63 research outputs found
Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
In this paper, we introduce a dictionary learning based approach applied to
the problem of real-time reconstruction of MR image sequences that are highly
undersampled in k-space. Unlike traditional dictionary learning, our method
integrates both global and patch-wise (local) sparsity information and
incorporates some priori information into the reconstruction process. Moreover,
we use a Dependent Hierarchical Beta-process as the prior for the group-based
dictionary learning, which adaptively infers the dictionary size and the
sparsity of each patch; and also ensures that similar patches are manifested in
terms of similar dictionary atoms. An efficient numerical algorithm based on
the alternating direction method of multipliers (ADMM) is also presented.
Through extensive experimental results we show that our proposed method
achieves superior reconstruction quality, compared to the other state-of-the-
art DL-based methods
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Magnetic resonance multitasking for motion-resolved quantitative cardiovascular imaging.
Quantitative cardiovascular magnetic resonance (CMR) imaging can be used to characterize fibrosis, oedema, ischaemia, inflammation and other disease conditions. However, the need to reduce artefacts arising from body motion through a combination of electrocardiography (ECG) control, respiration control, and contrast-weighting selection makes CMR exams lengthy. Here, we show that physiological motions and other dynamic processes can be conceptualized as multiple time dimensions that can be resolved via low-rank tensor imaging, allowing for motion-resolved quantitative imaging with up to four time dimensions. This continuous-acquisition approach, which we name cardiovascular MR multitasking, captures - rather than avoids - motion, relaxation and other dynamics to efficiently perform quantitative CMR without the use of ECG triggering or breath holds. We demonstrate that CMR multitasking allows for T1 mapping, T1-T2 mapping and time-resolved T1 mapping of myocardial perfusion without ECG information and/or in free-breathing conditions. CMR multitasking may provide a foundation for the development of setup-free CMR imaging for the quantitative evaluation of cardiovascular health
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