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10:30   Ultrasound Chair: Ali CAGDAS Akyildiz 10:30 15 mins Multivariate deconvolution of the functional ultrasound response using block-term decomposition Aybüke Erol, Pieter Kruizinga, Borbála Hunyadi Abstract: Functional ultrasound (fUS) is a relatively new neuroimaging modality that indirectly measures brain activity. Particularly, fUS detects subtle fluctuations in blood volume induced by the local variations in neuronal activity through the phenomenon of neurovascular coupling. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we propose a deconvolution method for multivariate fUS time-series that reveals both the region-specific HRFs, and the underlying source signals that induce the hemodynamic responses. To that end, we model the fUS time-series as convolutive mixtures and apply block-term decomposition (BTD) on the tensor of lagged fUS autocorrelation matrices based on two assumptions: (1) HRFs are parametrizable, and (2) source signals are uncorrelated. In order to avoid falling into local minima while optimizing the non-convex cost function of BTD, we repeat BTD multiple times with random initializations, and apply hierarchical clustering to determine a stable final solution for the deconvolution [1]. We test our approach on simulations and two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within three crucial regions of the mouse brain’s image-forming pathway; namely the lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources. [1] A. Erol, C. Soloukey, B. Generowicz, N. Van Dorp, S. Koekkoek, P. Kruizinga and B. Hunyadi, “Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition,” arXiv: 2204.03711, May 2022. 10:45 15 mins In vivo abdominal ultrasound imaging with multiple transducers using speed of sound correction Vera van Hal, Hein de Hoop, Jan-Willem Muller, Hans-Martin Schwab, Richard Lopata Abstract: Abdominal ultrasound (US) imaging is used to monitor rupture risk of abdominal aortic aneurysms. However, assessment of aortic geometry and wall deformation using conventional US is limited by the lateral lumen-wall contrast and resolution. We therefore introduce high frame rate multi-perspective (MP) bistatic imaging to improve aortic US. In MP bistatic imaging, two curved array transducers receive simultaneously on each transmit event. The advantage of such bistatic US imaging was investigated in US simulations and in an experimental study on ex vivo porcine aortas. Using MP bistatic imaging, the wall-lumen contrast-to-noise ratio was improved by up to 8 dB in vessel wall regions between transducers. This improved the accuracy of strain estimates in US simulations by 41-84% and resulted in more homogeneous strain results in ex vivo experiments. In vivo, MP image fusion is hampered by wavefront aberrations, caused by the strong speed-of-sound variations between muscle and fat in the abdominal wall. This limits abdominal US image quality by introducing distortions of the imaged structures, especially at deep imaging locations, such as the aorta. In MP US, these aberrations can be even more crucial, because image features from different probes can misalign severely. We developed a generic algorithm for aberration correction in US image reconstruction to improve image quality for both single-perspective (SP) and MP US. Its accuracy for abdominal imaging was evaluated in acoustic simulations and phantom experiments involving tissue-mimicking and ex vivo porcine material with large speed-of-sound contrast (~100 m/s). The lateral resolution was improved by up to 90% in US simulations and up to 65% in experiments compared to standard US image reconstruction. Moreover, geometric distortions were resolved in MP imaging, leading to a reduction in position error of around 80%. In conclusion, a more complete understanding of aortic geometry and wall motion can be retrieved by using high frame rate MP bistatic imaging with two curved array transducers. Moreover, results show that in vivo MP image fusion can be enabled by aberration correction using modelled arrival times in US image reconstruction. An in vivo study on bistatic MP aortic US imaging in healthy volunteers is ongoing. 