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Quantitative Cerebral Blood Volume Image Synthesis from Standard MRI Using Image-to-Image Translation for Brain

Bao Wang1, Yongsheng Pan1, Shangchen Xu1

  • 1From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.).

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Generative adversarial networks (GANs) create accurate synthetic cerebral blood volume (CBV) maps from standard MRI scans. These synthetic CBV maps improve brain tumor evaluation, aiding in grading, prognosis, and differential diagnosis.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Cerebral blood volume (CBV) maps from DSC-MRI are valuable for brain tumor assessment but not widely available.
  • Standard MRI sequences lack quantitative CBV information crucial for clinical decisions.

Purpose of the Study:

  • To develop and evaluate image-to-image translation techniques for synthesizing CBV maps from standard MRI sequences.
  • To use bookend technique DSC-MRI as the ground-truth reference for generating synthetic CBV maps.

Main Methods:

  • Retrospective study of 756 MRI examinations with DSC-MRI derived CBV maps.
  • Training of a 3D encoder-decoder network and a GAN for CBV map synthesis.
  • Quantitative (SSIM) and qualitative (neuroradiologist assessment) evaluation of synthesized maps.
  • Assessment of clinical utility in tumor grading, prognosis, and differential diagnosis using multicenter data.

Main Results:

  • The 3D encoder-decoder network achieved the highest synthetic performance (SSIM, 86.29%).
  • Neuroradiologists rated the synthesized CBV maps favorably (mean score, 2.63).
  • Combining synthetic CBV maps with standard MRI significantly improved diagnostic and predictive accuracy for glioma grading, prognosis, and differential diagnosis, as well as brain metastases.

Conclusions:

  • Image-to-image translation techniques, particularly GANs, can generate accurate synthetic CBV maps from standard MRI.
  • Synthetic CBV maps enhance the clinical evaluation of brain tumors, offering improved diagnostic and prognostic capabilities.
  • This approach holds promise for wider clinical adoption of CBV mapping in neuro-oncology.