Transcript segars_slides_MDCTx - Carl E Ravin Advanced Imaging

Population of 4D
Computational Phantoms for
CT Imaging Research and
Dosimetry
CARL E RAVIN
ADVANCED
I M A G I N G
LABORATORIES
W. Paul Segars, PhD
Carl E. Ravin Advanced Imaging
Labs
Duke University
Medical Imaging Simulation
Computer model of
SPECT scanner
Emission
Computed
Tomography
(ECT)
Myocardial SPECT
Digital phantom
Images reconstructed
from projections
Transmission
Computed
Tomography
(TCT)
Computer model
of X-ray CT
scanner
X-ray CT
Images reconstructed
from projections
Medical Imaging Simulation
• Advantages:
– Exact anatomy of the computer phantom is known
providing a gold standard to evaluate and improve
devices and techniques
– Computer phantoms can be altered easily to
model different anatomies and medical situations
providing a large population of subjects from
which to perform research
– No need to worry about overexposing the
phantom to radiation or being sued by the
phantom (IRB approval not needed)
4D eXtended CArdiac-Torso (XCAT) Phantoms
Segars el al, 4D XCAT phantom for multimodality imaging research, Medical Physics,
vol. 37 (9), 2010
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4D XCAT Phantom Anatomy
Detailed Brain Model based on MRI
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4D XCAT Phantom Anatomy
Detail in the hands and feet
Adding nervous and
lymphatic systems
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Cardiac and Respiratory Models
Cardiac Model Based on 4D Tagged MRI and CT
Respiratory Model Based on 4D CT
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Imaging Simulations using the
Computerized XCAT Phantoms
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Population of 4D XCAT Phantoms
•
Create a population of hundreds of detailed
phantoms to represent the public at large from
infancy to adulthood
– Optimize CT clinical applications, image quality vs. dose
•
Each model is based on patient CT data from Duke
Database
•
Include cardiac and
respiratory motions
for 4D simulations
•
First library of 4D
phantoms
Segars el al, Population of anatomically variable 4D XCAT adult phantoms for imaging
research and optimization, Medical Physics, vol. 40 (4), 2013
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Phantom Construction
• Segment CT data to define initial base
anatomy for the patient
• Map template to patient models using
the segmented framework as a guide
• Morph the template to define
unsegmented structures in the target
patients (blood vessels, muscles,
tendons, ligaments, etc)
• Check morphed phantom for
anatomical accuracy
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LDDMM Method to Map the XCAT to
the Patient
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Application of LDDMM to Create
Patient-Specific Phantom
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New XCAT Phantoms
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58 Adult Phantoms
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Adult Phantoms
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42 Pediatric Phantoms
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Pediatric Phantoms
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Simulation of Male CT Data
BMI: 21.9
BMI: 22.7
BMI: 28.5
BMI: 36.1
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Simulation of Female CT Data
BMI: 18.2
BMI: 22.3
BMI: 28.6
BMI: 35.5
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Simulation of 4D CT Data
Cardiac Model
End-systole
End-diastole
Respiratory Model
End-expiration
End-inspiration
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Accurate Dose Estimation from
CT Protocols
Chest Scan
Abdomen Scan
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Ultimate Goal
• Create hundreds of models
representing both genders with varying
ages, heights, and weights
encompassing the full range from
pediatric to adult patients
• Optimize CT clinical applications in
terms of image quality versus radiation
dose
• Distribute the phantoms for research
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Conclusions
• The phantoms developed in this work
will have a widespread use in CT
imaging research to quantitatively
evaluate and improve imaging devices
and techniques and to investigate the
effects of anatomy and motion
• They can also be used to investigate
patient and population-based dose
correlations in CT and to enable
prospective estimation of CT dose and
radiation risk
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