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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
Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting.
PURPOSE: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. METHODS: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on two different phantoms and three healthy volunteer brains in vivo. RESULTS: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (<4% deviation for T1 and <6% deviation for T2). The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual qualities of fine brain structures in the multi-parametric maps generated by multi-shot EPI-MRF and Spiral-MRF implementations were comparable. CONCLUSION: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral-MRF. This multi-shot approach achieved considerable k-space subsampling and comparatively short TRs in a similar manner to spirals and therefore provides an alternative for performing MRF using an accelerated Cartesian readout; thereby increasing the potential usability of MRF.The research leading to these results has received funding from the European Commission H2020 Framework Programme (H2020- MSCAITN- 2014), number 642685 MacSeNet, the Engineering and Physical Sciences Research Council (EPSRC) platform Compressed Quantitative MRI grant, number EP/M019802/1 and the Scottish Research Partnership in Engineering (SRPe) award, number SRPe PECRE1718/ 17
Cram\'er-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI
We extend the traditional framework for estimating subspace bases that
maximize the preserved signal energy to additionally preserve the Cram\'er-Rao
bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and
precision in the quantitative maps. To this end, we introduce an
\textit{approximate compressed CRB} based on orthogonalized versions of the
signal's derivatives with respect to the model parameters. This approximation
permits singular value decomposition (SVD)-based minimization of both the CRB
and signal losses during compression. Compared to the traditional SVD approach,
the proposed method better preserves the CRB across all biophysical parameters
with negligible cost to the preserved signal energy, leading to reduced bias
and variance of the parameter estimates in simulation. In vivo, improved
accuracy and precision are observed in two quantitative neuroimaging
applications, permitting the use of smaller basis sizes in subspace
reconstruction and offering significant computational savings
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Deep learning based parallel imaging (PI) has made great progresses in recent
years to accelerate magnetic resonance imaging (MRI). Nevertheless, it still
has some limitations, such as the robustness and flexibility of existing
methods have great deficiency. In this work, we propose a method to explore the
k-space domain learning via robust generative modeling for flexible
calibration-less PI reconstruction, coined weight-k-space generative model
(WKGM). Specifically, WKGM is a generalized k-space domain model, where the
k-space weighting technology and high-dimensional space augmentation design are
efficiently incorporated for score-based generative model training, resulting
in good and robust reconstructions. In addition, WKGM is flexible and thus can
be synergistically combined with various traditional k-space PI models, which
can make full use of the correlation between multi-coil data and
realizecalibration-less PI. Even though our model was trained on only 500
images, experimental results with varying sampling patterns and acceleration
factors demonstrate that WKGM can attain state-of-the-art reconstruction
results with the well-learned k-space generative prior.Comment: 11pages, 12 figure
Complex diffusion-weighted image estimation via matrix recovery under general noise models
We propose a patch-based singular value shrinkage method for diffusion
magnetic resonance image estimation targeted at low signal to noise ratio and
accelerated acquisitions. It operates on the complex data resulting from a
sensitivity encoding reconstruction, where asymptotically optimal signal
recovery guarantees can be attained by modeling the noise propagation in the
reconstruction and subsequently simulating or calculating the limit singular
value spectrum. Simple strategies are presented to deal with phase
inconsistencies and optimize patch construction. The pertinence of our
contributions is quantitatively validated on synthetic data, an in vivo adult
example, and challenging neonatal and fetal cohorts. Our methodology is
compared with related approaches, which generally operate on magnitude-only
data and use data-based noise level estimation and singular value truncation.
Visual examples are provided to illustrate effectiveness in generating denoised
and debiased diffusion estimates with well preserved spatial and diffusion
detail.Comment: 26 pages, 9 figure
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