46,538 research outputs found
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
Purpose: To introduce a novel deep learning based approach for fast and
high-quality dynamic multi-coil MR reconstruction by learning a complementary
time-frequency domain network that exploits spatio-temporal correlations
simultaneously from complementary domains.
Theory and Methods: Dynamic parallel MR image reconstruction is formulated as
a multi-variable minimisation problem, where the data is regularised in
combined temporal Fourier and spatial (x-f) domain as well as in
spatio-temporal image (x-t) domain. An iterative algorithm based on variable
splitting technique is derived, which alternates among signal de-aliasing steps
in x-f and x-t spaces, a closed-form point-wise data consistency step and a
weighted coupling step. The iterative model is embedded into a deep recurrent
neural network which learns to recover the image via exploiting spatio-temporal
redundancies in complementary domains.
Results: Experiments were performed on two datasets of highly undersampled
multi-coil short-axis cardiac cine MRI scans. Results demonstrate that our
proposed method outperforms the current state-of-the-art approaches both
quantitatively and qualitatively. The proposed model can also generalise well
to data acquired from a different scanner and data with pathologies that were
not seen in the training set.
Conclusion: The work shows the benefit of reconstructing dynamic parallel MRI
in complementary time-frequency domains with deep neural networks. The method
can effectively and robustly reconstruct high-quality images from highly
undersampled dynamic multi-coil data ( and yielding 15s
and 10s scan times respectively) with fast reconstruction speed (2.8s). This
could potentially facilitate achieving fast single-breath-hold clinical 2D
cardiac cine imaging.Comment: Accepted by Magnetic Resonance in Medicin
Training Data Generation Framework For Machine-Learning Based Classifiers
In this thesis, we propose a new framework for the generation of training data for machine learning techniques used for classification in communications applications. Machine learning-based signal classifiers do not generalize well when training data does not describe the underlying probability distribution of real signals. The simplest way to accomplish statistical similarity between training and testing data is to synthesize training data passed through a permutation of plausible forms of noise. To accomplish this, a framework is proposed that implements arbitrary channel conditions and baseband signals. A dataset generated using the framework is considered, and is shown to be appropriately sized by having lower entropy than state-of-the-art datasets. Furthermore, unsupervised domain adaptation can allow for powerful generalized training via deep feature transforms on unlabeled evaluation-time signals. A novel Deep Reconstruction-Classification Network (DRCN) application is introduced, which attempts to maintain near-peak signal classification accuracy despite dataset bias, or perturbations on testing data unforeseen in training. Together, feature transforms and diverse training data generated from the proposed framework, teaching a range of plausible noise, can train a deep neural net to classify signals well in many real-world scenarios despite unforeseen perturbations
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