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REVIEW 3 major objections 3 minor 88 references

Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement

T0 review · 3 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Di-Fusion claims that a single neighboring noisy dMRI volume can supervise a diffusion-model denoiser, achieving state-of-the-art downstream results without clean data or a separate noise model.

desk verdict Well-engineered dMRI denoising with a broken Noise2Self argument: Eq. (9) leaks the target into the network input, and adjacent volumes do not share a clean signal. read the letter →

arxiv 2501.13514 v3 pith:UF4T6TQ6 submitted 2025-01-23 eess.IV cs.CV

Chenxu Wu , Qingpeng Kong , Zihang Jiang , S. Kevin Zhou This is my paper · ORCID
classification eess.IVcs.CV
keywords diffusionMRIdenoisingself-supervisedlearningNoise2Selfmodelstractographymicrostructuremodelingadaptivesampling
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

Di-Fusion is a fully self-supervised method for denoising diffusion MRI volumes. It maps one noisy diffusion-weighted slice, $x'$, onto its neighboring slice $x$ through a $T$-step diffusion chain, and argues that because both are independent noisy readings of shared tissue signal, minimizing the squared error against $x$ also minimizes error against the clean underlying image. To keep the chain stable it introduces a Fusion process that linearly interpolates between $x'$ and $x$ at every step, and a "Di-" process that replaces Gaussian noise with spatially shuffled measured differences. Training only the last $T_c$ diffusion steps suppresses the generative diversity that causes hallucinations. The paper reports that this single-stage pipeline outperforms prior self-supervised methods on tractography, microstructure fitting, and simulated denoising benchmarks.

What carries the argument

The load-bearing object is the simplified training objective of Eq. (9), a Noise2Self-style noisy-target loss that makes a diffusion model learn denoising from one neighboring noisy volume. The Fusion interpolation of Eq. (6), $x_t^* = \lambda_t^1 x + \lambda_t^2 x'$, aligns the forward trajectory with the target slice and avoids drift; the "Di-" noise of Eq. (8), $\xi_{x-x'} = \mathrm{mess}((x-x') - \mu_{x-x'})$, supplies a non-Gaussian, empirically grounded noise distribution for both forward and reverse processes; and the adaptive termination rule of Section 3.3, comparing $d_x = \|x - x_{\mathrm{out}}\|_2 \cdot b_x$ against $C_{\mathrm{SNR}}$, makes the refinement iterative and controllable.

What would settle it

Acquire two sets of dMRI volumes: one with repeated identical diffusion-gradient acquisitions (true independent measurements of the same $y$) and one with adjacent different-gradient volumes as used in the paper, then train Di-Fusion with Eq. (9) on each. If the same-gradient pair performs markedly better, or if the different-gradient pair's denoised output is visibly biased toward the target gradient, then the shared-clean-signal assumption is the active ingredient and the Noise2Self justification is the part that matters.

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Extended reading notes

Core claim

The central claim is that the simplified training loss in Eq. (9) inherits the Noise2Self guarantee: with $x$ and $x'$ independent corrupted measurements of the same clean volume, minimizing $\mathbb{E}\|x - F_\theta(\sqrt{\bar\alpha_t}\,x_t^* + \sqrt{1-\bar\alpha_t}\,\xi_{x-x'}, t)\|^2$ equals minimizing the same loss against the clean signal $y$ up to a constant. The paper develops this in three moves. First, the Fusion process defines $x_t^* = \lambda_t^1 x + \lambda_t^2 x'$, so the forward trajectory is an interpolation toward the target rather than a pure noising of $x'$, preventing cumulative drift. Second, the "Di-" process sets $\xi_{x-x'} = \mathrm{mess}((x-x') - \mu_{x-x'})$, a zero-mean spatially shuffled version of the measured noise difference, which retains noise variance and better matches real dMRI noise than a Gaussian. Third, training only the latter diffusion steps ($t \le T_c$) restricts the model to conditional generation and reduces hallucinations. At sampling, Run-Walk accelerated sampling and an adaptive threshold $C_{\mathrm{SNR}}$ let each slice terminate early, giving fast and controllable denoising.

Load-bearing premise

Adjacent diffusion volumes $x$ and $x'$ are independent noisy measurements of the same clean signal $y$, so their difference carries only noise; on real dMRI scans adjacent volumes use different diffusion gradients and therefore encode different signals.

Editorial extensions

If this is right

  • Denoising dMRI needs only one neighboring volume rather than a large set of diffusion directions, so the method could apply to clinical scans with very few volumes.
  • No clean ground truth and no separately trained noise model are required, simplifying the pipeline to a single training stage.
  • The $C_{\mathrm{SNR}}$ threshold gives a user-controlled trade-off: lower values preserve anatomical detail, higher values remove more noise at the cost of some detail.
  • Improved downstream performance follows directly: higher $R^2$ on DTI and CSD microstructure fits, fewer spurious tractography streamlines, and better SNR/CNR on in-vivo data.
  • Simulated fastMRI experiments suggest the approach may extend to self-supervised denoising of other MRI contrasts, not only diffusion-weighted images.

