Towards Video Guidance for Ultrasound, Using a Prior High-Resolution 3D Surface Map of the External Anatomy

Wang, Jihang; Shivaprabhu, Vikas; Galeotti, John; Horvath, Samantha; Gorantla, Vijay S.; Stetten, George

doi:10.1007/978-3-319-10437-9_6

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…The ease-of-use, extensibility, and effectiveness of our temporal calibration are attested to by its usage on devices by many groups on a variety of devices, including the Ultrasonix, BK, Hitachi, and Fraunhofer DiPhAS ultrasound systems (through research interface, framegrabber, and OpenIGTLink interfaces), the Ascension trakSTAR, and NDI Aurora electromagnetic trackers, and the Claron MicronTracker, and NDI Polaris optical trackers (see, for example, [14][15][16][17][18][19][20]).…”

Section: Discussionmentioning

confidence: 99%

Improved Temporal Calibration of Tracked Ultrasound: An Open-Source Solution

Moult¹,

Lassó

Ungi

et al. 2017

J. Med. Robot. Res.

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In tracked ultrasound systems, temporal misalignment between image and tracker data results in incorrect image pose. We present a fully automatic temporal calibration. We image a flat plate in water with a tracked probe undergoing periodic uniaxial freehand translation. Using robust line detection scheme, we compute temporal misalignment as difference between probe and corresponding image position. From 240 sequences, standard deviation was under 5 ms for standard imaging parameters. Source code is available in Public Library for Ultrasound Research, PLUS ( www.plustoolkit.org ).

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Section: Discussionmentioning

confidence: 99%

Improved Temporal Calibration of Tracked Ultrasound: An Open-Source Solution

Moult¹,

Lassó

Ungi

et al. 2017

J. Med. Robot. Res.

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“…Researchers have applied different noise-removal techniques before registration. One technique is to apply Gaussian filtering [3][4][5][6][7][8][9][10] to reduce the speckle noise in the image by blurring. More significantly, certain characteristics of an image, such as edges and ridges, can be preserved and enhanced by applying a bank of filters including Gaussian smoothing filter, the first derivative filter (gradient operator), the second derivative filter, the Laplacian filter, and threshold operators [11].…”

Section: Noise Removalmentioning

confidence: 99%

“…Here the reference volume is not selected, so the similarity measure is the sample variance of a population, which represents the difference from the current mean intensity for each voxel in volume. The similarity metric proposed in [65] was adopted by [8,9,50,55].…”

Section: Adaptedmentioning

confidence: 99%

“…This can be a hindrance to the registration of images since the internal structures of the patient's anatomy can deform while being imaged, but the tracker cannot accurately quantify these movements as it provides only global motion estimates relative to the tracker coordinate system. Prior work has been done to instead directly track an ultrasound transducer relative to a patient's anatomy [8,35,[100][101][102][103].…”

Section: Physical Probe Trackingmentioning

confidence: 99%

“…Sometimes, the 3D spatial location and orientation of the ultrasound probe is also known through the use of ultrasound probe trackers [7,8]. However, probe tracking is also subject to error, especially for lower-accuracy electromagnetic (EM) trackers.…”

Section: Introductionmentioning

confidence: 99%

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Ultrasound registration: A review

Che

Mathai

Galeotti

2017

Methods

Self Cite

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This article is a review of registration algorithms for use between ultrasound images (monomodal image-based ultrasound registration). Ultrasound is safe, inexpensive, and real-time, providing many advantages for clinical and scientific use on both humans and animals, but ultrasound images are also notoriously noisy and subject to several unique artifacts/distortions. This paper introduces the topic and unique aspects of ultrasound-to-ultrasound image registration, providing a broad introduction and summary of the literature and the field. Both theoretical and practical aspects are introduced. The first half of the paper is theoretical, organized according to the basic components of a registration framework, namely preprocessing, image-similarity metrics, optimizers, etc. It further subdivides these methods between those suitable for elastic (non-rigid) vs. inelastic (matrix) transforms. The second half of the paper is organized by anatomy and is practical in nature, presenting and discussing the complete published systems that have been validated for registration in specific anatomic regions.

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Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance

Huang

Galeotti

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Towards Video Guidance for Ultrasound, Using a Prior High-Resolution 3D Surface Map of the External Anatomy

Cited by 4 publications

References 16 publications

Improved Temporal Calibration of Tracked Ultrasound: An Open-Source Solution

Improved Temporal Calibration of Tracked Ultrasound: An Open-Source Solution

Ultrasound registration: A review

Robust Skin-Feature Tracking in Free-Hand Video from Smartphone or Robot-Held Camera, to Enable Clinical-Tool Localization and Guidance

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