Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

Chenhe Du; Hongjiang Wei; Jingjing Shi; Lixuan Chen; Qing Wu; Xuanyu Tian; Yuyao Zhang

REVIEW 4 major objections 6 minor 32 references

Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction

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

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Pith's one-line read Imperfect prior images make implicit neural representations robust for sparse-view CT.

desk verdict Interesting idea, but the paper's own tables and figures contradict its headline claims; needs major revision before it can be believed. read the letter →

arxiv 2502.05445 v1 pith:JD6HREBK submitted 2025-02-08 eess.IV cs.CV

Xuanyu Tian , Lixuan Chen , Qing Wu , Chenhe Du , Jingjing Shi , Hongjiang Wei , Yuyao Zhang This is my paper · ORCID

classification eess.IVcs.CV

keywords sparse-viewCTimplicitneuralrepresentationself-priorembeddingunsupervisedreconstructionplug-and-playhalf-quadraticsplittingBM3Dregularizationout-of-domaingeneralizationlow-dose

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

Spener is an unsupervised method for sparse-view CT that asks a simple question: can a reconstruction network learn from its own previous, imperfect output? The paper argues yes. At each iteration, an implicit neural representation (INR) is conditioned on the prior image from the last iteration through a local image encoder, trained to match the measured sinogram, and then the result is passed through a denoiser (BM3D) to produce the next prior. The authors claim that this self-prior loop makes the unsupervised method competitive with supervised state-of-the-art reconstruction on in-domain CT data, better than them on out-of-domain data (COVID-19 chest CT and head CT), and markedly more robust to noisy low-dose sinograms. The paper matters because it suggests paired training data and per-scanner retraining may not be necessary for high-quality sparse-view CT reconstruction.

What carries the argument

The engine of the method is a plug-and-play half-quadratic splitting (PnP-HQS) loop, in which the data-fidelity step is not a closed-form update but the training of an implicit neural representation (INR). The INR maps spatial coordinates p and the previous prior image z_{t-1} to image intensity; the prior is injected through a two-layer CNN image encoder that produces local feature vectors V(p), which are concatenated with multiresolution hash-encoded coordinates before being fed to an MLP. The network is trained by minimizing a sinogram data-consistency loss plus a regularization loss that keeps the output close to the prior image (Eq. 12), and the new reconstruction is then denoised by BM3D to form the next prior (Eq. 13). This self-prior embedding is what carries the claim: the imperfect prior image explicitly constrains the solution space at every iteration, and the alternating loop is the mechanism that refines it.

What would settle it

Run Spener on a digital phantom with known ground truth at a fixed 60-view geometry and repeat with several random seeds and Poisson noise levels; if the iteration-by-iteration PSNR or SSIM curves do not stabilize to the same fixed point, or if starting from different initial priors (e.g., FBP vs a constant image) yields materially different final images, the central iteration assumption is falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that imperfect INR reconstructions are themselves a usable image-domain prior, and that embedding them explicitly stabilizes the neural representation. Spener alternates two subproblems: the data-fidelity subproblem is solved by training a coordinate-MLP network, whose input combines hash-encoded coordinates with local features extracted by a CNN from the previous reconstruction; the regularization subproblem is solved by applying a BM3D denoiser to the freshly rendered image to produce the next prior. The reported experiments show that on the AAPM dataset at 60 and 90 views, Spener reaches 34.47/37.16 dB PSNR, matching or beating the supervised RegFormer, while on COVID-19 and CMB-CRC head data the supervised methods degrade and Spener leads. Under low-dose Poisson noise, the advantage over supervised methods grows, with about 2.8–3.3 dB PSNR improvements at 90 views.

Load-bearing premise

The load-bearing premise is that restarting the INR at each iteration from the denoised previous reconstruction will keep improving the estimate, rather than accumulating errors; the paper shows this empirically but offers no proof of convergence or bound on error accumulation.

Editorial extensions

If this is right

If correct, supervised-level sparse-view CT reconstruction can be obtained without paired training data, removing a major practical barrier for new scanners and protocols.
Because no training data is tied to a specific anatomy or geometry, the same unsupervised method transfers to out-of-domain data where supervised models lose accuracy.
The alternating INR-plus-denoiser design gives a concrete robustness path for noisy and low-dose acquisitions, the setting closest to real clinical CT.
The same design may be applied to other linear inverse problems with differentiable forward models, though the paper presents that as future work.

