# iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

arXiv: https://arxiv.org/abs/2008.08730 · HTML: https://mlsanity.com/paper/2008.08730/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts](https://mlsanity.com/paper/2301.08719/) (2023)
- [Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography](https://mlsanity.com/paper/2203.03074/) (2022)
- [Quality or Quantity: Toward a Unified Approach for Multi-organ Segmentation in Body CT](https://mlsanity.com/paper/2203.01934/) (2022)
- [Scout-Net: Prospective Personalized Estimation of CT Organ Doses from Scout Views](https://mlsanity.com/paper/2312.15354/) (2023)
- [XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT Scans](https://mlsanity.com/paper/2405.11133/) (2024)

## Abstract (arXiv)

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: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs 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 full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed less than 10% dose errors 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 to the level of individual CHPs through automation, achieving a wider and precise organ localization, paving the way for clinical monitoring, and personalized optimization, and large-scale research.