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Updated: Oct 22, 2025

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
Published on: July 17, 2020
Anthropomorphic brain phantoms for use in MRI systems: a systematic review
Noelle Crasto1,2, Abirami Kirubarajan3, Dafna Sussman4,5,6,7,8
1Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON, M5B 2K3, Canada.
Objective:
To provide a systematic review of available brain MRI phantoms for comparison of structural and functional characteristics.
Materials And Methods:
Phantoms were identified from a literature search using two databases including Google Scholar and PubMed. Narrow inclusion criteria were followed for identification of only tissue-mimicking MRI phantoms excluding digital, computational, or numerical phantoms. Assessment criteria for the identified phantoms was based on three categories being anatomical accuracy, tissue-mimicking materials, and exhibiting relaxation times approximating in-vivo tissues. The available features and uses of each phantom were reported and discussed using the assessment criteria.
Results:
Ten phantoms were identified after screening; each proposed phantom was then summarized in a table (Table 2). Significant features and characteristics were shown in the comparisons of phantom type in each category, being anthropomorphic vs. traditional phantoms. Anthropomorphic phantoms had more anatomically accurate features than traditional phantoms. On the other hand, traditional phantoms commonly used effective tissue-mimicking materials and accurate electromagnetic properties.
Discussion:
The findings provide an overview of the different proposed tissue-mimicking MRI brain phantoms available. Various uses and features are highlighted by comparing criteria such as anatomical accuracy, tissue-mimicking material, and electromagnetic properties. Tissue-mimicking MRI phantoms are an extremely useful tool for researchers and clinicians. Future applications include personalized phantom technology and validation of MR imaging and segmentation methods.
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