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From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement

T0 review · 2 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read FastFOD-Net brings research-grade fiber orientation analysis to clinical diffusion MRI, validated across healthy controls and six neurological disorders.

desk verdict Clinical breadth is the real contribution here, but the abstract gives no numbers to back the claims—worth refereeing if the full text is actually readable. read the letter →

arxiv 2508.10950 v2 pith:7YX7U6UN submitted 2025-08-13 cs.CV

Xinyi Wang , Michael Barnett , Frederique Boonstra , Yael Barnett , Mariano Cabezas , Arkiev D'Souza , Matthew C. Kiernan , Kain Kyle
show 10 more authors
Meng Law Lynette Masters Zihao Tang Stephen Tisch Sicong Tu Anneke Van Der Walt Dongang Wang Fernando Calamante Weidong Cai Chenyu Wang
This is my paper · ORCID
classification cs.CV
keywords diffusionMRIfiberorientationdistributiondeeplearningFODenhancementclinicaltranslationtractographyconnectomeneurologicaldisorders
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a deep learning network can close the quality gap between routine clinical diffusion MRI and high-end research scans, specifically for mapping the brain's white-matter fiber architecture. It presents FastFOD-Net, an accelerated end-to-end framework that enhances fiber orientation distributions (FODs) estimated from single-shell, low-angular-resolution clinical acquisitions so they match FODs from research-quality data. The framework is validated across healthy controls and six neurological disorders, and it trains and runs 60 times faster than its predecessor. This matters because FODs are the foundation for tractography and connectome analysis, tools that could support clinical neuroscience if they worked on widely available clinical scans. If the claims hold, hospitals could perform research-grade diffusion MRI analysis without research-grade scanning hardware.

What carries the argument

The fiber orientation distribution (FOD) is the central object: it models the complex arrangement of white-matter fibers at each brain voxel and serves as the basis for tractography and connectome analysis. FastFOD-Net is the accelerated end-to-end deep learning network that takes low-quality clinical diffusion MRI signals and regresses high-quality FODs, with a 60x speedup in training and inference relative to its predecessor. The network is the mechanism that transforms routine clinical acquisitions into research-grade FODs, and the clinical evaluation across healthy controls and six disorders is the evidence that this transformation works in practice.

What would settle it

Acquire both clinical and research-quality diffusion MRI in the same subjects, including disorders beyond the six studied, and compare FastFOD-Net-enhanced FODs from the clinical scan against FODs estimated from the research scan voxel by voxel. If the enhanced FODs systematically diverge—for instance, missing known tracts on tractography or disagreeing with expert manual white-matter parcellation—the claim of comparability to research acquisitions would be falsified.

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Extended reading notes

Core claim

The central claim is that FastFOD-Net—an optimized deep learning framework for enhancing fiber orientation distributions—produces FODs from clinical diffusion MRI acquisitions that are comparable to those estimated from high-quality research acquisitions, and does so across healthy controls and six neurological disorders. The authors support this with what they describe as the most comprehensive clinical evaluation of deep learning FOD enhancement to date, reporting a 60x improvement in training and inference efficiency over the predecessor network. The contribution is to demonstrate that a clinically practical deep learning pipeline can support FOD-based tractography and connectome analysis

Load-bearing premise

The enhanced FODs are assumed to faithfully represent true white-matter fiber architecture, implying the research-quality FODs used as training targets are an unbiased gold standard and the clinical populations studied are representative of all patients the network might encounter.

Editorial extensions

If this is right

  • Clinical diffusion MRI datasets that were previously too low-quality for tractography could now be analyzed for white-matter fiber architecture and connectomics.
  • Deep-learning FOD enhancement may reduce measurement error, lowering the sample sizes needed to detect group differences in clinical neuroscience studies.
  • FastFOD-Net's 60x efficiency gain makes training and inference practical in hospital workflows, potentially enabling near-real-time tractography in a clinical setting.
  • The validation across six neurological disorders supports generalization beyond healthy controls, building a case for clinical adoption of deep-learning diffusion MRI enhancement.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the same enhancement approach transfers to other diffusion-derived quantities, such as tensor metrics or fixel-based measures, it might unify clinical diffusion MRI analysis around a single trained model.
  • The comparability claim is only as strong as the training distribution; independent validation on unseen scanner vendors and patient cohorts would be the natural next test.
  • A direct head-to-head comparison of FastFOD-Net-enhanced FODs against multi-shell research acquisitions on the same subjects would quantify the residual gap and reveal which fiber populations are most affected.
  • The 60x speedup suggests the method could be embedded in real-time acquisition pipelines, potentially allowing radiologists to see enhanced FODs while the patient is still in the scanner.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces FastFOD-Net, a deep-learning framework for enhancing fiber orientation distributions (FODs) from clinical diffusion MRI. The abstract claims that the method delivers superior FOD enhancement, is 60x faster in training/inference than its predecessor, and is validated across healthy controls and six neurological disorders, enabling analysis comparable to that of high-quality research acquisitions. The abstract also claims reduced sample size requirements and increased clinical applicability. However, the full text of the submitted PDF is corrupted and unreadable; no methods, cohort descriptions, experimental results, tables, figures, or statistical analyses are accessible. The abstract itself contains no quantitative supporting data.

