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REVIEW 4 major objections 5 minor 2 cited by

Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Cardiac cine MRI can be reconstructed spoke-by-spoke, without binning or non-uniform FFT, by learning spatial and temporal subspace bases with two small neural networks.

desk verdict A binning-free subspace-INR cardiac reconstruction with a sound mathematical core, but the fixed rank-6 assumption is unvalidated for the arrhythmia cases it motivates, and the evaluation is too weak to support the superiority claim. read the letter →

arxiv 2412.12742 v1 pith:3Z7IZQQA submitted 2024-12-17 eess.IV cs.AIcs.LG

Wenqi Huang , Veronika Spieker , Siying Xu , Gastao Cruz , Claudia Prieto , Julia Schnabel , Kerstin Hammernik , Thomas Kuestner
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Daniel Rueckert
This is my paper · ORCID
classification eess.IVcs.AIcs.LG
keywords imagereconstructionnon-CartesianMRIcardiaccineimplicitneuralrepresentationsdeeplearninglow-ranksubspaceradialsamplingFourierslicetheorem
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

Conventional real-time cardiac cine MRI trades temporal resolution for spatial quality by grouping radial k-space spokes into discrete motion states (binning) and relies on non-uniform FFT (NUFFT) to handle radial trajectories. The paper claims both steps can be skipped: the dynamic image is modeled as a product of six spatial and six temporal basis functions, each learned by a compact multilayer perceptron, and every acquired spoke is used directly as a training sample via the Fourier slice theorem. Initialized from a low-resolution binned reconstruction and fine-tuned spoke-by-spoke, the method reports higher SNR and comparable or better edge sharpness than binned NUFFT and GRASP reconstructions at acceleration factors of 10 and 20. If the claim holds, real-time cardiac MRI could capture beat-to-beat dynamics at the repetition-time level, which is exactly what standard gated acquisitions cannot do.

What carries the argument

The machinery has two linked parts. First is the subspace decomposition: the cardiac cine image is approximated as a rank-6 product of spatial and temporal bases, so the whole $x$–$y$–$t$ volume is stored in just two small MLPs rather than as a dense grid. Second is the Fourier-slice data consistency term: each radial spoke is computed as the 1D Fourier transform of the image projected along the spoke's vertical direction, which the INR allows to be evaluated on arbitrary rotated coordinates, turning each spoke into a clean training sample. The GRASP low-resolution reconstruction serves as the initializer: its SVD provides the top-6 spatial and temporal bases that both networks are first trained to match, and the authors show that without such initialization the networks fail to capture temporal dynamics.

What would settle it

Reconstruct arrhythmia or stress-perfusion cardiac data with k=6 and with k=12 under otherwise identical settings: if the k=12 reconstruction shows sharper x-t profiles or higher SNR in systole, the fixed rank-6 bottleneck is falsified for those data. A direct measurement would be computing the temporal singular value spectrum of a high-quality reference cine and checking whether the energy beyond the sixth singular value is negligible; if it is not, the subspace assumption fails on its own terms.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the low-rank subspace structure of cardiac cine data can be turned into a training signal for two implicit neural networks, so that each radial spoke constrains the reconstruction directly. The dynamic image is written as $x = G_{\theta_s}(s) \cdot G_{\theta_t}(t)$ with $G_{\theta_s}$ mapping 2D spatial coordinates to six complex spatial basis images and $G_{\theta_t}$ mapping time to six temporal coefficients; the networks have hash-grid encodings and small MLPs. Data consistency is evaluated spoke by spoke: by the Fourier slice theorem, the 1D Fourier transform of the image's projection along the spoke's perpendicular direction equals the measured spoke, and because the INR is sampled on a rotated grid the projection reduces to a summation, avoiding both binning and NUFFT. A low-resolution GRASP reconstruction is decomposed by SVD to initialize the bases, and fine-tuning on the continuous spokes is shown to recover the spatial and temporal detail that the initialization lacks. The experimental comparison reports higher SNR in systole and diastole, higher systolic edge sharpness, and clearly less temporal blurring on x-t profiles than binned NUFFT and GRASP at R=10 and R=20.

