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Wu Y, Wang Z, Chu Y, Peng R, Peng H, Yang H, Guo K, Zhang J. Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review. Biomimetics (Basel) 2024; 9:170. [PMID: 38534855 DOI: 10.3390/biomimetics9030170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/29/2024] [Accepted: 03/09/2024] [Indexed: 03/28/2024] Open
Abstract
Malignant tumors have become one of the serious public health problems in human safety and health, among which the chest and abdomen diseases account for the largest proportion. Early diagnosis and treatment can effectively improve the survival rate of patients. However, respiratory motion in the chest and abdomen can lead to uncertainty in the shape, volume, and location of the tumor, making treatment of the chest and abdomen difficult. Therefore, compensation for respiratory motion is very important in clinical treatment. The purpose of this review was to discuss the research and development of respiratory movement monitoring and prediction in thoracic and abdominal surgery, as well as introduce the current research status. The integration of modern respiratory motion compensation technology with advanced sensor detection technology, medical-image-guided therapy, and artificial intelligence technology is discussed and analyzed. The future research direction of intraoperative thoracic and abdominal respiratory motion compensation should be non-invasive, non-contact, use a low dose, and involve intelligent development. The complexity of the surgical environment, the constraints on the accuracy of existing image guidance devices, and the latency of data transmission are all present technical challenges.
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Affiliation(s)
- Yuwen Wu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Zhisen Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Yuyi Chu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Renyuan Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Haoran Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Hongbo Yang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Kai Guo
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Juzhong Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
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2
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Evaluation of PET List Data-Driven Gated Motion Correction Technique Applied in Lung Tumors. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00719-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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3
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Bundschuh L, Prokic V, Guckenberger M, Tanadini-Lang S, Essler M, Bundschuh RA. A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study. Diagnostics (Basel) 2022; 12:diagnostics12030576. [PMID: 35328128 PMCID: PMC8947476 DOI: 10.3390/diagnostics12030576] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 12/11/2022] Open
Abstract
Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.
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Affiliation(s)
- Lena Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, 53127 Bonn, Germany; (M.E.); (R.A.B.)
- Correspondence: ; Tel.: +49-228-287-16181
| | - Vesna Prokic
- Department of Physics, University Koblenz-Landau, 55118 Koblenz, Germany;
- RheinAhrCampus, University of Applied Science, 56075 Koblenz, Germany
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (M.G.); (S.T.-L.)
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; (M.G.); (S.T.-L.)
| | - Markus Essler
- Department of Nuclear Medicine, University Hospital Bonn, 53127 Bonn, Germany; (M.E.); (R.A.B.)
| | - Ralph A. Bundschuh
- Department of Nuclear Medicine, University Hospital Bonn, 53127 Bonn, Germany; (M.E.); (R.A.B.)
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Lamare F, Bousse A, Thielemans K, Liu C, Merlin T, Fayad H, Visvikis D. PET respiratory motion correction: quo vadis? Phys Med Biol 2021; 67. [PMID: 34915465 DOI: 10.1088/1361-6560/ac43fc] [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: 06/02/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022]
Abstract
Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.
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Affiliation(s)
- Frederic Lamare
- Nuclear Medicine Department, University Hospital Centre Bordeaux Hospital Group South, ., Bordeaux, Nouvelle-Aquitaine, 33604, FRANCE
| | - Alexandre Bousse
- LaTIM, INSERM UMR1101, Université de Bretagne Occidentale, ., Brest, Bretagne, 29285, FRANCE
| | - Kris Thielemans
- University College London Institute of Nuclear Medicine, UCL Hospital, Tower 5, 235 Euston Road, London, NW1 2BU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Chi Liu
- Department of Diagnostic Radiology, Yale University School of Medicine Department of Radiology and Biomedical Imaging, PO Box 208048, 801 Howard Avenue, New Haven, Connecticut, 06520-8042, UNITED STATES
| | - Thibaut Merlin
- LaTIM, INSERM UMR1101, Universite de Bretagne Occidentale, ., Brest, Bretagne, 29285, FRANCE
| | - Hadi Fayad
- Weill Cornell Medicine - Qatar, ., Doha, ., QATAR
| | - Dimitris Visvikis
- LaTIM, UMR1101, Universite de Bretagne Occidentale, INSERM, Brest, Bretagne, 29285, FRANCE
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Mohammadi I, Castro IF, Rahmim A, Veloso JFCA. Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques. Phys Med Biol 2021; 67. [PMID: 34826826 DOI: 10.1088/1361-6560/ac3dc7] [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: 07/03/2021] [Accepted: 11/26/2021] [Indexed: 11/12/2022]
Abstract
In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance.
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Affiliation(s)
- Iraj Mohammadi
- Department of Physics, University of Aveiro, Aveiro, PORTUGAL
| | - I Filipe Castro
- i3n Physics Department, Universidade de Aveiro, Aveiro, PORTUGAL
| | - Arman Rahmim
- Radiology and Physics, The University of British Columbia, Vancouver, British Columbia, CANADA
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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'.
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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
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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).
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Affiliation(s)
- Johannes Mayer
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany. Author to whom any correspondence should be addressed
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8
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Gallezot JD, Lu Y, Naganawa M, Carson RE. Parametric Imaging With PET and SPECT. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020. [DOI: 10.1109/trpms.2019.2908633] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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9
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Munoz C, Neji R, Kunze KP, Nekolla SG, Botnar RM, Prieto C. Respiratory- and cardiac motion-corrected simultaneous whole-heart PET and dual phase coronary MR angiography. Magn Reson Med 2019; 81:1671-1684. [PMID: 30320931 PMCID: PMC6492195 DOI: 10.1002/mrm.27517] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/25/2018] [Accepted: 08/13/2018] [Indexed: 01/07/2023]
Abstract
PURPOSE To develop a framework for efficient and simultaneous acquisition of motion-compensated whole-heart coronary MR angiography (CMRA) and left ventricular function by MR and myocardial integrity by PET on a 3T PET-MR system. METHODS An acquisition scheme based on a dual-phase CMRA sequence acquired simultaneously with cardiac PET data has been developed. The framework is integrated with a motion-corrected image reconstruction approach, so that non-rigid respiratory and cardiac deformation fields estimated from MR images are used to correct both the CMRA (respiratory motion correction for each cardiac phase) and the PET data (respiratory and cardiac motion correction). The proposed approach was tested in a cohort of 8 healthy subjects and 6 patients with coronary artery disease. Left ventricular (LV) function estimated from motion-corrected dual-phase CMRA was compared to the gold standard estimated from a stack of 2D CINE images for the healthy subjects. Relative increase of signal in motion-corrected PET images compared to uncorrected images was computed for standard 17-segment polar maps for each patient. RESULTS Motion-corrected dual-phase CMRA images allow for visualization of the coronary arteries in both systole and diastole for all healthy subjects and cardiac patients. LV functional indices from healthy subjects result in good agreement with the reference method, underestimating stroke volume by 3.07 ± 3.26 mL and ejection fraction by 0.30 ± 1.01%. Motion correction improved delineation of the myocardium in PET images, resulting in an increased 18 F-FDG signal of up to 28% in basal segments of the myocardial wall compared to uncorrected images. CONCLUSION The proposed motion-corrected dual-phase CMRA and cardiac PET produces co-registered good quality images in both modalities in a single efficient examination of ~13 min.
