๐จ Flipped b-vectors in your diffusion MRI data?
The Fiber Coherence Index (FCI) by Schilling et al. is here to help!
๐ Detect and correct B-table orientation errors with ease.
๐ Read the paper(https://doi.org/10.1016/j.mri.2019.01.018)
๐ Already integrated into DIPY, CLI feature coming soon!
Get involved and explore the implementation:
๐ GitHub PR(https://github.com/dipy/dipy/pull/3447)
A heartfelt thank you to everyone who made #DIPYWorkshop2025 so meaningful. To the speakers and attendeesโyour insights, curiosity, and energy made it truly inspiring. Grateful to be part of such a passionate, collaborative community. Until next time! ๐ง ๐โจ
#DIPY #DiffusionMRI #AI #MachineLearning #medicalimaging #MRI #Medicine #LLM #DeepLearning #Science #Neuroscience
๐จ Exciting news! Maxime Descoteaux (@maxdescoteaux) will overview the Handbook of Diffusion MR Tractography at DIPY Workshop 2025! ๐๐ฌ
With 33 chapters, itโs a great companion to DIPY, covering:
๐ง Brain & pathways
๐ Diffusion MRI: Fundamentals & techniques
๐ค Fiber tractography: Deterministic, probabilistic & geodesic
๐ Tractography validation & applications in neurodevelopment, aging & plasticity
Donโt miss it! #DIPY2025 #DiffusionMRI #BrainConnectomics #DIPYWorkshop25 #python
๐ 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! ๐ง โจ
#DIPY #Neuroimaging #DiffusionMRI #DIPYWorkshop25 #opensource #python #MedicalImaging #dMRI
๐ 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!
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.
The relationship between the human mind and body has been a subject that has challenged great thinkers for millennia, including the philosophers Aristotle and Descartes. The answer, however, appears to reside in the very structure of the brain.
Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it generates huge amounts of data, resulting from a large number of repeated 3D scans. Each volume samples a location in q-space, indicating the direction and strength of a diffusion sensitizing gradient during the measurement. This captures detailed information about the self-diffusion and the tissue microstructure that restricts it. Lossless compression with GZIP is widely used to reduce the memory requirements. We introduce a novel lossless codec for diffusion MRI data. It reduces file sizes by more than 30% compared to GZIP and also beats lossless codecs from the JPEG family. Our codec builds on recent work on lossless PDE-based compression of 3D medical images, but additionally exploits smoothness in q-space. We demonstrate that, compared to using only image space PDEs, q-space PDEs further improve compression rates. Moreover, implementing them with finite element methods and a custom acceleration significantly reduces computational expense. Finally, we show that our codec clearly benefits from integrating subject motion correction and slightly from optimizing the order in which the 3D volumes are coded.
Diffusion Imaging in Python
Geoffrey Adams
Hari Tulsidas
Thomas Schultz