101
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Meschini G, Vai A, Paganelli C, Molinelli S, Fontana G, Pella A, Preda L, Vitolo V, Valvo F, Ciocca M, Riboldi M, Baroni G. Virtual 4DCT from 4DMRI for the management of respiratory motion in carbon ion therapy of abdominal tumors. Med Phys 2020; 47:909-916. [PMID: 31880819 DOI: 10.1002/mp.13992] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/17/2019] [Accepted: 12/17/2019] [Indexed: 12/12/2022] Open
Abstract
PURPOSE To evaluate a method for generating virtual four-dimensional computed tomography (4DCT) from four-dimensional magnetic resonance imaging (4DMRI) data in carbon ion radiotherapy with pencil beam scanning for abdominal tumors. METHODS Deformable image registration is used to: (a) register each respiratory phase of the 4DMRI to the end-exhale MRI; (b) register the reference end-exhale CT to the end-exhale MRI volume; (c) generate the virtual 4DCT by warping the registered CT according to the obtained deformation fields. A respiratory-gated carbon ion treatment plan is optimized on the planning 4DCT and the corresponding dose distribution is recalculated on the virtual 4DCT. The method was validated on a digital anthropomorphic phantom and tested on eight patients (18 acquisitions). For the phantom, a ground truth dataset was available to assess the method performances from the geometrical and dosimetric standpoints. For the patients, the virtual 4DCT was compared with the planning 4DCT. RESULTS In the phantom, the method exhibits a geometrical accuracy within the voxel size and Dose Volume Histograms deviations up to 3.3% for target V95% (mean dose difference ≤ 0.2% of the prescription dose, gamma pass rate > 98%). For patients, the virtual and the planning 4DCTs show good agreement at end-exhale (3% median D95% difference), whereas other respiratory phases exhibit moderate motion variability with consequent dose discrepancies, confirming the need for motion mitigation strategies during treatment. CONCLUSIONS The virtual 4DCT approach is feasible to evaluate treatment plan robustness against intra- and interfraction motion in carbon ion therapy delivered at the abdominal site.
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Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy
| | - Alessandro Vai
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy
| | | | - Giulia Fontana
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Andrea Pella
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Lorenzo Preda
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy.,Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy
| | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Francesca Valvo
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
| | - Marco Riboldi
- Chair of Experimental Physics - Medical Physics, Ludwig-Maximilians-Universität (LMU), Munich, 80539, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, 20133, Italy.,Centro Nazionale di Adroterapia Oncologica, Pavia, 27100, Italy
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102
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Huttinga NRF, van den Berg CAT, Luijten PR, Sbrizzi A. MR-MOTUS: model-based non-rigid motion estimation for MR-guided radiotherapy using a reference image and minimal k-space data. Phys Med Biol 2020; 65:015004. [PMID: 31698354 DOI: 10.1088/1361-6560/ab554a] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Time-resolved motion estimation from MRI data has received an increasing amount of interest due to the advent of the MR-Linac. The combination of an MRI scanner and a linear accelerator enables radiation plan adaptation based on internal organ motion estimated from MRI data. However, time-resolved estimation of this motion from MRI data still remains a challenge. In light of this application, we propose MR-MOTUS, a framework to estimate non-rigid 3D motion from minimal k-space data. MR-MOTUS consists of two main components: (1) a signal model that explicitly relates the k-space signal of a deforming object to non-rigid motion-fields and a reference image, and (2) model-based reconstructions of the non-rigid motion-fields directly from k-space data. Using an a priori available reference image and the fact that internal body motion exhibits a high level of spatial correlation, we represent the motion-fields in a low-dimensional space and reconstruct them from minimal k-space data that can be acquired very rapidly. The signal model is validated through numerical experiments with a digital 3D phantom and motion-fields are reconstructed from retrospectively undersampled in vivo head and abdomen data using various undersampling strategies. A comparison is made with state-of-the-art image registration performed on images reconstructed from the same undersampled data. Results show that MR-MOTUS reconstructs in vivo 3D rigid head motion from 474-fold retrospectively downsampled k-space data, and in vivo non-rigid 3D respiratory motion from 63-fold retrospectively undersampled k-space data. Preliminary results on prospectively undersampled data acquired with a 2D golden angle acquisition during free-breathing demonstrate the practical feasibility of the method.
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103
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Ziegler M, Brandt T, Lettmaier S, Fietkau R, Bert C. Method for a motion model based automated 4D dose calculation. Phys Med Biol 2019; 64:225002. [PMID: 31618719 DOI: 10.1088/1361-6560/ab4e51] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The Vero system can treat intra-fractionally moving tumors with gimbaled dynamic tumor tracking (DTT) by rotating the treatment beam so that it follows the motion of the tumor. However, the changes in the beam geometry and the constant breathing motion of the patient influence the dose applied to the patient. This study aims to perform a full 4D dose reconstruction for thirteen patients treated with DTT at the Vero system at the Universitätsklinikum Erlangen and investigates the temporal resolution required to perform an accurate 4D dose reconstruction. For all patients, a 4DCT was used to train a 4D motion model, which is able to calculate pseudo-CT images for arbitrary breathing phases. A new CT image was calculated for every 100 ms of treatment and a dose calculation was performed according to the current beam geometry (i.e. the rotation of the treatment beam at this moment in time) by rotating according to the momentary beam rotation, which is extracted from log-files. The resulting dose distributions were accumulated on the planning CT and characteristic parameters were extracted and compared. [Formula: see text]-evaluations of dose accumulations with different spatial-temporal resolutions were performed to determine the minimal required resolution. In total 173 700 dose calculations were performed. The accumulated 4D dose distributions show a reduced mean GTV dose of 0.77% compared to the static treatment plan. For some patients larger deviations were observed, especially in the presence of a poor 4DCT quality. The [Formula: see text]-evaluation showed that a temporal resolution of 500 ms is sufficient for an accurate dose reconstruction. If the tumor motion is regarded as well, a spatial-temporal sampling of 1400 ms and 2 mm yields accurate results, which reduces the workload by 84%.
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Affiliation(s)
- Marc Ziegler
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitätsstraße 27, 91054 Erlangen, Germany
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104
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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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105
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Smith RL, Dasari P, Lindsay C, King M, Wells K. Dense motion propagation from sparse samples. Phys Med Biol 2019; 64:205023. [PMID: 31487702 DOI: 10.1088/1361-6560/ab41a0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
There are many applications for which sparse, or partial sampling of dynamic image data can be used for articulating or estimating motion within the medical imaging area. In this new work, we propose a generalized framework for dense motion propagation from sparse samples which represents an example of transfer learning and manifold alignment, allowing the transfer of knowledge across imaging sources of different domains which exhibit different features. Many such examples exist in medical imaging, from mapping 2D ultrasound or fluoroscopy to 3D or 4D data or monitoring dynamic dose delivery from partial imaging data in radiotherapy. To illustrate this approach we animate, or articulate, a high resolution static MR image with 4D free breathing respiratory motion derived from low resolution sparse planar samples of motion. In this work we demonstrate that sparse sampling of dynamic MRI may be used as a viable approach to successfully build models of free- breathing respiratory motion by constrained articulation. Such models demonstrate high contrast, and high temporal and spatial resolution that have so far been hitherto unavailable with conventional imaging methods. The articulation is based on using a propagation model, in the eigen domain, to estimate complete 4D motion vector fields from sparsely sampled free-breathing dynamic MRI data. We demonstrate that this approach can provide equivalent motion vector fields compared to fully sampled 4D dynamic data, whilst preserving the corresponding high resolution/high contrast inherent in the original static volume. Validation is performed on three 4D MRI datasets using eight extracted slices from a fast 4D acquisition (0.7 s per volume). The estimated deformation fields from sparse sampling are compared to the fully sampled equivalents, resulting in an rms error of the order of 2 mm across the entire image volume. We also present exemplar 4D high contrast, high resolution articulated volunteer datasets using this methodology. This approach facilitates greater freedom in the acquisition of free breathing respiratory motion sequences which may be used to inform motion modelling methods in a range of imaging modalities and demonstrates that sparse sampling of free breathing data may be used within a manifold alignment and transfer learning paradigm to estimate fully sampled motion. The method may also be applied to other examples of sparse sampling to produce dense motion propagation.
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Affiliation(s)
- Rhodri L Smith
- Centre for Vision Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom. Author to whom any correspondence should be addressed
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106
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Real-time control of respiratory motion: Beyond radiation therapy. Phys Med 2019; 66:104-112. [PMID: 31586767 DOI: 10.1016/j.ejmp.2019.09.241] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/23/2019] [Accepted: 09/26/2019] [Indexed: 12/16/2022] Open
Abstract
Motion management in radiation oncology is an important aspect of modern treatment planning and delivery. Special attention has been paid to control respiratory motion in recent years. However, other medical procedures related to both diagnosis and treatment are likely to benefit from the explicit control of breathing motion. Quantitative imaging - including increasingly important tools in radiology and nuclear medicine - is among the fields where a rapid development of motion control is most likely, due to the need for quantification accuracy. Emerging treatment modalities like focussed-ultrasound tumor ablation are also likely to benefit from a significant evolution of motion control in the near future. In the present article an overview of available respiratory motion systems along with ongoing research in this area is provided. Furthermore, an attempt is made to envision some of the most expected developments in this field in the near future.