Reading between the lines

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

  • The paper's Noise2Self justification is not literally satisfied by adjacent volumes with different diffusion gradients, since those volumes encode different diffusion-weighted signals; the empirical success may indicate that the diffusion prior and the redundancy of brain structure carry much of the load.
  • The "Di-" process is effectively an empirical noise sampler, and one could test whether injecting this shuffled-difference noise improves other self-supervised denoisers that currently assume Gaussian noise.
  • Training only the latter diffusion steps appears to be a transferable recipe for reducing hallucinations in conditional diffusion restoration beyond dMRI.
  • The global $C_{\mathrm{SNR}}$ threshold could be calibrated per slice or per region instead of per volume, potentially improving edge-slice performance.
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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

3 major / 3 minor

Summary. The paper proposes Di-Fusion, a single-stage self-supervised dMRI denoising method based on a diffusion model. The forward process uses a 'Fusion' interpolation between the target slice x and its neighboring volume slice x', and replaces Gaussian noise with a spatially shuffled difference volume ('Di-' process). Training minimizes Eq. (9) over the last Tc diffusion steps, and sampling uses a deterministic Run-Walk schedule with an adaptive termination threshold C_SNR. Experiments on Stanford HARDI, Sherbrooke 3-Shell, PPMI, and simulated fastMRI data report improved SNR/CNR, microstructure model R2, tractography quality, and PSNR/SSIM relative to several baselines, and the authors claim a Noise2Self-style guarantee for the training objective.

Significance. If the theoretical basis were valid, the method would be significant for clinical dMRI because it needs no clean data, no explicit noise-model training, and offers controllable iterative refinement with demonstrated downstream benefits. Strengths of the manuscript include released code, ablations of the main components, and simulated experiments evaluated against held-out ground truth, so the reported numbers are not circular. However, the central Noise2Self derivation is invalid: Eq. (9) feeds the regression target x into the network input, and the dMRI premise that adjacent volumes share the same clean signal fails. The empirical results may still indicate a useful learned mapping, but they do not establish the paper's core claim of a self-supervised denoising guarantee.

major comments (3)
  1. [Section 3.2, Eq. (9)] The objective in Eq. (9) does not inherit the Noise2Self guarantee because the network input contains the regression target x. The input is sqrt(ᾱ_t)(λ_t^1 x + λ_t^2 x') + sqrt(1−ᾱ_t) ξ_{x−x'}, and for small t the coefficient λ_t^1 approaches 1, so the input is nearly x. Noise2Self J-invariance requires the output for a pixel to be independent of the input at that pixel; here the whole target image is available as an input, so the network can minimize Eq. (9) by copying x, and the identity E||F_θ(input)−x||^2 = E||F_θ(input)−y||^2 + const no longer holds. This failure is independent of the dMRI premise and is sufficient to break the claimed self-supervised denoising derivation.
  2. [Section 3, first paragraph, and Eq. (8)] The assumption that x = X_{*,*,i,j} and x' = X_{*,*,i,j−1} are 'independent corrupted measurements of the clean ground truth y' is false for dMRI: adjacent volumes are acquired with different diffusion gradient directions, so their underlying diffusion-weighted signals differ. Noise2Noise and Noise2Self require pairs that share the same clean target y. On the real HARDI, Sherbrooke, and PPMI datasets, the training objective therefore minimizes toward a different diffusion-weighted signal rather than toward a common clean volume, which is a second, independent failure of the theoretical basis.
  3. [Section 3.3, Eq. (10), and Algorithm 2] The 'Di-' process is effectively absent from the reverse sampling update: Eq. (10) contains the term (σ_t · η) ξ_{x−x'}, and the experiments set η = 0. The shuffled noise ξ appears only in the initial x_Tc and during training. Thus the statements that the Di- process characterizes real-world noise during the iterative refinement (Q2 and Q5) overstate its role; the refinement steps themselves are deterministic. This is a consistency issue between the method description and the actual sampling procedure.
minor comments (3)
  1. [Section 2.1, Eq. (1)] Equation (1) is stated as an equality of argmin without explicitly listing the required conditions on x, x', and y; please state the independence and identical-clean-target assumptions explicitly, since the paper later relies on them.
  2. [Eq. (12), Appendix D.1] The symbols β1 and β2 are used both for the noise-schedule terms in Section 2.2 and for the brain-mask thresholds in Appendix D.1 (β1 = −0.93, β2 = −0.95); the main text also uses ρ1 and ρ2 for the same thresholds. Please rename to avoid confusion.
  3. [Section 3.2, first paragraph] The sentence 'we first consider x and x′ as J = {x, x′}' conflates Noise2Self's pixel-partition J-invariance with a pair of whole images; please clarify the intended partition and how it is implemented in the network.

