Investig Magn Reson Imaging.  2020 Dec;24(4):241-251. 10.13104/imri.2020.24.4.241.

qMTNet⁺ , an Improved qMTNet with Residual Connection for Accelerated Quantitative Magnetization Transfer Imaging

• Luu HM
• Kim DH
• Choi SH
• Park SH

• KMID: 2510454
• DOI: http://doi.org/10.13104/imri.2020.24.4.241

Abstract

Purpose
To develop qMTNet⁺ , an improved version of a recently proposed neural network called qMTNet, to accelerate quantitative magnetization transfer (qMT) imaging acquisition and processing.
Materials and Methods
Conventional and inter-slice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing networks and from four young and four older subjects for testing its generalizability. qMTNet⁺ was designed to incorporate residual and multi-task learning to improve its performance. qMTNet⁺ is composed of multiple fully-connected layers. It can simultaneously generate missing MT-weighted images and qMT parameters from undersampled MT images. The network was trained and validated with 7-fold cross-validation. Additional testing with unseen data was performed to assess the generalizability of the network. Performance of qMTNet⁺ was compared with that of qMTNet-1 and qMTNet-acq for fitting and MT images generation, respectively.
Results
qMTNet⁺ achieved quantitative results that were better than qMTNet across all metrics (peak signal-to-noise ratio, structural similarity index, normalized mean squared error) for both conventional and inter-slice MT data. Produced offset images were better quantitatively than those produced by qMTNet-acq.
Conclusion
qMTNet⁺ improves qMTNet, generating qMT parameters from undersampled MT data with higher agreement with ground truth values. Additionally, qMTNet⁺ can produce both qMT parameters and unsampled MT images with a single network in an end-to-end manner, obviating the need for separate networks required for qMTNet. qMTNet⁺ has the potential to accelerate qMT imaging for diagnostic and research purposes.

Keyword

Quantitative imaging; Magnetization transfer; Artificial neural network; Deep learning; Acceleration