Load-bearing premise

The load-bearing premise is that the whole cardiac cine sequence is well approximated by six spatial and six temporal basis functions; if the heart's motion during a scan contains more independent temporal patterns than that, the fixed rank-6 bottleneck will smooth them away regardless of training.

Editorial extensions

If this is right

  • If the method is correct, reconstructed temporal resolution reaches the repetition time (TR), so events that binning averages away, such as rapid systolic motion, become visible in continuous frames.
  • The method reports SNR of about 20 dB in both systole and diastole, substantially above binned NUFFT (7 dB) and GRASP (14 dB), and systolic edge sharpness that beats both baselines at R=10 and R=20.
  • Removing binning removes the fixed trade-off between number of motion states and motion blur; removing NUFFT removes density-compensation and interpolation error sources from radial trajectories.
  • The spoke-as-minimal-unit formulation extends beyond cardiac cine to multi-contrast and quantitative MRI, as the paper itself states.
  • Because each scan is reconstructed by its own per-scan training without a fully sampled training set, the method is self-supervised and avoids cross-domain hallucinations, but it requires about 15 minutes per slice before an image is available.

Reading between the lines

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

  • If the rank-6 subspace approximation is as accurate for arrhythmic and high-variability patients as it is for healthy subjects, the method could make real-time cine the default for patients where ECG gating fails; this is an unstated clinical consequence of the paper's healthy-subject experiments.
  • The Fourier-slice projection trick is trajectory-agnostic in principle: spiral and rosette sampling also produce spokes along known directions, so testing this reconstruction on those trajectories would show whether the mechanism generalizes or depends on the radial golden-angle geometry.
  • The fixed k=6 can be tested empirically: if the singular values of the initial low-resolution reconstruction decay slowly, an adaptive rank per scan would be a natural extension that the paper does not explore.
  • Since the paper reports that the fine-tuned spatial bases become sharper than the initialized ones, a controlled experiment varying the initialization bin size would reveal how much of the final quality is inherited from the GRASP prior versus learned from the continuous spokes.
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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

4 major / 5 minor

Summary. The paper proposes a reconstruction framework for real-time cardiac cine MRI from continuously sampled radial k-space spokes. The dynamic image is represented as a product of spatial and temporal bases, each modeled by a separate MLP (a 'subspace INR'), with a fixed rank of k=6. The networks are initialized from a low-resolution GRASP reconstruction of binned central spokes, then fine-tuned using a spoke-specific data consistency term based on the Fourier slice theorem, which allows the method to avoid explicit binning and NUFFT during the fine-tuning stage. The method is evaluated on 17 healthy subjects, comparing against NUFFT and GRASP at acceleration rates of 10 and 20, using estimated SNR and edge sharpness as quantitative metrics, plus qualitative x-t profiles. The paper reports superior SNR and comparable or better edge sharpness versus the baseline methods.

Significance. The combination of subspace learning and implicit neural representations for cardiac cine MRI is a sensible and potentially important idea, and the use of the Fourier slice theorem to derive a binning-free data consistency term is an elegant contribution that could reduce NUFFT-related artifacts. The method is scan-specific and unsupervised, which avoids the need for large fully sampled training datasets. However, the current evidence does not yet support the paper's stronger claims, particularly those about arrhythmia patients and 'eliminating' binning and NUFFT, because the evaluation relies on proxy metrics without ground truth and the motivating pathological scenario is not tested. If the evaluation is strengthened and the claims are tempered, the work could be of significant value to the real-time cardiac MRI community.

