Literature DB >> 26520715

Needle detection in curvilinear ultrasound images based on the reflection pattern of circular ultrasound waves.

Mohammad I Daoud1, Robert N Rohling2, Septimiu E Salcudean2, Purang Abolmaesumi2.   

Abstract

PURPOSE: Ultrasound imaging provides a low-cost, real-time modality to guide needle insertion procedures, but localizing the needle using conventional ultrasound images is often challenging. Estimating the needle trajectory can increase the success rate of ultrasound-guided needle interventions and improve patient comfort. In this study, a novel method is introduced to localize the needle trajectory in curvilinear ultrasound images based on the needle reflection pattern of circular ultrasound waves.
METHODS: A circular ultrasound wave was synthesized by sequentially firing the elements of a curvilinear transducer and recording the radio-frequency signals received by each element. Two features, namely, the large amplitude and repetitive reflection pattern, were used to identify the needle echoes in the received signals. The trajectory of the needle was estimated by fitting the arrival times of needle echoes to an equation that describes needle reflection of circular waves. The method was employed to estimate the trajectories of needles inserted in agar phantom, beef muscle, and porcine tissue specimens.
RESULTS: The maximum error rates of estimating the needle trajectories were on the order of 1 mm and 3° for the radial and azimuth coordinates, respectively.
CONCLUSIONS: These results suggest that the proposed method can improve the robustness and accuracy of needle segmentation methods by adding signature-based detection of the needle trajectory in curvilinear ultrasound images. The method can be implemented on conventional ultrasound imaging systems.

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Year:  2015        PMID: 26520715     DOI: 10.1118/1.4932214

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  4 in total

1.  Multi-Needle Detection in 3D Ultrasound Images Using Unsupervised Order-Graph Regularized Sparse Dictionary Learning.

Authors:  Yupei Zhang; Xiuxiu He; Zhen Tian; Jiwoong Jason Jeong; Yang Lei; Tonghe Wang; Qiulan Zeng; Ashesh B Jani; Walter J Curran; Pretesh Patel; Tian Liu; Xiaofeng Yang
Journal:  IEEE Trans Med Imaging       Date:  2020-01-22       Impact factor: 10.048

2.  Accurate model-based segmentation of gynecologic brachytherapy catheter collections in MRI-images.

Authors:  Andre Mastmeyer; Guillaume Pernelle; Ruibin Ma; Lauren Barber; Tina Kapur
Journal:  Med Image Anal       Date:  2017-07-18       Impact factor: 8.545

Review 3.  Enhancement of needle visualization and localization in ultrasound.

Authors:  Parmida Beigi; Septimiu E Salcudean; Gary C Ng; Robert Rohling
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-09-30       Impact factor: 2.924

4.  Accurate Needle Localization Using Two-Dimensional Power Doppler and B-Mode Ultrasound Image Analyses: A Feasibility Study.

Authors:  Mohammad I Daoud; Ahmad Shtaiyat; Adnan R Zayadeen; Rami Alazrai
Journal:  Sensors (Basel)       Date:  2018-10-16       Impact factor: 3.576

  4 in total

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