Reading between the lines

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

Editorial inference: because the self-prior comes entirely from the INR's own output, the procedure can be viewed as a fixed-point iteration; a formal convergence condition on the denoiser and INR step could turn the reported empirical stabilization into a theorem.
Editorial inference: the gains from the image encoder suggest that the method is doing more than denoising—the network is reading local structure of the prior to decide where to trust or override it, so artifact-removal behaviour may emerge without being trained.
Editorial inference: replacing BM3D with a stronger learned denoiser could push performance further, but would reintroduce an external prior and weaken the unsupervised claim; testing that trade-off would clarify how much of the gain comes from the loop vs the denoiser.
Editorial inference: the evaluation uses 2D fan-beam slices; the clinically decisive test is whether the same self-prior loop stabilizes helical or cone-beam 3D acquisitions with real patient noise, which is not demonstrated in the paper.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

The one thing you should know: this paper has a genuinely interesting procedural idea, but as supplied it is not evidence for its own headline claim. The numbers in Figure 2 and Table 1 contradict each other for the same settings, and the paper's OOD narrative is undermined by its own Table 1. That is not a nitpick; it is the core comparison.

What is new: Spener runs an INR with hash encoding in a PnP-HQS loop, using an image encoder to pull local features from the previous iteration's reconstruction and feeding them into the MLP, then applying BM3D as the denoiser. That is a sensible new combination for sparse-view CT, and the authors test it on three datasets and two dose levels. The iterative ablation shows steady improvement over iterations. The code is released. The method is not circular: the data consistency loss uses the measured sinogram, and the self-prior is just the previous reconstruction, not the target.

The soft spots are real and load-bearing. Figure 2 lists Spener at 23.42 dB / 0.4645 SSIM on AAPM 60-view; Table 1 says 34.47 / 0.9163 for the same row. If the figure is right, Spener sits near FBP and the whole comparison collapses; if the table is right, the figure is mislabeled. Either way, the paper does not give a reproducible number for its central result. Separately, the text says supervised methods drop out-of-domain, but Table 1 has RegFormer scoring higher on CMB-CRC head (34.15 dB at 60 views) than on AAPM (33.78 dB) under identical view counts. That directly contradicts the claim. And the RegFormer citation points to Liu et al. 2023, a point cloud registration paper, while the actual CT baseline is Xia et al. 2022. The baseline may be fine, but the paper doesn't show it.

Minor: the main tables lack error bars (the ablations have them), and the convergence of the alternating scheme is shown only empirically. That is acceptable for this type of paper.

Who should read it: anyone working on INR-based CT reconstruction or unsupervised inverse problems. The idea has merit and the code will help people test it. But the current manuscript would mislead a reader who trusts its tables.

My recommendation: send it to peer review, but with the explicit expectation of major revision. The referees should demand reconciliation of Figure 2 and Table 1, correction of the RegFormer citation, and error bars or per-slice variance for the headline comparisons. If the authors cannot fix those, the paper should not be accepted. The idea deserves a fair shot; the numbers as printed do not.

Referee Report

4 major / 6 minor

Summary. The paper proposes Spener, an unsupervised implicit neural representation (INR) method for sparse-view CT reconstruction. The method alternates between fitting an INR, conditioned on a prior image via a small CNN encoder and hash-encoded coordinates, and applying BM3D denoising within a plug-and-play half-quadratic splitting framework. The prior image for each iteration is the denoised reconstruction of the previous iteration. Experiments on AAPM, COVID-19, and CMB-CRC head datasets claim that Spener matches supervised state-of-the-art methods on in-domain data, outperforms them on out-of-domain data, and is more robust to low-dose noise than existing INR methods.

