Diffusion Imaging in the Post HCP Era

Dowdle

et al. 2020

Preprint

Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for investigating the human brain. However, virtually all fMRI studies have relatively poor signal-to-noise ratio (SNR). We introduce a novel fMRI denoising technique, which removes noise that is indistinguishable from zero-mean Gaussian-distributed noise. Thermal noise, falling in this category, is a major noise source in fMRI, particularly, but not exclusively, at high spatial and/or temporal resolutions. Using 7-Tesla high-resolution data, we demonstrate remarkable improvements in temporal-SNR, the detection of stimulus-induced signal changes, and functional maps, while leaving stimulus-induced signal change amplitudes, image spatial resolution, and functional point-spread-function unaltered. We also provide supplementary data demonstrating that the method is equally applicable to supra-millimeter resolution 3- and 7-Tesla fMRI data, different cortical regions, stimulation/task paradigms, and acquisition strategies. The proposed denoising approach is expected to have a transformative impact on the scope and applications of fMRI to study the brain.

Section: Methodsmentioning

confidence: 99%

Lowering the Thermal Noise Barrier in Functional Brain Mapping with Magnetic Resonance Imaging

Vizioli

Dowdle

et al. 2020

Preprint

“…For the first step, we utilize slice-GRAPPA reconstruction for the slice accelerated dataset, obtained with the simultaneous multislice (SMS)/Multiband (MB) approach (Moeller et al, 2020). A single kernel is constructed for SMS/MB with phase-encoding undersampling such that for each slice, j , and channel, ch , and the kernels are calculated similarly as in slice-GRAPPA from the measured individual slices SB i with .…”

Section: Methodsmentioning

confidence: 99%

“…For the second step, g-factors are calculated building on the approach outlined in (Breuer et al, 2009) for g-factor quantification in GRAPPA reconstructions and detailed in (Moeller et al, 2020) and the same ESPIRIT sensitivity profiles used for image reconstructions are also used for the determination of the quantitative g-factor. The g-factor is subsequently used to normalize the signal scaling in m ( r , τ), as m ( r , τ)/ g ( r ).…”

Section: Methodsmentioning

confidence: 99%

“…Magnetic Resonance Imaging (MRI) provides a collection of different approaches that currently occupy an indispensable role in the armamentarium of methods employed for studying the human brain. Diffusion-weighted MRI (dMRI) (review (Moeller et al, 2020), and references therein), is one of these critically important techniques; it is currently the only non-invasive imaging method available to map short and long-range anatomical connections in the brain and to extract information on the white matter microstructure (Alexander et al, 2019). Complementing dMRI, there exists other MRI techniques such as resting state functional magnetic resonance imaging (rfMRI) employed for inferring functional connectivity from correlations in spontaneous temporal fluctuations (Smith et al, 2013), task based fMRI (tfMRI) that depicts regional responses to specific cognitive processes and stimuli (Barch et al, 2013), and arterial spin labeling (ASL), which is a method that provides quantitative measurements of cerebral blood flow (CBF) without the use exogenous contrast agents (Alsaedi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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NOise Reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing

Pishardy

Ramanna

et al. 2020

Preprint

Self Cite

Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method.HighlightsWe propose a framework, NORDIC, for denoising complex valued dMRI data using Gaussian statisticsThe effectiveness of the proposed denoising method is distinguished by the ability to remove only signal which cannot be distinguished from thermal noiseThe proposed method outperforms a state-of-art method for denoising dMRI in terms of fiber orientation dispersionQuantitative evaluation of NORDIC across different resolutions and SNR using human connectome type acquisitions and analysis shows up to 6 fold improvement in apparent SNR for 0.9mm whole brain dMRI at 3T.

“…[24][25][26] Subsequently, GRAPPA-type techniques were utilized to disentangle the SMS-accelerated slices as k-space-based reconstructions. [27][28][29] The GRAPPA-type reconstructions are adapted in multiple large-scale neuroimaging projects like the Human Connectome Project 30 and in CMR 13 due to their superior g-factor and interslice leakage reducing performance. Among the k-space-based methods, slice-GRAPPA extends GRAPPA 5 to SMS imaging, projecting SMS-accelerated data into new k-spaces for each slice using convolutional kernels.…”

Section: Introductionmentioning

confidence: 99%

Improved simultaneous multislice cardiac MRI using readout concatenated k‐space SPIRiT (ROCK‐SPIRiT)

Demirel

Weingärtner

Magnetic Resonance in Med

et al. 2021

Self Cite

To develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI. Methods: ReadOut Concatenated k-space SPIRiT (ROCK-SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self-consistency as in SPIRiT-type reconstruction in an extended k-space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS-accelerated cine imaging with threefold SMS and twofold in-plane acceleration. ROCK-SPIRiT is compared with two leakage-blocking SMS reconstruction methods: readout-SENSE-GRAPPA and split slice-GRAPPA. Further evaluation and comparisons are performed using prospectively SMS-accelerated cine imaging. Results: Results on retrospectively threefold SMS and twofold in-plane accelerated cine imaging show that ROCK-SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout-SENSE-GRAPPA: