Cargando…

Software development for quantitative analysis of brain amyloid PET

INTRODUCTION: Centiloid (CL) scaling has become a standard quantitative measure in amyloid PET because it allows the direct comparison of results across sites, even when different analytical methods or PET tracers are used. METHODS: In the present study, we developed new standalone software to easil...

Descripción completa

Detalles Bibliográficos
Autores principales:	Matsuda, Hiroshi, Yamao, Tensho
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2022
Materias:	Original Articles
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933769/ https://www.ncbi.nlm.nih.gov/pubmed/35134278 http://dx.doi.org/10.1002/brb3.2499

Ejemplares similares

Amyloid PET quantification using low-dose CT-guided anatomic standardization
por: Matsuda, Hiroshi, et al.
Publicado: (2021)

Tau positron emission tomography in patients with cognitive impairment and suspected Alzheimer’s disease
por: Matsuda, Hiroshi, et al.
Publicado: (2023)

Development of software for measuring brain amyloid accumulation using (18)F‐florbetapir PET and calculating global Centiloid scale and regional Z‐score values
por: Matsuda, Hiroshi, et al.
Publicado: (2023)

Software compatibility analysis for quantitative measures of [(18)F]flutemetamol amyloid PET burden in mild cognitive impairment
por: Pemberton, Hugh G., et al.
Publicado: (2023)

Evaluation of quantitative accuracy among different scatter corrections for quantitative bone SPECT/CT imaging
por: Miwa, Kenta, et al.
Publicado: (2022)

A comparison of advanced semi-quantitative amyloid PET analysis methods
por: Peira, Enrico, et al.
Publicado: (2022)

Quantitative comparative analysis of amyloid PET images using three radiopharmaceuticals
por: Jeong, Young Jin, et al.
Publicado: (2023)

Quantitative Evaluation of (18)F-Flutemetamol PET in Patients With Cognitive Impairment and Suspected Alzheimer's Disease: A Multicenter Study
por: Matsuda, Hiroshi, et al.
Publicado: (2021)

NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis
por: Markiewicz, Pawel J., et al.
Publicado: (2017)

Gray matter structural networks related to (18)F-THK5351 retention in cognitively normal older adults and Alzheimer's disease patients
por: Shigemoto, Yoko, et al.
Publicado: (2021)

Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images
por: Miwa, Kenta, et al.
Publicado: (2023)

Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer’s disease
por: Imabayashi, Etsuko, et al.
Publicado: (2022)

Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software
por: Markiewicz, Pawel J., et al.
Publicado: (2022)

Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms
por: Zukić, Dženan, et al.
Publicado: (2018)

AMYQ: An index to standardize quantitative amyloid load across PET tracers
por: Pegueroles, Jordi, et al.
Publicado: (2021)

Development of brain-penetrable antibody radioligands for in vivo PET imaging of amyloid-β and tau
por: Banka, Vinay, et al.
Publicado: (2023)

High-resolution, quantitative 3D PET image reconstruction for the Siemens hybrid 3T MR/BrainPET scanner using the PET reconstruction software toolkit (PRESTO)
por: Scheins, Juergen, et al.
Publicado: (2014)

Development of a PET images reconstruction software
por: Stringhini, Gianluca
Publicado: (2018)

Florbetaben for PET Imaging of Beta-Amyloid Plaques in the Brain
por: Richards, Danielle, et al.
Publicado: (2014)

A multisite analysis of the concordance between visual image interpretation and quantitative analysis of [(18)F]flutemetamol amyloid PET images
por: Bucci, Marco, et al.
Publicado: (2021)

The feasibility of (11)C‐PIB‐PET/CT for amyloid plaque burden: validation of the effectiveness of CT‐based partial volume correction
por: Sasaki, Kei, et al.
Publicado: (2016)

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom
por: Yoshii, Tokiya, et al.
Publicado: (2020)

Quantitative amyloid PET in Alzheimer's disease: the AMYPAD prognostic and natural history study
por: Lopes Alves, Isadora, et al.
Publicado: (2020)

Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods
por: Jovalekic, Aleksandar, et al.
Publicado: (2023)

Predicting amyloid PET and tau PET stages with plasma biomarkers
por: Jack, Clifford R, et al.
Publicado: (2023)

Phantom criteria for qualification of brain FDG and amyloid PET across different cameras
por: Ikari, Yasuhiko, et al.
Publicado: (2016)

Software for quantitative trait analysis
por: Almasy, Laura, et al.
Publicado: (2005)

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants
por: Shigemoto, Yoko, et al.
Publicado: (2023)

Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software
por: Kang, Koung Mi, et al.
Publicado: (2020)

Plasma Amyloid-β Oligomerization Tendency Predicts Amyloid PET Positivity
por: Pyun, Jung-Min, et al.
Publicado: (2021)

Development of Amyloid PET Analysis Pipeline Using Deep Learning-Based Brain MRI Segmentation—A Comparative Validation Study
por: Lee, Jiyeon, et al.
Publicado: (2022)

Concordance in detecting amyloid positivity between (18)F-florbetaben and (18)F-flutemetamol amyloid PET using quantitative and qualitative assessments
por: Cho, Soo Hyun, et al.
Publicado: (2020)

Diagnostic value of amyloid-PET and tau-PET: a head-to-head comparison
por: Altomare, Daniele, et al.
Publicado: (2021)

Development and evaluation of an automated quantification tool for amyloid PET images
por: Tsubaki, Yuma, et al.
Publicado: (2020)

Classification of Aβ State From Brain Amyloid PET Images Using Machine Learning Algorithm
por: Simfukwe, Chanda, et al.
Publicado: (2023)

Clinical impact of digital and conventional PET control databases for semi-quantitative analysis of brain (18)F-FDG digital PET scans
por: Mairal, Elise, et al.
Publicado: (2020)

Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies
por: van der Weijden, Chris W. J., et al.
Publicado: (2023)

Fluorine-18-Labeled Antibody Ligands for PET Imaging of Amyloid-β in Brain
por: Syvänen, Stina, et al.
Publicado: (2020)

Spatial Patterns of Amyloid Deposition in Patients with Chronic Focal or Diffuse Traumatic Brain Injury Using (18)F-FPYBF-2 PET
por: Ubukata, Shiho, et al.
Publicado: (2020)

Using PET with (18)F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment
por: Camus, V., et al.
Publicado: (2012)

Cannot write session to /tmp/vufind_sessions/sess_q54irc2u2t4nb2oubmt6lt76jh