A neural tracking and motor control approach to improve rehabilitation of upper limb movements

<p>Abstract</p> <p>Background</p> <p>Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics model, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an <it>ad hoc </it>markerless motion analysis algorithm for the estimation of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics model able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.</p> <p>Methods</p> <p>The markerless motion tracking system aims at localizing and monitoring the arm movement by tracking its silhouette. It uses a specifically designed motion estimation method, that we named Neural Snakes, which predicts the arm contour deformation as a first step for a silhouette extraction algorithm. The starting and ending points of the arm movement feed an Artificial Neural Controller, enclosing the muscular Hill's model, which solves the inverse dynamics to obtain the FES patterns needed to move a simulated arm from the starting point to the desired point. Both position error with respect to the requested arm trajectory and comparison between curvature factors have been calculated in order to determine the accuracy of the system.</p> <p>Results</p> <p>The proposed method has been tested on real data acquired during the execution of planar goal-oriented arm movements. Main results concern the capability of the system to accurately recreate the movement task by providing a synthetic arm model with the stimulation patterns estimated by the inverse dynamics model. In the simulation of movements with a length of ± 20 cm, the model has shown an unbiased angular error, and a mean (absolute) position error of about 1.5 cm, thus confirming the ability of the system to reliably drive the model to the desired targets. Moreover, the curvature factors of the factual human movements and of the reconstructed ones are similar, thus encouraging future developments of the system in terms of reproducibility of the desired movements.</p> <p>Conclusion</p> <p>A novel FES-assisted rehabilitation system for the upper limb is presented and two parts of it have been designed and tested. The system includes a markerless motion estimation algorithm, and a biologically inspired neural controller that drives a biomechanical arm model and provides the stimulation patterns that, in a future development, could be used to drive a smart Functional Electrical Stimulation system (sFES). The system is envisioned to help in the rehabilitation of post stroke hemiparetic patients, by assisting the movement of the paretic upper limb, once trained with a set of movements performed by the therapist or in virtual reality. Future work will include the application and testing of the stimulation patterns in real conditions.</p

Shape registration in implicit spaces using information theory and free form deformations

We present a novel, variational and statistical approach for shape registration. Shapes of interest are implicitly embedded in a higher- dimensional space of distance transforms. In this implicit embedding space, registration is formulated in a hierarchical manner: the Mutual Information criterion supports various transformation models and is optimized to perform global registration; then, a B- spline- based Incremental Free Form Deformations ( IFFD) model is used to minimize a Sum- of- Squared- Differences ( SSD) measure and further recover a dense local nonrigid registration field. The key advantage of such framework is twofold: 1) it naturally deals with shapes of arbitrary dimension ( 2D, 3D, or higher) and arbitrary topology ( multiple parts, closed/ open) and 2) it preserves shape topology during local deformation and produces local registration fields that are smooth, continuous, and establish one- to- one correspondences. Its invariance to initial conditions is evaluated through empirical validation, and various hard 2D/ 3D geometric shape registration examples are used to show its robustness to noise, severe occlusion, and missing parts. We demonstrate the power of the proposed framework using two applications: one for statistical modeling of anatomical structures, another for 3D face scan registration and expression tracking. We also compare the performance of our algorithm with that of several other well- known shape registration algorithms

Clinical validation of angle-independent myocardial elastography using MRI tagging

In this paper, two-dimensional angle-independent myocardial elastography (2DME) was employed in order to assess and image myocardial deformation (or, strains) in an entire left-ventricular view and was further validated against tagged Magnetic Resonance Imaging (tMRI) in normal as well as abnormal human subjects. Both RF ultrasound and tMRI frames were acquired in a 2D short-axis (SA) view at the papillary muscle level. In 2DME, in-plane (lateral and axial) incremental displacements (i.e., between two consecutive RF frames) were iteratively estimated using 1D cross-correlation and recorrelation techniques in a 2D search with a 1D matching kernel. The incremental displacements starting from end-diastole (ED) to end-systole (ES) were then accumulated to obtain cumulative systolic displacements. In tMRI, cardiac motion was obtained using a template-matching algorithm on a 2D grid-shaped mesh. The entire displacement distribution within the myocardium was obtained by a cubic B-spline-based method. In both 2DME and tMRL 2D Lagrangian finite systolic strains were calculated from cumulative 2D displacements. Principal strains, which were angle-independent and less centroid dependent than polar (i.e., radial and circumferential) strains, were then computed from the 2D finite strains through our previously established strategy. Both qualitatively (or, full SA view) and quantitatively (or, temporal strain profiles), 2DME is shown capable of estimating myocardial deformation highly comparable to tMRI estimates in a clinical setting. © 2007 IEEE.link_to_subscribed_fulltex