major comments (4)
  1. [Abstract and Section 2.3] The abstract and Section 2.3 state that the method 'eliminates the need for binning and non-uniform FFT.' This is contradicted by the initialization procedure, which reconstructs a low-resolution image with GRASP using binned spoke centers and NUFFT (Section 3, 'Initialization Settings'). The contribution should be reframed as avoiding binning and NUFFT in the fine-tuning stage, not in the entire pipeline, or the initialization should be described as a preprocessing step that still relies on those operations.
  2. [Section 3 and Table 1] The central claim of 'superior spatial and temporal image quality' is supported only by estimated SNR and edge sharpness computed on the method's own reconstructions, with no fully sampled ground truth available. These metrics are known to be biased by the reconstruction's noise and smoothing properties, and no statistical significance testing is reported. The paper should include additional validation, such as a digital phantom or simulation with known ground truth, and should at least report confidence intervals or p-values for the quantitative comparisons.
  3. [Section 2.3 (k=6)] The fixed rank-6 subspace is a load-bearing assumption for the motivating use case of arrhythmias and beat-to-beat variations. The paper provides no evidence that dynamic cardiac images with irregular rhythms have intrinsic rank ≤ 6; for variable RR intervals, changing contractility, or through-plane motion, the temporal rank may be higher, and any such component is discarded by construction regardless of network training. The authors should explicitly discuss this limitation and ideally evaluate on arrhythmic data or a simulation with variable heart rates to justify the choice of k.
  4. [Section 4, Table 1] The reported SNR improvements (e.g., 20.21 ± 6.88 dB for the proposed method vs. 13.57 ± 2.79 dB for GRASP in systole) are accompanied by large standard deviations, yet no statistical tests are performed. Without significance testing, the 'considerable margin' claimed in the text is not established; the authors should apply paired tests (e.g., Wilcoxon signed-rank) across subjects and report effect sizes.
minor comments (5)
  1. [Section 3, Fine-Tuning Settings] There is a typo: 'initiaslization' should be 'initialization'.
  2. [Discussion] The word 'Similarily' should be 'Similarly'.
  3. [Figure 1] Figure 1 is dense and the flow from initialization to fine-tuning to inference is not immediately clear; consider simplifying or adding subfigure labels to guide the reader.
  4. [Discussion] The Discussion repeats the claim that the method 'eliminates' binning and NUFFT; this should be aligned with the revised wording suggested in the major comments.
  5. [Code availability] The code is listed as 'available upon acceptance' with a URL, but the URL is not provided in the manuscript text; please include the actual link or a repository identifier in the final version for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper proposes a self-supervised reconstruction fit; the fixed-rank and GRASP-initialization concerns are assumptions or overclaims, not circular reductions.

full rationale

The paper does not derive an external ground-truth quantity from first principles; it proposes a scan-specific self-supervised reconstruction algorithm. The optimization objective (Eq. 5 and Eq. 8) enforces data consistency against the acquired radial spokes, and the reported SNR and edge-sharpness values are internal image-quality metrics computed on the method's own reconstructions, with no fully sampled ground truth. This is an acknowledged evaluation limitation, not a circular reduction: the method never claims to predict a quantity that is defined in terms of the fitted parameters. The fixed rank k=6 is an explicit modeling assumption about cardiac cine low-rank structure, and the GRASP-based initialization uses binned spokes and NUFFT, which does undercut the headline that binning and NUFFT are 'eliminated' — but that is an overstatement or correctness risk, not a definitional or self-citational circularity. Self-citations such as [5] and [17] are used for context and inspiration; the load-bearing data-consistency step rests on the Fourier slice theorem and the explicit INR subspace parametrization, both stated in the paper. No specific equation reduces to its own input, and no fitted parameter is renamed as a prediction. Therefore no circular step is identifiable, and the appropriate score is 0.

