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Artificial intelligence in image reconstruction: The change is here.

Ramandeep Singh1, Weiwen Wu2, Ge Wang2

  • 1Department of Radiology, Division of Thoracic Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|November 27, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning (DL) image reconstruction in computed tomography (CT) offers improved image quality at lower radiation doses. DL techniques can also handle unique artifacts and reduce computational costs for advanced detectors.

Keywords:
Artificial IntelligenceComputed tomographyDeep learningImage reconstruction

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Radiology

Background:

  • Computed tomography (CT) technology has advanced significantly in hardware and software.
  • Multidetector-row CT scanners and faster gantry speeds have improved scanning range and speed.
  • Concerns about radiation dose and image quality have driven the evolution of CT reconstruction techniques.

Purpose of the Study:

  • To review deep learning (DL)-based image reconstruction methods in CT.
  • To discuss the techniques, applications, uses, and limitations of DL in CT image reconstruction.
  • To highlight DL's potential in improving image quality and reducing radiation dose.

Main Methods:

  • Evolution from filtered back projection (FBP) to iterative reconstruction (IR) and deep learning (DL).
  • DL-based reconstruction utilizes training data, reducing reliance on physical imaging models.
  • DL addresses unique artifacts in photon-counting detector CT (PCD-CT) and handles computational challenges.

Main Results:

  • DL reconstruction techniques achieve improved or retained image quality compared to FBP at lower radiation doses.
  • DL can manage specific artifacts in PCD-CT through data-driven approaches.
  • Deep networks offer faster processing for PCD-CT compared to traditional model-based IR methods.

Conclusions:

  • Deep learning-based image reconstruction represents a significant advancement in CT technology.
  • DL enables enhanced image quality, reduced radiation exposure, and efficient processing for advanced CT systems.
  • Future applications of DL in CT hold potential for new clinical capabilities and improved diagnostic accuracy.