• Journal of Korean Neurosurgical Society
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• v.37 no.3
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• pp.241-243
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• 2005

We report a case of recurrent dermatofibrosarcoma in a 30-years-old woman who had undergone operations three times during 60 months and had received post-operative radiotherapy. On neurological examination, no neurological deficits were noticed. In brain magnetic resonance image(MRI), there was right parieto-occipital scalp mass with high signal in T2-weighted image, low signal in T1-weighted image with homogeneous enhancement. The removal was done including about 2cm uninvolved margins and pathologic examination of the lesion revealed dermatofibrosarcoma protuberans(DFSP). The prognostic factors of local recurrence may be related to surgical margins for resection; the length from the grossly intact margins, and the microscopically controlled excision in margins.

• Journal of Korean Neurosurgical Society
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• v.38 no.2
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• pp.144-146
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• 2005

Herein, a case of missed upper lumbar disc herniation, diagnosed by thorough neurological examination, digital infrared thermographic imaging[DITI], and repeated magnetic resonance[MR] image study, is reported. A 36-year-old female presented with intractable leg pain on left anterior thigh. Although she underwent lumbar MR image at other hospital, she was misdiagnosed as acute sprain. Neurological examination suggested the possibility of upper lumbar disc herniation, which was confirmed by DITI, MRI, and selective root block. After operation, her leg pain was significantly improved. It should be considered that upper lumbar disc herniation might be misdiagnosed as an acute sprain, as in our case. A high index of suspicion based on thorough neurological examination is most important in such cases. Then, multi-access such as DITI, MR image, and selective block, base on thorough neurological examination, are warranted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2425-2431
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• 1997

The increased usage of Magnetic Resonance Image (MRI) required the method for automatic segmentation of medical image that is more useful so as to diagnose the dissecitive information of a atient quickly and effectively through MR scans.The use of neural networks may give much hep to solving the complex problems concerned the matter. This paper proposes the new method for automatic segmentation of magnetic resonance (MR) images of the brain by using neural networks brained by back-propagation algorithm. The trained neural networks by the segmenting MR images of a patient produce an output that networks can segment MR images of the other patients automatically, too and show a clear image of the brain.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.115-120
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• 2012

Metals used orthopedic in human magnetic resonance imaging scan of the metal to be inserted, The information to users about the image distortion is to propose a basis for judgment. Metals used orthopedic on Stainless, Titanium and Clip using ferromagnetic artifacts and distortion of the image were measured. Using Phantom "Effect of Metals used in Orthopedic on Magnetic Resonance ImagingI" pig in a paper subsequently was carried out using the same bone. Experimental results using a pure Titanium is a relatively high diagnostic value was found that.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.113-113
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• 2001

Purpose： In the gradient echo EPI the conventional T2＊-weighted image is poor in signal as well distorted by the field inhomogeneity. By acquiring a proton density image in addition to th T2＊-weighted image at the same scan, the fMRI processing can be improved. Method： The central region of the k space is acquired twice at different time points after th RF pulse while acquiring the other regions onc as described in Fig. 1. In Fig. 1 the segment numbers are chronological. Then, we can get two images of different contrast by interleaving th central region in the k space as done in the dua contrast FSE.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.269-270
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• 1998

Image-guided surgery (IGS) system has become well known in the field of neurosurgery and spine surgery. A patient's anatomy is first registered to preoperatively acquired CT/ MRI data using the point matching algorithm. A magnetic field digitizer was used to measure the physical space data and the system was based on Workstation of Unix system. To evaluate the spatial accuracy of interactive IGS system, the phantom consisting of rods varied height and known location was used. The RMS error value between CT/MR images and real location was 3-4mm. For the more convenience of the surgery, we provide various image display modules.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.169-172
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• 1996

In this paper, we present the evaluation of signal to noise ratio(SNR) and images of Magnetic resonance imaging system which is underdevelopement. For the evaluation of such parameters, we used two different phantoms, one for SNR and image homogeneity, and the other is for the slice thickness measurement. Further, comparison with other leading MR systems may be needed for the better image quality assessment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.432-435
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• 2019

Convolutional Neural Networks (CNNs) have recently been gaining popularity in the medical image analysis field because of their image segmentation capabilities. In this paper, we present a CNN that performs automated brain tumor segmentations of sparsely annotated 3D Magnetic Resonance Imaging (MRI) scans. Our CNN is based on 3D U-net architecture, and it includes separate Dilated and Depth-wise Convolutions. It is fully-trained on the BraTS 2018 data set, and it produces more accurate results even when compared to the winners of the BraTS 2017 competition despite having a significantly smaller amount of parameters.

• Progress in Medical Physics
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• v.2 no.1
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• pp.3-16
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• 1991

Abstract. An area of particularly rapid technological growth in the last 15 years has been medical imaging (conventional X-ray, ultrasound, X-ray computed tomography (CT), magnetic resonance imaging (MRI). As the number and complexity of imaging studies rises, it becomes ever more important to distribute these images and the associated diagnoses in a timely and cost-effective fashion. The purpose of this paper is to describe the requirements for a future digital radiology system which will efficiently handle the large volume of images that generated, add new functionality to improve productivity of physicians, technologists, and other health care providers, and provide enough flexibility to allow the system to grow as medical image technology grows.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.