Pith. sign in

REVIEW 4 major objections 6 minor 26 references

Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Q-CATN synthesizes diffusion-weighted images at arbitrary q-space points from b0, T1, and T2 images, and its derived parameter maps and fiber tracts are closer to ground truth than those from fixed-scheme baselines.

desk verdict The architecture is plausible and the code is public, but the headline comparison is confounded and the adversarial loss as written is backwards. read the letter →

arxiv 2505.09323 v1 pith:AZ6IYHLA submitted 2025-05-14 eess.IV cs.CV

Pengli Zhu , Yingji Fu , Nanguang Chen , Anqi Qiu This is my paper · ORCID
classification eess.IVcs.CV
keywords diffusion-weightedimagingsynthesisq-spaceconditioningmulti-shellhigh-angularresolutiondiffusioncollaborativeattentionconditionalgenerativeadversarialnetworkcentralbiasinginstancenormalizationparameterestimationtractography
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 proposes Q-CATN, a translation network that synthesizes diffusion-weighted images at arbitrary q-space coordinates (gradient direction and b-value) from b0, T1, and T2 structural images. The authors claim that, trained on 20 subjects from a public dataset, Q-CATN generates dense multi-shell HARDI data whose derived DTI, DKI, and NODDI parameter maps and fiber tracts are quantitatively and qualitatively closer to ground truth than those produced by 1D-qDL, 2D-qDL, MESC-SD, and QGAN. The practical point is that a single model, rather than a pipeline tied to one acquisition scheme, could supply the dense q-space sampling that advanced diffusion models require. This matters because clinical and multi-site protocols rarely share fixed gradient tables, and existing methods degrade or need retraining when the sampling changes.

What carries the argument

The load-bearing mechanism is a collaborative attention generator conditioned on q-space. Single-modal attention (SMA) reweights each modality's channels; the multi-modal attention fusion (MMAF) module computes a softmax attention matrix across modalities and produces a fused representation; and the q-space embedding module applies central biasing instance normalization (CBIN), which adds a bias learned from the q coordinate to instance-normalized features. A q-space conditional discriminator with both global and pixel-level realism branches, together with a pretrained perceptual network and a spatially-correlative anatomical loss, drives the synthesized DWI toward the reference. This combination is what lets one network produce flexible MS-HARDI rather than a fixed set of parameter maps.

What would settle it

Acquire or synthesize test data with b-values and gradient directions that lie outside the training range, train Q-CATN only on the inner range, and compare synthesized DWIs against actual acquisitions using RMSE, MS-SSIM, and derived DTI/NODDI maps. If accuracy at these unseen q-space coordinates falls to the level of a non-flexible baseline, the central claim of flexible synthesis is refuted; the same test can be run across sites or scanner vendors.

Watch

Extended reading notes

Core claim

Q-CATN's central claim is that the mapping from structural MRI (b0, T1, T2) plus a q-space coordinate (three gradient-direction components and a b-value) to the DWI intensity at that coordinate can be learned once and then queried at any direction or b-value. The network extracts per-modality features with single-modal attention, fuses them through a multi-modal attention matrix, and injects the q-space coordinate into residual blocks via central biasing instance normalization, so the same generator adapts its internal representations to the requested sampling point. Trained with reconstruction, adversarial, and anatomical-consistency losses, Q-CATN produces dense 270-direction multi-shell DWI, and the paper reports that DTI, DKI, NODDI maps and fiber orientation distributions and tractography derived from these synthesized volumes beat all compared baselines both quantitatively (RMSE, MS-SSIM, PSNR, LPIPS) and visually. This is an extension of earlier q-space translation work, removing the fixed-sampling restriction while adding cross-modal attention.

Load-bearing premise

The network can learn the DWI signal as a smooth function of q-space from the 20-subject training distribution, so that at inference it produces accurate images for gradient directions and b-values it never saw; the paper does not test this outside the training distribution, and if that generalization fails, the flexibility claim collapses.