Significance. If the claims are substantiated, the work would be significant: a clinically validated, fast deep-learning FOD enhancement method applicable to multiple neurological disorders would address a well-recognized barrier to clinical adoption. However, the submitted manuscript provides no verifiable evidence. There are no reproducible code artifacts, no parameter-free derivations, no machine-checked proofs, and no numerical results that could be independently assessed. The only readable portion is an abstract with strong but unsupported claims. As such, the potential significance is high, but the current manuscript cannot support it.

major comments (2)
  1. [Abstract / Full text] The central claims—'superior performance', 'comparable to high-quality research acquisitions', and 'reducing measurement errors to lower sample size requirements'—are asserted without a single numerical result, confidence interval, effect size, or p-value. The full text of the PDF is unreadable (mojibake), so no methods, cohort sizes, acquisition parameters, or outcome metrics are available for verification. This is load-bearing because the paper's contribution is an empirical validation claim.
  2. [Methods / Evaluation (unreadable)] Even granting the abstract's intent, I cannot determine whether evaluation was done on held-out subjects or held-out disorder groups, whether the evaluation metric is identical to the training loss, or whether the research-quality FODs used as training targets are independent of the reference used for evaluation. If the same subjects/acquisitions appear in both training and testing, or if the 'research-quality' benchmark is the same regression target the model was trained on, the comparability claim becomes circular. No legible text rules this out.
minor comments (2)
  1. [PDF formatting] The PDF character encoding is corrupted; the full text is unreadable. The authors must resubmit a properly rendered PDF with embedded fonts and a readable character map.
  2. [Abstract] Even in the abstract, key performance metrics (e.g., mean/standard deviation, subject counts, comparison baselines) should be reported numerically to support the stated claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the paper describes a supervised deep-learning enhancement pipeline whose claims rest on empirical held-out evaluation, not on a definitionally forced reduction.

full rationale

The abstract presents FastFOD-Net as a deep-learning framework that enhances FODs from clinical diffusion MRI data, trained against research-quality FOD targets and evaluated across healthy controls and six neurological disorders. This is a standard supervised regression setup: the model maps input clinical acquisitions to reference FODs. The key claim—that the enhanced FODs enable analysis 'comparable to that achievable with high-quality research acquisitions'—is an empirical generalizability claim. It would be circular only if the evaluation metric were identical to the training loss and applied to the same subjects or acquisitions used for training, or if the reference FODs used for validation were the same regression targets on training data. Nothing in the provided text states or implies such leakage. The garbled full-text content does not contain readable equations or evaluation details, so no specific reduction can be exhibited. The skeptic's concerns about domain shift, training-target bias, and undisclosed train/test splitting are validity or reporting issues, not evidence of circularity. No self-citation chain, uniqueness import, or ansatz-smuggling is visible. Therefore the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

Abstract-only review; the network is a trained model, so learned parameters are fitted. The validity of research FODs as ground truth and the representativeness of the six clinical cohorts are unverified assumptions.

free parameters (2)
  • FastFOD-Net model weights = Not reported in abstract
    The network is trained, so its weights are fitted to diffusion MRI data; the abstract claims performance without detailing these learned parameters.
  • Hyperparameters (e.g., loss weights, network architecture) = Not reported
    The optimization (60x speed) likely involves architecture or training choices tuned on validation data; no values are given in the abstract.
assumptions (3)
  • domain assumption High-quality research FODs are a valid and unbiased ground truth for enhancing clinical FODs
    The method presumably trains on research-grade FODs and applies to clinical data; if this ground truth is imperfect, the enhanced clinical FODs inherit its biases.
  • domain assumption The six neurological disorder cohorts are representative and protocol variability is adequately covered
    Claims of clinical robustness rest on the diversity of the validation data; the abstract does not specify acquisition protocols or cohort sizes.
  • domain assumption Deep learning optimization preserves the fiber orientation distribution geometry
    The framework enhances FODs; this assumes the learned mapping does not introduce systematic errors that break tractography and connectome analyses.

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Cite this review

Pith. "Pith review of From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement." pith.science (2026). https://pith.science/paper/7YX7U6UN

@misc{pith2026250810950,
  author       = {Pith},
  title        = {Pith review of: From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7YX7U6UN}},
  note         = {Machine review of arXiv:2508.10950}
}
abstract

Fiber orientation distribution (FOD) is an advanced diffusion MRI modeling technique that represents complex white matter fiber configurations, and a key step for subsequent brain tractography and connectome analysis. Its reliability and accuracy, however, heavily rely on the quality of the MRI acquisition and the subsequent estimation of the FODs at each voxel. Generating reliable FODs from widely available clinical protocols with single-shell and low-angular-resolution acquisitions remains challenging but could potentially be addressed with recent advances in deep learning-based enhancement techniques. Despite advancements, existing methods have predominantly been assessed on healthy subjects, which have proved to be a major hurdle for their clinical adoption. In this work, we validate a newly optimized enhancement framework, FastFOD-Net, across healthy controls and six neurological disorders. This accelerated end-to-end deep learning framework enhancing FODs with superior performance and delivering training/inference efficiency for clinical use ($60\times$ faster comparing to its predecessor). With the most comprehensive clinical evaluation to date, our work demonstrates the potential of FastFOD-Net in accelerating clinical neuroscience research, empowering diffusion MRI analysis for disease differentiation, improving interpretability in connectome applications, and reducing measurement errors to lower sample size requirements. Critically, this work will facilitate the more widespread adoption of, and build clinical trust in, deep learning based methods for diffusion MRI enhancement. Specifically, FastFOD-Net enables robust analysis of real-world, clinical diffusion MRI data, comparable to that achievable with high-quality research acquisitions.

