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Towards Improving the Accuracy of Sensorless Freehand 3D Ultrasound by Learning

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Machine Learning in Medical Imaging (MLMI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7588))

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Abstract

Sensorless freehand 3D ultrasound (US) exploits the correlation between pairs of images in order to track the rigid motion of the US probe without an external position sensor. Conventionally, in-plane motion is compensated by maximizing image correlation. Out-of-plane motion is then estimated using a calibrated model of elevational speckle decorrelation. This approach is prone to systematic error due to interactions between the effects of in-plane and out-of-plane motion components on speckle decorrelation. This paper proposes to establish an error correction model using relevance vector regression and a database of image pairs with known probe motion. Preliminary results on synthetic US image pairs show a statistically significant reduction in mean target registration error, illustrating the promise of the new approach.

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Author information

Authors and Affiliations

  1. Dept. of Electrical Engineering, École de technologie supérieure, Montreal, Canada

    Juliette Conrath & Catherine Laporte

Authors
  1. Juliette Conrath
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  2. Catherine Laporte
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Editor information

Editors and Affiliations

  1. IBM Research Almaden, 650 Harry Road, 95120, San Jose, CA, USA

    Fei Wang

  2. School of Medicine, Department of Radiology and Biomedical Reserach Imaging Center, University of North Carolina, 130 Mason Farm Road, 27599, Chapel Hill, NC, USA

    Dinggang Shen

  3. Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics, 17 Xinxi Road, New Industrial Park, Xi’an,, 710119, Shaanxi, China

    Pingkun Yan

  4. Department of Radiology, The University of Chicago, 5841, South Maryland Avenue, 60637, Chicago, IL, USA

    Kenji Suzuki

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© 2012 Springer-Verlag Berlin Heidelberg

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Conrath, J., Laporte, C. (2012). Towards Improving the Accuracy of Sensorless Freehand 3D Ultrasound by Learning. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_10

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  • DOI: https://doi.org/10.1007/978-3-642-35428-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35427-4

  • Online ISBN: 978-3-642-35428-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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