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

The central claim rests on a rank-6 subspace model, the Fourier slice theorem, and the ability of hash-grid MLPs to fit the bases. No new physical entities are introduced. The free parameters are the rank, network architecture settings, and initialization hyperparameters, all chosen by hand.

free parameters (5)
  • subspace rank k = 6
    Number of spatial and temporal basis components retained after SVD in Section 2.3; controls the expressiveness of the low-rank model.
  • hash grid encoding parameters = hashmap size 20, 16 levels, 2 features per level, base resolution 16, scale 1.26
    Architecture choices for both MLPs in Model Details; chosen by hand, not fitted to data.
  • GRASP initialization binning size = 20 spokes per phase
    Binning used in the low-resolution initialization (Section 2.3), contradicting the binning-free claim for the overall pipeline.
  • GRASP total variation weight = 0.025
    Regularization weight for the initialization reconstruction.
  • learning rates and iteration counts = 0.01 for 1000 steps, 3e-5 for 150 iterations
    Optimization settings chosen empirically; stopping criteria noted as empirically determined in the Discussion.
assumptions (4)
  • domain assumption Cardiac cine MRI dynamic image can be approximated as the product of a small number (k=6) of spatial and temporal basis functions.
    Invoked in Section 2.2 to justify subspace decomposition; the rank is fixed without validation against ground truth.
  • standard math The Fourier slice theorem applies to the acquired radial spokes with coil sensitivities, allowing each spoke to be modeled as a 1D Fourier transform of a projection of the image.
    Used in Section 2.2, Eq. 6-8; this is a standard theorem in tomography and MRI.
  • domain assumption Each radial spoke is acquired at a single time point with no significant intra-spoke motion during the 2.3 ms TR.
    Necessary for the spoke-wise data consistency in Section 2.2; motion within a spoke would invalidate the single-time-frame model.
  • domain assumption The two MLPs with hash-grid encoding can represent the true spatial and temporal bases accurately after fine-tuning.
    Relies on the capacity and generalization of the chosen network architectures; not proven.

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Cite this review

Pith. "Pith review of Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging." pith.science (2026). https://pith.science/paper/3Z7IZQQA

@misc{pith2026241212742,
  author       = {Pith},
  title        = {Pith review of: Subspace Implicit Neural Representations for Real-Time Cardiac Cine MR Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3Z7IZQQA}},
  note         = {Machine review of arXiv:2412.12742}
}
read the original abstract

Conventional cardiac cine MRI methods rely on retrospective gating, which limits temporal resolution and the ability to capture continuous cardiac dynamics, particularly in patients with arrhythmias and beat-to-beat variations. To address these challenges, we propose a reconstruction framework based on subspace implicit neural representations for real-time cardiac cine MRI of continuously sampled radial data. This approach employs two multilayer perceptrons to learn spatial and temporal subspace bases, leveraging the low-rank properties of cardiac cine MRI. Initialized with low-resolution reconstructions, the networks are fine-tuned using spoke-specific loss functions to recover spatial details and temporal fidelity. Our method directly utilizes the continuously sampled radial k-space spokes during training, thereby eliminating the need for binning and non-uniform FFT. This approach achieves superior spatial and temporal image quality compared to conventional binned methods at the acceleration rate of 10 and 20, demonstrating potential for high-resolution imaging of dynamic cardiac events and enhancing diagnostic capability.

Figures

Figures reproduced from arXiv: 2412.12742 by the authors.

Figure 1
Figure 1. Method overview: a) Continuous k-space sampling with tiny golden angle radial trajectory; b) Binning spoke centers to reconstruct a low-resolution image with GRASP, decomposing into spatial and temporal bases via SVD, retaining top-k components; c) Interpolating and fitting low-resolution bases to spatial and temporal networks; d) Inputting rotated spatial and accurate temporal coordinates into networks to obtain sp… view at source ↗
Figure 2
Figure 2. Comparison of reconstruction results. a) and b) show two subjects. The first row of each subfigure shows x−y images at a selected cardiac phase. The second row zooms into a region of interest (ROI). The third row presents x − t profiles along a chosen y-coordinate. Our method preserves details in the ROI (yellow arrow) and minimizes temporal blurring seen in binned methods (red arrow). The color bars indicate intens… view at source ↗
Figure 3
Figure 3. The first four components of the spatial and temporal bases for an example case. a) The spatial bases are shown across four columns, where the first row displays the initialized spatial bases derived from low-resolution reconstruction, the second row shows the fine-tuned representations obtained by training based on the low-resolution initialization, and the third row presents the bases directly learned without init… view at source ↗

