Exploring sequence transformation in magnetic resonance imaging via deep learning using data from a single asymptomatic patient. (27th September 2021)

• Record Type: Journal Article
• Authors: Grant-Jacob, James A; Everitt, Chris; Eason, Robert W; King, Leonard J; Mills, Ben
• Is Part Of: Journal of physics communications. Volume 5:Number 9(2021)
• Publication Date: 2021-09-27
• Subjects: deep learning -- optics -- MRI -- medical imaging; Physics -- Periodicals; 530.05
• DOI: 10.1088/2399-6528/ac24d8
• Languages: English
• ISSNs: 2399-6528

Abstract

Abstract: We investigate the potential for deep learning to create a transfer function from T1 to T2 magnetic resonance imaging sequences using data collected from an asymptomatic patient. Neural networks were trained on images of a human left hand, and then applied to convert T1 images to T2 images for the associated right hand. Analysis showed that the most accurate neural network considered the features in the surrounding ∼1 cm when converting to T2, hence indicating that the neural network was able to identify structural correlations between the sequences. However, some small features measuring <2 mm differed, and grid patterning was evident from the images. While using deep learning for sequence transformations could enable faster processing and diagnosis and in turn reduce patient waiting times, additional work, such as synergising physics-based modelling with neural networks, will likely be required to demonstrate that deep learning can be used to accurately create T2 characteristics from T1 images. In addition, since the present work was conducted using data collected from a single patient, further example datasets collected from patients with a range of different pathologies will be required in order to validate the proposed method.