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뇌인지과학과 뇌질환 원인규명 및 치료법 연구 ;

This document provides information about Namkug Kim, PhD and his research experience. It lists his academic and professional background, selected grants, collaborations, publications, areas of research including brain imaging, stroke, Parkinson's disease, lung diseases, and artificial intelligence for medical imaging. It also mentions his roles as founder or stockholder in several medical technology companies. The document is in both Korean and English.

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뇌인지과학과 뇌질환 원인규명 및 치료법 연구 Namkug Kim, PhD Medical Imaging N Robotics Lab. Convergence Medicine/Radiology Biomedical Engineering Center Asan Medical Center/Univ. of Ulsan College of Medicine
Researches with Hyundai Heavy Industries Co. Ltd. LG Electronics Coreline Soft Inc. Osstem Implant CGBio VUNO Kakaobrain 이해상충 Stockholder Coreline Soft, Inc. AnyMedi Co-Founder Somansa Inc. Cybermed Inc. Clinical Imaging Solution, Inc AnyMedi, Inc. Selected Grants as PI 한국연구재단, NRF, South Korea 7T용 4D 자기공명유속영상을이용한 심뇌혈관 질환의 in- vivo 유동 정량화 SW개발, 2016 4D flow MRI을 이용한 심혈관 질환의 in-vivo유동 연구, 2015-7 자기공명분광영상 및 MRI의 통합 분석 소프트웨어 개발 산업부, KEIT, South Korea 의료영상 인공지능 과제, 2016-20 3DP 척추 맞춤형 임플란트, 2016-20 3D 프린터 기반 무치악 및 두개악안면결손환자용 수복 보철물 제작, 재건 시스템 개발, 2015-9 근골격계 복구 수술 로봇 개발, 2012-7 영상중재시술 로봇시스템 개발, 2012-7 Spine및 Neurosurgery 수술보조용 항법 시스템 개발, 2001 의료용 3차원 모델 제작 S/W 기술 개발, 정통부, 2000 의료영상재구성에 의한 가상시술 소프트웨어개발, 중소기업기술혁신개발,중기청, 2001 보건복지부, KHIDI, South Korea 영상 뇌졸중 예후 예측 및 치료방침 결정 시스템 개발, 2012- 8 관동맥 관류 CT 의 자동 진단 프로그램을 활용한 허혈성 질환의 진단과 치료, 2013-6 RP를 이용한 척추나사못 삽입술 계획 프로그램 개발, 2000 산학협력 Hyundai Heavy Industry,Osstem Implant,S&G Biotech, Coreline soft, MidasIT, AnyMedi,Hitachi Medical, Japan,
Kim, Namkug, PhD Kim CY, Kim N, et al, Concur Eng Resear Appl 1998. 6(1) 53-70 Kim N, et al, Comp Ind Eng, 1997 33(3-4) 497-500 Cho NW, Kim N, et al Comp Ind Eng, 1997 33(3-4) 573-576 1995.2 2013.3 1998.11 2004.9 1997.2 1997.5 2008.9 2009.11 2006.9 2000.2 2007.12 2008.2 2010.4 STEP2VRML* 1991.3 2011.3 2011.4 2011.7 Visualization, Computer Aided Design Medical Image Visualization, Computer Aided Surgery Radiologic Application : Lung, Prostate Tumor, Etc Brain/Neuro Imaging, MR Spectroscopy 2011.10 2011.10 2012.4 Computer aided surgery/Robotics
MIRL@AMC Image Based Robotics Biopsy/Tunneling Artificial Joint Replacement/ Active Catheter DBS Robot Computer Aided Surgery Human computer interface Stereovision Contactless Interface Image guided Surgery Planning 3D Printing/Navigation Brain Stroke Parkinson DBS planner, ECoG, BMI Lung COPD DILD Asthma HT Cardiac CT Perfusion 4D Flow MRI 4DUS LCX Artificial Intelligence For Medical imaging Artificial Intelligence Segmentation/Registration Computer aided detection Content based image retrieval LAD RCA
5 MBC 다큐 스페셜 ‘미래인간 AI’, 2016.12.05 영상유도 중재 수술로봇 ; 현대중공업 폐영상 분석 SW ; 코어라인 소프트 3D 프린터 응용 ; 서울아산병원 Movie Clips
Always! Paradigm Shifts in Radiology Qualitative Dx Film Sharing Quantitative Dx Digital Sharing (Network) CAx Tech Technology Push Being Digital 3D Technology Computer Aided Tech. Artificial Intelligent Smart/Mobile Model based… Domain (Medical) Need Aging Society Evidence based Minimal invasive Quantification Fast Drug Development Safety… Rapid Development using high tech solution Greek & Roman Era 0 AD 1543 AD Copernican heliocentrism 1668AD Newton Opticks 1905AD Einstein relativity 1687AD Newton Principia 1930AD Quantum Mechanics 1st TV Broadcast 1950AD Plate Tectonics US DNA 1789AD Chemical revolution 1859AD Origin of species 1915AD Mendelian inheritance 1946AD Computer ENIAC 1995AD Being digital Internet 2005AD Ubiquitous & Smart 1907AD 1st Radio Broadcast 1970AD CT, MRI Nobel,PET 1895AD 1st Xray 1983AD PACS 1920AD AART 2000AD CAD
Always! Paradigm Shifts in Surgery Qualitative Dx Quantitative Dx Digital Sharing (Network) CAx Tech Technology Push Being Digital 3D Technology Computer Aided Tech. Artificial Intelligent Smart/Mobile Model based… Domain (Medical) Need Aging Society Evidence based Minimal invasive Quantification Fast Drug Development Safety… Rapid Development using high tech solution Greek & Roman Era 0 AD 1992AD 1995AD ISS, IS established 1997AD da Vinci FDA 1524 AD Barber surgeon ?BC Inca 1821AD MGH Endoscopy 1844AD Anesthesia NO, Ether Chloroform 1847AD Infection ctrl Washing hand 1940AD Disposable catheter 1910AD Laparoscopy Surgery,Lighted Endoscopy 1989AD Surgical Navigation 1993AD AESOP FDA First robot 2001AD CyberKnife FDA 2012AD AMIGO 1881AD First Gastroscopy 1989AD First lap. Appendect. 1956AD Cardiac Catheteri. Nobel 1912AD Transplanto Logy Nobel Greek & Roman Era 0 AD 1543 AD Copernican heliocentrism 1668AD Newton Opticks 1905AD Einstein relativity 1687AD Newton Principia 1930AD Quantum Mechanics 1st TV Broadcast 1950AD Plate Tectonics US DNA 1789AD Chemical revolution 1859AD Origin of species 1915AD Mendelian inheritance 1946AD Computer ENIAC 1995AD Being digital Internet 2005AD Ubiquitous & Smart 1907AD 1st Radio Broadcast 1970AD CT, MRI Nobel,PET 1895AD 1st Xray 1983AD PACS 1920AD AART 2000AD CAD
