Population of 4 D Computational Phantoms for CT

Population of 4 D Computational Phantoms for CT Imaging Research and Dosimetry W. Paul Segars, Ph. D 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)

4 D e. Xtended CArdiac-Torso (XCAT) Phantoms Segars el al, 4 D XCAT phantom for multimodality imaging research, Medical Physics, vol. 37 (9), 2010

4 D XCAT Phantom Anatomy Detailed Brain Model based on MRI

4 D XCAT Phantom Anatomy Detail in the hands and feet Adding nervous and lymphatic systems

Cardiac and Respiratory Models Cardiac Model Based on 4 D Tagged MRI and CT Respiratory Model Based on 4 D CT

Imaging Simulations using the Computerized XCAT Phantoms

Population of 4 D 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 4 D simulations • First library of 4 D phantoms Segars el al, Population of anatomically variable 4 D XCAT adult phantoms for imaging research and optimization, Medical Physics, vol. 40 (4), 2013

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

LDDMM Method to Map the XCAT to the Patient

Application of LDDMM to Create Patient-Specific Phantom

New XCAT Phantoms 13

58 Adult Phantoms

Adult Phantoms

42 Pediatric Phantoms

Pediatric Phantoms

Simulation of Male CT Data BMI: 21. 9 BMI: 22. 7 BMI: 28. 5 BMI: 36. 1 18

Simulation of Female CT Data BMI: 18. 2 BMI: 22. 3 BMI: 28. 6 BMI: 35. 5 19

Simulation of 4 D CT Data Cardiac Model End-systole End-diastole Respiratory Model End-expiration End-inspiration 20

Accurate Dose Estimation from CT Protocols Chest Scan Abdomen Scan 21

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

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