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Huang Y, Thielemans K, Price G, McClelland JR. Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data. Phys Med Biol 2024; 69:025020. [PMID: 38091611 PMCID: PMC10791594 DOI: 10.1088/1361-6560/ad1546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/23/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Objective.As the most common solution to motion artefact for cone-beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan.Approach.Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients.Results.For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans.Significance.The motion compensated reconstructions and motion models produced by our method will have direct clinical benefit by providing more accurate estimates of the delivered dose and ultimately facilitating more accurate radiotherapy treatments for lung cancer patients.
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Affiliation(s)
- Yuliang Huang
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
| | - Kris Thielemans
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Gareth Price
- Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Jamie R McClelland
- Centre for Medical Image Computing, University College London, London, United Kingdom
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
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2
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Wikström KA, Isacsson UM, Nilsson KM, Ahnesjö A. Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy. J Appl Clin Med Phys 2021; 22:103-112. [PMID: 34258853 PMCID: PMC8425865 DOI: 10.1002/acm2.13351] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/19/2021] [Accepted: 06/17/2021] [Indexed: 12/04/2022] Open
Abstract
Patient breathing during lung cancer radiotherapy reduces the ability to keep a sharp dose gradient between tumor and normal tissues. To mitigate detrimental effects, accurate information about the tumor position is required. In this work, we evaluate the errors in modeled tumor positions over several fractions of a simple tumor motion model driven by a surface surrogate measure and its time derivative. The model is tested with respect to four different surface surrogates and a varying number of surrogate and image acquisitions used for model training. Fourteen patients were imaged 100 times with cine CT, at three sessions mimicking a planning session followed by two treatment fractions. Patient body contours were concurrently detected by a body surface laser scanning system BSLS from which four surface surrogates were extracted; thoracic point TP, abdominal point AP, the radial distance mean RDM, and a surface derived volume SDV. The motion model was trained on session 1 and evaluated on sessions 2 and 3 by comparing modeled tumor positions with measured positions from the cine images. The number of concurrent surrogate and image acquisitions used in the training set was varied, and its impact on the final result was evaluated. The use of AP as a surface surrogate yielded the smallest error in modeled tumor positions. The mean deviation between modeled and measured tumor positions was 1.9 mm. The corresponding deviations for using the other surrogates were 2.0 mm (RDM), 2.8 mm (SDV), and 3.0 mm (TP). To produce a motion model that accurately models the tumor position over several fractions requires at least 10 simultaneous surrogate and image acquisitions over 1–2 minutes.
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Affiliation(s)
- Kenneth A Wikström
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden.,Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ulf M Isacsson
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden.,Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | - Anders Ahnesjö
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden.,Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
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Ranjbar M, Sabouri P, Mossahebi S, Leiser D, Foote M, Zhang J, Lasio G, Joshi S, Sawant A. Development and prospective in-patient proof-of-concept validation of a surface photogrammetry + CT-based volumetric motion model for lung radiotherapy. Med Phys 2019; 46:5407-5420. [PMID: 31518437 DOI: 10.1002/mp.13824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 07/22/2019] [Accepted: 08/28/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE We develop and validate a motion model that uses real-time surface photogrammetry acquired concurrently with four-dimensional computed tomography (4DCT) to estimate respiration-induced changes within the entire irradiated volume, over arbitrarily many respiratory cycles. METHODS A research, couch-mounted, VisionRT (VRT) system was used to acquire optical surface data (15 Hz, ROI = 15 × 20 cm2 ) from the thoraco-abdominal surface of a consented lung SBRT patient, concurrently with their standard-of-care 4DCT. The end-exhalation phase from the 4DCT was regarded as reference and for each remaining phase, deformation vector fields (DVFs) with respect to the reference phase were computed. To reduce dimensionality, the first two principal components (PCs) of the matrix of nine DVFs were calculated. In parallel, ten phase-averaged VRT surfaces were created. Surface DVFs and corresponding PCs were computed. A principal least squares regression was used to relate the PCs of surface DVF to those of volume DVFs, establishing a relationship between time-varying surface and the underlying time-varying volume. Proof-of-concept validation was performed during each treatment fraction by concurrently acquiring 30 s time series of real-time surface data and "ground truth" kV fluoroscopic data (FL). A ray-tracing algorithm was used to create a digitally reconstructed fluorograph (DRF), and motion trajectories of high-contrast, soft-tissue, anatomical features in the DRF were compared with those from kV FL. RESULTS For five of the six fluoroscopic acquisition sessions, the model out-performed 4DCT in predicting contour Dice coefficient with respect to fluoroscopy-derived contours. Similarly, the model exhibited a marked improvement over 4DCT for patch positions on the diaphragm. Model patch position errors varied from 5 to -15 mm while 4DCT errors ranged between 5 and -22.4 mm. For one fluoroscopic acquisition, a marked change in the a priori internal-external correlation resulted in model errors comparable to those of 4DCT. CONCLUSIONS We described the development and a proof-of-concept validation for a volumetric motion model that uses surface photogrammetry to correlate the time-varying thoraco-abdominal surface to the time-varying internal thoraco-abdominal volume. These early results indicate that the proposed approach can result in a marked improvement over 4DCT. While limited by the duration of the fluoroscopic acquisitions as well as the resolution of the acquired images, the DRF-based proof-of-concept technique developed here is model-agnostic, and therefore, has the potential to be used as an in-patient validation tool for other volumetric motion models.
