51
|
Chest Wall Reconstruction Using 3-Dimensional Printing: Functional and Mechanical Results. Ann Thorac Surg 2021; 114:979-988. [PMID: 34555374 DOI: 10.1016/j.athoracsur.2021.07.103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 05/12/2021] [Accepted: 07/14/2021] [Indexed: 11/21/2022]
Abstract
BACKGROUND Tumors involving the chest wall may require extensive resection and reconstruction. This study aims to evaluate functional, cosmetic results, and quality of life (QoL) in patients who had a reconstruction based on patient-specific 3-dimensional (3D) printing. METHODS The patient-specific chest wall prosthesis was created for 10 patients. The anatomical models were 3D printed and used to produce a silicone mold that was filled with methyl methacrylate to create the customized prosthesis. Evaluation of the reconstruction was completed with a QoL assessment and postoperative tracking of patients' chest motion, using infrared markers. The distance between plot points representing markers on the operated and contralateral sides was measured to assess symmetrical motion. RESULTS Twenty-three consecutive patients were enrolled, with the median age of 64 years. Thirteen patients underwent a nonrigid reconstruction, and 10 had a patient-specific rigid reconstruction with methyl methacrylate. The median number of ribs resected was 3. No postoperative complications or morbidity related to the prostheses were reported. The median hospital stay in the nonrigid reconstruction group was 8.5 days compared with 7.5 days (p = .167) in the rigid reconstruction group. Postoperatively, most patients had low levels of symptoms, with 82% experiencing chest pain and 53% experiencing dyspnea. Rigid reconstruction patients demonstrated more symmetrical breathing motion compared with nonrigid reconstruction patients. The mean distances were 2.32 ± 2.18 and 7.28 ± 5.87 (P < .00001), respectively. CONCLUSIONS This study shows that a 3D patient-specific prosthesis is feasible and safe, suggesting a possible trend toward improved breathing mechanics, QoL, and cosmetic results.
Collapse
|
52
|
Polycarpou I, Soultanidis G, Tsoumpas C. Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200207. [PMID: 34218675 PMCID: PMC8255946 DOI: 10.1098/rsta.2020.0207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 05/04/2023]
Abstract
Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
Collapse
Affiliation(s)
- Irene Polycarpou
- Department of Health Sciences, European University of Cyprus, Nicosia, Cyprus
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charalampos Tsoumpas
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Biomedical Imaging Science Department, University of Leeds, West Yorkshire, UK
- Invicro, London, UK
| |
Collapse
|
53
|
Brown R, Kolbitsch C, Delplancke C, Papoutsellis E, Mayer J, Ovtchinnikov E, Pasca E, Neji R, da Costa-Luis C, Gillman AG, Ehrhardt MJ, McClelland JR, Eiben B, Thielemans K. Motion estimation and correction for simultaneous PET/MR using SIRF and CIL. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200208. [PMID: 34218674 DOI: 10.1098/rsta.2020.0208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 05/10/2023]
Abstract
SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
Collapse
Affiliation(s)
- Richard Brown
- Institute of Nuclear Medicine, University College London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Christoph Kolbitsch
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | | | - Evangelos Papoutsellis
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
- Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK
| | - Johannes Mayer
- Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
| | - Evgueni Ovtchinnikov
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
| | - Edoardo Pasca
- Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Harwell Campus, Didcot, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare, Frimley, UK
| | - Casper da Costa-Luis
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Ashley G Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Townsville, Australia
| | - Matthias J Ehrhardt
- Department of Mathematical Sciences, University of Bath, Bath, UK
- Institute for Mathematical Innovation, University of Bath, UK
| | - Jamie R McClelland
- Centre for Medical Image Computing, University College London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Bjoern Eiben
- Centre for Medical Image Computing, University College London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, UK
| |
Collapse
|
54
|
Lei L, Huang L, Zhao B, Hu Y, Jiang Z, Zhang J, Li B. Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image-guided interventions. Med Phys 2021; 48:4160-4176. [PMID: 34115885 DOI: 10.1002/mp.15008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/24/2021] [Accepted: 05/25/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Percutaneous image-guided interventions are commonly used for the diagnosis and treatment of cancer. In practice, physiological breathing-induced motion increases the difficulty of accurately inserting needles into tumors without impairing the surrounding vital structures. In this work, we propose a data-driven patient-specific hierarchical respiratory motion estimation framework to accurately estimate the position of a tumor and surrounding vital tissues in real time. METHODS The motion of optical markers attached to the chest or abdomen skin is used as a surrogate signal to estimate tumor motion based on ɛ-support vector regression (ɛ-SVR). With the estimated tumor motion as the input, a novel respiratory motion model is developed to estimate the diffeomorphic deformation field of the whole organ (liver or lung) without intraoperative, iterative optimization computations. The respiratory motion model of the whole organ is established in Lie algebra space based on the kriging algorithm to ensure that the estimated deformation field is diffeomorphic, optimal, and unbiased. Preoperative prior knowledge for modeling the motion of whole organs is obtained by deformation registration between four-dimensional computed tomography (4D CT) images using a hybrid diffeomorphic registration method. RESULTS AND CONCLUSIONS Experimental results on an in vivo beagle dog show that the minimum value of the determinant of the Jacobian of the estimated deformation field is greater than zero, so the estimated deformation field of the whole liver with our method is diffeomorphic. The mean position error of the tumor is 1.2 mm corresponding to a mean accuracy improvement of 76.5%, and the mean position error of the whole liver is 2.1 mm, corresponding to a mean accuracy improvement of 37.9%. The experimental results based on public human subject data show that the mean position error of the tumor is 1.1 mm, corresponding to a mean accuracy improvement of 83.1%, and the mean position error of the whole lung is 2.1 mm, corresponding to a mean accuracy improvement of 41.4%. The positioning errors for the tumor and whole organ are hierarchical and consistent with clinical demand.
Collapse
Affiliation(s)
- Long Lei
- Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China.,Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Li Huang
- Department of Pancreatobiliary Surgery, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China
| | - Baoliang Zhao
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ying Hu
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.,SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518055, China
| | - Zhongliang Jiang
- Computer Aided Medical Procedures, Technische Universität München, Garching, 85748, Germany
| | | | - Bing Li
- Department of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China
| |
Collapse
|
55
|
Wikström KA, Isacsson UM, Nilsson KM, Ahnesjö A. Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy. J Appl Clin Med Phys 2021; 22:103-112. [PMID: 34258853 PMCID: PMC8425865 DOI: 10.1002/acm2.13351] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/19/2021] [Accepted: 06/17/2021] [Indexed: 12/04/2022] Open
Abstract
Patient breathing during lung cancer radiotherapy reduces the ability to keep a sharp dose gradient between tumor and normal tissues. To mitigate detrimental effects, accurate information about the tumor position is required. In this work, we evaluate the errors in modeled tumor positions over several fractions of a simple tumor motion model driven by a surface surrogate measure and its time derivative. The model is tested with respect to four different surface surrogates and a varying number of surrogate and image acquisitions used for model training. Fourteen patients were imaged 100 times with cine CT, at three sessions mimicking a planning session followed by two treatment fractions. Patient body contours were concurrently detected by a body surface laser scanning system BSLS from which four surface surrogates were extracted; thoracic point TP, abdominal point AP, the radial distance mean RDM, and a surface derived volume SDV. The motion model was trained on session 1 and evaluated on sessions 2 and 3 by comparing modeled tumor positions with measured positions from the cine images. The number of concurrent surrogate and image acquisitions used in the training set was varied, and its impact on the final result was evaluated. The use of AP as a surface surrogate yielded the smallest error in modeled tumor positions. The mean deviation between modeled and measured tumor positions was 1.9 mm. The corresponding deviations for using the other surrogates were 2.0 mm (RDM), 2.8 mm (SDV), and 3.0 mm (TP). To produce a motion model that accurately models the tumor position over several fractions requires at least 10 simultaneous surrogate and image acquisitions over 1–2 minutes.
Collapse
Affiliation(s)
- Kenneth A Wikström
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden.,Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ulf M Isacsson
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden.,Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | - Anders Ahnesjö
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden.,Medical Radiation Sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| |
Collapse
|
56
|
Abstract
PET/CT has become a preferred imaging modality over PET-only scanners in clinical practice. However, along with the significant improvement in diagnostic accuracy and patient throughput, pitfalls on PET/CT are reported as well. This review provides a general overview on the potential influence of the limitations with respect to PET/CT instrumentation and artifacts associated with the modality integration on the image appearance and quantitative accuracy of PET. Approaches proposed in literature to address the limitations or minimize the artifacts are discussed as well as their current challenges for clinical applications. Although the CT component can play an important role in assisting clinical diagnosis, we concentrate on the imaging scenarios where CT is used to provide auxiliary information for attenuation compensation and scatter correction in PET.
Collapse
Affiliation(s)
- Yu-Jung Tsai
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT; Department of Biomedical Engineering, Yale University, New Haven, CT.
| |
Collapse
|
57
|
He P, Li Q. Motion management with variable cycle-based respiratory guidance method for carbon-ion pencil beam scanning treatment. Phys Med 2021; 87:99-105. [PMID: 34134014 DOI: 10.1016/j.ejmp.2021.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/09/2021] [Accepted: 06/04/2021] [Indexed: 02/08/2023] Open
Abstract
PURPOSE A novel variable cycle-based respiratory guidance method was proposed to synchronize the patterns between patients' breathing and the magnetic excitation of synchrotron under the mode of full-energy depth scanning beam delivery, in order to improve the treatment precision and efficiency for carbon ion therapy. METHODS Audio-visual biofeedback system with variable cycle-based respiratory guidance method was developed. We enrolled 6 healthy volunteers and a simulation study of the fixed cycle-based and variable cycle-based respiratory guidance with three treatment fractions was performed. A total of 72 breathing curves were collected for 4D dose calculations with three 4DCT datasets of lung tumor cases. Target dose coverage (D95: the percent dose covering 95% of the target), dose homogeneity (D5-D95), and treatment time were analyzed. The Wilcoxon signed-rank test was used for statistical difference analysis, and p < 0.05 was considered significant. RESULTS With the variable cycle-based respiratory guidance method, the breath hold phase of breathing curve could be synchronized with the synchrotron flat-top phase over time. The dose homogeneity was improved by factors of 1.94-2.92 compared to the fixed cycle-based respiratory guidance maneuvers alone or in combination with gating technique. Moreover, the treatment efficiency increased by 11-23%, depending on the duty cycle settings of the gating window. CONCLUSIONS The proposed variable cycle-based respiratory guidance method could improve both the treatment efficiency and precision under the mode of the full-energy depth scanning beam delivery for synchrotron-based carbon ion therapy.
