In magnetic resonance electrical impedance tomography (MREIT), we measure the induced magnetic flux density inside an object subject to an externally injected current. This magnetic flux density is contaminated with noise, which ultimately limits the quality of reconstructed conductivity and current density images. By analysing and experimentally verifying the amount of noise in images gathered from two MREIT systems, we found that a carefully designed MREIT study will be able to reduce noise levels below 0.25 and 0.05 nT at main magnetic field strengths of 3 and 11 T, respectively, at a voxel size of 3 x 3 x 3 mm(3). Further noise level reductions can be achieved by optimizing MREIT pulse sequences and using signal averaging. We suggest two different methods to estimate magnetic flux noise levels, and the results are compared to validate the experimental setup of an MREIT system.

Electrical impedance tomography (EIT) is particularly well-suited to applications where its portability, rapid acquisition speed and sensitivity give it a practical advantage over other monitoring or imaging systems. An EIT system's patient interface can potentially be adapted to match the target environment, and thereby increase its utility. It may thus be appropriate to use different electrode positions from those conventionally used in EIT in these cases. One application that may require this is the use of EIT on emergency medicine patients; in particular those who have suffered blunt abdominal trauma. In patients who have suffered major trauma, it is desirable to minimize the risk of spinal cord injury by avoiding lifting them. To adapt EIT to this requirement, we devised and evaluated a new electrode topology (the 'hemiarray') which comprises a set of eight electrodes placed only on the subject's anterior surface. Images were obtained using a two-dimensional sensitivity matrix and weighted singular value decomposition reconstruction. The hemiarray method's ability to quantify bleeding was evaluated by comparing its performance with conventional 2D reconstruction methods using data gathered from a saline phantom. We found that without applying corrections to reconstructed images it was possible to estimate blood volume in a two-dimensional hemiarray case with an uncertainty of around 27 ml. In an approximately 3D hemiarray case, volume prediction was possible with a maximum uncertainty of around 38 ml in the centre of the electrode plane. After application of a QI normalizing filter, average uncertainties in a two-dimensional hemiarray case were reduced to about 15 ml. Uncertainties in the approximate 3D case were reduced to about 30 ml.

Magnetic resonance electrical impedance tomography (MREIT) has the potential to provide conductivity and current density images with high spatial resolution and accuracy. Recent experimental studies at a field strength of 3 T showed that the spatial resolution of conductivity and current density images may be similar to that of conventional MR images as long as enough current is injected, at least 20 mA when the object being imaged has a size similar to the human head. To apply the MREIT technique to image small conductivity changes using less injection current, we performed MREIT studies at 11 T field strength, where noise levels in measured magnetic flux density data are significantly lower. In this paper we present the experimental results of imaging biological tissues with different conductivity values using MREIT at 11 T. We describe technical difficulties encountered in using high-field MREIT systems and possible solutions. High-field MREIT is suggested as a research tool for obtaining accurate conductivity data from tissue samples and animal subjects.

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Fused deposition modeling (FDM) is the most popular technology among 3D printing technologies because of inexpensive and flexible extrusion systems with thermoplastic materials. However, thermal degradation phenomena of the 3D-printed thermoplastics is an inevitable problem for long-term reliability. In the current study, thermal degradation of 3D-printed thermoplastics of ABS and PLA was studied. A classification methodology using deep learning strategy was developed so that thermal degradation of the thermoplastics could be classified using FTIR and Artificial Neural Networks (ANNs). Under given data and predefined rules for ANNs, ANN models with nine hidden layers showed the best results in terms of accuracy. To extend this methodology, other thermoplastics, several new datasets for ANNs, and control parameters of ANNs could be further investigated.