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107
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Paganelli C, Portoso S, Garau N, Meschini G, Via R, Buizza G, Keall P, Riboldi M, Baroni G. Time-resolved volumetric MRI in MRI-guided radiotherapy: an in silico comparative analysis. Phys Med Biol 2019; 64:185013. [PMID: 31323645 DOI: 10.1088/1361-6560/ab33e5] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
MRI-treatment units enable 2D cine-MRI centred in the tumour for motion detection in radiotherapy, but they lack 3D information due to spatio-temporal limits. To derive time-resolved 3D information, different approaches have been proposed in the literature, but a rigorous comparison among these strategies has not yet been performed. The goal of this study is to quantitatively investigate five published strategies that derive time-resolved volumetric MRI in MRI-guided radiotherapy: Propagation, out-of-plane motion compensation, Fayad model, ROI-based model and Stemkens model. Comparisons were performed using an MRI digital phantom generated with six different patient-derived motion signals and tumour-shapes. An average 4D cycle was generated as well as 2D cine-MRI data with corresponding 3D in-room ground truth. Quantitative analysis was performed by comparing the estimated 3D volume to the ground truth available for each 2D cine-MRI sample. A grouped patient statistical analysis was performed to evaluate the performance of the selected methods, in case of tumour tracking or motion estimation of the whole anatomy. Analyses were also performed based on patient characteristics. Quantitative ranking of the investigated methods highlighted that Propagation and ROI-based model strategies achieved an overall median tumour centre of mass 3D distance from the ground truth of 1.1 mm and 1.3 mm, respectively, and a diaphragm distance below 1.6 mm. Higher errors and variabilities were instead obtained for other methods, which lack the ability to compensate for in-room variations and to account for regional changes. These results were especially evident when further analysing patient characteristics, where errors above 2 mm/5 mm in tumour/diaphragm were found for more irregular breathing patterns in case of out-of-plane motion compensation, Fayad and Stemkens models. These findings suggest the potential of the proposed in silico framework to develop and compare strategies to estimate time-resolved 3DMRI in MRI-guided radiotherapy.
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Affiliation(s)
- C Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Both authors contributed equally. Author to whom any correspondence should be addressed
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108
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Bertholet J, Knopf A, Eiben B, McClelland J, Grimwood A, Harris E, Menten M, Poulsen P, Nguyen DT, Keall P, Oelfke U. Real-time intrafraction motion monitoring in external beam radiotherapy. Phys Med Biol 2019; 64:15TR01. [PMID: 31226704 PMCID: PMC7655120 DOI: 10.1088/1361-6560/ab2ba8] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/10/2019] [Accepted: 06/21/2019] [Indexed: 12/25/2022]
Abstract
Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.
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Affiliation(s)
- Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
- Author to whom any correspondence should be
addressed
| | - Antje Knopf
- Department of Radiation Oncology,
University Medical Center
Groningen, University of Groningen, The
Netherlands
| | - Björn Eiben
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Jamie McClelland
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Alexander Grimwood
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Emma Harris
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Martin Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Per Poulsen
- Department of Oncology, Aarhus University Hospital, Aarhus,
Denmark
| | - Doan Trang Nguyen
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
- School of Biomedical Engineering,
University of Technology
Sydney, Sydney, Australia
| | - Paul Keall
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
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Dietze MMA, Bastiaannet R, Kunnen B, van der Velden S, Lam MGEH, Viergever MA, de Jong HWAM. Respiratory motion compensation in interventional liver SPECT using simultaneous fluoroscopic and nuclear imaging. Med Phys 2019; 46:3496-3507. [PMID: 31183868 PMCID: PMC6851796 DOI: 10.1002/mp.13653] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 06/03/2019] [Accepted: 06/04/2019] [Indexed: 01/22/2023] Open
Abstract
PURPOSE Quantitative accuracy of the single photon emission computed tomography (SPECT) reconstruction of the pretreatment procedure of liver radioembolization is crucial for dosimetry; visual quality is important for detecting doses deposited outside the planned treatment volume. Quantitative accuracy is limited by respiratory motion. Conventional gating eliminates motion by count rejection but increases noise, which degrades the visual reconstruction quality. Motion compensation using all counts can be performed if the motion signal and motion vector field over time are known. The measurement of the motion signal of a patient currently requires a device (such as a respiratory belt) attached to the patient, which complicates the acquisition. The motion vector field is generally extracted from a previously acquired four-dimensional scan and can differ from the motion in the scan performed during the intervention. The simultaneous acquisition of fluoroscopic and nuclear projections can be used to obtain both the motion vector field and the projections of the corresponding (moving) activity distribution. This eliminates the need for devices attached to the patient and provides an accurate motion vector field for SPECT reconstruction. Our approach to motion compensation would primarily be beneficial for interventional SPECT because the time-critical setting requires fast scans and no inconvenience of an external apparatus. The purpose of this work is to evaluate the performance of the motion compensation approach for interventional liver SPECT by means of simulations. METHODS Nuclear and fluoroscopic projections of a realistic digital human phantom with respiratory motion were generated using fast Monte Carlo simulators. Fluoroscopic projections were sampled at 1-5 Hz. Nuclear data were acquired continuously in list mode. The motion signal was extracted from the fluoroscopic projections by calculating the center-of-mass, which was then used to assign each photon to a corresponding motion bin. The fluoroscopic projections were reconstructed per bin and coregistered, resulting in a motion vector field that was used in the SPECT reconstruction. The influence of breathing patterns, fluoroscopic imaging dose, sampling rate, number of bins, and scanning time was studied. In addition, the motion compensation method was compared with conventional gating to evaluate the detectability of spheres with varying uptake ratios. RESULTS The liver motion signal was accurately extracted from the fluoroscopic projections, provided the motion was stable in amplitude and the sampling rate was greater than 2 Hz. The minimum total fluoroscopic dose for the proposed method to function in a 5-min scan was 10 µGy. Although conventional gating improved the quantitative reconstruction accuracy, substantial background noise was observed in the short scans because of the limited counts available. The proposed method similarly improved the quantitative accuracy, but generated reconstructions with higher visual quality. The proposed method provided better visualization of low-contrast features than when using gating. CONCLUSION The proposed motion compensation method has the potential to improve SPECT reconstruction quality. The method eliminates the need for external devices to measure the motion signal and generates an accurate motion vector field for reconstruction. A minimal increase in the fluoroscopic dose is required to substantially improve the results, paving the way for clinical use.
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Affiliation(s)
- Martijn M. A. Dietze
- Radiology and Nuclear MedicineUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
- Image Sciences InstituteUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
| | - Remco Bastiaannet
- Radiology and Nuclear MedicineUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
- Image Sciences InstituteUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
| | - Britt Kunnen
- Radiology and Nuclear MedicineUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
- Image Sciences InstituteUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
| | - Sandra van der Velden
- Radiology and Nuclear MedicineUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
- Image Sciences InstituteUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
| | - Marnix G. E. H. Lam
- Radiology and Nuclear MedicineUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
| | - Max A. Viergever
- Image Sciences InstituteUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
| | - Hugo W. A. M. de Jong
- Radiology and Nuclear MedicineUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
- Image Sciences InstituteUtrecht University and University Medical Center UtrechtP.O. Box 855003508 GAUtrechtthe Netherlands
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Meschini G, Seregni M, Molinelli S, Vai A, Phillips J, Sharp GC, Pella A, Valvo F, Ciocca M, Riboldi M, Paganetti H, Baroni G. Validation of a model for physical dose variations in irregularly moving targets treated with carbon ion beams. Med Phys 2019; 46:3663-3673. [PMID: 31206718 DOI: 10.1002/mp.13662] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 12/14/2022] Open
Abstract
PURPOSE In particle therapy, conventional treatment planning systems rely on an imaging representation of the irradiated region to compute the dose. For irregular breathing, when an imaging dataset describing the actual motion is not available, a different approach for dose estimation is needed. To this aim, we validate a method for the estimation of physical dose variations in gated carbon ion treatments, providing also a demonstration of the feasibility of physical dose metrics to assess the method performance. Finally, we describe a sample use case, in which this method is used to assess plan robustness with respect to undetected irregular tumor motion. METHODS The method entails the definition of a patient- and beam-specific water equivalent depth (WED) space, the simulation of motion as a translation equal to tumor displacement, and the reconstruction of the altered dose. We validated the approach using four-dimensional computed tomographies (4DCTs) and clinical plans in 12 patients, treated with respiratory gated carbon ion beams at the National Centre for Oncological Hadrontherapy (Pavia, Italy). Using the end-exhale CT and dose distribution as a reference, the physical dose delivered at the end-inhale tumor position was estimated and compared to the ground-truth dose recalculation on the end-inhale CT. Biologically effective and physical dose variations between the plan and the recalculation were compared as well. As a use case, we evaluated dose changes caused by simulated irregular tumor motion, that is, linear and nonlinear baseline shifts and/or amplitude variations with hysteresis. RESULTS The ratio between biologically effective and physical equivalent uniform dose (EUD) variations due to end-exhale to end-inhale motion was less than one for 96% of investigated structures. In the validation study, we found a median error corresponding to a 14% EUD overestimation for the tumor and 4% EUD underestimation for a subgroup of organs at risk, together with a high EUD variation due to motion [median 352% EUD variation between end-exhale and end-inhale doses in the planning tumor volume (PTV)]. Considering relevant dose-volume histogram (DVH) metrics, the median difference between estimated and ground truth doses was ≤ 4%. Gamma analysis between estimated and recalculated dose distributions resulted in a pass rate > 80% for 83% of the target volumes. For the two patients selected for the sample use case, a patient-specific assessment of the method performance was performed on the 4DCT and it was possible to relate EUD variations of both tumor and organs at risk to the simulated target motion. CONCLUSIONS The physical dose distribution was found to be more sensitive to motion with respect to the biologically effective one, suggesting the suitability of the physical dose metrics for the WED-space method validation. We showed that the method can compensate for intra-fractional tumor motion with proper accuracy in the selected patient group, although its use is recommended when limited deformations are expected. In conclusion, the WED-space method can provide simulations of dose alteration due to irregular breathing when imaging data are lacking, and, once integrated with relative biological effectiveness (RBE) modeling, it would be useful in evaluating the robustness of carbon ion treatment plans.