Collapse
Affiliation(s)
- Pengbo He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiang Li
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| |
Collapse
|
58
|
Xue P, Fu Y, Ji H, Cui W, Dong E. Lung Respiratory Motion Estimation Based on Fast Kalman Filtering and 4D CT Image Registration. IEEE J Biomed Health Inform 2021; 25:2007-2017. [PMID: 33044936 DOI: 10.1109/jbhi.2020.3030071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Respiratory motion estimation is an important part in image-guided radiation therapy and clinical diagnosis. However, most of the respiratory motion estimation methods rely on indirect measurements of external breathing indicators, which will not only introduce great estimation errors, but also bring invasive injury for patients. In this paper, we propose a method of lung respiratory motion estimation based on fast Kalman filtering and 4D CT image registration (LRME-4DCT). In order to perform dynamic motion estimation for continuous phases, a motion estimation model is constructed by combining two kinds of GPU-accelerated 4D CT image registration methods with fast Kalman filtering method. To address the high computational requirements of 4D CT image sequences, a multi-level processing strategy is adopted in the 4D CT image registration methods, and respiratory motion states are predicted from three independent directions. In the DIR-lab dataset and POPI dataset with 4D CT images, the average target registration error (TRE) of the LRME-4DCT method can reach 0.91 mm and 0.85 mm respectively. Compared with traditional estimation methods based on pair-wise image registration, the proposed LRME-4DCT method can estimate the physiological respiratory motion more accurately and quickly. Our proposed LRME-4DCT method fully meets the practical clinical requirements for rapid dynamic estimation of lung respiratory motion.
Collapse
|
59
|
Tsai YJ, Bousse A, Arridge S, Stearns CW, Hutton BF, Thielemans K. Penalized PET/CT Reconstruction Algorithms With Automatic Realignment for Anatomical Priors. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021. [DOI: 10.1109/trpms.2020.3025540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
60
|
Ippoliti M, Lukas M, Brenner W, Schatka I, Furth C, Schaeffter T, Makowski MR, Kolbitsch C. Respiratory motion correction for enhanced quantification of hepatic lesions in simultaneous PET and DCE-MR imaging. Phys Med Biol 2021; 66. [PMID: 33823503 DOI: 10.1088/1361-6560/abf51e] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/06/2021] [Indexed: 11/11/2022]
Abstract
Simultaneous positron-emission tomography (PET)-magnetic resonance (MR) imaging is a hybrid technique in oncological hepatic imaging combining soft-tissue and functional contrast of dynamic contrast enhanced MR (DCE-MR) with metabolic information from PET. In this context, respiratory motion represents a major challenge by introducing blurring, artifacts and misregistration in the liver. In this work, we propose a free-breathing 3D non-rigid respiratory motion correction framework for simultaneously acquired DCE-MR and PET data, which makes use of higher spatial resolution MR data to derive motion information used directly during image reconstruction to minimize image blurring and motion artifacts. The main aim was to increase contrast of hepatic metastases to improve their detection and characterization. DCE-MR data were acquired at 3T through a golden radial phase encoding scheme, enabling derivation of motion fields. These were used in the motion compensated image reconstruction of DCE-MR time-series (48 time-points, 6 s temporal resolution, 1.5 mm isotropic spatial resolution) and 3D PET activity map, which was subsequently interpolated to the DCE-MR resolution. The extended Tofts model was fitted to DCE-MR data, obtaining functional parametric maps related to perfusion such as the endothelial permeability (Kt). Fifty-seven hepatic metastases were identified and analyzed. Quantitative evaluations of motion correction in PET images demonstrated average percentage increases of 16% ± 5% (mean ± SD) in Contrast (C), 18% ± 6% in SUVmeanand 14% ± 2% in SUVmax, while DCE-MR andKtscored contrast-to-noise-ratio increases of 64% ± 3% and 90% ± 6%, respectively. Motion-corrected data visually showed improved image contrast of hepatic metastases and effectively reduced blurring and motion artefacts. Scatter plots of SUVmeanversusKtsuggested that the proposed framework improved differentiation ofKtmeasurements. The presented motion correction framework for simultaneously acquired PET-DCE-MR data provides accurately aligned images with increased contrast of hepatic lesions allowing for improved detection and characterization.
Collapse
Affiliation(s)
- Matteo Ippoliti
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Mathias Lukas
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.,Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany.,Siemens Healthcare GmbH, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Imke Schatka
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Furth
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Technische Universität Berlin, Berlin, Germany.,King's College London, London, United Kingdom
| | - Marcus R Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.,Klinikum rechts der Isar der TU München, Munich, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| |
Collapse
|
61
|
Mella H, Mura J, Wang H, Taylor MD, Chabiniok R, Tintera J, Sotelo J, Uribe S. HARP-I: A Harmonic Phase Interpolation Method for the Estimation of Motion From Tagged MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1240-1252. [PMID: 33434127 DOI: 10.1109/tmi.2021.3051092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We proposed a novel method called HARP-I, which enhances the estimation of motion from tagged Magnetic Resonance Imaging (MRI). The harmonic phase of the images is unwrapped and treated as noisy measurements of reference coordinates on a deformed domain, obtaining motion with high accuracy using Radial Basis Functions interpolations. Results were compared against Shortest Path HARP Refinement (SP-HR) and Sine-wave Modeling (SinMod), two harmonic image-based techniques for motion estimation from tagged images. HARP-I showed a favorable similarity with both methods under noise-free conditions, whereas a more robust performance was found in the presence of noise. Cardiac strain was better estimated using HARP-I at almost any motion level, giving strain maps with less artifacts. Additionally, HARP-I showed better temporal consistency as a new method was developed to fix phase jumps between frames. In conclusion, HARP-I showed to be a robust method for the estimation of motion and strain under ideal and non-ideal conditions.
Collapse
|
62
|
Meschini G, Paganelli C, Vai A, Fontana G, Molinelli S, Pella A, Vitolo V, Barcellini A, Orlandi E, Ciocca M, Riboldi M, Baroni G. An MRI framework for respiratory motion modelling validation. J Med Imaging Radiat Oncol 2021; 65:337-344. [PMID: 33773081 PMCID: PMC8251859 DOI: 10.1111/1754-9485.13175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/27/2021] [Accepted: 03/07/2021] [Indexed: 12/20/2022]
Abstract
Introduction Respiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability. Methods Repeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively. Results The median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm3), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred. Conclusions Results suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy.
Collapse
Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Alessandro Vai
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Giulia Fontana
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Silvia Molinelli
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Andrea Pella
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Viviana Vitolo
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | | | - Ester Orlandi
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Mario Ciocca
- National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität (LMU), Garching bei München, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.,National Centre for Oncological Hadrontherapy (CNAO), Pavia, Italy
| |
Collapse
|
63
|
Lauria M, Navaratna R, O'Connell D, Santhanam A, Lee P, Low DA. Technical Note: Investigating internal-external motion correlation using fast helical CT. Med Phys 2021; 48:1823-1831. [PMID: 33550622 DOI: 10.1002/mp.14759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 01/26/2021] [Accepted: 01/30/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE To quantify the use of anterior torso skin surface position measurement as a breathing surrogate. METHODS Fourteen patients were scanned 25 times in alternating directions using a free-breathing low-mA fast helical CT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. The imaged diaphragm dome position was used as a gold standard surrogate, characterized by localizing the most superior points of the diaphragm dome in each lung. These positions were correlated against the bellows signal acquired at the corresponding scan times. The bellows system has been shown to have a slow linear drift, and the bellows-to-CT synchronization process had a small uncertainty, so the drift and time offset were determined by maximizing the correlation coefficient between the craniocaudal diaphragm position and the drift-corrected bellows signal. The corresponding fit was used to model the real-time diaphragm position. To estimate the effectiveness of skin surface positions as surrogates, the anterior torso surface position was measured from the CT scans and correlated against the diaphragm position model. The residual error was defined as the root-mean-square correlation residual with the breathing amplitude normalized to the 5th to 95th breathing amplitude percentiles. The fit residual errors were analyzed over the surface for the fourteen studied patients and reported as percentages of the 5th to 95th percentile ranges. RESULTS A strong correlation was measured between the diaphragm motion and the abdominal bellows signal with an average residual error of 9.21% and standard deviation of 3.77%. In contrast, the correlations between the diaphragm position model and patient surface positions varied throughout the torso and from patient to patient. However, a consistently high correlation was found near the abdomen for each patient, and the average minimum residual error relating the skin surface to the diaphragm was 11.8% with a standard deviation of 4.61%. CONCLUSIONS The thoracic patient surface was found to be an accurate surrogate, but the accuracy varied across the surface sufficiently that care would need to be taken to use the surface as an accurate and reliable surrogate. Future studies will use surface imaging to determine surface patch algorithms that utilize the entire chest as well as thoracic and abdominal breathing relationships.
