Pith. sign in

REVIEW 3 major objections 5 minor 32 references

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read Hybrid reconstruction cuts limited-angle CT error by forcing data consistency

desk verdict DCAR is a credible, well-evaluated combination of a U-Net prior with data consistency for limited-angle CT; the headline RMSE gains are plausible, but the e2 choice and missing-range prior accuracy need a sensitivity check. read the letter →

arxiv 1908.06792 v2 pith:NWIFETB3 submitted 2019-08-19 eess.IV cs.CVcs.LG

Yixing Huang , Alexander Preuhs , Guenter Lauritsch , Michael Manhart , Xiaolin Huang , Andreas Maier This is my paper · ORCID
classification eess.IVcs.CVcs.LG MSC 68T0765J2292C55 PACS 87.57.Q87.57.nf
keywords limitedangletomographydataconsistencydeeplearningpriorU-NetartifactreductionPoissonnoiseiterativereconstructioncone-beamCT
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that purely deep-learning reconstructions for limited-angle CT are unreliable because they ignore the measured projection data and are sensitive to noise. To fix this, the authors propose DCAR, a method that uses a U-Net's output only as a prior image and then applies an iterative reconstruction that enforces agreement with the measured projections while gently steering the unmeasured angular range toward the U-Net prior. In 120-degree cone-beam experiments on patient data, DCAR reduces root-mean-square error by more than 10 percent in noise-free cases and more than 24 percent in Poisson-noise cases compared with the U-Net alone. The central point is that deep learning should complement, not replace, the physics-based reconstruction constraint.

What carries the argument

The central object is the constrained optimization problem with two data-fidelity terms: one for the measured angular range (using $A_m$ and $p_m$) and one for the unmeasured angular range (using $A_u$ and the forward projection of the U-Net prior $f_{\text{U-Net}}$). The mechanism carries the argument by allowing the final image to be initialized as the U-Net output and then iteratively corrected so that it agrees with measured data; a soft-thresholding operator $S_\tau$ handles error tolerance, and iterative reweighted total variation regularizes the result. The work this does is to confine the deep learning prior's influence to the unmeasured angles, where no true data exist, while measured rays directly correct any inconsistency the U-Net introduced.

What would settle it

Run a limited-angle reconstruction with a deliberately corrupted U-Net prior (for example, a prior containing a false dark hole in the unmeasured angular range) and check whether DCAR reproduces the false structure; if it does, the unmeasured-range constraint is not actually preventing the prior's bad influence, contradicting the paper's claim that inaccuracies lie only in unmeasured areas.

Watch

Extended reading notes

Core claim

The paper establishes that a deep-learning reconstruction that is not consistent with the measured projection data can be substantially improved by treating it as a prior inside a constrained iterative reconstruction. The authors define a reconstruction $f$ that must satisfy $\|A_m f - p_m\| < e_1$ for measured projections and $\|A_u f - A_u f_{\text{U-Net}}\| < e_2$ for the unmeasured angular range, initialized with $f_{\text{U-Net}}$. Solving this with SART plus iterative reweighted total variation yields images that have both the anatomical plausibility learned by the U-Net and the fidelity to actual measurements that the U-Net lacks. On leave-one-out cross-validation with 17 patient datasets, the mean RMSE improvement over the U-Net is more than 10 percent in the noise-free setting and more than 24 percent with Poisson noise, with the largest gains occurring where the U-Net produces dark holes or smoothed-out cavities.

Load-bearing premise

The method assumes that the U-Net's forward projection onto the unmeasured angular range is a reliable estimate of the true missing projections; if the U-Net hallucinates or smooths away structures in those angles, the final DCAR image inherits those errors because no measured data can correct them.

Editorial extensions

If this is right

  • DCAR-type hybrid reconstruction could make deep learning reconstructions viable in clinical limited-angle CT settings where data consistency is mandatory.
  • The method's noise robustness suggests it can be used with lower X-ray doses, since Poisson noise is suppressed by the iterative data-consistency framework more effectively than by the U-Net alone.
  • Because the U-Net prior only fills unmeasured angles, the approach should generalize better to unseen anatomies than direct image-to-image prediction, as the measured data remain the dominant constraint.
  • The same principle of using a learned prior inside a constrained reconstruction could be transferred to other ill-posed inverse problems, such as sparse-view CT or metal artifact reduction, wherever a measured subset of the data is reliable.

