Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth.
Phys Med
; 50: 26-36, 2018 Jun.
Article
em En
| MEDLINE
| ID: mdl-29891091
ABSTRACT
The purpose of this study was to examine the dependence of image texture features on MR acquisition parameters and reconstruction using a digital MR imaging phantom. MR signal was simulated in a parallel imaging radiofrequency coil setting as well as a single element volume coil setting, with varying levels of acquisition noise, three acceleration factors, and four image reconstruction algorithms. Twenty-six texture features were measured on the simulated images, ground truth images, and clinical brain images. Subtle algorithm-dependent errors were observed on reconstructed phantom images, even in the absence of added noise. Sources of image error include Gibbs ringing at image edge gradients (tissue interfaces) and well-known artifacts due to high acceleration; two of the iterative reconstruction algorithms studied were able to mitigate these image errors. The difference of the texture features from ground truth, and their variance over reconstruction algorithm and parallel imaging acceleration factor, were compared to the clinical "effect size", i.e., the feature difference between high- and low-grade tumors on T1- and T2-weighted brain MR images of twenty glioma patients. The measured feature error (difference from ground truth) was small for some features, but substantial for others. The feature variance due to reconstruction algorithm and acceleration factor were generally smaller than the clinical effect size. Certain texture features may be preserved by MR imaging, but adequate precautions need to be taken regarding their validity and reliability. We present a general simulation framework for assessing the robustness and accuracy of radiomic textural features under various MR acquisition/reconstruction scenarios.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Imageamento por Ressonância Magnética
/
Modelos Teóricos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Phys Med
Assunto da revista:
BIOFISICA
/
BIOLOGIA
/
MEDICINA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Estados Unidos