Editorial extensions

If this is right

  • A single Q-CATN model can synthesize DWI at arbitrary gradient directions and b-values, so diffusion analysis no longer requires retraining for each acquisition protocol.
  • Dense synthesized q-space samples can be fed into standard pipelines to estimate DTI, DKI, and NODDI parameters and fiber tracts from inputs that were never densely sampled.
  • Using only b0, T1, and T2 images as inputs means the approach starts from commonly acquired structural scans, which is feasible when dedicated diffusion acquisitions are short or unavailable.
  • Quantitative gains over 1D-qDL, 2D-qDL, MESC-SD, and QGAN on RMSE, MS-SSIM, PSNR, and LPIPS imply downstream parameter maps and tractography are closer to ground-truth estimates.

Reading between the lines

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

  • The flexibility claim is really an interpolation claim: the paper demonstrates queries inside the training distribution of b-values and directions, so the open test is whether accuracy holds for q-space points outside that envelope, such as much higher b-values than those seen during training.
  • Because training uses only 20 healthy subjects, portability to other acquisitions, scanners, or pathological brains is untested; a plausible extension would evaluate Q-CATN after fine-tuning on a small target-site dataset.
  • The same collaborative-attention and CBIN machinery could be applied to other q-space encodings, such as diffusion spectrum imaging, where flexible conditioning may matter as much as it does for HARDI.
  • If flexible synthesis holds, a practical consequence the authors leave implicit is retrospective harmonization: one model could regenerate consistent multi-shell data across sites that used different gradient tables, enabling pooled analysis.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript proposes Q-CATN, a conditional generative framework that synthesizes diffusion-weighted images (DWI) at arbitrary q-space coordinates (gradient direction and b-value) from b0, T1, and T2 structural images. The architecture combines single-modal and multi-modal collaborative attention, a q-space embedding via central biasing instance normalization, and a q-space conditional discriminator with global and local realism branches. Training uses 20 HCP subjects with random-direction slices; testing uses 30 subjects. The authors compare Q-CATN against 1D-qDL, 2D-qDL, MESC-SD, and QGAN in terms of DTI, DKI, and NODDI parameter maps and fiber tractography, reporting quantitative gains and qualitatively closer fiber tracts. The central claim is that a single model can replace fixed-scheme DWI synthesis pipelines while preserving anatomical fidelity and downstream diffusion-model accuracy.

Significance. If the central claim were established, Q-CATN would be a practically useful contribution: it targets a real limitation of qDL-style methods, which typically require fixed gradient schemes, and it is evaluated on downstream parameter maps and tractography, not only image similarity. The paper also ships code and uses a publicly available dataset, which is a strength. The architectural elements—collaborative attention, CBIN conditioning, and the two-level discriminator—are reasonable and clearly described. However, the headline quantitative comparisons are confounded by unequal numbers of q-space directions used for the competing methods, and the paper provides no error bars, statistical tests, or ablations. As written, the experiments do not support the abstract's superiority claim over the qDL baselines, even though the underlying method may be sound. The flexibility claim is also not tested on truly unseen q-space ranges or across acquisition protocols. These are load-bearing gaps that require additional experiments rather than mere editing.

