• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.218-228
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• 2021

Due to their high degree of freedom to transfer and acquire light, fiber optics can be used in the presence of strong magnetic fields. Hence, optical sensing and imaging based on fiber optics can be integrated with magnetic resonance imaging (MRI) diagnostic systems to acquire valuable information on biological tissues and organs based on a magnetic field. In this article, we explored the combination of MRI and optical sensing/imaging techniques by classifying them into the following topics: 1) functional near-infrared spectroscopy with functional MRI for brain studies and brain disease diagnoses, 2) integration of fiber-optic molecular imaging and optogenetic stimulation with MRI, and 3) optical therapeutic applications with an MRI guidance system. Through these investigations, we believe that a combination of MRI and optical sensing/imaging techniques can be employed as both research methods for multidisciplinary studies and clinical diagnostic/therapeutic devices.

• Investigative Magnetic Resonance Imaging
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• v.21 no.1
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• pp.43-50
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• 2017

Chronic expanding organizing hematoma (CEH) occasionally mimics a soft tissue tumor on MRI, which becomes more problematic in patients with a history of surgical resection for musculoskeletal malignancy. Herein, we present a case of CEH which we were able to differentiate from recurrent tumor through MRI follow-up, including diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE) imaging. A 66-year-old male visited our institution under suspicion of recurrent leiomyosarcoma of the thigh, 19 months after surgery and radiation therapy. Due to inconclusive results, three US-guided biopsies and 6 MRI examinations were performed over 2 years. In the end, we could diagnose a CEH using conventional and functional MRI techniques, and it was histopathologically confirmed after surgical resection. A CEH may occur remotely after an initiating event, and it may persist and expand over several years. Functional MR sequences, in addition to conventional sequences, are helpful in differentiating CEH from malignant neoplasms.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.147-147
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• 2006

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.9-15
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• 1994

In last few decades, medical imaging techniques have been developed startling progress. Especially in MRI (Magnetic Resonance Imaging), many imaging techniques such as chemical shift imaging, flow imaging, diffusion and perfusion imaging, fast imaging, susceptibility imaging and functional imaging have been studied and many of them were well known as useful diagnostic instruments. In this paper, recently developing techniques, i.e., NMR microscopy, fringe field imaging and functional imaging will be presented.

• Journal of Applied Reliability
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• v.15 no.1
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• pp.27-32
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• 2015

Functional safety is part of the overall safety relating to the equipment under control (EUC) and the EUC control system that depends on the correct functioning of the electrical/electronic/programmable electronic (E/E/PE) safety-related systems. Since the complexity of the medical equipment is increased, manufactures have to obtain functional safety as well as basic safety. This study proposes a perspective for applying functional safety to medical equipment. The research is carried out with respect to overall safety life-cycle of functional safety and essential performance of the medical equipment. The relationship between functional safety and essential performance is identified centered on the safety function. The essential performance using E/E/PE systems is defined as a safety function of functional safety. This approach is applied to a ultrasound imaging system as a case study.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.146-146
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• 2006

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.149-149
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• 2006

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2375-2378
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• 2014

Cancer, like any disease, is a pathologic biological process. Drugs are designed to interfere with the pathologic process and should therefore also be validated using a functional screening method directed at these processes. Screening for cancers at an appropriate time and also evaluating results is also very important. Volumetric measurement helps in better screening and evaluation of tumors. Volumetry is a process of quantification of the tumors by identification (pre-cancerous or target lesion) and measurement. Volumetric image analysis allows an accurate, precise, sensitive, and medically valuable assessment of tumor response. It also helps in identifying possible outcomes such disease progression (PD) or complete response as per Response Evaluation Criteria in Solid Tumors (RECIST).

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.110-116
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• 2006

Finite element method (FEM) provides several advantages over other numerical methods such as boundary element method, since it allows truly volumetric analysis and incorporation of realistic electrical conductivity values. Finite element mesh generation is the first requirement in such in FEM to represent the volumetric domain of interest with numerous finite elements accurately. However, conventional mesh generators and approaches offered by commercial packages do not generate meshes that are content-adaptive to the contents of given images. In this paper, we present software that has been implemented to generate content-adaptive finite element meshes (cMESHes) based on the contents of MR images. The software offers various computational tools for cMESH generation from multi-slice MR images. The software named as the Content-adaptive FE Mesh Generation Toolbox runs under the commercially available technical computation software called Matlab. The major routines in the toolbox include anisotropic filtering of MR images, feature map generation, content-adaptive node generation, Delaunay tessellation, and MRI segmentation for the head conductivity modeling. The presented tools should be useful to researchers who wish to generate efficient mesh models from a set of MR images. The toolbox is available upon request made to the Functional and Metabolic Imaging Center or Bio-imaging Laboratory at Kyung Hee University in Korea.