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. INR meets Multi-Contrast MRI Reconstruction

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    A joint implicit neural representation with complementary undersampling reconstructs MPnRAGE multi-contrast brain MRI with better SSIM/PSNR than PICS at R=8 and R=12.

Reference graph

Works this paper leans on

45 extracted references · 40 canonical work pages · cited by 2 Pith papers

  1. [1]

    Magnetic resonance in medicine74(5), 1266–1278 (2015)

    Ahmad, R., Xue, H., Giri, S., Ding, Y., Craft, J., Simonetti, O.P.: Variable density incoherent spatiotemporal acquisition (vista) for highly accelerated cardiac mri. Magnetic resonance in medicine74(5), 1266–1278 (2015)

    work page 2015

  2. [2]

    Magnetic resonance in medicine 81(1), 439–453 (2019)

    Akçakaya, M., Moeller, S., Weingärtner, S., Uğurbil, K.: Scan-specific ro- bust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging. Magnetic resonance in medicine 81(1), 439–453 (2019)

    work page 2019

  3. [3]

    Magnetic Resonance in Medicine92(6), 2447–2463 (2024)

    Blumenthal, M., Fantinato, C., Unterberg-Buchwald, C., Haltmeier, M., Wang, X., Uecker, M.: Self-supervised learning for improved calibrationless radial mri with nlinv-net. Magnetic Resonance in Medicine92(6), 2447–2463 (2024)

    work page 2024

  4. [4]

    Bracewell,R.N.:Stripintegrationinradioastronomy.AustralianJournalofPhysics 9(2), 198–217 (1956)

    work page 1956

  5. [5]

    Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields

    Catalán, T., Courdurier, M., Osses, A., Botnar, R., Costabal, F.S., Prieto, C.: Unsupervised reconstruction of accelerated cardiac cine mri using neural fields. arXiv preprint arXiv:2307.14363 (2023)

    work page Pith review arXiv 2023

  6. [6]

    In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition

    Chibane, J., Alldieck, T., Pons-Moll, G.: Implicit functions in feature space for 3D shape reconstruction and completion. In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition. pp. 6970–6981 (2020)

    work page 2020

  7. [7]

    IEEE Transactions on Biomedical Engineering61(9), 2451–2457 (2014)

    Christodoulou, A.G., Hitchens, T.K., Wu, Y.L., Ho, C., Liang, Z.P.: Improved subspace estimation for low-rank model-based accelerated cardiac imaging. IEEE Transactions on Biomedical Engineering61(9), 2451–2457 (2014)

    work page 2014

  8. [8]

    arXiv preprint arXiv:2209.00835 (2022)

    Cui, Z.X., Cao, C., Liu, S., Zhu, Q., Cheng, J., Wang, H., Zhu, Y., Liang, D.: Self-score: Self-supervised learning on score-based models for mri reconstruction. arXiv preprint arXiv:2209.00835 (2022)

    arXiv 2022

Show all 45 references
  1. [9]

    Magnetic Resonance in Medicine90(5), 2052–2070 (2023)

    Desai, A.D., Ozturkler, B.M., Sandino, C.M., Boutin, R., Willis, M., Vasanawala, S., Hargreaves, B.A., Ré, C., Pauly, J.M., Chaudhari, A.S.: Noise2recon: Enabling snr-robust mri reconstruction with semi-supervised and self-supervised learning. Magnetic Resonance in Medicine90(...

