• Journal of Biomedical Engineering Research
• /
• v.30 no.4
• /
• pp.279-287
• /
• 2009

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality providing cross-sectional images of a conductivity distribution inside an electrically conducting object. MREIT has rapidly progressed in its theory, algorithm and experimental technique and now reached the stage of in vivo animal and human experiments. Conductivity image reconstructions in MREIT require various steps of carefully implemented numerical computations. To facilitate MREIT research, there is a pressing need for an MREIT software package with an efficient user interface. In this paper, we present an example of such a software, called CoReHA which stands for conductivity reconstructor using harmonic algorithms. It offers various computational tools including preprocessing of MREIT data, identification of boundary geometry, electrode modeling, meshing and implementation of the finite element method. Conductivity image reconstruction methods based on the harmonic $B_z$ algorithm are used to produce cross-sectional conductivity images. After summarizing basics of MREIT theory and experimental method, we describe technical details of each data processing task for conductivity image reconstructions. We pay attention to pitfalls and cautions in their numerical implementations. The presented software will be useful to researchers in the field of MREIT for simulation as well as experimental studies.

• Imaging Science in Dentistry
• /
• v.44 no.1
• /
• pp.1-6
• /
• 2014

Professionals performing radiographic examinations are responsible for maintaining optimal image quality for accurate diagnoses. These professionals must competently execute techniques such as film manipulation and processing to minimize patient exposure to radiation. Improper performance by the professional and/or patient may result in a radiographic image of unsatisfactory quality that can also lead to a misdiagnosis and the development of an inadequate treatment plan. Currently, the most commonly performed extraoral examination is panoramic radiography. The invention of panoramic radiography has resulted in improvements in image quality with decreased exposure to radiation and at a low cost. However, this technique requires careful, accurate positioning of the patient's teeth and surrounding maxillofacial bone structure within the focal trough. Therefore, we reviewed the literature for the most common types of positioning errors in panoramic radiography to suggest the correct techniques. We would also discuss how to determine if the most common positioning errors occurred in panoramic radiography, such as in the positioning of the patient's head, tongue, chin, or body.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.9
• /
• pp.4336-4354
• /
• 2018

Fuzzy C-means (FCM) algorithm is a most usually technique for medical image segmentation. But conventional FCM fails to perform well enough on magnetic resonance imaging (MRI) data with the noise and intensity inhomogeneity (IIH). In the paper, we propose a Gamma correction conditional FCM algorithm with spatial information (GcsFCM) to solve this problem. Firstly, the pre-processing, Gamma correction, is introduced to enhance the details of images. Secondly, the spatial information is introduced to reduce the effect of noise. Then we introduce the effective neighborhood mechanism into the local space information to improve the robustness for the noise and inhomogeneity. And the mechanism describes the degree of participation in generating local membership values and building clusters. Finally, the adjustment mechanism and the spatial information are combined into the weighted membership function. Experimental results on four image volumes with noise and IIH indicate that the proposed GcsFCM algorithm is more effective and robust to noise and IIH than the FCM, sFCM and csFCM algorithms.

• Nuclear Engineering and Technology
• /
• v.49 no.4
• /
• pp.781-786
• /
• 2017

The presence of crust on the inner walls of metallic ducts impairs transportation because crust completely or partially hinders the passage of fluid to the processing unit and causes damage to equipment connected to the production line. Its localization is crucial. With the development of the electronic imaging system installed at the Argonauta/Nuclear Engineering Institute (IEN)/National Nuclear Energy Commission (CNEN) reactor, it became possible to visualize crust in the interior of metallic piping of small diameter using real-time neutron radiography images obtained with a low neutron flux. The obtained images showed the resistance offered by crust on the passage of water inside the pipe. No discrepancy of the flow profile at the bottom of the pipe, before the crust region, was registered. However, after the passage of liquid through the pipe, images of the disturbances of the flow were clear and discrepancies in the flow profile were steep. This shows that this technique added the assembled apparatus was efficient for the visualization of the crust and of the two-phase flows.

• Journal of Korea Multimedia Society
• /
• v.24 no.2
• /
• pp.178-185
• /
• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• The Journal of Korean Institute of Information Technology
• /
• v.17 no.5
• /
• pp.77-82
• /
• 2019

Diabetic retinopathy is a complicated form of diabetes due to circulatory disorder in the peripheral blood vessels of the retina. We segment the microvessel for diagnosing diabetic retinophathy. The conventional methods using filter and features can segment the thick blood vessels, but it has relatively weak for segmenting fine blood vessels. In pre-processing step, noise reduction filter and histogram equalization are applied to suppress the noise and enhance the image contrast. Then, deep learning technique is used for pixel-by-pixel segmentation. The accuracy of conventional methods is between 90% to 94%, while the proposed method has improved as 95% accuracy. There is a problem of segmentation error around the optic disc and exudate due to the network depth. However the accuracy can be improved by modifying the network architecture in the future.

