15,642 research outputs found
MR image reconstruction using deep density priors
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled
measurements exploit prior information to compensate for missing k-space data.
Deep learning (DL) provides a powerful framework for extracting such
information from existing image datasets, through learning, and then using it
for reconstruction. Leveraging this, recent methods employed DL to learn
mappings from undersampled to fully sampled images using paired datasets,
including undersampled and corresponding fully sampled images, integrating
prior knowledge implicitly. In this article, we propose an alternative approach
that learns the probability distribution of fully sampled MR images using
unsupervised DL, specifically Variational Autoencoders (VAE), and use this as
an explicit prior term in reconstruction, completely decoupling the encoding
operation from the prior. The resulting reconstruction algorithm enjoys a
powerful image prior to compensate for missing k-space data without requiring
paired datasets for training nor being prone to associated sensitivities, such
as deviations in undersampling patterns used in training and test time or coil
settings. We evaluated the proposed method with T1 weighted images from a
publicly available dataset, multi-coil complex images acquired from healthy
volunteers (N=8) and images with white matter lesions. The proposed algorithm,
using the VAE prior, produced visually high quality reconstructions and
achieved low RMSE values, outperforming most of the alternative methods on the
same dataset. On multi-coil complex data, the algorithm yielded accurate
magnitude and phase reconstruction results. In the experiments on images with
white matter lesions, the method faithfully reconstructed the lesions.
Keywords: Reconstruction, MRI, prior probability, machine learning, deep
learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages
tota
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
Parallel imaging is a commonly used technique to accelerate magnetic
resonance imaging (MRI) data acquisition. Mathematically, parallel MRI
reconstruction can be formulated as an inverse problem relating the sparsely
sampled k-space measurements to the desired MRI image. Despite the success of
many existing reconstruction algorithms, it remains a challenge to reliably
reconstruct a high-quality image from highly reduced k-space measurements.
Recently, implicit neural representation has emerged as a powerful paradigm to
exploit the internal information and the physics of partially acquired data to
generate the desired object. In this study, we introduced IMJENSE, a
scan-specific implicit neural representation-based method for improving
parallel MRI reconstruction. Specifically, the underlying MRI image and coil
sensitivities were modeled as continuous functions of spatial coordinates,
parameterized by neural networks and polynomials, respectively. The weights in
the networks and coefficients in the polynomials were simultaneously learned
directly from sparsely acquired k-space measurements, without fully sampled
ground truth data for training. Benefiting from the powerful continuous
representation and joint estimation of the MRI image and coil sensitivities,
IMJENSE outperforms conventional image or k-space domain reconstruction
algorithms. With extremely limited calibration data, IMJENSE is more stable
than supervised calibrationless and calibration-based deep-learning methods.
Results show that IMJENSE robustly reconstructs the images acquired at
5 and 6 accelerations with only 4 or 8
calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and
19.5% undersampling rates. The high-quality results and scanning specificity
make the proposed method hold the potential for further accelerating the data
acquisition of parallel MRI
Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning
Deep learning (DL) has emerged as a leading approach in accelerating MR
imaging. It employs deep neural networks to extract knowledge from available
datasets and then applies the trained networks to reconstruct accurate images
from limited measurements. Unlike natural image restoration problems, MR
imaging involves physics-based imaging processes, unique data properties, and
diverse imaging tasks. This domain knowledge needs to be integrated with
data-driven approaches. Our review will introduce the significant challenges
faced by such knowledge-driven DL approaches in the context of fast MR imaging
along with several notable solutions, which include learning neural networks
and addressing different imaging application scenarios. The traits and trends
of these techniques have also been given which have shifted from supervised
learning to semi-supervised learning, and finally, to unsupervised learning
methods. In addition, MR vendors' choices of DL reconstruction have been
provided along with some discussions on open questions and future directions,
which are critical for the reliable imaging systems.Comment: 46 pages, 5figures, 1 tabl
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