Identification of left ventricular model parameters

Simulations with a model of left ventricular pressure generation consisting of time-varying elastance, resistance, series-elastance, and deactivation were fitted to pressure curves measured in the isolated rabbit ventricle. For constant ejection flows, a fit with a RMS error of 2.78 mmHg was obtained provided that deactivation was actually incorporated in the model. Deactivation was assumed to depend linearly on end ejection pressure. Resistance was found to be independent of volum

Deactivation in the rabbit left ventricle induced by constant ejection flow

A study of pressure generated by the left ventricle after ejection with constant flow for different values of the ejection flow, flow duration, time of flow arrest, and ventricular volume is discussed. It was found that pressure after ejection, normalized with respect to isovolumic pressure, is regenerated according to a model consisting of an elastance, a resistance, a series elastance, and an additional deactivation component. Deactivation is defined as the difference between the value 1 and the plateau value of the normalized pressure after constant flow ejection. It is shown that this plateau value is constant after constant flow ejection until the minimum in isovolumic dP/dt, i.e. during physiological systole. The plateau value is uniquely related to the value of the normalized pressure with a time constant of 10.44±0.09 ms which agrees with the series-elastance time constant of 10.35±0.26 m

Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound

Ultrasound localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert microbubbles with low-concentration within the bloodstream reveal the vasculature with capillary resolution. Despite its high spatial resolution, low microbubble concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single super-resolved image. %since each echo is required to be well separated from adjacent microbubbles. Such long acquisition times and stringent constraints on microbubble concentration are undesirable in many clinical scenarios. To address these restrictions, sparsity-based approaches have recently been developed. These methods reduce the total acquisition time dramatically, while maintaining good spatial resolution in settings with considerable microbubble overlap. %Yet, non of the reported methods exploit the fact that microbubbles actually flow within the bloodstream. % to improve recovery. Here, we further improve sparsity-based super-resolution ultrasound imaging by exploiting the inherent flow of microbubbles and utilize their motion kinematics. While doing so, we also provide quantitative measurements of microbubble velocities. Our method relies on simultaneous tracking and super-localization of individual microbubbles in a frame-by-frame manner, and as such, may be suitable for real-time implementation. We demonstrate the effectiveness of the proposed approach on both simulations and {\it in-vivo} contrast enhanced human prostate scans, acquired with a clinically approved scanner.Comment: 11 pages, 9 figure

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p