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Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Adrian Thummerer1, Lukas Schmidt1, Jan Hofmaier1

  • 1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.

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|December 3, 2024
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This summary is machine-generated.

Deep learning super-resolution enhances low-dose cone-beam computed tomography (CBCT) imaging quality for radiotherapy. Image domain processing yielded superior results compared to projection domain processing, enabling safer, lower-radiation scans.

Keywords:
cone beam computed tomographydeep learningradiotherapysuper‐resolution

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Artificial Intelligence

Background:

  • Cone-beam computed tomography (CBCT) is essential in radiotherapy but poses a secondary cancer risk due to ionizing radiation, especially in pediatric patients.
  • Deep learning super-resolution (SR) has improved image resolution but hasn't been applied to reduce CBCT radiation dose.

Purpose of the Study:

  • To reduce CBCT imaging dose by employing an enhanced super-resolution generative adversarial network (ESRGAN).
  • To restore image quality in low-dose CBCT using ESRGAN in both projection and image domains.

Main Methods:

  • Trained two ESRGAN models on 2997 head and neck cancer CBCT scans: one in the projection domain (CBCTSRpro) and one in the image domain (CBCTSRimg).
  • Evaluated SR CBCTs for image similarity, noise, spatial resolution, and registration accuracy against original high-dose CBCT (CBCTHR).
  • Conducted a visual Turing test to assess perceptual differences between original and SR CBCTs.

Main Results:

  • Both projection and image domain SR improved low-dose CBCT quality; visual Turing tests showed minimal perceptual difference.
  • CBCTSRimg slightly outperformed CBCTSRpro in the visual Turing test.
  • SR methods significantly improved spatial resolution (CBCTSRpro: 0.88 lp/mm, CBCTSRimg: 0.95 lp/mm) compared to low-dose CBCT (CBCTLR: 0.66 lp/mm), approaching high-dose levels (CBCTHR: 1.01 lp/mm).
  • Noise characteristics and registration accuracy for SR CBCTs were comparable to high-dose CBCT.

Conclusions:

  • Deep learning super-resolution is a viable method for reducing CBCT dose in radiotherapy.
  • Image domain SR processing generates higher quality images than projection domain processing for low-dose CBCT.
  • This approach enables acquiring low-dose CBCTs while maintaining diagnostic image quality, crucial for patient safety.