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REVIEW 4 major objections 6 minor 1 cited by

TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read TIGRE v3 argues that one open-source GPU toolbox can run 23 iterative CT reconstruction algorithms on real clinical, synchrotron, proton, neutron, and industrial data, and that different algorithms yield genuinely different images.

desk verdict A competent, honest software-guide paper for a widely used CT reconstruction toolbox; the new contribution is the Python/PyTorch ecosystem and data-loaders, not new math, and the lack of quantitative validation is a real but proportionate limitation given the paper's carefully limited claims. read the letter →

arxiv 2412.10129 v1 pith:AU2P6VOS submitted 2024-12-13 physics.med-ph cs.MSmath.OC

Ander Biguri , Tomoyuki Sadakane , Reuben Lindroos , Yi Liu , Malena Sabaté Landman , Yi Du , Manasavee Lohvithee , Stefanie Kaser
show 6 more authors
Sepideh Hatamikia Robert Bryll Emilien Valat Sarinrat Wonglee Thomas Blumensath Carola-Bibiane Schönlieb
This is my paper · ORCID
classification physics.med-phcs.MSmath.OC
keywords computedtomographyiterativereconstructionopen-sourcesoftwareGPUcomputingmulti-GPUmemorycone-beamCTprotonneutron
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

The paper presents TIGRE v3, an open-source toolbox for iterative computed tomography, and argues that it makes advanced reconstruction algorithms usable on real scanner data rather than only simulated data. It claims that one GPU-based codebase can handle clinical cone-beam, synchrotron parallel-beam, proton, neutron, and industrial micro-CT acquisitions through a common geometry model, vendor-specific data loaders, and multi-GPU memory management. The only evaluative claim the paper makes about the reconstructions themselves is that different algorithms produce genuinely different images, and that these differences can matter for different downstream tasks. A sympathetic reading is that if TIGRE v3 works as described, then iterative CT is no longer confined to specialists with proprietary pipelines, and algorithm developers and clinical users can share one platform.

What carries the argument

The load-bearing mechanism is the discretized Radon-transform system matrix $A$ and its adjoint, implemented as GPU kernels for forward projection and backprojection; every iterative update in the toolbox, from row-action to Krylov-subspace to proximal methods, calls this pair. The geometry model is what gives the pair its reach: each projection can carry its own source-detector distance, detector rotation, center-of-rotation shift, and axis orientation, so one codebase covers circular, helical, curved-detector, laminography-style, and proton-CT geometries. Multi-GPU memory splitting then distributes these operations across devices and allows volumes larger than any single GPU's memory, with memory transfers overlapped with computation. Together these pieces turn the abstract equation $Ax + \tilde{e} = b$ into a practical tool for real measurements.

What would settle it

Acquire a calibration phantom with known dimensions and attenuation on a supported clinical cone-beam CT scanner, load the raw data with the vendor loader, and compare the reconstructed geometry and values against the phantom's ground truth; systematic edge shifts, scale errors, or attenuation bias would show that the geometric model or loader is not faithful.

Watch

Extended reading notes

Core claim

TIGRE v3 provides 23 standardized iterative algorithms built on GPU forward- and back-projection operators, a geometric model that allows per-projection variation (detector rotations, curved detectors, arbitrary rotation axes, center-of-rotation shifts, and helical trajectories), six vendor data loaders, a proton-CT preprocessing stage that rebins particle-track data into radiographs, and an optional binding to deep-learning libraries. The paper's demonstrations reconstruct a limited-arc clinical head scan, a noisy synchrotron time series, simulated proton CT of a phantom, a 10-projection neutron scan of a padlock, and a helical micro-CT scan of a battery, all on a desktop computer. The authors state explicitly that they make no claim about which algorithm is 'best'; the claim is that reconstructions obtained with different algorithms are different and may be of interest in different scenarios. If correct, this is the central fact that makes a multi-algorithm open toolbox valuable.

Load-bearing premise

The load-bearing premise is that the toolbox's GPU projectors, geometry definitions, and vendor-specific loaders faithfully represent each scanner's real imaging geometry, since every demonstrated reconstruction depends on that fidelity; the clinical loader in particular is reverse engineered from domain knowledge rather than verified against proprietary documentation.

Editorial extensions

If this is right

  • A clinical cone-beam reconstruction with 493 projections over a 200-degree arc can be produced in under five minutes on a personal desktop, with OS-SART or OS-ASD-POCS visibly less noisy than FDK.
  • Neutron tomography at a low-flux reactor can use 10 projections instead of 201 and still yield recognizable reconstructions, cutting acquisition time from around 14 hours to a fraction of that.
  • Proton CT data, once rebinned into optimized radiographs, flows through the same reconstruction algorithms as ordinary CT, so pCT development does not need a separate solver stack.
  • Images larger than the memory of a single GPU can be reconstructed by splitting the work across multiple GPUs and overlapping memory copies with computation.
  • A single line of loader code can bring raw scanner output from several manufacturers into a common geometry representation, removing a major barrier to trying iterative algorithms.

