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Magnetic Resonance in Medicine
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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PubMed Central
Other literature type . 2023
License: CC BY
Data sources: PubMed Central
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Toward a realistic in silico abdominal phantom for QSM

Authors: Javier Silva; Carlos Milovic; Mathias Lambert; Cristian Montalba; Cristóbal Arrieta; Pablo Irarrazaval; Sergio Uribe; +1 Authors

Toward a realistic in silico abdominal phantom for QSM

Abstract

PurposeQSM outside the brain has recently gained interest, particularly in the abdominal region. However, the absence of reliable ground truths makes difficult to assess reconstruction algorithms, whose quality is already compromised by additional signal contributions from fat, gases, and different kinds of motion. This work presents a realistic in silico phantom for the development, evaluation and comparison of abdominal QSM reconstruction algorithms.MethodsSynthetic susceptibility and maps were generated by segmenting and postprocessing the abdominal 3T MRI data from a healthy volunteer. Susceptibility and values in different tissues/organs were assigned according to literature and experimental values and were also provided with realistic textures. The signal was simulated using as input the synthetic QSM and maps and fat contributions. Three susceptibility scenarios and two acquisition protocols were simulated to compare different reconstruction algorithms.ResultsQSM reconstructions show that the phantom allows to identify the main strengths and limitations of the acquisition approaches and reconstruction algorithms, such as in‐phase acquisitions, water‐fat separation methods, and QSM dipole inversion algorithms.ConclusionThe phantom showed its potential as a ground truth to evaluate and compare reconstruction pipelines and algorithms. The publicly available source code, designed in a modular framework, allows users to easily modify the susceptibility, and TEs, and thus creates different abdominal scenarios.

Country
Chile
Keywords

Brain Mapping, Digital phantom, Abdomen, Image Processing, Computer-Assisted, 610, Humans, Brain, Medicina y salud, Computer Processing and Modeling, Magnetic Resonance Imaging, Algorithms

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
hybrid