Significance. If the reported results are correct, the core idea is valuable: imperfect INR reconstructions can serve as effective local image-domain priors, and combining this self-prior embedding with an iterative denoiser can improve unsupervised SVCT reconstruction, especially in sparse-view and noisy settings. The method requires no paired training data and the authors provide a public code link. However, the central claims currently rest on quantitative results that are internally inconsistent between the main tables and qualitative figures, and on a supervised baseline whose citation is ambiguous. Until these inconsistencies are resolved, the significance of the contribution cannot be reliably assessed.

major comments (4)

[Comparison with SOTA Methods, Figure 2 vs. Table 1] The quantitative annotations in Figure 2 directly contradict Table 1 for the same experimental settings. For AAPM 60 views, Figure 2 lists Spener at 23.42/0.4645 and FBP at 34.39/0.9274, whereas Table 1 lists Spener at 34.47/0.9163 and FBP at 23.61/0.4786. The COVID-19 90-view and CMB-CRC 90-view rows show similar reversals. These are not rounding differences; they invert the ranking of methods. Because the headline claim that Spener matches or outperforms supervised SOTA depends on Table 1, this inconsistency is load-bearing and must be corrected and verified.
[SVCT Reconstructions in Normal and Low Dose, Figure 3 vs. Table 2] The same type of contradiction appears in the noisy-dose experiments. For AAPM 90-view normal dose, Figure 3 reports Spener at 25.86/0.5720, while Table 2 reports Spener at 36.61/0.9424. For low dose, the figure reports 25.98/0.5627 and the table reports 36.17/0.9349. The qualitative figure and the quantitative table cannot both be accurate. Since the noise-robustness claim is a central contribution, the authors must reconcile these numbers and re-verify all reported values.
[Methods in Comparison & Metrics; Datasets] The supervised baseline RegFormer is cited inconsistently: the Datasets section cites it as Xia et al. 2022, while Methods in Comparison cites it as Liu et al. 2023, and the reference list entry for Liu et al. 2023 is a point-cloud registration paper unrelated to CT reconstruction. This ambiguity prevents the reader from knowing which model was actually used as the supervised comparator. Please correct the citation and state the exact architecture and training configuration of the RegFormer baseline.
[Comparison with SOTA Methods, Table 1 and Table 2] The out-of-domain and noise-robustness claims are supported by a very small test set: 10 slices per external dataset, with no error bars or statistical significance tests in Tables 1 and 2. Given that the reported differences between methods are sometimes small, the authors should report variance across test slices or otherwise justify that the conclusions are stable beyond a single 10-slice sample.

minor comments (6)

[Abstract] The abstract contains the typo "SVCsT" in "solving SVCsT inverse problems"; it should read "SVCT".
[Figure 1] The caption of Figure 1 is very terse and does not explain the meaning of the (a)-(d) panels or the symbols z_t, x_t, and F_Phi; please expand it to make the pipeline self-contained.
[Ablation Studies, Effectiveness of Iterative Reconstruction] The heading contains the typo "Reconsturction"; please correct it.
[Experimental Settings, Dataset Simulation] The paper states that detailed fan-beam geometry settings are provided in the supplementary materials, but the arXiv version does not include supplementary material; please either include it or move the geometry description into the main text.
[Ablation Studies, Tables 3-6] The ablation tables report means with standard deviations, but the number of test slices used to compute these statistics is not stated; please clarify.
[Figure 4] The performance curve in Figure 4 is not clearly labeled: it is unclear whether the vertical axis is PSNR, SSIM, or both, and which curve corresponds to which metric; please add axis labels and a legend.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: Spener's self-prior is a regularizer; reconstructions are constrained by external sinogram data and evaluated against ground truth.

full rationale

The derivation chain is self-contained. The network objective (Eqs. 10-12) combines a data-consistency loss LDC against the measured sinogram y with a regularization loss LReg that ties the network output to the previous prior image z_{t-1}; the prior itself is updated by BM3D denoising of the network's own output (Eq. 13). This is a standard plug-and-play HQS iteration, not a circular definition: the target reconstruction is not defined in terms of the fitted parameters, and all benchmark metrics compare against ground-truth CT slices external to the optimization. The 'self-prior' is a regularizer drawn from the previous iterate, and its benefit is an empirical ablation claim, not a quantity that is forced by construction. Hyperparameters such as λ and BM3D σ are chosen empirically but are not fitted to the reported PSNR/SSIM values, so there is no fitted-input-called-prediction pattern. Citations to prior INR and PnP work are background or baseline references, not load-bearing premises, and no uniqueness theorem is imported from the authors' own prior work. There are reproducibility concerns outside circularity: the PSNR/SSIM labels in Figure 2 disagree with Table 1 for the same settings, and the 'RegFormer (Liu et al. 2023)' citation in the methods points to a point-cloud registration paper rather than the described CT transformer baseline (which appears to be Xia et al. 2022). These issues affect the credibility of the headline comparison but do not make the derivation circular.

