Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Autor: Tara Barwick, Lesley Honeyfield, Eric O. Aboagye, Adam D. Waldman, Matthew Grech-Sollars, Katherine Ordidge, Marianna Inglese
Přispěvatelé: Imperial College Healthcare NHS Trust- BRC Funding, Imperial Health Charity
Rok vydání: 2019
Předmět:
Adult
Male
DCE-MRI
Coefficient of variation
Contrast Media
Image processing
Overfitting
Biology
computer.software_genre
Stability (probability)
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Voxel
Image Interpretation
Computer-Assisted
Humans
Radiology
Nuclear Medicine and imaging
Shutter speed model
Aged
Diagnostic Neuroradiology
Brain Neoplasms
business.industry
Model selection
Reproducibility of Results
1103 Clinical Sciences
Pattern recognition
Glioma
Middle Aged
Tofts model
Magnetic Resonance Imaging
Shutter speed
Nuclear Medicine & Medical Imaging
Primary brain tumour
Female
Neurology (clinical)
Artificial intelligence
Akaike information criterion
1109 Neurosciences
Cardiology and Cardiovascular Medicine
business
computer
030217 neurology & neurosurgery
Zdroj: Neuroradiology
Inglese, M, Ordidge, K L, Honeyfield, L, Barwick, T D, Aboagye, E O, Waldman, A D & Grech-Sollars, M 2019, ' Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models ', Neuroradiology . https://doi.org/10.1007/s00234-019-02265-2
ISSN: 1432-1920
0028-3940
Popis: Purpose The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (
Databáze: OpenAIRE
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