Collapse
Affiliation(s)
- Michael Lauria
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA
| | - Ruvini Navaratna
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA.,Department of Radiology and Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, 53706, USA
| | - Dylan O'Connell
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA
| | - Anand Santhanam
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA
| | - Percy Lee
- Department of Radiation Oncology, The University of Texas, M.D. Anderson Cancer Center, Houston Texas, 77030, USA
| | - Daniel A Low
- Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California, 90095, USA
| |
Collapse
|
64
|
Gottwald LM, Blanken CPS, Tourais J, Smink J, Planken RN, Boekholdt SM, Meijboom LJ, Coolen BF, Strijkers GJ, Nederveen AJ, van Ooij P. Retrospective Camera-Based Respiratory Gating in Clinical Whole-Heart 4D Flow MRI. J Magn Reson Imaging 2021; 54:440-451. [PMID: 33694310 PMCID: PMC8359364 DOI: 10.1002/jmri.27564] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/02/2021] [Accepted: 02/03/2021] [Indexed: 12/17/2022] Open
Abstract
Background Respiratory gating is generally recommended in 4D flow MRI of the heart to avoid blurring and motion artifacts. Recently, a novel automated contact‐less camera‐based respiratory motion sensor has been introduced. Purpose To compare camera‐based respiratory gating (CAM) with liver‐lung‐navigator‐based gating (NAV) and no gating (NO) for whole‐heart 4D flow MRI. Study Type Retrospective. Subjects Thirty two patients with a spectrum of cardiovascular diseases. Field Strength/Sequence A 3T, 3D‐cine spoiled‐gradient‐echo‐T1‐weighted‐sequence with flow‐encoding in three spatial directions. Assessment Respiratory phases were derived and compared against each other by cross‐correlation. Three radiologists/cardiologist scored images reconstructed with camera‐based, navigator‐based, and no respiratory gating with a 4‐point Likert scale (qualitative analysis). Quantitative image quality analysis, in form of signal‐to‐noise ratio (SNR) and liver‐lung‐edge (LLE) for sharpness and quantitative flow analysis of the valves were performed semi‐automatically. Statistical Tests One‐way repeated measured analysis of variance (ANOVA) with Wilks's lambda testing and follow‐up pairwise comparisons. Significance level of P ≤ 0.05. Krippendorff's‐alpha‐test for inter‐rater reliability. Results The respiratory signal analysis revealed that CAM and NAV phases were highly correlated (C = 0.93 ± 0.09, P < 0.01). Image scoring showed poor inter‐rater reliability and no significant differences were observed (P ≥ 0.16). The image quality comparison showed that NAV and CAM were superior to NO with higher SNR (P = 0.02) and smaller LLE (P < 0.01). The quantitative flow analysis showed significant differences between the three respiratory‐gated reconstructions in the tricuspid and pulmonary valves (P ≤ 0.05), but not in the mitral and aortic valves (P > 0.05). Pairwise comparisons showed that reconstructions without respiratory gating were different in flow measurements to either CAM or NAV or both, but no differences were found between CAM and NAV reconstructions. Data Conclusion Camera‐based respiratory gating performed as well as conventional liver‐lung‐navigator‐based respiratory gating. Quantitative image quality analysis showed that both techniques were equivalent and superior to no‐gating‐reconstructions. Quantitative flow analysis revealed local flow differences (tricuspid/pulmonary valves) in images of no‐gating‐reconstructions, but no differences were found between images reconstructed with camera‐based and navigator‐based respiratory gating. Level of Evidence 3 Technical Efficacy Stage 2
Collapse
Affiliation(s)
- Lukas M Gottwald
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - Carmen P S Blanken
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - João Tourais
- MR R&D-Clinical Science, Philips Healthcare, Best, The Netherlands.,Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Magnetic Resonance Systems Lab, Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Jouke Smink
- MR R&D-Clinical Science, Philips Healthcare, Best, The Netherlands
| | - R Nils Planken
- Cardiology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | | | - Lilian J Meijboom
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - Bram F Coolen
- Biomedical Engineering and Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Gustav J Strijkers
- Biomedical Engineering and Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Aart J Nederveen
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| | - Pim van Ooij
- Radiology and Nuclear Medicine, Amsterdam, Amsterdam University Medical Centers, location AMC, The Netherlands
| |
Collapse
|
65
|
Dietze MMA, Kunnen B, Lam MGEH, de Jong HWAM. Interventional respiratory motion compensation by simultaneous fluoroscopic and nuclear imaging: a phantom study. Phys Med Biol 2021; 66:065001. [PMID: 33571969 DOI: 10.1088/1361-6560/abe556] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections and SPECT/CBCT, was developed to aid fluoroscopy-guided interventional procedures involving the administration of radionuclides (e.g. hepatic radioembolization). However, as in conventional SPECT/CT, the acquired nuclear images may be deteriorated by patient respiratory motion. We propose to perform compensation for respiratory motion by extracting the motion signal from fluoroscopic projections so that the nuclear counts can be gated into motion bins. The purpose of this study is to quantify the performance of this motion compensation technique with phantom experiments. METHODS Anthropomorphic phantom configurations that are representative of distributions obtained during the pre-treatment procedure of hepatic radioembolization were placed on a stage that translated with three different motion patterns. Fluoroscopic projections and nuclear counts were simultaneously acquired under planar and SPECT/CBCT imaging. The planar projections were visually assessed. The SPECT reconstructions were visually assessed and quantitatively assessed by calculating the activity recovery of the spherical inserts in the phantom. RESULTS The planar nuclear projections of the translating anthropomorphic phantom were blurry when no motion compensation was applied. With motion compensation, the nuclear projections became representative of the stationary phantom nuclear projection. Similar behavior was observed for the visual quality of SPECT reconstructions. The mean error of the activity recovery in the uncompensated SPECT reconstructions was 15.8% ± 0.9% for stable motion, 11.9% ± 0.9% for small variations, and 11.0% ± 0.9% for large variations. When applying motion compensation, the mean error decreased to 1.8% ± 1.6% for stable motion, 2.2% ± 1.5% for small variations, and 5.2% ± 2.5% for large variations. CONCLUSION A compact and mobile hybrid c-arm scanner, capable of simultaneously acquiring nuclear and fluoroscopic projections, can perform compensation for respiratory motion. Such motion compensation results in sharper planar nuclear projections and increases the quantitative accuracy of the SPECT reconstructions.
Collapse
Affiliation(s)
- Martijn M A Dietze
- Radiology and Nuclear Medicine, Utrecht University and University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands. Image Sciences Institute, Utrecht University and University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands
| | | | | | | |
Collapse
|
66
|
Ranjbar M, Sabouri P, Mossahebi S, Sawant A, Mohindra P, Lasio G, Topoleski LDT. Validation of a CT-based motion model with in-situ fluoroscopy for lung surface deformation estimation. Phys Med Biol 2021; 66:045035. [PMID: 33207334 PMCID: PMC7906954 DOI: 10.1088/1361-6560/abcbcf] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Many surrogate-based motion models (SMMs), proposed to guide motion management in radiotherapy, are constructed by correlating motion of an external surrogate and internal anatomy during CT-simulation. Changes in this correlation define model break down. We validate a methodology that incorporates fluoroscopic images (FL) acquired during treatment for SMM construction and update. Under a prospective IRB, 4DCT scans, VisionRT surfaces, and orthogonal FLs were collected from five lung cancer patients. VisionRT surfaces and two FL time-series were acquired pre- and post-treatment. A simulated annealing optimization scheme was used to estimate optimal lung deformations by maximizing the mutual information between digitally reconstructed radiographs (DRRs) of the SMM-estimated 3D images and FLs. Our SMM used partial-least-regression and was trained using the optimal deformations and VisionRT surfaces from the first breathing-cycle. SMM performance was evaluated using the mutual information score between reference FLs and the corresponding SMM or phase-assigned 4DCT DRRs. The Hausdorff distance for contoured landmarks was used to evaluate target position estimation error. For four out of five patients, two principal components approximated lung surface deformations with submillimeter accuracy. Analysis of the mutual information score between more than 4,000 pairs of FL and DRR demonstrated that our model led to more similarity between the FL and DRR images compared to 4DCT and DRR images from a model based on an a priori correlation model. Our SMM consistently displayed lower mean and 95th percentile Hausdorff distances. For one patient, 95th percentile Hausdorff distance was reduced by 11mm. Patient-averaged reductions in mean and 95th percentile Hausdorff distances were 3.6mm and 7mm for right-lung, and 3.1mm and 4mm for left-lung targets. FL data were used to evaluate model performance and investigate the feasibility of model update. Despite variability in breathing, use of post-treatment FL preserved model fidelity and consistently outperformed 4DCT for position estimation.
