1
|
Whitehead AC, Su KH, Emond EC, Biguri A, Brusaferri L, Machado M, Porter JC, Garthwaite H, Wollenweber SD, McClelland JR, Thielemans K. Data driven surrogate signal extraction for dynamic PET using selective PCA: time windows versus the combination of components. Phys Med Biol 2024; 69:175008. [PMID: 38959903 PMCID: PMC11322562 DOI: 10.1088/1361-6560/ad5ef1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 06/18/2024] [Accepted: 07/03/2024] [Indexed: 07/05/2024]
Abstract
Objective.Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging.Approach.The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyeret al(2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system.Main results.The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods.Significance.This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used).
Collapse
Affiliation(s)
- Alexander C Whitehead
- Institute of Nuclear Medicine, University College London, London, Greater London, United Kingdom
- Centre for Medical Image Computing, University College London, London, Greater London, United Kingdom
- Department of Computer Science, University College London, London, Greater London, United Kingdom
| | - Kuan-Hao Su
- Molecular Imaging and Computed Tomography Engineering, GE Healthcare, Waukesha, WI, United States of America
| | - Elise C Emond
- Institute of Nuclear Medicine, University College London, London, Greater London, United Kingdom
| | - Ander Biguri
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Cambridgeshire, United Kingdom
| | - Ludovica Brusaferri
- Computer Science and Informatics, London South Bank University, London, Greater London, United Kingdom
| | - Maria Machado
- Institute of Nuclear Medicine, University College London, London, Greater London, United Kingdom
| | - Joanna C Porter
- Centre for Respiratory Medicine, University College London, London, Greater London, United Kingdom
| | - Helen Garthwaite
- Centre for Respiratory Medicine, University College London, London, Greater London, United Kingdom
| | - Scott D Wollenweber
- Molecular Imaging and Computed Tomography Engineering, GE Healthcare, Waukesha, WI, United States of America
| | - Jamie R McClelland
- Centre for Medical Image Computing, University College London, London, Greater London, United Kingdom
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, Greater London, United Kingdom
- Centre for Medical Image Computing, University College London, London, Greater London, United Kingdom
| |
Collapse
|
2
|
Leek F, Anderson C, Robinson AP, Moss RM, Porter JC, Garthwaite HS, Groves AM, Hutton BF, Thielemans K. Optimisation of the air fraction correction for lung PET/CT: addressing resolution mismatch. EJNMMI Phys 2023; 10:77. [PMID: 38049611 PMCID: PMC10695904 DOI: 10.1186/s40658-023-00595-y] [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: 09/07/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Increased pulmonary [Formula: see text]F-FDG metabolism in patients with idiopathic pulmonary fibrosis, and other forms of diffuse parenchymal lung disease, can predict measurements of health and lung physiology. To improve PET quantification, voxel-wise air fractions (AF) determined from CT can be used to correct for variable air content in lung PET/CT. However, resolution mismatches between PET and CT can cause artefacts in the AF-corrected image. METHODS Three methodologies for determining the optimal kernel to smooth the CT are compared with noiseless simulations and non-TOF MLEM reconstructions of a patient-realistic digital phantom: (i) the point source insertion-and-subtraction method, [Formula: see text]; (ii) AF-correcting with varyingly smoothed CT to achieve the lowest RMSE with respect to the ground truth (GT) AF-corrected volume of interest (VOI), [Formula: see text]; iii) smoothing the GT image to match the reconstruction within the VOI, [Formula: see text]. The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, [Formula: see text] was implemented on thorax phantom data measured on two clinical PET/CT scanners with various reconstruction protocols. RESULTS The simulations demonstrated that at [Formula: see text] iterations (200 i), the kernel width was dependent on iteration number and VOI position in the lung. The [Formula: see text] method estimated a lower, more uniform, kernel width in all parts of the lung investigated. However, all three methods resulted in approximately equivalent AF-corrected VOI RMSEs (<10%) at [Formula: see text]200i. The insensitivity of AF-corrected quantification to kernel width suggests that a single global kernel could be used. For all three methodologies, the computed global kernel resulted in an AF-corrected lung RMSE <10% at [Formula: see text]200i, while larger lung RMSEs were observed for the VOI-specific kernels. The global kernel approach was then employed with the [Formula: see text] method on measured data. The optimally smoothed GT emission matched the reconstructed image well, both within the VOI and the lung background. VOI RMSE was <10%, pre-AFC, for all reconstructions investigated. CONCLUSIONS Simulations for non-TOF PET indicated that around 200i were needed to approach image resolution stability in the lung. In addition, at this iteration number, a single global kernel, determined from several VOIs, for AFC, performed well over the whole lung. The [Formula: see text] method has the potential to be used to determine the kernel for AFC from scans of phantoms on clinical scanners.