11:00 15 mins Finite element regularization of ultrasound displacement estimates Jan-Willem Muller, Hans-Martin Schwab, Marcel Rutten, Marc van Sambeek, Richard Lopata Abstract: Ultrasound (US) strain imaging and elastography rely on displacement tracking to characterize the mechanical properties of tissue. The measured displacement field may suffer from several inaccuracies, due to e.g. signal decorrelation, peak-hopping, and a lack of phase information in the lateral direction. Regularization techniques have been described in literature to increase robustness of strain determination. Typically, constraints are imposed using pixel-based methods, which need an equidistant grid and a relatively fine sampling of the estimated displacement field. In this study, a regularization method is proposed that is based on a finite element method (FEM), which allows for flexible regularization of sparse displacement data. The performance of the developed method was investigated in various in vitro phantoms and in vivo for both line-by-line scanning and ultrafast imaging. Block-matching was used to estimate the displacements for every node in the FEM mesh. The regularization method was applied on the inter frame displacement estimates. The L1-norm between the estimated and measured displacements is minimized, which increases the method’s robustness against outliers. The minimization is regularized by constraining the root mean square curl, divergence, and bending energy (smoothness) of the displacement field. The method was applied in two in vitro set-ups and in vivo. A pulsating polyvinyl alcohol vessel was scanned line-by-line using a 3.5 MHz curved probe (Esaote CA431) at a framerate of 28 Hz. Secondly, a pulsating porcine aorta, and a human volunteer were scanned with an ultrafast acquisition (>130 Hz), using a 3.7 MHz Verasonics C5-2v probe. The method improves the quality of the tracking, for both the line-by-line, and the ultrafast techniques. Specially, the regularization avoids the mesh from intersecting itself in all datasets, which normally results in non-physical displacement fields. Due to the accumulation of drift errors, the strains values were significantly overestimated without regularization. The regularization method could reduce the drift from 741 μm to 168 μm after one cycle in the porcine aorta experiment. Similar reductions in drift were observed for the other datasets. A thorough analysis of the impact on strain resolution and contrast will be determined in heterogenous phantoms and simulations in future work. 11:15 15 mins Robust phase difference method for local shear wave viscoelastographic estimation Xueting Li, Simona Turco, Ronald M. Aarts, Hessel Wijkstra, Massimo Mischi Abstract: Shear wave (SW) elastography is an ultrasound imaging modality that provides quantitative measurements of tissue elasticity and viscosity, e.g., by k-space analysis of the SW velocity [1]. The estimated viscoelastic properties can be a suitable biomarker for the differentiation of benign and malignant tissue [2]. The phase difference method was introduced for the local assessment of the viscoelastic properties. In more detail, the phase difference of SWs measured at neighboring pixels is estimated through their temporal Fourier transform to derive a phase velocity dispersion plot, which is then interpreted by fitting a proper rheological model (e.g., Kelvin-Voigt model) [3]. While allowing for a local assessment of elasticity and viscosity, this method is very sensitive to noise. Therefore, a robust phase difference method, based on model-fitting in the time domain, is here proposed. Differently from the standard phase difference method, here a Prony fitting of the SW axial velocity is introduced [4], and a time delay is added to account for the SW arrival time at the measurement pixels. The dispersion plot is then calculated from the fitted signals at two laterally spaced pixels. By fitting a Kelvin-Voigt model to the dispersion plot, the elasticity and viscosity are estimated locally. A large fitting bandwidth is chosen to also enable the estimation of small viscosity, despite the higher noise levels. The proposed approach was tested and compared with the standard phase difference method in silico (MATLAB simulation) and in vitro, using a custom-made gelatin phantom measured by a Vantage 256 (Verasonics) system equipped with an L11-4v probe. The elasticity and viscosity with and without the proposed delayed Prony fitting in silico and in vitro were computed and compared. The reference values of the gelatin phantom were calculated by the standard k-space method, as the true values are unknown. In both cases, the delayed Prony fitting achieved smaller variations in both elasticity and viscosity compared with the standard phase difference method, which indicates its improved robustness to noise. [1] Palmeri et al., IEEE TUFFC, 2017 [2] Nenadic et al., Phys. Med. Biol., 2017. [3] Deffieux et al., IEEE TMI, 2009. [4] Kumaresan et al., Proc. IEEE, 1984. 