Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Markiewicz, Paweł; Matthews, Julian C.; Ashburner, John; Cash, David M.; Thomas, David L.; Vita, Enrico De; Barnes, Anna; Cardoso, M. Jorge; Modat, Marc; Brown, Richard; Thielemans, Kris; Costa-Luis, Casper da; Alves, Isadora Lopes; Gispert, Juan Domingo; Schmidt, Mark E.; Marsden, Paul; Hammers, Alexander; Ourselin, Sébastien; Barkhof, Frederik

doi:10.1016/j.neuroimage.2021.117821

Cited by 11 publications

(10 citation statements)

References 34 publications

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“…The routines are then embedded in Python C extensions to be readily available for high-level manipulation of PET data in Python. Using NiftyPET , it has been possible to accurately assess the precision of MR-PET image registration, critical for accurate quantification of amyloid PET data ( 45 ). Also, the software was used for comprehensive analysis of the American College of Radiology PET phantom to estimate the spatial resolution of PET scanners ( 46 ) – information which is essential for performing a robust partial volume correction of amyloid PET images.…”

Section: Toward Open Sourcementioning

confidence: 99%

The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact

Collij

Farrar²,

García

et al. 2023

Front. Neurol.

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BackgroundAmyloid-β (Aβ) accumulation is considered the earliest pathological change in Alzheimer's disease (AD). The Amyloid Imaging to Prevent Alzheimer's Disease (AMYPAD) consortium is a collaborative European framework across European Federation of Pharmaceutical Industries Associations (EFPIA), academic, and ‘Small and Medium-sized enterprises’ (SME) partners aiming to provide evidence on the clinical utility and cost-effectiveness of Positron Emission Tomography (PET) imaging in diagnostic work-up of AD and to support clinical trial design by developing optimal quantitative methodology in an early AD population.The AMYPAD studiesIn the Diagnostic and Patient Management Study (DPMS), 844 participants from eight centres across three clinical subgroups (245 subjective cognitive decline, 342 mild cognitive impairment, and 258 dementia) were included. The Prognostic and Natural History Study (PNHS) recruited pre-dementia subjects across 11 European parent cohorts (PCs). Approximately 1600 unique subjects with historical and prospective data were collected within this study. PET acquisition with [18F]flutemetamol or [18F]florbetaben radiotracers was performed and quantified using the Centiloid (CL) method.ResultsAMYPAD has significantly contributed to the AD field by furthering our understanding of amyloid deposition in the brain and the optimal methodology to measure this process. Main contributions so far include the validation of the dual-time window acquisition protocol to derive the fully quantitative non-displaceable binding potential (BPND), assess the value of this metric in the context of clinical trials, improve PET-sensitivity to emerging Aβ burden and utilize its available regional information, establish the quantitative accuracy of the Centiloid method across tracers and support implementation of quantitative amyloid-PET measures in the clinical routine.Future stepsThe AMYPAD consortium has succeeded in recruiting and following a large number of prospective subjects and setting up a collaborative framework to integrate data across European PCs. Efforts are currently ongoing in collaboration with ARIDHIA and ADDI to harmonize, integrate, and curate all available clinical data from the PNHS PCs, which will become openly accessible to the wider scientific community.

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Section: Toward Open Sourcementioning

confidence: 99%

The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact

Collij

Farrar²,

García

et al. 2023

Front. Neurol.

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“…Nevertheless, with the specifically designed registration templates, we were able to achieve robust registration, which was assessed by running independent registrations for each noisy bootstrap PET image realization, finding that the registration does not produce detectable uncertainty above the intrinsic PET noise. Furthermore, based on previous research 30 and the visual inspection of registration, it can be surmized that more precise registration is also likely to be more accurate.…”

Section: Discussionmentioning

confidence: 99%

“…Here we focus on evaluation of the precision (uncertainty) of the whole processing chain of acquisition, image reconstruction, registration, and analysis using bootstrap resampling of the list‐mode data. 29 , 30 Note that the bootstrap resampling does not account for the variability in filling or positioning of the phantom. The two phantom scans were resampled 50 times, resulting in 100 realizations for the two acquisitions.…”

Section: Methodsmentioning

confidence: 99%

“…The standard analysis of the ACR phantom can be straightforwardly performed using the provided sampling templates and will be available on our website https://niftypet.readthedocs.io, together with all the PET data and templates as open source. Here we focus on evaluation of the precision (uncertainty) of the whole processing chain of acquisition, image reconstruction, registration, and analysis using bootstrap resampling of the list‐mode data 29,30 . Note that the bootstrap resampling does not account for the variability in filling or positioning of the phantom.…”

Section: Methodsmentioning

confidence: 99%

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Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software

et al. 2022

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A novel phantom-imaging platform, a set of software tools, for automated and high-precision imaging of the American College of Radiology (ACR) positron emission tomography (PET) phantom for PET/magnetic resonance (PET/MR) and PET/computed tomography (PET/CT) systems is proposed. Methods:The key feature of this platform is the vector graphics design that facilitates the automated measurement of the knife-edge response function and hence image resolution, using composite volume of interest templates in a 0.5 mm resolution grid applied to all inserts of the phantom. Furthermore, the proposed platform enables the generation of an accurate 𝜇-map for PET/MR systems with a robust alignment based on two-stage image registration using specifically designed PET templates. The proposed platform is based on the open-source NiftyPET software package used to generate multiple list-mode data bootstrap realizations and image reconstructions to determine the precision of the two-stage registration and any image-derived statistics. For all the analyses, iterative image reconstruction was employed with and without modeled shift-invariant point spread function and with varying iterations of the ordered subsets expectation maximization (OSEM) algorithm. The impact of the activity outside the field of view (FOV) was assessed using two acquisitions of 30 min each, with and without the activity outside the FOV. Results: The utility of the platform has been demonstrated by providing a standard and an advanced phantom analysis including the estimation of This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…For this analysis, we used a newly-developed multi-platform software AmyPET (https://github.com/AMYPAD/AmyPET), extending NiftyPET 27 . AmyPET enables robust quantification of PET scans while using SPM12 for core MR-PET image registration 28 , estimating amyloid/tau load with high quantitative accuracy and precision from raw count PET data to accurately account for common head motion. Protocol details for amyloid and tau PET acquisition are provided in eMethods 2 (supplementary material).…”

Section: Methodsmentioning

confidence: 99%

Heterogeneity in Preclinical Alzheimer’s Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling

Shand

Markiewicz

Cash

et al. 2023

Preprint

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Importance: Undetected biological heterogeneity adversely impacts trials in Alzheimer's disease because rate of cognitive decline --- and perhaps response to treatment --- differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer's disease progression. The resulting stratification is yet to be leveraged in clinical trials. Objective: Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value. Design: Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used. Setting: The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America. Participants: Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI). Main Outcomes and Measures: Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET. Results: We included 1,240 Aβ+ participants (and 407 Aβ− controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes --- Typical, Cortical, Subcortical --- comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the Cortical subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the Subcortical subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the Typical (-0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and Cortical (-0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB (Typical: +0.09/yr, [0.06,0.12], P<.0001; and Cortical: +0.07/yr, [0.04,0.10], P<.0001). Conclusions and Relevance: In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance.

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Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Cited by 11 publications

References 34 publications

The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact

The amyloid imaging for the prevention of Alzheimer's disease consortium: A European collaboration with global impact

Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software

Heterogeneity in Preclinical Alzheimer’s Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling

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