Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging

Chenxi Li; Fenglin Zhan; Ganxi Du; Haoqi Wen; Haoyu Lu; Jianyong Jiang; Jiaru Ni; Jigang Yang; Jinyue Zhang; Kexin Wang

REVIEW 4 major objections 6 minor 35 references

Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read PET-DPC, a conditional diffusion model with posterior physical correction, reconstructs PET images with quantitative accuracy matching iterative OSEM while cutting brain reconstruction time by about half and whole-body time by about 85%.

desk verdict PET-DPC is a promising diffusion-based PET reconstruction with a physics-correction step that works empirically, but the correction is under-derived and the quantitative accuracy claim is anchored to QuanTOF rather than ground truth. read the letter →

arxiv 2508.14364 v1 pith:X6YUZH5K submitted 2025-08-20 physics.med-ph cs.CV

Yucun Hou , Fenglin Zhan , Chenxi Li , Ziquan Yuan , Haoyu Lu , Yue Chen , Yihao Chen , Kexin Wang

show 8 more authors

Runze Liao Haoqi Wen Ganxi Du Jiaru Ni Taoran Chen Jinyue Zhang Jigang Yang Jianyong Jiang

This is my paper · ORCID

classification physics.med-phcs.CV

keywords PETreconstructiondiffusionmodelposteriorcorrectionquantitativeaccuracytime-of-flightlist-modescatterattenuation

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 proposes PET-DPC, a conditional diffusion model that reconstructs PET images by combining a learned denoiser with explicit physical corrections applied during the reverse sampling process. The central claim is that this physics-constrained sampling yields images quantitatively comparable to fully corrected OSEM/QuanTOF reconstructions, while outperforming end-to-end deep learning baselines in PSNR, SSIM, NRMSE, and tumor contrast, and generalizing to out-of-distribution phantom and simulation data. If true, the method would let clinical PET workflows replace slow iterative reconstruction with a fast diffusion-based pipeline—about 50% faster for brain scans and about 85% faster for whole-body scans—without sacrificing quantitative fidelity. The authors also report that the posterior correction, not the diffusion prior alone, is what restores background uniformity and tumor-to-background ratios in the NEMA phantom.

What carries the argument

The central mechanism is Algorithm 1's posterior correction step: after Tweedie denoising produces x̂0′, the image is multiplied by an intensity-matching coefficient C_IM and multiplicatively updated by back-projecting the ratio of measured to expected prompt events, Σ Pᵀ (y / ȳ), a step claimed to inject scatter, attenuation, and random corrections into the sampling trajectory. The conditioning input is the GTP-image, a back-projected image where each list-mode line of response is spread by a TOF Gaussian and a geometric overlap weight, then normalized by a sensitivity map; this supplies raw-data geometry without storing sinograms. The correction step, not the neural denoiser, is what the a

What would settle it

In a Monte Carlo simulation with known true scatter/random sinograms, reconstruct the same list-mode data with PET-DPC using the correct estimate and with deliberately perturbed scatter/random estimates; if image metrics shift systematically with the perturbation, the correction is not robustly likelihood-based. More directly, compare the update direction in Algorithm 1 Step 5 against the numerical gradient of Eq. (14) at the same x̂0; a mismatch would show the 'posterior correction' is not the gradient it claims to be.

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Extended reading notes

Core claim

The paper's discovery is that a diffusion model whose sampling steps are interleaved with a posterior correction based on a Poisson measurement model can produce PET images with quantitative fidelity comparable to fully corrected iterative reconstruction. During sampling, the model uses list-mode data to apply scatter, attenuation, and random corrections in the image domain, and this correction step is what distinguishes PET-DPC from a plain conditional DDPM. On 100 brain validation datasets, PET-DPC reached PSNR 47.703, SSIM 0.994, and NRMSE 0.066, versus FastPET (37.749/0.941/0.213) and Conditional-DDPM (44.782/0.992/0.098); similar gains held on whole-body data. The authors further report

Load-bearing premise

The load-bearing premise is that the multiplicative update using measured-to-expected event ratios, combined with an intensity-matching coefficient, correctly approximates the Poisson-likelihood gradient; the paper does not derive this update and does not specify how the scatter and random sinograms used in the forward model are estimated.