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

The method introduces no new physical entities. It depends on standard INR components and on hand-tuned hyperparameters (lambda, sigma, iteration count, hash encoding settings), plus strong assumptions about the forward model, noise model, and the effectiveness of BM3D as a CT prior.

free parameters (4)

lambda (regularization weight) = 2.5
Balances data consistency and regularization loss in Eq. (12); chosen via ablation on AAPM 90-view reconstruction (Table 6) and then fixed for all experiments.
BM3D noise level sigma = 0.01
Denoising strength in Eq. (13); set by the authors without a reported sensitivity analysis for this exact value.
Iteration count = 30 (implied by Figure 4)
Number of PnP iterations; set to a value where performance stabilizes (around iteration 20), exact final number not explicitly stated.
Hash encoding parameters (Nmin, T, b) = Nmin=2, T=2^24, b=1.95
Instant-NGP resolution parameters, given in Implementation Details without reported sensitivity analysis.

assumptions (5)

domain assumption The CT forward model is a known linear Radon transform (torch-radon fan-beam geometry) and the noise model is Poisson with known I0.
Used to simulate sinograms and to compute data consistency loss L_DC in Eq. (10); real CT may include scatter, beam hardening, and unknown geometry.
domain assumption The BM3D denoiser approximates the proximal operator of a CT image prior within PnP-HQS.
Invoked in Eq. (13) and Section 'Plug-and-Play Half-Quadratic Splitting'; no convergence proof is given for this substitution.
ad hoc to paper The image encoder can extract useful local priors from imperfect reconstructions.
Central to the method; supported only by ablation Table 4, no theoretical guarantee.
domain assumption The optimization of Phi via L_DC + lambda * L_Reg converges to a good solution at each iteration.
Underlies Eq. (12); relies on standard SGD behavior, no convergence analysis.
ad hoc to paper Sparse-view artifacts can be removed by alternating data fidelity and denoising without error accumulation.
The iterative scheme assumes successive refinement improves the image, shown empirically in Figure 4 but not proven.

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Cite this review

Pith. "Pith review of Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction." pith.science (2026). https://pith.science/paper/JD6HREBK

@misc{pith2026250205445,
  author       = {Pith},
  title        = {Pith review of: Unsupervised Self-Prior Embedding Neural Representation for Iterative Sparse-View CT Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JD6HREBK}},
  note         = {Machine review of arXiv:2502.05445}
}

read the original abstract

Emerging unsupervised implicit neural representation (INR) methods, such as NeRP, NeAT, and SCOPE, have shown great potential to address sparse-view computed tomography (SVCT) inverse problems. Although these INR-based methods perform well in relatively dense SVCT reconstructions, they struggle to achieve comparable performance to supervised methods in sparser SVCT scenarios. They are prone to being affected by noise, limiting their applicability in real clinical settings. Additionally, current methods have not fully explored the use of image domain priors for solving SVCsT inverse problems. In this work, we demonstrate that imperfect reconstruction results can provide effective image domain priors for INRs to enhance performance. To leverage this, we introduce Self-prior embedding neural representation (Spener), a novel unsupervised method for SVCT reconstruction that integrates iterative reconstruction algorithms. During each iteration, Spener extracts local image prior features from the previous iteration and embeds them to constrain the solution space. Experimental results on multiple CT datasets show that our unsupervised Spener method achieves performance comparable to supervised state-of-the-art (SOTA) methods on in-domain data while outperforming them on out-of-domain datasets. Moreover, Spener significantly improves the performance of INR-based methods in handling SVCT with noisy sinograms. Our code is available at https://github.com/MeijiTian/Spener.

Figures

Figures reproduced from arXiv: 2502.05445 by the authors.

