Real‐time ultrasound‐tagging to track the 2D motion of the common carotid artery wall <i>in vivo</i>

Zahnd, Guillaume; Salles, Sébastien; Liebgott, Hervé; Vray, Didier; Sérusclat, André; Moulin, Philippe

doi:10.1118/1.4905376

Cited by 14 publications

(10 citation statements)

References 68 publications

(86 reference statements)

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“…It is important to note that these methods are not specifically applicable to IVUS, and no intraluminal pressure information was provided; therefore, their tracking errors were obtained from the literature. [18][19][20] The Pearson correlation for our method resulted in P = .999 for subsequent frames in both horizontal and vertical directions and P = .996 and .999 for distant frames in the horizontal and vertical directions. These levels of accuracy were comparable to some state-of-the-art methods 11 present in the literature.…”

Section: Resultsmentioning

confidence: 84%

“…The tracking errors for our proposed method as well as for 8 other methods present in the literature are displayed in Table . It is important to note that these methods are not specifically applicable to IVUS, and no intraluminal pressure information was provided; therefore, their tracking errors were obtained from the literature . The Pearson correlation for our method resulted in P = .999 for subsequent frames in both horizontal and vertical directions and P = .996 and .999 for distant frames in the horizontal and vertical directions.…”

Section: Resultsmentioning

confidence: 97%

“…Prior studies 12,15,[18][19][20][21][22][23] have shown promising results by applying pixel-tracking techniques in ultrasound images. However, none of these methods were designed specifically for, or evaluated with, IVUS images.…”

Section: Discussionmentioning

confidence: 99%

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Radial‐Biased Tracking Method to Assess Tissue Displacement in Intravascular Ultrasound Sequences: A Phantom Framework Evaluation

Grinet

Moraes

2019

J of Ultrasound Medicine

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Objectives We created and evaluated a pixel‐tracking method capable of accurately identify the displacement of tissue in intravascular ultrasound (IVUS) images. Methods Our proposed pixel‐tracking method assessed the horizontal and vertical displacement of tissue from a numerical phantom of IVUS sequences. The proposed tracking method is based on a block‐matching framework, comparing 2 distinct frames within a selected region by normalized cross‐correlation. Our method, specialized for IVUS applications, reduced the tracking area by implementing a limiting radius and a radial bias during the search. Results The method was evaluated by using 54 numerical phantom image sequences from 9 distinct arterial models, resulting in different arteries with atherosclerotic plaques under a range of pressures. The ground truth reference coordinates of the tracked tissue were extracted from each numerical phantom sequence. Our results were compared to 8 other methods present in the literature. The mean absolute tracking errors ± SD for our method were 15.56 ± 19.46 and 13.04 ± 13.82 μm for the horizontal and vertical directions, respectively, between 2 subsequent frames, and 162.58 ± 305.93 and 102.22 ± 130.61 μm from lower to higher pressures in the range of 6 frames (n = 42,036). Conclusions Our application‐specific pixel‐tracking method showed promising results and no statistically significant tracking error (P = .954), comparable to state‐of‐the‐art methods present in the literature. Application‐specific tracking methods have advantages over general methods by turning tissue‐specific behavior into a directional bias in the tracking algorithm.

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Section: Resultsmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 97%

See 1 more Smart Citation

Radial‐Biased Tracking Method to Assess Tissue Displacement in Intravascular Ultrasound Sequences: A Phantom Framework Evaluation

Grinet

Moraes

2019

J of Ultrasound Medicine

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“…Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality in industrialized nations. 1 The atherosclerotic process, which causes thickening of the artery wall and reduction of the artery lumen, is the early manifestation of the possible onset of CVDs. 2,3 Studies have shown that the increase in the intima-media thickness (IMT) of the carotid artery is one of the prominent clinical signs of an ongoing atherosclerotic process.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound intima‐media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet‐based orientation‐selective filter

Zhang

Ma³

et al. 2016

Medical Physics

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“…TO schemes are usually combined with phase-based motion estimation techniques [15], [16]. The validation and the feasibility of this method have already been done in conventional imaging [19], [20] and in 3D imaging [21]. This method was also validated in ultrafast imaging [22] where the PWV, and the Young's modulus of the artery phantom were estimate accurately with a mean errors of 2.2%.…”

Section: Introductionmentioning

confidence: 99%

Estimation of arterial wall motion using ultrafast imaging and transverse oscillations: in vivo study

Salles

Long

Petrusca

et al. 2016

2016 IEEE International Ultrasonics Symposium (IUS)

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International audienceEarly detection of cardiovascular diseases can be done by assessing the dynamic properties of arteries. In this study, two phenomena related to the carotid wall motion are presented. The 2D motion estimation method employs (i) ultrafast imaging which became a world-wide use modality, (ii) transverse oscillations technics which allow improving the motion estimation in transverse direction i.e. perpendicular to the beam, and (iii) a 2D phase shift estimator. First, using only the radial acceleration wall, the Wall Spatial Periodic Ripples (WSPR) is studied under a cold pressor test. The results show that the WSPR mean maximum amplitude decreased of 22.9% at the systolic wave and 33.2% at the dicrotic notch. So, the WSPR is a possible artery wall rigidity marker. Then, using a very high frame rate imaging, the longitudinal carotid wall motion of one healthy subject is studied. This second experimentation permits to estimate the propagation of a longitudinal motion on an in vivo carotid. Its mean velocity was evaluated at 16.1 m.s-1, which given a ratio of 3 between with the estimated PWV

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Real‐time ultrasound‐tagging to track the 2D motion of the common carotid artery wall in vivo

Abstract: The proposed framework enables tagged ultrasound images of in vivo tissues to be acquired in real-time. Such unconventional beamforming strategy contributes to improve tracking accuracy and could potentially benefit to the interpretation and diagnosis of biomedical images.

Cited by 14 publications

References 68 publications

Radial‐Biased Tracking Method to Assess Tissue Displacement in Intravascular Ultrasound Sequences: A Phantom Framework Evaluation

Radial‐Biased Tracking Method to Assess Tissue Displacement in Intravascular Ultrasound Sequences: A Phantom Framework Evaluation

Ultrasound intima‐media thickness measurement of the carotid artery using ant colony optimization combined with a curvelet‐based orientation‐selective filter

Estimation of arterial wall motion using ultrafast imaging and transverse oscillations: in vivo study

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