Ultra-fast-pitch acquisition and reconstruction in helical CT

Hao Gong, Liqiang Ren, Cynthia H. McCollough, Lifeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A fast scan with a high helical pitch is desirable for many CT exams, such as pediatric, chest, and some of cardiovascular exams, to suppress patient motion artifacts. However, on a single-source scanner, the pitch typically cannot exceed ~1.5 without generating image distortion within the entire scanning field of view due to insufficient data acquired in a fast pitch mode. In this work, we developed a deep convolutional neural network-based approach to reducing artifacts on images reconstructed from insufficient data acquired in an ultra-fast-pitch mode (PP = 2.0). This custom-designed neural network, referred to as Ultra-fast-pitch image reconstruction neural network (UFP-net) consists of functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the fast-pitch mode. The UFP-net was trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss. Projection data at a regular pitch (PP = 1.0) and a fast-pitch (PP = 3.0) were simulated using 10 patient CT cases to generate training and validation datasets. Compared to filtered-back-projection (FBP), the UFP-net largely suppressed image artifacts and restored anatomical details. The structural similarity index (SSIM) was significantly improved (Mean SSIM: UFP-net 0.9, FBP 0.6), and the root-mean-square-error (RMSE) was largely reduced (Mean RMSE: UFP-net 57 HU, FBP 273 HU). The proposed method has the potential to enable ultra-fast-pitch data acquisition on single-source CT scanners to improve scanning speed while maintaining image quality.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationPhysics of Medical Imaging
EditorsGuang-Hong Chen, Hilde Bosmans
PublisherSPIE
ISBN (Electronic)9781510633919
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Physics of Medical Imaging - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11312
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Physics of Medical Imaging
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • Helical CT
  • High pitch
  • Image reconstruction
  • – Deep learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Gong, H., Ren, L., McCollough, C. H., & Yu, L. (2020). Ultra-fast-pitch acquisition and reconstruction in helical CT. In G-H. Chen, & H. Bosmans (Eds.), Medical Imaging 2020: Physics of Medical Imaging [1131209] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11312). SPIE. https://doi.org/10.1117/12.2549315