Circularity Check

1 steps flagged · score 6.0 of 10

The Noise2Self equivalence claimed for Eq. (9) is self-referential: the input contains the target x via Eq. (6) and Eq. (8), so the 'self-supervised denoising' derivation is an artifact of the construction.

  1. self definitional [Section 3.1 Eqs. (6), (8); Section 3.2 Eq. (9); Algorithm 1]
    "Assuming that the noise distributions ofx and x′ are mutually independent, the model with x′ as input and x as the optimization target satisfies the property of input-output independence. ... x∗t = λt1x + λt2x′, (6); ξx−x′ = mess ((x − x′) − µx−x′) (8); Lsimple(θ) := Et,x∗t ,ξx−x′ h x − Fθ(√¯αtx∗t + √1 − ¯αtξx−x′, t) 2i (9)"

    The preceding Noise2Self argument requires Fθ's input to be independent of the regression target x given the clean signal y. But Eq. (9)'s input is √ᾱt x∗t + √(1−ᾱt)ξx−x′, where Eq. (6) defines x∗t = λt1 x + λt2 x′ and Eq. (8) defines ξx−x′ from x − x′. The target x therefore appears in the input even before any network parameters are chosen. Conditional independence fails, and the loss can be minimized by reading the x-component out of the input rather than by estimating the clean signal. Consequently, the claimed identity E∥Fθ(x′)−x∥² = E∥Fθ(x′)−y∥² + const does not hold for Eq. (9); the equivalence to clean-signal training is a consequence of the construction, not of Noise2Self.

full rationale

The empirical comparisons are not circular: Section 4.4 evaluates PSNR/SSIM on simulated data with a clean ground truth that is not used in training, and the downstream tractography and microstructure metrics are computed on data independent of the fitted parameters. There is also no load-bearing self-citation chain; the cited statistical denoising results (Noise2Self, Noise2Noise, DDM2, Patch2Self) are external to this paper's authors. The circularity is confined to the theoretical derivation of the training objective. The paper asserts that Eq. (9) inherits the Noise2Self guarantee because 'the model with x′ as input and x as the optimization target satisfies the property of input-output independence,' but the actual input in Eq. (9) contains x through both the Fusion interpolation (Eq. (6)) and the Di-noise term (Eq. (8)). The claimed guarantee is therefore self-referential by construction. This leaves the empirical results with independent content but invalidates the paper's central self-supervised-denoiser justification. The separate concern that adjacent dMRI volumes do not share the same clean signal (Section 3, first paragraph) is an assumption failure rather than a circularity, and the manuscript's own limitations paragraph acknowledges the additive-Gaussian scope.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central method rests on the assertion that adjacent dMRI volumes are repeated noisy measurements of the same clean image. This is false for volumes with different diffusion gradients, and it invalidates the Noise2Self-based derivation. Additional assumptions about additive Gaussian noise and the noise-like behavior of the shuffled difference are also load-bearing.

free parameters (5)
  • Tc (number of trained diffusion steps) = 300
    Cutoff for training the latter diffusion steps; ablation shows results are stable for Tc < 500.
  • C_SNR (adaptive termination threshold) = 0.040
    Controls denoising strength and stopping; chosen per experiment, with values 0.040-0.085 shown.
  • Tr and p (Run-Walk schedule) = Tr=50, p=10
    Defines the accelerated sampling sub-sequence; Tr=1 reduces to DDIM and Tr=Tc to DDPM.
  • rho1, rho2 (brain mask thresholds) = -0.93, -0.95
    Used in Eq. (12) to compute bx; authors state changing them has little impact.
  • Noise schedule beta = 5e-5 to 1e-2 (printed as 1e2)
    Reverse warm-up schedule from DDM2; the printed value 1e2 appears to be a typo.
assumptions (4)
  • domain assumption x and x' are independent corrupted measurements of the same clean ground truth y
    Stated in Section 3 first paragraph; violated when adjacent volumes have different diffusion gradients.
  • domain assumption Additive Gaussian noise model for dMRI
    Used throughout; acknowledged as a limitation in Section 5.
  • ad hoc to paper Noise2Self J-invariance applies to the constructed input
    The input in Eq. (9) contains the target x, so the required independence is not satisfied.
  • domain assumption Spatial shuffling of x-x' yields a zero-mean noise whose statistics are those of the measurement noise
    Appendix C.2 proves variance preservation but ignores the signal difference between volumes.

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Pith. "Pith review of Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement." pith.science (2026). https://pith.science/paper/UF4T6TQ6

@misc{pith2026250113514,
  author       = {Pith},
  title        = {Pith review of: Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UF4T6TQ6}},
  note         = {Machine review of arXiv:2501.13514}
}
read the original abstract

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a ``microscope'' for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://github.com/FouierL/Di-Fusion.

Figures

Figures reproduced from arXiv: 2501.13514 by the authors.