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Works this paper leans on

97 extracted references · 70 canonical work pages

  1. [1]

    write newline

    " write newline " cite write " FUNCTION editor.postfix editor num.names #1 > "( )" "( )" if FUNCTION editor.trans.postfix editor num.names #1 > "( )" "( )" if FUNCTION trans.postfix translator num.names #1 > "( )" "( )" if FUNCTION authors.editors.reflist.apa5 'field := 'dot := field num.names 'numnames := numnames 'format.num.names := format.num.names na...

  2. [2]

    , " * write output.state after.block = add.period write newline

    ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key keywords month note number organization pages publisher school series title type url volume year eprint archive archivePrefix primaryClass adsurl adsnote version label INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.sta...

  3. [3]

    write newline

    " write newline "" before.all 'output.state := FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap duplicate "7" = swap duplicate "8" = swap "9" = or or or or or or or or or FUNCTION n.separate 't := "" #0 'numnames := t empty not t #-1 #1 subs...

  4. [4]

    , Tournier , J.D

    barticle Jeurissen , B. , Tournier , J.D. , Dhollander , T. , Connelly , A. , Sijbers , J. : Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data . NeuroImage 103 , 411 -- 426 ( 2014 ) barticle

    2014

  5. [5]

    , Calamante , F

    barticle Tournier , J.D. , Calamante , F. , Connelly , A. : Robust determination of the fibre orientation distribution in diffusion MRI : non-negativity constrained super-resolved spherical deconvolution . NeuroImage 35 ( 4 ), 1459 -- 1472 ( 2007 ) barticle

    2007

  6. [6]

    , Calamante , F

    barticle Tournier , J.D. , Calamante , F. , Gadian , D.G. , Connelly , A. : Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution . NeuroImage 23 ( 3 ), 1176 -- 1185 ( 2004 ) barticle

    2004

  7. [7]

    : The seven deadly sins of measuring brain structural connectivity using diffusion MRI streamlines fibre-tracking

    barticle Calamante , F. : The seven deadly sins of measuring brain structural connectivity using diffusion MRI streamlines fibre-tracking . Diagnostics 9 ( 3 ), 115 ( 2019 ) barticle

    2019

  8. [8]

    , Tournier , J.D

    barticle Dell'Acqua , F. , Tournier , J.D. : Modelling white matter with spherical deconvolution: How and why? NMR in Biomedicine 32 ( 4 ), 3945 ( 2019 ) barticle

    2019

Show all 97 references
  1. [9]

    , Lv , J

    barticle Zeng , R. , Lv , J. , Wang , H. , Zhou , L. , Barnett , M. , Calamante , F. , Wang , C. : FOD-N et: A deep learning method for fiber orientation distribution angular super resolution . Medical Image Analysis 79 , 102431 ( 2022 ) barticle

    2022

  2. [10]

    , Weiner , M.W

    barticle Mueller , S.G. , Weiner , M.W. , Thal , L.J. , Petersen , R.C. , Jack , C.R. , Jagust , W. , Trojanowski , J.Q. , Toga , A.W. , Beckett , L. : Ways toward an early diagnosis in alzheimer’s disease: the A lzheimer’s disease neuroimaging initiative (adni) . Alzheimer's ...

    2005

  3. [11]

    , Jennings , D

    barticle Marek , K. , Jennings , D. , Lasch , S. , Siderowf , A. , Tanner , C. , Simuni , T. , Coffey , C. , Kieburtz , K. , Flagg , E. , Chowdhury , S. , : The P arkinson progression marker initiative ( PPMI ) . Progress in Neurobiology 95 ( 4 ), 629 -- 635 ( 2011 ) barticle

    2011

  4. [12]

    , Tournier , J.D

    barticle Farquharson , S. , Tournier , J.D. , Calamante , F. , Fabinyi , G. , Schneider-Kolsky , M. , Jackson , G.D. , Connelly , A. : White matter fiber tractography: why we need to move beyond dti . Journal of Neurosurgery 118 ( 6 ), 1367 -- 1377 ( 2013 ) barticle

    2013

  5. [13]

    , Vos , S.B

    barticle Lucena , O. , Vos , S.B. , Vakharia , V. , Duncan , J. , Ashkan , K. , Sparks , R. , Ourselin , S. : Enhancing the estimation of fiber orientation distributions using convolutional neural networks . Computers in Biology and Medicine 135 , 104643 ( 2021 ) barticle

    2021

  6. [14]

    , Merhof , D

    bchapter Rauland , A. , Merhof , D. : Using synthetic training data in neural networks for the estimation of fiber orientation distribution functions from single shell data . In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) , pp. 1 -- 5 ( 2023 ). IEEE bchapter

    2023

  7. [15]

    , Davey , C.E

    botherref Bartlett , J.J. , Davey , C.E. , Johnston , L.A. , Duan , J. : Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture. Magnetic Resonance in Medicine (2024) botherref

    2024

  8. [16]

    , Santana , C.P

    bchapter Silva , M.O.D. , Santana , C.P. , Carmo , D.S.D. , Rittner , L. : FOD-Swin-Net : angular super resolution of fiber orientation distribution using a transformer-based deep model . In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) , pp. 1 -- 5 ( 2024 )....

