IPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry

• Main Authors: Abadi, E. (Author), Fu, W. (Author), Iliopoulos, A.-S (Author), Lo, J.Y (Author), Samei, E. (Author), Segars, W.P (Author), Sharma, S. (Author), Sun, X. (Author), Wang, Q. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: accuracy; Article; automation; Automation; Baseline methods; bone; breast; calculation; classification algorithm; Clinical monitoring; Clinical research; comparative study; computational human phantom; Computational phantom; Computational phantoms; computer assisted tomography; computer model; Computerized tomography; conceptual framework; contour volume; CT; deformable registration; Dice Similarity Coefficient; diffeomorphic deformation; diffeomorphic registration model; Diffeomorphic registrations; digital imaging and communications in medicine; Digital twin; dosimetry; Dosimetry; gallbladder; geometry; health care planning; heart; human; Humans; image quality; image segmentation; Image segmentation; imaging phantom; individualized computational phantom; kidney; large scale production; Large-scale research; learning based model; Learning Based Models; liver; lung; Markov chain Monte Carlo method; Medical imaging; multichannel large deformation diffeomorphic metric mapping; normal distribution; organ dose; Phantoms; Phantoms, Imaging; photon transport; preliminary data; process optimization; radiation dose; radiation field; Radiosensitive organs; segmentation; Similarity coefficients; simulation; soft tissue; spleen; statistical shape model; thyroid gland; Tomography, X-Ray Computed; tube current modulation; validation process; x-ray computed tomography
• Online Access: View Fulltext in Publisher

 Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or 'digital-twins (DT)' using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). Conclusion: iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. Significance: The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research. © 2013 IEEE.