• Progress in Medical Physics
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• v.14 no.1
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• pp.34-42
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• 2003

In medical imaging, three-dimensional (3D) display using Virtual Reality Modeling Language (VRML) as a portable file format can give intuitive information more efficiently on the World Wide Web (WWW). The web-based 3D visualization of functional images combined with anatomical images has not studied much in systematic ways. The goal of this study was to achieve a simultaneous observation of 3D anatomic and functional models with planar images on the WWW, providing their locational information in 3D space with a measuring implement using VRML. MRI and ictal-interictal SPECT images were obtained from one epileptic patient. Subtraction ictal SPECT co-registered to MRI (SISCOM) was performed to improve identification of a seizure focus. SISCOM image volumes were held by thresholds above one standard deviation (1-SD) and two standard deviations (2-SD). SISCOM foci and boundaries of gray matter, white matter, and cerebrospinal fluid (CSF) in the MRI volume were segmented and rendered to VRML polygonal surfaces by marching cube algorithm. Line profiles of x and y-axis that represent real lengths on an image were acquired and their maximum lengths were the same as 211.67 mm. The real size vs. the rendered VRML surface size was approximately the ratio of 1 to 605.9. A VRML measuring tool was made and merged with previous VRML surfaces. User interface tools were embedded with Java Script routines to display MRI planar images as cross sections of 3D surface models and to set transparencies of 3D surface models. When transparencies of 3D surface models were properly controlled, a fused display of the brain geometry with 3D distributions of focal activated regions provided intuitively spatial correlations among three 3D surface models. The epileptic seizure focus was in the right temporal lobe of the brain. The real position of the seizure focus could be verified by the VRML measuring tool and the anatomy corresponding to the seizure focus could be confirmed by MRI planar images crossing 3D surface models. The VRML application developed in this study may have several advantages. Firstly, 3D fused display and control of anatomic and functional image were achieved on the m. Secondly, the vector analysis of a 3D surface model was defined by the VRML measuring tool based on the real size. Finally, the anatomy corresponding to the seizure focus was intuitively detected by correlations with MRI images. Our web based visualization of 3-D fusion image and its localization will be a help to online research and education in diagnostic radiology, therapeutic radiology, and surgery applications.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.275-280
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• 2003

The method of simultaneous data acquisition of arteries and veins(SAAV) was suggested to obtain MR angiography of arteries and veins at 0.3T low filed MRI system (Magfinder, AlLab. Korea). Two separated artery- and vein-images were put together using AVCM(Artery-Vein Color Mapping) algorithm and presented in the same image. In this study, artery- and vein-separated angiograms of volunteer's neck were obtained. Two dimensioal blood-enhanced images wre sequentially obtained using SAAV pulse sequence based on time-of-flight(TOF) method with flow compensation. Imaging parameters were TR/TE=70/12msec. FOV=230mm, slice thickness = 3mm, flip angle=90$^{\circ}$, matrix size=256${\times}$256${\times}$64mm. TSat TH/SPA=15/20mm, Ts_v=10msec and Ts_a=40ms. 3D MRA images were reconstructed using the maximum intensity projection(MIP) and the artery-vein color mapping(AVCM) algorithm. This study showed good possibility of clinical applications of MRA in 0.3T which provides valuable diagnostic information of clinical vascular diseases.

• Archives of Plastic Surgery
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• v.43 no.5
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• pp.430-437
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• 2016