History of Medical Imaging 1972 Hounsfield, CT, Nobel Prize in Medicine in 1979 1962 Kuhl, SPECT and PET 1968 Targeted contrast agents 1963 Wright, Meyerdirk, Ultrasound 1967 1977 The first clinical MRI Lauterbur, Mansfield , MRI, Nobel Prize in Medicine in 2003 1944 1952 1991 2002 Rabi, Nobel Prize in Physics for his resonance method for recording the magnetic properties of atomic nuclei Bloch, Purcell, Nobel Prize in Physics for nuclear magnetic precision measurements and discoveries in connection therewith Enrst, Nobel Prize in Chemistry for the methodology of high resolution NMRS Wüthrich, Nobel Prize in Chemistry for nMRS for determining the 3D structure of biological macromolecules in solution 2005 Smart, Mobile, Ubiquitous 1988 3D Computer Graphics 1986 Self expanding stent 2000 Computer Aided Diagnosis 1983 Picture Archiving & Communication System 1905 The first English book on Chest Radiography 1896 First clinical X-ray radiolgraph 1895 Nobel Prize in Physics 1901 for X-ray Discovery 1917 1915 1914 Von Laue, Nobel Prize in Physics for x-ray diffraction from crystals. Bragg and Bragg, Nobel Prize in Physics for crystal structure derived from x-ray diffraction Barkla. Nobel Prize in Physics for characteristic radiation of elements 1936 1927 1924 Siegbahn ,Nobel Prize in Physics for x-ray spectroscopy Compton , Nobel Prize in Physics for scattering of x-rays by electrons Debye , Nobel Prize in Chemistry for diffraction of x-rays and electrons in gases 1920 The first ASRT in Chicago
Challenges in Medicine(Radiology & Surgery)? 9 ClinicalNeeds Evidencebased Minimalinvasive Quantification FastDrugDevelopment Safety… TechnicalSupports BeingDigital 3 DTechnology ComputerAidedTech. ArtificialIntelligent Smart/Mobile Modelbased… Picture Archive & Communication System Minimal Invasive Surgery Computer aided Surgery Computer Aided Diagnosis (CAD) Quantitative Imaging Surgical Navigator Functional/ Molecular Imaging Robotic Simulator Imaging Biomarker CBIR Single Port Surgery Artificial implant Imaging genomics 7T MRI Full DNA sequencing Future Medicine Quantitative surgery Robot Surgery Personalized Medicine NOTESQIBA … Future Medicine Being digital, Computer aided X Evidence based Medicine Quantitative Medicine -> Imaging Biomarker, QIBA,Quantitative Surgery etc Molecular/Functional Imaging Personalized Medicine -> DNA Revolution, Full DNA Seqeuncing Minimal Invasive -> Single Port, NOTES, Robotic Interventions
Translational Research : Pasteur’s Quadrant 1 0 John M. Dudley, Defending Basic Research, Nature Photonics 7, 338–339 (2013) Diagram based on Stokes 1997
11 Engineering Supports for Smartness  Imagination (Demand) vs Economy  Practical aspect  Medical >> Engineering  MD (i.e. Surgeon) Supported by Engineering  Smarter Eye : Endo., CT, MRI, US, AR, 3D Vision, Navi., etc  Smarter Hand : robotic instrument, master slave robot, endo wrist, etc  Smarter Brain : Computer aided planner, surgery, etc  Smarter Tactile: Sensor, haptic feedback, etc  Smarter ??? Needs • Inherent Complexity of Nature and Society • Interdisciplinary Basic Research Problem • Solving Actual Problem • Developed Science and Technology’s Push Obstacles • No Common Terminology or Terminologies of Different Meanings • No Need of Collaboration • Co-authorship • (e.g. 2nd vs co-1st ; CA vs co-CA) * “Academic program or process seeking to synthesize broad perspectives, knowledge, skills, interconnections ”, – Wikipedia
Smart Interventional (needle insertion) Robot Smart Eye Smart Hand Dx image and Op image fusion for profound information Planner & Simulator CT / Conebeam CT Navigation Smart Needle End- Effector Master /Slave Tangibles, steerable smart needle, Automatic needle exchange, Process automation Smart Brain Image based planning, navigation, operation and patient safety monitoring Safety for patients and operators Accuracy enhancement Registration, Navigation, Augment reality realtime monitoring Haptic feedback, physical/chemical/ bio sensor, Tremble canceling Steerable needle Integrated Information aided System *Hyundai Heavy Industry, kFDA Clearance
Collaboration? Direction? 13 “Where is my puppy?” Difference of Medical Device Development Cell Phone, Automobile, etc : Could be a Developer and User, concurrently Medical Device : Could be a Developer but User Too hard to be an Expert without license
Imaging Revolutions : CT •Developed the first CT Scanner at EMI • With A.M. Cormack • Received the Nobel Prize for Medicine in 1979 CT scanning the early days ,E C BECKMANN, The British Journal of Radiology, 79 The original lathe bed model ©EMI Ltd Pages from Dr Perry's notebooks on the first EMI scanner First Brain CT Scan at Atkinson Morley’s Hospital on 1971-10-1 Mechanical drawing of the ACTA scanner from Dr. Ledley's patent
Leonardo da Vinci (1487 - 1489)
기존 X-Ray의 단점
Third & Fourth Generations (From Picker) (From Siemens)
Slip Ring Power Block : X-Ray Tube Signal Block : DAS(Data) Control Block : Collimator Rotating Time
True 0.5 mm Ultrahigh Resolution 24 LP/CM
Ultra-Low Dose
 Technical Development  Being digital  3D or 12bit Displays  Imaging Modality  PACS, EMR, etc  Quantitative Imaging, Imaging Biomarker MEGA-Trends in HealthCare Being digital
D I C O M 3 . 0 PACS (Pictures Archive and Communication System) DICOM 3.0 Standard for Digital Image Communication set by NEMA Most machines support this file type Applies to all image formats (X-Ray, CT, MRI, etc…)
Structure of a DICOM 3.0 File File Preamble DICOM Prefix Other Mandatory elem. Data SetData Set Data Element Data ElementData ElementData Element Tags (4 Bytes) Value Field (VL Bytes) Value Length (2 Bytes) Value Representation (2 Bytes) 파일 시작 Tags (4 Bytes) Value Field (VL Bytes) Value Length (2 Bytes) Value Representation (4 Bytes) Tags (4 Bytes) Value Field (VL Byte or Undefined Length) 32 bit UI Value Length (4 Bytes) Data Element Structure For Explicit VR Data Element Structure For Explicit VR of OB, OW, SQ Data Element Structure For Implicit VR DICOM 3.0 Meta header element ...