Reading between the lines

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

  • The paper's refusal to rank algorithms suggests a testable consequence it does not pursue: once many solvers run on the same data, the natural next step is task-specific benchmarking, where image quality is measured by the downstream task (segmentation, metrology, diagnosis) rather than by generic image metrics.
  • Because the clinical scanner loader is reverse engineered from domain knowledge and not verified against proprietary documentation, an unstated risk is that geometry errors in that loader would systematically distort clinical reconstructions; validating the loader against a calibration phantom would settle this.
  • The deep-learning binding, presented as a convenience for learned reconstruction, also makes TIGRE's geometric accuracy a determinant of learned-method performance: if the forward operator is slightly wrong, trained networks will absorb that bias.
  • If the multi-GPU splitting scales linearly as claimed, then the toolbox could become a de facto reference implementation for testing new iterative algorithms on industrially sized volumes, which would shift algorithm development from small simulated phantoms toward realistic large-scale data.
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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 / 6 minor

Summary. The paper presents TIGRE v3, an open-source GPU-accelerated iterative CT reconstruction toolbox, and describes its algorithmic contents, Python/MATLAB implementations, geometric flexibility, multi-GPU memory management, vendor data loaders, proton-CT preprocessing, and PyTorch bindings. The authors demonstrate the toolbox on five datasets (clinical Varian CBCT, synchrotron CT, simulated proton CT, neutron CT, and industrial helical micro-CT), each with code snippets. The authors explicitly limit the paper's scientific claim to the statement that reconstructions obtained using different algorithms are different, and that different algorithms may suit different downstream tasks.

Significance. If the toolbox functions as described, TIGRE v3 is a valuable open-source community resource that lowers the barrier to using iterative reconstruction on real CT data. The paper's strengths are its explicit, modest claim, the availability of the open-source code, and the inclusion of reproducible code snippets for all five examples. The descriptions cover a broad range of modalities and algorithms, and the self-citations point to prior peer-reviewed publications for the core methods. However, the demonstrations are entirely qualitative, and the correctness of the vendor data loaders, which is load-bearing for the 'real datasets' claim, is asserted rather than validated.

major comments (4)
  1. [§2.6, §4] The correctness of the vendor data loaders and their derived geometries is load-bearing for every example in Section 3, but the paper does not validate them. Section 2.6 states that the Varian loader is reverse engineered from domain knowledge and not verified against proprietary documentation, while Section 4 asserts that all seven loaders are 'verified on real scanners' without showing any verification result. If a loader mis-estimates a geometric parameter such as source-detector distance, detector tilt, or center-of-rotation shift, the forward/backprojector pair models the wrong system matrix and the displayed algorithm differences could be driven by model error. The authors should either provide explicit validation (e.g., phantom scans with known geometry, comparison against vendor reconstruction, or quantitative geometry calibration) or temper the title-level and Section 4 claims about support for real datasets.
  2. [§3] The five demonstrations are presented without any quantitative metrics. No residual norms, error norms with respect to a known phantom, or wall-clock timings are reported, so statements such as 'illustrate the performance of several of the available solvers' (Abstract) and 'highlighting the power of TIGRE to produce computationally fast results' (§3) are not directly supported by the data shown. The paper's disclaimer that it does not rank algorithms is reasonable, but a toolbox paper that emphasizes efficiency and usability should report at least objective convergence measures and timings for the examples shown.
  3. [§3.2, Figure 3, Snippet 7] There is a reproducible-code inconsistency: Snippet 7 runs FISTA with 50 iterations, while the Figure 3 caption states 'FISTA (80 iterations)'. Since the paper's demonstrations are qualitative and the snippets are meant to reproduce the figures, the iteration counts must match exactly for the examples to be reproducible from the paper alone.
  4. [§3.4, Snippet 9] The neutron tomography example uses a TINTDataLoader that is not part of TIGRE (the snippet itself comments that this function is not in TIGRE), and Section 2.6 does not list TINT among the supported loaders. This undermines the claim that the examples showcase the toolbox's integrated data-loading pipeline and means that this particular example cannot be reproduced using the toolbox as described.
minor comments (6)
  1. [§2.2.3] The phrase 'Discrepancy Principe' should be 'Discrepancy Principle'.
  2. [§2.1, Appendix A] The text uses 'Gradient Descend' and 'Adaptative' where 'Gradient Descent' and 'Adaptive' are standard; these should be corrected.
  3. [§2.6 vs. §4] Section 2.6 lists six manufacturers (Philips, Varian, Comet Yxlon, Nikon, Bruker, Diondo) plus the DXChange format, whereas Section 4 says 'seven different manufacturers'; DXChange is a data format, not a manufacturer, so the count is inconsistent.
  4. [§3.4] The lens is referred to as a 'Nikkon 50-mm/f1.2'; the correct brand name is Nikon.
  5. [§3.3] The proton-CT example is a Monte Carlo simulation rather than a real acquisition, so the title-level phrase 'real datasets' should be qualified in the abstract or introduction to avoid overclaiming.
  6. [§2.8] The PyTorch wrapper section would benefit from a statement that the forward and adjoint operators were tested for gradient correctness (e.g., by finite differences), since the wrapper is offered as a differentiable operator for research use.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is a software guide whose claims rest on open-source code and standard algorithms; self-citations are descriptive pointers, not load-bearing premises.

