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APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Network

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application across different contrasts, anatomies, or image sizes. To address this limitation, we propose an unsupervised, auto-calibrated k-space completion method, based on a uniquely designed neural network that reconstructs the full k-space from an undersampled k-space, exploiting the redundancy among the multiple channels in the receive coil in a parallel imaging acquisition. To achieve this, contrary to common convolutional network approaches, the proposed network has a decreasing number of feature maps of constant size. In contrast to conventional parallel imaging methods such as GRAPPA that estimate the prediction kernel from the fully sampled autocalibration signals in a linear way, our method is able to learn nonlinear relations between sampled and unsampled positions in k-space. The proposed method was compared to the start-of-the-art ESPIRiT and RAKI methods in terms of noise amplification and visual image quality in both phantom and in-vivo experiments. The experiments indicate that APIR-Net provides a promising alternative to the conventional parallel imaging methods, and results in improved image quality especially for low SNR acquisitions.

OriginalsprogEngelsk
TitelMachine Learning for Medical Image Reconstruction : Second International Workshop, MLMIR 2019 Held in Conjunction with MICCAI 2019 Shenzhen, China, October 17, 2019, Proceedings
RedaktørerFlorian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye
Antal sider11
ForlagSpringer VS
Publikationsdato1 jan. 2019
Sider36-46
ISBN (Trykt)9783030338428
DOI
StatusUdgivet - 1 jan. 2019
Begivenhed2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, Kina
Varighed: 17 okt. 201917 okt. 2019

Konference

Konference2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
LandKina
ByShenzhen
Periode17/10/201917/10/2019
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind11905 LNCS
ISSN0302-9743

ID: 233538398