Figure 1
Figure 1. (a) Fusion process (Section 3.1) aligns {x¯t} T 1 to {xt} T 1 and avoids drift (“Drift” means drifted results, “Final” means the denoised version of “Target”); (b) Training the latter diffusion steps (Section 3.2) imposes restrictions on the generation ability of diffusion models and decreases uncertainty; (c) Run-Walk accelerated sampling (Section 3.3) accelerates the entire sampling process. larger proportion of x… view at source ↗
Figure 2
Figure 2. Overview of our single-stage Di-Fusion. The training process does not involve any extra [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Density map of FBC projected on the streamlines of the OR bundles. The numbers [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Scatter plots of the microstructure model predictions against input data. The top-left of [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Qualitative results. “OURS” results are obtained [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: R2 of microstructure model fitting on CSD & DTI obtained when Tc is different. When Tc < 500, the performance is consistent. Furthermore, we balance the training epochs for different Tc 4 and show R2 of microstructure model fitting results in [PITH_FULL_IMAGE:figures/…

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Reference graph

Works this paper leans on

88 extracted references · 61 canonical work pages

  1. [1]

    Ssfd: Self-supervised feature distance as an mr image reconstruction quality metric

    Philip M Adamson, Beliz Gunel, Jeffrey Dominic, Arjun D Desai, Daniel Spielman, Shreyas Vasanawala, John M Pauly, and Akshay Chaudhari. Ssfd: Self-supervised feature distance as an mr image reconstruction quality metric. In NeurIPS 2021 Workshop on Deep Learning and Inverse Problems, 2021

    2021

  2. [2]

    Evaluation of the accuracy and precision of the diffusion parameter estimation with gibbs and noise removal pipeline

    Benjamin Ades-Aron, Jelle Veraart, Peter Kochunov, Stephen McGuire, Paul Sherman, Elias Kellner, Dmitry S Novikov, and Els Fieremans. Evaluation of the accuracy and precision of the diffusion parameter estimation with gibbs and noise removal pipeline. Neuroimage, 183: 0 532--543, 2018

    2018

  3. [3]

    Cold diffusion: Inverting arbitrary image transforms without noise

    Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Cold diffusion: Inverting arbitrary image transforms without noise. Advances in Neural Information Processing Systems, 36, 2024

    2024

  4. [4]

    Mr diffusion tensor spectroscopy and imaging

    Peter J Basser, James Mattiello, and Denis LeBihan. Mr diffusion tensor spectroscopy and imaging. Biophysical journal, 66 0 (1): 0 259--267, 1994

    1994

  5. [5]

    Noise2self: Blind denoising by self-supervision

    Joshua Batson and Loic Royer. Noise2self: Blind denoising by self-supervision. In International Conference on Machine Learning, pp.\ 524--533. PMLR, 2019

    2019

  6. [6]

    Mr diffusion tractography

    Timothy EJ Behrens, Stamatios N Sotiropoulos, and Saad Jbabdi. Mr diffusion tractography. In Diffusion MRI, pp.\ 429--451. Elsevier, 2014

    2014

  7. [7]

    Restore: robust estimation of tensors by outlier rejection

    Lin-Ching Chang, Derek K Jones, and Carlo Pierpaoli. Restore: robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 53 0 (5): 0 1088--1095, 2005

    2005

  8. [8]

    Informed restore: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts

    Lin-Ching Chang, Lindsay Walker, and Carlo Pierpaoli. Informed restore: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magnetic resonance in medicine, 68 0 (5): 0 1654--1663, 2012

    2012

Show all 88 references
  1. [9]

    Utility of deep learning super-resolution in the context of osteoarthritis mri biomarkers

    Akshay S Chaudhari, Kathryn J Stevens, Jeff P Wood, Amit K Chakraborty, Eric K Gibbons, Zhongnan Fang, Arjun D Desai, Jin Hyung Lee, Garry E Gold, and Brian A Hargreaves. Utility of deep learning super-resolution in the context of osteoarthritis mri biomarkers. Journal of Magn...

    2020

  2. [10]

    Xq-nlm: denoising diffusion mri data via x-q space non-local patch matching

    Geng Chen, Yafeng Wu, Dinggang Shen, and Pew-Thian Yap. Xq-nlm: denoising diffusion mri data via x-q space non-local patch matching. In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, P...