full rationale

The paper makes no fitted-input prediction and contains no derivation whose output is presupposed by its inputs. The central claims are that TIGRE v3 contains 23 algorithms, multi-GPU memory management, vendor data loaders, and PyTorch bindings, and that the Section 3 examples show different algorithms yield different reconstructions. The latter is stated explicitly as the only scientific claim (Section 3: "the only claim we make in this work is that reconstructions obtained using different algorithms are different, and that they may be of interest in different scenarios"), and the reconstructions are illustrative demonstrations rather than validated quantitative predictions. Descriptions of Krylov solvers, multi-GPU support, the pCT binning, and the Varian loader cite the authors' own prior papers ([45], [58], [63], [66]), but these citations are descriptive pointers to implementation details and prior method papers, and the underlying algorithms are standard literature with open-source code that is externally inspectable. The reverse-engineered Varian loader footnote (Section 2.6: "the functions here are reverse engineered from domain knowledge, not private information") and the unquantified "verified on real scanners" statement in Section 4 are evidence-quality limitations, not circularity: they affect whether the examples substantiate the 'real datasets' claim, but they do not make any stated result equivalent to its own input. No equation in the paper is fitted to the data it then predicts, and no uniqueness claim is imported from the authors' prior work. Therefore the circularity burden is zero.

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

The paper introduces no new mathematical entities or fitted parameters. It relies on standard CT modeling assumptions and on the correctness of its software, data loaders, and algorithm implementations, which are not independently validated within this paper.

assumptions (3)
  • domain assumption The discrete linear model Ax + e = b is an adequate model for CT reconstruction, with A representing the forward projection operator.
    Used throughout Section 2.1 as the foundation for all algorithms; standard in CT but not justified by this paper.
  • domain assumption The vendor-specific data loaders decode proprietary scanner formats and geometry parameters correctly.
    The Varian loader is stated in Section 2.6 to be reverse engineered from domain knowledge rather than proprietary documentation, so errors in geometry decoding would affect all reconstructions from that loader.
  • domain assumption The implementations of the cited algorithms are faithful to the original publications.
    The paper cites algorithm papers and prior TIGRE implementation papers but does not verify equivalence in this document.

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Cite this review

Pith. "Pith review of TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets." pith.science (2026). https://pith.science/paper/AU2P6VOS

@misc{pith2026241210129,
  author       = {Pith},
  title        = {Pith review of: TIGRE v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AU2P6VOS}},
  note         = {Machine review of arXiv:2412.10129}
}
read the original abstract

Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox was born almost a decade ago precisely in the gap between mathematics and high performance computing for real CT data, providing user-friendly open-source software tools for image reconstruction. However, since its inception, the tools' features and codebase have had over a twenty-fold increase, and are now including greater geometric flexibility, a variety of modern algorithms for image reconstruction, high-performance computing features and support for other CT modalities, like proton CT. The purpose of this work is two-fold: first, it provides a structured overview of the current version of the TIGRE toolbox, providing appropriate descriptions and references, and serving as a comprehensive and peer-reviewed guide for the user; second, it is an opportunity to illustrate the performance of several of the available solvers showcasing real CT acquisitions, which are typically not be openly available to algorithm developers.

Figures

Figures reproduced from arXiv: 2412.10129 by the authors.