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Affiliation(s)
- M Ranjbar
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
| | - P Sabouri
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
| | - S Mossahebi
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
| | - D Leiser
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
| | - M Foote
- Department of Biomedical Engineering, Scientific Computing and Imaging Institute, University of Utah, 72 South Central Campus Drive, Room 3750, Salt Lake City, UT, 84112, USA
| | - J Zhang
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
| | - G Lasio
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
| | - S Joshi
- Department of Biomedical Engineering, Scientific Computing and Imaging Institute, University of Utah, 72 South Central Campus Drive, Room 3750, Salt Lake City, UT, 84112, USA
| | - A Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, 22 S Greene Street, Baltimore, MD, 21201, USA
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Lafrenière M, Mahadeo N, Lewis J, Rottmann J, Williams CL. Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate. Phys Med 2019; 63:25-34. [PMID: 31221405 DOI: 10.1016/j.ejmp.2019.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 04/26/2019] [Accepted: 05/18/2019] [Indexed: 12/25/2022] Open
Abstract
We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.
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Affiliation(s)
- M Lafrenière
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02215, USA.
| | - N Mahadeo
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02215, USA
| | - J Lewis
- University of California, Los Angeles, CA 90095, USA
| | - J Rottmann
- Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen, Switzerland
| | - C L Williams
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02215, USA.
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Ranjbar M, Sabouri P, Repetto C, Sawant A. A novel deformable lung phantom with programably variable external and internal correlation. Med Phys 2019; 46:1995-2005. [PMID: 30919974 DOI: 10.1002/mp.13507] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 03/06/2019] [Accepted: 03/06/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Lung motion phantoms used to validate radiotherapy motion management strategies have fairly simplistic designs that do not adequately capture complex phenomena observed in human respiration such as external and internal deformation, variable hysteresis and variable correlation between different parts of the thoracic anatomy. These limitations make reliable evaluation of sophisticated motion management techniques quite challenging. In this work, we present the design and implementation of a programmable, externally and internally deformable lung motion phantom that allows for a reproducible change in external-internal and internal-internal correlation of embedded markers. METHODS An in-house-designed lung module, made from natural latex foam was inserted inside the outer shell of a commercially available lung phantom (RSD, Long Beach, CA, USA). Radiopaque markers were placed on the external surface and embedded into the lung module. Two independently programmable high-precision linear motion actuators were used to generate primarily anterior-posterior (AP) and primarily superior-inferior (SI) motion in a reproducible fashion in order to enable (a) variable correlation between the displacement of interior volume and the exterior surface, (b) independent changes in the amplitude of the AP and SI motions, and (c) variable hysteresis. The ability of the phantom to produce complex and variable motion accurately and reproducibly was evaluated by programming the two actuators with mathematical and patient-recorded lung tumor motion traces, and recording the trajectories of various markers using kV fluoroscopy. As an example application, the phantom was used to evaluate the performance of lung motion models constructed from kV fluoroscopy and 4DCT images. RESULTS The phantom exhibited a high degree of reproducibility and marker motion ranges were reproducible to within 0.5 mm. Variable correlation was observed between the displacements of internal-internal and internal-external markers. The SI and AP components of motion of a specific marker had a correlation parameter that varied from -11 to 17. Monitoring a region of interest on the phantom's surface to estimate internal marker motion led to considerably lower uncertainties than when a single point was monitored. CONCLUSIONS We successfully designed and implemented a programmable, externally and internally deformable lung motion phantom that allows for a reproducible change in external-internal and internal-internal correlation of embedded markers.