Background Accurate breast volume assessment is a prerequisite to preoperative planning, as well as intraoperative decision making in breast reconstruction surgery. The use of three-dimensional surface imaging (3D scanning) to assess breast volume has many advantages. However, before employing 3D scanning in the field, the tool's validity should be demonstrated. The purpose of this study was to confirm the validity of 3D-scanning technology for evaluating breast volume. Methods We reviewed the charts of 25 patients who underwent breast reconstruction surgery immediately after total mastectomy. Breast volumes using the Axis Three 3D scanner, water-displacement technique, and magnetic resonance imaging (MRI) were obtained bilaterally in the preoperative period. During the operation, the tissue removed during total mastectomy was weighed and the specimen volume was calculated from the weight. Then, we compared the volume obtained from 3D scanning with those obtained using the water-displacement technique, MRI, and the calculated volume of the tissue removed. Results The intraclass correlation coefficient (ICC) of breast volumes obtained from 3D scanning, as compared to the volumes obtained using the water-displacement technique and specimen weight, demonstrated excellent reliability. The ICC of breast volumes obtained using 3D scanning, as compared to those obtained by MRI, demonstrated substantial reliability. Passing-Bablok regression showed agreement between 3D scanning and the water-displacement technique, and showed a linear association of 3D scanning with MRI and specimen volume, respectively. Conclusions When compared with the classical water-displacement technique and MRI-based volumetry, 3D scanning showed significant reliability and a linear association with the other two methods.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.599-606
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• 2013

The purpose of this study is to know a clinical usefulness for delineation of articular cartilage compared with 2D TSE-SPIR and 3D FFE-PROSET technique. From January 2013 to september 2013, a total of 30 normal volunteers(12 men and 18 women aged between 35 and 55 years; mean 49.48 years) were studied on a philips 3.0T MRI scanner. As a quantitative analysis, SNRs and CNRs were evaluated by using two methods for delineation of articular cartilage. As a qualitative analysis, image quality was evaluated by special radiological technologist of MRI for image delineation on a three grade. As a results, SNRs and CNRs for articular cartilage were significantly greater for the 3D FFE-PROSET(SNRs: 8.40, 114.02, 9.53, CNRs: 104.49, 139.49) technique compared to 2D TSE-SPIR(SNRs: 4.41, 71.63, 7.34, CNRs: 64.30, 58.41) technique, image quality also was higher for evaluation of 3D FFE-PROSET(2.40) technique(p=0.0021). In conclusion, this study showed that a 3D FFE-PROSET MRI has improved SNRs and CNRs for evaluating of the articular cartilage, these conclusions in the future will be provided useful information in diagnosis of articular cartilage.

• Journal of radiological science and technology
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• v.41 no.5
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• pp.451-456
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• 2018

During last a decade, there has been increased demand for 3D-printed medical devices with significant improvement of 3D-Printer (also known as Additive. Manufacturing AM), which depend upon human body features. Especially, demand for personalized medical material is highly growing with being super-aged society. In this study, 3D-reconstructed 3D mesh image from CT/MRI-images is demonstrated to analyse each patients' personalized anatomical features by using in house, then to be able to manufacture its counterpart. Developed software is distributed free of charge, letting various researcher identify biological feature for each areas.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.55-66
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• 2012

Purpose : During brain MRI scanning, subject's head motion can adversely affect MRI images. To minimize MR image distortion by head movement, we developed an optical tracking system to detect the 3-D movement of subjects. Materials and Methods: The system consisted of 2 CCD cameras, two infrared illuminators, reflective sphere-type markers, and frame grabber with desktop PC. Using calibration which is the procedure to calculate intrinsic/extrinsic parameters of each camera and triangulation, the system was desiged to detect 3-D coordinates of subject's head movement. We evaluated the accuracy of 3-D position of reflective markers on both test board and the real MRI scans. Results: The stereo system computed the 3-D position of markers accurately for the test board and for the subject with glasses with attached optical reflective marker, required to make regular head motion during MRI scanning. This head motion tracking didn't affect the resulting MR images even in the environment varying magnetic gradient and several RF pulses. Conclusion: This system has an advantage to detect subject's head motion in real-time. Using the developed system, MRI operator is able to determine whether he/she should stop or intervene in MRI acquisition to prevent more image distortions.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Smart Media Journal
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• v.13 no.6
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• pp.90-97
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• 2024

