Dataset Information

Accelerated three-dimensional multispectral MRI with robust principal component analysis for separation of on- and off-resonance signals.

ABSTRACT: PURPOSE:To enable highly accelerated distortion-free MRI near metal by separating on- and off-resonance to exploit the redundancy of slice-phase encoding for the dominant on-resonance component. METHODS:Multispectral MRI techniques resolve off-resonance distortions by a combination of limited excitation bins and additional encoding. Inspired by robust principal component analysis, a novel compact representation of multispectral images as a sum of rank-one and sparse matrices corresponding to on- and off-resonance respectively is described. This representation is used in a calibration-free and model-free reconstruction for data with an undersampling pattern that varies between bins. Retrospective undersampling was used to compare the proposed reconstruction and bin-by-bin compressed sensing. Hip images were acquired in eight patients with standard and prospectively undersampled three-dimensional multispectral imaging, and image quality was evaluated by two radiologists on a 5-point scale. RESULTS:Experiments with retrospective undersampling showed that the enhanced sparsity afforded by the separation greatly reduces reconstruction errors and artifacts. Images from prospectively undersampled multispectral imaging offered 2.6-3.4-fold (18-24-fold overall) acceleration compared to standard multispectral imaging with parallel imaging and partial-Fourier acceleration with equivalence in all qualitative assessments within a tolerance of one point (P?

SUBMITTER: Levine E

PROVIDER: S-EPMC5756705 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Accelerated three-dimensional multispectral MRI with robust principal component analysis for separation of on- and off-resonance signals.

Levine Evan E Stevens Kathryn K Beaulieu Christopher C Hargreaves Brian B

Magnetic resonance in medicine 20170707 3

<h4>Purpose</h4>To enable highly accelerated distortion-free MRI near metal by separating on- and off-resonance to exploit the redundancy of slice-phase encoding for the dominant on-resonance component.<h4>Methods</h4>Multispectral MRI techniques resolve off-resonance distortions by a combination of limited excitation bins and additional encoding. Inspired by robust principal component analysis, a novel compact representation of multispectral images as a sum of rank-one and sparse matrices corre ...[more]

PMID: 28686800

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Project description:BACKGROUND:High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis. RESULTS:We report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes. CONCLUSIONS:rPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.

Dataset Information

Accelerated three-dimensional multispectral MRI with robust principal component analysis for separation of on- and off-resonance signals.

Publications

Accelerated three-dimensional multispectral MRI with robust principal component analysis for separation of on- and off-resonance signals.

Similar Datasets

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