Figure 1
Figure 1. Diagram of geometric parameters of TIGRE for a single X-ray projection. [PITH_FULL_IMAGE:figures/full_fig_p012_1.png] view at source ↗
Figure 2
Figure 2. Reconstruction from a Varian Edge machine, 200 [PITH_FULL_IMAGE:figures/full_fig_p018_2.png] view at source ↗
Figure 3
Figure 3. Synchrotron dataset acquired with settings for a dynamic in-situ experiment. [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Reconstructions of pCT projections of the Catphan high resolution phantom [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]
Figure 5
Figure 5. Figure 5: Neutron tomography dataset of the padlock taken at Thailand Institute of [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]
Figure 6
Figure 6. Figure 6: Scan of an Energizer AAAA battery acquired in a diondo d5 scanner at the [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Randomized and Inner-product Free Krylov Methods for Large-scale Inverse Problems

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Reference graph

Works this paper leans on

74 extracted references · 67 canonical work pages · cited by 1 Pith paper

  1. [1]

    Kak, Malcolm Slaney, IEEE Engineering in Medicine, and Biology Society

    Avinash C. Kak, Malcolm Slaney, IEEE Engineering in Medicine, and Biology Society. Principles of computerized tomographic imaging . IEEE Press, New York, 1988. Published under the sponsorship of the IEEE Engineering in Medicine and Biology Society

    work page 1988

  2. [2]

    Per Christian Hansen, Jakob Jørgensen, and William R. B. Lionheart. Computed Tomography: Algorithms, Insight, and Just Enough Theory . Society for Industrial and Applied Mathematics, Philadelphia, PA, 2021

    work page 2021

  3. [3]

    Discrete Inverse Problems

    Per Christian Hansen. Discrete Inverse Problems . SIAM, 2010. TIGRE v3 26

    work page 2010

  4. [4]

    Mueller and Samuli Siltanen

    Jennifer L. Mueller and Samuli Siltanen. Linear and Nonlinear Inverse Problems with Practical Applications. Society for Industrial and Applied Mathematics, Philadelphia, PA, 2012

    2012

  5. [5]

    Kalra, and Sarabjeet Singh

    Atul Padole, Ranish Deedar Ali Khawaja, Mannudeep K. Kalra, and Sarabjeet Singh. CT radiation dose and iterative reconstruction techniques. American Journal of Roentgenology , 204(4):W384–W392, 2015. PMID: 25794087

    work page 2015

  6. [6]

    Basics of iterative reconstruction methods in computed tomography: A vendor- independent overview

    Wolfram Stiller. Basics of iterative reconstruction methods in computed tomography: A vendor- independent overview. European Journal of Radiology, 109:147–154, 2018

    work page 2018

  7. [7]

    TIGRE: a MATLAB- GPU toolbox for CBCT image reconstruction

    Ander Biguri, Manjit Dosanjh, Steven Hancock, and Manuchehr Soleimani. TIGRE: a MATLAB- GPU toolbox for CBCT image reconstruction. Biomedical Physics & Engineering Express , 2(5):055010, 2016

    work page 2016

  8. [8]

    The ASTRA toolbox: A platform for advanced algorithm development in electron tomography

    Wim Van Aarle, Willem Jan Palenstijn, Jan De Beenhouwer, Thomas Altantzis, Sara Bals, K Joost Batenburg, and Jan Sijbers. The ASTRA toolbox: A platform for advanced algorithm development in electron tomography. Ultramicroscopy, 157:35–47, 2015

    work page 2015

Show all 74 references
  1. [9]

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

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu ...

    2019

  2. [10]

    Tomosipo: fast, flexible, and convenient 3d tomography for complex scanning geometries in Python

    Allard A Hendriksen, Dirk Schut, Willem Jan Palenstijn, Nicola Vigan´ o, Jisoo Kim, Dani¨ el M Pelt, Tristan Van Leeuwen, and K Joost Batenburg. Tomosipo: fast, flexible, and convenient 3d tomography for complex scanning geometries in Python. Optics Express, 29(24):40494–40513, 2021

    2021

  3. [11]

    Core Imaging Library-part I: a versatile Python framework for tomographic imaging

    Jakob S Jørgensen, Evelina Ametova, Genoveva Burca, Gemma Fardell, Evangelos Papoutsellis, Edoardo Pasca, Kris Thielemans, Martin Turner, Ryan Warr, William RB Lionheart, et al. Core Imaging Library-part I: a versatile Python framework for tomographic imaging. Philosophical Tr...

    2021

  4. [12]

    The reconstruction toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the insight toolkit (ITK)

    Simon Rit, M Vila Oliva, S´ ebastien Brousmiche, Rudi Labarbe, David Sarrut, and Gregory C Sharp. The reconstruction toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the insight toolkit (ITK). In Journal of Physics: Conference Series , volume 489, pag...

