Dr. Ali Pour Yazdanpanah Profile

Dr. Ali Pour Yazdanpanah

Post Doctoral Research Fellow

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Proceedings Article | 15 March 2019 Presentation + Paper

Non-learning based deep parallel MRI reconstruction (NLDpMRI)

Ali Pour Yazdanpanah, Onur Afacan, Simon Warfield

Proceedings Volume 10949, 1094904 (2019) https://doi.org/10.1117/12.2511653

KEYWORDS: Magnetic resonance imaging, Neural networks, Image quality, Data acquisition, Image restoration, Reconstruction algorithms

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Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. Recently, the deep learning-based MRI reconstruction techniques were suggested to accelerate MR image acquisition. The most common issues in any deep learning-based MRI reconstruction approaches are generalizability and transferability. For different MRI scanner configurations using these approaches, the network must be trained from scratch every time with new training dataset, acquired under new configurations, to be able to provide good reconstruction performance. Here, we propose a new generalized parallel imaging method based on deep neural networks called NLDpMRI to reduce any structured aliasing ambiguities related to the different k-space undersampling patterns for accelerated data acquisition. Two loss functions including non-regularized and regularized are proposed for parallel MRI reconstruction using deep network optimization and we reconstruct MR images by optimizing the proposed loss functions over the network parameters. Unlike any deep learning-based MRI reconstruction approaches, our method doesn’t include any training step that the network learns from a large number of training samples and it only needs the single undersampled multi-coil k-space data for reconstruction. Also, the proposed method can handle k-space data with different undersampling patterns, and the different number of coils. Experimental results show that the proposed method outperforms the current state-of-the-art GRAPPA method and the deep learning-based variational network method.

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