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Affiliation(s)
| | | | | | - Alessandro Vai
- Centro Nazionale Adroterapia Oncologica, Pavia, 27100, Italy
| | - Justin Phillips
- Alexian Brothers Medical Center, Elk Grove Village, IL, 60007, USA
| | | | - Andrea Pella
- Centro Nazionale Adroterapia Oncologica, Pavia, 27100, Italy
| | - Francesca Valvo
- Centro Nazionale Adroterapia Oncologica, Pavia, 27100, Italy
| | - Mario Ciocca
- Centro Nazionale Adroterapia Oncologica, Pavia, 27100, Italy
| | - Marco Riboldi
- Ludwig-Maximilians-Universität, Munich, 80539, Germany
| | | | - Guido Baroni
- Politecnico di Milano, Milan, 20133, Italy.,Centro Nazionale Adroterapia Oncologica, Pavia, 27100, Italy
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Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9132737] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Respiratory motion in living organisms is known to result in image blurring and loss of resolution, chiefly due to the lengthy acquisition times of the corresponding image acquisition methods. Optoacoustic tomography can effectively eliminate in vivo motion artifacts due to its inherent capacity for collecting image data from the entire imaged region following a single nanoseconds-duration laser pulse. However, multi-frame image analysis is often essential in applications relying on spectroscopic data acquisition or for scanning-based systems. Thereby, efficient methods to correct for image distortions due to motion are imperative. Herein, we demonstrate that efficient motion rejection in optoacoustic tomography can readily be accomplished by frame clustering during image acquisition, thus averting excessive data acquisition and post-processing. The algorithm’s efficiency for two- and three-dimensional imaging was validated with experimental whole-body mouse data acquired by spiral volumetric optoacoustic tomography (SVOT) and full-ring cross-sectional imaging scanners.
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Smith RL, Rahni AAA, Jones J, wells K. A Kalman-Based Approach With EM Optimization for Respiratory Motion Modeling in Medical Imaging. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2018.2879441] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Ebner M, Patel PA, Atkinson D, Caselton L, Firmin L, Amin Z, Bainbridge A, De Coppi P, Taylor SA, Ourselin S, Chouhan MD, Vercauteren T. Super-resolution for upper abdominal MRI: Acquisition and post-processing protocol optimization using brain MRI control data and expert reader validation. Magn Reson Med 2019; 82:1905-1919. [PMID: 31264270 PMCID: PMC6742507 DOI: 10.1002/mrm.27852] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/23/2019] [Accepted: 05/20/2019] [Indexed: 12/16/2022]
Abstract
PURPOSE Magnetic resonance (MR) cholangiopancreatography (MRCP) is an established specialist method for imaging the upper abdomen and biliary/pancreatic ducts. Due to limitations of either MR image contrast or low through-plane resolution, patients may require further evaluation with contrast-enhanced computed tomography (CT) images. However, CT fails to offer the high tissue-ductal-vessel contrast-to-noise ratio available on T2-weighted MR imaging. METHODS MR super-resolution reconstruction (SRR) frameworks have the potential to provide high-resolution visualizations from multiple low through-plane resolution single-shot T2-weighted (SST2W) images as currently used during MRCP studies. Here, we (i) optimize the source image acquisition protocols by establishing the ideal number and orientation of SST2W series for MRCP SRR generation, (ii) optimize post-processing protocols for two motion correction candidate frameworks for MRCP SRR, and (iii) perform an extensive validation of the overall potential of upper abdominal SRR, using four expert readers with subspeciality interest in hepato-pancreatico-biliary imaging. RESULTS Obtained SRRs show demonstrable advantages over traditional SST2W MRCP data in terms of anatomical clarity and subjective radiologists' preference scores for a range of anatomical regions that are especially critical for the management of cancer patients. CONCLUSIONS Our results underline the potential of using SRR alongside traditional MRCP data for improved clinical diagnosis.
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Affiliation(s)
- Michael Ebner
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Premal A Patel
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom
| | | | - Lucy Caselton
- Centre for Medical Imaging, UCL, London, United Kingdom
| | - Louisa Firmin
- Centre for Medical Imaging, UCL, London, United Kingdom
| | - Zahir Amin
- Centre for Medical Imaging, UCL, London, United Kingdom
| | - Alan Bainbridge
- Department of Medical Physics and Biomedical Engineering, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | | | | | - Sébastien Ourselin
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Tom Vercauteren
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London (UCL), London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Guo M, Chee G, O'Connell D, Dhou S, Fu J, Singhrao K, Ionascu D, Ruan D, Lee P, Low DA, Zhao J, Lewis JH. Reconstruction of a high-quality volumetric image and a respiratory motion model from patient CBCT projections. Med Phys 2019; 46:3627-3639. [PMID: 31087359 DOI: 10.1002/mp.13595] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 04/10/2019] [Accepted: 05/08/2019] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To develop and evaluate a method of reconstructing a patient- and treatment day- specific volumetric image and motion model from free-breathing cone-beam projections and respiratory surrogate measurements. This Motion-Compensated Simultaneous Algebraic Reconstruction Technique (MC-SART) generates and uses a motion model derived directly from the cone-beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter- and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery. METHODS The MC-SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC-SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements. RESULTS A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC-SART was also applied to measured patient cone-beam projections acquired with a linac-mounted CBCT system. Results from this patient data demonstrate the feasibility of MC-SART and showed qualitative image quality improvements compared to other state-of-the-art algorithms. CONCLUSION We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone-beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high-quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.
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Affiliation(s)
- Minghao Guo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.,Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Geraldine Chee
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Salam Dhou
- Department of Computer Science and Engineering, American University of Sharjah, Sharjah, 26666, United Arab Emirates
| | - Jie Fu
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Kamal Singhrao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Dan Ionascu
- Department of Radiation Oncology, College of Medicine, University of Cincinnati, Cincinnati, OH, 45221, USA
| | - Dan Ruan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Percy Lee
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - John H Lewis
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, de Marvao A, O'Regan DP, Cook S, Glocker B, Matthews PM, Rueckert D. Learning-Based Quality Control for Cardiac MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1127-1138. [PMID: 30403623 DOI: 10.1109/tmi.2018.2878509] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation; 2) inter-slice motion detection; 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method-integrating both regression and structured classification models-to extract landmarks and probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank and on 100 cases from the UK Digital Heart Project and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g., on UK Biobank, sensitivity and specificity, respectively, 88% and 99% for heart coverage estimation and 85% and 95% for motion detection), allowing their exclusion from the analyzed dataset or the triggering of a new acquisition.
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Chee G, O’Connell D, Yang YM, Singhrao K, Low DA, Lewis JH. McSART: an iterative model-based, motion-compensated SART algorithm for CBCT reconstruction. ACTA ACUST UNITED AC 2019; 64:095013. [DOI: 10.1088/1361-6560/ab07d6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Borman PTS, Bos C, Stemkens B, Moonen CTW, Raaymakers BW, Tijssen RHN. Assessment of 3D motion modeling performance for dose accumulation mapping on the MR-linac by simultaneous multislice MRI. Phys Med Biol 2019; 64:095004. [PMID: 30917353 DOI: 10.1088/1361-6560/ab13e3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Hybrid MR-linac systems enable intrafraction motion monitoring during radiation therapy. Since time-resolved 3D MRI is still challenging, various motion models have been developed that rely on time-resolved 2D imaging. Continuous validation of these models is important for accurate dose accumulation mapping. In this study we used 2D simultaneous multislice (SMS) imaging to improve the PCA-based motion modeling method developed previously (Stemkens et al 2016 Phys. Med. Biol. 61 5335-55). From the additional simultaneously acquired slices, several independent motion models could be generated, which allowed for an assessment of the sensitivity of the motion model to the location of the time-resolved 2D slices. Additionally, the best model could be chosen at every time-point, increasing the method's robustness. Imaging experiments were performed in six healthy volunteers using three simultaneous slices, which generated three independent models per volunteer. For each model the motion traces of the liver tip and both kidneys were estimated. We found that the location of the 2D slices influenced the model's error in five volunteers significantly with a p -value <0.05, and that selecting the best model at every time-point can improve the method. This allows for more accurate and robust motion characterization in MR-guided radiotherapy.
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Affiliation(s)
- P T S Borman
- Department of Radiotherapy, University Medical Center Utrecht. Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. Imaging Division, University Medical Center Utrecht. Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. Author to whom any correspondence should be addressed
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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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Chassagnon G, Martin C, Marini R, Vakalopolou M, Régent A, Mouthon L, Paragios N, Revel MP. Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis. Radiology 2019; 291:487-492. [PMID: 30835186 DOI: 10.1148/radiol.2019182099] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Current imaging methods are not sensitive to changes in pulmonary function resulting from fibrosis. MRI with ultrashort echo time can be used to image the lung parenchyma and lung motion. Purpose To evaluate elastic registration of inspiratory-to-expiratory lung MRI for the assessment of pulmonary fibrosis in study participants with systemic sclerosis (SSc). Materials and Methods This prospective study was performed from September 2017 to March 2018 and recruited healthy volunteers and participants with SSc and high-resolution CT (within the previous 3 months) of the chest for lung MRI. Two breath-hold, coronal, three-dimensional, ultrashort-echo-time, gradient-echo sequences of the lungs were acquired after full inspiration and expiration with a 3.0-T unit. Images were registered from inspiration to expiration by using an elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Similarity between areas with marked shrinkage and logarithm of Jacobian determinants less than -0.15 were compared between healthy volunteers and study participants with SSc. Receiver operating characteristic curve analysis was performed to determine the best Dice similarity coefficient threshold for diagnosis of fibrosis. Results Sixteen participants with SSc (seven with pulmonary fibrosis at high-resolution CT) and 11 healthy volunteers were evaluated. Areas of marked shrinkage during expiration with logarithm of Jacobian determinants less than -0.15 were found in the posterior lung bases of healthy volunteers and in participants with SSc without fibrosis, but not in participants with fibrosis. The sensitivity and specificity of MRI for presence of fibrosis at high-resolution CT were 86% and 75%, respectively (area under the curve, 0.81; P = .04) by using a threshold of 0.36 for Dice similarity coefficient. Conclusion Elastic registration of inspiratory-to-expiratory MRI shows less lung base respiratory deformation in study participants with systemic sclerosis-related pulmonary fibrosis compared with participants without fibrosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Biederer in this issue.
