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Schneider A, Munoz C, Hua A, Ellis S, Jeljeli S, Kunze KP, Neji R, Reader AJ, Reyes E, Ismail TF, Botnar RM, Prieto C. Non-rigid motion-compensated 3D whole-heart T 2 mapping in a hybrid 3T PET-MR system. Magn Reson Med 2024; 91:1951-1964. [PMID: 38181169 DOI: 10.1002/mrm.29973] [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: 06/07/2023] [Revised: 11/21/2023] [Accepted: 11/26/2023] [Indexed: 01/07/2024]
Abstract
PURPOSE Simultaneous PET-MRI improves inflammatory cardiac disease diagnosis. However, challenges persist in respiratory motion and mis-registration between free-breathing 3D PET and 2D breath-held MR images. We propose a free-breathing non-rigid motion-compensated 3D T2 -mapping sequence enabling whole-heart myocardial tissue characterization in a hybrid 3T PET-MR system and provides non-rigid respiratory motion fields to correct also simultaneously acquired PET data. METHODS Free-breathing 3D whole-heart T2 -mapping was implemented on a hybrid 3T PET-MRI system. Three datasets were acquired with different T2 -preparation modules (0, 28, 55 ms) using 3-fold undersampled variable-density Cartesian trajectory. Respiratory motion was estimated via virtual 3D image navigators, enabling multi-contrast non-rigid motion-corrected MR reconstruction. T2 -maps were computed using dictionary-matching. Approach was tested in phantom, 8 healthy subjects, 14 MR only and 2 PET-MR patients with suspected cardiac disease and compared with spin echo reference (phantom) and clinical 2D T2 -mapping (in-vivo). RESULTS Phantom results show a high correlation (R2 = 0.996) between proposed approach and gold standard 2D T2 mapping. In-vivo 3D T2 -mapping average values in healthy subjects (39.0 ± 1.4 ms) and patients (healthy tissue) (39.1 ± 1.4 ms) agree with conventional 2D T2 -mapping (healthy = 38.6 ± 1.2 ms, patients = 40.3 ± 1.7 ms). Bland-Altman analysis reveals bias of 1.8 ms and 95% limits of agreement (LOA) of -2.4-6 ms for healthy subjects, and bias of 1.3 ms and 95% LOA of -1.9 to 4.6 ms for patients. CONCLUSION Validated efficient 3D whole-heart T2 -mapping at hybrid 3T PET-MRI provides myocardial inflammation characterization and non-rigid respiratory motion fields for simultaneous PET data correction. Comparable T2 values were achieved with both 3D and 2D methods. Improved image quality was observed in the PET images after MR-based motion correction.
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Affiliation(s)
- Alina Schneider
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Alina Hua
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sam Ellis
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sami Jeljeli
- PET Centre, St Thomas' Hospital, King's College London & Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Karl P Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew J Reader
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Eliana Reyes
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Tevfik F Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millenium Institute for Intelligent Healthcare Engineering iHEALTH, Santiago, Chile
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Veronese M, Rizzo G, Belzunce M, Schubert J, Searle G, Whittington A, Mansur A, Dunn J, Reader A, Gunn RN. Reproducibility of findings in modern PET neuroimaging: insight from the NRM2018 grand challenge. J Cereb Blood Flow Metab 2021; 41:2778-2796. [PMID: 33993794 PMCID: PMC8504414 DOI: 10.1177/0271678x211015101] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/10/2021] [Accepted: 04/03/2021] [Indexed: 11/16/2022]
Abstract
The reproducibility of findings is a compelling methodological problem that the neuroimaging community is facing these days. The lack of standardized pipelines for image processing, quantification and statistics plays a major role in the variability and interpretation of results, even when the same data are analysed. This problem is well-known in MRI studies, where the indisputable value of the method has been complicated by a number of studies that produce discrepant results. However, any research domain with complex data and flexible analytical procedures can experience a similar lack of reproducibility. In this paper we investigate this issue for brain PET imaging. During the 2018 NeuroReceptor Mapping conference, the brain PET community was challenged with a computational contest involving a simulated neurotransmitter release experiment. Fourteen international teams analysed the same imaging dataset, for which the ground-truth was known. Despite a plurality of methods, the solutions were consistent across participants, although not identical. These results should create awareness that the increased sharing of PET data alone will only be one component of enhancing confidence in neuroimaging results and that it will be important to complement this with full details of the analysis pipelines and procedures that have been used to quantify data.