    2014

  5. [13]

    TomoPy: a framework for the analysis of synchrotron tomographic data

    Doga G¨ ursoy, Francesco De Carlo, Xianghui Xiao, and Chris Jacobsen. TomoPy: a framework for the analysis of synchrotron tomographic data. Journal of synchrotron radiation , 21(5):1188– 1193, 2014

    2014

  6. [14]

    Differentiable forward projector for x-ray computed tomography

    Hyojin Kim and Kyle Champley. Differentiable forward projector for x-ray computed tomography. arXiv preprint arXiv:2307.05801 , 2023

    2023 arXiv

  7. [15]

    Uses and abuses of EIDORS: an extensible software base for EIT

    Andy Adler and William RB Lionheart. Uses and abuses of EIDORS: an extensible software base for EIT. Physiological measurement, 27(5):S25, 2006

    2006

  8. [16]

    pyEIT: A python based framework for electrical impedance tomography

    Benyuan Liu, Bin Yang, Canhua Xu, Junying Xia, Meng Dai, Zhenyu Ji, Fusheng You, Xiuzhen Dong, Xuetao Shi, and Feng Fu. pyEIT: A python based framework for electrical impedance tomography. SoftwareX, 7:304–308, 2018

    2018

  9. [17]

    Stir: software for tomographic image reconstruction release 2

    Kris Thielemans, Charalampos Tsoumpas, Sanida Mustafovic, Tobias Beisel, Pablo Aguiar, Nikolaos Dikaios, and Matthew W Jacobson. Stir: software for tomographic image reconstruction release 2. Physics in Medicine & Biology , 57(4):867, 2012

    2012

  10. [18]

    SIRF: synergistic image reconstruction framework

    Evgueni Ovtchinnikov, Richard Brown, Christoph Kolbitsch, Edoardo Pasca, Casper da Costa- Luis, Ashley G Gillman, Benjamin A Thomas, Nikos Efthimiou, Johannes Mayer, Palak Wadhwa, et al. SIRF: synergistic image reconstruction framework. Computer Physics Communications, 249:107...

    2020

  11. [19]

    CASToR: a generic data organization and TIGRE v3 27 processing code framework for multi-modal and multi-dimensional tomographic reconstruction

    Thibaut Merlin, Simon Stute, Didier Benoit, Julien Bert, Thomas Carlier, Claude Comtat, Marina Filipovic, Fr´ ed´ eric Lamare, and Dimitris Visvikis. CASToR: a generic data organization and TIGRE v3 27 processing code framework for multi-modal and multi-dimensional tomographic...

    2018

  12. [20]

    Operator discretization library (ODL)

    Jonas Adler, Holger Kohr, and Ozan ¨Oktem. Operator discretization library (ODL). Zenodo, 2017

    2017

  13. [21]

    AIR Tools II: algebraic iterative reconstruction methods, improved implementation

    Per Christian Hansen and Jakob Sauer Jørgensen. AIR Tools II: algebraic iterative reconstruction methods, improved implementation. Numerical Algorithms, 79(1):107–137, 2018

    2018

  14. [22]

    IRtools: a MATLAB package of iterative regularization methods and large-scale test problems

    Silvia Gazzola, Per Christian Hansen, and James G Nagy. IRtools: a MATLAB package of iterative regularization methods and large-scale test problems. Numerical Algorithms , 81(3):773–811, 2019

    2019

  15. [23]

    Introduction to Bayesian Scientific Computing—Ten Lectures on Subjective Computing , volume 2

    Daniela Calvetti and Erkki Somersalo. Introduction to Bayesian Scientific Computing—Ten Lectures on Subjective Computing , volume 2. 01 2007

    2007

  16. [24]

    Practical cone-beam algorithm

    Lee A Feldkamp, Lloyd C Davis, and James W Kress. Practical cone-beam algorithm. Josa a , 1(6):612–619, 1984

    1984

  17. [25]

    X-ray micro-CT with a displaced detector array

    Ge Wang. X-ray micro-CT with a displaced detector array. Medical physics, 29(7):1634–1636, 2002

    2002

  18. [26]

    Parker weights revisited

    Stefan Wesarg, Matthias Ebert, and Thomas Bortfeld. Parker weights revisited. Medical physics, 29(3):372–378, 2002

    2002

  19. [27]

    Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography

    Richard Gordon, Robert Bender, and Gabor T Herman. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. Journal of theoretical Biology, 29(3):471–481, 1970

    1970

  20. [28]

    Andersen and A.C

    A.H. Andersen and A.C. Kak. Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrasonic imaging, 6(1):81–94, 1984

    1984

  21. [29]