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Affiliation(s)
- Guillaume Chassagnon
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Charlotte Martin
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Rafael Marini
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Maria Vakalopolou
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Alexis Régent
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Luc Mouthon
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Nikos Paragios
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
| | - Marie-Pierre Revel
- From the Department of Radiology, Groupe Hospitalier Cochin-Hôtel Dieu, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (G.C., C.M., M.P.R.); Center for Visual Computing, École CentraleSupélec, Gif-sur-Yvette, France (G.C., M.V., N.P.); TheraPanacea, Pépinière Santé Cochin, Paris, France (R.M., N.P.); and Department of Internal Medicine, Reference Center for Rare Systemic Autoimmune Diseases of Île de France, Hôpital Cochin, AP-HP, Université Paris Descartes, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France (A.R., L.M.)
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Garau N, Via R, Meschini G, Lee D, Keall P, Riboldi M, Baroni G, Paganelli C. A ROI-based global motion model established on 4DCT and 2D cine-MRI data for MRI-guidance in radiation therapy. Phys Med Biol 2019; 64:045002. [PMID: 30625459 DOI: 10.1088/1361-6560/aafcec] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In-room magnetic resonance imaging (MRI) allows the acquisition of fast 2D cine-MRI centered in the tumor for advanced motion management in radiotherapy. To achieve 3D information during treatment, patient-specific motion models can be considered the most viable solution. However, conventional global motion models are built using a single motion surrogate, independently from the anatomical location. In this work, we present a novel motion model based on regions of interest (ROIs) established on 4D computed tomography (4DCT) and 2D cine-MRI, aiming at accurately compensating for changes during treatment. In the planning phase, a motion model is built on a 4DCT dataset, through 3D deformable image registration (DIR). ROIs are then defined and correlated with motion fields derived by 2D DIR between CT slices centered in the tumor. In the treatment phase, the model is applied to in-room cine-MRI data to compensate for organ motion in a multi-modal framework, aiming at estimating a time-resolved 3DCT. The method is validated on a digital phantom and tested on two lung patients. Analysis is performed by considering different anatomical planes (coronal, sagittal and a combination of the two) and evaluating the performance of the method on tumor and diaphragm. For the phantom study, the ROI-based model results in a uniform median error on both diaphragm and tumor below 1.5 mm. For what concerns patients, median errors on both diaphragm and tumor are around 2 mm (maximum patient resolution), confirming the capability of the method to regionally compensate for motion. A novel ROI-based motion model is proposed as an integral part of an envisioned clinical MRI-guided workflow aiming at enhanced image guidance compared to conventional strategies.
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Affiliation(s)
- Noemi Garau
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Author to whom any correspondence should be addressed
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Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study. PREDICTIVE INTELLIGENCE IN MEDICINE 2019. [DOI: 10.1007/978-3-030-32281-6_2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Zhang J, Huang X, Shen Y, Chen Y, Cai J, Ge Y. Nearest Neighbor Method to Estimate Internal Target for Real-Time Tumor Tracking. Technol Cancer Res Treat 2018; 17:1533033818786597. [PMID: 30081745 PMCID: PMC6081758 DOI: 10.1177/1533033818786597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE This work proposed a nearest neighbor estimation method to track the respiration-induced tumor motion. METHODS Based on the simultaneously collected motion traces of external surrogate and internal target during the modeling phase prior to treatment, we first obtain the nearest neighbors of the current surrogate in external space. Subsequently, the concurrent targets in internal space are determined and used to estimate the current target position. The method was validated on 71 cases that were from 3 open access databases. In addition, to evaluate the method's estimation and prediction accuracy, we compared the method with other works. RESULTS Except for 2 cases, the nearest neighbor estimation achieved the root-mean-square error of <3 mm. The comparison indicated that the method had better estimation accuracy than polynomial model and good prediction performance. DISCUSSION The 2 exceptive cases were further analyzed for failure causes. We inferred that one was because of the lack of estimating new target in our method, and the other one was because of the mistake during data collection. Accordingly, the potential solutions were suggested. Besides, the method's estimation for surrogate outliers, effects of modeling length, calibration, and extension were discussed. CONCLUSION The results demonstrated nearest neighbor estimation's effectiveness. Except for this, the method imposes no restrictions on the modality of the pretreatment target images and does not assume a specific correspondence function between the surrogate and the target. With only 1 critical parameter, this nearest neighbor estimation method is easy to implement in clinical setting and thus has potential for broad applications.
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Affiliation(s)
- Jie Zhang
- 1 School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu Province, China
| | - Xiaolin Huang
- 1 School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu Province, China
| | - Yuxiaotong Shen
- 1 School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu Province, China
| | - Ying Chen
- 1 School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu Province, China
| | - Jing Cai
- 2 Department of Radiotherapy, Nantong Tumor Hospital, Nantong, Jiangsu Province, China
| | - Yun Ge
- 1 School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu Province, China
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Paganelli C, Whelan B, Peroni M, Summers P, Fast M, van de Lindt T, McClelland J, Eiben B, Keall P, Lomax T, Riboldi M, Baroni G. MRI-guidance for motion management in external beam radiotherapy: current status and future challenges. Phys Med Biol 2018; 63:22TR03. [PMID: 30457121 DOI: 10.1088/1361-6560/aaebcf] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.
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Affiliation(s)
- C Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy. Author to whom any correspondence should be addressed. www.cartcas.polimi.it
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Ginn JS, O'Connell D, Thomas DH, Low DA, Lamb JM. Model-Interpolated Gating for Magnetic Resonance Image-Guided Radiation Therapy. Int J Radiat Oncol Biol Phys 2018; 102:885-894. [PMID: 29970314 PMCID: PMC6542358 DOI: 10.1016/j.ijrobp.2018.05.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 04/03/2018] [Accepted: 05/02/2018] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments. METHODS AND MATERIALS The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported. RESULTS Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm. CONCLUSIONS This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.
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Affiliation(s)
- John S Ginn
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
| | - Dylan O'Connell
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - David H Thomas
- Department of Radiation Oncology, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado
| | - Daniel A Low
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - James M Lamb
- Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
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Bastiaannet R, Kappadath SC, Kunnen B, Braat AJAT, Lam MGEH, de Jong HWAM. The physics of radioembolization. EJNMMI Phys 2018; 5:22. [PMID: 30386924 PMCID: PMC6212377 DOI: 10.1186/s40658-018-0221-z] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Accepted: 06/19/2018] [Indexed: 12/11/2022] Open
Abstract
Radioembolization is an established treatment for chemoresistant and unresectable liver cancers. Currently, treatment planning is often based on semi-empirical methods, which yield acceptable toxicity profiles and have enabled the large-scale application in a palliative setting. However, recently, five large randomized controlled trials using resin microspheres failed to demonstrate a significant improvement in either progression-free survival or overall survival in both hepatocellular carcinoma and metastatic colorectal cancer. One reason for this might be that the activity prescription methods used in these studies are suboptimal for many patients.In this review, the current dosimetric methods and their caveats are evaluated. Furthermore, the current state-of-the-art of image-guided dosimetry and advanced radiobiological modeling is reviewed from a physics' perspective. The current literature is explored for the observation of robust dose-response relationships followed by an overview of recent advancements in quantitative image reconstruction in relation to image-guided dosimetry.This review is concluded with a discussion on areas where further research is necessary in order to arrive at a personalized treatment method that provides optimal tumor control and is clinically feasible.
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Affiliation(s)
- Remco Bastiaannet
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Room E01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
| | - S. Cheenu Kappadath
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1155 Pressler St, Unit 1352, Houston, TX 77030 USA
| | - Britt Kunnen
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Room E01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
| | - Arthur J. A. T. Braat
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Room E01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
| | - Marnix G. E. H. Lam
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Room E01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
| | - Hugo W. A. M. de Jong
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Room E01.132, P.O. Box 85500, 3508 GA Utrecht, The Netherlands
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126
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Kruggel F. A Simple Measure for Acuity in Medical Images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 27:5225-5233. [PMID: 29994711 DOI: 10.1109/tip.2018.2851673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
An automatic and objective assessment of image quality is important in an era, where large-scale processing of imaging data from multi-center studies becomes commonplace. Based on a comprehensive statistical image model that includes noise and blur, a measure for image acuity is derived here as the ratio of the maximal gradient magnitude and the intensity difference at a boundary. Acuity may be affected by the object under study, the image acquisition, reconstruction processes, and any post-processing steps. The acuity measure presented here is post-hoc, intuitive to understand, simple to compute, and easily integrates with other standard measures of image quality. Three applications in medical imaging are included where our acuity measure is useful in the objective and automatic assessment of image quality.
