Personalized Computational Human Phantoms via a Hybrid Model-based Deep Learning Method

Hamidreza Khodajou-Chokami, Adeleh Bitarafan, Dmitry V. Dylov, Mahdieh Soleymani Baghshah, Seyed Abolfazl Hosseini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Computed tomography (CT) simulators are versatile tools for scanning protocol evaluation, optimization of geometrical design parameters, assessment of image reconstruction algorithms, and evaluation of the impact of future innovations attempting to improve the performance of CT scanners. Computational human phantoms (CHPs) play a key role in simulators for the radiation dosimetry and assessment of image quality tasks in the medical x-ray systems. Since the construction of patient-specific CHPs can be both difficult and time-consuming, nominal standard/reference CHPs have been established, yielding significant discrepancies in the special design and optimization demands of patient dose and imaging protocols for most medical applications. Therefore, the aim of this work was to develop a personalized Monte-Carlo (MC) CT simulator equipped with a fast and well-structured tool-kit called DeepSegNet for automatic generation of patient-specific CHPs based on MRI images, working under two principal algorithms. To this end, we first developed a 3D convolutional neural network (3DCNN) for the automated segmentation of 3D MRI images to detect anatomical organs/tissues. Then, a 3D voxel merging (3DVM) algorithm constructing CHPs and making fast MC calculations were developed. The proposed 3DCNN benefits from the main merit of residual networks by designing a 15-layer model. Next, the 3DVM algorithm utilizes the segmented data acquired from the former step, to create realistic and optimized CHPs by material mapping and voxel size manipulating. The performance of our 3DCNN model on 20 patients as test cases was 84.54% and 74.52% in terms of average accuracy and Dice-Coefficient, respectively, outperforming SegNet, as a comparable method by 2%. Finally, we developed an MC CT simulator by implementing a set of our generated CHPs. The efficiency of our 3DVM algorithm in constructing CHPs was assessed in terms of MC execution time and the number of merged voxels representing occupied storage memory and compared to the existing lattice method. Besides, the accuracy of our 3DVM investigated through the estimation of patient dose maps and image reconstruction. Results demonstrated a significant reduction of about 96% in the number of voxels and a 15% reduction in MC execution time for x-ray photon transportation while keeping the same accuracy. Therefore, this software package has a strong potential in the optimization of therapeutic and radiological imaging procedures.

Original languageEnglish
Title of host publicationIEEE Medical Measurements and Applications, MeMeA 2020 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728153865
DOIs
Publication statusPublished - Jun 2020
Event15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020 - Bari, Italy
Duration: 1 Jun 20203 Jun 2020

Publication series

NameIEEE Medical Measurements and Applications, MeMeA 2020 - Conference Proceedings

Conference

Conference15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020
Country/TerritoryItaly
CityBari
Period1/06/203/06/20

Keywords

  • computational human phantoms
  • convolutional neural networks
  • follow-up CT imaging
  • lattice method
  • monte-carlo method
  • semantic segmentation
  • voxel merging algorithm

Fingerprint

Dive into the research topics of 'Personalized Computational Human Phantoms via a Hybrid Model-based Deep Learning Method'. Together they form a unique fingerprint.

Cite this