이화여대 뇌인지과학과 발표자료

1. 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

2. 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,

3. 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

4. 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. 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 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

7. 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

8. 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

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 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

12. 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

13. 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

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. 기존 X-Ray의 단점

19. Third & Fourth Generations
(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
Ultrahigh Resolution 24 LP/CM

26. Ultra-Low Dose

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 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…)

29. 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
...

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
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)

33. MEGA-Trends in Radiology
iPhone and iPad Radiology App, MIM Mobile FDA Approved
Park HJ, Harvard, 2003

34. 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

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
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

37. 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

38. 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

39. 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

40. SPHARM comparison
Women’s Amygdala
Modified SPHARM*, Chung, M.K., et al, IEEE TMI, 26:566-581

41. 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

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; 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

44. 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***

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. Chemical shifts

47. 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

48. 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

49. 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

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
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

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 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

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 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.

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. 70/4
3

62. 71/4
3

63. 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

64. Vessel-based brain parcellation
Voronoi diagram
Euclidean distance Manhattan distance Fuzzy boundary
• https://en.wikipedia.org/wiki/Voronoi_diagram

65. 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

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 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.

68. DWI/FLAIR mismatch
Algorithm (flipping)
ADC FLAIR
Infarct
segmentation
Registration
Infarct
Flipping

69. Deep Learning
Deep learning 방식의 neural network model.

70. T2 Hyperintensity 2D 또는 3D occurrence
map
2세부 김동억 교수님 협력연구