At Indiana University wanna be Ph.D. Works on image analysis/deep learning on neuroimaging. One of the biggest carbon emitting students using supercomputers.
๐ Week 23 of the #DiffusionMRIZeroToHero series!
Raw tractograms may contain 30-40% implausible streamlines.
Hence, post-processing (length filtering, ROI dissection, clustering, compression) and quality control (visual checks + quantitative metrics) are not optional, they're a part of the pipeline.
Full post: https://t.co/3HGWjCR6Vv
#DIPY #Neuroimaging #dMRI #Tractography
๐ Week 20 of our #DiffusionMRIZeroToHero series!
Welcome to the Tractography module!๐ง
We are moving from local voxel models to global brain wiring. Today we introduce the basics: how do we connect the dots using Deterministic vs. Probabilistic tracking?
Full post: https://t.co/mjfzxOkrjx
#DIPY #Neuroimaging #dMRI #Tractography
๐ Week 18 of #DiffusionMRIZeroToHero series!
Learn how CSD creates sharp fiber ODFs resolving crossings down to ~30-40ยฐ, and how Multi-Tissue CSD goes further by separating WM, GM & CSF for cleaner results.
Full post: https://t.co/NFCkC7lV6L
#DIPY#Neuroimaging#dMRI
๐ Week 16 of #DiffusionMRIZeroToHero!
Spherical Harmonics - Fourier Transform on the sphere & the math behind ODF reconstruction from HARDI data.
Full Post - https://t.co/lkBQliveAn
#DIPY#Neuroimaging#dMRI#ODF
๐ Week 15 of #DiffusionMRIZeroToHero! ๐
We explore the HARDI acquisition scheme and how it enables modeling of Orientation Distribution Functions (ODFs) to solve DTIโs crossing fiber problem.
Full Post: https://t.co/4HEQRyF2Gh
#DIPY#Neuroimaging#dMR
๐ Week 14 of #DiffusionMRIZeroToHero: Free Water Elimination DTI (FW-DTI)
Standard DTI can be ineffective when CSF and white matter share the same voxels. Learn how FW-DTI removes this fluid contamination to isolate the true tissue signal. ๐ง
Full post: https://t.co/1aMFa2iJ1U
#dMRI #DIPY #NeuroImaging
While DKI improves upon DTI, it can still struggle with noise and fiber orientations.
This week in #DiffusionMRIZeroToHero, we explore Mean Signal DKI (MS-DKI) to get stable, dispersion-free microstructure maps. ๐ง
Read More: https://t.co/IfCJIXW6HM
#DIPY#dMRI #MedicalImaging
This week in #DiffusionMRIZeroToHero we unpack Diffusion Tensor Imaging. ๐ง
See how multi-directional diffusion-weighted measurements per voxel are summarized into 3D diffusion tensors, turning raw DWI into interpretable diffusion metrics: https://t.co/QDExJVSvvh
#DIPY#DTI #Neuroimaging #MedicalImaging
Seeing ripples near sharp edges in your MRI? That's Gibbs Ringing. ๐
This week's #DiffusionMRIZeroToHero explains why this happens, how it affects dMRI metrics, and how to fix it without blurring your data.
Full Post - https://t.co/wLayxvfsGN
#dMRI#DIPY#MedicalImaging #NeuroImaging
#dMRI data is noisy, even after motion & distortion correction, obscuring subtle microstructural details.
Week 7 of #DiffusionMRIZeroToHero, we look at Denoising - the process of recovering the underlying signal without blurring.
Read: https://t.co/PizMNPzSeQ
#DIPY#Denoising
Week 6a of #DiffusionMRIZeroToHero: Brain extraction made easy with DIPY CLI ๐ง โ๏ธ
Run median_otsu for diffusion data or EVAC+ for T1 images in just one command. Simple, fast, and pipeline friendly.
Read: https://t.co/uZGy0OIdfN
#DIPY#DiffusionMRI#dMRI#NeuroImaging
This week in #DiffusionMRIZeroToHero ๐
We explain susceptibility distortion correction - the artifacts that make even stable scans look warped, and how to fix them to restore true brain geometry.
Full Post: https://t.co/BkURGcsiIH
#DIPY#dMRI#NeuroImaging#Neuroscience
Welcome to Week 4 of #DiffusionMRIZeroToHero ๐
Raw dMRI data is messy. Small head movements & scanner-induced eddy currents can distort the volumes.
We break down what these artifacts are & why you need to fix them.
Full Post: https://t.co/W2ZnL1liS0
#dMRI#Neuroscience#DIPY
In Week 3 of #DiffusionMRIZerotoHero, we examine the NIfTI format and how it consolidates sequential 3D diffusion acquisitions into a standardized 4D dataset.
Read more on: https://t.co/NLrR6Ud3qv
#dMRI#DIPY#Neuroimaging
Introducing our paper, FORCE: A forward modeling dMRI framework by @ShahAtharv57469 et al. It simulates signals to find the best match, unifying analysis in a single step. ๐ง
More on LinkedIn: https://t.co/8yEIJhoMbE
Paper: https://t.co/cwnE02m5x3
#dMRI#NeuroScience#DIPY
Our Diffusion MRI Zero to Hero series is here! ๐
What is dMRI?
How does it work?
What is its clinical significance?
๐ง Read the full post on LinkedIn: https://t.co/2VTjKnYz5D
๐ง Join us for the next edition of the DIPY Workshop!
๐ March 16โ20, 2026 | 100% Online
5 days of hands-on learning on preprocessing, reconstruction, tractography, advanced analytics, and much more.
Registrations now open โ https://t.co/WDDBrAgH0m
#DW2026#Neuroimaging#MRI
@mikolaj_pawlak@dipymri Yes it should. We are yet to test it beyond T1 T2 and diffusion images, but we are happy to try it on other images once the model is ready.