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Power Doppler ultrasound imaging with mechanical perturbation for improved intraoperative needle tip identification during prostate brachytherapy: a phantom study
Author(s): Nathan Orlando; Jonatan Snir; Kevin Barker; Douglas Hoover; Aaron Fenster
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Paper Abstract

Prostate cancer has the second highest noncutaneous cancer incidence in men worldwide. A common treatment technique for intermediate and high-risk localized prostate cancer is ultrasound (US)-guided high-dose-rate brachytherapy. This minimally invasive procedure uses a radioactive source passed through multiple needles to deliver radiation to the prostate and relies on accurate identification of needle tips to ensure patient safety and delivery of the prescribed doses. Image artifacts from nearby needles and the surrounding tissue often limit the accuracy of needle tip identification when using standard US imaging. To overcome these limitations and improve the accuracy of intraoperative needle tip identification, we propose the use of power Doppler (pD) US imaging while a mechanical perturbation is applied to the needle of interest. A mock procedure employing the standard clinical workflow was completed in a tissue-mimicking agar phantom. Inserted needles were imaged using standard US, followed by pD imaging of the same needles while a custom-made mechanical oscillator was used to perturb the needle. Physical measurements of the needle end lengths were used to estimate insertion depth errors (IDEs). 13 unobstructed needles and 10 shadowed needles were imaged using standard and pD US, resulting in mean IDEs ± standard deviation of 2.2 ± 0.9 mm and 1.3 ± 0.9 mm, respectively, for unobstructed needles, and 2.1 ± 1.6 mm and 1.6 ± 1.2 mm for shadowed needles. Mean IDEs were reduced in all cases when pD imaging was used, suggesting our method may be useful in improving HDR-BT treatment accuracy and patient safety.

Paper Details

Date Published: 8 March 2019
PDF: 7 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 1095131 (8 March 2019); doi: 10.1117/12.2513082
Show Author Affiliations
Nathan Orlando, Western Univ. (Canada)
Robarts Research Institute (Canada)
Jonatan Snir, London Regional Cancer Program (Canada)
Kevin Barker, Robarts Research Institute (Canada)
Douglas Hoover, London Regional Cancer Program (Canada)
Western Univ. (Canada)
Aaron Fenster, Western Univ. (Canada)
Robarts Research Institute (Canada)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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