Multiparametric quantification and visualization of liver fat using ultrasound

Objectives- Several ultrasound measures have shown promise for assessment of steatosis compared to traditional B-scan, however clinicians may be required to integrate information across the parameters. Here, we propose an integrated multiparametric approach, enabling simple clinical assessment of key information from combined ultrasound parameters. Methods- We have measured 13 parameters related to ultrasound and shear wave elastography. These were measured in 30 human subjects under a study of liver fat. The 13 individual measures are assessed for their predictive value using independent magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) measurements as a reference standard. In addition, a comprehensive and fine-grain analysis is made of all possible combinations of sub-sets of these parameters to determine if any subset can be efficiently combined to predict fat fraction. Results- We found that as few as four key parameters related to ultrasound propagation are sufficient to generate a linear multiparametric parameter with a correlation against MRI-PDFF values of greater than 0.93. This optimal combination was found to have a classification area under the curve (AUC) approaching 1.0 when applying a threshold for separating steatosis grade zero from higher classes. Furthermore, a strategy is developed for applying local estimates of fat content as a color overlay to produce a visual impression of the extent and distribution of fat within the liver. Conclusion- In principle, this approach can be applied to most clinical ultrasound systems to provide the clinician and patient with a rapid and inexpensive estimate of liver fat content.

Task-Invariant Learning of Continuous Joint Kinematics during Steady-State and Transient Ambulation Using Ultrasound Sensing

Natural control of limb motion is continuous and progressively adaptive to individual intent. While intuitive interfaces have the potential to rely on the neuromuscular input by the user for continuous adaptation, continuous volitional control of assistive devices that can generalize across various tasks has not been addressed. In this study, we propose a method to use spatiotemporal ultrasound features of the rectus femoris and vastus intermedius muscles of able-bodied individuals for task-invariant learning of continuous knee kinematics during steady-state and transient ambulation. The task-invariant learning paradigm was statistically evaluated against a task-specific paradigm for the steady-state (1) level-walk, (2) incline, (3) decline, (4) stair ascent, and (5) stair descent ambulation tasks. The transitions between steady-state stair ambulation and level-ground walking were also investigated. It was observed that the continuous knee kinematics can be learned using a task-invariant learning paradigm with statistically comparable accuracy to a task-specific paradigm. Statistical analysis further revealed that incorporating the temporal ultrasound features significantly improves the accuracy of continuous estimations (p < 0.05). The average root mean square errors (RMSEs) of knee angle and angular velocity estimation were 7.06{\deg} and 53.1{\deg}/sec, respectively, for the task-invariant learning compared to 6.00{\deg} and 51.8{\deg}/sec for the task-specific models. High accuracy of continuous task-invariant paradigms overcome the barrier of task-specific control schemes and motivate the implementation of direct volitional control of lower-limb assistive devices using ultrasound sensing, which may eventually enhance the intuitiveness and functionality of these devices towards a "free form" control approach.