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InNetGAN: Inception Network-Based Generative Adversarial Network for Denoising Low-Dose Computed Tomography.

K A Saneera Hemantha Kulathilake1,2, Nor Aniza Abdullah1, A M Randitha Ravimal Bandara3

  • 1Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.

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This study introduces InNetGAN, a novel deep learning approach for low-dose computed tomography (LDCT) denoising. InNetGAN effectively reduces noise while preserving crucial image details, improving diagnostic accuracy.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Low-dose computed tomography (LDCT) reduces radiation exposure but introduces noise and artifacts.
  • Image quality degradation in LDCT hinders accurate disease diagnosis and treatment planning.
  • Deep learning (DL) shows promise for LDCT denoising but often retains residual noise.

Purpose of the Study:

  • To develop an advanced DL model for effective LDCT denoising.
  • To address noise retention in skip connections of DL models.
  • To preserve subtle structures and texture details in denoised LDCT images.

Main Methods:

  • Proposed a Generative Adversarial Network with Inception network modules (InNetGAN).
  • Generator based on U-net architecture with modified skip connections.
  • Inception network modules integrated to filter noise in feature maps.

Main Results:

  • InNetGAN demonstrated superior performance in noise reduction compared to state-of-the-art methods.
  • The model effectively preserved subtle structures and texture details in LDCT images.
  • Quantitative and qualitative results validated the efficacy of InNetGAN.

Conclusions:

  • InNetGAN offers an effective solution for LDCT denoising by filtering noise transmission.
  • The proposed method enhances LDCT image quality, aiding clinical applications.
  • InNetGAN represents a significant advancement in deep learning for medical image enhancement.