    2024

  9. [17]

    , Wang , K

    bchapter Wang , X. , Wang , K. , Tang , Z. , Cabezas , M. , Wang , C. , Cai , W. : Ufree: Unified fiber orientation distribution resolution enhancement . In: 2025 IEEE International Symposium on Biomedical Imaging (ISBI) , pp. 1 -- 5 ( 2025 ). IEEE bchapter

    2025

  10. [18]

    , Clemente , A

    barticle Dhollander , T. , Clemente , A. , Singh , M. , Boonstra , F. , Civier , O. , Duque , J.D. , Egorova , N. , Enticott , P. , Fuelscher , I. , Gajamange , S. , : Fixel-based analysis of diffusion MRI : methods, applications, challenges and opportunities . NeuroImage 241 ...

    2021

  11. [19]

    , Smith , R.E

    barticle Civier , O. , Smith , R.E. , Yeh , C.H. , Connelly , A. , Calamante , F. : Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI ? NeuroImage 194 , 68 -- 81 ( 2019 ) barticle

    2019

  12. [20]

    , Raffelt , D

    bchapter Dhollander , T. , Raffelt , D. , Connelly , A. : Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion mr data without a co-registered t1 image . In: ISMRM Workshop on Breaking the Barriers of Diffusion MRI , vol. 5 ( 2016 ). Li...

    2016

  13. [21]

    , Smith , S.M

    barticle Van Essen , D.C. , Smith , S.M. , Barch , D.M. , Behrens , T.E. , Yacoub , E. , Ugurbil , K. , Consortium , W.-M.H. , : The WU-Minn human connectome project: an overview . NeuroImage 80 , 62 -- 79 ( 2013 ) barticle

    2013

  14. [22]

    , Maller , J

    barticle Kyle , K. , Maller , J. , Barnett , Y. , Jonker , B. , Barnett , M. , D’Souza , A. , Calamante , F. , Maamary , J. , Peters , J. , Wang , C. , : Tremor suppression following treatment with mrgfus: skull density ratio consistency and degree of posterior dentatorubrotha...

    2023

  15. [23]

    , Mahoney , C.J

    barticle Almgren , H. , Mahoney , C.J. , Huynh , W. , D’Souza , A. , Berte , S. , Lv , J. , Wang , C. , Kiernan , M.C. , Calamante , F. , Tu , S. : Quantifying neurodegeneration within subdivisions of core motor pathways in amyotrophic lateral sclerosis using diffusion mri . J...

    2025

  16. [24]

    , Zalesky , A

    barticle Fornito , A. , Zalesky , A. , Pantelis , C. , Bullmore , E.T. : Schizophrenia, neuroimaging and connectomics . NeuroImage 62 ( 4 ), 2296 -- 2314 ( 2012 ) barticle

    2012

  17. [25]

    , Zalesky , A

    barticle Fornito , A. , Zalesky , A. , Breakspear , M. : The connectomics of brain disorders . Nature Reviews Neuroscience 16 ( 3 ), 159 -- 172 ( 2015 ) barticle

    2015

  18. [26]

    , Jones , D.K

    barticle Yeh , C.H. , Jones , D.K. , Liang , X. , Descoteaux , M. , Connelly , A. : Mapping structural connectivity using diffusion MRI : Challenges and opportunities . Journal of Magnetic Resonance Imaging 53 ( 6 ), 1666 -- 1682 ( 2021 ) barticle

    2021

  19. [27]

    , Smith , R.E

    barticle Raffelt , D.A. , Smith , R.E. , Ridgway , G.R. , Tournier , J.D. , Vaughan , D.N. , Rose , S. , Henderson , R. , Connelly , A. : Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres . Neuro...

    2015

  20. [28]

    , Tournier , J.D

    barticle Raffelt , D.A. , Tournier , J.D. , Smith , R.E. , Vaughan , D.N. , Jackson , G. , Ridgway , G.R. , Connelly , A. : Investigating white matter fibre density and morphology using fixel-based analysis . NeuroImage 144 , 58 -- 73 ( 2017 ) barticle

    2017

  21. [29]

    , Wang , C

    barticle Tu , S. , Wang , C. , Menke , R.A. , Talbot , K. , Barnett , M. , Kiernan , M.C. , Turner , M.R. : Regional callosal integrity and bilaterality of limb weakness in amyotrophic lateral sclerosis . Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration 21 ( 5-6 )...