    Censor and T

    Y. Censor and T. Elfving. Block-iterative algorithms with diagonally scaled oblique projections for the linear feasibility problem. SIAM Journal on Matrix Analysis and Applications , 24(1):40–58, 2002

    2002

  22. [30]

    Sidky and X

    E.Y. Sidky and X. Pan. Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine and Biology , 53(17):4777, 2008

    2008

  23. [31]

    Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms

    Li Liu, Weikai Lin, and Mingwu Jin. Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms. Computers in biology and medicine , 56:97–106, 2015

    2015

  24. [32]

    Adaptive-weighted total variation minimization for sparse data toward low-dose X-ray computed tomography image reconstruction

    Yan Liu, Jianhua Ma, Yi Fan, and Zhengrong Liang. Adaptive-weighted total variation minimization for sparse data toward low-dose X-ray computed tomography image reconstruction. Physics in Medicine & Biology , 57(23):7923, 2012

    2012

  25. [33]

    Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

    Manasavee Lohvithee, Ander Biguri, and Manuchehr Soleimani. Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms. Physics in Medicine and Biology , 2017

    2017

  26. [34]

    H. Xue, L. Zhang, Z. Cheng, Y. Xing, and Y. Xiao. An improved tv minimization algorithm for incomplete data problem in computer tomography. In Nuclear Science Symposium Conference Record (NSS/MIC), 2010 IEEE , pages 2621–2624, Oct 2010

    2010

  27. [35]

    Methods of conjugate gradients for solving linear systems

    Magnus R Hestenes, Eduard Stiefel, et al. Methods of conjugate gradients for solving linear systems. Journal of research of the National Bureau of Standards , 49(6):409–436, 1952

    1952

  28. [36]

    Algorithm 583: LSQR: Sparse linear equations and least squares problems

    Christopher C Paige and Michael A Saunders. Algorithm 583: LSQR: Sparse linear equations and least squares problems. ACM Transactions on Mathematical Software (TOMS) , 8(2):195–209, 1982

    1982

  29. [37]

    GMRES methods for tomographic reconstruction with an unmatched back projector

    Per Christian Hansen, Ken Hayami, and Keiichi Morikuni. GMRES methods for tomographic reconstruction with an unmatched back projector. Journal of Computational and Applied Mathematics, 413:114352, 2022

    2022

  30. [38]

    LSMR: An iterative algorithm for sparse least- squares problems

    David Chin-Lung Fong and Michael Saunders. LSMR: An iterative algorithm for sparse least- squares problems. SIAM Journal on Scientific Computing , 33(5):2950–2971, 2011

    2011

  31. [39]

    An iteratively reweighted norm algorithm for minimization of total variation functionals

    Brendt Wohlberg and Paul Rodriguez. An iteratively reweighted norm algorithm for minimization of total variation functionals. IEEE Signal Processing Letters , 14(12):948–951, 2007

    2007

  32. [40]

    Flexible GMRES for total variation regularization

    Silvia Gazzola and Malena Sabat´ e Landman. Flexible GMRES for total variation regularization. TIGRE v3 28 BIT Numerical Mathematics , 59:721–746, 2019

    2019

  33. [41]

    Accelerated image reconstruction using ordered subsets of projection data

    H Malcolm Hudson and Richard S Larkin. Accelerated image reconstruction using ordered subsets of projection data. IEEE transactions on medical imaging , 13(4):601–609, 1994

    1994

  34. [42]

    A fast iterative shrinkage-thresholding algorithm for linear inverse problems

    Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences , 2(1):183–202, 2009

    2009

  35. [43]

    Faster FISTA, 2018

    Jingwei Liang and Carola-Bibiane Sch¨ onlieb. Faster FISTA, 2018

    2018

  36. [44]

    Iterative reconstruction and motion compensation in computed tomography on GPUs

    Ander Biguri. Iterative reconstruction and motion compensation in computed tomography on GPUs. PhD thesis, University of Bath, Bath, UK, 11 2017

    2017

  37. [45]

    On Krylov methods for large-scale CBCT reconstruction

    Malena Sabat´ e Landman, Ander Biguri, Sepideh Hatamikia, Richard Boardman, John Aston, and Carola-Bibiane Sch¨ onlieb. On Krylov methods for large-scale CBCT reconstruction. Physics in Medicine & Biology , 68(15), 2023

    2023

  38. [46]

    Parallel optimization: Theory, algorithms, and applications

    Yair Censor and Stavros Andrea Zenios. Parallel optimization: Theory, algorithms, and applications. Oxford University Press, USA, 1997

    1997

  39. [47]

    Ordered-subset simultaneous algebraic reconstruction techniques (OS- SART)

