Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: cs