Basic DICOM Protocols * DICOM 3.0의 base protocol Endian : 2 Byte (16 bit) number를 주고 받는데 있어서의 Ordering 방법 > Little Endian : Least significant byte is sent (stored) first. Ex. (0002,0002) -> 02 00 02 00 > Big Endian : Most significant byte is sent (stored) last. Ex. (0002,0002) -> 00 02 00 02
MEGA-Trends in Radiology iPhone and iPad Radiology App, MIM Mobile FDA Approved First Observation of the Chemical Shift**First NMR Spectra on Water* Nobel prizes 1944 Physics Rabi (Columbia) 1952 Physics Bloch (Stanford), Purcell (Harvard) 1991 Chemistry Ernst (ETH) 2002 Chemistry Wüthrich (ETH) 2003 Medicine Lauterbur (University of Illinois in Urbana ), Mansfield (University of Nottingham)
MEGA-Trends in Radiology iPhone and iPad Radiology App, MIM Mobile FDA Approved Park HJ, Harvard, 2003
Neuro/Brain Science 35 fMRI Realtime fMRI PerfusionDiffusion Brain computer interface Thinking Functional study Connectivity map Cortical thickness Whole body diffusion DTI Tractography 1st pass perfusion Two compartment perfusion Brain cortical thickness Etc postprocessing ASL DCEMR DSEMR Implementation Public domain Developed SPM Brain Voyager TBSS Tractography S/W Perfusion S/W MIL Diffusion MIL Tractography MIL ASL perfusion MIL perfusion Tracto-based segmentation B0 correction B1 correction High tesla MR Animal MR Manganese MR Brain registration STT/ Statistic CC Subdivision Response monitor FSL AFNI … Brain2 Connectome
Amygdala Shape Analysis  Sexual dimorphism  Effect of gender and sex  Normal Male, female (20, 20) 36Kim N, et al Shape analysis of amygdala - Effects of aging and gender difference, submitted to
Amygdala Shape Analysis 37 Statistical analysis & Visualization 3D Modeling Sub Nucleus Segmentation Kim HJ, Kim N, Amygdala shape alterations in bipolar disorder, ISAD 2008 Subregional shape difference of amygdala
Amygdala Subnucleus Shape Analysis ; Gender Difference ;  Phantom study  Forty healthy volunteers (20 men and 20 women)  Centromedial in left amygdala and laterobasal, superficial and CM in right amygdala (Men << Women) Overall Procedure Kim N, Kim HJ, Lyoo IK, et al, Neuroscience Letter, 2011 488(1):65-9 Amygdala Sex Difference Phantom Study
Shape change of centromedial nucleus after menopause  62 men and 59 women, age range 20-79 years  3D fast field echo pulse sequence MRI  ICV calibration Semi-automatic Segmentation ICC = 0.95 Overview of amygdala subregional analysis. Kim HJ , Kim N (co-first), et al, Neuroimage, In-press 2012/5, Cover
Shape change of centromedial nucleus after menopause  Radius of centromedial nucleus of women  steeper decline with age, while there was no significant difference between the age-related decrease of amygdala volume in men and women. Scatter plots of the adjusted mean radius of CM vs. age. Age-related change of amygdala shape in women and men. Kim HJ , Kim N (co-first), et al, Neuroimage, In-press 2012/5, Cover
SPHARM comparison Women’s Amygdala Modified SPHARM*, Chung, M.K., et al, IEEE TMI, 26:566-581
Nano Manganese fMRI Analysis; Gender Difference ; Rat Study  Nano-Maganese fMRI VBM result  Rat Sex Difference  N = 40 (M 20, F 20)  VBM result  Cortical Surface Projection  Visualization : 3D Texture Color Mapping (p-map) Nano-Maganese fMRI Analysis 3D Texture Color Mapping (FDR p map : blue, male, nose; red, female, eye, ear) Kim HJ , Kim N, et al, Unpublished
Cortical Thinning ; Bipolar Disorder (양극성장해, 조울증)  Bipolar disorder(25) vs control (21)  MRI : 3D spoiled gradient echo pulse sequence  Free surfer  Bipolar Disorder  Impairment of emotional, cognitive, and sensory processing P map of difference of cortical thickness Left : middle frontal cortex, cingulate cortex, postcentral cortex, and middle occipital cortex Right : frontal cortex, angular cortex, postcentral cortex, and lateral occipital cortex. -> Sensory and sensor association cortices Illness duration vs cortical thickness of left midfrontal cortex *Lyoo IK, Kim N, et al, Bipolar Disord, 2006. 8(1): p. 65-74. 86 citations (Google scholar)
FA VBM; Trauma survivor ;  PostTraumatic Stress Disorder (외상후 스트레스성 장애, PTSD) patients (Taegu subway fire incident) 20 vs healthy volunteers 20  FA map by VBM of SPM2 *Kim M, Kim N, et al, Neuroreport, 2005. 16(10): p. 1049-53. Low FA in left anterior cingulate regions PTSD symptom severity negatively correlated to anterior cingulate FA
NMR Theory  Nobel Prizes  Nucleus Spin Nobel prizes 1944 Physics Rabi (Columbia) 1952 Physics Bloch (Stanford), Purcell (Harvard) 1991 Chemistry Ernst (ETH) 2002 Chemistry Wüthrich (ETH) 2003 Medicine Lauterbur (University of Illinois in Urbana ), Mansfield (University of Nottingham) +2009 fMRI was nominated. First Observation of the Chemical Shift** **Arnold, J.T., S.S. Dharmatti, and M.E. Packard, J. Chem. Phys., 1951. 19: p. 507. ***http://www.steve.gb.com/science/spectroscopy.html *Bloch, F.; Hansen, W. W.; Packard, M. Physical Review (1946), 70 474-85. First NMR Spectra on Water* Usages 1) Structural (chemical) elucidation 2) Study of dynamic processes 3) Structural (three-dimensional) studies 4) Drug Design 5) Medicine -MRI OH CH2 CH3Ethanol***
NAA Peak Choline Peak Creatine Peak What’s MRS?  Provide a non- invasive method of studying metabolism in vivo.  The tissue’s chemical environment  determines the frequency of a “metabolite peak” Typical 1H MRS
Chemical shifts
What’s MRS?  Candidates for MRS include:  1H, 31P, 13C, 23Na, 7Li, 19F, 14N, 15N, 17O, 39K  The most commonly studied nuclei are 1H and 31P Nucleus I Resonanc e Freq (@7T) in MHz Natural Abunda nce (%) Abs Sensitivity at constant field (Product of rel sensitivity and Natural abundance) T1 T2 1 H 1/2 298 99.98 1 0.3-3s 1-600 ms 23 Na 3/2 78.9 100 9.3x10-2 20-50ms 0.5-20 ms 31 P 1/2 120.6 100 6.6x10-2 2-5s 50-1000 13 C 1/2 74.8 1.1 1.76x10-4 3-15 s 100-1000 ms 17 O 5/2 40.36 0.037 1.08x10-5 4-6 ms 1-2 ms 14 N 1 21.4 99.6 1.0x10-3 15 N 1/2 30.0 0.37 3.85x10-6 19 F 1/2 280 100 .83
MRI vs MRS MRI Imaging Voxel: • 1 – 5 mm3 • Signal from water (and fat) • Conc; • Pure Water • H2O : 55.6 M • 1H : 111 M • Brain • 70% water • 36M MRS Spectroscopic Voxel: • 1 – 8 cm3 • Signal from metabolites • Conc; • Brain • Metabolites • 1 – 10 mM vs
Water Suppression suppressing the water signal* a signal one hundred times smaller than that of the water peak, and without water suppression would be poorly resolved *© Scott & White 2004
Uspectra*  MRS Processing  Color overlay on CSI/MRI  ROI based Spectroscopy Averaging  Spectrum analysis software interface  Metabolites Quantitation (Gaussian, Lorentzian, Voigt)  Point Spread Function Correction  Grid Shifting  Utilz (SNR, Coil Inhomogeneity, etc) Uspectra; Matlab Siemens MRS DICOM Siemens MRS RDA Siemens MRS Raw File Philips MRS DICOM Bruker MRS MRS Etc LCModel AMARES FITMAN MRS File Import/Export MRS Analysis Import MRS Kim N. Brain Institute, Univ. of Utah, AMC
