71 research outputs found
Autocalibrating and Calibrationless Parallel Magnetic Resonance Imaging as a Bilinear Inverse Problem
Modern reconstruction methods for magnetic resonance imaging (MRI) exploit
the spatially varying sensitivity profiles of receive-coil arrays as additional
source of information. This allows to reduce the number of time-consuming
Fourier-encoding steps by undersampling. The receive sensitivities are a priori
unknown and influenced by geometry and electric properties of the (moving)
subject. For optimal results, they need to be estimated jointly with the image
from the same undersampled measurement data. Formulated as an inverse problem,
this leads to a bilinear reconstruction problem related to multi-channel blind
deconvolution. In this work, we will discuss some recently developed approaches
for the solution of this problem.Comment: 3 pages, 3 figures, 12th International Conference on Sampling Theory
and Applications, Tallinn 201
Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
Purpose: To develop a truly calibrationless reconstruction method that
derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep
learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction
technique, forms the images from undersampled MR k-space data using ESPIRiT
maps that effectively represents coil sensitivity information. Accurate ESPIRiT
map estimation requires quality coil sensitivity calibration or autocalibration
data. We present a U-Net based deep learning model to estimate the
multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel
multi-slice MR data. The model is trained using fully-sampled multi-slice axial
brain datasets from the same MR receiving coil system. To utilize subject-coil
geometric parameters available for each dataset, the training imposes a hybrid
loss on ESPIRiT maps at the original locations as well as their corresponding
locations within the standard reference multi-slice axial stack. The
performance of the approach was evaluated using publicly available T1-weighed
brain and cardiac data. Results: The proposed model robustly predicted
multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were
highly comparable to the reference ESPIRiT maps directly computed from 24
consecutive central k-space lines. Further, they led to excellent ESPIRiT
reconstruction performance even at high acceleration, exhibiting a similar
level of errors and artifacts to that by using reference ESPIRiT maps.
Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps
directly from uniformly-undersampled MR data. It presents a general strategy
for calibrationless parallel imaging reconstruction through learning from coil
and protocol specific data
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