PyDesigner: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Tensor and Diffusional Kurtosis Imaging

Dhiman, Siddhartha; Teves, Joshua B.; Thorn, Kathryn; McKinnon, Emilie T.; Moss, Hunter G.; Adisetiyo, Vitria; Ades‐Aron, Benjamin; Veraart, Jelle; Chen, Jenny; Fieremans, Els; Benitez, Andreana; Helpern, Joseph A.; Jensen, Jens Højgaard

doi:10.1101/2021.10.20.465189

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…Briefly, preprocessing was conducted using an open‐source program developed by our laboratory called PyDesigner (v1.0‐RC7; https://github.com/m-ama/PyDesigner). 28 PyDesigner computes all parametric maps reported here, including AWF and D e,⊥ (see Appendix S1 for a summary of their theory and equations, published previously 13 ). Conceptually, AWF is the ratio of intra‐axonal water over all magnetic resonance‐visible intra‐ and extra‐axonal water, whereas D e,⊥ represents diffusivity perpendicular to the primary diffusion tensor eigenvector of the extra‐axonal compartment.…”

Section: Methodsmentioning

confidence: 99%

Greater Diffusion Restriction in White Matter in Preclinical Alzheimer Disease

Benitez

Jensen

Thorn

et al. 2022

Annals of Neurology

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Objective: The Alzheimer's continuum is biologically defined by beta-amyloid deposition, which at the earliest stages is superimposed upon white matter degeneration in aging. However, the extent to which these co-occurring changes is characterized is relatively underexplored. The goal of this study was to use diffusional kurtosis imaging (DKI) and biophysical modeling to detect and describe amyloid-related white matter changes in preclinical Alzheimer disease. Methods: Cognitively unimpaired participants ages 45 to 85 years completed brain magnetic resonance imaging, amyloid positron emission tomography (florbetapir), neuropsychological testing, and other clinical measures at baseline in a cohort study. We tested whether beta-amyloid-negative (ABÀ) and -positive (AB+) participants differed on DKIbased conventional (ie, fractional anisotropy [FA], mean diffusivity [MD], mean kurtosis) and modeling (ie, axonal water fraction [AWF], extra-axonal radial diffusivity [D e,⊥ ]) metrics, and whether these metrics were associated with other biomarkers. Results: We found significantly greater diffusion restriction (higher FA/AWF, lower MD/D e,⊥ ) in white matter in AB+ than ABÀ (partial η 2 =0.08-0.19), more notably in the extra-axonal space within primarily late myelinating tracts. Diffusion metrics predicted amyloid status incrementally over age (area under the curve = 0.84) with modest yet selective associations, where AWF (a marker of axonal density) correlated with speed/executive functions and neurodegeneration, whereas D e,⊥ (a marker of gliosis/myelin repair) correlated with amyloid deposition and white matter hyperintensity volume. Interpretation: These results support prior evidence of a nonmonotonic change in diffusion behavior, where an early increase in diffusion restriction is hypothesized to reflect inflammation and myelin repair prior to an ensuing decrease in diffusion restriction, indicating glial and neuronal degeneration.

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Section: Methodsmentioning

confidence: 99%

Greater Diffusion Restriction in White Matter in Preclinical Alzheimer Disease

Benitez

Jensen

Thorn

et al. 2022

Annals of Neurology

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“…The primary motivation for developing PyDesigner was to implement the key elements of DESIGNER while replacing all MATLAB code with Python, thereby allowing greater portability and accessibility. PyDesigner and DESIGNER Dv1 yield nearly identical outputs 29 . Nonetheless, there are a few additional options, default settings and minor bug fixes included in PyDesigner.…”

Section: Discussionmentioning

confidence: 99%

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging

Dhiman,

Hickey,

Thorn

et al. 2024

JoVE

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PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for noncommercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

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“…The tibial part of the sciatic nerve was manually segmented on anatomical T2‐weighted sequences and regions of interest, then transferred on the central 14 slices of DKI parameter maps (FIJI/ImageJ [29]). In total, the following 14 parameter maps were calculated from the DKI dataset using DKI PyDesigner [30]: DTI (axial diffusivity [AD], radial diffusivity [RD], mean diffusivity, fractional anisotropy [FA]), DKI (axial kurtosis, radial kurtosis [RK], mean kurtosis, mean kurtosis tensor, kurtosis fractional anisotropy), and WMTI (axonal water fraction, extra‐axonal space AD, RD, tortuosity, intra‐axonal space AD). Similarly, signal intensities within the tibial nerve were extracted on the central 10 MTI slices and MTR was calculated using the following equation, as published previously [31]:

sans-serifMTR goodbreak= \frac{{MT}_{off} - {MT}_{on}}{{sans-serifMT}_{sans-serifoff}}

where MT off and MT on are the mean nerve signal intensities with and without off‐resonance saturation pulses.…”

Section: Methodsmentioning

confidence: 99%

Microstructural changes of peripheral nerves in early multiple sclerosis: A prospective magnetic resonance neurography study

Foesleitner,

Sturm,

Hayes

et al. 2023

Euro J of Neurology

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Background and purposeMultiple sclerosis (MS) is a demyelinating disorder of the central nervous system (CNS). However, there is increasing evidence of peripheral nerve involvement. This study aims to characterize the pattern of peripheral nerve changes in patients with newly diagnosed MS using quantitative magnetic resonance (MR) neurography.MethodsIn this prospective study, 25 patients first diagnosed with MS according to the revised McDonald criteria (16 female, mean age = 32.8 ± 10.6 years) and 14 healthy controls were examined with high‐resolution 3‐T MR neurography of the sciatic nerve using diffusion kurtosis imaging (DKI; 20 diffusional directions, b = 0, 700, 1200 s/mm2) and magnetization transfer imaging (MTI). In total, 15 quantitative MR biomarkers were analyzed and correlated with clinical symptoms, intrathecal immunoglobulin synthesis, electrophysiology, and lesion load on brain and spine MR imaging.ResultsPatients showed decreased fractional anisotropy (mean = 0.51 ± 0.04 vs. 0.56 ± 0.03, p < 0.001), extra‐axonal tortuosity (mean = 2.32 ± 0.17 vs. 2.49 ± 0.17, p = 0.008), and radial kurtosis (mean = 1.40 ± 0.23 vs. 1.62 ± 0.23, p = 0.014) and higher radial diffusivity (mean = 1.09 ∙ 10−3 mm2/s ± 0.16 vs. 0.98 ± 0.11 ∙ 10−3 mm2/s, p = 0.036) than controls. Groups did not differ in MTI. No significant association was found between MR neurography markers and clinical/laboratory parameters or CNS lesion load.ConclusionsThis study provides further evidence of peripheral nerve involvement in MS already at initial diagnosis. The characteristic pattern of DKI parameters indicates predominant demyelination and suggests a primary coaffection of the peripheral nervous system in MS. This first human study using DKI for peripheral nerves shows its potential and clinical feasibility in providing novel biomarkers.

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PyDesigner: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Tensor and Diffusional Kurtosis Imaging

Cited by 10 publications

References 24 publications

Greater Diffusion Restriction in White Matter in Preclinical Alzheimer Disease

Greater Diffusion Restriction in White Matter in Preclinical Alzheimer Disease

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging

Microstructural changes of peripheral nerves in early multiple sclerosis: A prospective magnetic resonance neurography study

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