Ultrasound myocardial elastography and registered 3D tagged MRI: Quantitative strain comparison

Ultrasound Myocardial Elastography (UME) and Tagged Magnetic Resonance Imaging (tMRI) are two imaging modalities that were developed in the recent years to quantitatively estimate the myocardial deformations. Tagged MRI is currently considered as the gold standard for myocardial strain mapping in vivo. However, despite the low SNR nature of ultrasound signals, echocardiography enjoys the widespread availability in the clinic, as well as its low cost and high temporal resolution. Comparing the strain estimation performances of the two techniques has been of great interests to the community. In order to assess the cardiac deformation across different imaging modalities, in this paper, we developed a semi-automatic intensity and gradient based registration framework that rigidly registers the 3D tagged MRIs with the 2D ultrasound images. Based on the two registered modalities, we conducted spatially and temporally more detailed quantitative strain comparison of the RF-based UME technique and tagged MRI. From the experimental results, we conclude that qualitatively the two modalities share similar overall trends. But error and variations in UME accumulate over time. Quantitatively tMRI is more robust and accurate than UME. © Springer-Verlag Berlin Heidelberg 2007.link_to_subscribed_fulltex

Preliminary Validation of Angle-Independent Myocardial Elastography Using MR Tagging in a Clinical Setting

Myocardial elastography (ME), a radio-frequency (RF) based speckle tracking technique, was employed in order to image the entire two-dimensional (2D) transmural deformation field in full echocardiographic views and was validated against tagged magnetic resonance imaging (tMRI) in normal as well as reperfused (i.e., treated myocardial infarction [MI]) human left ventricles. RF ultrasound and tMRI frames were acquired at the papillary muscle level in 2D short-axis (SA) views at the frame rates of 136 (fps; real-time) and 33 fps (electrocardiogram [ECG]-gated), respectively. In ME, in-plane, 2D (lateral and axial) incremental displacements were iteratively estimated using one-dimensional (1D) cross-correlation and recorrelation techniques in a 2D search with a 1D matching kernel. In tMRI, cardiac motion was estimated by a template-matching algorithm on a 2D grid-shaped mesh. In both ME and tMRI, cumulative 2D displacements were obtained and then used to estimate 2D Lagrangian finite systolic strains, from which polar (i.e., radial and circumferential) strains, namely angle-independent measures, were further obtained through coordinate transformation. Principal strains, which are angle-independent and less centroid-dependent than polar strains, were also computed and imaged based on the 2D finite strains using methodology previously established. Both qualitatively and quantitatively, angle-independent ME is shown to be capable of (1) estimating myocardial deformation in good agreement with tMRI estimates in a clinical setting and of (2) differentiating abnormal from normal myocardium in a full left-ventricular view. The principal strains were concluded to be a potential diagnostic measure for detection of cardiac disease with reduced centroid dependence. (E-mail: [email protected]). © 2008 World Federation for Ultrasound in Medicine & Biology.link_to_subscribed_fulltex

Validation of myocardial elastography using MR tagging in normal and abnormal human hearts in vivo

In this paper, Myocardial Elastography (ME), a radio-frequency (RF) based speckle tracking technique, was employed in order to assess the contractility of a myocardium, and validated against tagged Magnetic Resonance Imaging (tMRI) in vivo in normal as well as abnormal cases. Both RF ultrasound and tMRI frames were acquired in 2D short-axis (SA) views from two healthy subjects and one with a history of infarction. In-plane (lateral and axial) incremental displacements were iteratively estimated using ID cross-correlation and recorrelation techniques in a 2D search with a 1D matching kernel. The incremental displacements from end-diastole (ED) to end-systole (ES) were then accumulated to obtain cumulative systolic displacements. In tMRI, cardiac motion was obtained by a template-matching algorithm on a 2D grid-shaped mesh. The entire displacement distribution within the myocardium was obtained by a cubic B-spline-based method. In both ME and tMRI, 2D Lagrangian finite systolic strains were calculated from cumulative 2D displacements. Radial and circumferential strains were then computed from the 2D finite strains. Both qualitatively and quantitatively, ME is shown capable of measuring myocardial deformation in excellent agreement with tMRI estimates in normal and abnormal subjects.link_to_subscribed_fulltex