Control of whole-body FDG-positron emission tomography image quality by adjusting the acquisition time: A new physical image quality index and patientdependent parameters for clinical imaging

Objective: This study aimed to establish a methodology for obtaining visually equivalent image quality regardless of patient size by controlling the acquisition time of positron emission tomography (PET) studies. Methods: In Part 1, we determined the physical image quality index with the highest correlation with visual assessment in 30 patients. In Part 2, 100 patients were scanned to identify the patient-dependent parameters that were most correlated with the physical image quality index. These parameters were calculated from the combination of the administered activity of 18F-FDG and weight. We drew an approximate curve from these parameters and prepared a scatter plot of the physical image quality index. In Part 3, we checked whether the image quality was constant by controlling the acquisition time in 189 patients. The approximation formula we obtained under (2) was used to control the acquisition time. The physical image quality index was a constant value, and the patient-dependent parameter was calculated from the patient’s physique. Results: The physical image quality index with the highest correlation with visual evaluation was the noise equivalent count weight (NECweight) (correlation coefficient: 0.90). The patient-dependent index most correlated with NECweight was activity/weight3 (A/W3) (coefficient of determination: 0.978). The verification of the acquisition time to obtain a certain image quality showed an average of 0.60 ± 0.034 Mcounts/m∙kg, and a similar image quality was obtained independent of the individual physiques. Conclusions: Calculating NECweight and A/W3 enable the determination of the appropriate acquisition time for stable image quality before the PET study

Estimating Deficient Muscle Activity Using LSTM With Integrated Damping Neurons for EMG-Based Control of Robotic Prosthetic Fingers

Robotic prosthetic hands can help perform the intended sophisticated movements of the upper limb, which can assist amputees to perform their daily activities. Although a robotic prosthetic hand can be controlled in real-time using the user’s electromyography (EMG), which directly reflects the user’s motion intention, some important EMG signals are usually lost owing to muscle deficiency. This study proposes a muscle activity estimator that is inspired by the muscle synergy across subjects to estimate the activity of the missing muscles in amputees in real-time. The proposed estimator learns muscle synergy from the EMG balance, finger joint angles, and the grasping force of healthy persons. The proposed estimator is developed as an artificial neural network (ANN) with a novel cell structure that combines long-short-term memory and damping neurons to analyze muscle dynamics. Furthermore, to improve the accuracy of learning muscle synergy, the muscles to be input to the estimator are selected by focusing on the enslavement of muscles and anatomical relationships. The effectiveness of the proposed estimator is evaluated by experiments. The results showed that the proposed estimator can contribute well to the realization of the intended sophisticated motions of the user

Towards Control of a Transhumeral Prosthesis with EEG Signals

Robotic prostheses are expected to allow amputees greater freedom and mobility. However, available options to control transhumeral prostheses are reduced with increasing amputation level. In addition, for electromyography-based control of prostheses, the residual muscles alone cannot generate sufficiently different signals for accurate distal arm function. Thus, controlling a multi-degree of freedom (DoF) transhumeral prosthesis is challenging with currently available techniques. In this paper, an electroencephalogram (EEG)-based hierarchical two-stage approach is proposed to achieve multi-DoF control of a transhumeral prosthesis. In the proposed method, the motion intention for arm reaching or hand lifting is identified using classifiers trained with motion-related EEG features. For this purpose, neural network and k-nearest neighbor classifiers are used. Then, elbow motion and hand endpoint motion is estimated using a different set of neural-network-based classifiers, which are trained with motion information recorded using healthy subjects. The predictions from the classifiers are compared with residual limb motion to generate a final prediction of motion intention. This can then be used to realize multi-DoF control of a prosthesis. The experimental results show the feasibility of the proposed method for multi-DoF control of a transhumeral prosthesis. This proof of concept study was performed with healthy subjects