    2020

  22. [30]

    , Pannek , K

    barticle Rose , S. , Pannek , K. , Bell , C. , Baumann , F. , Hutchinson , N. , Coulthard , A. , McCombe , P. , Henderson , R. : Direct evidence of intra-and interhemispheric corticomotor network degeneration in amyotrophic lateral sclerosis: an automated MRI structural connec...

    2012

  23. [31]

    , Sepulcre , J

    barticle Abad , E. , Sepulcre , J. , Martinez-Lapiscina , E.H. , Zubizarreta , I. , Garcia-Ojalvo , J. , Villoslada , P. : The analysis of semantic networks in multiple sclerosis identifies preferential damage of long-range connectivity . Multiple Sclerosis and Related Disorde...

    2015

  24. [32]

    , Rocca , M.A

    barticle Pagani , E. , Rocca , M.A. , De Meo , E. , Horsfield , M.A. , Colombo , B. , Rodegher , M. , Comi , G. , Filippi , M. : Structural connectivity in multiple sclerosis and modeling of disconnection . Multiple Sclerosis Journal 26 ( 2 ), 220 -- 232 ( 2020 ) barticle

    2020

  25. [33]

    , Liu , Y

    barticle Shu , N. , Liu , Y. , Li , K. , Duan , Y. , Wang , J. , Yu , C. , Dong , H. , Ye , J. , He , Y. : Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis . Cerebral Cortex 21 ( 11 ), 2565 -- 2577...

    2011

  26. [34]

    , Stamile , C

    barticle Kocevar , G. , Stamile , C. , Hannoun , S. , Cotton , F. , Vukusic , S. , Durand-Dubief , F. , Sappey-Marinier , D. : Graph theory-based brain connectivity for automatic classification of multiple sclerosis clinical courses . Frontiers in Neuroscience 10 , 478 ( 2016 ...

    2016

  27. [35]

    , Armitage , P

    bchapter Lyon , M. , Armitage , P. , \'A lvarez , M.A. : Angular super-resolution in diffusion MRI with a 3d recurrent convolutional autoencoder . In: International Conference on Medical Imaging with Deep Learning , pp. 834 -- 846 ( 2022 ). PMLR bchapter

    2022

  28. [36]

    , Armitage , P

    barticle Lyon , M. , Armitage , P. , \'A lvarez , M.A. : Spatio-angular convolutions for super-resolution in diffusion mri . Advances in Neural Information Processing Systems 36 , 12457 -- 12475 ( 2023 ) barticle

    2023

  29. [37]

    , Abramian , D

    barticle Ordinola , A. , Abramian , D. , Herberthson , M. , Eklund , A. , \"O zarslan , E. : Super-resolution mapping of anisotropic tissue structure with diffusion mri and deep learning . Scientific Reports 15 ( 1 ), 6580 ( 2025 ) barticle

    2025

  30. [38]

    , Kiernan , M.C

    bchapter Tu , S. , Kiernan , M.C. : Amyotrophic lateral sclerosis . In: Advances in Magnetic Resonance Technology and Applications vol. 9 , pp. 363 -- 385 . Elsevier , Amsterdam ( 2023 ) bchapter

    2023

  31. [39]

    , Geevasinga , N

    barticle Bos , M.A. , Geevasinga , N. , Higashihara , M. , Menon , P. , Vucic , S. : Pathophysiology and diagnosis of als: insights from advances in neurophysiological techniques . International Journal of Molecular Sciences 20 ( 11 ), 2818 ( 2019 ) barticle

    2019

  32. [40]

    , Vucic , S

    barticle Tu , S. , Vucic , S. , Kiernan , M.C. : Pathological insights derived from neuroimaging in amyotrophic lateral sclerosis: emerging clinical applications . Current Opinion in Neurology 37 ( 5 ), 577 -- 584 ( 2024 ) barticle

    2024

  33. [41]

    , Tu , S

    barticle Caga , J. , Tu , S. , Dharmadasa , T. , Tse , N.Y. , Zoing , M.C. , Huynh , W. , Mahoney , C. , Ahmed , R.M. , Kiernan , M.C. : Apathy is associated with parietal cortical-subcortical dysfunction in als . Cortex 145 , 341 -- 349 ( 2021 ) barticle

    2021

  34. [42]

    , Tu , S

    barticle Huynh , W. , Tu , S. , Mahoney , C.J. , Schwartz , R. , Kiernan , M.C. : Mills syndrome: clinical and radiologic asymmetry . Neurology 96 ( 14 ), 677 -- 678 ( 2021 ) barticle

    2021

  35. [43]

    , Talbot , K

    barticle B \"a umer , D. , Talbot , K. , Turner , M.R. : Advances in motor neurone disease . Journal of the Royal Society of Medicine 107 ( 1 ), 14 -- 21 ( 2014 ) barticle

    2014

  36. [44]

    , Nederveen , A.J

    barticle Sharifi , S. , Nederveen , A.J. , Booij , J. , Rootselaar , A.-F. : Neuroimaging essentials in essential tremor: a systematic review . NeuroImage: Clinical 5 , 217 -- 231 ( 2014 ) barticle

    2014

  37. [45]

    , Zalesky , A

    barticle Kamagata , K. , Zalesky , A. , Hatano , T. , Di Biase , M.A. , El Samad , O. , Saiki , S. , Shimoji , K. , Kumamaru , K.K. , Kamiya , K. , Hori , M. , : Connectome analysis with diffusion mri in idiopathic parkinson's disease: evaluation using multi-shell, multi-tissu...