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Affiliation(s)
- Camila Munoz
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
| | - Radhouene Neji
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Siemens Healthcare, MR Research CollaborationsFrimleyUnited Kingdom
| | - Karl P. Kunze
- Technische Universität München, Nuklearmedizinische Klinik und PoliklinikMunichGermany
| | - Stephan G. Nekolla
- Technische Universität München, Nuklearmedizinische Klinik und PoliklinikMunichGermany
- DZHK (Deutsches Zentrum für Herz‐Kreislauf‐Forschung e.V.), partner site Munich Heart AllianceMunichGermany
| | - Rene M. Botnar
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Catolica de Chile, Escuela de IngenieriaSantiagoChile
| | - Claudia Prieto
- King’s College London, School of Biomedical Engineering and Imaging SciencesLondonUnited Kingdom
- Pontificia Universidad Catolica de Chile, Escuela de IngenieriaSantiagoChile
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Boada FE, Koesters T, Block KT, Chandarana H. Improved Detection of Small Pulmonary Nodules Through Simultaneous MR/PET Imaging. PET Clin 2018; 13:89-95. [PMID: 29157389 DOI: 10.1016/j.cpet.2017.09.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Magnetic resonance (MR)/PET scanners provide an imaging platform that enables simultaneous acquisition of MR and PET data in perfect spatial and temporal registration. This feature allows improving image quality for the MR and PET images obtained during the course of an examination. In this work the authors demonstrate the use of prospective MR-based motion tracking information for removing motion blur in MR/PET images of small pulmonary nodules. The theoretical basis for the algorithms is presented alongside clinical examples of its use.
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Affiliation(s)
- Fernando E Boada
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA.
| | - Thomas Koesters
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA
| | - Kai Tobias Block
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA
| | - Hersh Chandarana
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA
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Chan C, Onofrey J, Jian Y, Germino M, Papademetris X, Carson RE, Liu C. Non-Rigid Event-by-Event Continuous Respiratory Motion Compensated List-Mode Reconstruction for PET. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:504-515. [PMID: 29028189 PMCID: PMC7304524 DOI: 10.1109/tmi.2017.2761756] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with 18F-FPDTBZ and one with 18F-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The 18F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.
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12
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Hu M, Zhai G, Li D, Fan Y, Duan H, Zhu W, Yang X. Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation. PLoS One 2018; 13:e0190466. [PMID: 29304152 PMCID: PMC5755779 DOI: 10.1371/journal.pone.0190466] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 12/17/2017] [Indexed: 11/18/2022] Open
Abstract
To achieve the simultaneous and unobtrusive breathing rate (BR) and heart rate (HR) measurements during nighttime, we leverage a far-infrared imager and an infrared camera equipped with IR-Cut lens and an infrared lighting array to develop a dual-camera imaging system. A custom-built cascade face classifier, containing the conventional Adaboost model and fully convolutional network trained by 32K images, was used to detect the face region in registered infrared images. The region of interest (ROI) inclusive of mouth and nose regions was afterwards confirmed by the discriminative regression and coordinate conversions of three selected landmarks. Subsequently, a tracking algorithm based on spatio-temporal context learning was applied for following the ROI in thermal video, and the raw signal was synchronously extracted. Finally, a custom-made time-domain signal analysis approach was developed for the determinations of BR and HR. A dual-mode sleep video database, including the videos obtained under environment where illumination intensity ranged from 0 to 3 Lux, was constructed to evaluate the effectiveness of the proposed system and algorithms. In linear regression analysis, the determination coefficient (R2) of 0.831 had been observed for the measured BR and reference BR, and this value was 0.933 for HR measurement. In addition, the Bland-Altman plots of BR and HR demonstrated that almost all the data points located within their own 95% limits of agreement. Consequently, the overall performance of the proposed technique is acceptable for BR and HR estimations during nighttime.
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Affiliation(s)
- Menghan Hu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Guangtao Zhai
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Duo Li
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Yezhao Fan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Huiyu Duan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhan Zhu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaokang Yang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai Key Laboratory of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
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13
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MRI-assisted dual motion correction for myocardial perfusion defect detection in PET imaging. Med Phys 2017. [DOI: 10.1002/mp.12429] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Brock KK, Mutic S, McNutt TR, Li H, Kessler ML. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med Phys 2017; 44:e43-e76. [PMID: 28376237 DOI: 10.1002/mp.12256] [Citation(s) in RCA: 518] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 02/13/2017] [Accepted: 02/19/2017] [Indexed: 11/07/2022] Open
Abstract
Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.
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Affiliation(s)
- Kristy K Brock
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, FCT 14.6048, Houston, TX, 77030, USA
| | - Sasa Mutic
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Todd R McNutt
- Department of Radiation Oncology, Johns Hopkins Medical Institute, Baltimore, MD, USA
| | - Hua Li
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Marc L Kessler
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
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Hu MH, Zhai GT, Li D, Fan YZ, Chen XH, Yang XK. Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:36006. [PMID: 28264083 DOI: 10.1117/1.jbo.22.3.036006] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 02/21/2017] [Indexed: 06/06/2023]
Abstract
We present a dual-mode imaging system operating on visible and long-wave infrared wavelengths for achieving the noncontact and nonobtrusive measurements of breathing rate and pattern, no matter whether the subjects use the nose and mouth simultaneously, alternately, or individually when they breathe. The improved classifiers in tandem with the biological characteristics outperformed the custom cascade classifiers using the Viola–Jones algorithm for the cross-spectrum detection of face and nose as well as mouth. In terms of breathing rate estimation, the results obtained by this system were verified to be consistent with those measured by reference method via the Bland–Altman plot with 95% limits of agreement from ? 2.998 to 2.391 and linear correlation analysis with a correlation coefficient of 0.971, indicating that this method was acceptable for the quantitative analysis of breathing. In addition, the breathing waveforms extracted by the dual-mode imaging system were basically the same as the corresponding standard breathing sequences. Since the validation experiments were conducted under challenging conditions, such as the significant positional and abrupt physiological variations, we stated that this dual-mode imaging system utilizing the respective advantages of RGB and thermal cameras was a promising breathing measurement tool for residential care and clinical applications.