    2016

  3. [11]

    Noise reduction in diffusion mri using non-local self-similar information in joint x- q space

    Geng Chen, Yafeng Wu, Dinggang Shen, and Pew-Thian Yap. Noise reduction in diffusion mri using non-local self-similar information in joint x- q space. Medical image analysis, 53: 0 79--94, 2019

    2019

  4. [12]

    Wavegrad: Estimating gradients for waveform generation

    Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan. Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713, 2020

    2009 arXiv

  5. [13]

    Mr image denoising and super-resolution using regularized reverse diffusion

    Hyungjin Chung, Eun Sun Lee, and Jong Chul Ye. Mr image denoising and super-resolution using regularized reverse diffusion. IEEE Transactions on Medical Imaging, 42 0 (4): 0 922--934, 2022 a

    2022

  6. [14]

    Improving diffusion models for inverse problems using manifold constraints

    Hyungjin Chung, Byeongsu Sim, Dohoon Ryu, and Jong Chul Ye. Improving diffusion models for inverse problems using manifold constraints. Advances in Neural Information Processing Systems, 35: 0 25683--25696, 2022 b

    2022

  7. [15]

    Complex diffusion-weighted image estimation via matrix recovery under general noise models

    Lucilio Cordero-Grande, Daan Christiaens, Jana Hutter, Anthony N Price, and Jo V Hajnal. Complex diffusion-weighted image estimation via matrix recovery under general noise models. Neuroimage, 200: 0 391--404, 2019

    2019

  8. [16]

    An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images

    Pierrick Coup \'e , Pierre Yger, Sylvain Prima, Pierre Hellier, Charles Kervrann, and Christian Barillot. An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images. IEEE transactions on medical imaging, 27 0 (4): 0 425--441, 2008

    2008

  9. [17]

    Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising

    Pierrick Coup \'e , Jos \'e V Manj \'o n, Montserrat Robles, and D Louis Collins. Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising. IET image Processing, 6 0 (5): 0 558--568, 2012

    2012

  10. [18]

    Diffusion models in vision: A survey

    Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

    2023

  11. [19]

    Measuring robustness in deep learning based compressive sensing

    Mohammad Zalbagi Darestani, Akshay S Chaudhari, and Reinhard Heckel. Measuring robustness in deep learning based compressive sensing. In Marina Meila and Tong Zhang (eds.), Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machi...

    2021

  12. [20]

    Vortex: Physics-driven data augmentations for consistency training for robust accelerated mri reconstruction

    Arjun D Desai, Beliz Gunel, Batu Ozturkler, Harris Beg, Shreyas Vasanawala, Brian Hargreaves, Christopher R \'e , John M Pauly, and Akshay Chaudhari. Vortex: Physics-driven data augmentations for consistency training for robust accelerated mri reconstruction. Medical Imaging w...

    2022

  13. [21]

    Noise2recon: Enabling joint mri reconstruction and denoising with semi-supervised and self-supervised learning

    Arjun D Desai, Batu M Ozturkler, Christopher M Sandino, Robert Boutin, Marc Willis, Shreyas Vasanawala, Brian A Hargreaves, Christopher M R \'e , John M Pauly, and Akshay S Chaudhari. Noise2recon: Enabling joint mri reconstruction and denoising with semi-supervised and self-su...

    arXiv

  14. [22]

    Left-invariant diffusions on the space of positions and orientations and their application to crossing-preserving smoothing of hardi images

    Remco Duits and Erik Franken. Left-invariant diffusions on the space of positions and orientations and their application to crossing-preserving smoothing of hardi images. International Journal of Computer Vision, 92: 0 231--264, 2011

    2011

  15. [23]

    Patch2self: Denoising diffusion mri with self-supervised learning

    Shreyas Fadnavis, Joshua Batson, and Eleftherios Garyfallidis. Patch2self: Denoising diffusion mri with self-supervised learning. Advances in Neural Information Processing Systems, 33: 0 16293--16303, 2020 a

    2020

  16. [24]

    Patch2self: Denoising diffusion mri with self-supervised learning

    Shreyas Fadnavis, Joshua Batson, and Eleftherios Garyfallidis. Patch2self: Denoising diffusion mri with self-supervised learning. Advances in Neural Information Processing Systems, 33: 0 16293--16303, 2020 b

    2020

  17. [25]

    Patch2self2: Self-supervised denoising on coresets via matrix sketching

    Shreyas Fadnavis, Agniva Chowdhury, Joshua Batson, Petros Drineas, and Eleftherios Garyfallidis. Patch2self2: Self-supervised denoising on coresets via matrix sketching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 27641--27651, 2024

    2024

  18. [26]

    Generative diffusion prior for unified image restoration and enhancement

    Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, and Bo Dai. Generative diffusion prior for unified image restoration and enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 9935--9946, 2023

    2023

  19. [27]

    Corediff: Contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization

    Qi Gao, Zilong Li, Junping Zhang, Yi Zhang, and Hongming Shan. Corediff: Contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization. IEEE Transactions on Medical Imaging, 2023

    2023

  20. [28]

    Dipy, a library for the analysis of diffusion mri data

    Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stefan Van Der Walt, Maxime Descoteaux, Ian Nimmo-Smith, and Dipy Contributors. Dipy, a library for the analysis of diffusion mri data. Frontiers in neuroinformatics, 8: 0 8, 2014

    2014

  21. [29]

    Simultaneous noddi and gfa parameter map generation from subsampled q-space imaging using deep learning

    Eric K Gibbons, Kyler K Hodgson, Akshay S Chaudhari, Lorie G Richards, Jennifer J Majersik, Ganesh Adluru, and Edward VR DiBella. Simultaneous noddi and gfa parameter map generation from subsampled q-space imaging using deep learning. Magnetic resonance in medicine, 81 0 (4): ...

