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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Impact of PET Reconstruction on Amyloid-β Quantitation in Cross-Sectional and Longitudinal Analyses. J Nucl Med 2024; 65:781-787. [PMID: 38575189 PMCID: PMC11064829 DOI: 10.2967/jnumed.123.266188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/13/2024] [Indexed: 04/06/2024] Open
Abstract
Amyloid-β (Aβ) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aβ PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aβ-negative [Aβ-], 45 Aβ-positive [Aβ+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aβ- or Aβ+ visually. For each reconstruction, Aβ CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aβ- and Aβ+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aβ-, 33 Aβ+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aβ- or Aβ+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aβ CLs were calculated using CapAIBL. Since linear Aβ accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aβ accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aβ accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aβ- and the Aβ+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aβ- CLs increased for a FWHM of 4.5 mm or more, whereas Aβ+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aβ- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aβ accumulators in the Aβ- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aβ PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aβ- and Aβ+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aβ accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.
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Affiliation(s)
- Gihan P Ruwanpathirana
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Robert C Williams
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Colin L Masters
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
| | - Christopher C Rowe
- Florey Institute of Neurosciences and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
- Australian Dementia Network, Melbourne, Victoria, Australia; and
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
| | - Catherine E Davey
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia;
- Melbourne Brain Centre Imaging Unit, University of Melbourne, Melbourne, Victoria, Australia
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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Inter-scanner Aβ-PET harmonization using barrel phantom spatial resolution matching. Alzheimers Dement (Amst) 2024; 16:e12561. [PMID: 38476638 PMCID: PMC10927914 DOI: 10.1002/dad2.12561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 01/15/2024] [Accepted: 01/19/2024] [Indexed: 03/14/2024]
Abstract
INTRODUCTION The standardized uptake value ratio (SUVR) is used to measure amyloid beta-positron emission tomography (Aβ-PET) uptake in the brainDifferences in PET scanner technologies and image reconstruction techniques can lead to variability in PET images across scanners. This poses a challenge for Aβ-PET studies conducted in multiple centers. The aim of harmonization is to achieve consistent Aβ-PET measurements across different scanners. In this study, we propose an Aβ-PET harmonization method of matching spatial resolution, as measured via a barrel phantom, across PET scanners. Our approach was validated using paired subject data, for which patients were imaged on multiple scanners. METHODS In this study, three different PET scanners were evaluated: the Siemens Biograph Vision 600, Siemens Biograph molecular computed tomography (mCT), and Philips Gemini TF64. A total of five, eight, and five subjects were each scanned twice with [18F]-NAV4694 across Vision-mCT, mCT-Philips, and Vision-Philips scanner pairs. The Vision and mCT scans were reconstructed using various iterations, subsets, and post-reconstruction Gaussian smoothing, whereas only one reconstruction configuration was used for the Philips scans. The full-width at half-maximum (FWHM) of each reconstruction configuration was calculated using [18F]-filled barrel phantom scans with the Society of Nuclear Medicine and Molecular Imaging (SNMMI) phantom analysis toolkit. Regional SUVRs were calculated from 72 brain regions using the automated anatomical labelling atlas 3 (AAL3) atlas for each subject and reconstruction configuration. Statistical similarity between SUVRs was assessed using paired (within subject) t-tests for each pair of reconstructions across scanners; the higher the p-value, the greater the similarity between the SUVRs. RESULTS Vision-mCT harmonization: Vision reconstruction with FWHM = 4.10 mm and mCT reconstruction with FWHM = 4.30 mm gave the maximal statistical similarity (maximum p-value) between regional SUVRs. Philips-mCT harmonization: The FWHM of the Philips reconstruction was 8.2 mm and the mCT reconstruction with the FWHM of 9.35 mm, which gave the maximal statistical similarity between regional SUVRs. Philips-Vision harmonization: The Vision reconstruction with an FWHM of 9.1 mm gave the maximal statistical similarity between regional SUVRs when compared with the Philips reconstruction of 8.2 mm and were selected as the harmonized for each scanner pair. CONCLUSION Based on data obtained from three sets of participants, each scanned on a pair of PET scanners, it has been verified that using reconstruction configurations that produce matched-barrel, phantom spatial resolutions results in maximally harmonized Aβ-PET quantitation between scanner pairs. This finding is encouraging for the use of PET scanners in multi-center trials or updates during longitudinal studies. Highlights Question: Does the process of matching the barrel phantom-derived spatial resolution between scanners harmonize amyloid beta-standardized uptake value ratio (Aβ-SUVR) quantitation? Pertinent findings: It has been validated that reconstruction pairs with matched barrel phantom-derived spatial resolution maximize the similarity between subjects paired Aβ-PET (positron emission tomography) SUVR values recorded on two scanners. Implications for patient care: Harmonization between scanners in multi-center trials and PET camera updates in longitudinal studies can be achieved using a simple and efficient phantom measurement procedure, beneficial for the validity of Aβ-PET quantitation measurements.