    2023

  2. [10]

    arXiv preprint arXiv:2301.00127 (2022)

    Feng, J., Feng, R., Wu, Q., Zhang, Z., Zhang, Y., Wei, H.: Spatiotemporal implicit neural representation for unsupervised dynamic mri reconstruction. arXiv preprint arXiv:2301.00127 (2022)

    2022

  3. [11]

    Magnetic resonance in medicine72(3), 707– 717 (2014)

    Feng, L., Grimm, R., Block, K.T., Chandarana, H., Kim, S., Xu, J., Axel, L., Sodickson, D.K., Otazo, R.: Golden-angle radial sparse parallel mri: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric mri....

    2014

  4. [12]

    Magnetic resonance in medicine83(1), 94–108 (2020) 14 W

    Feng, L., Wen, Q., Huang, C., Tong, A., Liu, F., Chandarana, H.: Grasp-pro: improving grasp dce-mri through self-calibrating subspace-modeling and contrast phase automation. Magnetic resonance in medicine83(1), 94–108 (2020) 14 W. Huang et al

    2020

  5. [13]

    In: BVM Workshop

    Haft, P.T., Huang, W., Cruz, G., Rueckert, D., Zimmer, V.A., Hammernik, K.: Neural implicit k-space with trainable periodic activation functions for cardiac mr imaging. In: BVM Workshop. pp. 82–87. Springer (2024)

    2024

  6. [14]

    Magnetic resonance in medicine79(6), 3055–3071 (2018)

    Hammernik, K., Klatzer, T., Kobler, E., Recht, M.P., Sodickson, D.K., Pock, T., Knoll, F.: Learning a variational network for reconstruction of accelerated MRI data. Magnetic resonance in medicine79(6), 3055–3071 (2018)

    2018

  7. [15]

    In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part VI 24

    Hu, C., Li, C., Wang, H., Liu, Q., Zheng, H., Wang, S.: Self-supervised learning for mri reconstruction with a parallel network training framework. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, Se...

    2021

  8. [16]

    Medical Image Analysis 73, 102190 (2021)

    Huang, W., Ke, Z., Cui, Z.X., Cheng, J., Qiu, Z., Jia, S., Ying, L., Zhu, Y., Liang, D.: Deep low-rank plus sparse network for dynamic MR imaging. Medical Image Analysis 73, 102190 (2021)

    2021

  9. [17]

    Huang, W., Li, H.B., Pan, J., Cruz, G., Rueckert, D., Hammernik, K.: Neural implicitk-spaceforbinning-freenon-cartesiancardiacmrimaging.In:International Conference on Information Processing in Medical Imaging. pp. 548–560. Springer (2023)

    2023

  10. [18]

    Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine63(1), 68–78 (2010)

    Jung, H., Park, J., Yoo, J., Ye, J.C.: Radial k-t focuss for high-resolution cardiac cine mri. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine63(1), 68–78 (2010)

    2010

  11. [19]

    IEEE Transactions on Medical Imaging40(12), 3698–3710 (2021)

    Ke, Z., Huang, W., Cui, Z.X., Cheng, J., Jia, S., Wang, H., Liu, X., Zheng, H., Ying, L., Zhu, Y., et al.: Learned low-rank priors in dynamic mr imaging. IEEE Transactions on Medical Imaging40(12), 3698–3710 (2021)

    2021

  12. [20]

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

    Korkmaz, Y., Cukur, T., Patel, V.M.: Self-supervised mri reconstruction with un- rolled diffusion models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 491–501. Springer (2023)

    2023

  13. [21]

    IEEE Transactions on Com- putational Imaging (2024)

    Kunz, J.F., Ruschke, S., Heckel, R.: Implicit neural networks with fourier-feature inputs for free-breathing cardiac mri reconstruction. IEEE Transactions on Com- putational Imaging (2024)

    2024

  14. [22]

    Magnetic resonance in medicine84(4), 2018– 2033 (2020)

