30,275 research outputs found
High resolution in-vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm
MR-STAT is a recently proposed framework that allows the reconstruction of
multiple quantitative parameter maps from a single short scan by performing
spatial localisation and parameter estimation on the time domain data
simultaneously, without relying on the FFT. To do this at high-resolution,
specialized algorithms are required to solve the underlying large-scale
non-linear optimisation problem. We propose a matrix-free and parallelized
inexact Gauss-Newton based reconstruction algorithm for this purpose. The
proposed algorithm is implemented on a high performance computing cluster and
is demonstrated to be able to generate high-resolution (
in-plane resolution) quantitative parameter maps in simulation, phantom and
in-vivo brain experiments. Reconstructed and values for the gel
phantoms are in agreement with results from gold standard measurements and for
the in-vivo experiments the quantitative values show good agreement with
literature values. In all experiments short pulse sequences with robust
Cartesian sampling are used for which conventional MR Fingerprinting
reconstructions are shown to fail.Comment: Accepted by NMR in Biomedicine on 2019-12-0
Fat fraction mapping using bSSFP Signal Profile Asymmetries for Robust multi-Compartment Quantification (SPARCQ)
Purpose: To develop a novel quantitative method for detection of different
tissue compartments based on bSSFP signal profile asymmetries (SPARCQ) and to
provide a validation and proof-of-concept for voxel-wise water-fat separation
and fat fraction mapping. Methods: The SPARCQ framework uses phase-cycled bSSFP
acquisitions to obtain bSSFP signal profiles. For each voxel, the profile is
decomposed into a weighted sum of simulated profiles with specific
off-resonance and relaxation time ratios. From the obtained set of weights,
voxel-wise estimations of the fractions of the different components and their
equilibrium magnetization are extracted. For the entire image volume,
component-specific quantitative maps as well as banding-artifact-free images
are generated. A SPARCQ proof-of-concept was provided for water-fat separation
and fat fraction mapping. Noise robustness was assessed using simulations. A
dedicated water-fat phantom was used to validate fat fractions estimated with
SPARCQ against gold-standard 1H MRS. Quantitative maps were obtained in knees
of six healthy volunteers, and SPARCQ repeatability was evaluated in scan
rescan experiments. Results: Simulations showed that fat fraction estimations
are accurate and robust for signal-to-noise ratios above 20. Phantom
experiments showed good agreement between SPARCQ and gold-standard (GS) fat
fractions (fF(SPARCQ) = 1.02*fF(GS) + 0.00235). In volunteers, quantitative
maps and banding-artifact-free water-fat-separated images obtained with SPARCQ
demonstrated the expected contrast between fatty and non-fatty tissues. The
coefficient of repeatability of SPARCQ fat fraction was 0.0512. Conclusion: The
SPARCQ framework was proposed as a novel quantitative mapping technique for
detecting different tissue compartments, and its potential was demonstrated for
quantitative water-fat separation.Comment: 20 pages, 7 figures, submitted to Magnetic Resonance in Medicin
Deep grey matter volumetry as a function of age using a semi-automatic qMRI algorithm
Quantitative Magnetic Resonance has become more and more accepted for clinical trial in many fields. This technique not only can generate qMRI maps (such as T1/T2/PD) but also can be used for further postprocessing including segmentation of brain and characterization of different brain tissue. Another main application of qMRI is to measure the volume of the brain tissue such as the deep Grey Matter (dGM). The deep grey matter serves as the brain's "relay station" which receives and sends inputs between the cortical brain regions. An abnormal volume of the dGM is associated with certain diseases such as Fetal Alcohol Spectrum Disorders (FASD). The goal of this study is to investigate the effect of age on the volume change of the dGM using qMRI.
Thirteen patients (mean age= 26.7 years old and age range from 0.5 to 72.5 years old) underwent imaging at a 1.5T MR scanner. Axial images of the entire brain were acquired with the mixed Turbo Spin-echo (mixed -TSE) pulse sequence. The acquired mixed-TSE images were transferred in DICOM format image for further analysis using the MathCAD 2001i software (Mathsoft, Cambridge, MA). Quantitative T1 and T2-weighted MR images were generated. The image data sets were further segmented using the dual-space clustering segmentation. Then volume of the dGM matter was calculated using a pixel counting algorithm and the spectrum of the T1/T2/PD distribution were also generated. Afterwards, the dGM volume of each patient was calculated and plotted on scatter plot. The mean volume of the dGM, standard deviation, and range were also calculated.
The result shows that volume of the dGM is 47.5 ±5.3ml (N=13) which is consistent with former studies. The polynomial tendency line generated based on scatter plot shows that the volume of the dGM gradually increases with age at early age and reaches the maximum volume around the age of 20, and then it starts to decrease gradually in adulthood and drops much faster in elderly age. This result may help scientists to understand more about the aging of the brain and it can also be used to compare with the results from former studies using different techniques
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on
dictio-nary matching to map the temporal MRF signals to quantitative tissue
parameters. Such approaches suffer from inherent discretization errors, as well
as high computational complexity as the dictionary size grows. To alleviate
these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting
approach, referred to as HYDRA.
Methods: HYDRA involves two stages: a model-based signature restoration phase
and a learning-based parameter restoration phase. Signal restoration is
implemented using low-rank based de-aliasing techniques while parameter
restoration is performed using a deep nonlocal residual convolutional neural
network. The designed network is trained on synthesized MRF data simulated with
the Bloch equations and fast imaging with steady state precession (FISP)
sequences. In test mode, it takes a temporal MRF signal as input and produces
the corresponding tissue parameters.
Results: We validated our approach on both synthetic data and anatomical data
generated from a healthy subject. The results demonstrate that, in contrast to
conventional dictionary-matching based MRF techniques, our approach
significantly improves inference speed by eliminating the time-consuming
dictionary matching operation, and alleviates discretization errors by
outputting continuous-valued parameters. We further avoid the need to store a
large dictionary, thus reducing memory requirements.
Conclusions: Our approach demonstrates advantages in terms of inference
speed, accuracy and storage requirements over competing MRF method
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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