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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate

Kutsev Bengisu Ozyoruk1, Stephanie A Harmon1, Nathan S Lay1

  • 1Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.

Journal of Personalized Medicine
|October 25, 2024
PubMed
Summary

A new AI model synthesizes prostate Apparent Diffusion Coefficient (ADC) maps from T2-weighted MRI, overcoming motion and gas artifacts. This AI-ADC model shows superior performance in generating accurate ADC maps for improved prostate cancer diagnostics.

Keywords:
DWIT2W MRIdeep learninggenerative artificial intelligenceprostate cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Prostate MRI Apparent Diffusion Coefficient (ADC) maps are crucial for tumor characterization.
  • Image artifacts from patient motion or rectal gas can reduce ADC map accuracy.
  • Generative Adversarial Networks (GANs) offer a potential solution for synthesizing accurate ADC maps.

Purpose of the Study:

  • To develop a novel Generative Adversarial Network (GAN) approach for synthesizing ADC maps directly from T2-weighted MRI (T2W MRI).
  • To improve the accuracy and reliability of ADC maps in prostate MRI by mitigating common artifacts.
  • To enhance clinical diagnostics and radiological workflows through improved prostate imaging.

Main Methods:

  • A GAN model, AI-ADC, was developed using contrastive learning to map axial T2W MRI to ADC maps.
  • The model was trained on a dataset of 506 patients with unpaired T2W MRI and ADC maps.
  • AI-ADC was compared against state-of-the-art methods: CycleGAN, CUT, and StyTr2.

Main Results:

  • AI-ADC achieved a higher mean Structural Similarity Index (SSIM) of 0.863 compared to CycleGAN (0.855), CUT (0.797), and StyTr2 (0.824).
  • AI-ADC demonstrated a significantly lower Fréchet Inception Distance (FID) of 31.992, indicating superior generation quality.
  • External validation on the ProstateX dataset showed AI-ADC outperformed other models with an SSIM of 0.647 and FID of 113.876.

Conclusions:

  • The proposed AI-ADC model effectively generates high-quality ADC maps from T2W MRI.
  • This approach shows significant potential for improving the accuracy of prostate cancer diagnosis.
  • The AI-ADC model can enhance clinical diagnostics and streamline radiological workflows.