Reading between the lines

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

  • The paper's stated improvements are likely conservative: the U-Net is trained on only 400 slices, so a stronger prior could push DCAR gains even higher while keeping measured-data fidelity.
  • A natural testable extension not explored here is replacing the U-Net prior with an ensemble or a network trained on a larger dataset, then measuring whether the $e_2$ tolerance can be reduced to tighten the unmeasured-range constraint further.
  • The choice of $e_2=0.5$ is empirical; if the U-Net prior were known to be accurate in the missing angular range, a smaller $e_2$ would theoretically improve resolution, implying that adaptive tuning of $e_2$ per projection could be worthwhile.
  • DCAR's reliance on the U-Net prior to estimate unmeasured projections means its ceiling is bounded by the prior's accuracy; a principled uncertainty estimate for the prior could allow the method to automatically relax $e_2$ where the prior is unreliable.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes a data consistent artifact reduction (DCAR) method for limited angle cone-beam CT. A U-Net first predicts an artifact-reduced prior image from an FBP reconstruction; then an iterative SART+wTV reconstruction is run using the U-Net output as initialization, with data consistency enforced in the measured angular range and an additional soft-thresholded constraint that keeps the forward projection of the reconstruction close to that of the U-Net prior in the unmeasured angular range. The method is validated on 17 patients from the AAPM Low-Dose CT Grand Challenge with 120° limited angle data, using leave-one-out cross-validation. The reported mean RMSE improvements over the U-Net baseline are more than 10% in the noise-free case and more than 24% in the case with simulated Poisson noise.

Significance. If the results hold, DCAR is a practically valuable and conceptually clean way to combine a deep learning prior with analytic data consistency constraints, directly addressing the known robustness limitations of pure deep learning reconstructions. The paper has clear strengths: it uses a public dataset, a patient-level leave-one-out protocol, and a straightforward formulation that should be reproducible. The central idea of constraining the reconstruction in the unmeasured projection domain is plausible and interesting. However, the quantitative claims currently rest on an empirically chosen tolerance parameter without a sensitivity analysis, the noisy experiments use a single noise realization with no error bars or significance tests, and no ablation isolates the contribution of the proposed unmeasured-range constraint from the effect of better initialization. These issues are addressable and do not undermine the core concept, but they must be resolved before the stated improvement percentages can be considered robust.

major comments (3)
  1. [Section 2.2, Eq. (3) and Section 3] The central quantitative claim (more than 10% RMSE reduction noise-free and more than 24% in the noisy case) depends critically on the tolerance e2 in Eq. (3), which controls how much the final image may deviate from the forward projection of the U-Net prior in the unmeasured angular range. The manuscript states that e2=0.5 is chosen empirically, but it provides no sensitivity analysis and no direct evaluation of how accurately Au f_U-Net approximates the true unmeasured projections. If the prior is inaccurate in the missing angular range and e2 is too tight, the reconstruction inherits those errors; if e2 is too loose, the constraint is inactive and the method reduces to SART+wTV initialized with f_U-Net. Please report a sweep over e2 (including e2 near zero and e2 effectively infinite), a projection-domain residual analysis in the unmeasured angular range, and the aggregate mean RMSE improvement across the 17 patients.
  2. [Section 2.3 and Eq. (6)] The experimental comparison does not isolate the contribution of the proposed unmeasured-range constraint. The paper compares DCAR (50 iterations, initialized with f_U-Net, with the Au constraint) against SART+wTV from zero initialization (100 iterations), but not against SART+wTV initialized with f_U-Net and without the Au constraint. Without this ablation, the reported improvement cannot be attributed specifically to the constraint in Eq. (3); it may be explained by the improved initialization alone combined with wTV regularization. Please add an ablation that uses f_U-Net initialization and the same 50-iteration SART+wTV protocol but omits or loosens the e2 constraint.
  3. [Section 3, Figs. 4 and 5] The noisy-case evaluation is based on a single Poisson noise realization per patient, and no standard deviations, confidence intervals, or significance tests are reported. The claim of robustness to Poisson noise would be substantially strengthened by repeating the noisy experiment with multiple independent noise realizations and reporting mean ± standard deviation per patient or a paired statistical test (e.g., Wilcoxon signed-rank test) across the 17 patients. The current figures show only point estimates of mean RMSE per patient, which is not sufficient to support the 'significant' improvement claimed in the abstract.
minor comments (5)
  1. [Section 4] Discussion states 'only 13 slices are chosen from each patient' but Section 2.3 says 25 slices per patient are used for training; please correct this inconsistency.
  2. [Eq. (6)] The soft-thresholding operator S_tau is used in the SART update but is never explicitly defined; please state its definition and clarify the units of e1 and e2 (e.g., HU in projection domain) so the chosen values are meaningful to readers.
  3. [Figure 3 caption] In the caption of Fig. 3, 'DACR' should be 'DCAR'; also consider reporting the average relative improvement across all patients in the text in addition to the per-patient labels.
  4. [Section 1] There is a typo in the Introduction: 'consitent' should be 'consistent'.
  5. [Section 2.3] Please justify the different iteration counts for wTV (100 iterations) and DCAR (50 iterations) in the comparison; if the iteration count changes the outcome materially, report results at matched iteration numbers or explain why the chosen counts are appropriate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: DCAR's data-consistency refinement is validated against held-out reference volumes, and its U-Net prior is an input rather than the target of the derivation.

