Topology-Preserving Computed Tomography Super-Resolution Based on Dual-Stream Diffusion Model

Yuetan Chu, Longxi Zhou, Gongning Luo*, Zhaowen Qiu, Xin Gao

*Corresponding author for this work

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


X-ray computed tomography (CT) is indispensable for modern medical diagnosis, but the degradation of spatial resolution and image quality can adversely affect analysis and diagnosis. Although super-resolution (SR) techniques can help restore lost spatial information and improve imaging resolution for low-resolution CT (LRCT), they are always criticized for topology distortions and secondary artifacts. To address this challenge, we propose a dual-stream diffusion model for super-resolution with topology preservation and structure fidelity. The diffusion model employs a dual-stream structure-preserving network and an imaging enhancement operator in the denoising process for image information and structural feature recovery. The imaging enhancement operator can achieve simultaneous enhancement of vascular and blob structures in CT scans, providing the structure priors in the super-resolution process. The final super-resolved CT is optimized in both the convolutional imaging domain and the proposed vascular structure domain. Furthermore, for the first time, we constructed an ultra-high resolution CT scan dataset with a spatial resolution of 0.34 × 0.34 mm 2 and an image size of 1024 × 1024 as a super-resolution training set. Quantitative and qualitative evaluations show that our proposed model can achieve comparable information recovery and much better structure fidelity compared to the other state-of-the-art methods. The performance of high-level tasks, including vascular segmentation and lesion detection on super-resolved CT scans, is comparable to or even better than that of raw HRCT. The source code is publicly available at

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031439988
StatePublished - 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: Oct 8 2023Oct 12 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14229 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.


  • Computed tomography
  • Diffusion model
  • Image enhancement
  • Super resolution

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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