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A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics

Rohlén, Robin LU orcid ; Lundsberg, Jonathan LU ; Malesevic, Nebojsa LU and Antfolk, Christian LU (2023) In Journal of Neural Engineering 20(3).
Abstract
Objective. Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the
question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g. the active MU displacements, arterial pulsations, bones, connective tissue, and noise. Approach. This study proposes a... (More)
Objective. Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the
question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g. the active MU displacements, arterial pulsations, bones, connective tissue, and noise. Approach. This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against spatiotemporal independent component analysis (stICA), i.e. the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings. Main results. We found that the computational time for velBSS was at least 20 times less than for stICA, while the
twitch responses and spatial maps extracted from stICA and velBSS for the same MU reference were highly correlated (0.96 ± 0.05 and 0.81 ± 0.13). Significance. The present algorithm (velBSS) is computationally much faster than the currently available method (stICA) while maintaining the same performance. It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.
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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Neural Engineering
volume
20
issue
3
article number
034001
publisher
IOP Publishing
external identifiers
  • pmid:37172576
  • scopus:85159787445
ISSN
1741-2560
DOI
10.1088/1741-2552/acd4e9
language
English
LU publication?
yes
id
edf631d4-8c8a-4e3a-93c4-04d893c152aa
date added to LUP
2023-05-16 00:06:48
date last changed
2025-04-04 15:29:23
@article{edf631d4-8c8a-4e3a-93c4-04d893c152aa,
  abstract     = {{Objective. Ultrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the<br/>question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g. the active MU displacements, arterial pulsations, bones, connective tissue, and noise. Approach. This study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against spatiotemporal independent component analysis (stICA), i.e. the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings. Main results. We found that the computational time for velBSS was at least 20 times less than for stICA, while the<br/>twitch responses and spatial maps extracted from stICA and velBSS for the same MU reference were highly correlated (0.96 ± 0.05 and 0.81 ± 0.13). Significance. The present algorithm (velBSS) is computationally much faster than the currently available method (stICA) while maintaining the same performance. It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.<br/>}},
  author       = {{Rohlén, Robin and Lundsberg, Jonathan and Malesevic, Nebojsa and Antfolk, Christian}},
  issn         = {{1741-2560}},
  language     = {{eng}},
  month        = {{05}},
  number       = {{3}},
  publisher    = {{IOP Publishing}},
  series       = {{Journal of Neural Engineering}},
  title        = {{A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics}},
  url          = {{http://dx.doi.org/10.1088/1741-2552/acd4e9}},
  doi          = {{10.1088/1741-2552/acd4e9}},
  volume       = {{20}},
  year         = {{2023}},
}