Overall Development flow Brain tissue characterization SPM5 :unified segmentation White/gray matter Probabilistic volume map T1 or T2 Brain MRI USpectra MRS from *.rda, *.dcm Registration on MRI and MRS MRI volume or slice image SNR calc on MRS Inhomogeneity calc on MRS Deconvolution on Point Spread Function Clinical Data Set i.e., depression vs control Interface to LCModel, jMRUI, Fitman Own module : SVD : AMARES Regression study On PCr/b-ATP, White/gray matter ratio Comparing SNR, inhomogeneity btw McLean and UofU Same data set from McLean, UofU FSL Kim N. Brain Institute, Univ. of Utah, AMC
 Import MRS, MRI, Analysis Results (LCModel, jMRUI)  MRS processing  Spectroscopy measurement  Gaussian, Lorentzian ,Voigt  Interface to LCModel, jMRUI  Regression study of MRS,MRI  Color Coding, etc Regression measurement on HPRT data* Cho difference Regression of MRS on MRI USpectra : 1H/31P MRS Analysis Software *Sung YH , Kim N, Renshaw FP, et al, RSNA 2009 Gray/White matter segmentation using FSL
LCModel Accuracy; Phantom ; MRS NAAG concentration  5 phantoms + 5 scans  Various concentrations : NAAG, Glutamate  Constant concentration : other 10 metabolites NAAG conc. phantom & measurement NAAG measurement Glu measurement *Kim N, Renshaw FP, et al, ISMRM 2010
MEGAPRESS SNR increase ; MRS GABA  GABA : important neuro transmitter  In-vivo GABA measurement : unstable  SNR increase  Phase alignment  Optimal weights NAA GABA Cho Cr A C = B - A B MEGAPRESS for GABA measurement Phase registration, optimal weighting for MRS SNR increase SNR increase phase aligned method ; 28.3% optimized weight method ; 19.8% the combined method ; 62.8% LCModel fitting ; more concentration (paired t-test p<0.01) lower Cramér–Rao lower bounds (paired t-test p = 0.08). Simple diff mean with nSNR Hierarchical Phase registered with oSNR GABA GABA
MRS Longitudinal Study; CDP-Cho. Tx Methamphetamin Dependent ;  Cytidine-50-diphosphate choline (CDP-choline),  important intermediate for major membrane phospholipids  4-week CDP-choline treatment  urine sample 3 / week * 4 week : total 12 SVS VOI at midfrontal gray matter MR spectrum by LCModel Negative urine vs % Change of NAA over treatment *Yoon SJ, Kim N, et al, Neuropsychopharmacology, 2010. 35(5): p. 1165-73
MRS; Abstinent Methamphetamin Abuser  MA abusers (30) vs control (20)  MI conc. :  the MA abusers > healthy subjects  NAA conc. of white matter  MA ‘large’ dose < MA ‘small’ dose  MA ‘large’ dose < healthy subjects.  LCModel left frontal white matter Midfrontal gray matter NAA of white matter NAA of gray matter Abstinence day Cumulative dose *Sung YH, Kim N, et al, Drug Alcohol Depend, 2010. 35(5): p. 1165-73
Creatine Kinase Activity : 31P MRS*  31P MRS  Change in high energy phosphate levels  Change in Creatine kinase (CK) kinetics  In visual cortex during photic stimulation (normal)  Apparent unidirectional rate constant  Kf : forward direction PCr -> ATP synthesis  34% increase  Experimental Setting  Psychiatric Patients T1 = 4.515 KAB = 0.3977 0 1 1 ( ) 1 1 1 zA zA A A AB A M M R T k T     Long saturation times Typical 31P MRS Practical 31P MRS AMARES Fit
Suicide, Depression vs Altitude ; GIS ;MRS  High attitude sickness or Mild Hypoxia  ↑ Depression  ↑ Suicide  Why? Any Brain Metabolites change?Zip boundaries on 1:250,000 DEM (altitude, USGS), Arc/Gis *Kim N, Renshaw FP, et al, AJP, 2010-09-15 Epub **DelMastro K, Kim N, et al, ADO in press * ** Boston (alt :0m) Salt lake city (alt : 1280m) 9.4T Varian 31P, 1H Dual Channel Coil Cocaine Use in the Past Year is Associated with Altitude of Residence, Kristen DelMastro, Namkug Kim, Douglas G. Kondo, Perry Renshaw, Journal of Addiction Medicine, In press Open-label uridine for treatment of depressed adolescents with bipolar disorder. Kondo DG, Sung YH, Hellem TL, Delmastro KK, Jeong EK, Kim N, Shi X, Renshaw PF. J Child Adolesc Psychopharmacol. 2011 Apr;21(2):171-5. Epub 2011 Apr 12. Review: magnetic resonance spectroscopy studies of pediatric major depressive disorder. Kondo DG, Hellem TL, Sung YH, Kim N, Jeong EK, Delmastro KK, Shi X, Renshaw PF. Depress Res Treat. 2011;2011:650450. Epub 2010 Oct 4. Incidence of major depressive episode correlates with elevation of substate region of residence. DelMastro K, Hellem T, Kim N, Kondo D, Sung YH, Renshaw PF. J Affect Disord. 2011 Mar;129(1-3):376-9. Epub 2010 Nov 11. Altitude, gun ownership, rural areas, and suicide. Kim N, Mickelson JB, Brenner BE, Haws CA, Yurgelun-Todd DA, Renshaw PF. Am J Psychiatry. 2011 Jan;168(1):49-54. Epub 2010 Sep 15.
Parkinson DTI  Neurology : 이종식 교수님  Radiology : 김상준 교수님  Data  Control : N 86 / PD : N 100 / PDD : N 12 / MSAp : N 16 / MSAc : N 14 / MSAm : N 9  Processing  TBSS, FSL Kim SJ, et al, ASNR 2012 White matter abnormality in Parkinson disease: Evaluation with Diffusion tensor imaging using tract-based spatial statistics study FA map : PD vs Control FA map : MSAc vs ControlMD, PD vs Control (L39, R37)
Aphasia Analysis  Neurology : 권미선, 이재홍 교수님  Stroke -> Aphasia  Longitudinal study  Data  Control x15 / Aphasia Pts x7  Imaging  Philips 3T Achieva, 3DT1TFE 0.5mm SENSE  DWI x15, DTI, 3DT1TFE, T2  Clinical Data  Age, sex, edu, semantic, phnemic, generative, K-BNT, MoCA-K, Syll/min  Processing  FSL, TBSS
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뇌졸중 예후 인자 예측 시스템 개발 특징 추출을 위한 혈관 기반 뇌영역 분할 • http://radiopaedia.org/articles/cerebral-vascular-territories • https://en.wikipedia.org/wiki/Magnetic_resonance_angiograph y
Vessel-based brain parcellation Voronoi diagram Euclidean distance Manhattan distance Fuzzy boundary • https://en.wikipedia.org/wiki/Voronoi_diagram
혈관 기반 심근 분할 ; 돼지 동물실험 1-b) CT 영상 capture (size 동일, Target Territory의 Mask 반영)1-a) 병리사진 (Target Territory 마킹) 2) Control Point 지정 (최소 35~40개) 3) Registration 4) 평가 Resizing 필요(Optional: CT Image size에 맞춤) Left: Pie#0~20 Right: Pie#0~15 중첩: Pie 15개 Start point LAD RCA LCX Full Mask 확장된 심근 Mask LAD LCX RCA LAD RCA LCX LAD LCX RCA
Visual assessment • D. Leander Rimmele and GötzThomalla, “Wake-up stroke: clinical characteristics, imaging findings and treatment option – an update” Frontiers in Neurology, 2014. 뇌졸중 예후 인자 예측 시스템 개발 뇌졸중 발생 시점 추정 모델 DWI/FLAIR mismatch
뇌졸중 예후 인자 예측 시스템 개발 뇌졸중 발생 시점 추정 모델 DWI/FLAIR mismatch Identification patients with acute ischemic stroke within 4.5h (or 6h) of onset • GötzThomalla et al., “DWI-FLAIR mismatch for the identifi cation of patients with acute ischaemic stroke within 4·5 h of symptom onset (PRE-FLAIR): a multicentre observational study” Lancet Neurology, 2011.
DWI/FLAIR mismatch Algorithm (flipping) ADC FLAIR Infarct segmentation Registration Infarct Flipping
Deep Learning Deep learning 방식의 neural network model.
T2 Hyperintensity 2D 또는 3D occurrence map 2세부 김동억 교수님 협력연구

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뇌인지과학과 뇌질환 원인규명 및 치료법 연구 ;

  • 1.
    뇌인지과학과 뇌질환 원인규명및 치료법 연구 Namkug Kim, PhD Medical Imaging N Robotics Lab. Convergence Medicine/Radiology Biomedical Engineering Center Asan Medical Center/Univ. of Ulsan College of Medicine
  • 2.