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127
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De Luca V, Banerjee J, Hallack A, Kondo S, Makhinya M, Nouri D, Royer L, Cifor A, Dardenne G, Goksel O, Gooding MJ, Klink C, Krupa A, Le Bras A, Marchal M, Moelker A, Niessen WJ, Papiez BW, Rothberg A, Schnabel J, van Walsum T, Harris E, Lediju Bell MA, Tanner C. Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins. Med Phys 2018; 45:4986-5003. [PMID: 30168159 DOI: 10.1002/mp.13152] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 07/26/2018] [Accepted: 07/27/2018] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle. METHODS We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins. RESULTS Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%. CONCLUSIONS Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.
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Affiliation(s)
- Valeria De Luca
- Computer Vision Laboratory, ETH Zurich, Zürich, Switzerland
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Andre Hallack
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | | | - Maxim Makhinya
- Computer Vision Laboratory, ETH Zurich, Zürich, Switzerland
| | | | - Lucas Royer
- Institut de Recherche Technologique b-com, Rennes, France
| | | | | | - Orcun Goksel
- Computer Vision Laboratory, ETH Zurich, Zürich, Switzerland
| | | | - Camiel Klink
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | | | - Maud Marchal
- Institut de Recherche Technologique b-com, Rennes, France
| | - Adriaan Moelker
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | | | - Julia Schnabel
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Theo van Walsum
- Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Muyinatu A Lediju Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
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128
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Trnková P, Knäusl B, Actis O, Bert C, Biegun AK, Boehlen TT, Furtado H, McClelland J, Mori S, Rinaldi I, Rucinski A, Knopf AC. Clinical implementations of 4D pencil beam scanned particle therapy: Report on the 4D treatment planning workshop 2016 and 2017. Phys Med 2018; 54:121-130. [PMID: 30337001 DOI: 10.1016/j.ejmp.2018.10.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 09/18/2018] [Accepted: 10/02/2018] [Indexed: 12/14/2022] Open
Abstract
In 2016 and 2017, the 8th and 9th 4D treatment planning workshop took place in Groningen (the Netherlands) and Vienna (Austria), respectively. This annual workshop brings together international experts to discuss research, advances in clinical implementation as well as problems and challenges in 4D treatment planning, mainly in spot scanned proton therapy. In the last two years several aspects like treatment planning, beam delivery, Monte Carlo simulations, motion modeling and monitoring, QA phantoms as well as 4D imaging were thoroughly discussed. This report provides an overview of discussed topics, recent findings and literature review from the last two years. Its main focus is to highlight translation of 4D research into clinical practice and to discuss remaining challenges and pitfalls that still need to be addressed and to be overcome.
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Affiliation(s)
- Petra Trnková
- HollandPTC, P.O. Box 5046, 2600 GA Delft, the Netherlands; Erasmus MC, P.O. Box 5201, 3008 AE Rotterdam, the Netherlands
| | - Barbara Knäusl
- Department of Radiation Oncology, Division of Medical Radiation Physics, Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna/AKH Vienna, Austria
| | - Oxana Actis
- Paul Scherrer Institute (PSI), 5232 Villigen, Switzerland
| | - Christoph Bert
- Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
| | - Aleksandra K Biegun
- KVI-Center for Advanced Radiation Technology, University of Groningen, Groningen, the Netherlands
| | - Till T Boehlen
- Paul Scherrer Institute (PSI), 5232 Villigen, Switzerland
| | - Hugo Furtado
- Department of Radiation Oncology, Division of Medical Radiation Physics, Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Medical University of Vienna/AKH Vienna, Austria
| | - Jamie McClelland
- Centre for Medical Image Computing, Dept. Medical Physics and Biomedical, University College London, London, UK
| | - Shinichiro Mori
- National Institute of Radiological Sciences for Charged Particle Therapy, Chiba, Japan
| | - Ilaria Rinaldi
- Lyon 1 University and CNRS/IN2P3, UMR 5822, 69622 Villeurbanne, France; MAASTRO Clinic, P.O. Box 3035, 6202 NA Maastricht, the Netherlands
| | | | - Antje C Knopf
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, the Netherlands.
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129
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Karani N, Tanner C, Kozerke S, Konukoglu E. Reducing Navigators in Free-Breathing Abdominal MRI via Temporal Interpolation Using Convolutional Neural Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:2333-2343. [PMID: 29994024 DOI: 10.1109/tmi.2018.2831442] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Navigated 2-D multi-slice dynamic magnetic resonance imaging (MRI) acquisitions are essential for MR guided therapies. This technique yields time-resolved volumetric images during free-breathing, which are ideal for visualizing and quantifying breathing induced motion. To achieve this, navigated dynamic imaging requires acquiring multiple navigator slices. Reducing the number of navigator slices would allow for acquiring more data slices in the same time, and hence, increasing through-plane resolution or alternatively the overall acquisition time can be reduced while keeping resolution unchanged. To this end, we propose temporal interpolation of navigator slices using convolutional neural networks (CNNs). Our goal is to acquire fewer navigators and replace the missing ones with interpolation. We evaluate the proposed method on abdominal navigated dynamic MRI sequences acquired from 14 subjects. Investigations with several CNN architectures and training loss functions show favorable results for cost and a simple feed-forward network with no skip connections. When compared with interpolation by non-linear registration, the proposed method achieves higher interpolation accuracy on average as quantified in terms of root mean square error and residual motion. Analysis of the differences shows that the better performance is due to more accurate interpolation at peak exhalation and inhalation positions. Furthermore, the CNN-based approach requires substantially lower execution times than that of the registration-based method. At last, experiments on dynamic volume reconstruction reveal minimal differences between reconstructions with acquired and interpolated navigator slices.
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130
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Rao F, Li WL, Yin ZP. Non-rigid point cloud registration based lung motion estimation using tangent-plane distance. PLoS One 2018; 13:e0204492. [PMID: 30256830 PMCID: PMC6157875 DOI: 10.1371/journal.pone.0204492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 09/10/2018] [Indexed: 01/31/2023] Open
Abstract
Accurate estimation of motion field in respiration-correlated 4DCT images, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported treatment planning. However, the lung motion estimation often suffers from the sliding motion. In this paper, a novel lung motion method based on the non-rigid registration of point clouds is proposed, and the tangent-plane distance is used to represent the distance term, which describes the difference between two point clouds. Local affine transformation model is used to express the non-rigid deformation of the lung motion. The final objective function is expressed in the Frobenius norm formation, and matrix optimization scheme is carried out to find out the optimal transformation parameters that minimize the objective function. A key advantage of our proposed method is that it alleviates the requirement that the source point cloud and the reference point cloud should be in one-to-one corresponding relationship, and the requirement is difficult to be satisfied in practical application. Furthermore, the proposed method takes the sliding motion of the lung into consideration and improves the registration accuracy by reducing the constraint of the motion along the tangent direction. Non-rigid registration experiments are carried out to validate the performance of the proposed method using popi-model data. The results demonstrate that the proposed method outperforms the traditional method with about 20% accuracy increase.
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Affiliation(s)
- Fan Rao
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Wen-long Li
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- * E-mail:
| | - Zhou-ping Yin
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
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131
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Paganelli C, Meschini G, Molinelli S, Riboldi M, Baroni G. “Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats”. Med Phys 2018; 45:e908-e922. [DOI: 10.1002/mp.13162] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 07/30/2018] [Accepted: 08/24/2018] [Indexed: 12/26/2022] Open
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | - Giorgia Meschini
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
| | | | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universitat Munchen; Munich 80539 Germany
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano 20133 Italy
- Centro Nazionale di Adroterapia Oncologica; Pavia 27100 Italy
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Fahmi S, Simonis FFJ, Abayazid M. Respiratory motion estimation of the liver with abdominal motion as a surrogate. Int J Med Robot 2018; 14:e1940. [PMID: 30112864 PMCID: PMC6282606 DOI: 10.1002/rcs.1940] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 06/08/2018] [Accepted: 06/10/2018] [Indexed: 12/25/2022]
Abstract
Background: Respiratory‐induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates. Methods: A learning‐based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates. Accordingly, appropriate motion models (multivariate, Ridge and Lasso regression models) were designed to correlate the liver motion with the abdominal motion, and trained to estimate the superior–inferior (SI) motion of the liver. Three subjects volunteered for 6 sessions of such that liver images acquired by magnetic resonance imaging (MRI) were recorded alongside camera‐tracked external markers. Results and conclusions: The proposed machine learning approach was validated in MRI on human subjects and the results show that the approach could estimate the respiratory‐induced SI motion of the liver with a mean absolute error (MAE) accuracy below 2 mm.