• Investigative Magnetic Resonance Imaging
• /
• v.26 no.2
• /
• pp.96-103
• /
• 2022

Purpose: The survival of organisms critically depends on avoidance responses to life-threatening stimuli. Information about dangerous situations needs to be remembered to produce defensive behavior. To investigate underlying brain regions to process information of danger, manganese-enhanced MRI (MEMRI) was used in olfactory fear-conditioned rats. Materials and Methods: Fear conditioning was conducted in male Sprague-Dawley rats. The animals received nasal injections of manganese chloride solution to monitor brain activation for olfactory information processing. Twenty-four hours after manganese injection, rats were exposed to electric foot shocks with odor cue for one hour. Control rats were exposed to the same odor cue without foot shocks. Forty-eight hours after the conditioning, rats were anesthetized and their brains were scanned with 9.4T MRI. Acquired images were processed and statistical analyses were performed using AFNI. Results: Manganese injection enhanced brain areas involved in olfactory information pathways in T1 weighted images. Rats that received foot shocks showed higher brain activation in the central nucleus of the amygdala, septum, primary motor cortex, and preoptic area. In contrast, control rats displayed greater signals in the orbital cortex and nucleus accumbens. Conclusion: Nasal delivery of manganese solution enhanced olfactory signal pathways in rats. Odor cue paired with foot shocks activated amygdala, the central brain region in fear, and related brain circuits. Use of MEMRI in fear conditioning provides a reliable monitoring technique of brain activation for fear learning.

• Nuclear Medicine and Molecular Imaging
• /
• v.43 no.5
• /
• pp.459-467
• /
• 2009

Purpose: The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Materials and Methods: Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. Results: The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 see, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 see, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. Conclusion: The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries.

• Progress in Medical Physics
• /
• v.24 no.2
• /
• pp.108-118
• /
• 2013

Currently, an arterial spin labeling (ASL) magnetic resonance imaging (MRI) technique does not routinely used in clinical studies to measure perfusion in brain because optimization of imaging protocol is required to obtain optimal perfusion signals. Therefore, the objective of this study was to investigate changes of perfusion-weighed signal intensities with varying several parameters on a pulsed arterial spin labeling MRI technique obtained from a 3T MRI system. We especially evaluated alternations of ASL-MRI signal intensities on special brain areas, including in brain tissues and lobes. The signal targeting with alternating radiofrequency (STAR) pulsed ASL method was scanned on five normal subjects (mean age: 36 years, range: 29~41 years) on a 3T MRI system. Four parameters were evaluated with varying: 1) the labeling gap, 2) the labeling delay time, 3) the labeling thickness, and 4) the slice scan order. Signal intensities were obtained from the perfusion-weighted imaging on the gray and white matters and brain lobes of the frontal, parietal, temporal, and occipital areas. The results of this study were summarized: 1) Perfusion-weighted signal intensities were decreased with increasing the labeling gap in the bilateral gray matter areas and were least affected on the parietal lobe, but most affected on the occipital lobe. 2) Perfusion-weighted signal intensities were decreased with increasing the labeling delay time until 400 ms, but increased up to 1,000 ms in the bilateral gray matter areas. 3) Perfusion-weighted signal intensities were increased with increasing the labeling thickness until 120 mm in both the gray and white matter. 4) Perfusion-weighted signal intensities were higher descending scans than asending scans in both the gray and white matter. We investigated changes of perfusion-weighted signal intensities with varying several parameters in the STAR ASL method. It should require having protocol optimization processing before applying in patients. It has limitations to apply the ASL method in the white matter on a 3T MRI system.

• Journal of Internet Computing and Services
• /
• v.17 no.1
• /
• pp.101-107
• /
• 2016

Due to the recent advancement in digital imaging technology, development of intervention equipment has become generalize. Video arbitration procedure is a process to insert a tiny catheter and a guide wire in the body, so in order to enhance the effectiveness and safety of this treatment, the high-quality of x-ray of image should be used. However, the increasing of radiation has become the problem. Therefore, the studies to improve the performance of x-ray detectors are being actively processed. Moreover, this intervention is based on the reference of the angiographic imaging and 3D medical image processing. In this paper, we propose a guidance system to support this intervention. Through this intervention, it can solve the problem of the existing 2D medical images based vessel that has a formation of cerebrovascular disease, and guide the real-time tracking and optimal route to the target lesion by intervention catheter and guide wire tool. As a result, the system was completely composed for medical image acquisition unit and image processing unit as well as a display device. The experimental environment, guide services which are provided by the proposed system Brain Phantom (complete intracranial model with aneurysms, ref H+N-S-A-010) was taken with x-ray and testing. To generate a reference image based on the Laplacian algorithm for the image processing which derived from the cerebral blood vessel model was applied to DICOM by Volume ray casting technique. $A^*$ algorithm was used to provide the catheter with a guide wire tracking path. Finally, the result does show the location of the catheter and guide wire providing in the proposed system especially, it is expected to provide a useful guide for future intervention service.