Collapse
Affiliation(s)
- M Ranjbar
- Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD, United States of America. These authors have contributed equally. Author to whom any correspondence should be addressed
| | | | | | | | | | | | | |
Collapse
|
67
|
Rabe M, Paganelli C, Riboldi M, Bondesson D, Jörg Schneider M, Chmielewski T, Baroni G, Dinkel J, Reiner M, Landry G, Parodi K, Belka C, Kamp F, Kurz C. Porcine lung phantom-based validation of estimated 4D-MRI using orthogonal cine imaging for low-field MR-Linacs. Phys Med Biol 2021; 66:055006. [PMID: 33171458 DOI: 10.1088/1361-6560/abc937] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Real-time motion monitoring of lung tumors with low-field magnetic resonance imaging-guided linear accelerators (MR-Linacs) is currently limited to sagittal 2D cine magnetic resonance imaging (MRI). To provide input data for improved intrafractional and interfractional adaptive radiotherapy, the 4D anatomy has to be inferred from data with lower dimensionality. The purpose of this study was to experimentally validate a previously proposed propagation method that provides continuous time-resolved estimated 4D-MRI based on orthogonal cine MRI for a low-field MR-Linac. Ex vivo porcine lungs were injected with artificial nodules and mounted in a dedicated phantom that allows for the simulation of periodic and reproducible breathing motion. The phantom was scanned with a research version of a commercial 0.35 T MR-Linac. Respiratory-correlated 4D-MRI were reconstructed and served as ground truth images. Series of interleaved orthogonal slices in sagittal and coronal orientation, intersecting the injected targets, were acquired at 7.3 Hz. Estimated 4D-MRI at 3.65 Hz were created in post-processing using the propagation method and compared to the ground truth 4D-MRI. Eight datasets at different breathing frequencies and motion amplitudes were acquired for three porcine lungs. The overall median (95[Formula: see text] percentile) deviation between ground truth and estimated deformation vector fields was 2.3 mm (5.7 mm), corresponding to 0.7 (1.6) times the in-plane imaging resolution (3.5 × 3.5 mm2). Median (95[Formula: see text] percentile) estimated nodule position errors were 1.5 mm (3.8 mm) for nodules intersected by orthogonal slices and 2.1 mm (7.1 mm) for nodules located more than 2 cm away from either of the orthogonal slices. The estimation error depended on the breathing phase, the motion amplitude and the location of the estimated position with respect to the orthogonal slices. By using the propagation method, the 4D motion within the porcine lung phantom could be accurately and robustly estimated. The method could provide valuable information for treatment planning, real-time motion monitoring, treatment adaptation, and post-treatment evaluation of MR-guided radiotherapy treatments.
Collapse
Affiliation(s)
- Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Chiara Paganelli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - David Bondesson
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | - Moritz Jörg Schneider
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | | | - Guido Baroni
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy.,Bioengineering Unit, Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Julien Dinkel
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany.,Comprehensive Pneumology Center, German Center for Lung Research (DZL), Munich, Germany
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,German Cancer Consortium (DKTK), Partner Site Munich, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.,Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching (Munich), Germany
| |
Collapse
|
68
|
Zhang Y, Kashani R, Cao Y, Lawrence TS, Johansson A, Balter JM. A hierarchical model of abdominal configuration changes extracted from golden angle radial magnetic resonance imaging. Phys Med Biol 2021; 66:045018. [PMID: 33361579 PMCID: PMC7993537 DOI: 10.1088/1361-6560/abd66e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Abdominal organs are subject to a variety of physiological forces that superimpose their effects to influence local motion and configuration. These forces not only include breathing, but can also arise from cyclic antral contractions and a range of slow configuration changes. To elucidate each individual motion pattern as well as their combined effects, a hierarchical motion model was built for characterization of these 3 motion modes (characterized as deformation maps between states) using golden angle radial MR signals. Breathing motions are characterized first. Antral contraction states are then reconstructed after breathing motion-induced deformation are corrected; slow configuration change states are further extracted from breathing motion-corrected image reconstructions. The hierarchical model is established based on these multimodal states, which can be either individually shown or combined to demonstrate any arbitrary composited motion patterns. The model was evaluated using 20 MR scans acquired from 9 subjects. Poor reproducibility of breathing motions both within as well as between scan sessions was observed, with an average intra-subject difference of 1.6 cycles min-1 for average breathing frequencies of 12.0 cycles min-1. Antral contraction frequency distributions were more stable than breathing, but also presented poor reproducibility between scans with an average difference of 0.3 cycles min-1 for average frequencies of 3.2 cycles min-1. The magnitudes of motions beyond breathing were found to be significant, with 14.4 and 33.8 mm maximal motions measured from antral contraction and slow configuration changes, respectively. Hierarchical motion models have potential in multiple applications in radiotherapy, including improving the accuracy of dose delivery estimation, providing guidance for margin creation, and supporting advanced decisions and strategies for immobilization, treatment monitoring and gating.
Collapse
Affiliation(s)
- Yuhang Zhang
- Department of Radiation Oncology, University of Michigan, United States of America
- Department of Biomedical Engineering, University of Michigan, United States of America
| | - Rojano Kashani
- Department of Radiation Oncology, University of Michigan, United States of America
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, United States of America
- Department of Biomedical Engineering, University of Michigan, United States of America
- Department of Radiology, University of Michigan, United States of America
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, United States of America
| | - Adam Johansson
- Department of Surgical Sciences, Uppsala University, Sweden
| | - James M Balter
- Department of Radiation Oncology, University of Michigan, United States of America
- Department of Biomedical Engineering, University of Michigan, United States of America
| |
Collapse
|
69
|
Nie Z, Li C, Liu H, Yang X. Deformable Image Registration Based on Functions of Bounded Generalized Deformation. Int J Comput Vis 2021. [DOI: 10.1007/s11263-021-01439-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
70
|
Cai N, Chen H, Li Y, Peng Y, Li J. Adaptive Weighting Landmark-Based Group-Wise Registration on Lung DCE-MRI Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:673-687. [PMID: 33136541 DOI: 10.1109/tmi.2020.3035292] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Image registration of lung dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging because the rapid changes in intensity lead to non-realistic deformations of intensity-based registration methods. To address this problem, we propose a novel landmark-based registration framework by incorporating landmark information into a group-wise registration. Robust principal component analysis is used to separate motion from intensity changes caused by a contrast agent. Landmark pairs are detected on the resulting motion components and then incorporated into an intensity-based registration through a constraint term. To reduce the negative effect of inaccurate landmark pairs on registration, an adaptive weighting landmark constraint is proposed. The method for calculating landmark weights is based on an assumption that the displacement of a good matched landmark is consistent with those of its neighbors. The proposed method was tested on 20 clinical lung DCE-MRI image series. Both visual inspection and quantitative assessment are used for the evaluation. Experimental results show that the proposed method effectively reduces the non-realistic deformations in registration and improves the registration performance compared with several state-of-the-art registration methods.
Collapse
|
71
|
Czerska K, Emert F, Kopec R, Langen K, McClelland JR, Meijers A, Miyamoto N, Riboldi M, Shimizu S, Terunuma T, Zou W, Knopf A, Rucinski A. Clinical practice vs. state-of-the-art research and future visions: Report on the 4D treatment planning workshop for particle therapy - Edition 2018 and 2019. Phys Med 2021; 82:54-63. [PMID: 33588228 DOI: 10.1016/j.ejmp.2020.12.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/09/2020] [Accepted: 12/16/2020] [Indexed: 12/18/2022] Open
Abstract
The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of moving targets with scanned particle beams, started in 2009 and since then has been organized annually. The mission of the workshop is to create an informal ground for clinical medical physicists, medical physics researchers and medical doctors interested in the development of the 4D technology, protocols and their translation into clinical practice. The 10th and 11th editions of the workshop took place in Sapporo, Japan in 2018 and Krakow, Poland in 2019, respectively. This review report from the Sapporo and Krakow workshops is structured in two parts, according to the workshop programs. The first part comprises clinicians and physicists review of the status of 4D clinical implementations. Corresponding talks were given by speakers from five centers around the world: Maastro Clinic (The Netherlands), University Medical Center Groningen (The Netherlands), MD Anderson Cancer Center (United States), University of Pennsylvania (United States) and The Proton Beam Therapy Center of Hokkaido University Hospital (Japan). The second part is dedicated to novelties in 4D research, i.e. motion modelling, artificial intelligence and new technologies which are currently being investigated in the radiotherapy field.
Collapse
Affiliation(s)
- Katarzyna Czerska
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland.
| | - Frank Emert
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland
| | - Renata Kopec
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland
| | - Katja Langen
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Naoki Miyamoto
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Hokkaido, Japan; Faculty of Engineering, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Germany
| | - Shinichi Shimizu
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Hokkaido, Japan; Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Toshiyuki Terunuma
- Faculty of Medicine, University of Tsukuba, Japan; Proton Medical Research Center, University of Tsukuba Hospital, Japan
| | - Wei Zou
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Antje Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland
| |
Collapse
|
72
|
Alvarez P, Rouzé S, Miga MI, Payan Y, Dillenseger JL, Chabanas M. A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery. Med Image Anal 2021; 69:101983. [PMID: 33588119 DOI: 10.1016/j.media.2021.101983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 01/16/2021] [Accepted: 01/26/2021] [Indexed: 12/09/2022]
Abstract
The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.
Collapse
Affiliation(s)
- Pablo Alvarez
- Univ. Rennes 1, Inserm, LTSI - UMR 1099, Rennes F-35000, France; Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble F-38000, France.
| | - Simon Rouzé
- Univ. Rennes 1, Inserm, LTSI - UMR 1099, Rennes F-35000, France; CHU Rennes, Department of Cardio-Thoracic and Vascular Surgery, Rennes F-35000, France.
| | - Michael I Miga
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yohan Payan
- Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble F-38000, France.
| | | | - Matthieu Chabanas
- Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, Grenoble F-38000, France; Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, TN, USA.
| |
Collapse
|
73
|
Krieger M, Giger A, Jud C, Duetschler A, Salomir R, Bieri O, Bauman G, Nguyen D, Cattin PC, Weber DC, Lomax AJ, Zhang Y. Liver-ultrasound-guided lung tumour tracking for scanned proton therapy: a feasibility study. Phys Med Biol 2021; 66:035011. [PMID: 33238246 DOI: 10.1088/1361-6560/abcde6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Pencil beam scanned (PBS) proton therapy of lung tumours is hampered by respiratory motion and the motion-induced density changes along the beam path. In this simulation study, we aim to investigate the effectiveness of proton beam tracking for lung tumours both under ideal conditions and in conjunction with a respiratory motion model guided by real-time ultrasound imaging of the liver. Multiple-breathing-cycle 4DMRIs of the thorax and abdominal 2D ultrasound images were acquired simultaneously for five volunteers. Deformation vector fields extracted from the 4DMRI, referred to as ground truth motion, were used to generate 4DCT(MRI) data sets of two lung cancer patients, resulting in 10 data sets with variable motion patterns. Given the 4DCT(MRI) and the corresponding ultrasound images as surrogate data, a patient-specific motion model was built. The model consists of an autoregressive model and Gaussian process regression for the temporal and spatial prediction, respectively. Two-field PBS plans were optimised on the reference CTs, and 4D dose calculations (4DDC) were used to simulate dose delivery for (a) unmitigated motion, (b) ideal 2D and 3D tracking (both beam adaption and 4DDC based on ground truth motion), and (c) realistic 2D and 3D tracking (beam adaption based on motion predictions, 4DDC on ground truth motion). Model-guided tracking retrieved clinically acceptable target dose homogeneity, as seen in a substantial reduction of the D5%-D95% compared to the non-mitigated simulation. Tracking in 2D and 3D resulted in a similar improvement of the dose homogeneity, as did ideal and realistic tracking simulations. In some cases, however, the tracked deliveries resulted in a shift towards higher or lower dose levels, leading to unacceptable target over- or under-coverage. The presented motion modelling framework was shown to be an accurate motion prediction tool for the use in proton beam tracking. Tracking alone, however, may not always effectively mitigate motion effects, making it necessary to combine it with other techniques such as rescanning.
