Journal of medical engineering最新文献

Optical coherence tomography is a micrometer-scale imaging modality that permits label-free, cross-sectional imaging of biological tissue microstructure using tissue backscattering properties. After its invention in the 1990s, OCT is now being widely used in several branches of neuroscience as well as other fields of biomedical science. This review study reports an overview of OCT's applications in several branches or subbranches of neuroscience such as neuroimaging, neurology, neurosurgery, neuropathology, and neuroembryology. This study has briefly summarized the recent applications of OCT in neuroscience research, including a comparison, and provides a discussion of the remaining challenges and opportunities in addition to future directions. The chief aim of the review study is to draw the attention of a broad neuroscience community in order to maximize the applications of OCT in other branches of neuroscience too, and the study may also serve as a benchmark for future OCT-based neuroscience research. Despite some limitations, OCT proves to be a useful imaging tool in both basic and clinical neuroscience research.

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

Researchers and clinicians are increasingly using plantar pressure and force measurement system to evaluate foot functions. This research evaluates the quality and reliability of a Tekscan HR mat to study the plantar pressures and forces acting during walking, running, jumping, and standing of healthy subjects. The following regions of the foot were investigated: heel, mid foot, metatarsophalangeal joint, hallux, and the toes. The arches of both feet of the three healthy subjects in the gait analysis were presented which addresses the balancing issues of the body during locomotion. The results indicated that the peaks at the big toe (79.4 ± 8.5 N/cm², p = 0.0001) were the maximum compared to forefoot (40.3 ± 3.3 N/cm², p = 0.001), to midfoot (7.5 ± 1.3 N/cm², p = 0.001), and to heel (27.8 ± 3.9 N/cm², p = 0.0002) for jump activity. The running activity demonstrated similar results as jump where the maximum peak pressures were absorbed at the big toe region. The heel region during running (86.3 ± 12.6 N/cm², p = 0.001) showed three times the pressure peak compared to the jump land (27.8 ± 3.9 N/cm², p = 0.0002) activity. The measurement system proved to be highly capable of detecting heel strike and toe-off moments.

The longevity of hip prostheses is contingent on the stability of the implant within the cavity of the femur bone. The cemented fixation was mostly adopted owing to offering the immediate stability from cement-stem and cement-bone bonding interfaces after implant surgery. Yet cement damage and stress shielding of the bone were proven to adversely affect the lifelong stability of the implant, especially among younger subjects who tend to have an active lifestyle. The geometry and material distribution of the implant can be optimized more efficiently with a three-dimensional realistic design of a functionally graded material (FGM). We report an efficient numerical technique for achieving this objective, for maximum performance stress shielding and the rate of early accumulation of cement damage were concurrently minimized. Results indicated less stress shielding and similar cement damage rates with a 2D-FGM implant compared to 1D-FGM and Titanium alloy implants.

Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.

Most of the 3D printing applications of preoperative models have been focused on dental and craniomaxillofacial area. The purpose of this paper is to demonstrate the possibilities in other application areas and give examples of the current possibilities. The approach was to communicate with the surgeons with different fields about their needs related preoperative models and try to produce preoperative models that satisfy those needs. Ten different kinds of examples of possibilities were selected to be shown in this paper and aspects related imaging, 3D model reconstruction, 3D modeling, and 3D printing were presented. Examples were heart, ankle, backbone, knee, and pelvis with different processes and materials. Software types required were Osirix, 3Data Expert, and Rhinoceros. Different 3D printing processes were binder jetting and material extrusion. This paper presents a wide range of possibilities related to 3D printing of preoperative models. Surgeons should be aware of the new possibilities and in most cases help from mechanical engineering side is needed.

Clinical studies on application of functional electrical stimulation (FES) to motor rehabilitation have been increasing. However, muscle fatigue appears early in the course of repetitive movement production training by FES. Although M-wave variables were suggested to be reliable indices of muscle fatigue in long lasting constant electrical stimulation under the isometric condition, the ability of M-wave needs more studies under intermittent stimulation condition, because the intervals between electrical stimulations help recovery of muscle activation level. In this paper, M-waves elicited by double pulses were examined in muscle fatigue evaluation during repetitive movements considering rehabilitation training with surface electrical stimulation. M-waves were measured under the two conditions of repetitive stimulation: knee extension force production under the isometric condition and the dynamic movement condition by knee joint angle control. Amplitude of M-wave elicited by the 2nd pulse of a double pulse decreased during muscle fatigue in both measurement conditions, while the change in M-waves elicited by single pulses in a stimulation burst was not relevant to muscle fatigue in repeated activation with stimulation interval of 1 s. Fatigue index obtained from M-waves elicited by 2nd pulses was suggested to provide good estimation of muscle fatigue during repetitive movements with FES.

The development of an Automated System for Asthma Monitoring (ADAM) is described. This consists of a consumer electronics mobile platform running a custom application. The application acquires an audio signal from an external user-worn microphone connected to the device analog-to-digital converter (microphone input). This signal is processed to determine the presence or absence of cough sounds. Symptom tallies and raw audio waveforms are recorded and made easily accessible for later review by a healthcare provider. The symptom detection algorithm is based upon standard speech recognition and machine learning paradigms and consists of an audio feature extraction step followed by a Hidden Markov Model based Viterbi decoder that has been trained on a large database of audio examples from a variety of subjects. Multiple Hidden Markov Model topologies and orders are studied. Performance of the recognizer is presented in terms of the sensitivity and the rate of false alarm as determined in a cross-validation test.

Kaczmarz's alternating projection method has been widely used for solving mostly over-determined linear system of equations A x = b in various fields of engineering, medical imaging, and computational science. Because of its simple iterative nature with light computation, this method was successfully applied in computerized tomography. Since tomography generates a matrix A with highly coherent rows, randomized Kaczmarz algorithm is expected to provide faster convergence as it picks a row for each iteration at random, based on a certain probability distribution. Since Kaczmarz's method is a subspace projection method, the convergence rate for simple Kaczmarz algorithm was developed in terms of subspace angles. This paper provides analyses of simple and randomized Kaczmarz algorithms and explains the link between them. New versions of randomization are proposed that may speed up convergence in the presence of nonuniform sampling, which is common in tomography applications. It is anticipated that proper understanding of sampling and coherence with respect to convergence and noise can improve future systems to reduce the cumulative radiation exposures to the patient. Quantitative simulations of convergence rates and relative algorithm benchmarks have been produced to illustrate the effects of measurement coherency and algorithm performance, respectively, under various conditions in a real-time kernel.