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Affiliation(s)
- Maida Ranjbar
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Pouya Sabouri
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Carlo Repetto
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Amit Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
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Green OL, Rankine LJ, Cai B, Curcuru A, Kashani R, Rodriguez V, Li HH, Parikh PJ, Robinson CG, Olsen JR, Mutic S, Goddu SM, Santanam L. First clinical implementation of real-time, real anatomy tracking and radiation beam control. Med Phys 2018; 45:3728-3740. [PMID: 29807390 DOI: 10.1002/mp.13002] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Revised: 01/05/2018] [Accepted: 01/05/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE We describe the acceptance testing, commissioning, periodic quality assurance, and workflow procedures developed for the first clinically implemented magnetic resonance imaging-guided radiation therapy (MR-IGRT) system for real-time tracking and beam control. METHODS The system utilizes real-time cine imaging capabilities at 4 frames per second for real-time tracking and beam control. Testing of the system was performed using an in-house developed motion platform and a commercially available motion phantom. Anatomical tracking is performed by first identifying a target (a region of interest that is either tissue to be treated or a critical structure) and generating a contour around it. A boundary contour is also created to identify tracking margins. The tracking algorithm deforms the anatomical contour (target or a normal organ) on every subsequent cine frame and compares it to the static boundary contour. If the anatomy of interest moves outside the boundary, the radiation delivery is halted until the tracked anatomy returns to treatment portal. The following were performed to validate and clinically implement the system: (a) spatial integrity evaluation; (b) tracking accuracy; (c) latency; (d) relative point dose and spatial dosimetry; (e) development of clinical workflow for gating; and (f) independent verification by an outside credentialing service. RESULTS The spatial integrity of the MR system was found to be within 2 mm over a 45-cm diameter field-of-view. The tracking accuracy for geometric targets was within 1.2 mm. The average system latency was measured to be within 394 ms. The dosimetric accuracy using ionization chambers was within 1.3% ± 1.7%, and the dosimetric spatial accuracy was within 2 mm. The phantom irradiation for the outside credentialing service had satisfactory results, as well. CONCLUSIONS The first clinical MR-IGRT system was validated for real-time tracking and gating capabilities and shown to be reliable and accurate. Patient workflow methods were developed for efficient treatment. Periodic quality assurance tests can be efficiently performed with commercially available equipment to ensure accurate system performance.
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Affiliation(s)
- Olga L Green
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | - Leith J Rankine
- University of North Carolina at Chapel Hill, Chapel Hill, NC, 27713, USA
| | - Bin Cai
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | - Austen Curcuru
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | | | - Vivian Rodriguez
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | - H Harold Li
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | - Parag J Parikh
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | | | - Jeffrey R Olsen
- University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sasa Mutic
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | - S M Goddu
- Washington University School of Medicine, St. Louis, MO, 63130, USA
| | - Lakshmi Santanam
- Washington University School of Medicine, St. Louis, MO, 63130, USA
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Zhang K, Dai J, Hu Z, Niu C. Dosimetric impact of hysteresis on lung cancer tomotherapy: A moving phantom study. Phys Med 2018; 49:40-46. [DOI: 10.1016/j.ejmp.2018.04.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 03/15/2018] [Accepted: 04/04/2018] [Indexed: 12/25/2022] Open
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Li G, Wei J, Huang H, Chen Q, Gaebler CP, Lin T, Yuan A, Rimner A, Mechalakos J. Characterization of optical-surface-imaging-based spirometry for respiratory surrogating in radiotherapy. Med Phys 2016; 43:1348-60. [PMID: 26936719 DOI: 10.1118/1.4941951] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To provide a comprehensive characterization of a novel respiratory surrogate that uses optical surface imaging (OSI) for accurate tidal volume (TV) measurement, dynamic airflow (TV') calculation, and quantitative breathing pattern (BP) estimation during free breathing (FB), belly breathing (BB), chest breathing (CB), and breath hold (BH). METHODS Optical surface imaging, which captures all respiration-induced torso surface motion, was applied to measure respiratory TV, TV', and BP in