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Towards ultra-low-dose CT for detecting pulmonary nodules using DenseNet.

Ching-Ching Yang1,2

  • 1Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, Sanmin Dist., Kaohsiung, 80708, Taiwan. cyang@kmu.edu.tw.

Physical and Engineering Sciences in Medicine
|February 10, 2025
PubMed
Summary

Deep learning using DenseNet effectively reduces noise in ultra-low-dose CT scans, improving lung nodule detection for cancer screening while maintaining image quality comparable to full-dose scans.

Keywords:
DenseNetImage denoisingPulmonary noduleUltra-low-dose CT

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Ultra-low-dose CT (ULCT) is crucial for lung cancer screening but suffers from image noise, hindering nodule detection.
  • Reducing radiation dose is vital due to the linear no-threshold model, which posits no safe radiation level.

Purpose of the Study:

  • To investigate the feasibility of using DenseNet, a deep learning model, for noise suppression in ULCT images.
  • To assess if DenseNet can improve image quality and nodule detectability in ULCT for lung cancer screening.

Main Methods:

  • DenseNet was trained on CT images with varying radiation doses.
  • The model was tested on 14 patients (7 with solid nodules, 7 with subsolid nodules) not included in training.
  • Image quality was evaluated using Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Contrast-to-Noise Ratio (CNR), and subjective scoring.

Main Results:

  • Denoising with DenseNet significantly improved RMSE and PSNR values.
  • Lung nodules were more easily distinguishable in denoised ULCT images, supported by improved CNR and subjective assessments.
  • No statistically significant differences were found between full-dose CT and denoised ULCT in evaluating anatomical structures.

Conclusions:

  • DenseNet is a viable approach for effectively reducing image noise in ULCT scans.
  • This technique shows promise for enhancing lung cancer screening by improving nodule detection without compromising diagnostic accuracy.
  • Further dose reduction in CT imaging remains a critical area of research in radiology.