major comments (4)
  1. [Section 3, quantitative comparison] The comparison is not controlled with respect to angular sampling density. The text states that 1D-qDL, 2D-qDL, and MESC-SD are evaluated on 30 fixed-direction undersampled data, while Q-GAN and Q-CATN use sphere2cart to simulate 270 directional data before fitting DTI, DKI, and NODDI maps. Fitting any model on nine times more angular samples will tend to improve map smoothness and metric scores regardless of synthesis fidelity. The reported RMSE, MS-SSIM, PSNR, and LPIPS advantages over the qDL baselines therefore cannot be attributed to Q-CATN's architecture or to the fidelity of its synthesized DWI. Please rerun the comparison with matched angular sampling: evaluate the qDL baselines on the same 270 synthesized directions, or evaluate Q-CATN and QGAN on the 30 directions used for the qDL baselines, and report both settings.
  2. [Section 3.2, quantitative analysis] No error bars, confidence intervals, or statistical significance tests are reported for the quantitative metrics. Figure 4 appears to aggregate results across the 30 test subjects or a set of slices, but there is no stated variance. Without subject-level or slice-level variability, the reader cannot judge whether the observed differences are meaningful. Please report mean and standard deviation (or box plots) across test subjects, and perform paired significance tests for the main comparisons.
  3. [Section 2.6 and Section 3] The 'flexible q-space' generalization claim is not validated. Training uses random directions from the HCP sampling scheme, and the experiments report synthesis at directions that appear to lie within the same acquisition protocol. There is no held-out set of untrained b-values, untrained gradient directions, different shells, cross-site data, or different acquisition hardware. If the method fails to extrapolate outside the training q-space distribution, the central flexibility advantage collapses. Please define the training and inference q-space ranges explicitly, hold out a disjoint set of directions and b-values, and evaluate extrapolation performance.
  4. [Section 3.3, FOD and tractography] The tractography and FOD evaluation is purely qualitative. The text claims that Q-CATN results 'align more closely with the ground truth' and that fibers from undersampled data exhibit the poorest quality, but no quantitative tractography metrics (e.g., bundle overlap, Dice, streamline count, or distance measures) or FOD error metrics are given. Please add quantitative downstream metrics to support the tractography claim, or soften the claim accordingly.
minor comments (6)
  1. [Abstract] The first sentence reads 'This study, we propose'; it should be 'In this study, we propose'.
  2. [Section 2.2, Eq. under MMAF] The equation for zatt_n uses n both as the free index of the output and as the summation index in the same expression; this is likely a typo and should use separate indices (e.g., zatt_i = z_i + sum_j z_j ⊗ A_{ji}).
  3. [Section 2.5, Eq. (1)] The adversarial loss in Eq. (1) appears to have reversed roles: real samples ybn contribute log(1 - D(*)(ybn, q)) and synthesized samples xbn contribute log D(*)(xbn, q). If D outputs the probability of realness, the signs should be opposite. Please clarify the convention used for the discriminator output.
  4. [Figure 2] The units for b-values are written as 'mm/s2'; b-values should be in s/mm^2 (and the diffusion weighting is not an acceleration, so the displayed unit is incorrect).
  5. [Figure 4] The caption repeats 'DTI-FA DKI-RK NODDI-ODI' four times in a row, which appears to be a layout artifact; please clean up the caption and label each panel distinctly.
  6. [Section 2.6, optimization] The loss weights λrec and λac are fixed at 100; no sensitivity analysis or ablation is reported for these choices, so it is unclear how robust the results are to hyperparameter changes.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Q-CATN is supervised empirical learning with external baselines and ground-truth targets; no equation reduces to its inputs.

full rationale

The paper's central claim is that Q-CATN synthesizes flexible-q-space DWIs and that these support better DTI/DKI/NODDI maps and tractography than 1D-qDL, 2D-qDL, MESC-SD, and QGAN. This claim is supported by supervised training against ground-truth DWIs: the reconstruction loss (Eq. 2) compares the synthesized DWI x_bn to the reference DWI x*_bn at the targeted q-space coordinate, the adversarial loss (Eq. 1) distinguishes synthetic from real DWIs, and the anatomical consistency loss compares VGG features of synthetic and reference DWIs. None of these losses is derived from the downstream parameter maps or tracts being predicted, and the parameter maps are fitted afterwards with external tools (DIPY, AMICO, MRtrix3, FSL, DSI-Studio), not generated by the network from a fitted constant. The baselines are external published methods, not self-citations of the present authors. The CBIN module is cited to Yu et al. (2018), an external source, and QGAN is cited to Ren et al. (2021), also external; there is no self-citation chain or imported uniqueness theorem that forces the architecture. The flexible q-space conditioning is an architectural design choice, not a hidden restatement of the evaluation target. The main validity concern is experimental fairness rather than circularity: Section 3 states that 1D-qDL, 2D-qDL, and MESC-SD use 30 fixed directions while Q-CATN and QGAN use 270 synthesized directions via sphere2cart, so the reported superiority over the qDL family is confounded by angular resolution. This confound affects the strength of the empirical comparison and the generalizability of the flexibility claim (Section 2.3 and 2.6 give no validation on untrained q-space ranges or cross-site data), but it is not a circularity per the scoring rubric: the compared methods are external, the targets are ground-truth data, and no fitted parameter is renamed as a prediction. Under the hard rules, a fairness gap or a missing generalization test does not constitute circularity, so the appropriate score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities or free-form constants. The learnable q-space embedding and attention weights are ordinary network parameters trained by gradient descent. The only hand-set parameters that affect the objective are the two loss weights, and the key external premise is that structural MRI plus a q-space code carries enough information to synthesize DWI. The 270-direction evaluation choice is also a hand-set quantity that the reported advantage partly rests on.