three common breathing patterns. Eleven healthy volunteers participated in breathing experiments with concurrent OSI-based and conventional spirometric measurements under an institutional review board approved protocol. This OSI-based technique measures dynamic TV from torso volume change (ΔVtorso = TV) in reference to full exhalation and airflow (TV' = dTV/dt). Volume conservation, excluding exchanging air, was applied for OSI-based measurements under negligible pleural pressure variation in FB, BB, and CB. To demonstrate volume conservation, a constant TV was measured during BH while the chest and belly are moving ("pretended" respiration). To assess the accuracy of OSI-based spirometry, a conventional spirometer was used as the standard for both TV and TV'. Using OSI, BP was measured as BP(OSI) = ΔVchest/ΔVtorso and BP can be visualized using BP(SHI) = SHIchest/(SHIchest + SHIbelly), where surface height index (SHI) is defined as the mean vertical distance within a region of interest on the torso surface. A software tool was developed for OSI image processing, volume calculation, and BP visualization, and another tool was implemented for data acquisition using a Bernoulli-type spirometer. RESULTS The accuracy of the OSI-based spirometry is -21 ± 33 cm(3) or -3.5% ± 6.3% averaged from 11 volunteers with 76 ± 28 breathing cycles on average in FB. Breathing variations between two separate acquisitions with approximate 30-min intervals are substantial: -1% ± 34% (ranging from -64% to 40%) in TV, 4% ± 20% (ranging from -50% to 26%) in breathing period (T), and -1% ± 34% (ranging from -49% to 44%) in BP. The airflow accuracy and variation (between two exercises) are -1 ± 54 cm(3)/s and -5% ± 30%, respectively. The slope of linear regression between OSI-TV and spirometric TV is 0.93 (R(2) = 0.95) for FB, 0.96 (R(2) = 0.98) for BB, and 0.95 (R(2) = 0.95) for CB. The correlation between the two spirometric measurements is 0.98 ± 0.01. BP increases from BB, FB to CB, while TV increases from FB, BB, to CB. Under BH, 4% volume variation (range) on average was observed. CONCLUSIONS The OSI-based technique provides an accurate measurement of tidal volume, airflow rate, and breathing pattern; all affect internal organ motion. This technique can be applied to various breathing patterns, including FB, BB, and CB. Substantial breathing irregularities and irreproducibility were observed and quantified with the OSI-based technique. These breathing parameters are useful to quantify breathing conditions, which could be used for effective tumor motion predictions.
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Affiliation(s)
- Guang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - Jie Wei
- Department of Computer Science, City College of New York, New York, New York 10031
| | - Hailiang Huang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - Qing Chen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - Carl P Gaebler
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - Tiffany Lin
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - Amy Yuan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York 10065
| | - James Mechalakos
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York 10065
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Leni PE, Laurent R, Salomon M, Gschwind R, Makovicka L, Henriet J. Development of a 4D numerical chest phantom with customizable breathing. Phys Med 2016; 32:795-800. [PMID: 27184332 DOI: 10.1016/j.ejmp.2016.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Revised: 05/03/2016] [Accepted: 05/05/2016] [Indexed: 10/21/2022] Open
Abstract
Respiratory movement information is useful for radiation therapy, and is generally obtained using 4D scanners (4DCT). In the interest of patient safety, reducing the use of 4DCT could be a significant step in reducing radiation exposure, the effects of which are not well documented. The authors propose a customized 4D numerical phantom representing the organ contours. Firstly, breathing movement can be simulated and customized according to the patient's anthroporadiametric data. Using learning sets constituted by 4D scanners, artificial neural networks can be trained to interpolate the lung contours corresponding to an unknown patient, and then to simulate its respiration. Lung movement during the breathing cycle is modeled by predicting the lung contours at any respiratory phases. The interpolation is validated comparing the obtained lung contours with 4DCT via Dice coefficient. Secondly, a preliminary study of cardiac and œsophageal motion is also presented to demonstrate the flexibility of this approach. The application may simulate the position and volume of the lungs, the œsophagus and the heart at every phase of the respiratory cycle with a good accuracy: the validation of the lung modeling gives a Dice index greater than 0.93 with 4DCT over a breath cycle.
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Affiliation(s)
- Pierre-Emmanuel Leni
- IRMA/Chrono-Environnement Laboratory, UMR CNRS 6249, University of Bourgogne Franche-Comté, France.