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Affiliation(s)
- Meng-Han Hu
- Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai, China
| | - Guang-Tao Zhai
- Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai, China
| | - Duo Li
- Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai, China
| | - Ye-Zhao Fan
- Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai, China
| | - Xiao-Hui Chen
- Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai, China
| | - Xiao-Kang Yang
- Shanghai Jiao Tong University, Institute of Image Communication and Information Processing, Shanghai, China
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16
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Boada FE, Koesters T, Block KT, Chandarana H. Improved Detection of Small Pulmonary Nodules Through Simultaneous MR/PET Imaging. Magn Reson Imaging Clin N Am 2017; 25:273-279. [PMID: 28390528 DOI: 10.1016/j.mric.2016.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Magnetic resonance (MR)/PET scanners provide an imaging platform that enables simultaneous acquisition of MR and PET data in perfect spatial and temporal registration. This feature allows improving image quality for the MR and PET images obtained during the course of an examination. In this work the authors demonstrate the use of prospective MR-based motion tracking information for removing motion blur in MR/PET images of small pulmonary nodules. The theoretical basis for the algorithms is presented alongside clinical examples of its use.
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Affiliation(s)
- Fernando E Boada
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA.
| | - Thomas Koesters
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA
| | - Kai Tobias Block
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA
| | - Hersh Chandarana
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA
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17
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Kalantari F, Wang J. Attenuation correction in 4D-PET using a single-phase attenuation map and rigidity-adaptive deformable registration. Med Phys 2017; 44:522-532. [PMID: 27987223 DOI: 10.1002/mp.12063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 12/03/2016] [Accepted: 12/05/2016] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Four-dimensional positron emission tomography (4D-PET) imaging is a potential solution to the respiratory motion effect in the thoracic region. Computed tomography (CT)-based attenuation correction (AC) is an essential step toward quantitative imaging for PET. However, due to the temporal difference between 4D-PET and a single attenuation map from CT, typically available in routine clinical scanning, motion artifacts are observed in the attenuation-corrected PET images, leading to errors in tumor shape and uptake. We introduced a practical method to align single-phase CT with all other 4D-PET phases for AC. METHODS A penalized non-rigid Demons registration between individual 4D-PET frames without AC provides the motion vectors to be used for warping single-phase attenuation map. The non-rigid Demons registration was used to derive deformation vector fields (DVFs) between PET matched with the CT phase and other 4D-PET images. While attenuated PET images provide useful data for organ borders such as those of the lung and the liver, tumors cannot be distinguished from the background due to loss of contrast. To preserve the tumor shape in different phases, an ROI-covering tumor was excluded from nonrigid transformation. Instead the mean DVF of the central region of the tumor was assigned to all voxels in the ROI. This process mimics a rigid transformation of the tumor along with a nonrigid transformation of other organs. A 4D-XCAT phantom with spherical lung tumors, with diameters ranging from 10 to 40 mm, was used to evaluate the algorithm. The performance of the proposed hybrid method for attenuation map estimation was compared to (a) the Demons nonrigid registration only and (b) a single attenuation map based on quantitative parameters in individual PET frames. RESULTS Motion-related artifacts were significantly reduced in the attenuation-corrected 4D-PET images. When a single attenuation map was used for all individual PET frames, the normalized root-mean-square error (NRMSE) values in tumor region were 49.3% (STD: 8.3%), 50.5% (STD: 9.3%), 51.8% (STD: 10.8%) and 51.5% (STD: 12.1%) for 10-mm, 20-mm, 30-mm, and 40-mm tumors, respectively. These errors were reduced to 11.9% (STD: 2.9%), 13.6% (STD: 3.9%), 13.8% (STD: 4.8%), and 16.7% (STD: 9.3%) by our proposed method for deforming the attenuation map. The relative errors in total lesion glycolysis (TLG) values were -0.25% (STD: 2.87%) and 3.19% (STD: 2.35%) for 30-mm and 40-mm tumors, respectively, in proposed method. The corresponding values for Demons method were 25.22% (STD: 14.79%) and 18.42% (STD: 7.06%). Our proposed hybrid method outperforms the Demons method especially for larger tumors. For tumors smaller than 20 mm, nonrigid transformation could also provide quantitative results. CONCLUSION Although non-AC 4D-PET frames include insignificant anatomical information, they are still useful to estimate the DVFs to align the attenuation map for accurate AC. The proposed hybrid method can recover the AC-related artifacts and provide quantitative AC-PET images.
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Affiliation(s)
- Faraz Kalantari
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235-8808, USA
| | - Jing Wang
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235-8808, USA
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18
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Zhuang M, Dierckx RAJO, Zaidi H. Generic and robust method for automatic segmentation of PET images using an active contour model. Med Phys 2016; 43:4483. [PMID: 27487865 DOI: 10.1118/1.4954844] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. METHODS MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer's thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. RESULTS For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (-8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of -13.35% ± 11.87% and -11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. CONCLUSIONS The authors' results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors' study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.
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Affiliation(s)
- Mingzan Zhuang
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; Department of Radiation Oncology, Tumor Hospital of Shantou University Medical College, Shantou, Guangdong 515000, China; and The Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, Guangdong 515000, China
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland; and Department of Nuclear Medicine and Molecular Imaging,University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands
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19
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Lamare F, Fayad H, Fernandez P, Visvikis D. Local respiratory motion correction for PET/CT imaging: Application to lung cancer. Med Phys 2016; 42:5903-12. [PMID: 26429264 DOI: 10.1118/1.4930251] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications. METHODS Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions. RESULTS Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV. CONCLUSIONS The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.
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Affiliation(s)
- F Lamare
- INCIA, UMR 5287, University of Bordeaux, Talence F-33400, France and Nuclear Medicine Department, University Hospital, Bordeaux 33000, France
| | - H Fayad
- INSERM, UMR1101, LaTIM, Université de Bretagne Occidentale, Brest 29609, France
| | - P Fernandez
- INCIA, UMR 5287, University of Bordeaux, Talence F-33400, France and Nuclear Medicine Department, University Hospital, Bordeaux 33000, France
| | - D Visvikis
- INSERM, UMR1101, LaTIM, Université de Bretagne Occidentale, Brest 29609, France
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20
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Kalantari F, Li T, Jin M, Wang J. Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR). Phys Med Biol 2016; 61:5639-61. [PMID: 27385378 DOI: 10.1088/0031-9155/61/15/5639] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: (1) the reconstruction algorithms do not make full use of projection statistics; and (2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10-40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.
