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dc.contributor.author
Tiryaki, Mehmet Efe
dc.contributor.author
Demir, Sinan Ozgun
dc.contributor.author
Sitti, Metin
dc.date.accessioned
2022-06-21T08:20:04Z
dc.date.available
2022-06-21T03:11:18Z
dc.date.available
2022-06-21T08:20:04Z
dc.date.issued
2022-07
dc.identifier.issn
2377-3766
dc.identifier.other
10.1109/LRA.2022.3179509
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/553511
dc.description.abstract
Magnetic resonance imaging (MRI)-guided robots emerged as a promising tool for minimally invasive medical operations. Recently, MRI scanners have been proposed for actuating and localizing magnetic microrobots in the patient’s body using two-dimensional (2D) MR images. However, three-dimensional (3D) magnetic microrobots tracking during motion is still an untackled issue in MRI-powered microrobotics. Here, we present a deep learning-based 3D magnetic microrobot tracking method using 2D MR images during microrobot motion. The proposed method comprises a convolutional neural network (CNN) and complementary particle filter for 3D microrobot tracking. The CNN localizes the microrobot position relative to the 2D MRI slice and classifies the microrobot visibility in the MR images. First, we create an ultrasound (US) imaging-mentored MRI-based microrobot imaging and actuation system to train the CNN. Then, we trained the CNN using the MRI data generated by automated experiments using US image-based visual servoing of a microrobot with a 500μm-diameter magnetic core. We showed that the proposed CNN can localize the microrobot and classified its visibility in an in vitro environment with ±0.56 mm and 87.5% accuracy in 2D MR images, respectively. Furthermore, we demonstrated ex-vivo 3D microrobot tracking with ±1.43 mm accuracy, improving tracking accuracy by 60% compared to the previous studies. The presented tracking strategy will enable MRI-powered microrobots to be used in high-precision targeted medical applications in the future.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.subject
Deep learning methods
en_US
dc.subject
medical robots and systems
en_US
dc.subject
micro/nano robots
en_US
dc.subject
motion control
en_US
dc.subject
visual servoing
en_US
dc.title
Deep Learning-based 3D Magnetic Microrobot Tracking using 2D MR Images
en_US
dc.type
Journal Article
dc.date.published
2022-06-01
ethz.journal.title
IEEE Robotics and Automation Letters
ethz.journal.volume
7
en_US
ethz.journal.issue
3
en_US
ethz.pages.start
6982
en_US
ethz.pages.end
6989
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::09726 - Sitti, Metin (ehemalig) / Sitti, Metin (former)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::09726 - Sitti, Metin (ehemalig) / Sitti, Metin (former)
ethz.date.deposited
2022-06-21T03:12:41Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-06-21T08:20:12Z
ethz.rosetta.lastUpdated
2023-02-07T03:39:17Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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