Noise estimation in parallel MRI: GRAPPA and SENSE

Aja‐Fernández, Santiago; Vegas-Sánchez-Ferrero, Gonzalo; Tristán‐Vega, Antonio

doi:10.1016/j.mri.2013.12.001

Cited by 104 publications

(76 citation statements)

References 27 publications

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“…4-(b). This pattern follows the shape of some real patterns found in SENSE acquisitions (Aja-Fernández et al (2014); Aja-Fernández and Vegas-SanchezFerrero (2016)). …”

Section: Methodssupporting

confidence: 80%

“…The final value of the variance of noise at each point will depend on the covariance matrix between coils of the original data (prior to reconstruction) and on the sensitivity map of each coil, but not on the data themselves. This model has been observed for SENSE by different authors through experimental and theoretical studies (see for instance the studies in Pruessmann et al (1999); Thunberg and Zetterberg (2007); Robson et al (2008); Aja-Fernández et al (2014)). …”

Section: The Non-stationary Rician Noise Model In Mrimentioning

confidence: 58%

“…3-(a). An 8-coil system is simulated using artificial sensitivities for each coil, so that the image in the l-th coil can be seen as Aja-Fernández et al (2014) …”

Section: Experiments With Synthetic Datamentioning

confidence: 99%

“…Instead of assuming a single σ 2 value for each pixel within the image, the variance of noise varies with x, i.e. σ 2 (x) (Aja-Fernández et al (2014); Aja-Fernández et al (2015)). If SENSE is considered, the reconstruction process yields to the magnitude value of a complex Gaussian, and therefore, the final magnitude signal can still be considered Rician distributed, but with a different σ 2 (x) for each x (Dietrich et al (2008); Aja-Fernández et al (2014); Aja-Fernández et al (2015)).…”

Section: Introductionmentioning

confidence: 99%

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Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach

González-Jaime

Vegas-Sánchez-Ferrero

Kerre

et al. 2016

Knowledge-Based Systems

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In order to accelerate the acquisition process in multiple-coil Magnetic Resonance scanners, parallel techniques were developed. These techniques reduce the acquisition time via a subsampling of the k-space and a reconstruction process. From a signal and noise perspective, the use of a acceleration techniques modify the structure of the noise within the image. In the most common algorithms, like SENSE, the final magnitude image after the reconstruction is known to follow a Rician distribution for each pixel, just like single coil systems. However, the noise is spatially non-stationary, i.e. the variance of noise becomes x-dependent. This effect can also be found in magnitude images due to other processing inside the scanner. In this work we propose a method to adapt well-known noise filtering techniques initially designed to deal with stationary noise to the case of spatially variant Rician noise. The method copes with inaccurate estimates of variant noise patterns in the image, showing its robustness in realistic cases. The method employs a consensus strategy in conjunction with a set of aggregation functions and a penalty function. Multiple possible outputs are generated for each pixel assuming different unknown input parameters. The consensus approach merges them into a unique filtered image. As a filtering technique, we have selected the Linear Minimum Mean Square Error (LMMSE) estimator for Rician data, which has been used to test our methodology due to its simplicity and robustness. Results with synthetic and in vivo data confirm the good behavior of our approach.

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“…4-(b). This pattern follows the shape of some real patterns found in SENSE acquisitions (Aja-Fernández et al (2014); Aja-Fernández and Vegas-SanchezFerrero (2016)). …”

Section: Methodssupporting

confidence: 80%

Section: The Non-stationary Rician Noise Model In Mrimentioning

confidence: 58%

“…3-(a). An 8-coil system is simulated using artificial sensitivities for each coil, so that the image in the l-th coil can be seen as Aja-Fernández et al (2014) …”

Section: Experiments With Synthetic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach

González-Jaime

Vegas-Sánchez-Ferrero

Kerre

et al. 2016

Knowledge-Based Systems

Self Cite

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“…Finally, while the data energy of our global tractography method assumes Gaussian noise, the noise model in real data is Rice or noncentral χ distributed, depending on the acquisition, and may bias the reconstruction (Gudbjartsson and Patz, 1995;Aja-Fernández et al, 2014). However, by estimating the tissue response functions from the data under the same assumption, the kernels incorporate this noise bias as well, and represent not the actual tissue response, but 345 rather expectations of the actual response under non-Gaussian noise.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

et al. 2015

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Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.

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Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Maximov

Alnæs

Westlye

2019

Human Brain Mapping

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Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state‐of‐the‐art framework for data processing facilitates cross‐study harmonisation and reduces pipeline‐related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy‐current and motion‐induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.

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Noise estimation in parallel MRI: GRAPPA and SENSE

Cited by 104 publications

References 27 publications

Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach

Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

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