Literature DB >> 21317021

An MRI digital brain phantom for validation of segmentation methods.

Bruno Alfano1, Marco Comerci, Michele Larobina, Anna Prinster, Joseph P Hornak, S Easter Selvan, Umberto Amato, Mario Quarantelli, Gioacchino Tedeschi, Arturo Brunetti, Marco Salvatore.   

Abstract

Knowledge of the exact spatial distribution of brain tissues in images acquired by magnetic resonance imaging (MRI) is necessary to measure and compare the performance of segmentation algorithms. Currently available physical phantoms do not satisfy this requirement. State-of-the-art digital brain phantoms also fall short because they do not handle separately anatomical structures (e.g. basal ganglia) and provide relatively rough simulations of tissue fine structure and inhomogeneity. We present a software procedure for the construction of a realistic MRI digital brain phantom. The phantom consists of hydrogen nuclear magnetic resonance spin-lattice relaxation rate (R1), spin-spin relaxation rate (R2), and proton density (PD) values for a 24 × 19 × 15.5 cm volume of a "normal" head. The phantom includes 17 normal tissues, each characterized by both mean value and variations in R1, R2, and PD. In addition, an optional tissue class for multiple sclerosis (MS) lesions is simulated. The phantom was used to create realistic magnetic resonance (MR) images of the brain using simulated conventional spin-echo (CSE) and fast field-echo (FFE) sequences. Results of mono-parametric segmentation of simulations of sequences with different noise and slice thickness are presented as an example of possible applications of the phantom. The phantom data and simulated images are available online at http://lab.ibb.cnr.it/.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21317021     DOI: 10.1016/j.media.2011.01.004

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  5 in total

1.  Semiautomatic regional segmentation to measure orbital fat volumes in thyroid-associated ophthalmopathy. A validation study.

Authors:  M Comerci; A Elefante; D Strianese; R Senese; P Bonavolontà; B Alfano; B Bonavolontà; A Brunetti
Journal:  Neuroradiol J       Date:  2013-08-27

2.  Effects of slice thickness and head rotation when measuring glioma sizes on MRI: in support of volume segmentation versus two largest diameters methods.

Authors:  Pierre Schmitt; Emmanuel Mandonnet; Adrien Perdreau; Elsa D Angelini
Journal:  J Neurooncol       Date:  2013-02-09       Impact factor: 4.130

3.  An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom.

Authors:  Chengyue Wu; David A Hormuth; Ty Easley; Victor Eijkhout; Federico Pineda; Gregory S Karczmar; Thomas E Yankeelov
Journal:  Med Image Anal       Date:  2021-07-20       Impact factor: 13.828

4.  An Anthropomorphic Digital Reference Object (DRO) for Simulation and Analysis of Breast DCE MRI Techniques.

Authors:  Leah Henze Bancroft; James Holmes; Ryan Bosca-Harasim; Jacob Johnson; Pingni Wang; Frank Korosec; Walter Block; Roberta Strigel
Journal:  Tomography       Date:  2022-04-02

5.  D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.

Authors:  Daniele Perrone; Ben Jeurissen; Jan Aelterman; Timo Roine; Jan Sijbers; Aleksandra Pizurica; Alexander Leemans; Wilfried Philips
Journal:  PLoS One       Date:  2016-03-01       Impact factor: 3.240

  5 in total

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