    Researches with Hyundai HeavyIndustries Co. Ltd. LG Electronics Coreline Soft Inc. Osstem Implant CGBio VUNO Kakaobrain 이해상충 Stockholder Coreline Soft, Inc. AnyMedi Co-Founder Somansa Inc. Cybermed Inc. Clinical Imaging Solution, Inc AnyMedi, Inc. Selected Grants as PI 한국연구재단, NRF, South Korea 7T용 4D 자기공명유속영상을이용한 심뇌혈관 질환의 in- vivo 유동 정량화 SW개발, 2016 4D flow MRI을 이용한 심혈관 질환의 in-vivo유동 연구, 2015-7 자기공명분광영상 및 MRI의 통합 분석 소프트웨어 개발 산업부, KEIT, South Korea 의료영상 인공지능 과제, 2016-20 3DP 척추 맞춤형 임플란트, 2016-20 3D 프린터 기반 무치악 및 두개악안면결손환자용 수복 보철물 제작, 재건 시스템 개발, 2015-9 근골격계 복구 수술 로봇 개발, 2012-7 영상중재시술 로봇시스템 개발, 2012-7 Spine및 Neurosurgery 수술보조용 항법 시스템 개발, 2001 의료용 3차원 모델 제작 S/W 기술 개발, 정통부, 2000 의료영상재구성에 의한 가상시술 소프트웨어개발, 중소기업기술혁신개발,중기청, 2001 보건복지부, KHIDI, South Korea 영상 뇌졸중 예후 예측 및 치료방침 결정 시스템 개발, 2012- 8 관동맥 관류 CT 의 자동 진단 프로그램을 활용한 허혈성 질환의 진단과 치료, 2013-6 RP를 이용한 척추나사못 삽입술 계획 프로그램 개발, 2000 산학협력 Hyundai Heavy Industry,Osstem Implant,S&G Biotech, Coreline soft, MidasIT, AnyMedi,Hitachi Medical, Japan,
  • 3.
    Kim, Namkug, PhD KimCY, Kim N, et al, Concur Eng Resear Appl 1998. 6(1) 53-70 Kim N, et al, Comp Ind Eng, 1997 33(3-4) 497-500 Cho NW, Kim N, et al Comp Ind Eng, 1997 33(3-4) 573-576 1995.2 2013.3 1998.11 2004.9 1997.2 1997.5 2008.9 2009.11 2006.9 2000.2 2007.12 2008.2 2010.4 STEP2VRML* 1991.3 2011.3 2011.4 2011.7 Visualization, Computer Aided Design Medical Image Visualization, Computer Aided Surgery Radiologic Application : Lung, Prostate Tumor, Etc Brain/Neuro Imaging, MR Spectroscopy 2011.10 2011.10 2012.4 Computer aided surgery/Robotics
  • 4.
    MIRL@AMC Image Based Robotics Biopsy/Tunneling ArtificialJoint Replacement/ Active Catheter DBS Robot Computer Aided Surgery Human computer interface Stereovision Contactless Interface Image guided Surgery Planning 3D Printing/Navigation Brain Stroke Parkinson DBS planner, ECoG, BMI Lung COPD DILD Asthma HT Cardiac CT Perfusion 4D Flow MRI 4DUS LCX Artificial Intelligence For Medical imaging Artificial Intelligence Segmentation/Registration Computer aided detection Content based image retrieval LAD RCA
  • 5.
    5 MBC 다큐 스페셜‘미래인간 AI’, 2016.12.05 영상유도 중재 수술로봇 ; 현대중공업 폐영상 분석 SW ; 코어라인 소프트 3D 프린터 응용 ; 서울아산병원 Movie Clips
  • 6.
    Always! Paradigm Shiftsin Radiology Qualitative Dx Film Sharing Quantitative Dx Digital Sharing (Network) CAx Tech Technology Push Being Digital 3D Technology Computer Aided Tech. Artificial Intelligent Smart/Mobile Model based… Domain (Medical) Need Aging Society Evidence based Minimal invasive Quantification Fast Drug Development Safety… Rapid Development using high tech solution Greek & Roman Era 0 AD 1543 AD Copernican heliocentrism 1668AD Newton Opticks 1905AD Einstein relativity 1687AD Newton Principia 1930AD Quantum Mechanics 1st TV Broadcast 1950AD Plate Tectonics US DNA 1789AD Chemical revolution 1859AD Origin of species 1915AD Mendelian inheritance 1946AD Computer ENIAC 1995AD Being digital Internet 2005AD Ubiquitous & Smart 1907AD 1st Radio Broadcast 1970AD CT, MRI Nobel,PET 1895AD 1st Xray 1983AD PACS 1920AD AART 2000AD CAD
  • 7.
    Always! Paradigm Shiftsin Surgery Qualitative Dx Quantitative Dx Digital Sharing (Network) CAx Tech Technology Push Being Digital 3D Technology Computer Aided Tech. Artificial Intelligent Smart/Mobile Model based… Domain (Medical) Need Aging Society Evidence based Minimal invasive Quantification Fast Drug Development Safety… Rapid Development using high tech solution Greek & Roman Era 0 AD 1992AD 1995AD ISS, IS established 1997AD da Vinci FDA 1524 AD Barber surgeon ?BC Inca 1821AD MGH Endoscopy 1844AD Anesthesia NO, Ether Chloroform 1847AD Infection ctrl Washing hand 1940AD Disposable catheter 1910AD Laparoscopy Surgery,Lighted Endoscopy 1989AD Surgical Navigation 1993AD AESOP FDA First robot 2001AD CyberKnife FDA 2012AD AMIGO 1881AD First Gastroscopy 1989AD First lap. Appendect. 1956AD Cardiac Catheteri. Nobel 1912AD Transplanto Logy Nobel Greek & Roman Era 0 AD 1543 AD Copernican heliocentrism 1668AD Newton Opticks 1905AD Einstein relativity 1687AD Newton Principia 1930AD Quantum Mechanics 1st TV Broadcast 1950AD Plate Tectonics US DNA 1789AD Chemical revolution 1859AD Origin of species 1915AD Mendelian inheritance 1946AD Computer ENIAC 1995AD Being digital Internet 2005AD Ubiquitous & Smart 1907AD 1st Radio Broadcast 1970AD CT, MRI Nobel,PET 1895AD 1st Xray 1983AD PACS 1920AD AART 2000AD CAD
  • 8.
    History of MedicalImaging 1972 Hounsfield, CT, Nobel Prize in Medicine in 1979 1962 Kuhl, SPECT and PET 1968 Targeted contrast agents 1963 Wright, Meyerdirk, Ultrasound 1967 1977 The first clinical MRI Lauterbur, Mansfield , MRI, Nobel Prize in Medicine in 2003 1944 1952 1991 2002 Rabi, Nobel Prize in Physics for his resonance method for recording the magnetic properties of atomic nuclei Bloch, Purcell, Nobel Prize in Physics for nuclear magnetic precision measurements and discoveries in connection therewith Enrst, Nobel Prize in Chemistry for the methodology of high resolution NMRS Wüthrich, Nobel Prize in Chemistry for nMRS for determining the 3D structure of biological macromolecules in solution 2005 Smart, Mobile, Ubiquitous 1988 3D Computer Graphics 1986 Self expanding stent 2000 Computer Aided Diagnosis 1983 Picture Archiving & Communication System 1905 The first English book on Chest Radiography 1896 First clinical X-ray radiolgraph 1895 Nobel Prize in Physics 1901 for X-ray Discovery 1917 1915 1914 Von Laue, Nobel Prize in Physics for x-ray diffraction from crystals. Bragg and Bragg, Nobel Prize in Physics for crystal structure derived from x-ray diffraction Barkla. Nobel Prize in Physics for characteristic radiation of elements 1936 1927 1924 Siegbahn ,Nobel Prize in Physics for x-ray spectroscopy Compton , Nobel Prize in Physics for scattering of x-rays by electrons Debye , Nobel Prize in Chemistry for diffraction of x-rays and electrons in gases 1920 The first ASRT in Chicago
  • 9.
