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Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic

E-Nae Cheong1, Geunu Jeong2, Jiyeon Park2

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Deep learning reconstruction (DLR) effectively reduces noise in brain tumor MRI scans. This technique preserves quantitative accuracy for key physiologic parameters, enabling robust imaging.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Neuro-oncology

Background:

  • Deep learning reconstruction (DLR) shows promise for enhancing MRI image quality.
  • Its impact on quantitative parameters from diffusion-weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion in brain tumor imaging is not well-established.

Purpose of the Study:

  • To assess the effect of DLR on quantitative parameters derived from DWI and DSC in brain tumor patients.
  • To evaluate DLR's potential for improving quantitative physiological MRI in neuro-oncology.

Main Methods:

  • Retrospective analysis of 62 patients with post-radiation brain metastasis using 3.0T MRI (T2, FLAIR, T1WI, DWI, DSC).
  • DWI and DSC images were reconstructed using three DLR levels (high, medium, low).
  • Quantitative parameters (ADC, CBV, CBF, MTT, TTP) were compared between original and DLR images using statistical tests.

Main Results:

  • DLR significantly reduced noise in DSC imaging (lowest RMSE and MAE with high-level DLR) without affecting CBV quantification.
  • No significant differences were observed between original and DLR images for ADC, CBV, CBF, and MTT.
  • High-level DLR showed a significant increase in TTP compared to original images, with high reproducibility across DLR levels for all tested parameters.

Conclusions:

  • DLR effectively reduces noise in DWI and DSC MRI for brain tumors.
  • It preserves the quantitative accuracy of essential physiologic parameters like ADC, CBV, CBF, and MTT.
  • DLR holds potential for robust physiological MRI applications in brain tumor imaging.