free parameters (3)
  • lambda_rec = 100
    Reconstruction loss weight in Section 2.6; hand-chosen, no sensitivity analysis reported.
  • lambda_ac = 100 (text groups lambda_rec and lambda_ac together)
    Anatomical consistency loss weight in Section 2.6; hand-chosen, no ablation reported.
  • number_of_synthesized_q-space_directions = 270
    Chosen in Section 3; Q-CATN and QGAN use 270 simulated directions while the qDL baselines use 30, creating an unequal comparison.
assumptions (4)
  • domain assumption b0, T1, and T2 images contain sufficient information to synthesize DWI at arbitrary q-space coordinates.
    The whole encoder-decoder design in Sections 2.1-2.3 assumes this mapping is learnable from structural inputs plus the q-space code.
  • domain assumption A model trained on 20 HCP subjects generalizes to unseen subjects and to q-space coordinates outside the training set.
    Training and inference in Section 2.6 rely on this; no cross-site or out-of-distribution q-space validation is provided.
  • domain assumption VGG16 features pretrained on natural images provide a valid anatomical self-similarity prior for DWI.
    The anatomical consistency loss in Section 2.5 uses Gac from a VGG16 network; this transfer is assumed but not validated.
  • domain assumption Parameter maps fitted from 270 synthesized directions with DIPY/AMICO are a fair proxy for maps fitted from real dense acquisitions.
    Section 3 uses sphere2cart-simulated 270-direction data for QGAN and Q-CATN and treats the resulting DTI/DKI/NODDI maps as comparable to ground-truth fits.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis." pith.science (2026). https://pith.science/paper/AZ6IYHLA

@misc{pith2026250509323,
  author       = {Pith},
  title        = {Pith review of: Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AZ6IYHLA}},
  note         = {Machine review of arXiv:2505.09323}
}
read the original abstract

This study, we propose a novel Q-space Guided Collaborative Attention Translation Networks (Q-CATN) for multi-shell, high-angular resolution DWI (MS-HARDI) synthesis from flexible q-space sampling, leveraging the commonly acquired structural MRI data. Q-CATN employs a collaborative attention mechanism to effectively extract complementary information from multiple modalities and dynamically adjust its internal representations based on flexible q-space information, eliminating the need for fixed sampling schemes. Additionally, we introduce a range of task-specific constraints to preserve anatomical fidelity in DWI, enabling Q-CATN to accurately learn the intrinsic relationships between directional DWI signal distributions and q-space. Extensive experiments on the Human Connectome Project (HCP) dataset demonstrate that Q-CATN outperforms existing methods, including 1D-qDL, 2D-qDL, MESC-SD, and QGAN, in estimating parameter maps and fiber tracts both quantitatively and qualitatively, while preserving fine-grained details. Notably, its ability to accommodate flexible q-space sampling highlights its potential as a promising toolkit for clinical and research applications. Our code is available at https://github.com/Idea89560041/Q-CATN.

Figures

Figures reproduced from arXiv: 2505.09323 by the authors.