    Ge Wang and Ming Jiang. Ordered-subset simultaneous algebraic reconstruction techniques (OS- SART). Journal of X-ray Science and Technology , 12(3):169–177, 2004

    2004

  40. [48]

    Block-iterative algorithms with diagonally scaled oblique projections for the linear feasibility problem

    Yair Censor and Tommy Elfving. Block-iterative algorithms with diagonally scaled oblique projections for the linear feasibility problem. SIAM Journal on Matrix Analysis and Applications, 24(1):40–58, 2002

    2002

  41. [49]

    Computational methods for large-scale inverse problems: A survey on hybrid projection methods

    Julianne Chung and Silvia Gazzola. Computational methods for large-scale inverse problems: A survey on hybrid projection methods. SIAM Review, 66(2):205–284, 2024

    2024

  42. [50]

    Vasily A. Morozov. On the solution of functional equations by the method of regularization. Soviet Math. Doklady , page 414–417, 1966

    1966

  43. [51]

    Golub, Michael Heath, and Grace Wahba

    Gene H. Golub, Michael Heath, and Grace Wahba. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21(2):215–223, 1979

    1979

  44. [52]

    Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique

    Joaquim G Sanctorum, Sam Van Wassenbergh, Van Nguyen, Jan De Beenhouwer, Jan Sijbers, and Joris JJ Dirckx. Extended imaging volume in cone-beam x-ray tomography using the weighted simultaneous iterative reconstruction technique. Physics in Medicine & Biology , 66(16):165008, 2021

    2021

  45. [53]

    Optimization-based image reconstruction from sparse-view data in offset-detector cbct

    Junguo Bian, Jiong Wang, Xiao Han, Emil Y Sidky, Lingxiong Shao, and Xiaochuan Pan. Optimization-based image reconstruction from sparse-view data in offset-detector cbct. Physics in Medicine & Biology , 58(2):205, 2012

    2012

  46. [54]

    Digital breast tomosynthesis: state of the art

    Srinivasan Vedantham, Andrew Karellas, Gopal R Vijayaraghavan, and Daniel B Kopans. Digital breast tomosynthesis: state of the art. Radiology, 277(3):663–684, 2015

    2015

  47. [55]

    Recent advances in X-ray cone-beam computed laminography

    Neil S O’Brien, Richard P Boardman, Ian Sinclair, and Thomas Blumensath. Recent advances in X-ray cone-beam computed laminography. Journal of X-ray Science and Technology, 24(5):691– 707, 2016

    2016

  48. [56]

    A geometric calibration method for cone beam CT systems

    Kai Yang, Alexander LC Kwan, DeWitt F Miller, and John M Boone. A geometric calibration method for cone beam CT systems. Medical physics, 33(6Part1):1695–1706, 2006

    2006

  49. [57]

    Toward on-the-fly trajectory optimization for C-arm CBCT under strong kinematic constraints

    Sepideh Hatamikia, Ander Biguri, Gernot Kronreif, Michael Figl, Tom Russ, Joachim Kettenbach, Martin Buschmann, and Wolfgang Birkfellner. Toward on-the-fly trajectory optimization for C-arm CBCT under strong kinematic constraints. Plos one , 16(2):e0245508, 2021

    2021

  50. [58]

    Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox

    Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Ibrahim El khalil Harrane, Richard Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, et al. Arbitrarily large tomography with iterative algorithms on multiple GPUs using the TIGRE toolbox. ...

    2020

  51. [59]

    The realisation of fast X-ray computed tomography using a limited number of projection images for dimensional metrology

    Wenjuan Sun, Stephan Chretien, Ander Biguri, Manuchehr Soleimani, Thomas Blumensath, and Jessica Talbott. The realisation of fast X-ray computed tomography using a limited number of projection images for dimensional metrology. NDT & E International , 137:102852, 2023

    2023

  52. [60]

    4D imaging of two-phase flow in porous media using laboratory-based micro-computed tomography

    KR Tekseth and DW Breiby. 4D imaging of two-phase flow in porous media using laboratory-based micro-computed tomography. Water Resources Research, 60(4):e2023WR036514, 2024

    2024

  53. [61]

    X-ray TIGRE v3 29 microcomputed tomography in additive manufacturing: a review of the current technology and applications

    Anton Du Plessis, Igor Yadroitsev, Ina Yadroitsava, and Stephan G Le Roux. X-ray TIGRE v3 29 microcomputed tomography in additive manufacturing: a review of the current technology and applications. 3D Printing and Additive Manufacturing , 5(3):227–247, 2018

    2018

  54. [62]

    Scientific data exchange: a schema for HDF5-based storage of raw and analyzed data

    Francesco De Carlo, Doga G¨ ursoy, Federica Marone, Mark Rivers, Dilworth Y Parkinson, Faisal Khan, Nicholas Schwarz, David J Vine, Stefan Vogt, S-C Gleber, et al. Scientific data exchange: a schema for HDF5-based storage of raw and analyzed data. Journal of synchrotron radiat...

