2019/01/15 by Andrew S. Nencka, Nencka, Andrew S., Andrew G. Klein +17
Medicine · #Advanced MRI Techniques and Applications #FOS: Physical sciences #MRI in cancer diagnosis #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.48550/arxiv.1901.04871
openalex publication_date 2019/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Purpose: Magnetic resonance imaging (MRI) exams include multiple series with varying contrast and redundant information. For instance, T2-FLAIR contrast is based upon tissue T2 decay and the presence of water, also present in T2- and diffusion-weighted contrasts. T2-FLAIR contrast can be hypothetically modeled through deep learning models trained with diffusion- and T2-weighted acquisitions. Methods: Diffusion-, T2-, T2-FLAIR-, and T1-weighted brain images were acquired in 15 individuals. A convolutional neural network was developed to generate a T2-FLAIR image from other contrasts. Two datasets were withheld from training for validation. Results: Inputs with physical relationships to T2-FLAIR contrast most significantly impacted performance. The best model yielded results similar to acquired T2-FLAIR images, with a structural similarity index of 0.909, and reproduced pathology excluded from training. Synthetic images qualitatively exhibited lower noise and increased smoothness compared to acquired images. Conclusion: This suggests that with optimal inputs, deep learning based contrast generation performs well with creating synthetic T2-FLAIR images. Feature engineering on neural network inputs, based upon the physical basis of contrast, impacts the generation of synthetic contrast images. A larger, prospective clinical study is needed.