11:30 15 mins Calibrated 2D ultrasound image analysis for classifying hepatic steatosis compared to liver biopsy analyzed quantitatively using novel and automatic method on digitized HE scans Gert Weijers, Eric Tjwa, Chris de Korte Abstract: Purpose Test if the Calibrated Ultrasound (CAUS) predictive performance increases in the detection and staging of hepatic steatosis, when using a novel quantitative histology method for staging the liver fat content instead of traditional histological steatosis staging (NALFD Activity score, further called NAS). Background and methods Nonalcoholic fatty liver disease (NAFLD) is the most common liver disorder in developed countries with a global prevalence of approximately 25%. Liver biopsy still is the gold standard for staging steatosis, however are invasive and prone to misclassification by qualitative scoring. CAUS is able to detect and classify steatosis accurately compared to classical steatosis scoring (Weijers et al., Radiology, 2022). CAUS’ Residual Attenuation Coefficient (RAC), which is a depth independent relative attenuation measure, showed the highest predictive value. To test the effect of possible histological misclassification we compared CAUS to a novel and quantitative method for automatic interpretation of digitized HE stains (Munsterman et al,. Cytometry B, 2019) Results In this study retrospectively 214 consecutive patients were enrolled from which 195 patient with a successful liver biopsy and 2D ultrasound examinations could be included. HE stains were digitized and quantitatively analyzed by determining the fat area percentage (QNAS, %). Area under the ROC (AUROC) were performed to assess the predictive performance of steatosis grading. Highest correlation to QNAS was found for CAUS’ RAC parameter (R=0.77, p<.001). Boosted performances of CAUS to QNAS (AUROC mild; moderate; severe steatosis respectively: 0.99; 0.96; 0.95) were found compared to traditional histological staging (AUROC: 0.97; 0.93; 0.93). Conclusion CAUS is able to classify HS accurately using calibrated 2D ultrasound images. The use of quantitative instead of qualitative histology boosted the predictive performance, which indicate that HS misclassification by pathologist partly might be overcome. 11:45 15 mins Quantitative evaluation of fast free-hand volumetric ultrasound Anton Nikolaev, Hendrik H. G. Hansen, Thomas J. J. Maal, Nens van Alfen, Chris L. de Korte Abstract: Free-hand volumetric ultrasound (FVUS) facilitates 3D US imaging of large anatomical areas. However, this method is user-dependent and image quality, especially in the scan direction (elevational direction), depends on the number of US images acquired per distance unit. This might affect clinical decision making for example in quantitative ultrasound muscle imaging. This study addresses three goals. First, to determine quantitatively below which number of acquisitions per cm (acq/cm) image quality is affected: the acquisition limit. Second, to determine the translation speed used naturally by sonographers. Third, to demonstrate in vivo possible benefits of utilizing plane wave imaging for FVUS, so-called fast FVUS in order to boost translation speed while maintaining quantitative image information. Fast FVUS enables imaging at much higher framerates and hence the acquisition limit is easier met which allows for much faster transducer translation. From an analysis of the contrast and elevational resolution in a phantom, the average acquisition limit was determined to be 33 acq/cm. Above this limit, the quantitative ultrasound information remained unchanged. This would imply that when imaging at 30 frames per second, a common frame-rate of current 2D ultrasound devices, suboptimal imaging quality is obtained above transducer translation speeds of 9.1 mm/s. The median and maximum transducer translation speed observed in 10 sonographers were 15.8 mm/s and 30.1 mm/s, thus above this limit. Finally, we presented a design of fast FVUS that enabled acquiring 200 fps, and hence, would allow imaging up to speeds of 60.6 mm/s. We demonstrated in vivo in tibialis anterior muscles that more anatomical details were visible with fast FVUS which were lost at the typical framerate. These observations support our hypothesis that fast FVUS would be an ideal method for 3D quantitative muscle ultrasound.

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