Towards optimal linear estimation of orientation distribution functions with arbitrarily sampled diffusion MRI data

Varadarajan, Divya; Haldar, Justin P.

doi:10.1109/isbi.2018.8363680

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…If FRT is computed, then a volumetric map of generalized fractional anisotropy (GFA) is also calculated from the FRT ODFs. BDP also provides implementations of 3D-SHORE (Özarslan et al, 2013), generalized q-space imaging (GQI) (Yeh et al, 2010), and ERFO (Varadarajan & Haldar, 2018, 2017). 3D-SHORE can estimate ODFs from single-shell and multi-shell data, while GQI can be applied to grid, single-shell, and multi-shell data.…”

Section: Methodsmentioning

confidence: 99%

“…Default: 1.25. estimateODF_ERFO: If enabled, estimates ODFs using the ERFO method. (Varadarajan & Haldar, 2018). Options: {0,1}.…”

Section: Participant-level Workflowmentioning

confidence: 99%

“…In this paper, we present the BrainSuite BIDS App, which encapsulates components of BrainSuite into the BIDS App framework. BrainSuite is a collection of open-source software tools for analyzing brain imaging data, which we have been developing for the past two decades (e.g., Shattuck et al, 2001; Shattuck & Leahy, 2002; Joshi et al, 2007, 2012; Haldar & Leahy, 2013; Bhushan et al, 2015, 2016; Li et al, 2018; Varadarajan & Haldar, 2018; Joshi et al, 2018a,b, 2022). The BrainSuite BIDS App implements a comprehensive workflow comprising three pipelines to analyze anatomical T1-weighted (T1w) MRI, diffusion MRI (dMRI), and functional MRI (fMRI) data.…”

Section: Introductionmentioning

confidence: 99%

“…The BrainSuite Diffusion Pipeline (BDP) (Varadarajan et al, 2020) performs distortion correction and registration of the diffusion MRI data to the anatomical T1w image (Bhushan et al, 2012, 2015), fits diffusion tensor models to the distortion-corrected diffusion MRI, and generate maps of diffusion parameters (e.g., fractional anisotropy, mean diffusivity). BDP also provides a selection of methods for estimating orientation distribution functions for more complicated diffusion sampling schemes (e.g., Haldar & Leahy, 2013; Varadarajan & Haldar, 2018). The BrainSuite Functional Pipeline (BFP) provides methods for analyzing resting-state and task-based fMRI data using a combination of tools from BrainSuite (Bhushan et al, 2016; Li et al, 2018), FSL (Smith et al, 2004) and AFNI (Cox, 1996).…”

Section: Introductionmentioning

confidence: 99%

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BrainSuite BIDS App: Containerized Workflows for MRI Analysis

Kim¹,

Joshi

Choi

et al. 2023

Preprint

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There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

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Section: Methodsmentioning

confidence: 99%

“…Default: 1.25. estimateODF_ERFO: If enabled, estimates ODFs using the ERFO method. (Varadarajan & Haldar, 2018). Options: {0,1}.…”

Section: Participant-level Workflowmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

Kim¹,

Joshi

Choi

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has shown advantages over other methods ( 39 ). Furthermore, ERFO can model single-shell (and multi-shell) data and has the capacity of rendering crossing fibers with the most negligible false positives ( 40 ).…”

Section: Methodsmentioning

confidence: 99%

Chronic anemia: The effects on the connectivity of white matter

Gonzalez-Zacarias

Choi

et al. 2022

Front. Neurol.

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Chronic anemia is commonly observed in patients with hemoglobinopathies, mainly represented by disorders of altered hemoglobin (Hb) structure (sickle cell disease, SCD) and impaired Hb synthesis (e.g. thalassemia syndromes, non-SCD anemia). Both hemoglobinopathies have been associated with white matter (WM) alterations. Novel structural MRI research in our laboratory demonstrated that WM volume was diffusely lower in deep, watershed areas proportional to anemia severity. Furthermore, diffusion tensor imaging analysis has provided evidence that WM microstructure is disrupted proportionally to Hb level and oxygen saturation. SCD patients have been widely studied and demonstrate lower fractional anisotropy (FA) in the corticospinal tract and cerebellum across the internal capsule and corpus callosum. In the present study, we compared 19 SCD and 15 non-SCD anemia patients with a wide range of Hb values allowing the characterization of the effects of chronic anemia in isolation of sickle Hb. We performed a tensor analysis to quantify FA changes in WM connectivity in chronic anemic patients. We calculated the volumetric mean of FA along the pathway of tracks connecting two regions of interest defined by BrainSuite's BCI-DNI atlas. In general, we found lower FA values in anemic patients; indicating the loss of coherence in the main diffusion direction that potentially indicates WM injury. We saw a positive correlation between FA and hemoglobin in these same regions, suggesting that decreased WM microstructural integrity FA is highly driven by chronic hypoxia. The only connection that did not follow this pattern was the connectivity within the left middle-inferior temporal gyrus. Interestingly, more reductions in FA were observed in non-SCD patients (mainly along with intrahemispheric WM bundles and watershed areas) than the SCD patients (mainly interhemispheric).

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Diffusion MRI with machine learning

Karimi,

Warfield

2024

Imaging Neuroscience

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Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

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Towards optimal linear estimation of orientation distribution functions with arbitrarily sampled diffusion MRI data

Cited by 3 publications

References 14 publications

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

BrainSuite BIDS App: Containerized Workflows for MRI Analysis

Chronic anemia: The effects on the connectivity of white matter

Diffusion MRI with machine learning

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