3 research outputs found
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Deep neural networks have enabled improved image quality and fast inference
times for various inverse problems, including accelerated magnetic resonance
imaging (MRI) reconstruction. However, such models require a large number of
fully-sampled ground truth datasets, which are difficult to curate, and are
sensitive to distribution drifts. In this work, we propose applying
physics-driven data augmentations for consistency training that leverage our
domain knowledge of the forward MRI data acquisition process and MRI physics to
achieve improved label efficiency and robustness to clinically-relevant
distribution drifts. Our approach, termed VORTEX, (1) demonstrates strong
improvements over supervised baselines with and without data augmentation in
robustness to signal-to-noise ratio change and motion corruption in
data-limited regimes; (2) considerably outperforms state-of-the-art purely
image-based data augmentation techniques and self-supervised reconstruction
methods on both in-distribution and out-of-distribution data; and (3) enables
composing heterogeneous image-based and physics-driven data augmentations. Our
code is available at https://github.com/ad12/meddlr.Comment: Accepted to MIDL 202
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Deep learning (DL) has shown promise for faster, high quality accelerated MRI
reconstruction. However, supervised DL methods depend on extensive amounts of
fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD)
shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate
this challenge, we propose Noise2Recon, a model-agnostic, consistency training
method for joint MRI reconstruction and denoising that can use both
fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised
and self-supervised settings. With limited or no labeled training data,
Noise2Recon outperforms compressed sensing and deep learning baselines,
including supervised networks, augmentation-based training, fine-tuned
denoisers, and self-supervised methods, and matches performance of supervised
models, which were trained with 14x more fully-sampled scans. Noise2Recon also
outperforms all baselines, including state-of-the-art fine-tuning and
augmentation techniques, among low-SNR scans and when generalizing to other OOD
factors, such as changes in acceleration factors and different datasets.
Augmentation extent and loss weighting hyperparameters had negligible impact on
Noise2Recon compared to supervised methods, which may indicate increased
training stability. Our code is available at https://github.com/ad12/meddlr
Coil Sketching for computationally-efficient MR iterative reconstruction
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI
datasets often have a prohibitive computational cost that bottlenecks clinical
deployment, especially for 3D non-Cartesian acquisitions. One common approach
is to reduce the number of coil channels actively used during reconstruction as
in coil compression. While effective for Cartesian imaging, coil compression
inherently loses signal energy, producing shading artifacts that compromise
image quality for 3D non-Cartesian imaging. We propose coil sketching, a
general and versatile method for computationally-efficient iterative MR image
reconstruction.
Theory and Methods: We based our method on randomized sketching algorithms, a
type of large-scale optimization algorithms well established in the fields of
machine learning and big data analysis. We adapt the sketching theory to the
MRI reconstruction problem via a structured sketching matrix that, similar to
coil compression, reduces the number of coils concurrently used during
reconstruction, but unlike coil compression, is able to leverage energy from
all coils.
Results: First, we performed ablation experiments to validate the sketching
matrix design on both Cartesian and non-Cartesian datasets. The resulting
design yielded both improved computational efficiency and preserved
signal-to-noise ratio (SNR) as measured by the inverse g-factor. Then, we
verified the efficacy of our approach on high-dimensional non-Cartesian 3D
cones datasets, where coil sketching yielded up to three-fold faster
reconstructions with equivalent image quality.
Conclusion: Coil sketching is a general and versatile reconstruction
framework for computationally fast and memory-efficient reconstruction.Comment: 16 pages, 7 figures, 2 table