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Published on: January 11, 2020
Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully
Evan Calabrese1, Jeffrey D Rudie1, Andreas M Rauschecker1
1Department of Radiology and Biomedical Imaging (E.C., J.D.R., A.M.R., J.E.V.M., S.C.) and Center for Intelligent Imaging (E.C.), University of California at San Francisco, 350 Parnassus Ave, Suite 307H, San Francisco, CA 94143-0628.
Purpose:
To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma.
Materials And Methods:
In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade.
Results:
Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87).
Conclusion:
The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.