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Affiliation(s)
- Shamel Fahmi
- Robotics and Mechatronics group (RaM), the faculty of Electrical Engineering Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, 7500AE, the Netherlands.,Advanced Robotics Department, Istituto Italiano di Tecnologia, Genova, 16163, Italy
| | - Frank F J Simonis
- Magnetic Detection and Imaging Department, Faculty of Science and Technology, University of Twente, Enschede, 7500AE, the Netherlands
| | - Momen Abayazid
- Robotics and Mechatronics group (RaM), the faculty of Electrical Engineering Mathematics and Computer Science, Technical Medical Centre, University of Twente, Enschede, 7500AE, the Netherlands
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133
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Kincaid Jr. RE, Hertanto AE, Hu YC, Wu AJ, Goodman KA, Pham HD, Yorke ED, Zhang Q, Chen Q, Mageras GS. Evaluation of respiratory motion-corrected cone-beam CT at end expiration in abdominal radiotherapy sites: a prospective study. Acta Oncol 2018; 57:1017-1024. [PMID: 29350579 DOI: 10.1080/0284186x.2018.1427885] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
BACKGROUND Cone beam computed tomography (CBCT) for radiotherapy image guidance suffers from respiratory motion artifacts. This limits soft tissue visualization and localization accuracy, particularly in abdominal sites. We report on a prospective study of respiratory motion-corrected (RMC)-CBCT to evaluate its efficacy in localizing abdominal organs and improving soft tissue visibility at end expiration. MATERIAL AND METHODS In an IRB approved study, 11 patients with gastroesophageal junction (GEJ) cancer and five with pancreatic cancer underwent a respiration-correlated CT (4DCT), a respiration-gated CBCT (G-CBCT) near end expiration and a one-minute free-breathing CBCT scan on a single treatment day. Respiration was recorded with an external monitor. An RMC-CBCT and an uncorrected CBCT (NC-CBCT) were computed from the free-breathing scan, based on a respiratory model of deformations derived from the 4DCT. Localization discrepancy was computed as the 3D displacement of the GEJ region (GEJ patients), or gross tumor volume (GTV) and kidneys (pancreas patients) in the NC-CBCT and RMC-CBCT relative to their positions in the G-CBCT. Similarity of soft-tissue features was measured using a normalized cross correlation (NCC) function. RESULTS Localization discrepancy from the end-expiration G-CBCT was reduced for RMC-CBCT compared to NC-CBCT in eight of eleven GEJ cases (mean ± standard deviation, respectively, 0.21 ± 0.11 and 0.43 ± 0.28 cm), in all five pancreatic GTVs (0.26 ± 0.21 and 0.42 ± 0.29 cm) and all ten kidneys (0.19 ± 0.13 and 0.51 ± 0.25 cm). Soft-tissue feature similarity around GEJ was higher with RMC-CBCT in nine of eleven cases (NCC =0.48 ± 0.20 and 0.43 ± 0.21), and eight of ten kidneys (0.44 ± 0.16 and 0.40 ± 0.17). CONCLUSIONS In a prospective study of motion-corrected CBCT in GEJ and pancreas, RMC-CBCT yielded improved organ visibility and localization accuracy for gated treatment at end expiration in the majority of cases.
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Affiliation(s)
- Russell E. Kincaid Jr.
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Agung E. Hertanto
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yu-Chi Hu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Abraham J. Wu
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Karyn A. Goodman
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hai D. Pham
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ellen D. Yorke
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Qinghui Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Qing Chen
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Gig S. Mageras
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Muller M, Paganelli C, Keall P. A phantom study to create synthetic CT from orthogonal twodimensional cine MRI and evaluate the effect of irregular breathing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:4162-4165. [PMID: 30441272 DOI: 10.1109/embc.2018.8513236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
An exciting innovation in radiotherapy is the use of real-time MRI for treatment adaptation. This study proposes an in-silico framework for the generation of 3D synthetic CT (sCT) from orthogonal interleaved 2D cine MRI data to overcome the lack of electron density information in MR images. The method uses pre-treatment data to build a patient breathing motion model. This model is then driven by surrogates extracted from cine MR images during the treatment. The effect of irregular breathing on the motion model is also evaluated by simulating different motion components related to uncorrelated diaphragm, chest and tumor motion. 3D sCT were successfully created for each of the 512 cine MRI pairs in the digital phantom study. The analysis showed that the diaphragm position was a good surrogate to rescale the 3D breathing motion for the current regular breathing phase. However, respiratory and tumor motion were correlated in only 59% of the phases, resulting in tumor position uncertainties of up to 3mm. The inclusion of additional chest and tumor motion information improved the accuracy for irregular changes in breathing pattern and enhanced the tumor position uncertainties to less than 1mm. This study successfully demonstrated a proof-ofprinciple for a digital phantom dataset based on patient parameters, providing a way to create real-time 3D electron density volumes and enhancing the need to account for irregular breathing pattern.
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Fayad H, Gilles M, Pan T, Visvikis D. A 4D global respiratory motion model of the thorax based on CT images: A proof of concept. Med Phys 2018; 45:3043-3051. [PMID: 29772057 DOI: 10.1002/mp.12982] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 04/26/2018] [Accepted: 05/07/2018] [Indexed: 11/06/2022] Open
Abstract
PURPOSE Respiratory motion reduces the sensitivity and specificity of medical images especially in the thoracic and abdominal areas. It may affect applications such as cancer diagnostic imaging and/or radiation therapy (RT). Solutions to this issue include modeling of the respiratory motion in order to optimize both diagnostic and therapeutic protocols. Personalized motion modeling required patient-specific four-dimensional (4D) imaging which in the case of 4D computed tomography (4D CT) acquisition is associated with an increased dose. The goal of this work was to develop a global respiratory motion model capable of relating external patient surface motion to internal structure motion without the need for a patient-specific 4D CT acquisition. METHODS The proposed global model is based on principal component analysis and can be adjusted to a given patient anatomy using only one or two static CT images in conjunction with a respiratory synchronized patient external surface motion. It is based on the relation between the internal motion described using deformation fields obtained by registering 4D CT images and patient surface maps obtained either from optical imaging devices or extracted from CT image-based patient skin segmentation. 4D CT images of six patients were used to generate the global motion model which was validated by adapting it on four different patients having skin segmented surfaces and two other patients having time of flight camera acquired surfaces. The reproducibility of the proposed model was also assessed on two patients with two 4D CT series acquired within 2 weeks of each other. RESULTS Profile comparison shows the efficacy of the global respiratory motion model and an improvement while using two CT images in order to adapt the model. This was confirmed by the correlation coefficient with a mean correlation of 0.9 and 0.95 while using one or two CT images respectively and when comparing acquired to model generated 4D CT images. For the four patients with segmented surfaces, expert validation indicates an error of 2.35 ± 0.26 mm compared to 6.07 ± 0.76 mm when using a simple interpolation between full inspiration (FI) and full expiration (FE) CT only; i.e., without specific modeling of the respiratory motion. For the two patients with acquired surfaces, this error was of 2.48 ± 0.18 mm. In terms of reproducibility, model error changes of 0.12 and 0.17 mm were measured for the two patients concerned. CONCLUSIONS The framework for the derivation of a global respiratory motion model was developed. A single or two static CT images and associated patient surface motion, as a surrogate measure, are only needed to personalize the model. This model accuracy and reproducibility were assessed by comparing acquired vs model generated 4D CT images. Future work will consist of assessing extensively the proposed model for radiotherapy applications.
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Affiliation(s)
- Hadi Fayad
- OHS PET/CT Hamad Medical Corporation, Doha, Qatar
| | | | - Tinsu Pan
- Department of Imaging Physics, M.D. Anderson Cancer Center, Houston, TX, USA
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Blessing M, Hofmann J, Vogel L, Boda-Heggemann J, Lohr F, Wenz F, Stieler F, Simeonova-Chergou A. An offline technique to evaluate residual motion of the diaphragm during deep inspiratory breath-hold from cone-beam CT datasets. Strahlenther Onkol 2018; 194:855-860. [DOI: 10.1007/s00066-018-1313-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/25/2018] [Indexed: 12/25/2022]
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Mogadas N, Sothmann T, Knopp T, Gauer T, Petersen C, Werner R. Influence of deformable image registration on 4D dose simulation for extracranial SBRT: A multi-registration framework study. Radiother Oncol 2018; 127:225-232. [PMID: 29606523 DOI: 10.1016/j.radonc.2018.03.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 03/14/2018] [Accepted: 03/14/2018] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE To evaluate the influence of deformable image registration approaches on correspondence model-based 4D dose simulation in extracranial SBRT by means of open source deformable image registration (DIR) frameworks. MATERIAL AND METHODS Established DIR algorithms of six different open source DIR frameworks were considered and registration accuracy evaluated using freely available 4D image data. Furthermore, correspondence models (regression-based correlation of external breathing signal measurements and internal structure motion field) were built and model accuracy evaluated. Finally, the DIR algorithms were applied for motion field estimation in radiotherapy planning 4D CT data of five lung and five liver lesion patients, correspondence model formation, and model-based 4D dose simulation. Deviations between the original, statically planned and the 4D-simulated VMAT dose distributions were analyzed and correlated to DIR accuracy differences. RESULTS Registration errors varied among the DIR approaches, with lower DIR accuracy translating into lower correspondence modeling accuracy. Yet, for lung metastases, indices of 4D-simulated dose distributions widely agreed, irrespective of DIR accuracy differences. In contrast, liver metastases 4D dose simulation results strongly vary for the different DIR approaches. CONCLUSIONS Especially in treatment areas with low image contrast (e.g. the liver), DIR-based 4D dose simulation results strongly depend on the applied DIR algorithm, drawing resulting dose simulations and indices questionable.
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Affiliation(s)
- Nik Mogadas
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Germany
| | - Thilo Sothmann
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Germany; Department of Radiotherapy and Radio-Oncology, University Medical Center Hamburg-Eppendorf, Germany.
| | - Tobias Knopp
- Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Germany
| | - Tobias Gauer
- Department of Radiotherapy and Radio-Oncology, University Medical Center Hamburg-Eppendorf, Germany
| | - Cordula Petersen
- Department of Radiotherapy and Radio-Oncology, University Medical Center Hamburg-Eppendorf, Germany
| | - René Werner
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Germany
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O'Connell D, Thomas DH, Lamb JM, Lewis JH, Dou T, Sieren JP, Saylor M, Hofmann C, Hoffman EA, Lee PP, Low DA. Dependence of subject-specific parameters for a fast helical CT respiratory motion model on breathing rate: an animal study. Phys Med Biol 2018; 63:04NT04. [PMID: 29360098 DOI: 10.1088/1361-6560/aaaa15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
To determine if the parameters relating lung tissue displacement to a breathing surrogate signal in a previously published respiratory motion model vary with the rate of breathing during image acquisition. An anesthetized pig was imaged using multiple fast helical scans to sample the breathing cycle with simultaneous surrogate monitoring. Three datasets were collected while the animal was mechanically ventilated with different respiratory rates: 12 bpm (breaths per minute), 17 bpm, and 24 bpm. Three sets of motion model parameters describing the correspondences between surrogate signals and tissue displacements were determined. The model error was calculated individually for each dataset, as well asfor pairs of parameters and surrogate signals from different experiments. The values of one model parameter, a vector field denoted [Formula: see text] which related tissue displacement to surrogate amplitude, determined for each experiment were compared. The mean model error of the three datasets was 1.00 ± 0.36 mm with a 95th percentile value of 1.69 mm. The mean error computed from all combinations of parameters and surrogate signals from different datasets was 1.14 ± 0.42 mm with a 95th percentile of 1.95 mm. The mean difference in [Formula: see text] over all pairs of experiments was 4.7% ± 5.4%, and the 95th percentile was 16.8%. The mean angle between pairs of [Formula: see text] was 5.0 ± 4.0 degrees, with a 95th percentile of 13.2 mm. The motion model parameters were largely unaffected by changes in the breathing rate during image acquisition. The mean error associated with mismatched sets of parameters and surrogate signals was 0.14 mm greater than the error achieved when using parameters and surrogate signals acquired with the same breathing rate, while maximum respiratory motion was 23.23 mm on average.