Collapse
Affiliation(s)
- Francesca Leek
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK.
- Nuclear Medicine Metrology, National Physical Laboratory, Teddington, UK.
| | - Cameron Anderson
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
| | - Andrew P Robinson
- Nuclear Medicine Metrology, National Physical Laboratory, Teddington, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
- Schuster Laboratory, School of Physics and Astronomy, University of Manchester, Manchester, UK
| | - Robert M Moss
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Joanna C Porter
- UCL Respiratory, University College London and Interstitial Lung Disease Service, University College London Hospitals NHS Trust, London, UK
| | - Helen S Garthwaite
- UCL Respiratory, University College London and Interstitial Lung Disease Service, University College London Hospitals NHS Trust, London, UK
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London Hospitals NHS Trust, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| |
Collapse
|
3
|
Shiyam Sundar LK, Lassen ML, Gutschmayer S, Ferrara D, Calabrò A, Yu J, Kluge K, Wang Y, Nardo L, Hasbak P, Kjaer A, Abdelhafez YG, Wang G, Cherry SR, Spencer BA, Badawi RD, Beyer T, Muzik O. Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies. J Nucl Med 2023; 64:1145-1153. [PMID: 37290795 DOI: 10.2967/jnumed.122.265362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/09/2023] [Indexed: 06/10/2023] Open
Abstract
We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Methods: Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment. Moreover, the frames suited to successful motion correction were automatically estimated by calculating the initial normalized cross-correlation metric between the reference frame and the other moving frames. To evaluate motion correction performance, WB dynamic image sequences from 3 different PET/CT systems (Biograph mCT, Biograph Vision 600, and uEXPLORER) using 6 different tracers (18F-FDG, 18F-fluciclovine, 68Ga-PSMA, 68Ga-DOTATATE, 11C-Pittsburgh compound B, and 82Rb) were considered. Motion correction accuracy was assessed using 4 different measures: change in volume mismatch between individual WB image volumes to assess gross body motion, change in displacement of a large organ (liver dome) within the torso due to respiration, change in intensity in small tumor nodules due to motion blur, and constancy of activity concentration levels. Results: Motion correction decreased gross body motion artifacts and reduced volume mismatch across dynamic frames by about 50%. Moreover, large-organ motion correction was assessed on the basis of correction of liver dome motion, which was removed entirely in about 70% of all cases. Motion correction also improved tumor intensity, resulting in an average increase in tumor SUVs by 15%. Large deformations seen in gated cardiac 82Rb images were managed without leading to anomalous distortions or substantial intensity changes in the resulting images. Finally, the constancy of activity concentration levels was reasonably preserved (<2% change) in large organs before and after motion correction. Conclusion: FALCON allows fast and accurate correction of rigid and nonrigid WB motion artifacts while being insensitive to scanner hardware or tracer distribution, making it applicable to a wide range of PET imaging scenarios.