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Affiliation(s)
- Mattia Veronese
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | - Martin Belzunce
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London, UK
| | - Julia Schubert
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | | | | | - Ayla Mansur
- Invicro LLC, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Joel Dunn
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London, UK
- King's College London & Guy's and St. Thomas' PET Centre, London, UK
| | - Andrew Reader
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London, UK
| | - Roger N Gunn
- Invicro LLC, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - and the Grand Challenge Participants#
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Invicro LLC, London, UK
- School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
- King's College London & Guy's and St. Thomas' PET Centre, London, UK
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PET NEMA IQ Phantom dataset: image reconstruction settings for quantitative PET imaging. Data Brief 2021; 37:107231. [PMID: 34195308 PMCID: PMC8237526 DOI: 10.1016/j.dib.2021.107231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/09/2021] [Accepted: 06/10/2021] [Indexed: 01/15/2023] Open
Abstract
The data presented here provide information about the role of reconstruction parameters on Positron Emission Tomography (PET) image quantification. Multiple phantom measurements in four different Spheres to Background Ratio (SBR) were performed on Biograph 6 TruePoint TrueV PET/CT scanner. PET raw data were reconstructed with/without resolution recovery algorithm using six various iteration x subsets with five different Full-Width Half-Maximum (FWHM) values of Gaussian post-smoothing filter. The Recovery Coefficient (RC) of six spheres using three common Volume of Interest (VOI) methods (max, 3D-50% Isocontour, and peak) were calculated. Moreover, SUVmax, SUVmean, and SUVpeak and volumetric indices, such as metabolic tumor volume (MTV), volume recovery coefficient (VRC), and total lesion glycolysis (TLG) were measured. RCmax, RC50%, and RCpeak as a function of sphere size were plotted in all reconstruction methods considering different SBRs. The data could be noticeable for standardization and optimization of quantitative metrics in PET imaging.
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Munoz C, Ellis S, Nekolla SG, Kunze KP, Vitadello T, Neji R, Botnar RM, Schnabel JA, Reader AJ, Prieto C. MR-guided motion-corrected PET image reconstruction for cardiac PET-MR. J Nucl Med 2021; 62:jnumed.120.254235. [PMID: 34049978 PMCID: PMC8612202 DOI: 10.2967/jnumed.120.254235] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022] Open
Abstract
Simultaneous PET-MR imaging has shown potential for the comprehensive assessment of myocardial health from a single examination. Furthermore, MR-derived respiratory motion information has been shown to improve PET image quality by incorporating this information into the PET image reconstruction. Separately, MR-based anatomically guided PET image reconstruction has been shown to perform effective denoising, but this has been so far demonstrated mainly in brain imaging. To date the combined benefits of motion compensation and anatomical guidance have not been demonstrated for myocardial PET-MR imaging. This work addresses this by proposing a single cardiac PET-MR image reconstruction framework which fully utilises MR-derived information to allow both motion compensation and anatomical guidance within the reconstruction. Methods: Fifteen patients underwent a 18F-FDG cardiac PET-MR scan with a previously introduced acquisition framework. The MR data processing and image reconstruction pipeline produces respiratory motion fields and a high-resolution respiratory motion-corrected MR image with good tissue contrast. This MR-derived information was then included in a respiratory motion-corrected, cardiac-gated, anatomically guided image reconstruction of the simultaneously acquired PET data. Reconstructions were evaluated by measuring myocardial contrast and noise and compared to images from several comparative intermediate methods using the components of the proposed framework separately. Results: Including respiratory motion correction, cardiac gating, and anatomical guidance significantly increased contrast. In particular, myocardium-to-blood pool contrast increased by 143% on average (p<0.0001) compared to conventional uncorrected, non-guided PET images. Furthermore, anatomical guidance significantly reduced image noise compared to non-guided image reconstruction by 16.1% (p<0.0001). Conclusion: The proposed framework for MR-derived motion compensation and anatomical guidance of cardiac PET data was shown to significantly improve image quality compared to alternative reconstruction methods. Each component of the reconstruction pipeline was shown to have a positive impact on the final image quality. These improvements have the potential to improve clinical interpretability and diagnosis based on cardiac PET-MR images.