    2019

  22. [30]

    Towards quantitative connectivity analysis: reducing tractography biases

    Gabriel Girard, Kevin Whittingstall, Rachid Deriche, and Maxime Descoteaux. Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage, 98: 0 266--278, 2014

    2014

  23. [31]

    The elements of statistical learning: data mining, inference, and prediction, volume 2

    Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009

    2009

  24. [32]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33: 0 6840--6851, 2020

    2020

  25. [33]

    Neighbor2neighbor: Self-supervised denoising from single noisy images

    Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 14781--14790, 2021

    2021

  26. [34]

    Estimation of non-normalized statistical models by score matching

    Aapo Hyv \"a rinen and Peter Dayan. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research, 6 0 (4), 2005

    2005

  27. [35]

    Retrospective harmonization of multi-site diffusion mri data acquired with different acquisition parameters

    Suheyla Cetin Karayumak, Sylvain Bouix, Lipeng Ning, Anthony James, Tim Crow, Martha Shenton, Marek Kubicki, and Yogesh Rathi. Retrospective harmonization of multi-site diffusion mri data acquired with different acquisition parameters. Neuroimage, 184: 0 180--200, 2019

    2019

  28. [36]

    Denoising diffusion restoration models

    Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song. Denoising diffusion restoration models. Advances in Neural Information Processing Systems, 35: 0 23593--23606, 2022

    2022

  29. [37]

    a ggstr \

    Elena A Kaye, Emily A Aherne, Cihan Duzgol, Ida H \"a ggstr \"o m, Erich Kobler, Yousef Mazaheri, Maggie M Fung, Zhigang Zhang, Ricardo Otazo, Hebert A Vargas, et al. Accelerating prostate diffusion-weighted mri using a guided denoising convolutional neural network: retrospect...

    2020

  30. [38]

    Noise2score: tweedie’s approach to self-supervised image denoising without clean images

    Kwanyoung Kim and Jong Chul Ye. Noise2score: tweedie’s approach to self-supervised image denoising without clean images. Advances in Neural Information Processing Systems, 34: 0 864--874, 2021

    2021

  31. [39]

    Noise2void-learning denoising from single noisy images

    Alexander Krull, Tim-Oliver Buchholz, and Florian Jug. Noise2void-learning denoising from single noisy images. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 2129--2137, 2019

    2019

  32. [40]

    High-quality self-supervised deep image denoising

    Samuli Laine, Tero Karras, Jaakko Lehtinen, and Timo Aila. High-quality self-supervised deep image denoising. Advances in Neural Information Processing Systems, 32, 2019

    2019

  33. [41]

    Looking into the functional architecture of the brain with diffusion mri

    Denis Le Bihan. Looking into the functional architecture of the brain with diffusion mri. Nature reviews neuroscience, 4 0 (6): 0 469--480, 2003

    2003

  34. [42]

    Artifacts and pitfalls in diffusion mri

    Denis Le Bihan, Cyril Poupon, Alexis Amadon, and Franck Lethimonnier. Artifacts and pitfalls in diffusion mri. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 24 0 (3): 0 478--488, 2006

    2006

  35. [43]

    Noise2noise: Learning image restoration without clean data

    Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2noise: Learning image restoration without clean data. arXiv preprint arXiv:1803.04189, 2018

    2018 arXiv

  36. [44]

    Diffusion weighted image denoising using overcomplete local pca

    Jos \'e V Manj \'o n, Pierrick Coup \'e , Luis Concha, Antonio Buades, D Louis Collins, and Montserrat Robles. Diffusion weighted image denoising using overcomplete local pca. PloS one, 8 0 (9): 0 e73021, 2013

    2013

  37. [45]

    Zero-shot noise2noise: Efficient image denoising without any data

    Youssef Mansour and Reinhard Heckel. Zero-shot noise2noise: Efficient image denoising without any data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 14018--14027, 2023

    2023

  38. [46]

    The parkinson progression marker initiative (ppmi)

    Kenneth Marek, Danna Jennings, Shirley Lasch, Andrew Siderowf, Caroline Tanner, Tanya Simuni, Chris Coffey, Karl Kieburtz, Emily Flagg, Sohini Chowdhury, et al. The parkinson progression marker initiative (ppmi). Progress in neurobiology, 95 0 (4): 0 629--635, 2011

    2011

  39. [47]

    Comparison of objective image quality metrics to expert radiologists’ scoring of diagnostic quality of mr images

    Allister Mason, James Rioux, Sharon E Clarke, Andreu Costa, Matthias Schmidt, Valerie Keough, Thien Huynh, and Steven Beyea. Comparison of objective image quality metrics to expert radiologists’ scoring of diagnostic quality of mr images. IEEE transactions on medical imaging, ...