**Figure 1.** Overview of Spener model, including (a) iterative reconstruction using an image embedding neural network FΦ, (b) architecture of the image embedding neural network FΦ, (c) solving the data fidelity subproblem via the image embedding neural network FΦ, and (d) solving regularization subproblem via a denoiser Dσ. Proposed Method Overview Spener [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 1.** (d) illustrates the process for solving the regulariza [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

**Figure 2.** Qualitative results of CT images reconstructed by the compared methods on three datasets. The top two rows show [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

Figures from the paper (3 more)

**Figure 3.** Figure 3: Qualitative results of CT image reconstructed by the compared methods under two dose settings, with both results [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]

**Figure 4.** Figure 4: Qualitative and quantitative results of Spener [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Performance curves of Spener with different [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 28 canonical work pages

[1]

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

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
[2]

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 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Cancer Moonshot Biobank . 2022. Cancer Moonshot Biobank - Colorectal Cancer Collection (CMB-CRC)

work page 2022
[4]

Chen, H.; Zhang, Y.; Chen, Y.; Zhang, J.; Zhang, W.; Sun, H.; Lv, Y.; Liao, P.; Zhou, J.; and Wang, G. 2018. LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT. IEEE transactions on medical imaging, 37(6): 1333--1347

work page 2018
[5]

Dabov, K.; Foi, A.; Katkovnik, V.; and Egiazarian, K. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8): 2080--2095

work page 2007
[6]

A.; and Fessler, J

Elbakri, I. A.; and Fessler, J. A. 2002. Statistical image reconstruction for polyenergetic X-ray computed tomography. IEEE transactions on medical imaging, 21(2): 89--99

work page 2002
[7]

Geman, D.; and Yang, C. 1995. Nonlinear image recovery with half-quadratic regularization. IEEE transactions on Image Processing, 4(7): 932--946

work page 1995
[8]

H.; McCann, M

Jin, K. H.; McCann, M. T.; Froustey, E.; and Unser, M. 2017. Deep convolutional neural network for inverse problems in imaging. IEEE transactions on image processing, 26(9): 4509--4522

work page 2017

Show all 32 references

[9]

C.; and Slaney, M

Kak, A. C.; and Slaney, M. 2001. Principles of computerized tomographic imaging. SIAM

2001
[10]

S.; Bouman, C

Kamilov, U. S.; Bouman, C. A.; Buzzard, G. T.; and Wohlberg, B. 2023. Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications. IEEE Signal Processing Magazine, 40(1): 85--97

2023
[11]

Lee, H.; Lee, J.; Kim, H.; Cho, B.; and Cho, S. 2018. Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2): 109--119

2018
[12]

Lin, Y.; Luo, Z.; Zhao, W.; and Li, X. 2023. Learning deep intensity field for extremely sparse-view CBCT reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 13--23. Springer

2023
[13]

Lin, Y.; Yang, J.; Wang, H.; Ding, X.; Zhao, W.; and Li, X. 2024. C\^ 2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11205--11214

2024
[14]

Liu, J.; Wang, G.; Liu, Z.; Jiang, C.; Pollefeys, M.; and Wang, H. 2023. RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 8417--8426

2023
[15]

Ma, C.; Li, Z.; Zhang, J.; Zhang, Y.; and Shan, H. 2023. FreeSeed: Frequency-band-aware and self-guided network for sparse-view CT reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 250--259. Springer

2023
[16]

H.; Bartley, A

McCollough, C. H.; Bartley, A. C.; Carter, R. E.; Chen, B.; Drees, T. A.; Edwards, P.; Holmes III, D. R.; Huang, A. E.; Khan, F.; Leng, S.; et al. 2017. Low-dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challeng...

2017
[17]

M \"u ller, T.; Evans, A.; Schied, C.; and Keller, A. 2022. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4): 1--15

2022
[18]

Rahaman, N.; Baratin, A.; Arpit, D.; Draxler, F.; Lin, M.; Hamprecht, F.; Bengio, Y.; and Courville, A. 2019. On the spectral bias of neural networks. In International Conference on Machine Learning, 5301--5310. PMLR

2019
[19]

W.; Kim, H.; Anirudh, R.; Mohan, K

Reed, A. W.; Kim, H.; Anirudh, R.; Mohan, K. A.; Champley, K.; Kang, J.; and Jayasuriya, S. 2021. Dynamic ct reconstruction from limited views with implicit neural representations and parametric motion fields. In Proceedings of the IEEE/CVF International Conference on Computer...