This paper focuses on detecting Alzheimer's Disease (AD). The most usual form of dementia is Alzheimer's disease, which causes permanent cause memory cell damage. Alzheimer's disease, a neurodegenerative disease, increases slowly over time. For this matter, early detection of Alzheimer's disease is important. The purpose of this work is using Magnetic Resonance Imaging (MRI) to diagnose AD. A Convolution Neural Network (CNN) model, Reset, and VGG the pre-trained learning models are used. Performing analysis and validation of layers affects the effectiveness of the model. T1-weighted MRI images are taken for preprocessing from ADNI. The Dataset images are taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). 3D MRI scans into 2D image slices shows the optimization method in the training process while achieving 96% and 94% accuracy in VGG 16 and ResNet 18 respectively. This study aims to classify AD from brain 3D MRI images and obtain better results.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.251-254
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• 2004

환자의 체동은 MRl에 의해 제공된 화질을 저하시키는 주된 원인이 되고 있다. 본 연구에서는 MRI에 있어서 3차원 강체운동에 기인한 아티팩트를 수정하는 기법을 제안한다. 이러한 목표를 달성하기위해 MR 화상 데이터를 얻기위한 2차원 다-슬라이스 기법(a multiple 2-D slice technique)이 사용되어왔다. 대상물체의 운동에 해당하는 수집된 MRI 데이터는 불균일 표본화와 위상오차에 의해 영향을 받게된다. 3차원 운동에 대해 주어진 운동 파라메타와 장면간의 영향이라는 가정하에 양선형보간법과 중첩법으로 다-슬라이스 데이터를 사용하는 방법에 기반한 재구성 알고리즘을 MRI 아티팩트를 수정하는데 사용한다. 미지의 체동 파라메타들을 추정하기위해 3차원 강체운동은 다-슬라이스 취득기법의 각 영상과 결합된 관심영역 바깥쪽에서의 측정된 에너지를 증가시킨다는 사실을 이용하는 최소에너지법을 적용한다.

• Investigative Magnetic Resonance Imaging
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• v.13 no.2
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• pp.161-170
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• 2009

Purpose : A parallel imaging method provides us to improve temporal resolution to obtain three-dimensional (3D) MR images. The objective of this study was to optimize three 3D MRI techniques by adjusting 2D SESNE factors of the parallel imaging method in phantom and human brain. Materials and Methods : With a 3 Tesla MRI system and an 8-channel phase-array sensitivity-encoding (SENSE) coil, three 3D MRI techniques of 3D T1-weighted imaging (3D T1WI), 3D T2-weighted imaging (3D T2WI) and 3D fluid attenuated inversion recovery (3D FLAIR) imaging were optimized with adjusting SESNE factors in a water phantom and three human brains. The 2D SENSE factor was applied on the phase-encoding and the slice-encoding directions. Signal-to-noise ratio(SNR), percent signal reduction rate(%R), and contrast-to-noise ratio(CNR) were calculated by using signal intensities obtained in specific regions-of-interest (ROI). Results : In the phantom study, SENSE factor = 3 was provided in 0.2% reduction of signals against without using SENSE with imaging within 5 minutes for 3D T1WI. SENSE factor = 2 was provided in 0.98% signal reduction against without using SENSE with imaging within 5 minutes for 3D T2WI. SENSE factor = 4 was provided in 0.2% signal reduction against without using SENSE with imaging around 6 minutes for 3D FLAIR. In the human brain study, SNR and CNR were higher with SENSE factors = 3 than 4 for all three imaging techniques. Conclusion : This study was performed to optimize 2D SENSE factors in the three 3D MRI techniques that can be scanned in clinical time limitations with minimizing SNR reductions. Without compromising SNR and CNR, the optimum 2D SENSE factors were 3 and 4, yielding the scan time of about 5 to 6 minutes. Further studies are necessary to optimize 3D MRI techniques in other areas in human body.