington University, St. Louis, Missouri 63110, USA
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93
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Colgan R, McClelland J, McQuaid D, Evans PM, Hawkes D, Brock J, Landau D, Webb S. Planning lung radiotherapy using 4D CT data and a motion model. Phys Med Biol 2008; 53:5815-30. [DOI: 10.1088/0031-9155/53/20/017] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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94
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Chavarrías C, Vaquero JJ, Sisniega A, Rodríguez-Ruano A, Soto-Montenegro ML, García-Barreno P, Desco M. Extraction of the respiratory signal from small-animal CT projections for a retrospective gating method. Phys Med Biol 2008; 53:4683-95. [DOI: 10.1088/0031-9155/53/17/015] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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95
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Riegel AC, Chang JY, Vedam SS, Johnson V, Chi PCM, Pan T. Cine computed tomography without respiratory surrogate in planning stereotactic radiotherapy for non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2008; 73:433-41. [PMID: 18644683 DOI: 10.1016/j.ijrobp.2008.04.047] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2007] [Revised: 04/17/2008] [Accepted: 04/21/2008] [Indexed: 12/25/2022]
Abstract
PURPOSE To determine whether cine computed tomography (CT) can serve as an alternative to four-dimensional (4D)-CT by providing tumor motion information and producing equivalent target volumes when used to contour in radiotherapy planning without a respiratory surrogate. METHODS AND MATERIALS Cine CT images from a commercial CT scanner were used to form maximum intensity projection and respiratory-averaged CT image sets. These image sets then were used together to define the targets for radiotherapy. Phantoms oscillating under irregular motion were used to assess the differences between contouring using cine CT and 4D-CT. We also retrospectively reviewed the image sets for 26 patients (27 lesions) at our institution who had undergone stereotactic radiotherapy for Stage I non-small-cell lung cancer. The patients were included if the tumor motion was >1 cm. The lesions were first contoured using maximum intensity projection and respiratory-averaged CT image sets processed from cine CT and then with 4D-CT maximum intensity projection and 10-phase image sets. The mean ratios of the volume magnitude were compared with intraobserver variation, the mean centroid shifts were calculated, and the volume overlap was assessed with the normalized Dice similarity coefficient index. RESULTS The phantom studies demonstrated that cine CT captured a greater extent of irregular tumor motion than did 4D-CT, producing a larger tumor volume. The patient studies demonstrated that the gross tumor defined using cine CT imaging was similar to, or slightly larger than, that defined using 4D-CT. CONCLUSION The results of our study have shown that cine CT is a promising alternative to 4D-CT for stereotactic radiotherapy planning.
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Affiliation(s)
- Adam C Riegel
- Department of Imaging Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
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Samant SS, Xia J, Muyan-Ozcelik P, Owens JD. High performance computing for deformable image registration: Towards a new paradigm in adaptive radiotherapy. Med Phys 2008; 35:3546-53. [PMID: 18777915 DOI: 10.1118/1.2948318] [Citation(s) in RCA: 95] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Sanjiv S Samant
- Department of Nuclear and Radiological Engineering, University of Florida, Gainesville, Florida 32611-8300, USA.
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McQuaid D, Webb S. Target-tracking deliveries using conventional multileaf collimators planned with 4D direct-aperture optimization. Phys Med Biol 2008; 53:4013-29. [DOI: 10.1088/0031-9155/53/15/001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Hughes S, McClelland J, Chandler A, Adams M, Boutland J, Withers D, Ahmad S, Blackall J, Tarte S, Hawkes D, Landau D. A Comparison of Internal Target Volume Definition by Limited Four-dimensional Computed Tomography, the Addition of Patient-specific Margins, or the Addition of Generic Margins when Planning Radical Radiotherapy for Lymph Node-positive Non-small Cell Lung Cancer. Clin Oncol (R Coll Radiol) 2008; 20:293-300. [DOI: 10.1016/j.clon.2007.12.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2007] [Revised: 11/20/2007] [Accepted: 12/03/2007] [Indexed: 10/22/2022]
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Wu Z, Rietzel E, Boldea V, Sarrut D, Sharp GC. Evaluation of deformable registration of patient lung 4DCT with subanatomical region segmentations. Med Phys 2008; 35:775-81. [DOI: 10.1118/1.2828378] [Citation(s) in RCA: 116] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Webb S. Adapting IMRT delivery fraction-by-fraction to cater for variable intrafraction motion. Phys Med Biol 2007; 53:1-21. [DOI: 10.1088/0031-9155/53/1/001] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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