opennog

mrtrix3

MRtrix3 provides a set of tools to perform various advanced diffusion MRI analyses, including constrained spherical deconvolution (CSD), probabilistic tractography, track-density imaging, and apparent fibre density

matlab-toolboxes

Contains all my Matlab toolboxes.

ODGD

Diffusion-Weighted MRI often suffers from signal attenuation due to long TE, sensitivity to physiological motion, and dephasing due to concomitant gradients (CGs). These challenges complicate image interpretation and may introduce bias in quantitative diffusion measurements. Motion moment-nulled diffusion-weighting gradients have been proposed to compensate motion, however, they frequently result in high TE and suffer from CG effects. In this work [1], we present a novel Optimed Diffusion-weighting Gradient waveform Design (ODGD) method for diffusion-weighting gradient waveform design for any diffusion-weighting direction that seeks to overcome the limitations of previous methods. The proposed ODGD method consists of: 1) a constrained optimization formulation that minimizes the TE for a given b-value subject to both, moment-nulling and/or CG-nulling constraints, and 2) a quadratic optimization algorithm that directly solves the formulation without introducing approximations.

PD-Longitudinal-Connectome

Identify and quantify a longitudinal degenerative Parkinson's disease pattern from the diffusion magnetic resonance imaging connectivity information.

PulseqDiffusion

pypulseq

Pulseq in Python

pydicom

Read, modify and write DICOM files with python code

dcmqi

dcmqi (DICOM for Quantitative Imaging) is a free, open source C++ library for conversion between imaging research formats and the standard DICOM representation for image analysis results

dcm2niix

dcm2nii DICOM to NIfTI converter: compiled versions available from NITRC

scikit-learn

scikit-learn: machine learning in Python

opennog/ODGD

Diffusion-Weighted MRI often suffers from signal attenuation due to long TE, sensitivity to physiological motion, and dephasing due to concomitant gradients (CGs). These challenges complicate image interpretation and may introduce bias in quantitative diffusion measurements. Motion moment-nulled diffusion-weighting gradients have been proposed to compensate motion, however, they frequently result in high TE and suffer from CG effects. In this work [1], we present a novel Optimed Diffusion-weighting Gradient waveform Design (ODGD) method for diffusion-weighting gradient waveform design for any diffusion-weighting direction that seeks to overcome the limitations of previous methods. The proposed ODGD method consists of: 1) a constrained optimization formulation that minimizes the TE for a given b-value subject to both, moment-nulling and/or CG-nulling constraints, and 2) a quadratic optimization algorithm that directly solves the formulation without introducing approximations.

opennog/matlab-toolboxes

Contains all my Matlab toolboxes.

opennog/PD-Longitudinal-Connectome

Identify and quantify a longitudinal degenerative Parkinson's disease pattern from the diffusion magnetic resonance imaging connectivity information.

opennog/PulseqDiffusion

opennog/pypulseq

Pulseq in Python