Deformable regions of interest with multiple points for tissue tracking in echocardiography

Cui, Xiaoke; Washio, Takumi; Chono, Tomoaki; Baba, Hirotaka; Okada, Junichi; Sugiura, Seiryo; Hisada, Toshiaki

doi:10.1016/j.media.2016.08.002

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…Tracking in ultrasound sequences under respiratory-induced motion is challenging (Sawada et al 2004). Various methods have been proposed for tracking a moving object in ultrasound sequences (Cifor et al 2013, Liang et al 2013, O'Shea et al 2014, Banerjee et al 2016, Ipsen et al 2016, Cui et al 2017. Vessels are ideal landmarks to identify and track in the ultrasound images because they contain high contrast and well-defined shapes (Kubota et al 2014, Banerjee et al 2015, Makhinya and Goksel 2015, Pan et al 2015, Worm et al 2016.…”

Section: Introductionmentioning

confidence: 99%

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning

Huang¹,

Su²,

Chen³

et al. 2019

Phys. Med. Biol.

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We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultrasound image processing, our previous system could only track abdominal motions under breath-hold. To overcome this limitation, a novel 2D-based image processing method for tracking intra-fraction respiratory motion is proposed. Fifty-seven different anatomical features acquired from 27 sets of 2D ultrasound sequences were used in this study. Three 2D ultrasound sequences were acquired with the robotic ultrasound system from three healthy volunteers. The remaining datasets were provided by the 2015 MICCAI Challenge on Liver Ultrasound Tracking. All datasets were preprocessed to extract the feature point, and a patientspecific motion pattern was extracted by principal component analysis and slow feature analysis (SFA). The tracking finds the most similar frame (or indexed frame) by a k-dimensional-tree-based nearest neighbor search for estimating the tracked object location. A template image was updated dynamically through the indexed frame to perform a fast template matching (TM) within a learned smaller search region on the incoming frame. The mean tracking error between manually annotated landmarks and the location extracted from the indexed training frame is 1.80 ± 1.42 mm. Adding a fast TM procedure within a small search region reduces the mean tracking error to 1.14 ± 1.16 mm. The tracking time per frame is 15 ms, which is well below the frame acquisition time. Furthermore, the anatomical reproducibility was measured by analyzing the location's anatomical landmark relative to the probe; the position-controlled probe has better reproducibility and yields a smaller mean error across all three volunteer cases, compared to the force-controlled probe (2.69 versus 11.20 mm in the superior-inferior direction and 1.19 versus 8.21 mm in the anterior-posterior direction). Our method reduces the processing time for tracking respiratory motion significantly, which can reduce the delivery uncertainty.

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Section: Introductionmentioning

confidence: 99%

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning

Huang¹,

Su²,

Chen³

et al. 2019

Phys. Med. Biol.

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“…Echocardiogram is a non-invasive procedure to estimate the morphology and function of the heart; two-dimensional (2D) speckle-tracking echocardiography (STE) is an echocardiographic technique that is used as a tool to measure longitudinal, circumferential, radial and global deformation of both ventricles [1][2][3] . STE uses natural acoustic markers that are speckles in a gray-scale seen in 2D-mode images, as a consequence of the sound pulse random backscattering in a tissue plane explored by echography in structures smaller than the ultrasound wavelength 4 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the global longitudinal strain and segmental strain of the right ventricle with two-dimensional speckle-tracking echocardiography with elevation of the legs

García-López

Vargas-Barrón

2019

ACME

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“…In our work, within the field of basic physiology, we also developed a three-dimensional cardiomyocyte-based model with the reproduction of detailed subcellular structures, including metabolism [56], a rational formula for accurate analysis of cardiac excitation propagation to overcome self-contraction of the bidomain equation [7], an excitation-contraction coupling model with the cooperativity effect [89], and an algorithm that optimizes the ventricular fiber structure of the human heart [10]. Moreover, we also investigated how the sinus of Valsalva relieves abnormal stress on aortic valve leaflets [11], developed a new echo-tracking method with deformable regions of interest aimed at rational pattern matching [12], and clarified the mechanism by which line block is generated in a non-contact mapping system using a heart simulator [13].…”

Section: Introduction To Ut-heartmentioning

confidence: 99%

Clinical and pharmacological application of multiscale multiphysics heart simulator, UT-Heart

Okada

Washio

Sugiura³

et al. 2019

Korean J Physiol Pharmacol

Self Cite

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A heart simulator, UT-Heart, is a finite element model of the human heart that can reproduce all the fundamental activities of the working heart, including propagation of excitation, contraction, and relaxation and generation of blood pressure and blood flow, based on the molecular aspects of the cardiac electrophysiology and excitation-contraction coupling. In this paper, we present a brief review of the practical use of UT-Heart. As an example, we focus on its application for predicting the effect of cardiac resynchronization therapy (CRT) and evaluating the proarrhythmic risk of drugs. Patient-specific, multiscale heart simulation successfully predicted the response to CRT by reproducing the complex pathophysiology of the heart. A proarrhythmic risk assessment system combining in vitro channel assays and in silico simulation of cardiac electrophysiology using UT-Heart successfully predicted druginduced arrhythmogenic risk. The assessment system was found to be reliable and efficient. We also developed a comprehensive hazard map on the various combinations of ion channel inhibitors. This in silico electrocardiogram database (now freely available at http://ut-heart.com/ ) can facilitate proarrhythmic risk assessment without the need to perform computationally expensive heart simulation. Based on these results, we conclude that the heart simulator, UT-Heart, could be a useful tool in clinical medicine and drug discovery.

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Deformable regions of interest with multiple points for tissue tracking in echocardiography

Cited by 7 publications

References 44 publications

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning

Evaluation of the global longitudinal strain and segmental strain of the right ventricle with two-dimensional speckle-tracking echocardiography with elevation of the legs

Clinical and pharmacological application of multiscale multiphysics heart simulator, UT-Heart

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