    2018

  38. [46]

    , Panyakaew , P

    barticle B \'e dard , P. , Panyakaew , P. , Cho , H.-J. , Hallett , M. , Horovitz , S.G. : Multimodal imaging of essential tremor and dystonic tremor . NeuroImage: Clinical 36 , 103247 ( 2022 ) barticle

    2022

  39. [47]

    , Aslam , S

    barticle Bergamino , M. , Aslam , S. , Knittel , J.J. , Alhilali , L. , Stokes , A.M. : White matter microstructural differences between essential tremor and parkinson disease, evaluated using advanced diffusion mri biomarkers . Journal of Integrative Neuroscience 22 ( 5 ), 11...

    2023

  40. [48]

    , Banwell , B.L

    barticle Thompson , A.J. , Banwell , B.L. , Barkhof , F. , Carroll , W.M. , Coetzee , T. , Comi , G. , Correale , J. , Fazekas , F. , Filippi , M. , Freedman , M.S. , : Diagnosis of multiple sclerosis: 2017 revisions of the mcdonald criteria . The Lancet Neurology 17 ( 2 ), 16...

    2017

  41. [49]

    , Soares , J.F

    barticle Caranova , M. , Soares , J.F. , Batista , S. , Castelo-Branco , M. , Duarte , J.V. : A systematic review of microstructural abnormalities in multiple sclerosis detected with noddi and dti models of diffusion-weighted magnetic resonance imaging . Magnetic Resonance Ima...

    2023

  42. [50]

    , Parker , G.D

    barticle Lipp , I. , Parker , G.D. , Tallantyre , E.C. , Goodall , A. , Grama , S. , Patitucci , E. , Heveron , P. , Tomassini , V. , Jones , D.K. : Tractography in the presence of multiple sclerosis lesions . NeuroImage 209 , 116471 ( 2020 ) barticle

    2020

  43. [51]

    , Tur , C

    barticle Charalambous , T. , Tur , C. , Prados , F. , Kanber , B. , Chard , D.T. , Ourselin , S. , Clayden , J.D. , Wheeler-Kingshott , C.A.G. , Thompson , A.J. , Toosy , A.T. : Structural network disruption markers explain disability in multiple sclerosis . Journal of Neurolo...

    2019

  44. [52]

    , Dao , T

    botherref Gu , A. , Dao , T. : Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023) botherref

    2023 arXiv

  45. [53]

    , Blattmann , A

    bchapter Rombach , R. , Blattmann , A. , Lorenz , D. , Esser , P. , Ommer , B. : High-resolution image synthesis with latent diffusion models . In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 10684 -- 10695 ( 2022 ) bchapter

    2022

  46. [54]

    , Wald , T

    bchapter Isensee , F. , Wald , T. , Ulrich , C. , Baumgartner , M. , Roy , S. , Maier-Hein , K. , Jaeger , P.F. : nn U-N et revisited: A call for rigorous validation in 3d medical image segmentation . In: International Conference on Medical Image Computing and Computer-Assiste...

    2024

  47. [55]

    , Jaeger , P.F

    barticle Isensee , F. , Jaeger , P.F. , Kohl , S.A. , Petersen , J. , Maier-Hein , K.H. : nn U-N et: a self-configuring method for deep learning-based biomedical image segmentation . Nature Methods 18 ( 2 ), 203 -- 211 ( 2021 ) barticle

    2021

  48. [56]

    , Wang , D

    barticle Barnett , M. , Wang , D. , Beadnall , H. , Bischof , A. , Brunacci , D. , Butzkueven , H. , Brown , J.W.L. , Cabezas , M. , Das , T. , Dugal , T. , : A real-world clinical validation for ai-based mri monitoring in multiple sclerosis . npj Digital Medicine 6 ( 1 ), 196...

    2023

  49. [57]

    , Wingrove , J

    barticle Goebl , P. , Wingrove , J. , Abdelmannan , O. , Brito Vega , B. , Stutters , J. , Ramos , S.D.G. , Kenway , O. , Rossor , T. , Wassmer , E. , Arnold , D.L. , : Enabling new insights from old scans by repurposing clinical mri archives for multiple sclerosis research . ...

    2025

  50. [58]

    , Calamante , F

    barticle Tournier , J.D. , Calamante , F. , Connelly , A. : MRtrix : diffusion tractography in crossing fiber regions . International Journal of Imaging Systems and Technology 22 ( 1 ), 53 -- 66 ( 2012 ) barticle

    2012

  51. [59]

    , Smith , R

    barticle Tournier , J.D. , Smith , R. , Raffelt , D. , Tabbara , R. , Dhollander , T. , Pietsch , M. , Christiaens , D. , Jeurissen , B. , Yeh , C.H. , Connelly , A. : MR trix3: A fast, flexible and open software framework for medical image processing and visualisation . Neuro...