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Affiliation(s)
- Camila Munoz
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam Ellis
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Stephan G. Nekolla
- Nuklearmedizinische Klinik und Poliklinik, Technische Technical University of Munich, Munich, Germany
| | - Karl P. Kunze
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom
| | - Teresa Vitadello
- Department of Internal Medicine I, University Hospital Rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany; and
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare, Frimley, United Kingdom
| | - Rene M. Botnar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Julia A. Schnabel
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew J. Reader
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile
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Ehrhardt MJ, Markiewicz P, Schönlieb CB. Faster PET reconstruction with non-smooth priors by randomization and preconditioning. Phys Med Biol 2019; 64:225019. [PMID: 31430733 DOI: 10.1088/1361-6560/ab3d07] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Uncompressed clinical data from modern positron emission tomography (PET) scanners are very large, exceeding 350 million data points (projection bins). The last decades have seen tremendous advancements in mathematical imaging tools many of which lead to non-smooth (i.e. non-differentiable) optimization problems which are much harder to solve than smooth optimization problems. Most of these tools have not been translated to clinical PET data, as the state-of-the-art algorithms for non-smooth problems do not scale well to large data. In this work, inspired by big data machine learning applications, we use advanced randomized optimization algorithms to solve the PET reconstruction problem for a very large class of non-smooth priors which includes for example total variation, total generalized variation, directional total variation and various different physical constraints. The proposed algorithm randomly uses subsets of the data and only updates the variables associated with these. While this idea often leads to divergent algorithms, we show that the proposed algorithm does indeed converge for any proper subset selection. Numerically, we show on real PET data (FDG and florbetapir) from a Siemens Biograph mMR that about ten projections and backprojections are sufficient to solve the MAP optimisation problem related to many popular non-smooth priors; thus showing that the proposed algorithm is fast enough to bring these models into routine clinical practice.
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Affiliation(s)
- Matthias J Ehrhardt
- Institute for Mathematical Innovation, University of Bath, Bath BA2 7JU, United Kingdom
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Bland J, Mehranian A, Belzunce MA, Ellis S, da Costa‐Luis C, McGinnity CJ, Hammers A, Reader AJ. Intercomparison of MR-informed PET image reconstruction methods. Med Phys 2019; 46:5055-5074. [PMID: 31494961 PMCID: PMC6899618 DOI: 10.1002/mp.13812] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 08/23/2019] [Accepted: 08/23/2019] [Indexed: 12/28/2022] Open
Abstract
PURPOSE Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET-unique regions). To address this, further developments for MR-informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade-off between the suppression of noise and the retention of unique features present in the PET data. METHODS The reconstruction methods investigated were: the MR-informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR-informed Bowsher and Gaussian MR-guided MAP methods; and the PET-MR-informed hybrid kernel and anato-functional MAP methods. The trade-off between improving the reconstruction of the whole brain region and the PET-unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18 F] fluorodeoxyglucose (FDG) three-dimensional datasets were used. The real [18 F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET-MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. RESULTS For the high-count simulated and real data studies, the anato-functional MAP method performed better than the other methods under investigation (MR-informed, PET-MR-informed and postsmoothed MLEM), in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato-functional MAP method enables the reconstruction of PET-unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato-functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade-off for the reconstruction of the whole brain and PET-unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. CONCLUSION For the reconstruction of noisy data, multiple MR-informed methods produce favorable whole brain vs PET-unique region trade-off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of postsmoothed MLEM.