full rationale

The paper's central claim is that adding data-consistency constraints to a deep-learning prior improves limited-angle reconstructions beyond the prior alone. This is not circular. The DCAR objective (Eq. 5) combines a measured-data constraint (Eq. 1), an unmeasured-prior constraint (Eq. 3), and iterative reweighted total variation (Eq. 4); the U-Net output f_U-Net is an input to the optimization, not the quantity being derived or predicted. The reported >10% and >24% RMSE improvements are computed against held-out patient volumes using leave-one-out cross-validation, so the U-Net baseline is an external benchmark rather than a fitted target. The tolerance e2=0.5 is chosen empirically, but it is a hyperparameter of the algorithm, not a parameter fitted to reproduce the reported improvement. Self-citations to the authors' prior work ([15], [19]) supply the wTV solver and prior robustness motivation, but the paper re-demonstrates the relevant behavior with its own experiments, so these citations are not load-bearing. No equation reduces to another by construction, and no fitted input is renamed as a prediction.

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

The central claim depends on a handful of manually chosen reconstruction tolerances (e1, e2) and standard imaging assumptions (ideal forward model, gradient sparsity, accurate geometry). No new physical entities are introduced. The main non-standard assumption is that the U-Net prior is trustworthy in the unmeasured angular range, which is exactly what DCAR relies on for the missing data.

free parameters (5)
  • e1 (data consistency tolerance) = 0.001 (noise-free), 0.01 (noisy)
    Hand-selected error tolerance for the measured projections in Eq. (1). Controls how strictly the final image reproduces the measured data; no sensitivity analysis provided.
  • e2 (prior consistency tolerance) = 0.5
    Called 'relatively large tolerance value chosen empirically' in Sec. 2.3. Balances the influence of the U-Net prior in the missing angular range; the reported RMSE improvements likely depend on this value.
  • lambda (SART relaxation) = 0.8
    Relaxation parameter in the SART update (Eq. 6); a standard value but chosen by hand.
  • epsilon (wTV weight update) = 5 HU
    Small value to avoid division by zero in the reweighted total variation (Eq. 4); affects resolution and convergence speed.
  • iteration count = 50
    Number of SART + wTV iterations; convergence to the constraints is not proven and the choice is empirical.
assumptions (5)
  • domain assumption The forward projection model pm = Am f exactly describes the measured data, aside from a tolerance e1.
    Standard idealization in CT simulation; ignores scatter, beam hardening, and detector effects.
  • domain assumption The deep learning prior f_U-Net, when forward projected, is a usable substitute for the unmeasured projections in the missing angular range.
    The entire DCAR logic depends on this: Eq. (3) replaces the unknown pu with Au f_U-Net. If this prior is inaccurate in the missing angles, the reconstruction will be biased.
  • domain assumption The image is approximately piecewise constant, so the reweighted total variation prior is appropriate.
    wTV regularization (Eq. 4) is used to suppress noise and artifacts; this assumes gradient sparsity.
  • domain assumption The iterative SART + wTV algorithm converges to a solution satisfying constraints (1) and (3) within 50 iterations.
    No convergence analysis is provided; 50 iterations are an empirical choice.
  • domain assumption The system geometry (angular range, distances, detector size) is accurately known for both measured and unmeasured ranges.
    The system matrices Am and Au are treated as exact; errors in geometry would break data consistency.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior." pith.science (2026). https://pith.science/paper/NWIFETB3

@misc{pith2026190806792,
  author       = {Pith},
  title        = {Pith review of: Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NWIFETB3}},
  note         = {Machine review of arXiv:1908.06792}
}
read the original abstract

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.

Figures

Figures reproduced from arXiv: 1908.06792 by the authors.