| | - Rémy Laurent
- IRMA/Chrono-Environnement Laboratory, UMR CNRS 6249, University of Bourgogne Franche-Comté, France
| | - Michel Salomon
- FEMTO-ST Laboratory, UMR CNRS 6174, University of Bourgogne Franche-Comté, France
| | - Régine Gschwind
- IRMA/Chrono-Environnement Laboratory, UMR CNRS 6249, University of Bourgogne Franche-Comté, France
| | - Libor Makovicka
- IRMA/Chrono-Environnement Laboratory, UMR CNRS 6249, University of Bourgogne Franche-Comté, France
| | - Julien Henriet
- IRMA/Chrono-Environnement Laboratory, UMR CNRS 6249, University of Bourgogne Franche-Comté, France
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Dou TH, Thomas DH, O'Connell D, Bradley JD, Lamb JM, Low DA. Technical Note: Simulation of 4DCT tumor motion measurement errors. Med Phys 2015; 42:6084-9. [PMID: 26429283 PMCID: PMC4592437 DOI: 10.1118/1.4931416] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 08/30/2015] [Accepted: 09/09/2015] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To determine if and by how much the commercial 4DCT protocols under- and overestimate tumor breathing motion. METHODS 1D simulations were conducted that modeled a 16-slice CT scanner and tumors moving proportionally to breathing amplitude. External breathing surrogate traces of at least 5-min duration for 50 patients were used. Breathing trace amplitudes were converted to motion by relating the nominal tumor motion to the 90th percentile breathing amplitude, reflecting motion defined by the more recent 5DCT approach. Based on clinical low-pitch helical CT acquisition, the CT detector moved according to its velocity while the tumor moved according to the breathing trace. When the CT scanner overlapped the tumor, the overlapping slices were identified as having imaged the tumor. This process was repeated starting at successive 0.1 s time bin in the breathing trace until there was insufficient breathing trace to complete the simulation. The tumor size was subtracted from the distance between the most superior and inferior tumor positions to determine the measured tumor motion for that specific simulation. The effect of the scanning parameter variation was evaluated using two commercial 4DCT protocols with different pitch values. Because clinical 4DCT scan sessions would yield a single tumor motion displacement measurement for each patient, errors in the tumor motion measurement were considered systematic. The mean of largest 5% and smallest 5% of the measured motions was selected to identify over- and underdetermined motion amplitudes, respectively. The process was repeated for tumor motions of 1-4 cm in 1 cm increments and for tumor sizes of 1-4 cm in 1 cm increments. RESULTS In the examined patient cohort, simulation using pitch of 0.06 showed that 30% of the patients exhibited a 5% chance of mean breathing amplitude overestimations of 47%, while 30% showed a 5% chance of mean breathing amplitude underestimations of 36%; with a separate simulation using pitch of 0.1 showing, respectively, 37% overestimation and 61% underestimation. CONCLUSIONS The simulation indicates that commercial low-pitch helical 4DCT processes potentially yield large tumor motion measurement errors, both over- and underestimating the tumor motion.
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Affiliation(s)
- Tai H Dou
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095
| | - David H Thomas
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095
| | - Dylan O'Connell
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095
| | - Jeffrey D Bradley
- Department of Radiation Oncology, Washington University of St. Louis School of Medicine, St. Louis, Missouri 63110
| | - James M Lamb
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095
| | - Daniel A Low
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California 90095
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11
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White BM, Santhanam A, Thomas D, Min Y, Lamb JM, Neylon J, Jani S, Gaudio S, Srinivasan S, Ennis D, Low DA. Modeling and incorporating cardiac-induced lung tissue motion in a breathing motion model. Med Phys 2014; 41:043501. [PMID: 24694158 DOI: 10.1118/1.4866888] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model. METHODS The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement y(X0) of each voxel (X0) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow. RESULTS The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement. CONCLUSIONS The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.
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Affiliation(s)
- Benjamin M White
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
| | - Anand Santhanam
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
| | - David Thomas
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
| | - Yugang Min
- Department of Radiation Oncology, University of California, Los Angeles, California 90095
| | - James M Lamb
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
| | - Jack Neylon
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
| | - Shyam Jani
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
| | - Sergio Gaudio
- Department of Radiation Oncology, University of California, Los Angeles, California 90095
| | - Subashini Srinivasan
- Biomedical Engineering IDP, University of California, Los Angeles, California 90095 and Department of Radiological Sciences, University of California, Los Angeles, California 90095
| | - Daniel Ennis
- Biomedical Physics IDP, University of California, Los Angeles, California 90095; Biomedical Engineering IDP, University of California, Los Angeles, California 90095; and Department of Radiological Sciences, University of California, Los Angeles, California 90095