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Affiliation(s)
- Dylan O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA 90095, United States of America. Author to whom any correspondence should be addressed
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O'Connell D, Ruan D, Thomas DH, Dou TH, Lewis JH, Santhanam A, Lee P, Low DA. A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT. Phys Med Biol 2018; 63:04NT03. [PMID: 29350191 DOI: 10.1088/1361-6560/aaa90f] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Breathing motion modeling requires observation of tissues at sufficiently distinct respiratory states for proper 4D characterization. This work proposes a method to improve sampling of the breathing cycle with limited imaging dose. We designed and tested a prospective free-breathing acquisition protocol with a simulation using datasets from five patients imaged with a model-based 4DCT technique. Each dataset contained 25 free-breathing fast helical CT scans with simultaneous breathing surrogate measurements. Tissue displacements were measured using deformable image registration. A correspondence model related tissue displacement to the surrogate. Model residual was computed by comparing predicted displacements to image registration results. To determine a stopping criteria for the prospective protocol, i.e. when the breathing cycle had been sufficiently sampled, subsets of N scans where 5 ⩽ N ⩽ 9 were used to fit reduced models for each patient. A previously published metric was employed to describe the phase coverage, or 'spread', of the respiratory trajectories of each subset. Minimum phase coverage necessary to achieve mean model residual within 0.5 mm of the full 25-scan model was determined and used as the stopping criteria. Using the patient breathing traces, a prospective acquisition protocol was simulated. In all patients, phase coverage greater than the threshold necessary for model accuracy within 0.5 mm of the 25 scan model was achieved in six or fewer scans. The prospectively selected respiratory trajectories ranked in the (97.5 ± 4.2)th percentile among subsets of the originally sampled scans on average. Simulation results suggest that the proposed prospective method provides an effective means to sample the breathing cycle with limited free-breathing scans. One application of the method is to reduce the imaging dose of a previously published model-based 4DCT protocol to 25% of its original value while achieving mean model residual within 0.5 mm.
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Affiliation(s)
- D O'Connell
- Department of Radiation Oncology, University of California, Los Angeles, CA 90095, United States of America
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Paganelli C, Lee D, Kipritidis J, Whelan B, Greer PB, Baroni G, Riboldi M, Keall P. Feasibility study on 3D image reconstruction from 2D orthogonal cine-MRI for MRI-guided radiotherapy. J Med Imaging Radiat Oncol 2018; 62:389-400. [DOI: 10.1111/1754-9485.12713] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 01/12/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano Italy
| | - Danny Lee
- Department of Radiation Oncology; Calvary Mater Newcastle; Newcastle New South Wales Australia
| | - John Kipritidis
- Northern Sydney Cancer Centre; Royal North Shore Hospital; Sydney New South Wales Australia
- ACRF Image X Institute; Sydney Medical School; University of Sydney; Sydney New South Wales Australia
| | - Brendan Whelan
- ACRF Image X Institute; Sydney Medical School; University of Sydney; Sydney New South Wales Australia
| | - Peter B Greer
- Department of Radiation Oncology; Calvary Mater Newcastle; Newcastle New South Wales Australia
- School of Mathematical and Physical Sciences; University of Newcastle; Newcastle New South Wales Australia
| | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria; Politecnico di Milano; Milano Italy
- Bioengineering Unit; Centro Nazionale di Adroterapia Oncologica; Pavia Italy
| | - Marco Riboldi
- Department of Medical Physics; Ludwig-Maximilians-Universität München; Munich Germany
| | - Paul Keall
- ACRF Image X Institute; Sydney Medical School; University of Sydney; Sydney New South Wales Australia
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141
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Sauppe S, Kuhm J, Brehm M, Paysan P, Seghers D, Kachelrieß M. Motion vector field phase-to-amplitude resampling for 4D motion-compensated cone-beam CT. Phys Med Biol 2018; 63:035032. [PMID: 29235989 DOI: 10.1088/1361-6560/aaa16d] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We propose a phase-to-amplitude resampling (PTAR) method to reduce motion blurring in motion-compensated (MoCo) 4D cone-beam CT (CBCT) image reconstruction, without increasing the computational complexity of the motion vector field (MVF) estimation approach. PTAR is able to improve the image quality in reconstructed 4D volumes, including both regular and irregular respiration patterns. The PTAR approach starts with a robust phase-gating procedure for the initial MVF estimation and then switches to a phase-adapted amplitude gating method. The switch implies an MVF-resampling, which makes them amplitude-specific. PTAR ensures that the MVFs, which have been estimated on phase-gated reconstructions, are still valid for all amplitude-gated reconstructions. To validate the method, we use an artificially deformed clinical CT scan with a realistic breathing pattern and several patient data sets acquired with a TrueBeamTM integrated imaging system (Varian Medical Systems, Palo Alto, CA, USA). Motion blurring, which still occurs around the area of the diaphragm or at small vessels above the diaphragm in artifact-specific cyclic motion compensation (acMoCo) images based on phase-gating, is significantly reduced by PTAR. Also, small lung structures appear sharper in the images. This is demonstrated both for simulated and real patient data. A quantification of the sharpness of the diaphragm confirms these findings. PTAR improves the image quality of 4D MoCo reconstructions compared to conventional phase-gated MoCo images, in particular for irregular breathing patterns. Thus, PTAR increases the robustness of MoCo reconstructions for CBCT. Because PTAR does not require any additional steps for the MVF estimation, it is computationally efficient. Our method is not restricted to CBCT but could rather be applied to other image modalities.
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Affiliation(s)
- Sebastian Sauppe
- German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. Medical Faculty, Ruprecht-Karls-University, Im Neuenheimer Feld 672, 69120 Heidelberg, Germany
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142
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Fuin N, Catalano OA, Scipioni M, Canjels LPW, Izquierdo-Garcia D, Pedemonte S, Catana C. Concurrent Respiratory Motion Correction of Abdominal PET and Dynamic Contrast-Enhanced-MRI Using a Compressed Sensing Approach. J Nucl Med 2018; 59:1474-1479. [PMID: 29371404 DOI: 10.2967/jnumed.117.203943] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 01/15/2018] [Indexed: 01/23/2023] Open
Abstract
We present an approach for concurrent reconstruction of respiratory motion-compensated abdominal dynamic contrast-enhanced (DCE)-MRI and PET data in an integrated PET/MR scanner. The MR and PET reconstructions share the same motion vector fields derived from radial MR data; the approach is robust to changes in respiratory pattern and does not increase the total acquisition time. Methods: PET and DCE-MRI data of 12 oncologic patients were simultaneously acquired for 6 min on an integrated PET/MR system after administration of 18F-FDG and gadoterate meglumine. Golden-angle radial MR data were continuously acquired simultaneously with PET data and sorted into multiple motion phases on the basis of a respiratory signal derived directly from the radial MR data. The resulting multidimensional dataset was reconstructed using a compressed sensing approach that exploits sparsity among respiratory phases. Motion vector fields obtained using the full 6-min (MC6-min) and only the last 1 min (MC1-min) of data were incorporated into the PET reconstruction to obtain motion-corrected PET images and in an MR iterative reconstruction algorithm to produce a series of motion-corrected DCE-MR images (moco_GRASP). The motion-correction methods (MC6-min and MC1-min) were evaluated by qualitative analysis of the MR images and quantitative analysis of SUVmax and SUVmean, contrast, signal-to-noise ratio (SNR), and lesion volume in the PET images. Results: Motion-corrected MC6-min PET images demonstrated 30%, 23%, 34%, and 18% increases in average SUVmax, SUVmean, contrast, and SNR and an average 40% reduction in lesion volume with respect to the non-motion-corrected PET images. The changes in these figures of merit were smaller but still substantial for the MC1-min protocol: 19%, 10%, 15%, and 9% increases in average SUVmax, SUVmean, contrast, and SNR; and a 28% reduction in lesion volume. Moco_GRASP images were deemed of acceptable or better diagnostic image quality with respect to conventional breath-hold Cartesian volumetric interpolated breath-hold examination acquisitions. Conclusion: We presented a method that allows the simultaneous acquisition of respiratory motion-corrected diagnostic quality DCE-MRI and quantitatively accurate PET data in an integrated PET/MR scanner with negligible prolongation in acquisition time compared with routine PET/DCE-MRI protocols.
