17 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
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
Long scan duration remains a challenge for high-resolution MRI. Deep learning
has emerged as a powerful means for accelerated MRI reconstruction by providing
data-driven regularizers that are directly learned from data. These data-driven
priors typically remain unchanged for future data in the testing phase once
they are learned during training. In this study, we propose to use a transfer
learning approach to fine-tune these regularizers for new subjects using a
self-supervision approach. While the proposed approach can compromise the
extremely fast reconstruction time of deep learning MRI methods, our results on
knee MRI indicate that such adaptation can substantially reduce the remaining
artifacts in reconstructed images. In addition, the proposed approach has the
potential to reduce the risks of generalization to rare pathological
conditions, which may be unavailable in the training data
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
Complexities of deep learning-based undersampled MR image reconstruction
Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.Relevance statementIncreasingly complex algorithms output reconstructed images that are difficult to assess for robustness and diagnostic quality, necessitating high-quality datasets and collaboration with radiologists.Key points• Deep learning-based image reconstruction algorithms are increasing both in complexity and performance.• The evaluation of reconstructed images may mistake perceived image quality for diagnostic value.• Collaboration with radiologists is crucial for advancing deep learning technology.</p