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, California 90095 and Biomedical Physics IDP, University of California, Los Angeles, California 90095
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White BM, Zhao T, Lamb JM, Bradley JD, Low DA. Physiologically guided approach to characterizing respiratory motion. Med Phys 2014; 40:121723. [PMID: 24320509 DOI: 10.1118/1.4830423] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To characterize radiation therapy patient breathing patterns based on measured external surrogate information. METHODS Breathing surrogate data were collected during 4DCT from a cohort of 50 patients including 28 patients with lung cancer and 22 patients without lung cancer. A spirometer and an abdominal pneumatic bellows were used as the surrogates. The relationship between these measurements was assumed to be linear within a small phase difference. The signals were correlated and drift corrected using a previously published method to convert the signal into tidal volume. The airflow was calculated with a first order time derivative of the tidal volume using a window centered on the point of interest and with a window length equal to the CT gantry rotation period. The airflow was compared against the tidal volume to create ellipsoidal patterns that were binned into 25 ml × 25 ml∕s bins to determine the relative amount of time spent in each bin. To calculate the variability of the maximum inhalation tidal volume within a free-breathing scan timeframe, a metric based on percentile volume ratios was defined. The free breathing variability metric (κ) was defined as the ratio between extreme inhalation tidal volumes (defined as >93 tidal volume percentile of the measured tidal volume) and normal inhalation tidal volume (defined as >80 tidal volume percentile of the measured tidal volume). RESULTS There were three observed types of volume-flow curves, labeled Types 1, 2, and 3. Type 1 patients spent a greater duration of time during exhalation with κ = 1.37 ± 0.11. Type 2 patients had equal time duration spent during inhalation and exhalation with κ = 1.28 ± 0.09. The differences between the mean peak exhalation to peak inhalation tidal volume, breathing period, and the 85th tidal volume percentile for Type 1 and Type 2 patients were statistically significant at the 2% significance level. The difference between κ and the 98th tidal volume percentile for Type 1 and Type 2 patients was found to be statistically significant at the 1% significance level. Three patients did not display a breathing stability curve that could be classified as Type 1 or Type 2 due to chaotic breathing patterns. These patients were classified as Type 3 patients. CONCLUSIONS Based on an observed volume-flow curve pattern, the cohort of 50 patients was divided into three categories called Type 1, Type 2, and Type 3. There were statistically significant differences in breathing characteristics between Type 1 and Type 2 patients. The use of volume-flow curves to classify patients has been demonstrated as a physiological characterization metric that has the potential to optimize gating windows in radiation therapy.
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Affiliation(s)
- Benjamin M White
- University of California Los Angeles, Westwood, California 90095
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Thomas D, Lamb J, White B, Jani S, Gaudio S, Lee P, Ruan D, McNitt-Gray M, Low D. A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases. Int J Radiat Oncol Biol Phys 2014; 89:191-8. [PMID: 24613815 PMCID: PMC4097042 DOI: 10.1016/j.ijrobp.2014.01.016] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Revised: 12/22/2013] [Accepted: 01/13/2014] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts. METHODS AND MATERIALS Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling. RESULTS Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm. CONCLUSIONS The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.
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Affiliation(s)
- David Thomas
- Department of Radiation Oncology, University of California, Los Angeles, California.
| | - James Lamb
- Department of Radiation Oncology, University of California, Los Angeles, California
| | - Benjamin White
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Shyam Jani
- Department of Radiation Oncology, University of California, Los Angeles, California
| | - Sergio Gaudio
- Department of Radiation Oncology, University of California, Los Angeles, California
| | - Percy Lee
- Department of Radiation Oncology, University of California, Los Angeles, California
| | - Dan Ruan
- Department of Radiation Oncology, University of California, Los Angeles, California
| | - Michael McNitt-Gray
- Department of Radiological Sciences, University of California, Los Angeles, California
| | - Daniel Low
- Department of Radiation Oncology, University of California, Los Angeles, California
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White B, Zhao T, Lamb J, Wuenschel S, Bradley J, El Naqa I, Low D. Distribution of lung tissue hysteresis during free breathing. Med Phys 2013; 40:043501. [PMID: 23556925 DOI: 10.1118/1.4794504] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To characterize and quantify free breathing lung tissue motion distributions. METHODS Forty seven patient data sets were acquired using a 4DCT protocol consisting of 25 ciné scans at abutting couch positions on a 16-slice scanner. The tidal volume of each scan was measured by simultaneously acquiring spirometry and an abdominal pneumatic bellows. The concept of a characteristic breath was developed to manage otherwise natural breathing pattern variations. The characteristic breath was found by first dividing the breathing traces into individual breaths, from maximum exhalation to maximum exhalation. A linear breathing drift model was assumed and