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Affiliation(s)
- James Bland
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Abolfazl Mehranian
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Martin A. Belzunce
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Sam Ellis
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Casper da Costa‐Luis
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
| | - Colm J. McGinnity
- King's College London & Guy's and St Thomas' PET CentreSt Thomas' HospitalLondonSE1 7EHUK
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET CentreSt Thomas' HospitalLondonSE1 7EHUK
| | - Andrew J. Reader
- School of Biomedical Engineering and Imaging SciencesKing's College LondonSt Thomas' HospitalLondonSE1 7EHUK
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Belzunce MA, Mehranian A, Reader AJ. Enhancement of Partial Volume Correction in MR-Guided PET Image Reconstruction by Using MRI Voxel Sizes. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 3:315-326. [PMID: 31245657 PMCID: PMC6528651 DOI: 10.1109/trpms.2018.2881248] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 06/29/2018] [Accepted: 11/05/2018] [Indexed: 01/08/2023]
Abstract
Positron emission tomography (PET) suffers from poor spatial resolution which results in quantitative bias when evaluating the radiotracer uptake in small anatomical regions, such as the striatum in the brain which is of importance in this paper of neurodegenerative diseases. These partial volume effects need to be compensated for by employing partial volume correction (PVC) methods in order to achieve quantitatively accurate images. Two important PVC methods applied during the reconstruction are resolution modeling, which suffers from Gibbs artifacts, and penalized likelihood using anatomical priors. The introduction of clinical simultaneous PET-MR scanners has attracted new attention for the latter methods and brought new opportunities to use MRI information to assist PET image reconstruction in order to improve image quality. In this context, MR images are usually down-sampled to the PET resolution before being used in MR-guided PET reconstruction. However, the reconstruction of PET images using the MRI voxel size could achieve a better utilization of the high resolution anatomical information and improve the PVC obtained with these methods. In this paper, we evaluate the importance of the use of MRI voxel sizes when reconstructing PET images with MR-guided maximum a posteriori (MAP) methods, specifically the modified Bowsher method. We also propose a method to avoid the artifacts that arise when PET reconstructions are performed in a higher resolution matrix than the standard for a given scanner. The MR-guided MAP reconstructions were implemented with a modified Lange prior that included anatomical information with the Bowsher method. The methods were evaluated with and without resolution modeling for simulated and real brain data. We show that the use of the MRI voxel sizes when reconstructing PET images with MR-guided MAP enhances PVC by improving the contrast and reducing the bias in six different regions of the brain using regional metrics for a single simulated data set and ensemble metrics for ten noise realizations. Similar results were obtained for real data, where a good enhancement of the contrast was achieved. The combination of MR-guided MAP reconstruction with point-spread function modeling and MRI voxel sizes proved to be an attractive method to achieve considerable enhancement of PVC, while reducing and controlling the noise level and Gibbs artifacts.
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Affiliation(s)
- Martin A Belzunce
- School of Biomedical Engineering and Imaging SciencesKing's College London - St. Thomas' HospitalLondonSE1 7EHU.K
| | - Abolfazl Mehranian
- School of Biomedical Engineering and Imaging SciencesKing's College London - St. Thomas' HospitalLondonSE1 7EHU.K
| | - Andrew J Reader
- School of Biomedical Engineering and Imaging SciencesKing's College London - St. Thomas' HospitalLondonSE1 7EHU.K
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Ellis S, Mallia A, McGinnity CJ, Cook GJR, Reader AJ. Multi-Tracer Guided PET Image Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2018; 2:499-509. [PMID: 30215028 PMCID: PMC6130802 DOI: 10.1109/trpms.2018.2856581] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Multi-tracer positron emission tomography (PET) has the potential to enhance PET imaging by providing complementary information from different physiological processes. However, one or more of the images may present high levels of noise. Guided image reconstruction methods transfer information from a guide image into the PET image reconstruction to encourage edge-preserving noise reduction. In this work we aim to reduce noise in poorer quality PET datasets via guidance from higher quality ones by using a weighted quadratic penalty approach. In particular, we applied this methodology to [18F]fluorodeoxyglucose (FDG) and [11C]methionine imaging of gliomas. 3D simulation studies showed that guiding the reconstruction of methionine datasets using pre-existing FDG images reduced reconstruction errors across the whole-brain (-8%) and within a tumour (-36%) compared to maximum likelihood expectation-maximisation (MLEM). Furthermore, guided reconstruction outperformed a comparable non-local means filter, indicating that regularising during reconstruction is preferable to post-reconstruction approaches. Hyperparameters selected from the 3D simulation study were applied to real data, where it was observed that the proposed FDG-guided methionine reconstruction allows for better edge preservation and noise reduction than standard MLEM. Overall, the results in this work demonstrate that transferring information between datasets in multi-tracer PET studies improves image quality and quantification performance.
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Affiliation(s)
- Sam Ellis
- School of Biomedical Engineering and Imaging Sciences, King's College London
| | - Andrew Mallia
- School of Biomedical Engineering and Imaging Sciences, King's College London, and the King's College London and Guy's and St Thomas' PET Centre
| | - Colm J McGinnity
- School of Biomedical Engineering and Imaging Sciences, King's College London, and the King's College London and Guy's and St Thomas' PET Centre
| | - Gary J R Cook
- School of Biomedical Engineering and Imaging Sciences, King's College London, and the King's College London and Guy's and St Thomas' PET Centre
| | - Andrew J Reader
- School of Biomedical Engineering and Imaging Sciences, King's College London
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