    Challenges in Medicine(Radiology& Surgery)? 9 ClinicalNeeds Evidencebased Minimalinvasive Quantification FastDrugDevelopment Safety… TechnicalSupports BeingDigital 3 DTechnology ComputerAidedTech. ArtificialIntelligent Smart/Mobile Modelbased… Picture Archive & Communication System Minimal Invasive Surgery Computer aided Surgery Computer Aided Diagnosis (CAD) Quantitative Imaging Surgical Navigator Functional/ Molecular Imaging Robotic Simulator Imaging Biomarker CBIR Single Port Surgery Artificial implant Imaging genomics 7T MRI Full DNA sequencing Future Medicine Quantitative surgery Robot Surgery Personalized Medicine NOTESQIBA … Future Medicine Being digital, Computer aided X Evidence based Medicine Quantitative Medicine -> Imaging Biomarker, QIBA,Quantitative Surgery etc Molecular/Functional Imaging Personalized Medicine -> DNA Revolution, Full DNA Seqeuncing Minimal Invasive -> Single Port, NOTES, Robotic Interventions
  • 10.
    Translational Research :Pasteur’s Quadrant 1 0 John M. Dudley, Defending Basic Research, Nature Photonics 7, 338–339 (2013) Diagram based on Stokes 1997
  • 11.
    11 Engineering Supports forSmartness  Imagination (Demand) vs Economy  Practical aspect  Medical >> Engineering  MD (i.e. Surgeon) Supported by Engineering  Smarter Eye : Endo., CT, MRI, US, AR, 3D Vision, Navi., etc  Smarter Hand : robotic instrument, master slave robot, endo wrist, etc  Smarter Brain : Computer aided planner, surgery, etc  Smarter Tactile: Sensor, haptic feedback, etc  Smarter ??? Needs • Inherent Complexity of Nature and Society • Interdisciplinary Basic Research Problem • Solving Actual Problem • Developed Science and Technology’s Push Obstacles • No Common Terminology or Terminologies of Different Meanings • No Need of Collaboration • Co-authorship • (e.g. 2nd vs co-1st ; CA vs co-CA) * “Academic program or process seeking to synthesize broad perspectives, knowledge, skills, interconnections ”, – Wikipedia
  • 12.
    Smart Interventional (needleinsertion) Robot Smart Eye Smart Hand Dx image and Op image fusion for profound information Planner & Simulator CT / Conebeam CT Navigation Smart Needle End- Effector Master /Slave Tangibles, steerable smart needle, Automatic needle exchange, Process automation Smart Brain Image based planning, navigation, operation and patient safety monitoring Safety for patients and operators Accuracy enhancement Registration, Navigation, Augment reality realtime monitoring Haptic feedback, physical/chemical/ bio sensor, Tremble canceling Steerable needle Integrated Information aided System *Hyundai Heavy Industry, kFDA Clearance
  • 13.
    Collaboration? Direction? 13 “Where ismy puppy?” Difference of Medical Device Development Cell Phone, Automobile, etc : Could be a Developer and User, concurrently Medical Device : Could be a Developer but User Too hard to be an Expert without license
  • 14.
    Imaging Revolutions :CT •Developed the first CT Scanner at EMI • With A.M. Cormack • Received the Nobel Prize for Medicine in 1979 CT scanning the early days ,E C BECKMANN, The British Journal of Radiology, 79 The original lathe bed model ©EMI Ltd Pages from Dr Perry's notebooks on the first EMI scanner First Brain CT Scan at Atkinson Morley’s Hospital on 1971-10-1 Mechanical drawing of the ACTA scanner from Dr. Ledley's patent
  • 16.
    Leonardo da Vinci(1487 - 1489)
  • 17.
  • 19.
    Third & FourthGenerations (From Picker) (From Siemens)
  • 21.
    Slip Ring Power Block: X-Ray Tube Signal Block : DAS(Data) Control Block : Collimator Rotating Time
  • 25.
    True 0.5 mm UltrahighResolution 24 LP/CM
  • 26.
  • 27.
     Technical Development Being digital  3D or 12bit Displays  Imaging Modality  PACS, EMR, etc  Quantitative Imaging, Imaging Biomarker MEGA-Trends in HealthCare Being digital
  • 28.
    D I CO M 3 . 0 PACS (Pictures Archive and Communication System) DICOM 3.0 Standard for Digital Image Communication set by NEMA Most machines support this file type Applies to all image formats (X-Ray, CT, MRI, etc…)
  • 29.
    Structure of aDICOM 3.0 File File Preamble DICOM Prefix Other Mandatory elem. Data SetData Set Data Element Data ElementData ElementData Element Tags (4 Bytes) Value Field (VL Bytes) Value Length (2 Bytes) Value Representation (2 Bytes) 파일 시작 Tags (4 Bytes) Value Field (VL Bytes) Value Length (2 Bytes) Value Representation (4 Bytes) Tags (4 Bytes) Value Field (VL Byte or Undefined Length) 32 bit UI Value Length (4 Bytes) Data Element Structure For Explicit VR Data Element Structure For Explicit VR of OB, OW, SQ Data Element Structure For Implicit VR DICOM 3.0 Meta header element ...
  • 30.
    Basic DICOM Protocols *DICOM 3.0의 base protocol Endian : 2 Byte (16 bit) number를 주고 받는데 있어서의 Ordering 방법 > Little Endian : Least significant byte is sent (stored) first. Ex. (0002,0002) -> 02 00 02 00 > Big Endian : Most significant byte is sent (stored) last. Ex. (0002,0002) -> 00 02 00 02
  • 32.
    MEGA-Trends in Radiology iPhoneand iPad Radiology App, MIM Mobile FDA Approved First Observation of the Chemical Shift**First NMR Spectra on Water* Nobel prizes 1944 Physics Rabi (Columbia) 1952 Physics Bloch (Stanford), Purcell (Harvard) 1991 Chemistry Ernst (ETH) 2002 Chemistry Wüthrich (ETH) 2003 Medicine Lauterbur (University of Illinois in Urbana ), Mansfield (University of Nottingham)
  • 33.
    MEGA-Trends in Radiology iPhoneand iPad Radiology App, MIM Mobile FDA Approved Park HJ, Harvard, 2003
  • 34.
    Neuro/Brain Science 35 fMRI Realtime fMRI PerfusionDiffusion Braincomputer interface Thinking Functional study Connectivity map Cortical thickness Whole body diffusion DTI Tractography 1st pass perfusion Two compartment perfusion Brain cortical thickness Etc postprocessing ASL DCEMR DSEMR Implementation Public domain Developed SPM Brain Voyager TBSS Tractography S/W Perfusion S/W MIL Diffusion MIL Tractography MIL ASL perfusion MIL perfusion Tracto-based segmentation B0 correction B1 correction High tesla MR Animal MR Manganese MR Brain registration STT/ Statistic CC Subdivision Response monitor FSL AFNI … Brain2 Connectome
  • 35.
    Amygdala Shape Analysis Sexual dimorphism  Effect of gender and sex  Normal Male, female (20, 20) 36Kim N, et al Shape analysis of amygdala - Effects of aging and gender difference, submitted to
  • 36.