Editorial extensions

If this is right

Clinical PET could be reconstructed in roughly half the time for brain scans and one seventh of the time for whole-body CBM scans, compared with the QuanTOF iterative baseline.
End-to-end deep learning PET methods that omit explicit physical modeling should expect degraded quantitative performance when applied out of distribution; physics-corrected diffusion sampling is the proposed remedy.
The number of posterior correction steps is a practical control knob: more steps improve PSNR/SSIM/NRMSE on brain validation data, at increased compute cost.
A single model trained on brain and whole-body data can transfer to NEMA phantom and simulated data without retraining, provided the posterior correction is active.
The quantitative gap between conditional diffusion and iterative reconstruction can be closed without giving up the speed advantage of deep learning.

Reading between the lines

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

Editorial extension: The multiplicative update in Algorithm 1 Step 5 has the same ratio structure as MLEM updates. If validated as a likelihood-gradient surrogate, similar corrections could be grafted onto faster generative samplers, such as consistency or flow-matching models, to enforce PET physics at even lower step counts.
Editorial extension: Since the correction stage back-projects list-mode data in the image domain and the diffusion prior supplies regularization, the scheme may extend naturally to SPECT or to joint activity/attenuation estimation when a μ-map is available.
Editorial extension: A direct test is to run PET-DPC on simulated data with known true scatter/random sinograms and compare against reconstructions using the estimated ones; this would separate errors from the correction approximation from errors in scatter/random estimation.
Editorial extension: The ablation's large drop from one correction step to zero suggests the correction steps carry most of the physics; a systematic study trading denoiser steps for correction iterations could yield a faster protocol.

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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

Quick take: this is a real step forward for fast quantitative PET reconstruction, but the central physics-correction step is a heuristic wearing a likelihood's clothes, and the claims about absolute quantitative accuracy need a careful look.

What's genuinely new: the GTP-image input (TOF + geometric backprojection with sensitivity normalization) is a sensible way to feed raw list-mode information into a network, and the idea of alternating a DDPM predictor with a measurement-based corrector during sampling is a clean combination of existing pieces (DPS, MLEM-style updates) that hasn't been applied to PET this way before. The experimental work is serious: 300 brain and 50 whole-body clinical datasets, a physical NEMA phantom, 20 GATE simulations, ablations on the number of correction steps, and OOD generalization tests. That's more than most papers in this space do. The ablation showing performance degrading from 5 correction steps down to 1 is convincing evidence that the correction term is doing something real.

The soft spots are in the physics and the evaluation. Algorithm 1 Step 5 is written as a multiplicative MLEM-style update, but it's not derived as a gradient of the Poisson log-likelihood in Eq. (14) — the gradient would be an additive term, and no step size or equivalence argument is given. The paper also never says how the scatter and random sinograms (r̅ and s̅ in Eq. 10) are estimated for the correction step. That matters because if those estimates are wrong, the "physics correction" could be baking in errors. The strongest evidence of this is Table III: on simulated data where the true gray/white matter ratio is 3.846, QuanTOF recovers 1.898 and PET-DPC recovers 1.868. The paper calls this "strong quantitative consistency," but it's actually consistency with QuanTOF, not with the ground truth. The method is inheriting QuanTOF's bias. That's fine if you only claim to match QuanTOF, but the abstract says "quantitative accuracy" and "surpassing traditional iterative methods," which overreaches. Also, the whole-body validation is only 5 cases, and no code or data is provided, so the reproducibility is limited.