the drift removed for each breath. Breaths that exceeded one standard deviation in period or amplitude were removed from further analysis. A characteristic breath was defined by normalizing each breath to a common amplitude, aligning the peak inhalation times for all of the breaths, and determining the average time at each tidal volume, keeping inhalation and exhalation separate. Breathing motion trajectories were computed using a previously published five-dimensional lung tissue trajectory model which expresses the position of internal lung tissue, X, as: X(v,f:X0)=X0+α(X0)v+β(X0)f, where X0 is the internal lung tissue position at zero tidal volume and zero airflow, the scalar values v and f are the measured tidal volume and airflow, respectively, and the vectors α and β are fitted free parameters. In order to characterize the motion patterns, the trajectory elongations were examined throughout the subject's lungs. Elongation was defined here by generating a rectangular bounding box with one side parallel to the α vector and the box oriented in the plane defined by the α and β motion vectors. Hysteresis motion was defined as the ratio of the box dimensions aligned orthogonal to and parallel to the α vector. The 15th and 85th percentile of the elongation were used to characterize tissue trajectory hysteresis. RESULTS The 15th and 85th percentile bounding box elongations were 0.090 ± 0.005 and 0.083 ± 0.013 in the upper left lung and 0.187 ± 0.037 and 0.203 ± 0.053, in the lower left lung. The 15th and 85th percentiles for the upper right lung were 0.092 ± 0.006 and 0.085 ± 0.013, and 0.184 ± 0.038, and 0.196 ± 0.043 in the lower right lung. Both percentiles were calculated for tidal volume displacements between 5 and 15 mm. In the left lung, the average elongations in the upper and lower lung were ζ=0.120 ± 0.064 and ζ=0.090 ± 0.055, respectively. The average elongations in the upper and lower right lung were ζ=0.107 ± 0.060 and ζ=0.082 ± 0.048, respectively. The elongation varied smoothly throughout the lungs. CONCLUSIONS The hysteresis motion was relatively small compared to the volume-filling motion, contributing between 8% and 20% of the overall motion. Statistically significant differences were observed in the range of hysteresis contribution for upper and lower lung regions. The characteristic breath process provided an excellent method for defining an average breath. The characteristic breath had continuous tidal volume and airflow characteristics when the breath was continuously repeated,useful for generating patterns representative of realistic motion for breathing motion studies.
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Affiliation(s)
- Benjamin White
- Department of Radiation Oncology, University of California Los Angeles, Westwood, 200 Medical Plaza, Suite B265, Los Angeles, California 90095, USA.
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Estimating Internal Respiratory Motion from Respiratory Surrogate Signals Using Correspondence Models. 4D MODELING AND ESTIMATION OF RESPIRATORY MOTION FOR RADIATION THERAPY 2013. [DOI: 10.1007/978-3-642-36441-9_9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
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16
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Respiratory motion models: A review. Med Image Anal 2013; 17:19-42. [DOI: 10.1016/j.media.2012.09.005] [Citation(s) in RCA: 271] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2012] [Revised: 08/15/2012] [Accepted: 09/17/2012] [Indexed: 12/25/2022]
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17
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Sadeghi-Naini A, Shirzadi Z, Samani A. Towards modeling tumor motion in the deflated lung for minimally invasive ablative procedures. ACTA ACUST UNITED AC 2012; 17:211-20. [DOI: 10.3109/10929088.2012.708788] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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18
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Dubsky S, Hooper SB, Siu KKW, Fouras A. Synchrotron-based dynamic computed tomography of tissue motion for regional lung function measurement. J R Soc Interface 2012; 9:2213-24. [PMID: 22491972 DOI: 10.1098/rsif.2012.0116] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
During breathing, lung inflation is a dynamic process involving a balance of mechanical factors, including trans-pulmonary pressure gradients, tissue compliance and airway resistance. Current techniques lack the capacity for dynamic measurement of ventilation in vivo at sufficient spatial and temporal resolution to allow the spatio-temporal patterns of ventilation to be precisely defined. As a result, little is known of the regional dynamics of lung inflation, in either health or disease. Using fast synchrotron-based imaging (up to 60 frames s(-1)), we have combined dynamic computed tomography (CT) with cross-correlation velocimetry to measure regional time constants and expansion within the mammalian lung in vivo. Additionally, our new technique provides estimation of the airflow distribution throughout the bronchial tree during the ventilation cycle. Measurements of lung expansion and airflow in mice and rabbit pups are shown to agree with independent measures. The ability to measure lung function at a regional level will provide invaluable information for studies into normal and pathological lung dynamics, and may provide new pathways for diagnosis of regional lung diseases. Although proof-of-concept data were acquired on a synchrotron, the methodology developed potentially lends itself to clinical CT scanning and therefore offers translational research opportunities.
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Affiliation(s)
- Stephen Dubsky
- Division of Biological Engineering, Monash University, Victoria, Australia.
