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A Generative Model of Lung CT Conditioned on Radiomics Features.

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  • 1Johns Hopkins University, Baltimore, MD, USA.

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This study introduces a deep learning model using a diffusion transformer architecture to generate lung CT images with controlled texture features. The model accurately replicates user-specified autocorrelation and inverse difference values, enabling precise medical image synthesis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Traditional deep learning image generation models lack control over specific output properties.
  • Controlling image texture features is crucial for applications like medical data synthesis.

Purpose of the Study:

  • To develop a deep learning model capable of generating images with user-specified texture features.
  • To utilize a diffusion transformer architecture conditioned on texture features for controlled image synthesis.

Main Methods:

  • A diffusion transformer architecture was employed for image generation.
  • Texture features (autocorrelation, inverse difference from Gray-Level Co-Occurrence Matrix) were used as conditional inputs.
  • The model was trained and evaluated on lung patches from a public CT database.
Keywords:
Conditional generationdiffusion modelradiomics

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Main Results:

  • The model successfully generated lung parenchyma-like image patches.
  • Generated images showed strong agreement and low variability with conditional texture features (autocorrelation, inverse difference).
  • High concordance correlation coefficients (0.9962 for autocorrelation, 0.9402 for inverse difference) were achieved.

Conclusions:

  • The diffusion transformer model effectively generates images with controlled texture features.
  • This approach supports highly controlled data generation for diverse applications, particularly in medical imaging.
  • The model's ability to align generated image textures with conditional inputs is validated.