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Affiliation(s)
- Faraz Kalantari
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
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21
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Evaluation of motion-correction methods for dual-gated cardiac positron emission tomography/computed tomography imaging. Nucl Med Commun 2016; 37:956-68. [PMID: 27258990 DOI: 10.1097/mnm.0000000000000539] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Dual gating is a method of dividing the data of a cardiac PET scan into smaller bins according to the respiratory motion and the ECG of the patient. It reduces the undesirable motion artefacts in images, but produces several images for interpretation and decreases the quality of single images. By using motion-correction techniques, the motion artefacts in the dual-gated images can be corrected and the images can be combined into a single motion-free image with good statistics. AIM The aim of the present study is to develop and evaluate motion-correction methods for cardiac PET studies. We have developed and compared two different methods: computed tomography (CT)/PET-based and CT-only methods. METHODS The methods were implemented and tested with a cardiac phantom and three patient datasets. In both methods, anatomical information of CT images is used to create models for the cardiac motion. RESULTS In the patient study, the CT-only method reduced motion (measured as the centre of mass of the myocardium) on average 43%, increased the contrast-to-noise ratio on average 6.0% and reduced the target size on average 10%. Slightly better figures (51, 6.9 and 28%) were obtained with the CT/PET-based method. Even better results were obtained in the phantom study for both the CT-only method (57, 68 and 43%) and the CT/PET-based method (61, 74 and 52%). CONCLUSION We conclude that using anatomical information of CT for motion correction of cardiac PET images, both respiratory and pulsatile motions can be corrected with good accuracy.
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22
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Attenberger U, Catana C, Chandarana H, Catalano OA, Friedman K, Schonberg SA, Thrall J, Salvatore M, Rosen BR, Guimaraes AR. Whole-body FDG PET-MR oncologic imaging: pitfalls in clinical interpretation related to inaccurate MR-based attenuation correction. ACTA ACUST UNITED AC 2016; 40:1374-86. [PMID: 26025348 DOI: 10.1007/s00261-015-0455-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Simultaneous data collection for positron emission tomography and magnetic resonance imaging (PET/MR) is now a reality. While the full benefits of concurrently acquiring PET and MR data and the potential added clinical value are still being evaluated, initial studies have identified several important potential pitfalls in the interpretation of fluorodeoxyglucose (FDG) PET/MRI in oncologic whole-body imaging, the majority of which being related to the errors in the attenuation maps created from the MR data. The purpose of this article was to present such pitfalls and artifacts using case examples, describe their etiology, and discuss strategies to overcome them. Using a case-based approach, we will illustrate artifacts related to (1) Inaccurate bone tissue segmentation; (2) Inaccurate air cavities segmentation; (3) Motion-induced misregistration; (4) RF coils in the PET field of view; (5) B0 field inhomogeneity; (6) B1 field inhomogeneity; (7) Metallic implants; (8) MR contrast agents.
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Affiliation(s)
- Ulrike Attenberger
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim, Germany
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23
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Dutta J, Huang C, Li Q, El Fakhri G. Pulmonary imaging using respiratory motion compensated simultaneous PET/MR. Med Phys 2016; 42:4227-40. [PMID: 26133621 DOI: 10.1118/1.4921616] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies. METHODS The authors have developed an MCIR framework based on maximum a posteriori or MAP estimation. For fast acquisition of high quality 4D MR images, the authors developed a novel Golden-angle RAdial Navigated Gradient Echo (GRANGE) pulse sequence and used it in conjunction with sparsity-enforcing k-t FOCUSS reconstruction. The authors use a 1D slice-projection navigator signal encapsulated within this pulse sequence along with a histogram-based gate assignment technique to retrospectively sort the MR and PET data into individual gates. The authors compute deformation fields for each gate via nonrigid registration. The deformation fields are incorporated into the PET data model as well as utilized for generating dynamic attenuation maps. The framework was validated using simulation studies on the 4D XCAT phantom and three clinical patient studies that were performed on the Biograph mMR, a simultaneous whole body PET/MR scanner. RESULTS The authors compared MCIR (MC) results with ungated (UG) and one-gate (OG) reconstruction results. The XCAT study revealed contrast-to-noise ratio (CNR) improvements for MC relative to UG in the range of 21%-107% for 14 mm diameter lung lesions and 39%-120% for 10 mm diameter lung lesions. A strategy for regularization parameter selection was proposed, validated using XCAT simulations, and applied to the clinical studies. The authors' results show that the MC image yields 19%-190% increase in the CNR of high-intensity features of interest affected by respiratory motion relative to UG and a 6%-51% increase relative to OG. CONCLUSIONS Standalone MR is not the traditional choice for lung scans due to the low proton density, high magnetic susceptibility, and low T2 (∗) relaxation time in the lungs. By developing and validating this PET/MR pulmonary imaging framework, the authors show that simultaneous PET/MR, unique in its capability of combining structural information from MR with functional information from PET, shows promise in pulmonary imaging.
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Affiliation(s)
- Joyita Dutta
- Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Chuan Huang
- Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114; Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115; and Departments of Radiology and Psychiatry, Stony Brook Medicine, Stony Brook, New York 11794
| | - Quanzheng Li
- Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
| | - Georges El Fakhri
- Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114 and Department of Radiology, Harvard Medical School, Boston, Massachusetts 02115
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Smyczynski MS, Gifford HC, Dey J, Lehovich A, McNamara JE, Segars WP, King MA. LROC Investigation of Three Strategies for Reducing the Impact of Respiratory Motion on the Detection of Solitary Pulmonary Nodules in SPECT. IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2016; 63:130-139. [PMID: 27182080 PMCID: PMC4863469 DOI: 10.1109/tns.2015.2481825] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPN) in single photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this non-uniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99m NeoTect. Similarly, spherical phantoms of 1.0 cm diameter were generated to model small SPN for each of 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of: 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one-fourth of the 32 frames centered around EE (Quarter-Binning), 4) one-half of the 32 frames centered around EE (Half-Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human-observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter-Binning and Half-Binning strategies resulted in SPN detection accuracy statistically significantly below (P < 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction.
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Affiliation(s)
- Mark S Smyczynski
- Department of Radiology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655
| | - Howard C Gifford
- Biomedical Engineering Dept., University of Houston, Houston, TX
| | - Joyoni Dey
- Department of Physics & Astronomy, Louisiana State University, LA
| | - Andre Lehovich
- Department of Radiology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655
| | | | - W Paul Segars
- Duke Advanced Imaging Laboratories, Duke University Medical Center, Durham, NC
| | - Michael A King
- Department of Radiology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655 (telephone: 508-856-4255, )
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25
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Manescu P, Ladjal H, Azencot J, Beuve M, Shariat B. Motion compensation for PET image reconstruction using deformable tetrahedral meshes. Phys Med Biol 2015; 60:9269-93. [DOI: 10.1088/0031-9155/60/24/9269] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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26
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Ahmed MAA, Xiao P, Xie Q. New approach for simultaneous respiratory and cardiac motion correction in cardiac PET (NAMC-CPET). Phys Med Biol 2015; 60:7779-804. [PMID: 26393382 DOI: 10.1088/0031-9155/60/19/7779] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Respiratory and cardiac motions are inevitable during the relatively long acquisition time of cardiac positron emission tomography (PET) scan. The correction of the resultant motion blur has become a significant challenge due to recent spatial resolution improvement of the PET scanners. The majority of current motion compensation algorithms are based on gating as a primary step. A new approach based on temporal basis functions is developed to correct respiratory and cardiac motion simultaneously in cardiac PET within the normal scanning time (NAMC-CPET). Simulation and experimental studies are conducted to evaluate and validate the final outputs in comparison to the existing gating methods. A dynamic digital phantom is used to simulate realistic human thorax and abdomen with respiratory and cardiac motions. GATE simulation was run at China National Grid Center to obtain realistic PET data in a reasonable time. Moreover, Tibet minipig experiments were conducted using a preclinical small animal PET scanner developed at HUST to validate the performance of the NAMC-CPET in real data. The results reveal that NAMC-CPET outperformed the existing gating methods (respiratory, cardiac, and dual) in cardiac imaging in term of noise reduction and contrast, especially in short acquisition duration. NAMC-CPET obtained better results in the conducted experiments in terms of contrast and the visibility of the heart. In contrast, the dual gating failed to obtain valuable images in the normal scan time due to the low 18F-FDG uptake. NAMC-CPET is advantageous in the low-statistic situation. The results are promising with great potential implications in cardiac PET imaging in terms of the radioactive dose and scan time reduction.
