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Modelling side to side intestinal anastomosis.

Javier CIVIT ; Fernando DE LA PORTILLA ; Jose Luis SEVILLANO ; Anton CIVIT

Biomedical Engineering Letters.2017;7(3):267-271. doi:10.1007/s13534-017-0032-5

Side-to-side intestinal anastomosis is a surgical procedure where an incision is performed between two parallel segments of gut and then they are sutured together. The purpose of this paper is to investigate if the standard surgical practice diameter used in anastomosis leads to undesirable closed circulatory flows which may be harmful to the gut tissue. A finite element model for the chyme flow in a side by side anastomosis with realistic user configurable parameters is developed and solved in a wide range of situations. We analyze the flow crossing the anastomosis, the normalized pressure difference in the gut section and the streamlines that show the presence or absence of closed flow regions for a set of surgically feasible anastomosis diameter values. In contrast with the findings of simpler analytical models, closed flows do not appear in any of these cases. The study shows that the current standard surgical practice where the anastomosis diameter is similar to the gut diameter does not lead to undesirable effects predicted by some simple analytical models.
Intestines ; Anastomosis, Surgical

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Design of a stimulation protocol to predict temperature distribution in subcutaneous tissue using the finite element model.

Hyoun Seok MYOUNG ; Dong Hyun KIM ; Han Sung KIM ; Kyoung Joung LEE

Biomedical Engineering Letters.2017;7(3):261-266. doi:10.1007/s13534-017-0029-0

Moxibustion is a traditional Oriental medicine therapy that treats the symptoms of a disease with thermal stimulation. However, it is difficult to control the strength of the thermal or chemical stimulus generated by the various types and amounts of moxa and to prevent energy loss through the skin. To overcome these problems, we previously developed a method to efficiently provide RF thermal stimulation to subcutaneous tissue. In this paper, we propose a finite element model (FEM) to predict temperature distributions in subcutaneous tissue after radio-frequency thermal stimulation. To evaluate the performance of the developed FEM, temperature distributions were obtained from the FEM, and in vivo experiments were conducted using the RF stimulation system at subcutaneous tissue depths of 5 and 10 mm in the femoral region of a rabbit model. High correlation coefficients between simulated and actual temperature distributions—0.98 at 5 mm and 0.99 at 10 mm—were obtained, despite some slight errors in the temperature distribution at each depth. These results demonstrate that the FEM described here can be used to determine thermal stimulation profiles produced by RF stimulation of subcutaneous tissue.
Finite Element Analysis ; Medicine, East Asian Traditional ; Methods ; Moxibustion ; Skin ; Subcutaneous Tissue*

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Primary blast waves induced brain dynamics influenced by head orientations.

Yi HUA ; Yugang WANG ; Linxia GU

Biomedical Engineering Letters.2017;7(3):253-259. doi:10.1007/s13534-017-0027-2

There is controversy regarding the directional dependence of head responses subjected to blast loading. The goal of this work is to characterize the role of head orientation in the mechanics of blast wave-head interactions as well as the load transmitting to the brain. A three-dimensional human head model with anatomical details was reconstructed from computed tomography images. Three different head orientations with respect to the oncoming blast wave, i.e., front-on with head facing blast, back-on with head facing away from blast, and side-on with right side exposed to blast, were considered. The reflected pressure at the blast wave-head interface positively correlated with the skull curvature. It is evidenced by the maximum reflected pressure occurring at the eye socket with the largest curvature on the skull. The reflected pressure pattern along with the local skull areas could further influence the intracranial pressure distributions within the brain. We did find out that the maximum coup pressure of 1.031 MPa in the side-on case as well as the maximum contrecoup pressure of −0.124 MPa in the back-on case. Moreover, the maximum principal strain (MPS) was also monitored due to its indication to diffuse brain injury. It was observed that the peak MPS located in the frontal cortex region regardless of the head orientation. However, the local peak MPS within each individual function region of the brain depended on the head orientation. The detailed interactions between blast wave and head orientations provided insights for evaluating the brain dynamics, as well as biomechanical factors leading to traumatic brain injury.
Brain Injuries ; Brain* ; Frontal Lobe ; Head* ; Humans ; Intracranial Pressure ; Mechanics ; Orbit ; Skull

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Application of computer-aided approaches to the PUMC classification of scoliosis.

Junhua ZHANG ; Hongjian LI ; Yufeng ZHANG

Biomedical Engineering Letters.2017;7(3):245-251. doi:10.1007/s13534-017-0022-7

Surgical planning for scoliosis relies on the classification of the spinal curve pattern. To improve the reliability of the Peking Union Medical College (PUMC) classification system for scoliotic spinal curves, a computer-aided system is proposed and its reliability is evaluated. First, the reliability of curve measurements was improved by the computer-aided Cobb measurement approach. Second, judgmental errors were reduced by the computer program through the automation of the PUMC classification procedure. Four observers divided into an expert group and a resident group participated in the experiments. The kappa statistic was used to evaluate the variability. Classifications of 65 scoliotic cases by the four observers showed that with the computer's aid, the average intraobserver and interobserver kappa values were improved from 0.86 to 0.93 and from 0.75 to 0.86, respectively. The results indicate that the proposed computerized system can assist a surgeon in the PUMC classification of scoliosis and is especially useful for inexperienced surgeons.
Automation ; Beijing ; Classification* ; Judgment ; Scoliosis* ; Surgeons

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A bench-top micro-CT capable of simulating head motions.

