2,455 research outputs found
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data
Deep learning (DL) has emerged as a tool for improving accelerated MRI
reconstruction. A common strategy among DL methods is the physics-based
approach, where a regularized iterative algorithm alternating between data
consistency and a regularizer is unrolled for a finite number of iterations.
This unrolled network is then trained end-to-end in a supervised manner, using
fully-sampled data as ground truth for the network output. However, in a number
of scenarios, it is difficult to obtain fully-sampled datasets, due to
physiological constraints such as organ motion or physical constraints such as
signal decay. In this work, we tackle this issue and propose a self-supervised
learning strategy that enables physics-based DL reconstruction without
fully-sampled data. Our approach is to divide the acquired sub-sampled points
for each scan into training and validation subsets. During training, data
consistency is enforced over the training subset, while the validation subset
is used to define the loss function. Results show that the proposed
self-supervised learning method successfully reconstructs images without
fully-sampled data, performing similarly to the supervised approach that is
trained with fully-sampled references. This has implications for physics-based
inverse problem approaches for other settings, where fully-sampled data is not
available or possible to acquire.Comment: 5 Pages, 5 Figure
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
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI
Purpose: To develop an improved self-supervised learning strategy that
efficiently uses the acquired data for training a physics-guided reconstruction
network without a database of fully-sampled data.
Methods: Currently self-supervised learning for physics-guided reconstruction
networks splits acquired undersampled data into two disjoint sets, where one is
used for data consistency (DC) in the unrolled network and the other to define
the training loss. The proposed multi-mask self-supervised learning via data
undersampling (SSDU) splits acquired measurements into multiple pairs of
disjoint sets for each training sample, while using one of these sets for DC
units and the other for defining loss, thereby more efficiently using the
undersampled data. Multi-mask SSDU is applied on fully-sampled 3D knee and
prospectively undersampled 3D brain MRI datasets, which are retrospectively
subsampled to acceleration rate (R)=8, and compared to CG-SENSE and single-mask
SSDU DL-MRI, as well as supervised DL-MRI when fully-sampled data is available.
Results: Results on knee MRI show that the proposed multi-mask SSDU
outperforms SSDU and performs closely with supervised DL-MRI, while
significantly outperforming CG-SENSE. A clinical reader study further ranks the
multi-mask SSDU higher than supervised DL-MRI in terms of SNR and aliasing
artifacts. Results on brain MRI show that multi-mask SSDU achieves better
reconstruction quality compared to SSDU and CG-SENSE. Reader study demonstrates
that multi-mask SSDU at R=8 significantly improves reconstruction compared to
single-mask SSDU at R=8, as well as CG-SENSE at R=2.
Conclusion: The proposed multi-mask SSDU approach enables improved training
of physics-guided neural networks without fully-sampled data, by enabling
efficient use of the undersampled data with multiple masks
JSSL: Joint Supervised and Self-supervised Learning for MRI Reconstruction
Magnetic Resonance Imaging represents an important diagnostic modality;
however, its inherently slow acquisition process poses challenges in obtaining
fully sampled k-space data under motion in clinical scenarios such as
abdominal, cardiac, and prostate imaging. In the absence of fully sampled
acquisitions, which can serve as ground truth data, training deep learning
algorithms in a supervised manner to predict the underlying ground truth image
becomes an impossible task. To address this limitation, self-supervised methods
have emerged as a viable alternative, leveraging available subsampled k-space
data to train deep learning networks for MRI reconstruction. Nevertheless,
these self-supervised approaches often fall short when compared to supervised
methodologies. In this paper, we introduce JSSL (Joint Supervised and
Self-supervised Learning), a novel training approach for deep learning-based
MRI reconstruction algorithms aimed at enhancing reconstruction quality in
scenarios where target dataset(s) containing fully sampled k-space measurements
are unavailable. Our proposed method operates by simultaneously training a
model in a self-supervised learning setting, using subsampled data from the
target dataset(s), and in a supervised learning manner, utilizing data from
other datasets, referred to as proxy datasets, where fully sampled k-space data
is accessible. To demonstrate the efficacy of JSSL, we utilized subsampled
prostate parallel MRI measurements as the target dataset, while employing fully
sampled brain and knee k-space acquisitions as proxy datasets. Our results
showcase a substantial improvement over conventional self-supervised training
methods, thereby underscoring the effectiveness of our joint approach. We
provide a theoretical motivation for JSSL and establish a practical
"rule-of-thumb" for selecting the most appropriate training approach for deep
MRI reconstruction.Comment: 26 pages, 11 figures, 6 table
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast,
albeit it is an inherently slow imaging modality. Promising deep learning
methods have recently been proposed to reconstruct accelerated MRI scans.
However, existing methods still suffer from various limitations regarding image
fidelity, contextual sensitivity, and reliance on fully-sampled acquisitions
for model training. To comprehensively address these limitations, we propose a
novel self-supervised deep reconstruction model, named Self-Supervised
Diffusion Reconstruction (SSDiffRecon). SSDiffRecon expresses a conditional
diffusion process as an unrolled architecture that interleaves cross-attention
transformers for reverse diffusion steps with data-consistency blocks for
physics-driven processing. Unlike recent diffusion methods for MRI
reconstruction, a self-supervision strategy is adopted to train SSDiffRecon
using only undersampled k-space data. Comprehensive experiments on public brain
MR datasets demonstrates the superiority of SSDiffRecon against
state-of-the-art supervised, and self-supervised baselines in terms of
reconstruction speed and quality. Implementation will be available at
https://github.com/yilmazkorkmaz1/SSDiffRecon
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