1,723 research outputs found
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction
Purpose: Acquiring fully-sampled MRI -space data is time-consuming, and
collecting accelerated data can reduce the acquisition time. Employing 2D
Cartesian-rectilinear subsampling schemes is a conventional approach for
accelerated acquisitions; however, this often results in imprecise
reconstructions, even with the use of Deep Learning (DL), especially at high
acceleration factors. Non-rectilinear or non-Cartesian trajectories can be
implemented in MRI scanners as alternative subsampling options. This work
investigates the impact of the -space subsampling scheme on the quality of
reconstructed accelerated MRI measurements produced by trained DL models.
Methods: The Recurrent Variational Network (RecurrentVarNet) was used as the
DL-based MRI-reconstruction architecture. Cartesian, fully-sampled multi-coil
-space measurements from three datasets were retrospectively subsampled with
different accelerations using eight distinct subsampling schemes: four
Cartesian-rectilinear, two Cartesian non-rectilinear, and two non-Cartesian.
Experiments were conducted in two frameworks: scheme-specific, where a distinct
model was trained and evaluated for each dataset-subsampling scheme pair, and
multi-scheme, where for each dataset a single model was trained on data
randomly subsampled by any of the eight schemes and evaluated on data
subsampled by all schemes.
Results: In both frameworks, RecurrentVarNets trained and evaluated on
non-rectilinearly subsampled data demonstrated superior performance,
particularly for high accelerations. In the multi-scheme setting,
reconstruction performance on rectilinearly subsampled data improved when
compared to the scheme-specific experiments.
Conclusion: Our findings demonstrate the potential for using DL-based
methods, trained on non-rectilinearly subsampled measurements, to optimize scan
time and image quality.Comment: 24 pages, 12 figures, 5 table
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
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