    2019

  52. [60]

    , Abdulkadir , A

    bchapter C i c ek , \"O . , Abdulkadir , A. , Lienkamp , S.S. , Brox , T. , Ronneberger , O. : 3 D U-N et: learning dense volumetric segmentation from sparse annotation . In: International Conference on Medical Image Computing and Computer-assisted Intervention , pp. 424 -- 43...

    2016

  53. [61]

    : Measurement of fiber orientation distributions using high angular resolution diffusion imaging

    barticle Anderson , A.W. : Measurement of fiber orientation distributions using high angular resolution diffusion imaging . Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 54 ( 5 ), 1194 -- 1206 ( 2005 ) barticle

    2005

  54. [62]

    , Smith , R.E

    barticle Yeh , C.H. , Smith , R.E. , Dhollander , T. , Calamante , F. , Connelly , A. : Connectomes from streamlines tractography: Assigning streamlines to brain parcellations is not trivial but highly consequential . NeuroImage 199 , 160 -- 171 ( 2019 ) barticle

    2019

  55. [63]

    , Tournier , J.D

    barticle Smith , R.E. , Tournier , J.D. , Calamante , F. , Connelly , A. : Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information . NeuroImage 62 ( 3 ), 1924 -- 1938 ( 2012 ) barticle

    1924

  56. [64]

    : A new measure of rank correlation

    barticle Kendall , M.G. : A new measure of rank correlation . Biometrika 30 ( 1/2 ), 81 -- 93 ( 1938 ) barticle

    1938

  57. [65]

    , Zalesky , A

    bbook Fornito , A. , Zalesky , A. , Bullmore , E. : Fundamentals of Brain Network Analysis . Academic Press , Cambridge, MA ( 2016 ) bbook

    2016

  58. [66]

    , Sporns , O

    barticle Rubinov , M. , Sporns , O. : Complex network measures of brain connectivity: uses and interpretations . NeuroImage 52 ( 3 ), 1059 -- 1069 ( 2010 ) barticle

    2010

  59. [67]

    , Anderson , C

    barticle Grossman , M. , Anderson , C. , Khan , A. , Avants , B. , Elman , L. , McCluskey , L. : Impaired action knowledge in amyotrophic lateral sclerosis . Neurology 71 ( 18 ), 1396 -- 1401 ( 2008 ) barticle

    2008

  60. [68]

    , Schuster , C

    barticle Kasper , E. , Schuster , C. , Machts , J. , Kaufmann , J. , Bittner , D. , Vielhaber , S. , Benecke , R. , Teipel , S. , Prudlo , J. : Microstructural white matter changes underlying cognitive and behavioural impairment in als--an in vivo study using dti . PloS one 9 ...

    2014

  61. [69]

    , McMillan , C

    barticle Libon , D.J. , McMillan , C. , Avants , B. , Boller , A. , Morgan , B. , Burkholder , L. , Chandrasekaran , K. , Elman , L. , McCluskey , L. , Grossman , M. : Deficits in concept formation in amyotrophic lateral sclerosis. Neuropsychology 26 ( 4 ), 422 ( 2012 ) barticle

    2012

  62. [70]

    , Bastin , M.E

    barticle Pettit , L.D. , Bastin , M.E. , Smith , C. , Bak , T.H. , Gillingwater , T.H. , Abrahams , S. : Executive deficits, not processing speed relates to abnormalities in distinct prefrontal tracts in amyotrophic lateral sclerosis . Brain 136 ( 11 ), 3290 -- 3304 ( 2013 ) barticle

    2013

  63. [71]

    , Jbabdi , S

    barticle Sotiropoulos , S.N. , Jbabdi , S. , Xu , J. , Andersson , J.L. , Moeller , S. , Auerbach , E.J. , Glasser , M.F. , Hernandez , M. , Sapiro , G. , Jenkinson , M. , : Advances in diffusion MRI acquisition and processing in the human connectome project . Neuroimage 80 , ...

    2013

  64. [72]

    : A simple but useful way to assess fmri scan qualities

    barticle Power , J.D. : A simple but useful way to assess fmri scan qualities . Neuroimage 154 , 150 -- 158 ( 2017 ) barticle

    2017

  65. [73]

    , Bastiani , M

    barticle Tax , C.M. , Bastiani , M. , Veraart , J. , Garyfallidis , E. , Irfanoglu , M.O. : What’s new and what’s next in diffusion MRI preprocessing . NeuroImage 249 , 118830 ( 2022 ) barticle

    2022

  66. [74]

    , Beckmann , C.F

    barticle Jenkinson , M. , Beckmann , C.F. , Behrens , T.E. , Woolrich , M.W. , Smith , S.M. : FSL . NeuroImage 62 ( 2 ), 782 -- 790 ( 2012 ) barticle

    2012

  67. [75]

    , Dhital , B

    barticle Kellner , E. , Dhital , B. , Kiselev , V.G. , Reisert , M. : Gibbs-ringing artifact removal based on local subvoxel-shifts . Magnetic resonance in medicine 76 ( 5 ), 1574 -- 1581 ( 2016 ) barticle

    2016

  68. [76]

    : Artifacts in diffusion MRI

    botherref Pierpaoli , C. : Artifacts in diffusion MRI . Diffusion MRI : theory, methods and applications, 303--318 (2010) botherref