Collapse
Affiliation(s)
- Lalith Kumar Shiyam Sundar
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Martin Lyngby Lassen
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Gutschmayer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Daria Ferrara
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Anna Calabrò
- Department of Radiology, University of California-Davis, Davis, California
| | - Josef Yu
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Kilian Kluge
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Yiran Wang
- Department of Radiology, University of California-Davis, Davis, California
| | - Lorenzo Nardo
- Department of Radiology, University of California-Davis, Davis, California
| | - Philip Hasbak
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Kjaer
- Department of Clinical Physiology, Nuclear Medicine, and PET and Cluster for Molecular Imaging Section 4011, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | | | - Guobao Wang
- Department of Radiology, University of California-Davis, Davis, California
| | - Simon R Cherry
- Department of Radiology, University of California-Davis, Davis, California
- Department of Biomedical Engineering, University of California-Davis, Davis, California; and
| | - Benjamin A Spencer
- Department of Radiology, University of California-Davis, Davis, California
| | - Ramsey D Badawi
- Department of Radiology, University of California-Davis, Davis, California
- Department of Biomedical Engineering, University of California-Davis, Davis, California; and
| | - Thomas Beyer
- Quantitative Imaging and Medical Physics Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Otto Muzik
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University School of Medicine, Detroit, Michigan
| |
Collapse
|
4
|
Sari H, Teimoorisichani M, Mingels C, Alberts I, Panin V, Bharkhada D, Xue S, Prenosil G, Shi K, Conti M, Rominger A. Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners. Eur J Nucl Med Mol Imaging 2022; 49:4490-4502. [PMID: 35852557 PMCID: PMC9606046 DOI: 10.1007/s00259-022-05909-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 07/10/2022] [Indexed: 12/19/2022]
Abstract
Purpose Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to construct attenuation maps (µ-maps) at 511 keV. In this work, the increased sensitivity of long axial field-of-view (LAFOV) PET scanners was exploited to develop and evaluate a deep learning (DL) and joint reconstruction-based method to generate µ-maps utilizing background radiation from lutetium-based (LSO) scintillators. Methods Data from 18 subjects were used to train convolutional neural networks to enhance initial µ-maps generated using joint activity and attenuation reconstruction algorithm (MLACF) with transmission data from LSO background radiation acquired before and after the administration of 18F-fluorodeoxyglucose (18F-FDG) (µ-mapMLACF-PRE and µ-mapMLACF-POST respectively). The deep learning-enhanced µ-maps (µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST) were compared against MLACF-derived and CT-based maps (µ-mapCT). The performance of the method was also evaluated by assessing PET images reconstructed using each µ-map and computing volume-of-interest based standard uptake value measurements and percentage relative mean error (rME) and relative mean absolute error (rMAE) relative to CT-based method. Results No statistically significant difference was observed in rME values for µ-mapDL-MLACF-PRE and µ-mapDL-MLACF-POST both in fat-based and water-based soft tissue as well as bones, suggesting that presence of the radiopharmaceutical activity in the body had negligible effects on the resulting µ-maps. The rMAE values µ-mapDL-MLACF-POST were reduced by a factor of 3.3 in average compared to the rMAE of µ-mapMLACF-POST. Similarly, the average rMAE values of PET images reconstructed using µ-mapDL-MLACF-POST (PETDL-MLACF-POST) were 2.6 times smaller than the average rMAE values of PET images reconstructed using µ-mapMLACF-POST. The mean absolute errors in SUV values of PETDL-MLACF-POST compared to PETCT were less than 5% in healthy organs, less than 7% in brain grey matter and 4.3% for all tumours combined. Conclusion We describe a deep learning-based method to accurately generate µ-maps from PET emission data and LSO background radiation, enabling CT-free attenuation and scatter correction in LAFOV PET scanners. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05909-3.
Collapse
Affiliation(s)
- Hasan Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland.
| | | | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | | | - Song Xue
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - George Prenosil
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, Bern, Switzerland
| |
Collapse
|
5
|
Are Quantitative Errors Reduced with Time-of-Flight Reconstruction When Using Imperfect MR-Based Attenuation Maps for 18F-FDG PET/MR Neuroimaging? APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12094605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone 18F-FDG PET neuroimaging were included. Attenuation maps using a single value (0.100 cm−1) with and without air, and a 3-class attenuation map with soft tissue (0.096 cm−1), air and bone (0.151 cm−1) were used. CT-based attenuation correction was used as a reference. Volume-of-interest (VOI) analysis was conducted. Mean bias and standard deviation across the brain was studied. Regional correlations and concordance were evaluated. Statistical testing was conducted. Average bias and standard deviation were slightly reduced in the majority (23–26 out of 35) of the VOI with TOF. Bias was reduced near the cortex, nasal sinuses, and in the mid-brain with TOF. Bland–Altman and regression analysis showed small improvements with TOF. However, the overall effect of TOF to quantitative accuracy was small (3% at maximum) and significant only for two attenuation maps out of three at 525 ps timing resolution. In conclusion, TOF might reduce the quantitative errors due to attenuation correction in PET/MR neuroimaging, but this effect needs to be further investigated on systems with better timing resolution.
