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- Valiente-Dobon, J. J., et al.
(författare)
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Conceptual design of the AGATA 2 pi array at LNL
- 2023
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Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 1049
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Tidskriftsartikel (refereegranskat)abstract
- The Advanced GAmma Tracking Array (AGATA) has been installed at Laboratori Nazionali di Legnaro (LNL), Italy. In this installation, AGATA will consist, at the beginning, of 13 AGATA triple clusters (ATCs) with an angular coverage of 1n,and progressively the number of ATCs will increase up to a 2 pi angular coverage. This setup will exploit both stable and radioactive ion beams delivered by the Tandem-PIAVE-ALPI accelerator complex and the SPES facility. The new implementation of AGATA at LNL will be used in two different configurations, firstly one coupled to the PRISMA large-acceptance magnetic spectrometer and lately a second one at Zero Degrees, along the beam line. These two configurations will allow us to cover a broad physics program, using different reaction mechanisms, such as Coulomb excitation, fusion-evaporation, transfer and fission at energies close to the Coulomb barrier. These setups have been designed to be coupled with a large variety of complementary detectors such as charged particle detectors, neutron detectors, heavy-ion detectors, high-energy gamma-ray arrays, cryogenic and gasjet targets and the plunger device for lifetime measurements. We present in this paper the conceptual design, characteristics and performance figures of this implementation of AGATA at LNL.
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- Lane, C. A., et al.
(författare)
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Study protocol: Insight 46-a neuroscience sub-study of the MRC National Survey of Health and Development
- 2017
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Ingår i: Bmc Neurology. - : Springer Science and Business Media LLC. - 1471-2377. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- Background: Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment -including a-amyloid depostion, vascular disease, network breakdown and atrophy -to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms. Methods/design: This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that similar to 1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, beta-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73). Discussion: Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials.
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- Coath, W., et al.
(författare)
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Operationalizing the centiloid scale for F-18 florbetapir PET studies on PET/MRI
- 2023
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Ingår i: Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring. - 2352-8729. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTIONThe Centiloid scale aims to harmonize amyloid beta (A beta) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODSWe transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for A beta PET positivity were converted. RESULTSThe Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. DISCUSSIONTransformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTSCentiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids. White matter referenced values may be less generalizable between datasets.
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