    2014

  55. [63]

    TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research

    Yi Du, Ruoxi Wang, Ander Biguri, Xuzhi Zhao, Yahui Peng, and Hao Wu. TIGRE-VarianCBCT for on-board cone-beam computed tomography, an open-source toolkit for imaging, dosimetry and clinical research. Physica Medica, 102:33–45, 2022

    2022

  56. [64]

    Dosimetric accuracy and radiobiological implications of ion computed tomography for proton therapy treatment planning

    S Meyer, F Kamp, T Tessonnier, A Mairani, et al. Dosimetric accuracy and radiobiological implications of ion computed tomography for proton therapy treatment planning. Physics in Medicine & Biology , 64(12):125008, 2019

    2019

  57. [65]

    Conceptual design of a proton computed tomography system for applications in proton radiation therapy

    Reinhard Schulte, Vladimir Bashkirov, Tianfang Li, Zhengrong Liang, Klaus Mueller, Jason Heimann, Leah R Johnson, Brian Keeney, HF-W Sadrozinski, Abraham Seiden, et al. Conceptual design of a proton computed tomography system for applications in proton radiation therapy. IEEE ...

    2004

  58. [66]

    Extension of the open- source TIGRE toolbox for proton imaging

    Stefanie Kaser, Thomas Bergauer, Ander Biguri, Wolfgang Birkfellner, Sepideh Hatamikia, Albert Hirtl, Christian Irmler, Benjamin Kirchmayer, and Felix Ulrich-Pur. Extension of the open- source TIGRE toolbox for proton imaging. Zeitschrift f¨ ur Medizinische Physik, 2022

    2022

  59. [67]

    A maximum likelihood method for high resolution proton radiography/proton CT

    Charles-Antoine Collins-Fekete, S´ ebastien Brousmiche, Stephen KN Portillo, Luc Beaulieu, and Joao Seco. A maximum likelihood method for high resolution proton radiography/proton CT. Physics in Medicine & Biology , 61(23):8232, 2016

    2016

  60. [68]

    Learned primal-dual reconstruction

    Jonas Adler and Ozan ¨Oktem. Learned primal-dual reconstruction. IEEE transactions on medical imaging, 37(6):1322–1332, 2018

    2018

  61. [69]

    Tomobank: a tomographic data repository for computational x-ray science

    Francesco De Carlo, Do˘ ga G¨ ursoy, Daniel J Ching, K Joost Batenburg, Wolfgang Ludwig, Lucia Mancini, Federica Marone, Rajmund Mokso, Dani¨ el M Pelt, Jan Sijbers, et al. Tomobank: a tomographic data repository for computational x-ray science. Measurement Science and Technol...

    2018

  62. [70]

    Catphan 600 ® Manual

    The Phantom Laboratory. Catphan 600 ® Manual. https://www.phantomlab.com/catphan-600, Accessed: 2024-10-26

    2024

  63. [71]

    GEANT4—a simulation toolkit

    Sea Agostinelli et al. GEANT4—a simulation toolkit. Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 506(3):250–303, 2003

    2003

  64. [72]

    Applications of neutron computed tomography to thermal-hydraulics research

    Manasavee Lohvithee, Somboon Rassame, and Takashi Hibiki. Applications of neutron computed tomography to thermal-hydraulics research. Progress in Nuclear Energy, 2022

    2022

  65. [73]

    A study of why we need to reassess full reference image quality assessment with medical images

    Anna Breger, Ander Biguri, Malena Sabat´ e Landman, Ian Selby, Nicole Amberg, Elisabeth Brunner, Janek Gr¨ ohl, Sepideh Hatamikia, Clemens Karner, Lipeng Ning, et al. A study of why we need to reassess full reference image quality assessment with medical images. arXiv preprint...

    2024 arXiv

  66. [74]

    A study on the adequacy of common iqa measures for medical images

    Anna Breger, Clemens Karner, Ian Selby, Janek Gr¨ ohl, S¨ oren Dittmer, Edward Lilley, Judith Babar, Jake Beckford, Timothy J Sadler, Shahab Shahipasand, et al. A study on the adequacy of common iqa measures for medical images. arXiv preprint arXiv:2405.19224 , 2024. TIGRE v3 ...

    2024 arXiv

Pith tools

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