7T-guided super-resolution of 3T MRI

By Khosro Bahrami, Feng Shi, Islem Rekik, Yaozong Gao, Dinggang Shen. 2017

57 citations recorded by the source corpus.

Purpose High‐resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super‐resolution 3T MRI that resembles 7T MRI, which is called as 7T‐like MR image in this paper. Methods First, we propose a mapping from 3T MRI to 7T MRI space, using regression random forest. The mapped 3T MR images serve as intermediate results with similar appearance as 7T MR images. Second, we predict the final higher resolution 7T‐like MR images based on sparse representation, using paired local dictionaries for both the mapped 3T MR images and 7T MR images. Results Based on 15 subjects with both 3T and 7T MR images, the predicted 7T‐like MR images by our method can best match the ground‐truth 7T MR images, compared to other methods. Meanwhile, the experiment on brain tissue segmentation shows that our 7T‐like MR images lead to the highest accuracy in the segmentation of WM, GM, and CSF brain tissues, compared to segmentations of 3T MR images as well as the reconstructed 7T‐like MR images by other methods. Conclusions We propose a novel method for prediction of high‐resolution 7T‐like MR images from low‐resolution 3T MR images. Our predicted 7T‐like MR images demonstrate better spatial resolution compared to 3T MR images, as well as prediction results by other comparison methods. Such high‐quality 7T‐like MR images could better facilitate disease diagnosis and intervention.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “31 Super-resolution for MRI have been around for almost 10 years [191,192] and can be used to improve the trade-off between resolution, SNR, and acquisition time [193], generate 7T-like MR images on 3T MRI scanners [194], or obtain super-resolution T1 maps from a set of low resolution T1”
Super-resolution reconstruction of MR image with a novel residual learning network algorithm (2018, methodology): “Typical SR methods include the sparse representation-based SR algorithms (Rueda et al 2013, Wang et al 2014, Lu et al 2015, Zhang et al 2015, Jia et al 2016, 2017) and the regression learning-based algorithms (Alexander et al 2014, Schulter et al 2015, Jog et al 2016, Bahrami et al 2017).”
DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping (2018, methodology): “Recently, deep neural networks have alternative to iterative methods for solving (41– 44) and have shown impressive results in applications such as denoising (45), deconvolution (46), image reconstruction (47–49) and super-resolution (50–53).”