pcamach2

7T-mri-pipeline-hpc

Readthedocs compatible

7T_DWI_denoising

Testing compute performance and comparing denoising methods for 7 Tesla Diffusion Weighted Imaging

BrainSpace

BrainSpace is an open-access toolbox that allows for the identification and analysis of gradients from neuroimaging and connectomics datasets | available in both Python and Matlab |

containers_demo

Demo for containerizing applications with Docker and Singularity

dipy

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

FastSurfer

PyTorch implementation of FastSurferCNN

fmriprep

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

fs-data-utils

Shell and python utilities for gathering Freesurfer stats into csv format

lesion-mapper

Automated mapping of T2 white matter hyperintensities using FSL

qsm_method_comparison_report

BIDS-compatible quantitative susceptibility mapping report generation to compare SWI sequences

pcamach2/qsm_method_comparison_report

BIDS-compatible quantitative susceptibility mapping report generation to compare SWI sequences

pcamach2/7T-mri-pipeline-hpc

Readthedocs compatible

pcamach2/7T_DWI_denoising

Testing compute performance and comparing denoising methods for 7 Tesla Diffusion Weighted Imaging

pcamach2/BrainSpace

BrainSpace is an open-access toolbox that allows for the identification and analysis of gradients from neuroimaging and connectomics datasets | available in both Python and Matlab |

pcamach2/containers_demo

Demo for containerizing applications with Docker and Singularity

pcamach2/dipy

DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.

pcamach2/FastSurfer

PyTorch implementation of FastSurferCNN

pcamach2/fmriprep

fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.

pcamach2/fs-data-utils

Shell and python utilities for gathering Freesurfer stats into csv format

pcamach2/lesion-mapper

Automated mapping of T2 white matter hyperintensities using FSL

pcamach2/mappertrac

Massively Parallel, Portable, and Reproducible Tractography (MaPPeRTrac). A probabilistic diffusion MRI tractography pipeline for high performance computing. Generates edge-centric connectomes and edge density images. Highly extensible.

pcamach2/mrfil.github.io

GitHub Pages for MRFIL Lab

pcamach2/nipype

Workflows and interfaces for neuroimaging packages

pcamach2/micaopen

Open Scripts and pipelines from the Multimodal Imaging and Connectome Analysis Lab at the Montreal Neurological Institute

pcamach2/pepolar-fmap-intendedfor-populate

pcamach2/persim-rips-singularity

Example code for generating Vietoris-Rips complex persistent homology plots from resting-state functional connectivity NxN Pearson correlation matrices

pcamach2/pydeface

defacing utility for MRI images

pcamach2/qsiprep

Preprocessing and reconstruction of diffusion MRI

pcamach2/qsiprep_build

Builds the dependencies for qsiprep

pcamach2/rsp

modified Duke BIAC resting-state fc pipeline with SUIT parcellations added

pcamach2/SF_predict

pcamach2/slurm-utils

Convenient bash utilities for slurm

pcamach2/smriprep

Structural MRI PREProcessing (sMRIPrep) workflows for NIPreps (NeuroImaging PREProcessing tools)