Mohamed A A HEGAZY ; Mohamed Elsayed ELDIB ; Yang Ji MUN ; Myung Hye CHO ; Min Hyoung CHO ; Soo Yeol LEE

Biomedical Engineering Letters.2017;7(3):237-244. doi:10.1007/s13534-017-0023-6

Computational three-dimensional (3D) models of a dental structure generated from 3D dental computed tomography (CT) images are now widely used in digital dentistry. To generate precise 3D models, high-resolution imaging of the dental structure with a dental CT is required. However, a small head motion of the patient during the dental CT scan could degrade the spatial resolution of CT images to the extent that digital dentistry is no longer possible. A bench-top micro-CT has been built to evaluate the head motion effects on the dental CT images. A micro-CT has been built on an optic table with a micro-focus x-ray source and a flat-panel detector. A rotation stage, placed in between the x-ray source and the detector, is mounted on two-directional goniometers that can rotate the rotation stage in two orthogonal directions while the rotation stage is performing the CT scan. The goniometers can make object motions of an arbitrary waveform to simulate head tilting or head nodding. CT images of a phantom have been taken with and without introducing the motions, and the motion effects on the CT images have been evaluated. Object motions parallel to the detector plane have greater effects on the CT images than those against the detector plane. With the bench-top micro-CT, the motion effects have been visually seen at a tiny rotational motion as small as 0.3°. The bench-top micro-CT can be used to evaluate head motion effects on the dental CT images. The projection data, taken with the motion effects, would be used to develop motion artifact correction methods for a high-resolution dental-CT.
Artifacts ; Dentistry ; Head* ; Humans ; Tomography, X-Ray Computed

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Non-magnetic compliant finger sensor for continuous fine motor movement detection.

Anterpal SANDHU ; Yasong LI ; Nicholas PEATFIELD ; Xin Yi YONG ; Ryan D'ARCY ; Carlo MENON ; Teresa P L CHEUNG

Biomedical Engineering Letters.2017;7(3):215-219. doi:10.1007/s13534-017-0031-6

A non-magnetic MEG compatible device has been developed that provides continuous force and velocity information. Combined with MEG, this device may find utility in characterizing brain regions associated with force and velocity relative to individual digits or movement pattern. 15 healthy right-handed participants were given visual cues to perform random finger movements on the prototype finger sensor for 21 s and then rest for 21 s (7 times). Respective finger flexion data were obtained, during 151-channel MEG brain scanning, by feeding the signal from finger sensor into four input Analog to Digital Converter (ADC) channels in the MEG hardware. The source activity was reconstructed in beta band using a Linearly Constrained Minimum Variance (LCMV) beamformer in the beta band. The ADC channels were used as regressors for a continuous time General Linear Model (GLM) and a Region of Interest (ROI) was identified to examine activity. MEG analysis showed bilateral activation in the primary motor cortex region. Because individual digits could be isolated in the ADC data, somatotopy of the fingers were observed consistent with the homunculus except pinky finger. The total span was calculated to be 5.5662 mm. The study confirms that the finger sensor is magnetically compatible with MEG measurements and may potentially provide a means to study complex sensorimotor functions. Improved isolation of individual digit information along with the use of machine learning algorithms can help retrieve more accurate results.
Brain ; Cues ; Fingers* ; Linear Models ; Machine Learning ; Motor Cortex

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MEG and EEG dipole clusters from extended cortical sources.

Manfred FUCHS ; Jörn KASTNER ; Reyko TECH ; Michael WAGNER ; Fernando GASCA

Biomedical Engineering Letters.2017;7(3):185-191. doi:10.1007/s13534-017-0019-2

Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.
Artifacts ; Dataset ; Electroencephalography* ; Head ; Magnetic Fields ; Magnetoencephalography ; Neurons ; Noise

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Statistical non-parametric mapping in sensor space.

Michael WAGNER ; Reyko TECH ; Manfred FUCHS ; Jörn KASTNER ; Fernando GASCA

Biomedical Engineering Letters.2017;7(3):193-203. doi:10.1007/s13534-017-0015-6

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
Electroencephalography ; Magnetoencephalography ; Methods ; Random Allocation

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Erratum to: A real time surface electromyography signal driven prosthetic hand model using PID controlled DC motor.

Retheep RAJ ; Rejith RAMAKRISHNAN ; Karingannikalam Subramanian SIVANANDAN

Biomedical Engineering Letters.2017;7(1):55-55. doi:10.1007/s13534-017-0011-x

There are typos in the second author's last name and third author's first name in the published article. The corrected names are shown in this erratum.

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A new approach for multi-channel surface EMG signal simulation.

Yong NING ; Yingchun ZHANG

Biomedical Engineering Letters.2017;7(1):45-53. doi:10.1007/s13534-017-0009-4

Simulation models are necessary for testing the performance of newly developed approaches before they can be applied to interpreting experimental data, especially when biomedical signals such as surface electromyogram (SEMG) signals are involved. A new and easily implementable surface EMG simulation model was developed in this study to simulate multi-channel SEMG signals. A single fiber action potential (SFAP) is represented by the sum of three Gaussian functions. SFAP waveforms can be modified by adjusting the amplitude and bandwidth of the Gaussian functions. SEMG signals were successfully simulated at different detected locations. Effects of the fiber depth, electrode position and conduction velocity of SFAP on motor unit action potential (MUAP) were illustrated. Results demonstrate that the easily implementable SEMG simulation approach developed in this study can be used to effectively simulate SEMG signals.
Action Potentials ; Electrodes

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