🚨 Flipped b-vectors in your diffusion MRI data?
The Fiber Coherence Index (FCI) by Schilling et al. is here to help!
🔎 Detect and correct B-table orientation errors with ease.
📄 Read the paper(https://doi.org/10.1016/j.mri.2019.01.018)
🔗 Already integrated into DIPY, CLI feature coming soon!
Get involved and explore the implementation:
👉 GitHub PR(https://github.com/dipy/dipy/pull/3447)
A heartfelt thank you to everyone who made #DIPYWorkshop2025 so meaningful. To the speakers and attendees—your insights, curiosity, and energy made it truly inspiring. Grateful to be part of such a passionate, collaborative community. Until next time! 🧠📊✨
#DIPY #DiffusionMRI #AI #MachineLearning #medicalimaging #MRI #Medicine #LLM #DeepLearning #Science #Neuroscience
🚀 2 DAYS TO GO! 🚀
Join us at DIPY Workshop 2025 – a masterclass in structural & diffusion imaging! 🧠✨
📅 March 17-21, 2025
📍 Online (Zoom & Discord)
🔬 Hands-on tutorials, expert talks, and networking
💡 Register & learn more: 👉 https://workshop.dipy.org
#DIPYWorkshop #Neuroimaging #DiffusionMRI #python #dmri #opensource
🚨 Exciting news! Maxime Descoteaux (@maxdescoteaux) will overview the Handbook of Diffusion MR Tractography at DIPY Workshop 2025! 📚🔬
With 33 chapters, it’s a great companion to DIPY, covering:
🧠 Brain & pathways
🌀 Diffusion MRI: Fundamentals & techniques
🛤 Fiber tractography: Deterministic, probabilistic & geodesic
🔍 Tractography validation & applications in neurodevelopment, aging & plasticity
Don’t miss it! #DIPY2025 #DiffusionMRI #BrainConnectomics #DIPYWorkshop25 #python
🚀 DIPY 1.11 brings 3 powerful new volume extraction methods:
dipy_extract_b0: Quickly get b0 images & averages.
dipy_extract_shell: Effortlessly select b-value ranges.
dipy_extract_volume: Easily pull specific 3D volumes from your 4D data.
Explore these and more at the DIPY Workshop 2025! 🧠✨
#DIPY #Neuroimaging #DiffusionMRI #DIPYWorkshop25 #opensource #python #MedicalImaging #dMRI
🎉 Exciting news! The Dipy team is heading to #ISMRM2025! Join us for oral presentations by Jongsung Park and Bramsh Chandio, as they share cutting-edge research.
Don't miss it!
🚀 DIPY v1.11.0 – Tractography at Ultra Speed! ⚡
DIPY v1.11.0 is bringing Ultra Fast Tracking – up to 100x faster! 🔥
Supercharge your tractography workflows with unmatched speed & precision!
🌟 Speakers Gabriel Girard & John Kruper will introduce the Ultra Fast Tracking API at DIPY Workshop 2025! 🎤
🔗 Learn more & register: workshop.dipy.org
#DIPY #DiffusionMRI #Tractography #Neuroimaging #AI #opensource #programming #MRI #DMRI #python
Is it just me, or are #MRI people really bad at keeping the difference between afferent and efferent connections straight? There's a whole lot of papers that seem to just assume that if they do #DiffusionTensorImaging, and pick a seed region, that all of the streamlines they get correspond to efferents from that site. And if they cite another paper as evidence, it's often another #DiffusionMRI paper that just asserts the same thing without obvious reference to anatomical ground truth.
If you don't reference an actual #Neuroanatomy paper that worked out which direction(s) your fibers are projecting, you can't claim anything about whether your seed projects to, from, or merely passes through any other ROI along your streamlines! I think a lot of times the answer is indeed known, but cite some real anatomy papers so your readers can tell. This problem isn't universal, but it does seem weirdly common.
Scientists have found a new way to map the insular cortex, a part of the human brain that is involved in mind-body interactions. They used a technique called diffusion MRI to trace the connections between different regions of the insular cortex and other brain areas. This could help us understand how the brain integrates sensory, emotional, and cognitive information.
Making smart use of PDEs beats plain GZIP for lossless compression of #DiffusionMRI data by more than 30%.
Work by Ikram Jumakulyyev, recently published in JMIV.
OA paper: https://link.springer.com/article/10.1007/s10851-023-01144-z
Many have used bootstrapping for probabilistic tractography, but have you ever considered computing a bootstrap consensus to reduce uncertainty in #diffusionMRI? Our OA journal paper on this is now out http://doi.org/10.1111/cgf.14724 extending last year's VCBM paper on fiber tracking with model averaging.
FYI scientists esp #radiologists and those involved in #neurosciences
Clinica: An Open-Source Software Platform for Reproducible Clinical #Neuroscience Studies
https://www.frontiersin.org/articles/10.3389/fninf.2021.689675/full
Clinica is a set of automatic pipelines for processing and analysis of multimodal #neuroimaging data (T1-weighted #MRI, #DiffusionMRI, and #PET data) & tools for statistics, #MachineLearning, and #DeepLearining
It relies on the #BrainImaging data structure (BIDS)
