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DtiStudio

Date post: 01-Jan-2016
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DtiStudio. Susumu Mori Johns Hopkins University. Overall direction of DTI research. Smaller b (60), lower resolution, longer time. High Angular-Resolution Diffusion Imaging. - PowerPoint PPT Presentation
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DtiStudio Susumu Mori Johns Hopkins University
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Page 1: DtiStudio

DtiStudio

Susumu MoriJohns Hopkins University

Page 2: DtiStudio

Overall direction of DTI research

DWIs

Smaller b (<1,200), fewer directions (<30), 2mm, 5 min

Larger b (> 3,000), more directions (>60), lower resolution, longer time

Tensor calculation

High Angular-Resolution Diffusion Imaging

Page 3: DtiStudio

Overall direction of DTI research

DWIs

Smaller b (<1,200), fewer directions (<30), 2mm, 5 min

Tensor calculation

Page 4: DtiStudio

Overall direction of DTI research

DWIs

Smaller b (<1,200), fewer directions (<30), 2mm, 5 min

Tensor calculation

• Automated and quantitative quality control pipeline• Fully automated cloud-based web-based pipeline• Integration of image quantification schemes• Dynamic programming for fiber tracking• Automated fiber tracking

Page 5: DtiStudio

DtiStudio

Page 6: DtiStudio

How DTI studies could go wrong

Motion

Eddy-current distortion

Outlier pixels (dropout, ghost)

Registration

Slice rejectionPixel rejection

Motion monitoringEddy current monitoring

Page 7: DtiStudio

Motion monitoring

Page 8: DtiStudio

Non-physiological motion

20 40 60 80 100 120 140 160-3

-2

-1

0

1

2

3

Rep 1 Rep 2 Rep 3 Rep 4 Rep 5

XMR-SNR-test-AH: dtRegVoltranslationy

DW

I tansl

atio

ny:

mm

# of DWI20 40 60 80 100 120 140 160

0

0.5

1

1.5

2

2.5

3

3.5

4

Rep 1 Rep 2 Rep 3 Rep 4 Rep 5

XMR-SNR-test-AH: cumulateddtrotationmetric

DW

I rota

tionm

etric

: degre

e

# of DWI

Page 9: DtiStudio

Eddy current monitoring

Page 10: DtiStudio

Importance of tensor fitting quality

Page 11: DtiStudio

Images that CAN’T be registered

0 0.5 1 1.5 2 2.5 30

0.5

1

1.5

2

2.5

3

3.5

4

4.5

apparent translation: mm

abso

lute

fitti

ng e

rror b

efor

e re

gist

ratio

n:

0 0.5 1 1.5 2 2.5 30

0.5

1

1.5

2

2.5

3

3.5

4

4.5

apparent translation: mm

abso

lute

fitti

ng e

rror a

fter r

egis

tratio

n:

A B

C

D E

Page 12: DtiStudio

Outlier rejection

50 100 150

50

100

150

0

20

40

60

80

50 100 150

50

100

150

0

20

40

60

80

50 100 150

50

100

150

0

0.5

1

1.5

2

a b c d

e f g h

i j k l

Page 13: DtiStudio

Original image

After registrationArtifact masked

Select subject to show results

Population report Matlab code

Page 14: DtiStudio

MriStudio Pipeline

DWIs

DtiStudio

DiffeoMapRoiEditor

254 structures

Page 15: DtiStudio

Example of multi-modal analysis

Page 16: DtiStudio

Automated pipelineRead DICOM data

Tensor calculation QC report

Scalar map calculation Save images

Skull-strip

Linear registration

Atlas-based parcellation

Save images

Save tables

Page 17: DtiStudio

Database solution

Initiate image analysis pipeline

Specify the data for analysis

Calculation startsResults become available

Page 18: DtiStudio

Fiber tracking: path-generation

Automated parcellation of the brain

Page 19: DtiStudio

Fiber tracking: path-generation

Page 20: DtiStudio

Overall direction of DTI research

DWIs

Smaller b (<1,200), fewer directions (<30), 2mm, 5 min

Tensor calculation

• Automated and quantitative quality control pipeline• Fully automated cloud-based web-based pipeline• Integration of image quantification schemes• Dynamic programming for fiber tracking• Automated fiber tracking

Page 21: DtiStudio

Acknowledgment• Program development

– Hangyi Jiang– Xin Li

• Server implementation– Anthony Kolasny– Can Ceritoglu– Bill Schneider

• Quantification module– Michael Miller– Yue Li– Xiaoying Tang

• Atlas generation– Kenichi Oishi– Anderia Faria