Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model

Targeted delivery of magnetic nanoparticles (MNPs) to an area of a blood vessel with fluidic flow is hampered by the lack of a suitable real‐time imaging modality for MNPs, the control system complexity, and low targeting performance. Herein, an offline programming guidance (OLPG) scheme for aggrega...

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Main Authors: Myungjin Park, Seungjun Oh, Tuan Anh Le, Jungwon Yoon
Format: Article
Language:English
Published: Wiley 2023-09-01
Series:Advanced Intelligent Systems
Subjects:
Online Access:https://doi.org/10.1002/aisy.202300128
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author Myungjin Park
Seungjun Oh
Tuan Anh Le
Jungwon Yoon
author_facet Myungjin Park
Seungjun Oh
Tuan Anh Le
Jungwon Yoon
author_sort Myungjin Park
collection DOAJ
description Targeted delivery of magnetic nanoparticles (MNPs) to an area of a blood vessel with fluidic flow is hampered by the lack of a suitable real‐time imaging modality for MNPs, the control system complexity, and low targeting performance. Herein, an offline programming guidance (OLPG) scheme for aggregated MNPs is proposed based on a real‐time aggregation volume estimator. The proposed aggregation volume estimator based on a magnetic drug‐targeting simulator reflects volume changes of aggregated MNPs; hence, it can model a magnetic force acting on aggregated MNPs in real time while enhancing targeting performance. The proposed guidance system is evaluated using a simulation testbed and in vitro model of the rat brain, which yields comparable results at different fluid viscosities, flow velocities, target areas, and flow types. The OLPG with the aggregation volume estimator improves targeting performance by 116%–409% compared with the default mode, and by 111%–180% compared to the performance without the aggregation volume estimator. Furthermore, a guidance margin predicts enhanced targeting performance (root‐mean‐square error < 5%) irrespective of the flow environment. The proposed guidance strategy has the potential to overcome the problems caused by the lack of an imaging modality, control‐system complexity, and low targeting performance.
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spelling doaj.art-7f89958ecd27426da9a6332d41c6a8582023-09-23T07:09:23ZengWileyAdvanced Intelligent Systems2640-45672023-09-0159n/an/a10.1002/aisy.202300128Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature ModelMyungjin Park0Seungjun Oh1Tuan Anh Le2Jungwon Yoon3School of Integrated Technology Gwangju Institute of Science and Technology Institute of Integrated Technology 123 Cheomdan Gwagiro Gwangju 61005 Republic of KoreaSchool of Integrated Technology Gwangju Institute of Science and Technology Institute of Integrated Technology 123 Cheomdan Gwagiro Gwangju 61005 Republic of KoreaDepartment of Physiology and Biomedical Engineering Mayo Clinic Scottsdale 13400 E Shea Blvd Scottsdale AZ 85259 USASchool of Integrated Technology Gwangju Institute of Science and Technology Institute of Integrated Technology 123 Cheomdan Gwagiro Gwangju 61005 Republic of KoreaTargeted delivery of magnetic nanoparticles (MNPs) to an area of a blood vessel with fluidic flow is hampered by the lack of a suitable real‐time imaging modality for MNPs, the control system complexity, and low targeting performance. Herein, an offline programming guidance (OLPG) scheme for aggregated MNPs is proposed based on a real‐time aggregation volume estimator. The proposed aggregation volume estimator based on a magnetic drug‐targeting simulator reflects volume changes of aggregated MNPs; hence, it can model a magnetic force acting on aggregated MNPs in real time while enhancing targeting performance. The proposed guidance system is evaluated using a simulation testbed and in vitro model of the rat brain, which yields comparable results at different fluid viscosities, flow velocities, target areas, and flow types. The OLPG with the aggregation volume estimator improves targeting performance by 116%–409% compared with the default mode, and by 111%–180% compared to the performance without the aggregation volume estimator. Furthermore, a guidance margin predicts enhanced targeting performance (root‐mean‐square error < 5%) irrespective of the flow environment. The proposed guidance strategy has the potential to overcome the problems caused by the lack of an imaging modality, control‐system complexity, and low targeting performance.https://doi.org/10.1002/aisy.202300128aggregation modelaggregation volumeguidance marginmagnetic drug targetingmicro/nanorobotics
spellingShingle Myungjin Park
Seungjun Oh
Tuan Anh Le
Jungwon Yoon
Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model
Advanced Intelligent Systems
aggregation model
aggregation volume
guidance margin
magnetic drug targeting
micro/nanorobotics
title Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model
title_full Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model
title_fullStr Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model
title_full_unstemmed Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model
title_short Aggregation Volume Estimator–Based Offline Programming Guidance of Magnetic Nanoparticles in the Realistic Rat‐Brain Vasculature Model
title_sort aggregation volume estimator based offline programming guidance of magnetic nanoparticles in the realistic rat brain vasculature model
topic aggregation model
aggregation volume
guidance margin
magnetic drug targeting
micro/nanorobotics
url https://doi.org/10.1002/aisy.202300128
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