A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.

Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artifici...

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Autores principales:	Huixuan Wu, Pan Du, Rohan Kokate, Jian-Xun Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/83b2b85fe5da4e2db7c0a758290d340a
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id	oai:doaj.org-article:83b2b85fe5da4e2db7c0a758290d340a
record_format	dspace
spelling	oai:doaj.org-article:83b2b85fe5da4e2db7c0a758290d340a2021-12-02T20:09:23ZA semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.1932-620310.1371/journal.pone.0254051https://doaj.org/article/83b2b85fe5da4e2db7c0a758290d340a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254051https://doaj.org/toc/1932-6203Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.Huixuan WuPan DuRohan KokateJian-Xun WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254051 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Huixuan Wu Pan Du Rohan Kokate Jian-Xun Wang A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.
description	Magnetic particle tracking is a recently developed technology that can measure the translation and rotation of a particle in an opaque environment like a turbidity flow and fluidized-bed flow. The trajectory reconstruction usually relies on numerical optimization or filtering, which involve artificial parameters or thresholds. Existing analytical reconstruction algorithms have certain limitations and usually depend on the gradient of the magnetic field, which is not easy to measure accurately in many applications. This paper discusses a new semi-analytical solution and the related reconstruction algorithm. The new method can be used for an arbitrary sensor arrangement. To reduce the measurement uncertainty in practical applications, deep neural network (DNN)-based models are developed to denoise the reconstructed trajectory. Compared to traditional approaches such as wavelet-based filtering, the DNN-based denoisers are more accurate in the position reconstruction. However, they often over-smooth the velocity signal, and a hybrid method that combines the wavelet and DNN model provides a more accurate velocity reconstruction. All the DNN-based and wavelet methods perform well in the orientation reconstruction.
format	article
author	Huixuan Wu Pan Du Rohan Kokate Jian-Xun Wang
author_facet	Huixuan Wu Pan Du Rohan Kokate Jian-Xun Wang
author_sort	Huixuan Wu
title	A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.
title_short	A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.
title_full	A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.
title_fullStr	A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.
title_full_unstemmed	A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.
title_sort	semi-analytical solution and ai-based reconstruction algorithms for magnetic particle tracking.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/83b2b85fe5da4e2db7c0a758290d340a
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A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking.

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