Figure 1
Figure 1. The U-Net architecture for limited angle tomography (modified from [22]). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Reconstruction results of three example slices by U-Net and DCAR in [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Comparison of the mean RMSE values by wTV, U-Net, and DCAR for [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Reconstruction results of three example slices by U-Net and DCAR in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Comparison of the mean RMSE values by wTV, U-Net, and DCAR for [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 31 canonical work pages

  1. [1]

    A gentle introduction to deep learning in medical image processing,

    A. Maier, C. Syben, T. Lasser, and C. Riess, “A gentle introduction to deep learning in medical image processing,” Zeitschrift f¨ ur Medizinische Physik, vol. 29, no. 2, pp. 86–101, 2019

    work page 2019

  2. [2]

    Image reconstruction is a new frontier of machine learning,

    G. Wang, J. C. Ye, K. Mueller, and J. A. Fessler, “Image reconstruction is a new frontier of machine learning,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1289–1296, 2018

    work page 2018

  3. [3]

    A deep convolutional neural network using di- rectional wavelets for low-dose X-ray CT reconstruction,

    E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using di- rectional wavelets for low-dose X-ray CT reconstruction,” Medical physics, vol. 44, no. 10, 2017

    work page 2017

  4. [4]

    Low-dose CT via convolutional neural network,

    H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang, “Low-dose CT via convolutional neural network,” Biomedical optics express, vol. 8, no. 2, pp. 679–694, 2017

    work page 2017

  5. [5]

    Low dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss,

    Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, “Low dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss,” IEEE transactions on medical imaging, 2018

    work page 2018

  6. [6]

    Deep residual learning for compressed sensing ct reconstruction via persistent homology analysis,

    Y. S. Han, J. Yoo, and J. C. Ye, “Deep residual learning for compressed sensing ct reconstruction via persistent homology analysis,” arXiv preprint, 2016

    work page 2016

  7. [7]

    Framing U-Net via deep convolutional framelets: Application to sparse-view CT,

    Y. Han and J. C. Ye, “Framing U-Net via deep convolutional framelets: Application to sparse-view CT,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1418–1429, 2018

    work page 2018

  8. [8]

    LEARN: Learned experts assessment-based reconstruction network for sparse-data CT,

    H. Chen, Y. Zhang, Y. Chen, J. Zhang, W. Zhang, H. Sun, Y. Lv, P. Liao, J. Zhou, and G. Wang, “LEARN: Learned experts assessment-based reconstruction network for sparse-data CT,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1333–1347, 2018

    work page 2018

Show all 32 references
  1. [9]

    Deep learning methods to guide CT image reconstruction and reduce metal artifacts,

    L. Gjesteby, Q. Yang, Y. Xi, Y. Zhou, J. Zhang, and G. Wang, “Deep learning methods to guide CT image reconstruction and reduce metal artifacts,” in Medical Imaging 2017: Physics of Medical Imaging , vol. 10132. International Society for Optics and Photonics, 2017, p. 101322W

    2017

  2. [10]

    Convolutional neural network based metal artifact reduction in X-ray computed tomography,

    Y. Zhang and H. Yu, “Convolutional neural network based metal artifact reduction in X-ray computed tomography,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1370–1381, 2018

    2018

  3. [11]

    Picture reconstruction from projections in restricted range,

    A. K. Louis and W. T¨ ornig, “Picture reconstruction from projections in restricted range,” Mathematical Methods in the Applied Sciences , vol. 2, no. 2, pp. 209–220, 1980

    1980

  4. [12]

    Restoration of missing data in limited angle tomography based on Helgason–Ludwig consistency conditions,

    Y. Huang, X. Huang, O. Taubmann, Y. Xia, V. Haase, J. Hornegger, G. Lauritsch, and A. Maier, “Restoration of missing data in limited angle tomography based on Helgason–Ludwig consistency conditions,” Biomedical Physics & Engineering Express, vol. 3, no. 3, p. 035015, 2017

    2017

  5. [13]

    Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,

    E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Physics in Medicine & Biology, vol. 53, no. 17, p. 4777, 2008

    2008

  6. [14]

    A limited-angle CT reconstruction method based on anisotropic TV minimization,

    Z. Chen, X. Jin, L. Li, and G. Wang, “A limited-angle CT reconstruction method based on anisotropic TV minimization,” Physics in Medicine & Biology, vol. 58, no. 7, p. 2119, 2013. 12

    2013

  7. [15]

    Scale- space anisotropic total variation for limited angle tomography,

    Y. Huang, O. Taubmann, X. Huang, V. Haase, G. Lauritsch, and A. Maier, “Scale- space anisotropic total variation for limited angle tomography,”IEEE Transactions on Radiation and Plasma Medical Sciences , vol. 2, no. 4, pp. 307–314, 2018

    2018

  8. [16]