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Affiliation(s)
- Niccolo Fuin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Onofrio A Catalano
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Michele Scipioni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts.,Department of Information Engineering, University of Pisa, Pisa, Italy; and
| | - Lisanne P W Canjels
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - David Izquierdo-Garcia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Stefano Pedemonte
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts
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143
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Thomas DH, Santhanam A, Kishan AU, Cao M, Lamb J, Min Y, O'Connell D, Yang Y, Agazaryan N, Lee P, Low D. Initial clinical observations of intra- and interfractional motion variation in MR-guided lung SBRT. Br J Radiol 2018; 91:20170522. [PMID: 29166129 DOI: 10.1259/bjr.20170522] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To evaluate variations in intra- and interfractional tumour motion, and the effect on internal target volume (ITV) contour accuracy, using deformable image registration of real-time two-dimensional-sagittal cine-mode MRI acquired during lung stereotactic body radiation therapy (SBRT) treatments. METHODS Five lung tumour patients underwent free-breathing SBRT treatments on the ViewRay system, with dose prescribed to a planning target volume (defined as a 3-6 mm expansion of the 4DCT-ITV). Sagittal slice cine-MR images (3.5 × 3.5 mm2 pixels) were acquired through the centre of the tumour at 4 frames per second throughout the treatments (3-4 fractions of 21-32 min). Tumour gross tumour volumes (GTVs) were contoured on the first frame of the MR cine and tracked for the first 20 min of each treatment using offline optical-flow based deformable registration implemented on a GPU cluster. A ground truth ITV (MR-ITV20 min) was formed by taking the union of tracked GTV contours. Pseudo-ITVs were generated from unions of the GTV contours tracked over 10 s segments of image data (MR-ITV10 s). RESULTS Differences were observed in the magnitude of median tumour displacement between days of treatments. MR-ITV10 s areas were as small as 46% of the MR-ITV20 min. CONCLUSION An ITV offers a "snapshot" of breathing motion for the brief period of time the tumour is imaged on a specific day. Real-time MRI over prolonged periods of time and over multiple treatment fractions shows that ITV size varies. Further work is required to investigate the dosimetric effect of these results. Advances in knowledge: Five lung tumour patients underwent free-breathing MRI-guided SBRT treatments, and their tumours tracked using deformable registration of cine-mode MRI. The results indicate that variability of both intra- and interfractional breathing amplitude should be taken into account during planning of lung radiotherapy.
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Affiliation(s)
- David H Thomas
- 1 Department of Radiation Oncology, University of Colorado School of Medicine , Aurora, CO , USA
| | - Anand Santhanam
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Amar U Kishan
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Minsong Cao
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - James Lamb
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Yugang Min
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Dylan O'Connell
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Yingli Yang
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Nzhde Agazaryan
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Percy Lee
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
| | - Daniel Low
- 2 Department of Radiation Oncology, University of California , Los Angeles, CA , USA
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Fischer P, Faranesh A, Pohl T, Maier A, Rogers T, Ratnayaka K, Lederman R, Hornegger J. An MR-Based Model for Cardio-Respiratory Motion Compensation of Overlays in X-Ray Fluoroscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:47-60. [PMID: 28692969 PMCID: PMC5750091 DOI: 10.1109/tmi.2017.2723545] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time magnetic resonance (MR) imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration. The motion model itself is a linear direct correspondence model using the same surrogate signals as slice stacking. In X-ray fluoroscopy, only the surrogate signals need to be extracted to apply the motion model and animate the overlay in real time. For evaluation, points are manually annotated in oblique MR slices and in contrast-enhanced X-ray images. The 2-D Euclidean distance of these points is reduced from 3.85 to 2.75 mm in MR and from 3.0 to 1.8 mm in X-ray compared with the static baseline. Furthermore, the motion-compensated overlays are shown qualitatively as images and videos.
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145
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Abayazid M, Kato T, Silverman SG, Hata N. Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions. Int J Comput Assist Radiol Surg 2018; 13:125-133. [PMID: 28766177 PMCID: PMC5754381 DOI: 10.1007/s11548-017-1644-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 07/10/2017] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. MATERIALS AND METHODS The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. RESULTS The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. CONCLUSION The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.
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Affiliation(s)
- Momen Abayazid
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA.
- MIRA-Institute for Biomedical Technology and Technical Medicine (Robotics and Mechatronics), University of Twente, Enschede, The Netherlands.
| | - Takahisa Kato
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
- Healthcare Optics Research Laboratory, Canon U.S.A., Inc., Cambridge, MA, USA
| | - Stuart G Silverman
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
| | - Nobuhiko Hata
- Department of Radiology, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
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Respiratory Motion Modelling Using cGANs. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2018 2018. [DOI: 10.1007/978-3-030-00937-3_10] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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147
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Sothmann T, Gauer T, Wilms M, Werner R. Correspondence model-based 4D VMAT dose simulation for analysis of local metastasis recurrence after extracranial SBRT. ACTA ACUST UNITED AC 2017; 62:9001-9017. [DOI: 10.1088/1361-6560/aa955b] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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148
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Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol. Pract Radiat Oncol 2017; 8:e175-e183. [PMID: 29429921 DOI: 10.1016/j.prro.2017.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 10/28/2017] [Accepted: 11/08/2017] [Indexed: 11/22/2022]
Abstract
PURPOSE To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol. METHODS AND MATERIALS Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts. RESULTS Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images. CONCLUSION Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods.
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Gillman A, Smith J, Thomas P, Rose S, Dowson N. PET motion correction in context of integrated PET/MR: Current techniques, limitations, and future projections. Med Phys 2017; 44:e430-e445. [DOI: 10.1002/mp.12577] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 06/23/2017] [Accepted: 08/21/2017] [Indexed: 12/18/2022] Open
Affiliation(s)
- Ashley Gillman
- Australian e-Health Research Centre; CSIRO; Brisbane Australia
- Faculty of Medicine; University of Queensland; Brisbane Australia
| | - Jye Smith
- Department of Radiation Oncology; Royal Brisbane and Women's Hospital; Brisbane Australia
| | - Paul Thomas
- Faculty of Medicine; University of Queensland; Brisbane Australia
- Herston Imaging Research Facility and Specialised PET Services Queensland; Royal Brisbane and Women's Hospital; Brisbane Australia
| | - Stephen Rose
- Australian e-Health Research Centre; CSIRO; Brisbane Australia
| | - Nicholas Dowson
- Australian e-Health Research Centre; CSIRO; Brisbane Australia
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Freedman JN, Collins DJ, Bainbridge H, Rank CM, Nill S, Kachelrieß M, Oelfke U, Leach MO, Wetscherek A. T2-Weighted 4D Magnetic Resonance Imaging for Application in Magnetic Resonance-Guided Radiotherapy Treatment Planning. Invest Radiol 2017; 52:563-573. [PMID: 28459800 PMCID: PMC5581953 DOI: 10.1097/rli.0000000000000381] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 03/20/2017] [Indexed: 12/13/2022]
Abstract
OBJECTIVES The aim of this study was to develop and verify a method to obtain good temporal resolution T2-weighted 4-dimensional (4D-T2w) magnetic resonance imaging (MRI) by using motion information from T1-weighted 4D (4D-T1w) MRI, to support treatment planning in MR-guided radiotherapy. MATERIALS AND METHODS Ten patients with primary non-small cell lung cancer were scanned at 1.5 T axially with a volumetric T2-weighted turbo spin echo sequence gated to exhalation and a volumetric T1-weighted stack-of-stars spoiled gradient echo sequence with golden angle spacing acquired in free breathing. From the latter, 20 respiratory phases were reconstructed using the recently developed 4D joint MoCo-HDTV algorithm based on the self-gating signal obtained from the k-space center. Motion vector fields describing the respiratory cycle were obtained by deformable image registration between the respiratory phases and projected onto the T2-weighted image volume. The resulting 4D-T2w volumes were verified against the 4D-T1w volumes: an edge-detection method was used to measure the diaphragm positions; the locations of anatomical landmarks delineated by a radiation oncologist were compared and normalized mutual information was calculated to evaluate volumetric image similarity. RESULTS High-resolution 4D-T2w MRI was obtained. Respiratory motion was preserved on calculated 4D-T2w MRI, with median diaphragm positions being consistent with less than 6.6 mm (2 voxels) for all patients and less than 3.3 mm (1 voxel) for 9 of 10 patients. Geometrical positions were coherent between 4D-T1w and 4D-T2w MRI as Euclidean distances between all corresponding anatomical landmarks agreed to within 7.6 mm (Euclidean distance of 2 voxels) and were below 3.8 mm (Euclidean distance of 1 voxel) for 355 of 470 pairs of anatomical landmarks. Volumetric image similarity was commensurate between 4D-T1w and 4D-T2w MRI, as mean percentage differences in normalized mutual information (calculated over all respiratory phases and patients), between corresponding respiratory phases of 4D-T1w and 4D-T2w MRI and the tie-phase of 4D-T1w and 3-dimensional T2w MRI, were consistent to 0.41% ± 0.37%. Four-dimensional T2w MRI displayed tumor extent, structure, and position more clearly than corresponding 4D-T1w MRI, especially when mobile tumor sites were adjacent to organs at risk. CONCLUSIONS A methodology to obtain 4D-T2w MRI that retrospectively applies the motion information from 4D-T1w MRI to 3-dimensional T2w MRI was developed and verified. Four-dimensional T2w MRI can assist clinicians in delineating mobile lesions that are difficult to define on 4D-T1w MRI, because of poor tumor-tissue contrast.
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Affiliation(s)
- Joshua N. Freedman
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David J. Collins
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hannah Bainbridge
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christopher M. Rank
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Simeon Nill
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marc Kachelrieß
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Uwe Oelfke
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin O. Leach
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andreas Wetscherek
- From the *Joint Department of Physics, †CR UK Cancer Imaging Centre, and ‡Joint Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom; and §Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
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