On Feature Motion Decorrelation in Ultrasound Speckle Tracking

Liang, Tianzhu; Yung, Lingsing; Yu, Weichuan

doi:10.1109/tmi.2012.2230016

Cited by 22 publications

(5 citation statements)

References 36 publications

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“…Because pattern similarities are averaged over larger areas, accuracy may be decreased [ 26 ]. Furthermore, feature patterns in images may be less comparable after a longer period of time and larger distance covered, called image de-correlation [ 32 ]. In return, higher temporal resolution may allow for smaller interrogation windows resulting in higher accuracy.…”

Section: Discussionmentioning

confidence: 99%

Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking

Backhaus

Metschies

Billing

et al. 2021

Journal of Cardiovascular Magnetic Resonance

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Background Myocardial deformation analyses using cardiovascular magnetic resonance (CMR) feature tracking (CMR-FT) have incremental value in the assessment of cardiac function beyond volumetric analyses. Since guidelines do not recommend specific imaging parameters, we aimed to define optimal spatial and temporal resolutions for CMR cine images to enable reliable post-processing. Methods Intra- and inter-observer reproducibility was assessed in 12 healthy subjects and 9 heart failure (HF) patients. Cine images were acquired with different temporal (20, 30, 40 and 50 frames/cardiac cycle) and spatial resolutions (high in-plane 1.5 × 1.5 mm through-plane 5 mm, standard 1.8 × 1.8 x 8mm and low 3.0 × 3.0 x 10mm). CMR-FT comprised left ventricular (LV) global and segmental longitudinal/circumferential strain (GLS/GCS) and associated systolic strain rates (SR), and right ventricular (RV) GLS. Results Temporal but not spatial resolution did impact absolute strain and SR. Maximum absolute changes between lowest and highest temporal resolution were as follows: 1.8% and 0.3%/s for LV GLS and SR, 2.5% and 0.6%/s for GCS and SR as well as 1.4% for RV GLS. Changes of strain values occurred comparing 20 and 30 frames/cardiac cycle including LV and RV GLS and GCS (p < 0.001–0.046). In contrast, SR values (LV GLS/GCS SR) changed significantly comparing all successive temporal resolutions (p < 0.001–0.013). LV strain and SR reproducibility was not affected by either temporal or spatial resolution, whilst RV strain variability decreased with augmentation of temporal resolution. Conclusion Temporal but not spatial resolution significantly affects strain and SR in CMR-FT deformation analyses. Strain analyses require lower temporal resolution and 30 frames/cardiac cycle offer consistent strain assessments, whilst SR measurements gain from further increases in temporal resolution.

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Section: Discussionmentioning

confidence: 99%

Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking

Backhaus

Metschies

Billing

et al. 2021

Journal of Cardiovascular Magnetic Resonance

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“…If too low, larger displacements will necessitate larger search areas, and the local patterns could become less comparable, an effect known as image de-correlation [8]. On the other hand, a very high temporal resolution must be accompanied by a high spatial resolution, otherwise frame to frame displacements may become smaller than the dimensions of pixels and harder to detect [3].…”

Section: Technology Of Tissue Trackingmentioning

confidence: 99%

Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use

Pedrizzetti

Claus

Kilner

et al. 2016

Journal of Cardiovascular Magnetic Resonance

304

295

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Tissue tracking technology of routinely acquired cardiovascular magnetic resonance (CMR) cine acquisitions has increased the apparent ease and availability of non-invasive assessments of myocardial deformation in clinical research and practice. Its widespread availability thanks to the fact that this technology can in principle be applied on images that are part of every CMR or echocardiographic protocol. However, the two modalities are based on very different methods of image acquisition and reconstruction, each with their respective strengths and limitations. The image tracking methods applied are not necessarily directly comparable between the modalities, or with those based on dedicated CMR acquisitions for strain measurement such as tagging or displacement encoding. Here we describe the principles underlying the image tracking methods for CMR and echocardiography, and the translation of the resulting tracking estimates into parameters suited to describe myocardial mechanics. Technical limitations are presented with the objective of suggesting potential solutions that may allow informed and appropriate use in clinical applications.

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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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On Feature Motion Decorrelation in Ultrasound Speckle Tracking

Cited by 22 publications

References 36 publications

Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking

Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking

Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use

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

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