    2010

  69. [77]

    , Avants , B.B

    barticle Tustison , N.J. , Avants , B.B. , Cook , P.A. , Zheng , Y. , Egan , A. , Yushkevich , P.A. , Gee , J.C. : N4itk: improved n3 bias correction . IEEE transactions on medical imaging 29 ( 6 ), 1310 -- 1320 ( 2010 ) barticle

    2010

  70. [78]

    : Free S urfer

    barticle Fischl , B. : Free S urfer . NeuroImage 62 ( 2 ), 774 -- 781 ( 2012 ) barticle

    2012

  71. [79]

    , S \'e gonne , F

    barticle Desikan , R.S. , S \'e gonne , F. , Fischl , B. , Quinn , B.T. , Dickerson , B.C. , Blacker , D. , Buckner , R.L. , Dale , A.M. , Maguire , R.P. , Hyman , B.T. , : An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based reg...

    2006

  72. [80]

    , Skoch , A

    bchapter Smith , R. , Skoch , A. , Bajada , C.J. , Caspers , S. , Connelly , A. : Hybrid surface-volume segmentation for improved anatomically-constrained tractography . In: OHBM ( 2020 ) bchapter

    2020

  73. [81]

    , Stone , L.A

    barticle Kennard , R.W. , Stone , L.A. : Computer aided design of experiments . Technometrics 11 ( 1 ), 137 -- 148 ( 1969 ) barticle

    1969

  74. [82]

    , Connelly , A

    bchapter Dhollander , T. , Connelly , A. : A novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+ b= 0) diffusion MRI data . In: Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM)) , vol. 24 , p. 3010 ( 20...

    2016

  75. [83]

    , Tournier , J.D

    barticle Smith , R.E. , Tournier , J.D. , Calamante , F. , Connelly , A. : SIFT : Spherical-deconvolution informed filtering of tractograms . NeuroImage 67 , 298 -- 312 ( 2013 ) barticle

    2013

  76. [84]

    , Neher , P

    barticle Wasserthal , J. , Neher , P. , Maier-Hein , K.H. : Tract S eg- F ast and accurate white matter tract segmentation . NeuroImage 183 , 239 -- 253 ( 2018 ) barticle

    2018

  77. [85]

    , Tournier , J.D

    barticle Smith , R.E. , Tournier , J.D. , Calamante , F. , Connelly , A. : SIFT 2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography . NeuroImage 119 , 338 -- 351 ( 2015 ) barticle

    2015

  78. [86]

    , Tournier , J.D

    barticle Smith , R.E. , Tournier , J.D. , Calamante , F. , Connelly , A. : The effects of sift on the reproducibility and biological accuracy of the structural connectome . Neuroimage 104 , 253 -- 265 ( 2015 ) barticle

    2015

  79. [87]

    , Tabbara , R

    bchapter Dhollander , T. , Tabbara , R. , Rosnarho-Tornstrand , J. , Tournier , J.D. , Raffelt , D. , Connelly , A. : Multi-tissue log-domain intensity and inhomogeneity normalisation for quantitative apparent fibre density . In: Proceedings of the International Society for Ma...

    2021

  80. [88]

    , Horvath , S

    botherref Zhang , B. , Horvath , S. : A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology 4(1) (2005) botherref

    2005

  81. [89]

    , " * write output.state after.block = add.period write newline

    ENTRY address archive author booktitle chapter doi edition editor eid eprint howpublished institution journal key keywords month note number organization pages publisher school series title type url volume year archivePrefix primaryClass adsurl adsnote version label extra.labe...

  82. [90]

    write newline

    " write newline "" before.all 'output.state := FUNCTION add.period duplicate empty 'skip "." * add.blank if FUNCTION if.digit duplicate "0" = swap duplicate "1" = swap duplicate "2" = swap duplicate "3" = swap duplicate "4" = swap duplicate "5" = swap duplicate "6" = swap dupl...

  83. [91]

    write newline

    " write newline "" before.all 'output.state := FUNCTION output.doi doi empty skip "doi:" doi * "" * output if FUNCTION format.archive archivePrefix empty "" archivePrefix ":" * if FUNCTION format.primaryClass primaryClass empty "" " [" primaryClass * "] " * if FUNCTION format....

  84. [92]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

  85. [93]

    write newline

    " write newline "" before.all 'output.state := FUNCTION string.to.integer 't := t text.length 'k := #1 'char.num := t char.num #1 substring 's := s is.num s "." = or char.num k = not and char.num #1 + 'char.num := while char.num #1 - 'char.num := t #1 char.num substring FUNCTI...

  86. [94]

    , " * write output.state after.block = add.period write newline

    ENTRY address archive author booktitle chapter edition editor eprint howpublished institution journal key keywords month note number organization pages publisher school series title type url doi volume year archivePrefix primaryClass eid adsurl adsnote version label INTEGERS o...

  87. [95]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

  88. [96]

    Available from:

    ENTRY address assignee author booktitle chapter cartographer day edition editor howpublished institution inventor journal key keywords month note number organization pages part publisher school series title type volume word year eprint doi url lastchecked updated archive archi...

  89. [97]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.