Collapse
Affiliation(s)
- Miriam Krieger
- Center for Proton Therapy, Paul Scherrer Institute (PSI), Villigen PSI, Switzerland. Department of Physics, ETH Zurich, Zurich, Switzerland
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
74
|
Mayer J, Wurster TH, Schaeffter T, Landmesser U, Morguet A, Bigalke B, Hamm B, Brenner W, Makowski MR, Kolbitsch C. Imaging coronary plaques using 3D motion-compensated [ 18F]NaF PET/MR. Eur J Nucl Med Mol Imaging 2021; 48:2455-2465. [PMID: 33474584 PMCID: PMC8241750 DOI: 10.1007/s00259-020-05180-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 12/26/2020] [Indexed: 12/19/2022]
Abstract
Background Cardiac PET has recently found novel applications in coronary atherosclerosis imaging using [18F]NaF as a radiotracer, highlighting vulnerable plaques. However, the resulting uptakes are relatively small, and cardiac motion and respiration-induced movement of the heart can impair the reconstructed images due to motion blurring and attenuation correction mismatches. This study aimed to apply an MR-based motion compensation framework to [18F]NaF data yielding high-resolution motion-compensated PET and MR images. Methods Free-breathing 3-dimensional Dixon MR data were acquired, retrospectively binned into multiple respiratory and cardiac motion states, and split into fat and water fraction using a model-based reconstruction framework. From the dynamic MR reconstructions, both a non-rigid cardiorespiratory motion model and a motion-resolved attenuation map were generated and applied to the PET data to improve image quality. The approach was tested in 10 patients and focal tracer hotspots were evaluated concerning their target-to-background ratio, contrast-to-background ratio, and their diameter. Results MR-based motion models were successfully applied to compensate for physiological motion in both PET and MR. Target-to-background ratios of identified plaques improved by 7 ± 7%, contrast-to-background ratios by 26 ± 38%, and the plaque diameter decreased by −22 ± 18%. MR-based dynamic attenuation correction strongly reduced attenuation correction artefacts and was not affected by stent-related signal voids in the underlying MR reconstructions. Conclusions The MR-based motion correction framework presented here can improve the target-to-background, contrast-to-background, and width of focal tracer hotspots in the coronary system. The dynamic attenuation correction could effectively mitigate the risk of attenuation correction artefacts in the coronaries at the lung-soft tissue boundary. In combination, this could enable a more reproducible and reliable plaque localisation. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05180-4.
Collapse
Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany.
| | - Thomas-Heinrich Wurster
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany.,School of Biomedical Imaging Sciences, King's College London, London, UK.,Department of Medical Engineering, Technische Universität Berlin, Berlin, Germany
| | - Ulf Landmesser
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Morguet
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Boris Bigalke
- Klinik für Kardiologie, Charité Campus Benjamin Franklin, Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Marcus R Makowski
- Department of Medical Engineering, Technische Universität Berlin, Berlin, Germany.,Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, München, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Berlin, Germany.,School of Biomedical Imaging Sciences, King's College London, London, UK
| |
Collapse
|
75
|
Wang G, Li Z, Li G, Dai G, Xiao Q, Bai L, He Y, Liu Y, Bai S. Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy. Radiat Oncol 2021; 16:13. [PMID: 33446245 PMCID: PMC7807524 DOI: 10.1186/s13014-020-01729-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 12/06/2020] [Indexed: 02/08/2023] Open
Abstract
Background Surface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context. Methods Seven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, Dv. Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model. Results The models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update. Conclusions The LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.
Collapse
Affiliation(s)
- Guangyu Wang
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Zhibin Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
| | - Guyu Dai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Xiao
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Long Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yisong He
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yaxin Liu
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.,College of Physics, Sichuan University, Chengdu, 610065, China
| | - Sen Bai
- Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
76
|
Qi H, Fuin N, Cruz G, Pan J, Kuestner T, Bustin A, Botnar RM, Prieto C. Non-Rigid Respiratory Motion Estimation of Whole-Heart Coronary MR Images Using Unsupervised Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:444-454. [PMID: 33021937 DOI: 10.1109/tmi.2020.3029205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Non-rigid motion-corrected reconstruction has been proposed to account for the complex motion of the heart in free-breathing 3D coronary magnetic resonance angiography (CMRA). This reconstruction framework requires efficient and accurate estimation of non-rigid motion fields from undersampled images at different respiratory positions (or bins). However, state-of-the-art registration methods can be time-consuming. This article presents a novel unsupervised deep learning-based strategy for fast estimation of inter-bin 3D non-rigid respiratory motion fields for motion-corrected free-breathing CMRA. The proposed 3D respiratory motion estimation network (RespME-net) is trained as a deep encoder-decoder network, taking pairs of 3D image patches extracted from CMRA volumes as input and outputting the motion field between image patches. Using image warping by the estimated motion field, a loss function that imposes image similarity and motion smoothness is adopted to enable training without ground truth motion field. RespME-net is trained patch-wise to circumvent the challenges of training a 3D network volume-wise which requires large amounts of GPU memory and 3D datasets. We perform 5-fold cross-validation with 45 CMRA datasets and demonstrate that RespME-net can predict 3D non-rigid motion fields with subpixel accuracy (0.44 ± 0.38 mm) within ~10 seconds, being ~20 times faster than a GPU-implemented state-of-the-art non-rigid registration method. Moreover, we perform non-rigid motion-compensated CMRA reconstruction for 9 additional patients. The proposed RespME-net has achieved similar motion-corrected CMRA image quality to the conventional registration method regarding coronary artery length and sharpness.
Collapse
|
77
|
de Senneville BD, Coupé P, Ries M, Facq L, Moonen CTW. Deep correction of breathing-related artifacts in real-time MR-thermometry. Comput Med Imaging Graph 2020; 87:101834. [PMID: 33352524 DOI: 10.1016/j.compmedimag.2020.101834] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 11/10/2020] [Accepted: 11/17/2020] [Indexed: 11/28/2022]
Abstract
Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information. However, proton resonance frequency based thermometry of moving targets remains challenging since temperature artifacts are induced by the respiratory as well as physiological motion. If left uncorrected, these artifacts lead to severe errors in temperature estimates and impair therapy guidance. In this study, we evaluated deep learning for on-line correction of motion related errors in abdominal MR-thermometry. For this, a convolutional neural network (CNN) was designed to learn the apparent temperature perturbation from images acquired during a preparative learning stage prior to hyperthermia. The input of the designed CNN is the most recent magnitude image and no surrogate of motion is needed. During the subsequent hyperthermia procedure, the recent magnitude image is used as an input for the CNN-model in order to generate an on-line correction for the current temperature map. The method's artifact suppression performance was evaluated on 12 free breathing volunteers and was found robust and artifact-free in all examined cases. Furthermore, thermometric precision and accuracy was assessed for in vivo ablation using high intensity focused ultrasound. All calculations involved at the different stages of the proposed workflow were designed to be compatible with the clinical time constraints of a therapeutic procedure.
Collapse
Affiliation(s)
- B Denis de Senneville
- University of Bordeaux, IMB, UMR CNRS 5251, Talence, France, Talence Cedex, F-33405, France; INRIA Project Team Monc, Talence, France, Talence Cedex, F-33405, France; Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3508 GA, The Netherlands.
| | - P Coupé
- CNRS, University of Bordeaux, Bordeaux INP, "Laboratoire Bordelais de la Recherche Informatique" (LaBRI), UMR5800, Talence, F-33400, France
| | - M Ries
- Imaging Division, UMC Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands
| | - L Facq
- University of Bordeaux, IMB, UMR CNRS 5251, Talence, France, Talence Cedex, F-33405, France
| | - C T W Moonen
- Imaging Division, UMC Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands
| |
Collapse
|
78
|
Alina G, Krieger M, Jud C, Duetschler A, Salomir R, Bieri O, Bauman G, Nguyen D, Weber DC, Lomax AJ, Zhang Y, Cattin PC. Liver-ultrasound based motion modelling to estimate 4D dose distributions for lung tumours in scanned proton therapy. ACTA ACUST UNITED AC 2020; 65:235050. [DOI: 10.1088/1361-6560/abaa26] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
|
79
|
Rehouma H, Noumeir R, Essouri S, Jouvet P. Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration. SENSORS (BASEL, SWITZERLAND) 2020; 20:E7252. [PMID: 33348827 PMCID: PMC7766256 DOI: 10.3390/s20247252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 12/09/2020] [Accepted: 12/15/2020] [Indexed: 01/22/2023]
Abstract
Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.
