66 research outputs found
k-Space Deep Learning for Reference-free EPI Ghost Correction
Nyquist ghost artifacts in EPI are originated from phase mismatch between the
even and odd echoes. However, conventional correction methods using reference
scans often produce erroneous results especially in high-field MRI due to the
non-linear and time-varying local magnetic field changes. Recently, it was
shown that the problem of ghost correction can be reformulated as k-space
interpolation problem that can be solved using structured low-rank Hankel
matrix approaches. Another recent work showed that data driven Hankel matrix
decomposition can be reformulated to exhibit similar structures as deep
convolutional neural network. By synergistically combining these findings, we
propose a k-space deep learning approach that immediately corrects the phase
mismatch without a reference scan in both accelerated and non-accelerated EPI
acquisitions. To take advantage of the even and odd-phase directional
redundancy, the k-space data is divided into two channels configured with even
and odd phase encodings. The redundancies between coils are also exploited by
stacking the multi-coil k-space data into additional input channels. Then, our
k-space ghost correction network is trained to learn the interpolation kernel
to estimate the missing virtual k-space data. For the accelerated EPI data, the
same neural network is trained to directly estimate the interpolation kernels
for missing k-space data from both ghost and subsampling. Reconstruction
results using 3T and 7T in-vivo data showed that the proposed method
outperformed the image quality compared to the existing methods, and the
computing time is much faster.The proposed k-space deep learning for EPI ghost
correction is highly robust and fast, and can be combined with acceleration, so
that it can be used as a promising correction tool for high-field MRI without
changing the current acquisition protocol.Comment: To appear in Magnetic Resonance in Medicin
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We present simple reconstruction networks for multi-coil data by extending
deep cascade of CNN's and exploiting the data consistency layer. In particular,
we propose two variants, where one is inspired by POCSENSE and the other is
calibration-less. We show that the proposed approaches are competitive relative
to the state of the art both quantitatively and qualitatively.Comment: Presented at ISMRM 27th Annual Meeting & Exhibition (Abstract #4663
ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
Image reconstruction using deep learning algorithms offers improved
reconstruction quality and lower reconstruction time than classical compressed
sensing and model-based algorithms. Unfortunately, clean and fully sampled
ground-truth data to train the deep networks is often not available in several
applications, restricting the applicability of the above methods. This work
introduces the ENsemble Stein's Unbiased Risk Estimate (ENSURE) framework as a
general approach to train deep image reconstruction algorithms without fully
sampled and noise-free images. The proposed framework is the generalization of
the classical SURE and GSURE formulation to the setting where the images are
sampled by different measurement operators, chosen randomly from a set. We show
that the ENSURE loss function, which only uses the measurement data, is an
unbiased estimate for the true mean-square error. Our experiments show that the
networks trained with this loss function can offer reconstructions comparable
to the supervised setting. While we demonstrate this framework in the context
of MR image recovery, the ENSURE framework is generally applicable to arbitrary
inverse problems
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