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Fayad H, Schmidt H, Wuerslin C, Visvikis D. Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications. J Nucl Med 2015; 56:884-9. [PMID: 25908830 DOI: 10.2967/jnumed.114.153007] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 04/02/2015] [Indexed: 01/04/2023] Open
Abstract
UNLABELLED Simultaneous PET and MR imaging is a promising new technique allowing the fusion of functional (PET) and anatomic/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames that lead to low signal-to-noise ratio considering the associated low statistics. More advanced correction approaches, previously developed for PET/CT imaging, consist of either registering all the reconstructed gated frames to the reference frame or incorporating motion parameters into the iterative reconstruction process to produce a single motion-compensated PET image. The goal of this work was to compare these two—previously implemented in PET/CT—correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4-dimensional PET/MR acquisitions. Two different correction approaches were evaluated comparing the incorporation of elastic transformations extracted from 4-dimensional MR imaging datasets during PET list-mode image reconstruction to a postreconstruction image-based approach. METHODS Eleven patient datasets acquired on a PET/MR system were used. T1-weighted 4D MR images were registered to the end-expiration image using a nonrigid B-spline registration algorithm to derive deformation matrices accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space [RS] method). The corrected images were compared with those produced by applying the deformation matrices in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion-averaged images. RESULTS Both correction techniques led to significant improvement in accounting for respiratory motion artifacts when compared with uncorrected motion-averaged images. These improvements included signal-to-noise ratio (mean increase of 28.0% and 24.2% for the RS and IS methods, respectively), lesion size (reduction of 60.4% and 47.9%, respectively), lesion contrast (increase of 70.1% and 57.2%, respectively), and lesion position (changes of 60.9% and 46.7%, respectively). CONCLUSION Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D PET RS approach.
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Affiliation(s)
- Hadi Fayad
- INSERM, UMR1101, LaTIM, CHRU Morvan, Université de Bretagne Occidentale, Brest, France; and
| | - Holger Schmidt
- Diagnostic and Interventional Radiology, Department of Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Christian Wuerslin
- Diagnostic and Interventional Radiology, Department of Radiology, University Hospital of Tübingen, Tübingen, Germany
| | - Dimitris Visvikis
- INSERM, UMR1101, LaTIM, CHRU Morvan, Université de Bretagne Occidentale, Brest, France; and
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Fayad H, Odille F, Schmidt H, Würslin C, Küstner T, Felblinger J, Visvikis D. The use of a generalized reconstruction by inversion of coupled systems (GRICS) approach for generic respiratory motion correction in PET/MR imaging. Phys Med Biol 2015; 60:2529-46. [DOI: 10.1088/0031-9155/60/6/2529] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Vauclin S, Michel C, Buvat I, Doyeux K, Edet-Sanson A, Vera P, Gardin I, Hapdey S. Monte-Carlo simulations of clinically realistic respiratory gated (18)F-FDG PET: application to lesion detectability and volume measurements. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 118:84-93. [PMID: 25459525 DOI: 10.1016/j.cmpb.2014.10.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 10/02/2014] [Accepted: 10/03/2014] [Indexed: 06/04/2023]
Abstract
In PET/CT thoracic imaging, respiratory motion reduces image quality. A solution consists in performing respiratory gated PET acquisitions. The aim of this study was to generate clinically realistic Monte-Carlo respiratory PET data, obtained using the 4D-NCAT numerical phantom and the GATE simulation tool, to assess the impact of respiratory motion and respiratory-motion compensation in PET on lesion detection and volume measurement. To obtain reconstructed images as close as possible to those obtained in clinical conditions, a particular attention was paid to apply to the simulated data the same correction and reconstruction processes as those applied to real clinical data. The simulations required 140,000h (CPU) generating 1.5 To of data (98 respiratory gated and 49 ungated scans). Calibration phantom and patient reconstructed images from the simulated data were visually and quantitatively very similar to those obtained in clinical studies. The lesion detectability was higher when the better trade-off between lesion movement limitation (compared to ungated acquisitions) and image statistic preservation is considered (respiratory cycle sampling in 3 frames). We then compared the lesion volumes measured on conventional PET acquisitions versus respiratory gated acquisitions, using an automatic segmentation method and a 40%-threshold approach. A time consuming initial manual exclusion of noisy structures needed with the 40%-threshold was not necessary when the automatic method was used. The lesion detectability along with the accuracy of tumor volume estimates was largely improved with the gated compared to ungated PET images.
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Affiliation(s)
- S Vauclin
- QuantIF-Litis, EA4108 - FR CNRS 3638, Rouen University, Rouen, France; Siemens Medical, Saint-Denis, France
| | - C Michel
- Siemens Medical, Knoxville, TN, USA
| | - I Buvat
- IMNC, UMR 8165 CNRS, Universités Paris 7 & 11, Orsay, France
| | - K Doyeux
- QuantIF-Litis, EA4108 - FR CNRS 3638, Rouen University, Rouen, France; Radiotherapy Department, Henri Becquerel Center, Rouen, France
| | - A Edet-Sanson
- QuantIF-Litis, EA4108 - FR CNRS 3638, Rouen University, Rouen, France; Nuclear Medicine Department, Henri Becquerel Center, Rouen, France; Rouen University Hospital, Rouen, France
| | - P Vera
- QuantIF-Litis, EA4108 - FR CNRS 3638, Rouen University, Rouen, France; Nuclear Medicine Department, Henri Becquerel Center, Rouen, France; Rouen University Hospital, Rouen, France
| | - I Gardin
- QuantIF-Litis, EA4108 - FR CNRS 3638, Rouen University, Rouen, France; Nuclear Medicine Department, Henri Becquerel Center, Rouen, France; Rouen University Hospital, Rouen, France
| | - S Hapdey
- QuantIF-Litis, EA4108 - FR CNRS 3638, Rouen University, Rouen, France; Nuclear Medicine Department, Henri Becquerel Center, Rouen, France; Rouen University Hospital, Rouen, France.