Collapse
Affiliation(s)
- Haythem Rehouma
- École de Technologie Supérieure, Montreal, QC H3T 1C5, Canada;
| | - Rita Noumeir
- École de Technologie Supérieure, Montreal, QC H3T 1C5, Canada;
| | - Sandrine Essouri
- CHU Sainte-Justine, Montreal, QC H3T 1C5, Canada; (S.E.); (P.J.)
| | - Philippe Jouvet
- CHU Sainte-Justine, Montreal, QC H3T 1C5, Canada; (S.E.); (P.J.)
| |
Collapse
|
80
|
Ludwig J, Speier P, Seifert F, Schaeffter T, Kolbitsch C. Pilot tone-based motion correction for prospective respiratory compensated cardiac cine MRI. Magn Reson Med 2020; 85:2403-2416. [PMID: 33226699 DOI: 10.1002/mrm.28580] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/24/2020] [Accepted: 10/12/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE To evaluate prospective motion correction using the pilot tone (PT) as a quantitative respiratory motion signal with high temporal resolution for cardiac cine images during free breathing. METHODS Before cine data acquisition, a short prescan was performed, calibrating the PT to the respiratory-induced heart motion using respiratory-resolved real-time images. The calibrated PT was then applied for nearly real-time prospective motion correction of cine MRI through slice tracking (ie, updating the slice position before every readout). Additionally, in-plane motion correction was performed retrospectively also based on the calibrated PT data. The proposed method was evaluated in a moving phantom and 10 healthy volunteers. RESULTS The PT showed very good correlation to the phantom motion. In volunteer studies using a long-term scan over 7.96 ± 1.40 min, the mean absolute error between registered and predicted motion from the PT was 1.44 ± 0.46 mm in head-feet and 0.46 ± 0.07 mm in anterior-posterior direction. Irregular breathing could also be corrected well with the PT. The PT motion correction leads to a significant improvement of contrast-to-noise ratio by 68% (P ≤ .01) between blood pool and myocardium and sharpness of endocardium by 24% (P = .04) in comparison to uncorrected data. The image score, which refers to the cine image quality, has improved with the utilization of the proposed PT motion correction. CONCLUSION The proposed approach provides respiratory motion-corrected cine images of the heart with improved image quality and a high scan efficiency using the PT. The PT is independent of the MR acquisition, making this a very flexible motion-correction approach.
Collapse
Affiliation(s)
- Juliane Ludwig
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | | | - Frank Seifert
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.,Technische Universität Berlin, Biomedical Engineering, Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| |
Collapse
|
81
|
Huttinga NRF, Bruijnen T, van den Berg CAT, Sbrizzi A. Nonrigid 3D motion estimation at high temporal resolution from prospectively undersampled k-space data using low-rank MR-MOTUS. Magn Reson Med 2020; 85:2309-2326. [PMID: 33169888 PMCID: PMC7839760 DOI: 10.1002/mrm.28562] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 09/30/2020] [Accepted: 09/30/2020] [Indexed: 12/25/2022]
Abstract
Purpose With the recent introduction of the MR‐LINAC, an MR‐scanner combined with a radiotherapy LINAC, MR‐based motion estimation has become of increasing interest to (retrospectively) characterize tumor and organs‐at‐risk motion during radiotherapy. To this extent, we introduce low‐rank MR‐MOTUS, a framework to retrospectively reconstruct time‐resolved nonrigid 3D+t motion fields from a single low‐resolution reference image and prospectively undersampled k‐space data acquired during motion. Theory Low‐rank MR‐MOTUS exploits spatiotemporal correlations in internal body motion with a low‐rank motion model, and inverts a signal model that relates motion fields directly to a reference image and k‐space data. The low‐rank model reduces the degrees‐of‐freedom, memory consumption, and reconstruction times by assuming a factorization of space‐time motion fields in spatial and temporal components. Methods Low‐rank MR‐MOTUS was employed to estimate motion in 2D/3D abdominothoracic scans and 3D head scans. Data were acquired using golden‐ratio radial readouts. Reconstructed 2D and 3D respiratory motion fields were, respectively, validated against time‐resolved and respiratory‐resolved image reconstructions, and the head motion against static image reconstructions from fully sampled data acquired right before and right after the motion. Results Results show that 2D+t respiratory motion can be estimated retrospectively at 40.8 motion fields per second, 3D+t respiratory motion at 7.6 motion fields per second and 3D+t head‐neck motion at 9.3 motion fields per second. The validations show good consistency with image reconstructions. Conclusions The proposed framework can estimate time‐resolved nonrigid 3D motion fields, which allows to characterize drifts and intra and inter‐cycle patterns in breathing motion during radiotherapy, and could form the basis for real‐time MR‐guided radiotherapy.
Collapse
Affiliation(s)
- Niek R F Huttinga
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tom Bruijnen
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
82
|
Nicolò A, Massaroni C, Schena E, Sacchetti M. The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6396. [PMID: 33182463 PMCID: PMC7665156 DOI: 10.3390/s20216396] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/05/2020] [Accepted: 11/08/2020] [Indexed: 12/11/2022]
Abstract
Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions (e.g., adverse cardiac events, pneumonia, and clinical deterioration) and stressors, including emotional stress, cognitive load, heat, cold, physical effort, and exercise-induced fatigue. The sensitivity of respiratory rate to these conditions is superior compared to that of most of the other vital signs, and the abundance of suitable technological solutions measuring respiratory rate has important implications for healthcare, occupational settings, and sport. However, respiratory rate is still too often not routinely monitored in these fields of use. This review presents a multidisciplinary approach to respiratory monitoring, with the aim to improve the development and efficacy of respiratory monitoring services. We have identified thirteen monitoring goals where the use of the respiratory rate is invaluable, and for each of them we have described suitable sensors and techniques to monitor respiratory rate in specific measurement scenarios. We have also provided a physiological rationale corroborating the importance of respiratory rate monitoring and an original multidisciplinary framework for the development of respiratory monitoring services. This review is expected to advance the field of respiratory monitoring and favor synergies between different disciplines to accomplish this goal.
Collapse
Affiliation(s)
- Andrea Nicolò
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy;
| | - Carlo Massaroni
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy; (C.M.); (E.S.)
| | - Emiliano Schena
- Unit of Measurements and Biomedical Instrumentation, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Rome, Italy; (C.M.); (E.S.)
| | - Massimo Sacchetti
- Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, 00135 Rome, Italy;
| |
Collapse
|
83
|
Madesta F, Sentker T, Gauer T, Werner R. Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Med Phys 2020; 47:5619-5631. [DOI: 10.1002/mp.14441] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 06/02/2020] [Accepted: 07/16/2020] [Indexed: 12/25/2022] Open
Affiliation(s)
- Frederic Madesta
- Department of Computational Neuroscience University Medical Center Hamburg‐Eppendorf Hamburg20246 Germany
| | - Thilo Sentker
- Department of Computational Neuroscience University Medical Center Hamburg‐Eppendorf Hamburg20246 Germany
- Department of Radiotherapy and Radio‐Oncology University Medical Center Hamburg‐Eppendorf Hamburg20246 Germany
| | - Tobias Gauer
- Department of Radiotherapy and Radio‐Oncology University Medical Center Hamburg‐Eppendorf Hamburg20246 Germany
| | - René Werner
- Department of Computational Neuroscience University Medical Center Hamburg‐Eppendorf Hamburg20246 Germany
| |
Collapse
|
84
|
Image-guided Radiotherapy to Manage Respiratory Motion: Lung and Liver. Clin Oncol (R Coll Radiol) 2020; 32:792-804. [PMID: 33036840 DOI: 10.1016/j.clon.2020.09.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/26/2020] [Accepted: 09/18/2020] [Indexed: 12/25/2022]
Abstract
Organ motion as a result of respiratory and cardiac motion poses significant challenges for the accurate delivery of radiotherapy to both the thorax and the upper abdomen. Modern imaging techniques during radiotherapy simulation and delivery now permit better quantification of organ motion, which in turn reduces tumour and organ at risk position uncertainty. These imaging advances, coupled with respiratory correlated radiotherapy delivery techniques, have led to the development of a range of approaches to manage respiratory motion. This review summarises the key strategies of image-guided respiratory motion management with a focus on lung and liver radiotherapy.
Collapse
|
85
|
Eiben B, Bertholet J, Menten MJ, Nill S, Oelfke U, McClelland JR. Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom. Phys Med Biol 2020; 65:165005. [PMID: 32235043 DOI: 10.1088/1361-6560/ab8533] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum [Formula: see text]-percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy.
Collapse
Affiliation(s)
- Björn Eiben
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom of Great Britain and Northern Ireland
- Authors contributed equally
| | - Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
- Authors contributed equally
| | - Martin J Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom of Great Britain and Northern Ireland
| | - Simeon Nill
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland
| | - Jamie R McClelland
- Centre for Medical Image Computing, Radiotherapy Image Computing Group, Department of Medical Physics and Biomedical Engineering University College London, London, United Kingdom of Great Britain and Northern Ireland
| |
Collapse
|
86
|
Vergalasova I, Cai J. A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy. Med Phys 2020; 47:e988-e1008. [PMID: 32506452 DOI: 10.1002/mp.14312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.
