61,175 research outputs found
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
Complex-valued Time Series Modeling for Improved Activation Detection in fMRI Studies
A complex-valued data-based model with th order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model. Such improved accuracy in mapping the left functional central sulcus has important implications in neurosurgical planning for tumor and epilepsy patients. Additionally, we develop magnitude and phase detrending procedures for complex-valued time series and examine the effect of spatial smoothing. These methods improve the power of complex-valued data-based activation statistics. Our results advocate for the use of the complex-valued data and the modeling of its dependence structures as a more efficient and reliable tool in fMRI experiments over the current practice of using only magnitude-valued datasets
Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI
Purpose: This prospective clinical study assesses the feasibility of training
a deep neural network (DNN) for intravoxel incoherent motion (IVIM) model
fitting to diffusion-weighted magnetic resonance imaging (DW-MRI) data and
evaluates its performance. Methods: In May 2011, ten male volunteers (age
range: 29 to 53 years, mean: 37 years) underwent DW-MRI of the upper abdomen on
1.5T and 3.0T magnetic resonance scanners. Regions of interest in the left and
right liver lobe, pancreas, spleen, renal cortex, and renal medulla were
delineated independently by two readers. DNNs were trained for IVIM model
fitting using these data; results were compared to least-squares and Bayesian
approaches to IVIM fitting. Intraclass Correlation Coefficients (ICC) were used
to assess consistency of measurements between readers. Intersubject variability
was evaluated using Coefficients of Variation (CV). The fitting error was
calculated based on simulated data and the average fitting time of each method
was recorded. Results: DNNs were trained successfully for IVIM parameter
estimation. This approach was associated with high consistency between the two
readers (ICCs between 50 and 97%), low intersubject variability of estimated
parameter values (CVs between 9.2 and 28.4), and the lowest error when compared
with least-squares and Bayesian approaches. Fitting by DNNs was several orders
of magnitude quicker than the other methods but the networks may need to be
re-trained for different acquisition protocols or imaged anatomical regions.
Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to
DW-MRI data. Suitable software is available at (1)
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