Figure 1
Figure 1. Overview of Q-CATN. Panel (A) illustrates the high-level structure of the model. Panel (B) details the architecture of the single-modal attention module. Panel (C) shows the multi-modal attention fusion mechanism. Panel (D) outlines the struc￾ture of the conditional discriminator. 2.2 Collaborative Attention Mechanism To enable the generation of precise and realistic DWI outputs, the proposed Q￾CATN model employs a … view at source ↗
Figure 2
Figure 2. DWI synthesis re￾sults under different b￾values configurations. On the left, all potential in￾put channels (b0, T1, T2) are shown, while the right side displays the predicted results, with a standard DWI slice serving as the reference [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Qualitative com￾parison of different meth￾ods for undersampling DWI parameter fitting. The first six columns show the various diffusion maps and the last column is the ODI error map, each row represents differ￾ent comparison methods, with the reference map at the bottom. baseline models across four metrics, demonstrating its superior ability to adapt to proposed collaborative attention and flexible q-space sampling … view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Quantitative comparison of estimated parameter maps in different methods. Undersampled Q-GAN Ours Ground Truth ROI 1 ROI 2 Undersampled Q-GAN Ours Ground Truth FOD Tractography [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 22 canonical work pages

  1. [1]

    Medical image analysis85, 102742 (2023)

    Chen, G., Hong, Y., Huynh, K.M., Yap, P.T.: Deep learning prediction of diffusion mri data with microstructure-sensitive loss functions. Medical image analysis85, 102742 (2023)

    work page 2023

  2. [2]

    Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI

    Cicimen, A.G., Tregidgo, H.F., Figini, M., Messaritaki, E., McNabb, C.B., Palombo, M., Evans, C.J., Cercignani, M., Jones, D.K., Alexander, D.C.: Image quality transfer of diffusion mri guided by high-resolution structural mri. arXiv preprint arXiv:2408.03216 (2024)

    work page Pith review arXiv 2024

  3. [3]

    Neuroimage105, 32–44 (2015)

    Daducci, A., Canales-Rodríguez, E.J., Zhang, H., Dyrby, T.B., Alexander, D.C., Thiran, J.P.: Accelerated microstructure imaging via convex optimization (amico) from diffusion mri data. Neuroimage105, 32–44 (2015)

    work page 2015

  4. [4]

    Frontiers in neuroinformatics8, 8 (2014)

    Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., De- scoteaux, M., Nimmo-Smith, I., Contributors, D.: Dipy, a library for the analysis of diffusion mri data. Frontiers in neuroinformatics8, 8 (2014)

    work page 2014

  5. [5]

    Magnetic resonance in medicine 81(4), 2399–2411 (2019)

    Gibbons, E.K., Hodgson, K.K., Chaudhari, A.S., Richards, L.G., Majersik, J.J., Adluru, G., DiBella, E.V.: Simultaneous noddi and gfa parameter map genera- tion from subsampled q-space imaging using deep learning. Magnetic resonance in medicine 81(4), 2399–2411 (2019)

    work page 2019

  6. [6]

    IEEE transactions on medical imaging35(5), 1344–1351 (2016)

    Golkov, V., Dosovitskiy, A., Sperl, J.I., Menzel, M.I., Czisch, M., Sämann, P., Brox, T.,Cremers,D.:Q-spacedeeplearning:twelve-foldshorterandmodel-freediffusion mri scans. IEEE transactions on medical imaging35(5), 1344–1351 (2016)

    2016

  7. [7]

    In: Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings 21

    Gu, X., Knutsson, H., Nilsson, M., Eklund, A.: Generating diffusion mri scalar maps from t1 weighted images using generative adversarial networks. In: Image Analysis: 21st Scandinavian Conference, SCIA 2019, Norrköping, Sweden, June 11–13, 2019, Proceedings 21. pp. 489–498. Springer (2019)

    work page 2019

  8. [8]

    Advances in neural information processing systems33, 6840–6851 (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems33, 6840–6851 (2020)

    2020

Show all 26 references
  1. [9]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with condi- tional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1125–1134 (2017) 10 P. Zhu et al

    2017

  2. [10]

    Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine53(6), 1432–1440 (2005)

    Jensen, J.H., Helpern, J.A., Ramani, A., Lu, H., Kaczynski, K.: Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magneti...