Collapse
Affiliation(s)
- Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| |
Collapse
|
87
|
Dasnoy‐Sumell D, Souris K, Van Ooteghem G, Macq B. Continuous real time 3D motion reproduction using dynamic MRI and precomputed 4DCT deformation fields. J Appl Clin Med Phys 2020; 21:236-248. [PMID: 32614497 PMCID: PMC7484834 DOI: 10.1002/acm2.12953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 05/15/2020] [Accepted: 05/23/2020] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy of mobile tumors requires specific imaging tools and models to reduce the impact of motion on the treatment. Online continuous nonionizing imaging has become possible with the recent development of magnetic resonance imaging devices combined with linear accelerators. This opens the way to new guided treatment methods based on the real-time tracking of anatomical motion. In such devices, 2D fast MR-images are well-suited to capture and predict the real-time motion of the tumor. To be used effectively in an adaptive radiotherapy, these MR images have to be combined with X-ray images such as CT, which are necessary to compute the irradiation dose deposition. We therefore developed a method combining both image modalities to track the motion on MR images and reproduce the tracked motion on a sequence of 3DCT images in real-time. It uses manually placed navigators to track organ interfaces in the image, making it possible to select anatomical object borders that are visible on both MRI and CT modalities and giving the operator precise control of the motion tracking quality. Precomputed deformation fields extracted from the 4DCT acquired in the planning phase are then used to deform existing 3DCT images to match the tracked object position, creating a new set of 3DCT images encompassing irregularities in the breathing pattern for the complete duration of the MRI acquisition. The final continuous reconstructed 4DCT image sequence reproduces the motion captured by the MRI sequence with high precision (difference below 2 mm).
Collapse
Affiliation(s)
- Damien Dasnoy‐Sumell
- Institute of Information and Communication TechnologiesElectronics and Applied MathematicsUniversite Catholique de LouvainLouvain‐la‐NeuveBelgium
| | - Kevin Souris
- Institut de Recherche Experimentale et Clinique (IREC)Molecular Imaging, Radiotherapy and Oncology (MIRO)Universite Catholique de LouvainBrusselsBelgium
| | - G. Van Ooteghem
- Institut de Recherche Experimentale et Clinique (IREC)Molecular Imaging, Radiotherapy and Oncology (MIRO)Universite Catholique de LouvainBrusselsBelgium
| | - Benoit Macq
- Institute of Information and Communication TechnologiesElectronics and Applied MathematicsUniversite Catholique de LouvainLouvain‐la‐NeuveBelgium
| |
Collapse
|
88
|
Romaguera LV, Plantefève R, Romero FP, Hébert F, Carrier JF, Kadoury S. Prediction of in-plane organ deformation during free-breathing radiotherapy via discriminative spatial transformer networks. Med Image Anal 2020; 64:101754. [DOI: 10.1016/j.media.2020.101754] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 02/06/2023]
|
89
|
Mayer J, Brown R, Thielemans K, Ovtchinnikov E, Pasca E, Atkinson D, Gillman A, Marsden P, Ippoliti M, Makowski M, Schaeffter T, Kolbitsch C. Flexible numerical simulation framework for dynamic PET-MR data. Phys Med Biol 2020; 65:145003. [PMID: 32692725 DOI: 10.1088/1361-6560/ab7eee] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms. Contrast and tracer kinetics can also be simulated and are available as ground truth information. To closely emulate medical examination, input and output of the simulation are files in standardised open-source raw data formats. This enables the use of existing raw data as a template input and ensures seamless integration of the output into existing reconstruction pipelines. The proposed framework was validated in PET-MR and image registration applications. It was used to simulate a FDG-PET-MR scan with cardiac and respiratory motion. Ground truth motion information could be utilised to optimise parameters for PET and synergistic PET-MR image registration. In addition, a free-breathing dynamic contrast enhancement (DCE) abdominal scan of a patient with hepatic lesions was simulated. In order to correct for breathing motion, a motion-corrected image reconstruction scheme was used and a Toft's model was fit to the DCE data to obtain quantitative DCE-MRI parameters. Utilising the ground truth motion information, the dependency of quantitative DCE-MR images on the accuracy of the motion estimation was evaluated. We demonstrated that respiratory motion had to be available with an average accuracy of at least the spatial resolution of the DCE-MR images in order to ensure an improvement in lesions visualisation and quantification compared to no motion correction. The proposed framework provides a valuable tool with a wide range of scientific PET and MR applications and will be available as part of the open-source project Synergistic Image Reconstruction Framework (SIRF).
Collapse
Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany. Author to whom any correspondence should be addressed
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
90
|
Tran EH, Eiben B, Wetscherek A, Oelfke U, Meedt G, Hawkes DJ, McClelland JR. Evaluation of MRI-derived surrogate signals to model respiratory motion. Biomed Phys Eng Express 2020; 6:045015. [PMID: 33194224 PMCID: PMC7655234 DOI: 10.1088/2057-1976/ab944c] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/07/2020] [Accepted: 05/19/2020] [Indexed: 12/25/2022]
Abstract
An MR-Linac can provide motion information of tumour and organs-at-risk before, during, and after beam delivery. However, MR imaging cannot provide real-time high-quality volumetric images which capture breath-to-breath variability of respiratory motion. Surrogate-driven motion models relate the motion of the internal anatomy to surrogate signals, thus can estimate the 3D internal motion from these signals. Internal surrogate signals based on patient anatomy can be extracted from 2D cine-MR images, which can be acquired on an MR-Linac during treatment, to build and drive motion models. In this paper we investigate different MRI-derived surrogate signals, including signals generated by applying principal component analysis to the image intensities, or control point displacements derived from deformable registration of the 2D cine-MR images. We assessed the suitability of the signals to build models that can estimate the motion of the internal anatomy, including sliding motion and breath-to-breath variability. We quantitatively evaluated the models by estimating the 2D motion in sagittal and coronal slices of 8 lung cancer patients, and comparing them to motion measurements obtained from image registration. For sagittal slices, using the first and second principal components on the control point displacements as surrogate signals resulted in the highest model accuracy, with a mean error over patients around 0.80 mm which was lower than the in-plane resolution. For coronal slices, all investigated signals except the skin signal produced mean errors over patients around 1 mm. These results demonstrate that surrogate signals derived from 2D cine-MR images, including those generated by applying principal component analysis to the image intensities or control point displacements, can accurately model the motion of the internal anatomy within a single sagittal or coronal slice. This implies the signals should also be suitable for modelling the 3D respiratory motion of the internal anatomy.
Collapse
Affiliation(s)
- Elena H Tran
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Björn Eiben
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Uwe Oelfke
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Gustav Meedt
- Elekta, Medical Intelligence Medizintechnik GmbH, Schwabmünchen, Germany
| | - David J Hawkes
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| |
Collapse
|
91
|
Navest RJM, Mandija S, Zijlema SE, Stemkens B, Andreychenko A, Lagendijk JJW, van den Berg CAT. The noise navigator for MRI-guided radiotherapy: an independent method to detect physiological motion. Phys Med Biol 2020; 65:12NT01. [PMID: 32330921 DOI: 10.1088/1361-6560/ab8cd8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motion is problematic during radiotherapy as it could lead to potential underdosage of the tumor, and/or overdosage in organs-at-risk. A solution is adaptive radiotherapy guided by magnetic resonance imaging (MRI). MRI allows for imaging of target volumes and organs-at-risk before and during treatment delivery with superb soft tissue contrast in any desired orientation, enabling motion management by means of (real-time) adaptive radiotherapy. The noise navigator, which is independent of the MR signal, could serve as a secondary motion detection method in synergy with MR imaging. The feasibility of respiratory motion detection by means of the noise navigator was demonstrated previously. Furthermore, from electromagnetic simulations we know that the noise navigator is sensitive to tissue displacement and thus could in principle be used for the detection of various types of motion. In this study we demonstrate the detection of various types of motion for three anatomical use cases of MRI-guided radiotherapy, i.e. torso (bulk movement and variable breathing), head-and-neck (swallowing) and cardiac. Furthermore, it is shown that the noise navigator can detect bulk movement, variable breathing and swallowing on a hybrid 1.5 T MRI-linac system. Cardiac activity detection through the noise navigator seems feasible in an MRI-guided radiotherapy setting, but needs further optimization. The noise navigator is a versatile and fast (millisecond temporal resolution) motion detection method independent of MR signal that could serve as an independent verification method to detect the occurrence of motion in synergy with real-time MRI-guided radiotherapy.
Collapse
Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands. Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, Universiy Medical Center Utrecht, Utrecht, Netherlands
| | | | | | | | | | | | | |
Collapse
|
92
|
Gratz M, Ruhlmann V, Umutlu L, Fenchel M, Hong I, Quick HH. Impact of respiratory motion correction on lesion visibility and quantification in thoracic PET/MR imaging. PLoS One 2020; 15:e0233209. [PMID: 32497135 PMCID: PMC7272064 DOI: 10.1371/journal.pone.0233209] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 04/30/2020] [Indexed: 01/04/2023] Open
Abstract
The impact of a method for MR-based respiratory motion correction of PET data on lesion visibility and quantification in patients with oncologic findings in the lung was evaluated. Twenty patients with one or more lesions in the lung were included. Hybrid imaging was performed on an integrated PET/MR system using 18F-FDG as radiotracer. The standard thoracic imaging protocol was extended by a free-breathing self-gated acquisition of MR data for motion modelling. PET data was acquired simultaneously in list-mode for 5-10 mins. One experienced radiologist and one experienced nuclear medicine specialist evaluated and compared the post-processed data in consensus regarding lesion visibility (scores 1-4, 4 being best), image noise levels (scores 1-3, 3 being lowest noise), SUVmean and SUVmax. Motion-corrected (MoCo) images were additionally compared with gated images. Non-motion-corrected free-breathing data served as standard of reference in this study. Motion correction generally improved lesion visibility (3.19 ± 0.63) and noise ratings (2.95 ± 0.22) compared to uncorrected (2.81 ± 0.66 and 2.95 ± 0.22, respectively) or gated PET data (2.47 ± 0.93 and 1.30 ± 0.47, respectively). Furthermore, SUVs (mean and max) were compared for all methods to estimate their respective impact on the quantification. Deviations of SUVmax were smallest between the uncorrected and the MoCo lesion data (average increase of 9.1% of MoCo SUVs), while SUVmean agreed best for gated and MoCo reconstructions (MoCo SUVs increased by 1.2%). The studied method for MR-based respiratory motion correction of PET data combines increased lesion sharpness and improved lesion activity quantification with high signal-to-noise ratio in a clinical setting. In particular, the detection of small lesions in moving organs such as the lung and liver may thus be facilitated. These advantages justify the extension of the PET/MR imaging protocol by 5-10 minutes for motion correction.