2021
[20]

Ronchetti, M. 2020. TorchRadon: Fast Differentiable Routines for Computed Tomography. arXiv:2009.14788

2020 arXiv
[21]

A.; Layegh, P.; Kiani, B.; Masoumi, F.; Ataei Nakhaei, S.; and Mostafavi, S

Shakouri, S.; Bakhshali, M. A.; Layegh, P.; Kiani, B.; Masoumi, F.; Ataei Nakhaei, S.; and Mostafavi, S. M. 2021. COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis. BMC research notes, 14(1): 178

2021
[22]

Shen, L.; Pauly, J.; and Xing, L. 2022. NeRP: implicit neural representation learning with prior embedding for sparsely sampled image reconstruction. IEEE Transactions on Neural Networks and Learning Systems

2022
[23]

Sun, Y.; Liu, J.; Xie, M.; Wohlberg, B.; and Kamilov, U. S. 2021. Coil: Coordinate-based internal learning for tomographic imaging. IEEE Transactions on Computational Imaging, 7: 1400--1412

2021
[24]

Wang, G.; Yu, H.; and De Man, B. 2008. An outlook on x-ray CT research and development. Medical physics, 35(3): 1051--1064

2008
[25]

Wu, Q.; Feng, R.; Wei, H.; Yu, J.; and Zhang, Y. 2023. Self-Supervised Coordinate Projection Network for Sparse-View Computed Tomography. IEEE Transactions on Computational Imaging, 9: 517--529

2023
[26]

Wu, W.; Hu, D.; Niu, C.; Yu, H.; Vardhanabhuti, V.; and Wang, G. 2021. DRONE: Dual-domain residual-based optimization network for sparse-view CT reconstruction. IEEE Transactions on Medical Imaging, 40(11): 3002--3014

2021
[27]

Xia, W.; Yang, Z.; Zhou, Q.; Lu, Z.; Wang, Z.; and Zhang, Y. 2022. A transformer-based iterative reconstruction model for sparse-view ct reconstruction. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 790--800. Springer

2022
[28]

Zang, G.; Idoughi, R.; Li, R.; Wonka, P.; and Heidrich, W. 2021. IntraTomo: self-supervised learning-based tomography via sinogram synthesis and prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 1960--1970

2021
[29]

Zha, R.; Zhang, Y.; and Li, H. 2022. NAF: neural attenuation fields for sparse-view CBCT reconstruction. In Medical Image Computing and Computer Assisted Intervention--MICCAI 2022: 25th International Conference, Singapore, September 18--22, 2022, Proceedings, Part VI, 442--452...

2022
[30]

Zhang, K.; Li, Y.; Zuo, W.; Zhang, L.; Van Gool, L.; and Timofte, R. 2021. Plug-and-play image restoration with deep denoiser prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10): 6360--6376

2021
[31]

Zhang, K.; Zuo, W.; Gu, S.; and Zhang, L. 2017. Learning deep CNN denoiser prior for image restoration. In Proceedings of the IEEE conference on computer vision and pattern recognition, 3929--3938

2017
[32]

Zhang, Z.; Liang, X.; Dong, X.; Xie, Y.; and Cao, G. 2018. A sparse-view CT reconstruction method based on combination of DenseNet and deconvolution. IEEE transactions on medical imaging, 37(6): 1407--1417

2018

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

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

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

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 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Cancer Moonshot Biobank . 2022. Cancer Moonshot Biobank - Colorectal Cancer Collection (CMB-CRC)

work page 2022

[4] [4]

Chen, H.; Zhang, Y.; Chen, Y.; Zhang, J.; Zhang, W.; Sun, H.; Lv, Y.; Liao, P.; Zhou, J.; and Wang, G. 2018. LEARN: Learned experts’ assessment-based reconstruction network for sparse-data CT. IEEE transactions on medical imaging, 37(6): 1333--1347

work page 2018

[5] [5]

Dabov, K.; Foi, A.; Katkovnik, V.; and Egiazarian, K. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on image processing, 16(8): 2080--2095

work page 2007

[6] [6]

A.; and Fessler, J

Elbakri, I. A.; and Fessler, J. A. 2002. Statistical image reconstruction for polyenergetic X-ray computed tomography. IEEE transactions on medical imaging, 21(2): 89--99

work page 2002

[7] [7]

Geman, D.; and Yang, C. 1995. Nonlinear image recovery with half-quadratic regularization. IEEE transactions on Image Processing, 4(7): 932--946

work page 1995

[8] [8]

H.; McCann, M

Jin, K. H.; McCann, M. T.; Froustey, E.; and Unser, M. 2017. Deep convolutional neural network for inverse problems in imaging. IEEE transactions on image processing, 26(9): 4509--4522

work page 2017