Clinical Use of DTI

transcript

MIDAG@UNC

Clinical Use of DTI

Guido Gerig

Early Brain Development MIDAG@UNC

Contributors:

• Andy Alexander• Susumo Mori • The University of North Carolina Chapel Hill (UNC)

National Alliance for Medical Image Computing (NIH U54EB005149)

Acknowledgments

ASNR 2003 –Washington,DC DT-MRI Alexander 6

DTI Applications• Normal brain development and aging• Congenital anomalies and diseases of white matter• Traumatic brain injury • Encephalopathies – toxic, metabolic, infectious • Demyelinating and neurodegenerative diseases• Ischemia and stroke• Neoplasm, preoperative planning• Epilepsy • Dementia, schizophrenia, depression• Developmental disorders - fragile X, autism• Spinal Cord

Courtesy J. Neil Washington Univ., St. Louis

Normal Brain Development

WM AnisotropyChanges with Age

frontalpericallosal

centrumsemiovale

GenuLeft pericallosalRight pericallosal

20 30 40 50 60 70 80Age

Posterior

20 30 40 50 60 70 80Age

Centrum Semiovale

parietalpericallosal

splenium

Pfefferbaum et al.MRM 2000

Courtesy K. Lim, Univ. Minn.

ASNR 2003 –Washington,DC DT-MRI Alexander 9R. Bammer, F. Fazekas, Neuroim Clinics N Am, Feb. 2002.

S T A N F O R D S C H O O L OF M E D I C I N E

Lucas MRS/I Center

Multiple Sclerosis

DTI in Cerebral NeoplasmsDeviated

Edematous Destroyed

Infiltrated

Tract Displacement T2W ADC

FA FA 1

Cerebral Palsy

Courtesy of Susumu Mori

MICCAI 2005MICCAI 2005 - 17 - October 27, 2005

Fiber Tract-Oriented Statistics for Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Quantitative Diffusion Tensor MRI AnalysisAnalysis

11Depts of Computer Science, Depts of Computer Science, 22Psychiatry, Psychiatry, 33Radiation Oncology, UNC-Chapel Hill, USARadiation Oncology, UNC-Chapel Hill, USA44Scientific Computing and Imaging Institute, University of Utah, USAScientific Computing and Imaging Institute, University of Utah, USA

Isabelle CorougeIsabelle Corouge1,21,2, , P. Thomas FletcherP. Thomas Fletcher44, , Sarang JoshiSarang Joshi33, , John H. GilmoreJohn H. Gilmore22, , Guido GerigGuido Gerig1,21,2

Quantitative Tractography

- Tractography for ROI definition

- Tensor-math. for statistics along tracts

FA along tractsTract ROIs

Tensors statisics along spines

FA motor tract MD motor tract

Fiber Tract Modeling and Analysis

Experiments and Results• Data

– 3Tesla high resolution (2x2x2 mm3) DT MRI database– 8 subjects: 4 neonates at 2 weeks-old, 4 one year-old– Fiber tracts: genu and splenium

Neonate at 2 weeks-old One year-old

Experiments and Results

• Average of diffusion tensors in cross-sections along tracts

2 weeks-old One year-old

Experiments and Results• Diffusion properties along fiber tracts

Eigenvalues Mean Diffusivity Fractional Anistropy

Neonate Krabbe’s - Motor• Internal capsule

from brain stem to superior cortex

• Left and right hemispheric bundles

• FA values on bundle

Motor cortex

VL thalamus

Pontine nucleiCerebellar cortex

Deep cerebellar nuc-dentate

middle cerebellarpeduncle

superior cerebellarpeduncle

corona radiata

internal capsule cerebral

peduncle

Fallon

Premotor cortex

Prefrontal cortex

Visualization - Left Bundle

Controls

• At 1.5y• Walks• Good

• At 1y• Weak

trunk• Bad

• At 3m• Ok so

FA seems to correlate with outcome

Visualization - Right Bundle

Controls

• At 1.5y• Walks• Good

• At 3m• Ok so

• At 1y• Weak

trunk• Bad

FA higher in Cnt than Krabbe

FA Statistics along Fibers• Lower FA across most of

bundle for Krabbe• Bad motor case has

consistently lowest FA on left bundle

• Possible predictor for outcome?

LEFT: FA along Fiber

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

444595k4k5k6

RIGHT: FA along Fiber

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

444595k4k5k6

Q ui ckTi me™ and aTI FF (LZW) decompressor

are needed to see thi s pi cture.

Controlsgood motorbad motorgood motor

Clinical Use of DTI

Documents