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Zhao T, White B, Moore KL, Lamb J, Yang D, Lu W, Mutic S, Low DA. Biomechanical interpretation of a free-breathing lung motion model. Phys Med Biol 2011; 56:7523-40. [PMID: 22079895 PMCID: PMC4295720 DOI: 10.1088/0031-9155/56/23/012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The purpose of this paper is to develop a biomechanical model for free-breathing motion and compare it to a published heuristic five-dimensional (5D) free-breathing lung motion model. An ab initio biomechanical model was developed to describe the motion of lung tissue during free breathing by analyzing the stress-strain relationship inside lung tissue. The first-order approximation of the biomechanical model was equivalent to a heuristic 5D free-breathing lung motion model proposed by Low et al in 2005 (Int. J. Radiat. Oncol. Biol. Phys. 63 921-9), in which the motion was broken down to a linear expansion component and a hysteresis component. To test the biomechanical model, parameters that characterize expansion, hysteresis and angles between the two motion components were reported independently and compared between two models. The biomechanical model agreed well with the heuristic model within 5.5% in the left lungs and 1.5% in the right lungs for patients without lung cancer. The biomechanical model predicted that a histogram of angles between the two motion components should have two peaks at 39.8° and 140.2° in the left lungs and 37.1° and 142.9° in the right lungs. The data from the 5D model verified the existence of those peaks at 41.2° and 148.2° in the left lungs and 40.1° and 140° in the right lungs for patients without lung cancer. Similar results were also observed for the patients with lung cancer, but with greater discrepancies. The maximum-likelihood estimation of hysteresis magnitude was reported to be 2.6 mm for the lung cancer patients. The first-order approximation of the biomechanical model fit the heuristic 5D model very well. The biomechanical model provided new insights into breathing motion with specific focus on motion trajectory hysteresis.
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Affiliation(s)
- Tianyu Zhao
- University of Florida Proton Therapy Institute, Jacksonville, FL 32206, USA.
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Smith RL, Yang D, Lee A, Mayse ML, Low DA, Parikh PJ. The correlation of tissue motion within the lung: implications on fiducial based treatments. Med Phys 2011; 38:5992-7. [PMID: 22047363 PMCID: PMC3298561 DOI: 10.1118/1.3643028] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2011] [Revised: 08/24/2011] [Accepted: 09/03/2011] [Indexed: 11/07/2022] Open
Abstract
In radiation therapy many motion management and alignment techniques rely on the accuracy of an internal fiducial acting as a surrogate for target motion within the lung. Although fiducials are routinely used as surrogates for tumor motion, the extent to which varying spatial locations in the lung move similarly to other locations has yet to be quantitatively analyzed. In an attempt to analyze the motion correlation throughout the lung, ten primary lung cancer patients underwent IRB-approved 4DCT scans in the supine position. Deformable registration produced motion vectors for each voxel between exhalation and inhalation. Modeling was performed for each vector and all surrounding vectors within the lung in order to determine the mean 3D Euclidean distance necessary for an implanted fiducial to correlate with surrounding tissue motion to within 3 mm (left lower: 1.7 cm, left upper: 2.1 cm, right lower 1.6 cm, and right upper 2.9 cm). No general implantation rule of where to position a fiducial with respect to the tumor was found as the motion is highly patient and lobe specific. Correlation maps are presented showcasing spatial anisotropy of the motion of tissue surrounding the tumor.
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Affiliation(s)
- Ryan L Smith
- Washington University Medical School, St. Louis, MO 63110, USA
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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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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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Kesner AL, Kuntner C. A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods. Med Phys 2010; 37:5550-9. [PMID: 21089790 DOI: 10.1118/1.3483784] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
PURPOSE Respiratory gating in PET is an approach used to minimize the negative effects of respiratory motion on spatial resolution. It is based on an initial determination of a patient's respiratory movements during a scan, typically using hardware based systems. In recent years, several fully automated databased algorithms have been presented for extracting a respiratory signal directly from PET data, providing a very practical strategy for implementing gating in the clinic. In this work, a new method is presented for extracting a respiratory signal from raw PET sinogram data and compared to previously presented automated techniques. METHODS The acquisition of respiratory signal from PET data in the newly proposed method is based on rebinning the sinogram data into smaller data structures and then analyzing the time activity behavior in the elements of these structures. From this analysis, a 1D respiratory trace is produced, analogous to a hardware derived respiratory trace. To assess the accuracy of this fully automated method, respiratory signal was extracted from a collection of 22 clinical FDG-PET scans using this method, and compared to signal derived from several other software based methods as well as a signal derived from a hardware system. RESULTS The method presented required approximately 9 min of processing time for each 10 min scan (using a single 2.67 GHz processor), which in theory can be accomplished while the scan is being acquired and therefore allowing a real-time respiratory signal acquisition. Using the mean correlation between the software based and hardware based respiratory traces, the optimal parameters were determined for the presented algorithm. The mean/median/range of correlations for the set of scans when using the optimal parameters was found to be 0.58/0.68/0.07-0.86. The speed of this method was within the range of real-time while the accuracy surpassed the most accurate of the previously presented algorithms. CONCLUSIONS PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.
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