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Ouyang J, Petibon Y, Huang C, Reese TG, Kolnick AL, El Fakhri G. Quantitative simultaneous positron emission tomography and magnetic resonance imaging. J Med Imaging (Bellingham) 2014; 1:033502. [PMID: 26158055 DOI: 10.1117/1.jmi.1.3.033502] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2014] [Revised: 08/19/2014] [Accepted: 10/02/2014] [Indexed: 01/29/2023] Open
Abstract
Simultaneous positron emission tomography and magnetic resonance imaging (PET-MR) is an innovative and promising imaging modality that is generating substantial interest in the medical imaging community, while offering many challenges and opportunities. In this study, we investigated whether MR surface coils need to be accounted for in PET attenuation correction. Furthermore, we integrated motion correction, attenuation correction, and point spread function modeling into a single PET reconstruction framework. We applied our reconstruction framework to in vivo animal and patient PET-MR studies. We have demonstrated that our approach greatly improved PET image quality.
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Affiliation(s)
- Jinsong Ouyang
- Massachusetts General Hospital , Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston, Massachusetts 02114, United States ; Harvard Medical School , Department of Radiology, Boston, Massachusetts 02114, United States
| | - Yoann Petibon
- Massachusetts General Hospital , Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston, Massachusetts 02114, United States ; Sorbonne Universités , UPMC Univ Paris 06, Paris 75634, France
| | - Chuan Huang
- Massachusetts General Hospital , Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston, Massachusetts 02114, United States ; Harvard Medical School , Department of Radiology, Boston, Massachusetts 02114, United States
| | - Timothy G Reese
- Harvard Medical School , Department of Radiology, Boston, Massachusetts 02114, United States ; Massachusetts General Hospital , Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts 02129, United States
| | - Aleksandra L Kolnick
- Massachusetts General Hospital , Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston, Massachusetts 02114, United States
| | - Georges El Fakhri
- Massachusetts General Hospital , Center for Advanced Medical Imaging Sciences, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Boston, Massachusetts 02114, United States ; Harvard Medical School , Department of Radiology, Boston, Massachusetts 02114, United States
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Koivumäki T, Nekolla SG, Fürst S, Loher S, Vauhkonen M, Schwaiger M, Hakulinen MA. An integrated bioimpedance—ECG gating technique for respiratory and cardiac motion compensation in cardiac PET. Phys Med Biol 2014; 59:6373-85. [DOI: 10.1088/0031-9155/59/21/6373] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
Combined PET/computed tomography (CT) is of value in cancer diagnosis, follow-up, and treatment planning. For cancers located in the thorax or abdomen, the patient’s breathing causes artifacts and errors in PET and CT images. Many different approaches for artifact avoidance or correction have been developed; most are based on gated acquisition and synchronization between the respiratory signal and PET acquisition. The respiratory signal is usually produced by an external sensor that tracks a physiological characteristic related to the patient’s breathing. Respiratory gating is a compensation technique in which time or amplitude binning is used to exclude the motion in reconstructed PET images. Although this technique is performed in routine clinical practice, it fails to adequately correct for respiratory motion because each gate can mix several tissue positions. Researchers have suggested either selecting PET events from gated acquisitions or performing several PET acquisitions (corresponding to a breath-hold CT position). However, the PET acquisition time must be increased if adequate counting statistics are to be obtained in the different gates after binning. Hence, other researchers have assessed correction techniques that take account of all the counting statistics (without increasing the acquisition duration) and integrate motion information before, during, or after the reconstruction process. Here, we provide an overview of how motion is managed to overcome respiratory motion in PET/CT images.
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Respiratory-induced errors in tumor quantification and delineation in CT attenuation-corrected PET images: effects of tumor size, tumor location, and respiratory trace: a simulation study using the 4D XCAT phantom. Mol Imaging Biol 2014; 15:655-65. [PMID: 23780352 DOI: 10.1007/s11307-013-0656-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
PURPOSE We investigated the magnitude of respiratory-induced errors in tumor maximum standardized uptake value (SUVmax), localization, and volume for different respiratory motion traces and various lesion sizes in different locations of the thorax and abdomen in positron emission tomography (PET) images. PROCEDURES Respiratory motion traces were simulated based on the common patient breathing cycle and three diaphragm motions used to drive the 4D XCAT phantom. Lesions with different diameters were simulated in different locations of lungs and liver. The generated PET sinograms were subsequently corrected using computed tomography attenuation correction involving the end exhalation, end inhalation, and average of the respiratory cycle. By considering respiration-averaged computed tomography as a true value, the lesion volume, displacement, and SUVmax were measured and analyzed for different respiratory motions. RESULTS Respiration with 35-mm diaphragm motion results in a mean lesion SUVmax error of 24 %, a mean superior inferior displacement of 7.6 mm and a mean lesion volume overestimation of 129 % for a 9-mm lesion in the liver. Respiratory motion results in lesion volume overestimation of 50 % for a 9-mm lower lung lesion near the liver with just 15-mm diaphragm motion. Although there are larger errors in lesion SUVmax and volume for 35-mm motion amplitudes, respiration-averaged computed tomography results in smaller errors than the other two phases, except for the lower lung region. CONCLUSIONS The respiratory motion-induced errors in tumor quantification and delineation are highly dependent upon the motion amplitude, tumor location, tumor size, and choice of the attenuation map for PET image attenuation correction.
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Lamare F, Le Maitre A, Dawood M, Schäfers KP, Fernandez P, Rimoldi OE, Visvikis D. Evaluation of respiratory and cardiac motion correction schemes in dual gated PET/CT cardiac imaging. Med Phys 2014; 41:072504. [DOI: 10.1118/1.4881099] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Chan C, Jin X, Fung EK, Naganawa M, Mulnix T, Carson RE, Liu C. Event-by-event respiratory motion correction for PET with 3D internal-1D external motion correlation. Med Phys 2013; 40:112507. [DOI: 10.1118/1.4826165] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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37
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Kruis MF, van de Kamer JB, Houweling AC, Sonke JJ, Belderbos JS, van Herk M. PET Motion Compensation for Radiation Therapy Using a CT-Based Mid-Position Motion Model: Methodology and Clinical Evaluation. Int J Radiat Oncol Biol Phys 2013; 87:394-400. [DOI: 10.1016/j.ijrobp.2013.06.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2012] [Revised: 06/04/2013] [Accepted: 06/07/2013] [Indexed: 10/26/2022]
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38
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Ouyang J, Li Q, El Fakhri G. Magnetic resonance-based motion correction for positron emission tomography imaging. Semin Nucl Med 2013. [PMID: 23178089 DOI: 10.1053/j.semnuclmed.2012.08.007] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Positron emission tomography (PET) image quality is limited by patient motion. Emission data are blurred owing to cardiac and/or respiratory motion. Although spatial resolution is 4 mm for standard clinical whole-body PET scanners, the effective resolution can be as low as 1 cm owing to motion. Additionally, the deformation of attenuation medium causes image artifacts. Previously, gating has been used to "freeze" the motion, but led to significantly increased noise level. Simultaneous PET/magnetic resonance (MR) modality offers a new way to perform PET motion correction. MR can be used to measure 3-dimensional motion fields, which can then be incorporated into the iterative PET reconstruction to obtain motion-corrected PET images. In this report, we present MR imaging techniques to acquire dynamic images, a nonrigid image registration algorithm to extract motion fields from acquired MR images, and a PET reconstruction algorithm with motion correction. We also present results from both phantom and in vivo animal PET/MR studies. We demonstrate that MR-based PET motion correction using simultaneous PET/MR improves image quality and lesion detectability compared with gating and no motion correction.