    Küstner,T.,Bustin,A.,Jaubert,O.,Hajhosseiny,R.,Masci,P.G.,Neji,R.,Botnar, R., Prieto, C.: Isotropic 3d cartesian single breath-hold cine mri with multi-bin patch-based low-rank reconstruction. Magnetic resonance in medicine84(4), 2018– 2033 (2020)

    2020

  15. [23]

    IEEE transactions on medical imaging 30(5), 1042–1054 (2011)

    Lingala, S.G., Hu, Y., DiBella, E., Jacob, M.: Accelerated dynamic mri exploiting sparsity and low-rank structure: kt slr. IEEE transactions on medical imaging 30(5), 1042–1054 (2011)

    2011

  16. [24]

    In: NeurIPS 2023 Workshop on Deep Learning and Inverse Problems (2023)

    Mancu, A., Huang, W., da Cruz, G.L., Rueckert, D., Hammernik, K.: Self- supervised low-rank plus sparse network for radial mri reconstruction. In: NeurIPS 2023 Workshop on Deep Learning and Inverse Problems (2023)

    2023

  17. [25]

    Com- munications of the ACM65(1), 99–106 (2021)

    Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: Representing scenes as neural radiance fields for view synthesis. Com- munications of the ACM65(1), 99–106 (2021)

    2021

  18. [26]

    ACM transactions on graphics (TOG)41(4), 1–15 (2022)

    Müller,T.,Evans,A.,Schied,C.,Keller,A.:Instantneuralgraphicsprimitiveswith a multiresolution hash encoding. ACM transactions on graphics (TOG)41(4), 1–15 (2022)

    2022

  19. [27]

    Lulu.com (2010)

    Nishimura, D.G.: Principles of Magnetic Resonance Imaging. Lulu.com (2010)

    2010

  20. [28]

    Magnetic resonance in medicine73(3), 1125–1136 (2015) Subspace INRs for Real-Time Cardiac Cine MR Imaging 15

    Otazo, R., Candes, E., Sodickson, D.K.: Low-rank plus sparse matrix decompo- sition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic resonance in medicine73(3), 1125–1136 (2015) Subspace INRs for Real-Time Cardiac Cine MR Imaging 15

    2015

  21. [29]

    Medical Image Analysis 91, 103017 (2024)

    Pan, J., Hamdi, M., Huang, W., Hammernik, K., Kuestner, T., Rueckert, D.: Un- rolled and rapid motion-compensated reconstruction for cardiac cine mri. Medical Image Analysis 91, 103017 (2024)

    2024

  22. [30]

    IEEE Trans- actions on Medical Imaging (2024)

    Pan, J., Huang, W., Rueckert, D., Küstner, T., Hammernik, K.: Reconstruction- driven motion estimation for motion-compensated mr cine imaging. IEEE Trans- actions on Medical Imaging (2024)

    2024

  23. [31]

    Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine41(1), 179–186 (1999)

    Pipe, J.G., Menon, P.: Sampling density compensation in MRI: rationale and an iterative numerical solution. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine41(1), 179–186 (1999)

    1999

  24. [32]

    IEEE transactions on medical imaging38(1), 280–290 (2018)

    Qin, C., Schlemper, J., Caballero, J., Price, A.N., Hajnal, J.V., Rueckert, D.: Con- volutional recurrent neural networks for dynamic MR image reconstruction. IEEE transactions on medical imaging38(1), 280–290 (2018)

    2018

  25. [33]

    Magnetic Resonance in Medicine 91(5), 1978–1993 (2024)

    Qiu, Z., Hu, S., Zhao, W., Sakaie, K., Sun, J.E., Griswold, M.A., Jones, D.K., Ma, D.: Self-calibrated subspace reconstruction for multidimensional mr fingerprinting for simultaneous relaxation and diffusion quantification. Magnetic Resonance in Medicine 91(5), 1978–1993 (2024)