Collapse
Affiliation(s)
- Marcel Gratz
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg Essen, Essen, Germany
- High Field and Hybrid MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Verena Ruhlmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | | | - Inki Hong
- Siemens Medical Solutions Inc, Knoxville, Tennessee, United States of America
| | - Harald H. Quick
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg Essen, Essen, Germany
- High Field and Hybrid MR Imaging, University of Duisburg-Essen, Essen, Germany
| |
Collapse
|
93
|
Kurz C, Buizza G, Landry G, Kamp F, Rabe M, Paganelli C, Baroni G, Reiner M, Keall PJ, van den Berg CAT, Riboldi M. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol 2020; 15:93. [PMID: 32370788 PMCID: PMC7201982 DOI: 10.1186/s13014-020-01524-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/24/2020] [Indexed: 12/18/2022] Open
Abstract
The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.
Collapse
Affiliation(s)
- Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
- German Cancer Consortium (DKTK), 81377, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Privata Campeggi 53, 27100, Pavia, Italy
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, NSW, 2006, Australia
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Centre Utrecht, PO box 85500, 3508 GA, Utrecht, The Netherlands
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany.
| |
Collapse
|
94
|
Nabavi S, Abdoos M, Moghaddam ME, Mohammadi M. Respiratory Motion Prediction Using Deep Convolutional Long Short-Term Memory Network. JOURNAL OF MEDICAL SIGNALS & SENSORS 2020; 10:69-75. [PMID: 32676442 PMCID: PMC7359959 DOI: 10.4103/jmss.jmss_38_19] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/04/2019] [Accepted: 10/09/2019] [Indexed: 12/24/2022]
Abstract
Background Pulmonary movements during radiation therapy can cause damage to healthy tissues. It is necessary to adapt treatment planning based on tumor motion to avoid damage to healthy tissues. A range of approaches has been proposed to monitor the issue. A treatment planning based on fourdimensional computed tomography (4D CT) images can be addressed as one of the most achievable options. Although several methods proposed to predict pulmonary movements based on mathematical algorithms, the use of deep artificial neural networks has recently been considered. Methods In the current study, convolutional long shortterm memory networks are applied to predict and generate images throughout the breathing cycle. A total of 3295 CT images of six patients in three different views was considered as reference images. The proposed method was evaluated in six experiments based on a leaveonepatientout method similar to crossvalidation. Results The weighted average results of the experiments in terms of the rootmeansquared error and structural similarity index measure are 9 × 10^-3 and 0.943, respectively. Conclusion Utilizing the proposed method, because of its generative nature, which results in the generation of CT images during the breathing cycle, improves the radiotherapy treatment planning in the lack of access to 4D CT images.
Collapse
Affiliation(s)
- Shahabedin Nabavi
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | - Monireh Abdoos
- Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran
| | | | - Mohammad Mohammadi
- Department of Medical Physics, Royal Adelaide Hospital, Adelaide, Australia.,Department of Medical Physics, School of Physical Sciences, The University of Adelaide, Adelaide, Australia
| |
Collapse
|
95
|
Morton N, Sykes J, Barber J, Hofmann C, Keall P, O’Brien R. Reducing 4D CT imaging artifacts at the source: first experimental results from the respiratory adaptive computed tomography (REACT) system. ACTA ACUST UNITED AC 2020; 65:075012. [DOI: 10.1088/1361-6560/ab7abe] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
|
96
|
Ma H, Smal I, Daemen J, Walsum TV. Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering. Med Image Anal 2020; 61:101634. [DOI: 10.1016/j.media.2020.101634] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2019] [Revised: 10/26/2019] [Accepted: 01/02/2020] [Indexed: 10/25/2022]
|
97
|
Menten MJ, Mohajer JK, Nilawar R, Bertholet J, Dunlop A, Pathmanathan AU, Moreau M, Marshall S, Wetscherek A, Nill S, Tree AC, Oelfke U. Automatic reconstruction of the delivered dose of the day using MR-linac treatment log files and online MR imaging. Radiother Oncol 2020; 145:88-94. [PMID: 31931291 PMCID: PMC7191265 DOI: 10.1016/j.radonc.2019.12.010] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 12/11/2019] [Accepted: 12/15/2019] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND PURPOSE Anatomical changes during external beam radiotherapy prevent the accurate delivery of the intended dose distribution. Resolving the delivered dose, which is currently unknown, is crucial to link radiotherapy doses to clinical outcomes and ultimately improve the standard of care. MATERIAL AND METHODS In this study, we present a dose reconstruction workflow based on data routinely acquired during MR-guided radiotherapy. It employs 3D MR images, 2D cine MR images and treatment machine log files to calculate the delivered dose taking intrafractional motion into account. The developed pipeline was used to measure anatomical changes and assess their dosimetric impact in 89 prostate radiotherapy fractions delivered with a 1.5 T MR-linac at our institute. RESULTS Over the course of radiation delivery, the CTV shifted 0.6 mm ± 2.1 mm posteriorly and 1.3 mm ± 1.5 mm inferiorly. When extrapolating the dose changes in each case to 20 fractions, the mean clinical target volume D98% and clinical target volume D50% dose-volume metrics decreased by 1.1 Gy ± 1.6 Gy and 0.1 Gy ± 0.2 Gy, respectively. Bladder D3% did not change (0.0 Gy ± 1.2 Gy), while rectum D3% decreased by 1.0 Gy ± 2.0 Gy. Although anatomical changes and their dosimetric impact were small in the majority of cases, large intrafractional motion caused the delivered dose to substantially deviate from the intended plan in some fractions. CONCLUSIONS The presented end-to-end workflow is able to reliably, non-invasively and automatically reconstruct the delivered prostate radiotherapy dose by processing MR-linac treatment log files and online MR images. In the future, we envision this workflow to be adapted to other cancer sites and ultimately to enter widespread clinical use.
Collapse
Affiliation(s)
- Martin J Menten
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
| | - Jonathan K Mohajer
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Rahul Nilawar
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Jenny Bertholet
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Alex Dunlop
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Angela U Pathmanathan
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | | | | | - Andreas Wetscherek
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Simeon Nill
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Alison C Tree
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Uwe Oelfke
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| |
Collapse
|
98
|
Meschini G, Kamp F, Hofmaier J, Reiner M, Sharp G, Paganetti H, Belka C, Wilkens JJ, Carlson DJ, Parodi K, Baroni G, Riboldi M. Modeling RBE-weighted dose variations in irregularly moving abdominal targets treated with carbon ion beams. Med Phys 2020; 47:2768-2778. [PMID: 32162332 DOI: 10.1002/mp.14135] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 03/09/2020] [Accepted: 03/09/2020] [Indexed: 01/01/2023] Open
Abstract
PURPOSE To model four-dimensional (4D) relative biological effectiveness (RBE)-weighted dose variations in abdominal lesions treated with scanned carbon ion beam in case of irregular breathing motion. METHODS The proposed method, referred to as bioWED method, combines the simulation of tumor motion in a patient- and beam-specific water equivalent depth (WED)-space with RBE modeling, aiming at the estimation of RBE-weighted dose changes due to respiratory motion. The method was validated on a phantom, simulating gated and free breathing dose delivery, and on a patient case, for which free breathing irradiation was assumed and both amplitude and baseline breathing irregularities were simulated through a respiratory motion model. We quantified (a) the effect of motion on the equivalent uniform dose (EUD) and the RBE-weighted dose-volume histograms (DVH), by comparing the planned dose distribution with "ground truth" 4D RBE-weighted doses computed using 4D computed tomography data, and (ii) the estimation error, by comparing the doses estimated with the bioWED method to "ground truth" 4D RBE-weighted doses. RESULTS In the phantom validation, the estimation error on the EUD was limited with respect to the motion effect and the median estimation error on relevant RBE-weighted DVH metrics remained within 5%. In the patient study, the estimation error as computed on the EUD was smaller than the corresponding motion effect, exhibiting the largest values in the baseline irregularity simulation. However, the median estimation error over all simulations was below 3.2% considering relevant DVH metrics. CONCLUSIONS In the evaluated cases, the bioWED method showed proper accuracy when compared to deformable image registration-based 4D dose calculation. Therefore, it can be seen as a tool to test treatment plan robustness against irregular breathing motion, although its accuracy decreases as a function of increasing soft tissue deformation and should be evaluated on a larger patient dataset.
Collapse
Affiliation(s)
- Giorgia Meschini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Jan Hofmaier
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Gregory Sharp
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Harald Paganetti
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Jan J Wilkens
- Department of Radiation Oncology, School of Medicine, Technical University of Munich, Klinikum rechts der Isar, Munich, Germany
| | - David J Carlson
- Yale University, New Haven, CT, USA.,University of Pennsylvania, Philadelphia, PA, USA
| | - Katia Parodi
- Department of Experimental Physics -Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Munich, Germany
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.,Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Marco Riboldi
- Department of Experimental Physics -Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Munich, Germany
| |
Collapse
|
99
|
Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank. Sci Rep 2020; 10:2408. [PMID: 32051456 PMCID: PMC7015892 DOI: 10.1038/s41598-020-58212-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/20/2019] [Indexed: 12/02/2022] Open
Abstract
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes.
Collapse
|
100
|
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.
Collapse
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
| |
Collapse
|