Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas

Zach Eidex; Mojtaba Safari; Jacob Wynne; Richard L J Qiu; Tonghe Wang; David Viar Hernandez; Hui-Kuo Shu; Hui Mao; Xiaofeng Yang

doi:10.1002/mp.17509

Deep learning based apparent diffusion coefficient map generation from multi-parametric MR images for patients with diffuse gliomas

Med Phys. 2024 Nov 8. doi: 10.1002/mp.17509. Online ahead of print.

Authors

Zach Eidex¹, Mojtaba Safari¹, Jacob Wynne¹, Richard L J Qiu¹, Tonghe Wang², David Viar Hernandez¹, Hui-Kuo Shu^{1

3}, Hui Mao^{3

4}, Xiaofeng Yang^{1

3

5}

Affiliations

¹ Department of Radiation Oncology, Emory, University, Atlanta, Georgia, USA.
² Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
³ Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
⁴ Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA.
⁵ School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

PMID: 39514841
DOI: 10.1002/mp.17509

Abstract

Purpose: Apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance imaging (DWI MRI) provides functional measurements about the water molecules in tissues. However, DWI is time consuming and very susceptible to image artifacts, leading to inaccurate ADC measurements. This study aims to develop a deep learning framework to synthesize ADC maps from multi-parametric MR images.

Methods: We proposed the multiparametric residual vision transformer model (MPR-ViT) that leverages the long-range context of vision transformer (ViT) layers along with the precision of convolutional operators. Residual blocks throughout the network significantly increasing the representational power of the model. The MPR-ViT model was applied to T1w and T2-fluid attenuated inversion recovery images of 501 glioma cases from a publicly available dataset including preprocessed ADC maps. Selected patients were divided into training (N = 400), validation (N = 50), and test (N = 51) sets, respectively. Using the preprocessed ADC maps as ground truth, model performance was evaluated and compared against the Vision Convolutional Transformer (VCT) and residual vision transformer (ResViT) models with the peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE).

Results: The results are as follows using T1w + T2-FLAIR MRI as inputs: MPR-ViT-PSNR: 31.0 ± 2.1, MSE: 0.009 ± 0.0005, SSIM: 0.950 ± 0.015. In addition, ablation studies showed the relative impact on performance of each input sequence. Both qualitative and quantitative results indicate that the proposed MR-ViT model performs favorably against the ground truth data.

Conclusion: We show that high-quality ADC maps can be synthesized from structural MRI using a MPR-ViT model. Our predicted images show better conformality to the ground truth volume than ResViT and VCT predictions. These high-quality synthetic ADC maps would be particularly useful for disease diagnosis and intervention, especially when ADC maps have artifacts or are unavailable.

Keywords: DWI; MRI; deep learning; glioma; intramodal MRI synthesis.

Abstract

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