    2024

  26. [34]

    Radiology 307(3), e223008 (2023)

    Rajiah, P.S., François, C.J., Leiner, T.: Cardiac mri: state of the art. Radiology 307(3), e223008 (2023)

    2023

  27. [35]

    In: International conference on information processing in medical imaging

    Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D.: A deep cascade of convolutional neural networks for MR image reconstruction. In: International conference on information processing in medical imaging. pp. 647–658. Springer (2017)

    2017

  28. [36]

    Journal of Magnetic Resonance Imaging: AnOfficialJournaloftheInternationalSocietyforMagneticResonanceinMedicine 13(2), 301–307 (2001)

    Shea, S.M., Kroeker, R.M., Deshpande, V., Laub, G., Zheng, J., Finn, J.P., Li, D.: Coronary artery imaging: 3d segmented k-space data acquisition with multiple breath-holds and real-time slab following. Journal of Magnetic Resonance Imaging: AnOfficialJournaloftheInternational...

    2001

  29. [37]

    IEEE Transactions on Neural Networks and Learning Systems35(1), 770–782 (2022)

    Shen, L., Pauly, J., Xing, L.: Nerp: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction. IEEE Transactions on Neural Networks and Learning Systems35(1), 770–782 (2022)

    2022

  30. [38]

    Advances in Neural Information Processing Systems 33, 7462–7473 (2020)

    Sitzmann, V., Martel, J., Bergman, A., Lindell, D., Wetzstein, G.: Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems 33, 7462–7473 (2020)

    2020

  31. [39]

    IEEE Transactions on Medical Imaging (2023)

    Spieker, V., Eichhorn, H., Hammernik, K., Rueckert, D., Preibisch, C., Karampinos, D.C., Schnabel, J.A.: Deep learning for retrospective motion cor- rection in mri: a comprehensive review. IEEE Transactions on Medical Imaging (2023)

    2023

  32. [40]

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

    Spieker, V., Eichhorn, H., Stelter, J.K., Huang, W., Braren, R.F., Rückert, D., Sahli Costabal, F., Hammernik, K., Prieto, C., Karampinos, D.C., et al.: Self- supervised k-space regularization for motion-resolved abdominal mri using neural implicit k-space representations. In:...

    2024

  33. [41]

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

    Spieker, V., Huang, W., Eichhorn, H., Stelter, J., Weiss, K., Zimmer, V.A., Braren, R.F., Karampinos, D.C., Hammernik, K., Schnabel, J.A.: Iconik: Gen- erating respiratory-resolved abdominal mr reconstructions using neural implicit representations in k-space. In: International...

    2023

  34. [42]

    In: Proc Intl Soc Magn Reson Med

    Uecker, M., Tamir, J.I., Ong, F., Lustig, M.: The bart toolbox for computational magnetic resonance imaging. In: Proc Intl Soc Magn Reson Med. vol. 24, p. 1 (2016) 16 W. Huang et al

    2016

  35. [43]

    Investigative Magnetic Resonance Imaging25(4), 252–265 (2021)

    Wang, X., Uecker, M., Feng, L.: Fast real-time cardiac mri: A review of current techniques and future directions. Investigative Magnetic Resonance Imaging25(4), 252–265 (2021)

    2021

  36. [44]

    Journal of Magnetic Resonance Imaging 40(5), 1022–1040 (2014)

    Wright, K.L., Hamilton, J.I., Griswold, M.A., Gulani, V., Seiberlich, N.: Non- Cartesian parallel imaging reconstruction. Journal of Magnetic Resonance Imaging 40(5), 1022–1040 (2014)

    2014

  37. [45]

    Magnetic resonance in medicine84(6), 3172–3191 (2020)

    Yaman, B., Hosseini, S.A.H., Moeller, S., Ellermann, J., Uğurbil, K., Akçakaya, M.: Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magnetic resonance in medicine84(6), 3172–3191 (2020)

    2020

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.