Journal Article Ppublish
PubMed: 40545512 DOI: 10.1002/hbm.70246
Cross-Modality Image Translation of 3 Tesla Magnetic Resonance Imaging to 7 Tesla Using Generative Adversarial Networks.
Hum Brain Mapp 46(9):e70246
Authors: Eduardo Diniz, Tales Santini, Helmet Karim, Howard J Aizenstein, Tamer S Ibrahim
Affiliations
- Department of Psychology, Carnegie Mellon University, Pennsylvania, USA.
- Department of Bioengineering, University of Pittsburgh, Pennsylvania, USA.
- Department of Psychiatry, University of Pittsburgh, Pennsylvania, USA.
Abstract
The rapid advancements in magnetic resonance imaging (MRI) technology have precipitated a new paradigm wherein cross-modality data translation across diverse imaging platforms, field strengths, and different sites is increasingly challenging. This issue is particularly accentuated when transitioning from 3 Tesla (3T) to 7 Tesla (7T) MRI systems. This study proposes a novel solution to these challenges using generative adversarial networks (GANs)-specifically, the CycleGAN architecture-to create synthetic 7T images from 3T data. Employing a dataset of 1112 and 490 unpaired 3T and 7T MR images, respectively, we trained a 2-dimensional (2D) CycleGAN model, evaluating its performance on a paired dataset of 22 participants scanned at 3T and 7T. Independent testing on 22 distinct participants affirmed the model's proficiency in accurately predicting various tissue types, encompassing cerebral spinal fluid, gray matter, and white matter. Our approach provides a reliable and efficient methodology for synthesizing 7T images, achieving a median Dice coefficient of 83.62% for cerebral spinal fluid (CSF), 81.42% for gray matter (GM), and 89.75% for White Matter (WM), while the corresponding median Percentual Area Differences (PAD) were 6.82%, 7.63%, and 4.85% for CSF, GM, and WM, respectively, in the testing dataset, thereby aiding in harmonizing heterogeneous datasets. Furthermore, it delineates the potential of GANs in amplifying the contrast-to-noise ratio (CNR) from 3T, potentially enhancing the diagnostic capability of the images. While acknowledging the risk of model overfitting, our research underscores a promising progression toward harnessing the benefits of 7T MR systems in research investigations while preserving compatibility with existing 3T MR data. This work was previously presented at the ISMRM 2021 conference.
MeSH
Research Design, Gray Matter, Neuroimaging, Magnetic Resonance Imaging, Brain Mapping, Deep Learning, Work, Research, Spine, Generative Adversarial Networks