Improved Tracking Performance of Lagrangian Block-Matching Methodologies Using Block Expansion in the Time Domain: In Silico, Phantom and in Vivo Evaluations

Albinsson, John; Brorsson, Sofia; Ahlgren, Åsa Rydén; Cinthio, Magnus

doi:10.1016/j.ultrasmedbio.2014.05.010

Cited by 16 publications

(19 citation statements)

References 61 publications

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“…The in-house block-matching method, developed to estimate the location of the target in a given frame, builds on works by Albinsson et al [26,28] and will be summarized below. Here, we propose a novel tracking scheme that is based on the re-sampling of the cine loop along the time axis.…”

Section: Methodsmentioning

confidence: 99%

“…The method denoted as the "basic method" in [28] is a sparse iterative block-matching method, that uses the sum of absolute differences as the matching criterion and an unlimited search area. In this work, the sub-sample method has been replaced with the method presented below.…”

Section: Proposed Tracking Schemementioning

confidence: 99%

“…In this work, the sub-sample method has been replaced with the method presented below. The method denoted "basic method using an extra reference block" in [28] uses two independent kernels from two consecutive frames. The search area of the second kernel is limited to a small area around a position determined by the "basic method".…”

Section: Proposed Tracking Schemementioning

confidence: 99%

“…The position of each kernel is estimated with a Lagrangian viewpoint between every frame in the temporary cine loop (solid lines in Figure 1a). The position of the kernel in each frame is estimated using a block-matching method with an extra kernel described in [28] (where the method is denoted as the "basic method using an extra reference block"). This method was developed to minimize estimation errors when using a Lagrangian viewpoint.…”

Section: Proposed Tracking Schemementioning

confidence: 99%

“…The iterations continued until the position of the kernel is estimated in every frame (dashed lines in Figure 2c), i.e., m = 1. The position of the kernel is estimated using the block-matching method denoted "basic method" in [28].…”

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confidence: 99%

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Iterative 2D Tissue Motion Tracking in Ultrafast Ultrasound Imaging

et al. 2018

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Abstract:In order to study longitudinal movement and intramural shearing of the arterial wall with a Lagrangian viewpoint using ultrafast ultrasound imaging, a new tracking scheme is required. We propose the use of an iterative tracking scheme based on temporary down-sampling of the frame-rate, anteroposterior tracking, and unbiased block-matching using two kernels per position estimate. The tracking scheme was evaluated on phantom B-mode cine loops and considered both velocity and displacement for a range of down-sampling factors (k = 1-128) at the start of the iterations. The cine loops had a frame rate of 1300-1500 Hz and were beamformed using delay-and-sum. The evaluation on phantom showed that both the mean estimation errors and the standard deviations decreased with an increasing initial down-sampling factor, while they increased with an increased velocity or larger pitch. A limited in vivo study shows that the major pattern of movement corresponds well with state-of-the-art low frame rate motion estimates, indicating that the proposed tracking scheme could enable the study of longitudinal movement of the intima-media complex using ultrafast ultrasound imaging, and is one step towards estimating the propagation velocity of the longitudinal movement of the arterial wall.

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Section: Methodsmentioning

confidence: 99%

Section: Proposed Tracking Schemementioning

confidence: 99%

Section: Proposed Tracking Schemementioning

confidence: 99%

Section: Proposed Tracking Schemementioning

confidence: 99%

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confidence: 99%

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Iterative 2D Tissue Motion Tracking in Ultrafast Ultrasound Imaging

et al. 2018

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A combination of parabolic and grid slope interpolation for 2D tissue displacement estimations

Albinsson

Ahlgren

Jansson

et al. 2016

Med Biol Eng Comput

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Parabolic sub-sample interpolation for 2D block-matching motion estimation is computationally efficient. However, it is well known that the parabolic interpolation gives a biased motion estimate for displacements greater than |y.2| samples (y = 0, 1, …). Grid slope sub-sample interpolation is less biased, but it shows large variability for displacements close to y.0. We therefore propose to combine these sub-sample methods into one method (GS15PI) using a threshold to determine when to use which method. The proposed method was evaluated on simulated, phantom, and in vivo ultrasound cine loops and was compared to three sub-sample interpolation methods. On average, GS15PI reduced the absolute sub-sample estimation errors in the simulated and phantom cine loops by 14, 8, and 24% compared to sub-sample interpolation of the image, parabolic sub-sample interpolation, and grid slope sub-sample interpolation, respectively. The limited in vivo evaluation of estimations of the longitudinal movement of the common carotid artery using parabolic and grid slope sub-sample interpolation and GS15PI resulted in coefficient of variation (CV) values of 6.9, 7.5, and 6.8%, respectively. The proposed method is computationally efficient and has low bias and variance. The method is another step toward a fast and reliable method for clinical investigations of longitudinal movement of the arterial wall.

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