    Deep learning computed tomography,

    T. W¨ urfl, F. C. Ghesu, V. Christlein, and A. Maier, “Deep learning computed tomography,” in Procs MICCAI. Springer, 2016, pp. 432–440

    2016

  9. [17]

    Image prediction for limited-angle tomography via deep learning with convolutional neural network,

    H. Zhang, L. Li, K. Qiao, L. Wang, B. Yan, L. Li, and G. Hu, “Image prediction for limited-angle tomography via deep learning with convolutional neural network,” arXiv preprint, 2016

    2016

  10. [18]

    Multi-scale wavelet domain residual learning for limited-angle CT reconstruction,

    J. Gu and J. C. Ye, “Multi-scale wavelet domain residual learning for limited-angle CT reconstruction,” in Procs Fully3D, 2017, pp. 443–447

    2017

  11. [19]

    Some investigations on robustness of deep learning in limited angle tomography,

    Y. Huang, T. W¨ urfl, K. Breininger, L. Liu, G. Lauritsch, and A. Maier, “Some investigations on robustness of deep learning in limited angle tomography,” in Procs MICCAI. Cham: Springer International Publishing, 2018, pp. 145–153

    2018

  12. [20]

    Deep learning computed tomography: Learning projection-domain weights from image domain in limited angle problems,

    T. W¨ urfl, M. Hoffmann, V. Christlein, K. Breininger, Y. Huang, M. Unberath, and A. K. Maier, “Deep learning computed tomography: Learning projection-domain weights from image domain in limited angle problems,” IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1454–1463, 2018

    2018

  13. [21]

    Learning the invisible: a hybrid deep learning-shearlet framework for lim- ited angle computed tomography,

    T. A. Bubba, G. Kutyniok, M. Lassas, M. Mrz, W. Samek, S. Siltanen, and V. Srini- vasan, “Learning the invisible: a hybrid deep learning-shearlet framework for lim- ited angle computed tomography,” Inverse Problems, vol. 35, no. 6, p. 064002, jun 2019

    2019

  14. [22]

    U-Net: Convolutional networks for biomedical image segmentation,

    O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Procs MICCAI. Springer, 2015, pp. 234–241

    2015

  15. [23]

    Explaining and harnessing adversarial examples,

    I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint, 2014

    2014

  16. [24]

    Intriguing properties of neural networks,

    C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” arXiv preprint, 2013

    2013

  17. [25]

    Adversarial examples: Attacks and defenses for deep learning,

    X. Yuan, P. He, Q. Zhu, R. R. Bhat, and X. Li, “Adversarial examples: Attacks and defenses for deep learning,” arXiv preprint, 2017

    2017

  18. [26]

    On instabilities of deep learning in image reconstruction-does AI come at a cost?

    V. Antun, F. Renna, C. Poon, B. Adcock, and A. C. Hansen, “On instabilities of deep learning in image reconstruction-does AI come at a cost?” arXiv preprint arXiv:1902.05300, 2019

    1902 arXiv

  19. [27]

    Precision Learning: Reconstruction Filter Kernel Discretization,

    C. Syben, B. Stimpel, K. Breininger, T. W¨ urfl, R. Fahrig, A. D¨ orfler, and A. Maier, “Precision Learning: Reconstruction Filter Kernel Discretization,” in Procs CT Meeting, F. Noo, Ed., 2018, pp. 386–390

    2018

  20. [28]

    Learning with known operators reduces maximum training error bounds,

    A. K. Maier, C. Syben, B. Stimpel, T. W¨ urfl, M. Hoffmann, F. Schebesch, W. Fu, L. Mill, L. Kling, and S. Christiansen, “Learning with known operators reduces maximum training error bounds,” arXiv preprint , 2019, paper conditionally ac- cepted in Nature Machine Intelligence

    2019

  21. [29]

    Tv or not tv? that is the question,

    C. Riess, M. Berger, H. Wu, M. Manhart, R. Fahrig, and A. Maier, “Tv or not tv? that is the question,” Proceedings Fully3D, pp. 341–344, 2013

    2013

  22. [30]

    Sparse regularization in limited angle tomography,

    J. Frikel, “Sparse regularization in limited angle tomography,” Applied and Com- putational Harmonic Analysis , vol. 34, no. 1, pp. 117–141, 2013

    2013

  23. [31]

    Densely connected convolutional networks,

    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Procs CVPR, vol. 1, no. 2, 2017, p. 3

    2017

  24. [32]

    Low-dose CT for the detection and classification of metastatic liver lesions: Results of the 2016 low dose CT grand challenge,

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

    2016

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.