    Amygdala Shape Analysis 37 Statisticalanalysis & Visualization 3D Modeling Sub Nucleus Segmentation Kim HJ, Kim N, Amygdala shape alterations in bipolar disorder, ISAD 2008 Subregional shape difference of amygdala
  • 37.
    Amygdala Subnucleus ShapeAnalysis ; Gender Difference ;  Phantom study  Forty healthy volunteers (20 men and 20 women)  Centromedial in left amygdala and laterobasal, superficial and CM in right amygdala (Men << Women) Overall Procedure Kim N, Kim HJ, Lyoo IK, et al, Neuroscience Letter, 2011 488(1):65-9 Amygdala Sex Difference Phantom Study
  • 38.
    Shape change ofcentromedial nucleus after menopause  62 men and 59 women, age range 20-79 years  3D fast field echo pulse sequence MRI  ICV calibration Semi-automatic Segmentation ICC = 0.95 Overview of amygdala subregional analysis. Kim HJ , Kim N (co-first), et al, Neuroimage, In-press 2012/5, Cover
  • 39.
    Shape change ofcentromedial nucleus after menopause  Radius of centromedial nucleus of women  steeper decline with age, while there was no significant difference between the age-related decrease of amygdala volume in men and women. Scatter plots of the adjusted mean radius of CM vs. age. Age-related change of amygdala shape in women and men. Kim HJ , Kim N (co-first), et al, Neuroimage, In-press 2012/5, Cover
  • 40.
    SPHARM comparison Women’s Amygdala ModifiedSPHARM*, Chung, M.K., et al, IEEE TMI, 26:566-581
  • 41.
    Nano Manganese fMRIAnalysis; Gender Difference ; Rat Study  Nano-Maganese fMRI VBM result  Rat Sex Difference  N = 40 (M 20, F 20)  VBM result  Cortical Surface Projection  Visualization : 3D Texture Color Mapping (p-map) Nano-Maganese fMRI Analysis 3D Texture Color Mapping (FDR p map : blue, male, nose; red, female, eye, ear) Kim HJ , Kim N, et al, Unpublished
  • 42.
    Cortical Thinning ;Bipolar Disorder (양극성장해, 조울증)  Bipolar disorder(25) vs control (21)  MRI : 3D spoiled gradient echo pulse sequence  Free surfer  Bipolar Disorder  Impairment of emotional, cognitive, and sensory processing P map of difference of cortical thickness Left : middle frontal cortex, cingulate cortex, postcentral cortex, and middle occipital cortex Right : frontal cortex, angular cortex, postcentral cortex, and lateral occipital cortex. -> Sensory and sensor association cortices Illness duration vs cortical thickness of left midfrontal cortex *Lyoo IK, Kim N, et al, Bipolar Disord, 2006. 8(1): p. 65-74. 86 citations (Google scholar)
  • 43.
    FA VBM; Traumasurvivor ;  PostTraumatic Stress Disorder (외상후 스트레스성 장애, PTSD) patients (Taegu subway fire incident) 20 vs healthy volunteers 20  FA map by VBM of SPM2 *Kim M, Kim N, et al, Neuroreport, 2005. 16(10): p. 1049-53. Low FA in left anterior cingulate regions PTSD symptom severity negatively correlated to anterior cingulate FA
  • 44.
    NMR Theory  NobelPrizes  Nucleus Spin Nobel prizes 1944 Physics Rabi (Columbia) 1952 Physics Bloch (Stanford), Purcell (Harvard) 1991 Chemistry Ernst (ETH) 2002 Chemistry Wüthrich (ETH) 2003 Medicine Lauterbur (University of Illinois in Urbana ), Mansfield (University of Nottingham) +2009 fMRI was nominated. First Observation of the Chemical Shift** **Arnold, J.T., S.S. Dharmatti, and M.E. Packard, J. Chem. Phys., 1951. 19: p. 507. ***http://www.steve.gb.com/science/spectroscopy.html *Bloch, F.; Hansen, W. W.; Packard, M. Physical Review (1946), 70 474-85. First NMR Spectra on Water* Usages 1) Structural (chemical) elucidation 2) Study of dynamic processes 3) Structural (three-dimensional) studies 4) Drug Design 5) Medicine -MRI OH CH2 CH3Ethanol***
  • 45.
    NAA Peak Choline Peak Creatine Peak What’s MRS? Provide a non- invasive method of studying metabolism in vivo.  The tissue’s chemical environment  determines the frequency of a “metabolite peak” Typical 1H MRS
  • 46.
  • 47.
    What’s MRS?  Candidates forMRS include:  1H, 31P, 13C, 23Na, 7Li, 19F, 14N, 15N, 17O, 39K  The most commonly studied nuclei are 1H and 31P Nucleus I Resonanc e Freq (@7T) in MHz Natural Abunda nce (%) Abs Sensitivity at constant field (Product of rel sensitivity and Natural abundance) T1 T2 1 H 1/2 298 99.98 1 0.3-3s 1-600 ms 23 Na 3/2 78.9 100 9.3x10-2 20-50ms 0.5-20 ms 31 P 1/2 120.6 100 6.6x10-2 2-5s 50-1000 13 C 1/2 74.8 1.1 1.76x10-4 3-15 s 100-1000 ms 17 O 5/2 40.36 0.037 1.08x10-5 4-6 ms 1-2 ms 14 N 1 21.4 99.6 1.0x10-3 15 N 1/2 30.0 0.37 3.85x10-6 19 F 1/2 280 100 .83
  • 48.
    MRI vs MRS MRI ImagingVoxel: • 1 – 5 mm3 • Signal from water (and fat) • Conc; • Pure Water • H2O : 55.6 M • 1H : 111 M • Brain • 70% water • 36M MRS Spectroscopic Voxel: • 1 – 8 cm3 • Signal from metabolites • Conc; • Brain • Metabolites • 1 – 10 mM vs
  • 49.
    Water Suppression suppressing thewater signal* a signal one hundred times smaller than that of the water peak, and without water suppression would be poorly resolved *© Scott & White 2004
  • 50.
    Uspectra*  MRS Processing Color overlay on CSI/MRI  ROI based Spectroscopy Averaging  Spectrum analysis software interface  Metabolites Quantitation (Gaussian, Lorentzian, Voigt)  Point Spread Function Correction  Grid Shifting  Utilz (SNR, Coil Inhomogeneity, etc) Uspectra; Matlab Siemens MRS DICOM Siemens MRS RDA Siemens MRS Raw File Philips MRS DICOM Bruker MRS MRS Etc LCModel AMARES FITMAN MRS File Import/Export MRS Analysis Import MRS Kim N. Brain Institute, Univ. of Utah, AMC
  • 51.
    Overall Development flow Braintissue characterization SPM5 :unified segmentation White/gray matter Probabilistic volume map T1 or T2 Brain MRI USpectra MRS from *.rda, *.dcm Registration on MRI and MRS MRI volume or slice image SNR calc on MRS Inhomogeneity calc on MRS Deconvolution on Point Spread Function Clinical Data Set i.e., depression vs control Interface to LCModel, jMRUI, Fitman Own module : SVD : AMARES Regression study On PCr/b-ATP, White/gray matter ratio Comparing SNR, inhomogeneity btw McLean and UofU Same data set from McLean, UofU FSL Kim N. Brain Institute, Univ. of Utah, AMC
  • 52.
     Import MRS,MRI, Analysis Results (LCModel, jMRUI)  MRS processing  Spectroscopy measurement  Gaussian, Lorentzian ,Voigt  Interface to LCModel, jMRUI  Regression study of MRS,MRI  Color Coding, etc Regression measurement on HPRT data* Cho difference Regression of MRS on MRI USpectra : 1H/31P MRS Analysis Software *Sung YH , Kim N, Renshaw FP, et al, RSNA 2009 Gray/White matter segmentation using FSL
  • 53.