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Affiliation(s)
- Gihan P. Ruwanpathirana
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVICAustralia
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
| | - Robert C. Williams
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
| | - Colin L. Masters
- Florey Institute of Neurosciences and Mental HealthThe University of MelbourneMelbourneVICAustralia
- The Australian Dementia Network (ADNET)MelbourneAustralia
| | - Christopher C. Rowe
- Florey Institute of Neurosciences and Mental HealthThe University of MelbourneMelbourneVICAustralia
- The Australian Dementia Network (ADNET)MelbourneAustralia
- Department of Molecular Imaging & TherapyAustin HealthMelbourneVICAustralia
| | - Leigh A. Johnston
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVICAustralia
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
| | - Catherine E. Davey
- Department of Biomedical EngineeringThe University of MelbourneMelbourneVICAustralia
- Melbourne Brain Centre Imaging UnitThe University of MelbourneMelbourneVICAustralia
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Ruwanpathirana GP, Williams RC, Masters CL, Rowe CC, Johnston LA, Davey CE. Mapping the association between tau-PET and Aβ-amyloid-PET using deep learning. Sci Rep 2022; 12:14797. [PMID: 36042256 PMCID: PMC9427855 DOI: 10.1038/s41598-022-18963-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/23/2022] [Indexed: 11/26/2022] Open
Abstract
In Alzheimer’s disease, the molecular pathogenesis of the extracellular Aβ-amyloid (Aβ) instigation of intracellular tau accumulation is poorly understood. We employed a high-resolution PET scanner, with low detection thresholds, to examine the Aβ-tau association using a convolutional neural network (CNN), and compared results to a standard voxel-wise linear analysis. The full range of Aβ Centiloid values was highly predicted by the tau topography using the CNN (training R2 = 0.86, validation R2 = 0.75, testing R2 = 0.72). Linear models based on tau-SUVR identified widespread positive correlations between tau accumulation and Aβ burden throughout the brain. In contrast, CNN analysis identified focal clusters in the bilateral medial temporal lobes, frontal lobes, precuneus, postcentral gyrus and middle cingulate. At low Aβ levels, information from the middle cingulate, frontal lobe and precuneus regions was more predictive of Aβ burden, while at high Aβ levels, the medial temporal regions were more predictive of Aβ burden. The data-driven CNN approach revealed new associations between tau topography and Aβ burden.
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Affiliation(s)
- Gihan P Ruwanpathirana
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
| | - Robert C Williams
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
| | - Colin L Masters
- Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, VIC, Australia.,Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia.,Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
| | - Catherine E Davey
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia. .,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia.
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Ruwanpathirana GP, Plett DC, Williams RC, Davey CE, Johnston LA, Kronzucker HJ. Continuous monitoring of plant sodium transport dynamics using clinical PET. Plant Methods 2021; 17:8. [PMID: 33468197 PMCID: PMC7814562 DOI: 10.1186/s13007-021-00707-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 01/04/2021] [Indexed: 05/11/2023]
Abstract
BACKGROUND The absorption, translocation, accumulation and excretion of substances are fundamental processes in all organisms including plants, and have been successfully studied using radiotracers labelled with 11C, 13N, 14C and 22Na since 1939. Sodium is one of the most damaging ions to the growth and productivity of crops. Due to the significance of understanding sodium transport in plants, a significant number of studies have been carried out to examine sodium influx, compartmentation, and efflux using 22Na- or 24Na-labeled salts. Notably, however, most of these studies employed destructive methods, which has limited our understanding of sodium flux and distribution characteristics in real time, in live plants. Positron emission tomography (PET) has been used successfully in medical research and diagnosis for decades. Due to its ability to visualise and assess physiological and metabolic function, PET imaging has also begun to be employed in plant research. Here, we report the use of a clinical PET scanner with a 22Na tracer to examine 22Na-influx dynamics in barley plants (Hordeum vulgare L. spp. Vulgare-cultivar Bass) under variable nutrient levels, alterations in the day/night light cycle, and the presence of sodium channel inhibitors. RESULTS 3D dynamic PET images of whole plants show readily visible 22Na translocation from roots to shoots in each examined plant, with rates influenced by both nutrient status and channel inhibition. PET images show that plants cultivated in low-nutrient media transport more 22Na than plants cultivated in high-nutrient media, and that 22Na uptake is suppressed in the presence of a cation-channel inhibitor. A distinct diurnal pattern of 22Na influx was discernible in curves displaying rates of change of relative radioactivity. Plants were found to absorb more 22Na during the light period, and anticipate the change in the light/dark cycle by adjusting the sodium influx rate downward in the dark period, an effect not previously described experimentally. CONCLUSIONS We demonstrate the utility of clinical PET/CT scanners for real-time monitoring of the temporal dynamics of sodium transport in plants. The effects of nutrient deprivation and of ion channel inhibition on sodium influx into barley plants are shown in two proof-of-concept experiments, along with the first-ever 3D-imaging of the light and dark sodium uptake cycles in plants. This method carries significant potential for plant biology research and, in particular, in the context of genetic and treatment effects on sodium acquisition and toxicity in plants.
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Affiliation(s)
- Gihan P Ruwanpathirana
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Darren C Plett
- Australian Plant Phenomics Facility, The Plant Accelerator, School of Agriculture, Food & Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Robert C Williams
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
| | - Catherine E Davey
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Leigh A Johnston
- Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, VIC, Australia.
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.
| | - Herbert J Kronzucker
- Faculty of Veterinary and Agriculture Sciences, School of Agriculture and Food, The University of Melbourne, Melbourne, VIC, Australia
- Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
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