A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models
This work addresses the need for high-quality MRI conversion in medical imaging, but it is incremental as it primarily compares and adapts existing methods.
This paper tackled the problem of generating whole-brain 3T-like MRI images from clinical 1.5T MRIs by comparing deep neural network models, including a novel FCN method and existing super-resolution solutions, with results evaluated through experiments and metrics.
In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully convolutional network (FCN) method and three state-of-the-art super-resolution solutions, ESPCN [26], SRGAN [17] and PRSR [7]. The FCN solution, U-Convert-Net, carries out mapping of 1.5T-to-3T slices through a U-Net-like architecture, with 3D neighborhood information integrated through a multi-view ensemble. The pros and cons of the models, as well the associated evaluation metrics, are measured with experiments and discussed in depth. To the best of our knowledge, this study is the first work to evaluate multiple deep learning solutions for whole-brain MRI conversion, as well as the first attempt to utilize FCN/U-Net-like structure for this purpose.