    2005

  3. [11]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)

    2019

  4. [12]

    Elsevier (2007)

    Mori, S.: Introduction to diffusion tensor imaging. Elsevier (2007)

    2007

  5. [13]

    IEEE Transactions on Medical Imaging41(2), 491–499 (2021)

    Park, J., Jung, W., Choi, E.J., Oh, S.H., Jang, J., Shin, D., An, H., Lee, J.: Diffnet: diffusion parameter mapping network generalized for input diffusion gra- dient schemes and b-value. IEEE Transactions on Medical Imaging41(2), 491–499 (2021)

    2021

  6. [14]

    Ren, M., Kim, H., Dey, N., Gerig, G.: Q-space conditioned translation networks for directional synthesis of diffusion weighted images from multi-modal structural mri. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Str...

    2021

  7. [15]

    Neuroimage 23, S208–S219 (2004)

    Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., et al.: Advances in functional and structural mr image analysis and implementation as fsl. Neuroimage 23, S208–S219 (2004)

    2004

  8. [16]

    NeuroImage219, 117017 (2020)

    Tian,Q.,Bilgic,B.,Fan,Q.,Liao,C.,Ngamsombat,C.,Hu,Y.,Witzel,T.,Setsom- pop, K., Polimeni, J.R., Huang, S.Y.: Deepdti: High-fidelity six-direction diffusion tensor imaging using deep learning. NeuroImage219, 117017 (2020)

    2020

  9. [17]

    Neu- roimage 202, 116137 (2019)

    Tournier, J.D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.H., Connelly, A.: Mrtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neu- roimage 202, 116137 (2019)

    2019

  10. [18]

    Neuroimage 80, 62–79 (2013)

    Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K., Consortium, W.M.H., et al.: The wu-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)

    2013

  11. [19]

    Neuroinformatics14, 339–351 (2016)

    Yan, C.G., Wang, X.D., Zuo, X.N., Zang, Y.F.: Dpabi: data processing & analysis for (resting-state) brain imaging. Neuroinformatics14, 339–351 (2016)

    2016

  12. [20]

    In: International Conference on Medical Image Computing and Computer-Assisted Intervention

    Yang, J., Jiang, H., Tassew, T., Sun, P., Ma, J., Xia, Y., Yap, P.T., Chen, G.: Towards accurate microstructure estimation via 3d hybrid graph transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 25–34. Springer (2023)

    2023

  13. [21]

    Medical image analysis61, 101650 (2020)

    Ye, C., Li, Y., Zeng, X.: An improved deep network for tissue microstructure esti- mation with uncertainty quantification. Medical image analysis61, 101650 (2020)

    2020

  14. [22]

    PloS one 8(11), e80713 (2013)

    Yeh, F.C., Verstynen, T.D., Wang, Y., Fernández-Miranda, J.C., Tseng, W.Y.I.: Deterministic diffusion fiber tracking improved by quantitative anisotropy. PloS one 8(11), e80713 (2013)

    2013

  15. [23]

    arXiv preprint arXiv:1806.10050 (2018)

    Yu, X., Ying, Z., Li, T., Liu, S., Li, G.: Multi-mapping image-to-image translation with central biasing normalization. arXiv preprint arXiv:1806.10050 (2018)

    2018 arXiv

  16. [24]

    Neuroimage 61(4), 1000–1016 (2012)

    Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: Noddi: prac- tical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000–1016 (2012)

    2012

  17. [25]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Zheng, C., Cham, T.J., Cai, J.: The spatially-correlative loss for various image translation tasks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 16407–16417 (2021) Q-CATN 11

    2021

  18. [26]

    IEEE Journal of Biomedical and Health Informatics (2024)

    Zong, F., Zhu, Z., Zhang, J., Deng, X., Li, Z., Ye, C., Liu, Y.: Attention-based q- space deep learning generalized for accelerated diffusion magnetic resonance imag- ing. IEEE Journal of Biomedical and Health Informatics (2024)

    2024

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.