Power-Doppler Ultrasonic Imaging of Peripheral Perfusion in Diabetic Mice.

Objective: We explored the capabilities of power-Doppler ultrasonic (PD-US) imaging without contrast enhancement for monitoring changes in muscle perfusion over time.

Methods: Ischemic recovery was observed in healthy and type II diabetic male and female mice with and without exercise. In separate studies, perfusion was measured during and after 5-min ischemic periods and during four-week recovery periods following irreversible femoral ligation.

Deep Q-learning to globally optimize a -D parameter search for medical imaging.

Background: Estimation of the global optima of multiple model parameters is valuable for precisely extracting parameters that characterize a physical environment. This is especially useful for imaging purposes, to form reliable, meaningful physical images with good reproducibility. However, it is challenging to avoid different local minima when the objective function is nonconvex.

Monitoring Muscle Perfusion in Rodents During Short-Term Ischemia Using Power Doppler Ultrasound.

Objective: The aim of this work was to evaluate the reliability of power Doppler ultrasound (PD-US) measurements made without contrast enhancement to monitor temporal changes in peripheral blood perfusion.

Methods: On the basis of pre-clinical rodent studies, we found that combinations of spatial registration and clutter filtering techniques applied to PD-US signals reproducibly tracked blood perfusion in skeletal muscle. Perfusion is monitored while modulating hindlimb blood flow.

Combining Spatial Registration With Clutter Filtering for Power-Doppler Imaging in Peripheral Perfusion Applications.

Power-Doppler ultrasonic (PD-US) imaging is sensitive to echoes from blood cell motion in the microvasculature but generally nonspecific because of difficulties with filtering nonblood-echo sources. We are studying the potential for using PD-US imaging for routine assessments of peripheral blood perfusion without contrast media. The strategy developed is based on an experimentally verified computational model of tissue perfusion that simulates typical in vivo conditions.

A data-driven approach to characterizing nonlinear elastic behavior of soft materials.

The Autoprogressive (AutoP) method is a data-driven inverse method that leverages finite element analysis (FEA) and machine learning (ML) techniques to build constitutive relationships from measured force and displacement data. Previous applications of AutoP in tissue-like media have focused on linear elastic mechanical behavior as the target object is infinitesimally compressed. In this study, we extended the application of AutoP in characterizing nonlinear elastic mechanical behavior as the target object undergoes finite compressive deformation.