    2019

  40. [48]

    Cleaning output of tractography via fiber to bundle coherence, a new open source implementation

    SPL Meesters, GR Sanguinetti, Eleftherios Garyfallidis, JM Portegies, PPW Ossenblok, and Remco Duits. Cleaning output of tractography via fiber to bundle coherence, a new open source implementation. a new open source implementation, 2016

    2016

  41. [49]

    Blind/referenceless image spatial quality evaluator

    Anish Mittal, Anush K Moorthy, and Alan C Bovik. Blind/referenceless image spatial quality evaluator. In 2011 conference record of the forty fifth asilomar conference on signals, systems and computers (ASILOMAR), pp.\ 723--727. IEEE, 2011

    2011

  42. [50]

    Learning in implicit generative models

    Shakir Mohamed and Balaji Lakshminarayanan. Learning in implicit generative models. arXiv preprint arXiv:1610.03483, 2016

    2016 arXiv

  43. [51]

    Noisier2noise: Learning to denoise from unpaired noisy data

    Nick Moran, Dan Schmidt, Yu Zhong, and Patrick Coady. Noisier2noise: Learning to denoise from unpaired noisy data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 12064--12072, 2020

    2020

  44. [52]

    On modeling

    Dmitry S Novikov, Valerij G Kiselev, and Sune N Jespersen. On modeling. Magnetic resonance in medicine, 79 0 (6): 0 3172--3193, 2018

    2018

  45. [53]

    A threshold selection method from gray-level histograms

    Nobuyuki Ostu. A threshold selection method from gray-level histograms. IEEE Trans SMC, 9: 0 62, 1979

    1979

  46. [54]

    Restoring vision in adverse weather conditions with patch-based denoising diffusion models

    Ozan \"O zdenizci and Robert Legenstein. Restoring vision in adverse weather conditions with patch-based denoising diffusion models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023

    2023

  47. [55]

    Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising

    Tongyao Pang, Huan Zheng, Yuhui Quan, and Hui Ji. Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 2043--2052, 2021

    2021

  48. [56]

    Pytorch: An imperative style, high-performance deep learning library

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019

    2019

  49. [57]

    Improving fiber alignment in hardi by combining contextual pde flow with constrained spherical deconvolution

    Jorg M Portegies, Rutger Henri Jacques Fick, Gonzalo R Sanguinetti, Stephan PL Meesters, Gabriel Girard, and Remco Duits. Improving fiber alignment in hardi by combining contextual pde flow with constrained spherical deconvolution. PloS one, 10 0 (10): 0 e0138122, 2015

    2015

  50. [58]

    Snr-enhanced diffusion mri with structure-preserving low-rank denoising in reproducing kernel hilbert spaces

    Gabriel Ramos-Llord \'e n, Gonzalo Vegas-S \'a nchez-Ferrero, Congyu Liao, Carl-Fredrik Westin, Kawin Setsompop, and Yogesh Rathi. Snr-enhanced diffusion mri with structure-preserving low-rank denoising in reproducing kernel hilbert spaces. Magnetic resonance in medicine, 86 0...

    2021

  51. [59]

    Stanford hardi surfaces, 2016

    Ariel Rokem. Stanford hardi surfaces, 2016

    2016

  52. [60]

    U-net: Convolutional networks for biomedical image segmentation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention--MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part...

    2015

  53. [61]

    Palette: Image-to-image diffusion models

    Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In ACM SIGGRAPH 2022 conference proceedings, pp.\ 1--10, 2022 a

    2022

  54. [62]

    Image super-resolution via iterative refinement

    Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image super-resolution via iterative refinement. IEEE transactions on pattern analysis and machine intelligence, 45 0 (4): 0 4713--4726, 2022 b

    2022

  55. [63]

    Limits to anatomical accuracy of diffusion tractography using modern approaches

    Kurt G Schilling, Vishwesh Nath, Colin Hansen, Prasanna Parvathaneni, Justin Blaber, Yurui Gao, Peter Neher, Dogu Baran Aydogan, Yonggang Shi, Mario Ocampo-Pineda, et al. Limits to anatomical accuracy of diffusion tractography using modern approaches. Neuroimage, 185: 0 1--11, 2019

    2019

  56. [64]

    Deep unsupervised learning using nonequilibrium thermodynamics

    Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pp.\ 2256--2265. PMLR, 2015

    2015

  57. [65]

    Diffusionblend: Learning 3d image prior through position-aware diffusion score blending for 3d computed tomography reconstruction

    Bowen Song, Jason Hu, Zhaoxu Luo, Jeffrey A Fessler, and Liyue Shen. Diffusionblend: Learning 3d image prior through position-aware diffusion score blending for 3d computed tomography reconstruction. arXiv preprint arXiv:2406.10211, 2024

    2024

  58. [66]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020 a

    2010 arXiv

  59. [67]

    Generative modeling by estimating gradients of the data distribution

    Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019

    2019

  60. [68]