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Affiliation(s)
- Jinsong Ouyang
- Center for Advanced Radiological Sciences, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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39
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Brendle CB, Schmidt H, Fleischer S, Braeuning UH, Pfannenberg CA, Schwenzer NF. Simultaneously Acquired MR/PET Images Compared with Sequential MR/PET and PET/CT: Alignment Quality. Radiology 2013; 268:190-9. [DOI: 10.1148/radiol.13121838] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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40
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Schmidt H, Brendle C, Schraml C, Martirosian P, Bezrukov I, Hetzel J, Müller M, Sauter A, Claussen CD, Pfannenberg C, Schwenzer NF. Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System. Invest Radiol 2013; 48:247-55. [DOI: 10.1097/rli.0b013e31828d56a1] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Fayad HJ, Lamare F, Le Rest CC, Bettinardi V, Visvikis D. Generation of 4-dimensional CT images based on 4-dimensional PET-derived motion fields. J Nucl Med 2013; 54:631-8. [PMID: 23471313 DOI: 10.2967/jnumed.112.110809] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
UNLABELLED Respiratory motion can potentially reduce accuracy in anatomic and functional image fusion from multimodality systems. It can blur the uptake of small lesions and lead to significant activity underestimation. Solutions presented to date include respiration-synchronized anatomic and functional acquisitions. To increase the signal-to-noise ratio of the synchronized PET images, methods using nonrigid transformations during the reconstruction process have been proposed. In most of these methods, 4-dimensional (4D) CT images were used to derive the required deformation matrices. However, variations between acquired 4D PET and corresponding CT image series due to differences in respiratory conditions during PET and CT acquisitions have been reported. In addition, the radiation dose burden resulting from a 4D CT acquisition may not be justifiable for every patient. METHODS In this paper, we present a method for the generation of dynamic CT images from the combination of one reference CT image and deformation matrices obtained from the elastic registration of 4D PET images not corrected for attenuation. On the one hand, our approach eliminates the need for the acquisition of dynamic CT. On the other hand, it also ensures a good match between CT and PET images, allowing accurate attenuation correction to be performed for respiration-synchronized PET acquisitions. RESULTS The proposed method was first validated on Monte Carlo-simulated datasets, and then on patient datasets (n = 4) by comparing generated 4D CT images with the corresponding acquired original CT images. Different levels of PET image statistical quality were considered in order to investigate the impact of image noise in the derivation of the 4D CT series. CONCLUSION Our results suggest that clinically relevant PET acquisition times can be used for the implementation of such an approach, making this an even more attractive solution considering the absence of the extra dose given by a standard 4D CT acquisition. Finally, this approach may be applicable to other multimodality devices such as PET/MR.
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Affiliation(s)
- Hadi J Fayad
- INSERM, UMR1101, LaTIM, CHRU Morvan, Brest, France.
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Introduction to the analysis of PET data in oncology. J Pharmacokinet Pharmacodyn 2013; 40:419-36. [PMID: 23443280 DOI: 10.1007/s10928-013-9307-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Accepted: 02/13/2013] [Indexed: 12/22/2022]
Abstract
Several reviews on specific topics related to positron emission tomography (PET) ranging in complexity from introductory to highly technical have already been published. This introduction to the analysis of PET data was written as a simple guide of the different phases of analysis of a given PET dataset, from acquisition to preprocessing, to the final data analysis. Although sometimes issues specific to PET in neuroimaging will be mentioned for comparison, most of the examples and applications provided will refer to oncology. Due to the limitations of space we couldn't address each issue comprehensively but, rather, we provided a general overview of each topic together with the references that the interested reader should consult. We will assume a familiarity with the basic principles of PET imaging.
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Blume M, Navab N, Rafecas M. Joint image and motion reconstruction for PET using a B-spline motion model. Phys Med Biol 2012. [PMID: 23190499 DOI: 10.1088/0031-9155/57/24/8249] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
We present a novel joint image and motion reconstruction method for PET. The method is based on gated data and reconstructs an image together with a motion function. The motion function can be used to transform the reconstructed image to any of the input gates. All available events (from all gates) are used in the reconstruction. The presented method uses a B-spline motion model, together with a novel motion regularization procedure that does not need a regularization parameter (which is usually extremely difficult to adjust). Several image and motion grid levels are used in order to reduce the reconstruction time. In a simulation study, the presented method is compared to a recently proposed joint reconstruction method. While the presented method provides comparable reconstruction quality, it is much easier to use since no regularization parameter has to be chosen. Furthermore, since the B-spline discretization of the motion function depends on fewer parameters than a displacement field, the presented method is considerably faster and consumes less memory than its counterpart. The method is also applied to clinical data, for which a novel purely data-driven gating approach is presented.
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Affiliation(s)
- Moritz Blume
- Instituto de Física Corpuscular-IFIC, Universidad de Valencia/CSIC, E-46071 Valencia, Spain.
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Four-Dimensional Image Reconstruction Strategies in Cardiac-Gated and Respiratory-Gated PET Imaging. PET Clin 2012; 8:51-67. [PMID: 27157815 DOI: 10.1016/j.cpet.2012.10.005] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Cardiac and respiratory movements pose significant challenges to image quality and quantitative accuracy in PET imaging. Cardiac and/or respiratory gating attempt to address this issue, but instead lead to enhanced noise levels. Direct four-dimensional (4D) PET image reconstruction incorporating motion compensation has the potential to minimize noise amplification while removing considerable motion blur. A wide-ranging choice of such techniques is reviewed in this work. Future opportunities and the challenges facing the adoption of 4D PET reconstruction and its role in basic and clinical research are also discussed.
