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Deep Few-view High-resolution Photon-counting Extremity CT at Halved Dose for a Clinical Trial.

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  • 1Biomedical Imaging Center, Rensselaer Polytechnic, Troy, NY, 12180 USA.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Science

Background:

  • Photon-counting computed tomography (PCCT) offers high-resolution (HR) imaging for extremity tissue characterization.
  • Current PCCT methods require dose and speed improvements for clinical applications like contrast-enhanced studies.
  • Deep learning (DL) for 2D reconstruction is successful, but HR volumetric reconstruction faces GPU memory, data scarcity, and domain gap challenges.

Purpose of the Study:

  • To develop a deep learning-based approach for improving PCCT image reconstruction in extremity scans.
  • To achieve halved radiation dose and doubled imaging speed without compromising diagnostic image quality.
  • To address GPU memory constraints, training data scarcity, and domain gap issues in PCCT reconstruction.

Main Methods:

  • A patch-based volumetric refinement network was developed to manage GPU memory limitations.
  • Network training utilized synthetic data, with model-based iterative refinement employed to bridge the synthetic-real data gap.
  • The approach was validated through simulations, phantom experiments, and a clinical trial involving 8 patients.

Main Results:

  • Simulations and phantom experiments showed consistent improvements across various acquisition conditions and structures.
  • Radiologist evaluation of 8 patient scans indicated diagnostic image quality comparable or superior to the clinical benchmark.
  • The proposed method demonstrated effectiveness using a fixed network across different scenarios.

Conclusions:

  • The proposed DL-based reconstruction method significantly enhances PCCT safety and efficiency for extremity imaging.
  • This approach maintains or improves diagnostic image quality, paving the way for wider clinical adoption.
  • Further research could explore broader applications of this DL technique in medical imaging.