Collapse
|
6
|
Chen DL, Ballout S, Chen L, Cheriyan J, Choudhury G, Denis-Bacelar AM, Emond E, Erlandsson K, Fisk M, Fraioli F, Groves AM, Gunn RN, Hatazawa J, Holman BF, Hutton BF, Iida H, Lee S, MacNee W, Matsunaga K, Mohan D, Parr D, Rashidnasab A, Rizzo G, Subramanian D, Tal-Singer R, Thielemans K, Tregay N, van Beek EJR, Vass L, Vidal Melo MF, Wellen JW, Wilkinson I, Wilson FJ, Winkler T. Consensus Recommendations on the Use of 18F-FDG PET/CT in Lung Disease. J Nucl Med 2020; 61:1701-1707. [PMID: 32948678 PMCID: PMC9364897 DOI: 10.2967/jnumed.120.244780] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 09/09/2020] [Indexed: 01/04/2023] Open
Abstract
PET with 18F-FDG has been increasingly applied, predominantly in the research setting, to study drug effects and pulmonary biology and to monitor disease progression and treatment outcomes in lung diseases that interfere with gas exchange through alterations of the pulmonary parenchyma, airways, or vasculature. To date, however, there are no widely accepted standard acquisition protocols or imaging data analysis methods for pulmonary 18F-FDG PET/CT in these diseases, resulting in disparate approaches. Hence, comparison of data across the literature is challenging. To help harmonize the acquisition and analysis and promote reproducibility, we collated details of acquisition protocols and analysis methods from 7 PET centers. From this information and our discussions, we reached the consensus recommendations given here on patient preparation, choice of dynamic versus static imaging, image reconstruction, and image analysis reporting.
Collapse
Affiliation(s)
- Delphine L Chen
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington
| | - Safia Ballout
- School of Physics and Astronomy, University of Leeds, Leeds, United Kingdom
| | - Laigao Chen
- Worldwide Research, Development, and Medical, Pfizer Inc., Cambridge, Massachusetts
| | - Joseph Cheriyan
- Cambridge University Hospitals, NHS Foundation Trust, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Gourab Choudhury
- Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Elise Emond
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Kjell Erlandsson
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Marie Fisk
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Francesco Fraioli
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Roger N Gunn
- inviCRO, London, United Kingdom
- Department of Medicine, Imperial College London, London, United Kingdom
| | - Jun Hatazawa
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University, Osaka, Japan
| | - Beverley F Holman
- Nuclear Medicine Department, Royal Free Hospital, London, United Kingdom
| | - Brian F Hutton
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Hidehiro Iida
- Faculty of Biomedicine and Turku PET Center, University of Turku, Turku, Finland
| | - Sarah Lee
- Amallis Consulting Ltd., London, United Kingdom
| | - William MacNee
- Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Keiko Matsunaga
- Department of Nuclear Medicine and Tracer Kinetics, Osaka University, Osaka, Japan
| | - Divya Mohan
- Medical Innovation, Value Evidence, and Outcomes, GlaxoSmithKline R&D, Collegeville, Pennsylvania
| | - David Parr
- University Hospitals Coventry and Warwickshire, Coventry, United Kingdom
| | - Alaleh Rashidnasab
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Gaia Rizzo
- inviCRO, London, United Kingdom
- Department of Medicine, Imperial College London, London, United Kingdom
| | | | - Ruth Tal-Singer
- Medical Innovation, Value Evidence, and Outcomes, GlaxoSmithKline R&D, Collegeville, Pennsylvania
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, London, United Kingdom
| | - Nicola Tregay
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Edwin J R van Beek
- Edinburgh Imaging, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Laurence Vass
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Marcos F Vidal Melo
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jeremy W Wellen
- Research and Early Development, Celgene, Cambridge, Massachusetts; and
| | - Ian Wilkinson
- Cambridge University Hospitals, NHS Foundation Trust, Cambridge, United Kingdom
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Frederick J Wilson
- Clinical Imaging, Clinical Pharmacology, and Experimental Medicine, GlaxoSmithKline, Stevenage, United Kingdom
| | - Tilo Winkler
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
7
|
Wallstén E, Axelsson J, Jonsson J, Karlsson CT, Nyholm T, Larsson A. Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images. EJNMMI Phys 2020; 7:68. [PMID: 33226495 PMCID: PMC7683750 DOI: 10.1186/s40658-020-00336-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 11/04/2020] [Indexed: 11/29/2022] Open
Abstract
Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.
Collapse
Affiliation(s)
- Elin Wallstén
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden.
| | - Jan Axelsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| | - Anne Larsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| |
Collapse
|
8
|
Emond EC, Bousse A, Brusaferri L, Hutton BF, Thielemans K. Improved PET/CT Respiratory Motion Compensation by Incorporating Changes in Lung Density. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020. [DOI: 10.1109/trpms.2020.3001094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|