    Score-based generative modeling through stochastic differential equations

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020 b

    2011 arXiv

  61. [69]

    Solving inverse problems in medical imaging with score-based generative models

    Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. Solving inverse problems in medical imaging with score-based generative models. arXiv preprint arXiv:2111.08005, 2021

    2021 arXiv

  62. [70]

    FastMRI Prostate : A publicly available, biparametric MRI dataset to advance machine learning for prostate cancer imaging, 2023

    Radhika Tibrewala, Tarun Dutt, Angela Tong, Luke Ginocchio, Mahesh B Keerthivasan, Steven H Baete, Sumit Chopra, Yvonne W Lui, Daniel K Sodickson, Hersh Chandarana, and Patricia M Johnson. FastMRI Prostate : A publicly available, biparametric MRI dataset to advance machine lea...

    2023

  63. [71]

    Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution

    J-Donald Tournier, Fernando Calamante, and Alan Connelly. Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution. Neuroimage, 35 0 (4): 0 1459--1472, 2007

    2007

  64. [72]

    Score-based self-supervised mri denoising

    Jiachen Tu, Yaokun Shi, and Fan Lam. Score-based self-supervised mri denoising. In International Conference on Learning Representations, 2025

    2025

  65. [73]

    Deep image prior

    Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 9446--9454, 2018

    2018

  66. [74]

    Denoising of diffusion mri using random matrix theory

    Jelle Veraart, Dmitry S Novikov, Daan Christiaens, Benjamin Ades-Aron, Jan Sijbers, and Els Fieremans. Denoising of diffusion mri using random matrix theory. Neuroimage, 142: 0 394--406, 2016

    2016

  67. [75]

    Effects of physiological noise in population analysis of diffusion tensor mri data

    Lindsay Walker, Lin-Ching Chang, Cheng Guan Koay, Nik Sharma, Leonardo Cohen, Ragini Verma, and Carlo Pierpaoli. Effects of physiological noise in population analysis of diffusion tensor mri data. Neuroimage, 54 0 (2): 0 1168--1177, 2011

    2011

  68. [76]

    Learning fast samplers for diffusion models by differentiating through sample quality

    Daniel Watson, William Chan, Jonathan Ho, and Mohammad Norouzi. Learning fast samplers for diffusion models by differentiating through sample quality. In International Conference on Learning Representations, 2021

    2021

  69. [77]

    Q-space trajectory imaging for multidimensional diffusion mri of the human brain

    Carl-Fredrik Westin, Hans Knutsson, Ofer Pasternak, Filip Szczepankiewicz, Evren \"O zarslan, Danielle van Westen, Cecilia Mattisson, Mats Bogren, Lauren J O'Donnell, Marek Kubicki, et al. Q-space trajectory imaging for multidimensional diffusion mri of the human brain. Neuroi...

    2016

  70. [78]

    No-reference image quality metrics for structural mri

    Jeffrey P Woodard and Monica P Carley-Spencer. No-reference image quality metrics for structural mri. Neuroinformatics, 4: 0 243--262, 2006

    2006

  71. [79]

    Diffir: Efficient diffusion model for image restoration

    Bin Xia, Yulun Zhang, Shiyin Wang, Yitong Wang, Xinglong Wu, Yapeng Tian, Wenming Yang, and Luc Van Gool. Diffir: Efficient diffusion model for image restoration. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 13095--13105, 2023

    2023

  72. [80]

    Ddm ^2 : Self-supervised diffusion mri denoising with generative diffusion models

    Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop, and Akshay Chaudhari. Ddm ^2 : Self-supervised diffusion mri denoising with generative diffusion models. arXiv preprint arXiv:2302.03018, 2023

    2023 arXiv

  73. [81]

    Noise2same: Optimizing a self-supervised bound for image denoising

    Yaochen Xie, Zhengyang Wang, and Shuiwang Ji. Noise2same: Optimizing a self-supervised bound for image denoising. Advances in neural information processing systems, 33: 0 20320--20330, 2020

    2020

  74. [82]

    Noisy-as-clean: Learning self-supervised denoising from corrupted image

    Jun Xu, Yuan Huang, Ming-Ming Cheng, Li Liu, Fan Zhu, Zhou Xu, and Ling Shao. Noisy-as-clean: Learning self-supervised denoising from corrupted image. IEEE Transactions on Image Processing, 29: 0 9316--9329, 2020

    2020

  75. [83]

    Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J

    Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael...

    2018

  76. [84]

    Denoising diffusion bridge models

    Linqi Zhou, Aaron Lou, Samar Khanna, and Stefano Ermon. Denoising diffusion bridge models. In The Twelfth International Conference on Learning Representations, 2024

    2024

  77. [85]

    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...

  78. [86]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should ...

  79. [87]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@firs...

  80. [88]

    ,# (7),01444 '9=82<.342C 2! !22222222222222222222222222222222222222222222222222 Qj

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibset...

    2022

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.