Enhancement of instrumented ultrasonic tracking images using deep learning

Efthymios Maneas; Andreas Hauptmann; Erwin J Alles; Wenfeng Xia; Sacha Noimark; Anna L David; Simon Arridge; Adrien E Desjardins

doi:10.1007/s11548-022-02728-7

Enhancement of instrumented ultrasonic tracking images using deep learning

Efthymios Maneas, Andreas Hauptmann, Erwin J Alles, Wenfeng Xia, Sacha Noimark, Anna L David, Simon Arridge, Adrien E Desjardins

Surgical & Interventional Engineering

School of Biomedical Engineering and Imaging Sciences
Univ London, Birkbeck University London, University College London, University of London Royal Veterinary College, University of London, St Georges University London, Imperial College London, Kings College London, Royal Holloway University London, Queen Mary University London, Div Populat Hlth Sci & Educ

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

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Abstract

PURPOSE: Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.

METHODS: The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.

RESULTS: Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.

CONCLUSION: The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.

Original language	English
Journal	International Journal of Computer Assisted Radiology and Surgery
DOIs	https://doi.org/10.1007/s11548-022-02728-7
Publication status	E-pub ahead of print - 3 Sept 2022

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s11548-022-02728-7Final published version, 873 KBLicence: CC BY

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@article{9b1273ae2cfb4a84a597406272568958,

title = "Enhancement of instrumented ultrasonic tracking images using deep learning",

abstract = "PURPOSE: Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.METHODS: The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.RESULTS: Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.CONCLUSION: The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.",

author = "Efthymios Maneas and Andreas Hauptmann and Alles, {Erwin J} and Wenfeng Xia and Sacha Noimark and David, {Anna L} and Simon Arridge and Desjardins, {Adrien E}",

note = "Funding Information: This work was funded by the Wellcome (WT101957; 203145Z/16/Z; 203148/Z/16/Z) and the Engineering and Physical Sciences Research Council (EPSRC) (NS/A000027/1; NS/A000050/1; NS/A000049/1; EP/L016478/1; EP/M020533/1; EP/S001506/1), by the European Research Council (ERC-2012-StG, Proposal 310970 MOPHIM), by the Rosetrees Trust (PGS19-2/10006) and by the Academy of Finland (336796; 338408). A.L.D. is supported by the UCL/UCL Hospital National Institute for Health Research Comprehensive Biomedical Research Centre. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = sep,

day = "3",

doi = "10.1007/s11548-022-02728-7",

language = "English",

journal = "International Journal of Computer Assisted Radiology and Surgery",

issn = "1861-6410",

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T1 - Enhancement of instrumented ultrasonic tracking images using deep learning

AU - Maneas, Efthymios

AU - Hauptmann, Andreas

AU - Alles, Erwin J

AU - Xia, Wenfeng

AU - Noimark, Sacha

AU - David, Anna L

AU - Arridge, Simon

AU - Desjardins, Adrien E

N1 - Funding Information: This work was funded by the Wellcome (WT101957; 203145Z/16/Z; 203148/Z/16/Z) and the Engineering and Physical Sciences Research Council (EPSRC) (NS/A000027/1; NS/A000050/1; NS/A000049/1; EP/L016478/1; EP/M020533/1; EP/S001506/1), by the European Research Council (ERC-2012-StG, Proposal 310970 MOPHIM), by the Rosetrees Trust (PGS19-2/10006) and by the Academy of Finland (336796; 338408). A.L.D. is supported by the UCL/UCL Hospital National Institute for Health Research Comprehensive Biomedical Research Centre. Publisher Copyright: © 2022, The Author(s).

PY - 2022/9/3

Y1 - 2022/9/3

N2 - PURPOSE: Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.METHODS: The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.RESULTS: Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.CONCLUSION: The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.

AB - PURPOSE: Instrumented ultrasonic tracking provides needle localisation during ultrasound-guided minimally invasive percutaneous procedures. Here, a post-processing framework based on a convolutional neural network (CNN) is proposed to improve the spatial resolution of ultrasonic tracking images.METHODS: The custom ultrasonic tracking system comprised a needle with an integrated fibre-optic ultrasound (US) transmitter and a clinical US probe for receiving those transmissions and for acquiring B-mode US images. For post-processing of tracking images reconstructed from the received fibre-optic US transmissions, a recently-developed framework based on ResNet architecture, trained with a purely synthetic dataset, was employed. A preliminary evaluation of this framework was performed with data acquired from needle insertions in the heart of a fetal sheep in vivo. The axial and lateral spatial resolution of the tracking images were used as performance metrics of the trained network.RESULTS: Application of the CNN yielded improvements in the spatial resolution of the tracking images. In three needle insertions, in which the tip depth ranged from 23.9 to 38.4 mm, the lateral resolution improved from 2.11 to 1.58 mm, and the axial resolution improved from 1.29 to 0.46 mm.CONCLUSION: The results provide strong indications of the potential of CNNs to improve the spatial resolution of ultrasonic tracking images and thereby to increase the accuracy of needle tip localisation. These improvements could have broad applicability and impact across multiple clinical fields, which could lead to improvements in procedural efficiency and reductions in risk of complications.

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U2 - 10.1007/s11548-022-02728-7

DO - 10.1007/s11548-022-02728-7

M3 - Article

C2 - 36057759

SN - 1861-6410

JO - International Journal of Computer Assisted Radiology and Surgery

JF - International Journal of Computer Assisted Radiology and Surgery

ER -

Enhancement of instrumented ultrasonic tracking images using deep learning

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