Abstract:Background
Shear waves are generated by the closure of the heart valves. Significant differences in shear wave velocity have been found recently between normal myocardium and disease models of diffusely increased muscle stiffness. In this study we correlate in vivo myocardial shear wave imaging (SWI) with presence of scarred tissue, as model for local increase of stiffness. Stiffness variation is hypothesized to appear as velocity variation.
Methods
Ten healthy volunteers (group 1), 10 hypertrophic cardiomyo… Show more
“…In the third example, we observe a complex pattern of wave propagation resulting in a wave projection with two distinct velocity regions. This wave projection in the Mmode image is consistent with previous observations [22], [23] and is documented in [17]. Two wave branches are observed, roughly perpendicular to each other.…”
Section: D Velocity -Pipeline Efficiencysupporting
confidence: 92%
“…However, increasing the reported metadata may complicate the diagnostic process and reduce clinical practicality. Without incorporating 3D wave visualization and velocity estimation, our understanding of phenomena like non-linear wave projection ( [17], [22], [23]) would be limited. This could restrict the clinical utility of MW imaging.…”
The mechanical wave (MW) propagation velocity in the heart is related to the tissue stiffness and its measurement mainly relies on manual evaluation of the 1D wave projection. This study presents an automated method for 3D wave visualization and velocity estimation in the heart using 3D ultrasound imaging of the left ventricle (LV). High-quality (HQ, 19 vps) and high-frame-rate (HFR, 823 vps) volumes were acquired. Deep learning models automatically segmented the LV and extracted the apical standard views from the HQ data which were used to derive the anatomical M-lines and myocardial segmentation. The clutter filter wave imaging (CFWI) and tissue Doppler imaging (TDI) generated wave propagation maps from HFR data, and the aortic valve closure (AVC) and atrial contraction/kick (AK) waves were automatically detected. LV segmentation and anatomical M-lines were used for 3D wave propagation extraction and its 1D projection, respectively. The 1D wave propagation velocity was determined through automatic slope detection, while the 3D velocity map was derived from the gradient of the time-offlight (TOF) map. Results showed varying 1D velocity across views and myocardial regions, with the AVC propagation velocity surpassing that of the AK wave. The pipeline remained stable and generated results consistent with expert measurements. Comparing 3D and 1D propagation highlighted errors from 1D projection and demonstrated the benefits of the 3D method in assessing regional velocities and the validity of the 1D approach. This study demonstrated an automatic evaluation of 3D MW propagation velocities in the entire LV, leading to improved accuracy and standardized measurements of myocardial tissue properties.INDEX TERMS Aortic valve closure wave, Atrial kick wave, Cardiac elstography, Mechanical wave imaging, Natural mechanical waves, Shear wave imaging.
“…In the third example, we observe a complex pattern of wave propagation resulting in a wave projection with two distinct velocity regions. This wave projection in the Mmode image is consistent with previous observations [22], [23] and is documented in [17]. Two wave branches are observed, roughly perpendicular to each other.…”
Section: D Velocity -Pipeline Efficiencysupporting
confidence: 92%
“…However, increasing the reported metadata may complicate the diagnostic process and reduce clinical practicality. Without incorporating 3D wave visualization and velocity estimation, our understanding of phenomena like non-linear wave projection ( [17], [22], [23]) would be limited. This could restrict the clinical utility of MW imaging.…”
The mechanical wave (MW) propagation velocity in the heart is related to the tissue stiffness and its measurement mainly relies on manual evaluation of the 1D wave projection. This study presents an automated method for 3D wave visualization and velocity estimation in the heart using 3D ultrasound imaging of the left ventricle (LV). High-quality (HQ, 19 vps) and high-frame-rate (HFR, 823 vps) volumes were acquired. Deep learning models automatically segmented the LV and extracted the apical standard views from the HQ data which were used to derive the anatomical M-lines and myocardial segmentation. The clutter filter wave imaging (CFWI) and tissue Doppler imaging (TDI) generated wave propagation maps from HFR data, and the aortic valve closure (AVC) and atrial contraction/kick (AK) waves were automatically detected. LV segmentation and anatomical M-lines were used for 3D wave propagation extraction and its 1D projection, respectively. The 1D wave propagation velocity was determined through automatic slope detection, while the 3D velocity map was derived from the gradient of the time-offlight (TOF) map. Results showed varying 1D velocity across views and myocardial regions, with the AVC propagation velocity surpassing that of the AK wave. The pipeline remained stable and generated results consistent with expert measurements. Comparing 3D and 1D propagation highlighted errors from 1D projection and demonstrated the benefits of the 3D method in assessing regional velocities and the validity of the 1D approach. This study demonstrated an automatic evaluation of 3D MW propagation velocities in the entire LV, leading to improved accuracy and standardized measurements of myocardial tissue properties.INDEX TERMS Aortic valve closure wave, Atrial kick wave, Cardiac elstography, Mechanical wave imaging, Natural mechanical waves, Shear wave imaging.
“…This results in under-sampling of fast and short-lived events, e.g., in the isovolumic phases of the cardiac cycle and the in accuracy of temporal derivatives of motion and strain, i.e., velocity and strain rate. [57,58] Recently, dedicated high-frame-rate STE algorithms have been developed in order to combine the high temporal resolution of ultrafast ultrasound imaging with the comfort of 2D speckle tracking algorithms. [57,[59][60][61] Joos et al applied STE on high-frame-rate B-mode images at 500 fps that were obtained by special coherent compounding methods based on motion compensation.…”
Continuous developments in cardiovascular imaging, software, and hardware have led to technological advancements that open new ways for assessing myocardial mechanics, hemodynamics, and function. The technical shift from clinical ultrasound machines that rely on conventional line-per-line beam transmissions to ultrafast imaging based on plane or diverging waves provides very high frame rates of up to 5000 Hz with a wide variety of potential new applications, including shear wave imaging, ultrafast speckle tracking, intracardiac flow imaging, and myocardial perfusion imaging. This review provides an overview of these advances and demonstrates potential applications and their possible added value in clinical practice.
“…Lastly, emerging echocardiographic methods use high frame rate imaging in determining the myocardial stiffness 24,25 and even the presence of scars. 26 This has yet to be tested in apical forms of HCM.…”
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