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Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI.

ABSTRACT: PURPOSE:To evaluate the potentials of compressed sensing (CS) in MRI quantification of glycosaminoglycan (GAG) concentration in articular cartilage at microscopic resolution. METHODS:T1 -weighted 2D experiments of cartilage were fully sampled in k-space with five inversion times at 17.6??m resolution. These fully sampled k-space data were re-processed, by undersampling at various 1D and 2D CS undersampling factors (UFs). The undersampled data were reconstructed individually into 2D images using nonlinear reconstruction, which were used to calculate 2D maps of T1 and GAG concentration. The values of T1 and GAG in cartilage were evaluated at different UFs (up to 16, which used 6.25% of the data). K-space sampling pattern and zonal variations were also investigated. RESULTS:Using 2D variable density sampling pattern, the T1 images at UFs up to eight preserved major visual information and produced negligible artifacts. The GAG concentration remained accurate for different sub-tissue zones at various UFs. The variation of the mean GAG concentration through the whole tissue depth was 1.20%, compared to the fully sampled results. The maximum variation was 2.24% in the deep zone of cartilage. Using 1D variable density sampling pattern, the quantitative T1 mapping and GAG concentration at UFs up to 4 showed negligible variations. CONCLUSION:This study demonstrates that CS could be beneficial in microscopic MRI (µMRI) studies of cartilage by acquiring less data, without losing significant accuracy in the quantification of GAG concentration. Magn Reson Med 79:3163-3171, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

SUBMITTER: Wang N

PROVIDER: S-EPMC5843514 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI.

Wang Nian N Badar Farid F Xia Yang Y

Magnetic resonance in medicine 20171030 6

<h4>Purpose</h4>To evaluate the potentials of compressed sensing (CS) in MRI quantification of glycosaminoglycan (GAG) concentration in articular cartilage at microscopic resolution.<h4>Methods</h4>T<sub>1</sub> -weighted 2D experiments of cartilage were fully sampled in k-space with five inversion times at 17.6 μm resolution. These fully sampled k-space data were re-processed, by undersampling at various 1D and 2D CS undersampling factors (UFs). The undersampled data were reconstructed individu ...[more]

PMID: 29083096

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Project description:BACKGROUND:One of the fundamental problems of modern genomics is to extract the genetic architecture of a complex trait from a data set of individual genotypes and trait values. Establishing this important connection between genotype and phenotype is complicated by the large number of candidate genes, the potentially large number of causal loci, and the likely presence of some nonlinear interactions between different genes. Compressed Sensing methods obtain solutions to under-constrained systems of linear equations. These methods can be applied to the problem of determining the best model relating genotype to phenotype, and generally deliver better performance than simply regressing the phenotype against each genetic variant, one at a time. We introduce a Compressed Sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. RESULTS:The computational and data resource requirements for our method are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data. A phase transition (i.e., dramatic and qualitative change) in the behavior of the algorithm indicates when sufficient data is available for its successful application. CONCLUSION:Our results indicate that predictive models for many complex traits, including a variety of human disease susceptibilities (e.g., with additive heritability h (2)?0.5), can be extracted from data sets comprised of n ??100s individuals, where s is the number of distinct causal variants influencing the trait. For example, given a trait controlled by ?10 k loci, roughly a million individuals would be sufficient for application of the method.

Dataset Information

Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI.

Publications

Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI.

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