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A set of 4D pediatric XCAT reference phantoms for multimodality research
Hannah Norris1, Yakun Zhang1, Jason Bond1
1Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27705.
Insights
Researchers developed 4D pediatric extended cardiac-torso (XCAT) phantoms for CT imaging research. These patient-derived models simulate childhood anatomy and motion, aiding in optimizing pediatric CT protocols and improving imaging devices.
Area of Science:
- Medical Imaging
- Computational Anatomy
- Radiological Physics
Background:
- Previous development of adult 4D extended cardiac-torso (XCAT) phantoms for multimodality imaging.
- Need for pediatric-specific computational models for research and protocol optimization.
Purpose of the Study:
- To develop a reference set of 4D pediatric XCAT phantoms for male and female subjects across various ages (newborn to 15 years).
- To establish a foundation for creating a large population of pediatric phantoms for optimizing CT imaging protocols.
Main Methods:
- Utilized CT data from normal patients, matched to ICRP Publication 89 reference values for height and weight.
- Segmented organs and structures, creating initial models using NURBS surfaces and completing bodies with scaled adult or cadaver data.
- Applied a large deformation diffeomorphic metric mapping algorithm to incorporate 4D cardiac and respiratory motion models.
Main Results:
- Created ten detailed pediatric reference phantoms mirroring adult XCAT functionality, including thousands of anatomical structures.
- Phantoms capture pediatric anatomic variations, bone development, and organ growth based on patient data.
- Phantoms integrate with simulation packages to generate realistic pediatric imaging data for various modalities.
Conclusions:
- Patient-derived pediatric computational phantoms offer valuable variable anatomies for simulation.
- Expansion of this phantom set will enable evaluation and improvement of pediatric imaging devices.
- Optimized CT protocols for pediatric patients can be developed to enhance image quality and reduce radiation dose.
Purpose:
The authors previously developed an adult population of 4D extended cardiac-torso (XCAT) phantoms for multimodality imaging research. In this work, the authors develop a reference set of 4D pediatric XCAT phantoms consisting of male and female anatomies at ages of newborn, 1, 5, 10, and 15 years. These models will serve as the foundation from which the authors will create a vast population of pediatric phantoms for optimizing pediatric CT imaging protocols.
Methods:
Each phantom was based on a unique set of CT data from a normal patient obtained from the Duke University database. The datasets were selected to best match the reference values for height and weight for the different ages and genders according to ICRP Publication 89. The major organs and structures were segmented from the CT data and used to create an initial pediatric model defined using nonuniform rational B-spline surfaces. The CT data covered the entire torso and part of the head. To complete the body, the authors manually added on the top of the head and the arms and legs using scaled versions of the XCAT adult models or additional models created from cadaver data. A multichannel large deformation diffeomorphic metric mapping algorithm was then used to calculate the transform from a template XCAT phantom (male or female 50th percentile adult) to the target pediatric model. The transform was applied to the template XCAT to fill in any unsegmented structures within the target phantom and to implement the 4D cardiac and respiratory models in the new anatomy. The masses of the organs in each phantom were matched to the reference values given in ICRP Publication 89. The new reference models were checked for anatomical accuracy via visual inspection.
Results:
The authors created a set of ten pediatric reference phantoms that have the same level of detail and functionality as the original XCAT phantom adults. Each consists of thousands of anatomical structures and includes parameterized models for the cardiac and respiratory motions. Based on patient data, the phantoms capture the anatomic variations of childhood, such as the development of bone in the skull, pelvis, and long bones, and the growth of the vertebrae and organs. The phantoms can be combined with existing simulation packages to generate realistic pediatric imaging data from different modalities.
Conclusions:
The development of patient-derived pediatric computational phantoms is useful in providing variable anatomies for simulation. Future work will expand this ten-phantom base to a host of pediatric phantoms representative of the public at large. This can provide a means to evaluate and improve pediatric imaging devices and to optimize CT protocols in terms of image quality and radiation dose.