    LCModel Accuracy; Phantom; MRS NAAG concentration  5 phantoms + 5 scans  Various concentrations : NAAG, Glutamate  Constant concentration : other 10 metabolites NAAG conc. phantom & measurement NAAG measurement Glu measurement *Kim N, Renshaw FP, et al, ISMRM 2010
  • 54.
    MEGAPRESS SNR increase; MRS GABA  GABA : important neuro transmitter  In-vivo GABA measurement : unstable  SNR increase  Phase alignment  Optimal weights NAA GABA Cho Cr A C = B - A B MEGAPRESS for GABA measurement Phase registration, optimal weighting for MRS SNR increase SNR increase phase aligned method ; 28.3% optimized weight method ; 19.8% the combined method ; 62.8% LCModel fitting ; more concentration (paired t-test p<0.01) lower Cramér–Rao lower bounds (paired t-test p = 0.08). Simple diff mean with nSNR Hierarchical Phase registered with oSNR GABA GABA
  • 55.
    MRS Longitudinal Study;CDP-Cho. Tx Methamphetamin Dependent ;  Cytidine-50-diphosphate choline (CDP-choline),  important intermediate for major membrane phospholipids  4-week CDP-choline treatment  urine sample 3 / week * 4 week : total 12 SVS VOI at midfrontal gray matter MR spectrum by LCModel Negative urine vs % Change of NAA over treatment *Yoon SJ, Kim N, et al, Neuropsychopharmacology, 2010. 35(5): p. 1165-73
  • 56.
    MRS; Abstinent MethamphetaminAbuser  MA abusers (30) vs control (20)  MI conc. :  the MA abusers > healthy subjects  NAA conc. of white matter  MA ‘large’ dose < MA ‘small’ dose  MA ‘large’ dose < healthy subjects.  LCModel left frontal white matter Midfrontal gray matter NAA of white matter NAA of gray matter Abstinence day Cumulative dose *Sung YH, Kim N, et al, Drug Alcohol Depend, 2010. 35(5): p. 1165-73
  • 57.
    Creatine Kinase Activity: 31P MRS*  31P MRS  Change in high energy phosphate levels  Change in Creatine kinase (CK) kinetics  In visual cortex during photic stimulation (normal)  Apparent unidirectional rate constant  Kf : forward direction PCr -> ATP synthesis  34% increase  Experimental Setting  Psychiatric Patients T1 = 4.515 KAB = 0.3977 0 1 1 ( ) 1 1 1 zA zA A A AB A M M R T k T     Long saturation times Typical 31P MRS Practical 31P MRS AMARES Fit
  • 58.
    Suicide, Depression vsAltitude ; GIS ;MRS  High attitude sickness or Mild Hypoxia  ↑ Depression  ↑ Suicide  Why? Any Brain Metabolites change?Zip boundaries on 1:250,000 DEM (altitude, USGS), Arc/Gis *Kim N, Renshaw FP, et al, AJP, 2010-09-15 Epub **DelMastro K, Kim N, et al, ADO in press * ** Boston (alt :0m) Salt lake city (alt : 1280m) 9.4T Varian 31P, 1H Dual Channel Coil Cocaine Use in the Past Year is Associated with Altitude of Residence, Kristen DelMastro, Namkug Kim, Douglas G. Kondo, Perry Renshaw, Journal of Addiction Medicine, In press Open-label uridine for treatment of depressed adolescents with bipolar disorder. Kondo DG, Sung YH, Hellem TL, Delmastro KK, Jeong EK, Kim N, Shi X, Renshaw PF. J Child Adolesc Psychopharmacol. 2011 Apr;21(2):171-5. Epub 2011 Apr 12. Review: magnetic resonance spectroscopy studies of pediatric major depressive disorder. Kondo DG, Hellem TL, Sung YH, Kim N, Jeong EK, Delmastro KK, Shi X, Renshaw PF. Depress Res Treat. 2011;2011:650450. Epub 2010 Oct 4. Incidence of major depressive episode correlates with elevation of substate region of residence. DelMastro K, Hellem T, Kim N, Kondo D, Sung YH, Renshaw PF. J Affect Disord. 2011 Mar;129(1-3):376-9. Epub 2010 Nov 11. Altitude, gun ownership, rural areas, and suicide. Kim N, Mickelson JB, Brenner BE, Haws CA, Yurgelun-Todd DA, Renshaw PF. Am J Psychiatry. 2011 Jan;168(1):49-54. Epub 2010 Sep 15.
  • 59.
    Parkinson DTI  Neurology: 이종식 교수님  Radiology : 김상준 교수님  Data  Control : N 86 / PD : N 100 / PDD : N 12 / MSAp : N 16 / MSAc : N 14 / MSAm : N 9  Processing  TBSS, FSL Kim SJ, et al, ASNR 2012 White matter abnormality in Parkinson disease: Evaluation with Diffusion tensor imaging using tract-based spatial statistics study FA map : PD vs Control FA map : MSAc vs ControlMD, PD vs Control (L39, R37)
  • 60.
    Aphasia Analysis  Neurology: 권미선, 이재홍 교수님  Stroke -> Aphasia  Longitudinal study  Data  Control x15 / Aphasia Pts x7  Imaging  Philips 3T Achieva, 3DT1TFE 0.5mm SENSE  DWI x15, DTI, 3DT1TFE, T2  Clinical Data  Age, sex, edu, semantic, phnemic, generative, K-BNT, MoCA-K, Syll/min  Processing  FSL, TBSS
  • 61.
  • 62.
  • 63.
    뇌졸중 예후 인자예측 시스템 개발 특징 추출을 위한 혈관 기반 뇌영역 분할 • http://radiopaedia.org/articles/cerebral-vascular-territories • https://en.wikipedia.org/wiki/Magnetic_resonance_angiograph y
  • 64.
    Vessel-based brain parcellation Voronoidiagram Euclidean distance Manhattan distance Fuzzy boundary • https://en.wikipedia.org/wiki/Voronoi_diagram
  • 65.
    혈관 기반 심근분할 ; 돼지 동물실험 1-b) CT 영상 capture (size 동일, Target Territory의 Mask 반영)1-a) 병리사진 (Target Territory 마킹) 2) Control Point 지정 (최소 35~40개) 3) Registration 4) 평가 Resizing 필요(Optional: CT Image size에 맞춤) Left: Pie#0~20 Right: Pie#0~15 중첩: Pie 15개 Start point LAD RCA LCX Full Mask 확장된 심근 Mask LAD LCX RCA LAD RCA LCX LAD LCX RCA
  • 66.
    Visual assessment • D.Leander Rimmele and GötzThomalla, “Wake-up stroke: clinical characteristics, imaging findings and treatment option – an update” Frontiers in Neurology, 2014. 뇌졸중 예후 인자 예측 시스템 개발 뇌졸중 발생 시점 추정 모델 DWI/FLAIR mismatch
  • 67.
    뇌졸중 예후 인자예측 시스템 개발 뇌졸중 발생 시점 추정 모델 DWI/FLAIR mismatch Identification patients with acute ischemic stroke within 4.5h (or 6h) of onset • GötzThomalla et al., “DWI-FLAIR mismatch for the identifi cation of patients with acute ischaemic stroke within 4·5 h of symptom onset (PRE-FLAIR): a multicentre observational study” Lancet Neurology, 2011.
  • 68.
    DWI/FLAIR mismatch Algorithm (flipping) ADCFLAIR Infarct segmentation Registration Infarct Flipping
  • 69.
    Deep Learning Deep learning방식의 neural network model.
  • 70.
    T2 Hyperintensity 2D또는 3D occurrence map 2세부 김동억 교수님 협력연구