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Hatt M, Maitre AL, Wallach D, Fayad H, Visvikis D. Comparison of different methods of incorporating respiratory motion for lung cancer tumor volume delineation on PET images: a simulation study. Phys Med Biol 2012; 57:7409-30. [PMID: 23093372 DOI: 10.1088/0031-9155/57/22/7409] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The interest of PET complementary information for the delineation of the target volume in radiotherapy of lung cancer is increasing. However, respiratory motion requires the determination of a functional internal target volume (ITV) on PET images for which several strategies have been proposed. The purpose of this study was the comparison of these strategies for taking into account respiratory motion and deriving the ITV: (1) adding fixed margins to the volume defined on a single binned image, (2) segmenting a motion averaged image and (3) considering the union of volumes delineated on binned frames. For this third strategy, binned frames were either non-corrected for motion, or corrected using two different methods: elastic registration or super resolution. The strategies' performances were assessed on realistic simulated datasets combining the NCAT phantom with a PET Philips GEMINI scanner model in GATE, and containing various configurations of tumor to background contrast, with both regular and irregular respiratory motion (with a range of motion amplitudes). The obtained ITVs' sensitivity (SE) and positive predictive value (PVE) with respect to the known true ITV were significantly higher (from 0.8 to 0.95) than all other techniques when using binned frames corrected for motion, independently of motion regularity, amplitude, or tumor to background contrast. Although the absolute difference was small and not always significant, images corrected using super resolution led to systematically better results than using elastic registration. The worst results were obtained when using the motion averaged image for SE (around 0.5-0.6) and using the margins added to a single frame for PPV (0.6-0.7), respectively. The best strategy to account for breathing motion for tumor ITV delineation in radiotherapy planning is to rely on the use of the union of volumes delineated on super resolution-corrected binned images.
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Affiliation(s)
- Mathieu Hatt
- INSERM, UMR 1101 LaTIM, CHRU Morvan, Brest, France.
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Fayad H, Pan T, Pradier O, Visvikis D. Patient specific respiratory motion modeling using a 3D patient's external surface. Med Phys 2012; 39:3386-95. [PMID: 22755719 PMCID: PMC4032399 DOI: 10.1118/1.4718578] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2011] [Revised: 04/30/2012] [Accepted: 05/01/2012] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal. METHODS Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient. RESULTS Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only. CONCLUSIONS The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.
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Affiliation(s)
- Hadi Fayad
- INSERM UMR1101, LaTIM, CHU Morvan, Brest F-29200, France.
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Gigengack F, Ruthotto L, Burger M, Wolters CH, Jiang X, Schäfers KP. Motion correction in dual gated cardiac PET using mass-preserving image registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:698-712. [PMID: 22084048 DOI: 10.1109/tmi.2011.2175402] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Respiratory and cardiac motion leads to image degradation in positron emission tomography (PET) studies of the human heart. In this paper we present a novel approach to motion correction based on dual gating and mass-preserving hyperelastic image registration. Thereby, we account for intensity modulations caused by the highly nonrigid cardiac motion. This leads to accurate and realistic motion estimates which are quantitatively validated on software phantom data and carried over to clinically relevant data using a hardware phantom. For patient data, the proposed method is first evaluated in a high statistic (20 min scans) dual gating study of 21 patients. It is shown that the proposed approach properly corrects PET images for dual-cardiac as well as respiratory-motion. In a second study the list mode data of the same patients is cropped to a scan time reasonable for clinical practice (3 min). This low statistic study not only shows the clinical applicability of our method but also demonstrates its robustness against noise obtained by hyperelastic regularization.
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Affiliation(s)
- Fabian Gigengack
- European Institute for Molecular Imaging (EIMI), University of Münster, Münster, Germany.
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Wallach D, Lamare F, Kontaxakis G, Visvikis D. Super-resolution in respiratory synchronized positron emission tomography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:438-48. [PMID: 21997249 DOI: 10.1109/tmi.2011.2171358] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Respiratory motion is a major source of reduced quality in positron emission tomography (PET). In order to minimize its effects, the use of respiratory synchronized acquisitions, leading to gated frames, has been suggested. Such frames, however, are of low signal-to-noise ratio (SNR) as they contain reduced statistics. Super-resolution (SR) techniques make use of the motion in a sequence of images in order to improve their quality. They aim at enhancing a low-resolution image belonging to a sequence of images representing different views of the same scene. In this work, a maximum a posteriori (MAP) super-resolution algorithm has been implemented and applied to respiratory gated PET images for motion compensation. An edge preserving Huber regularization term was used to ensure convergence. Motion fields were recovered using a B-spline based elastic registration algorithm. The performance of the SR algorithm was evaluated through the use of both simulated and clinical datasets by assessing image SNR, as well as the contrast, position and extent of the different lesions. Results were compared to summing the registered synchronized frames on both simulated and clinical datasets. The super-resolution image had higher SNR (by a factor of over 4 on average) and lesion contrast (by a factor of 2) than the single respiratory synchronized frame using the same reconstruction matrix size. In comparison to the motion corrected or the motion free images a similar SNR was obtained, while improvements of up to 20% in the recovered lesion size and contrast were measured. Finally, the recovered lesion locations on the SR images were systematically closer to the true simulated lesion positions. These observations concerning the SNR, lesion contrast and size were confirmed on two clinical datasets included in the study. In conclusion, the use of SR techniques applied to respiratory motion synchronized images lead to motion compensation combined with improved image SNR and contrast, without any increase in the overall acquisition times.
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Dikaios N, Fryer TD. Improved motion-compensated image reconstruction for PET using sensitivity correction per respiratory gate and an approximate tube-of-response backprojector. Med Phys 2011; 38:4958-70. [PMID: 21978040 DOI: 10.1118/1.3611041] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE One limitation of positron emission tomography (PET) imaging of the torso is patient motion. Motion-compensated image reconstruction (MCIR) is one method employed to reduce the deleterious effects of motion. Existing MCIR algorithms use a single sensitivity correction term, which provides inexact normalization for multigate data. Consequently, in this study, the authors derive and examine the performance of an MCIR algorithm with sensitivity correction per gate. In addition, they demonstrate an approximate tube-of-response (TOR) backprojector. METHODS Simulated data from the NCAT phantom with six lesions added were used to compare MCIR algorithms with and without the incorporation of sensitivity correction per gate and TOR backprojection to postreconstruction registration (PRR) and images reconstructed without motion correction. To make the simulations more realistic, intragate motion was included. Deformation fields were determined from NCAT anatomical images using a free-form deformation approach with bending energy regularization. RESULTS Sensitivity correction per gate and TOR backprojection improved mean lesion contrast-to-noise ratio by 6%-8%, with the maximum increase (21%-23%) found for the smallest lesion. These increases were obtained despite a small increase (3%) in noise as measured by standard deviation in a uniform lung region. Sensitivity correction per gate comes at no extra computational cost, whilst replacing line-of-response backprojection with TOR backprojection increased the overall computation time by ∼20%. In addition, MCIR was found to be superior to PRR, with one factor contributing to this difference being the differential impact of interpolation following deformation. MCIR was also shown to exhibit super-resolution. CONCLUSIONS Replacing a single sensitivity correction term in MCIR with sensitivity correction per gate improves lesion detectability. For a small increase in computational expense, further improvements are achieved using an approximate TOR backprojector rather than line-of-response backprojection.
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Affiliation(s)
- Nikolaos Dikaios
- Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
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