Who is this for? Anyone working on deep learning for PET reconstruction, especially on making diffusion models clinically practical. It deserves peer review — the flaws are addressable and the empirical finding that the correction steps matter is useful even if the derivation tightens up. My recommendation: send it out, but ask the authors to either derive the update or explicitly label it as an empirical heuristic, specify the scatter/random estimation, and soften the quantitative-accuracy claims to be relative to QuanTOF rather than absolute.

I'd bring this to a reading group and likely cite it as related work, though I'd want the correction step clarified before building on it.

Referee Report

4 major / 6 minor

Summary. The paper proposes PET-DPC, a conditional denoising diffusion probabilistic model for PET reconstruction. List-mode data are first back-projected into a Geometric TOF Probabilistic Image (GTP-image) that serves as the conditioning input; during the reverse diffusion sampling process, a posterior correction step updates the current estimate using the measured list-mode data and the Poisson forward model (Eq. 10), intended to apply scatter, attenuation, and random corrections. The model is trained on 300 brain and 50 whole-body clinical datasets and evaluated on 100 brain validation cases, 5 whole-body validation cases, a NEMA phantom, and 20 simulated brain datasets, with QuanTOF reconstructions used as training labels and as the main reference. The paper reports that PET-DPC outperforms FastPET and a conditional DDPM without correction, closely matches QuanTOF, and reduces reconstruction time substantially.

Significance. If the posterior correction mechanism is sound, the paper would make a useful contribution by embedding explicit physical corrections into a fast diffusion-based reconstruction pipeline, addressing a recognized limitation of purely data-driven PET reconstruction. The evidence base is unusually broad for this literature: clinical brain and whole-body data, a physical phantom scanned on the same system, and GATE simulations, plus ablations on the number of correction steps and a runtime comparison. However, the validity of the central correction step is not established, scatter/random estimation is unspecified, and the quantitative claims are largely measured as agreement with QuanTOF rather than against physical ground truth, so the significance is contingent on resolving these issues.

major comments (4)

[Section II-B, Algorithm 1 Step 5] The paper states that Eq. (14) is used to compute the gradient with respect to x̂0 and that the measurement data update implements the posterior correction. However, Step 5 applies x̂0 ← C_IM x̂0' · (Σ_i y_i/ȳ_i P_ij)/(Σ_i P_ij), which is an MLEM-style multiplicative update. The gradient of Eq. (14) with respect to x_j is Σ_i P_ij(y_i/ȳ_i − 1), a different expression. No step size, derivation, or equivalence argument is provided. Since the entire quantitative-accuracy claim rests on this correction step, the method is at present an unexplained heuristic and the name 'posterior correction' is not justified.
[Section II-B, Eq. (10)] The forward model in Eq. (10) includes random and scatter means r̄_i and s̄_i, but the paper never specifies how these are estimated during posterior correction. The correction step requires an accurate ȳ_i, including these components; without specifying the scatter and random estimation method (e.g., SSS, delayed-window, or simulation-based), the algorithm is not reproducible and the risk of bias from inaccurate estimates is not assessed. The brief mention in the Discussion of treating each 10 s of whole-body acquisition as a bed position for correction is not a methods-level description.
[Table III and Section IV-D] In the simulation study, the ground-truth gray-to-white matter activity ratio is 3.846, but QuanTOF recovers 1.898 and PET-DPC 1.868. The text calls this 'strong quantitative consistency,' which conflates agreement with QuanTOF with absolute quantitative accuracy. By this measure PET-DPC simply inherits QuanTOF's substantial bias. The central claim of quantitative accuracy is therefore not supported by the simulation study; the authors should either use the true activity map to compute errors or moderate the claim to 'close agreement with the QuanTOF reference.'
[Section III.A.2 and Table II] The whole-body study uses only 5 validation cases, and the reported NRMSE standard deviations are large (e.g., 0.663 ± 0.248 for FastPET). The strong conclusions about whole-body performance and generalization rest on a very small sample. Confidence intervals or per-case results should be provided, and the corresponding claims should be framed as preliminary.

minor comments (6)

[Section II-C, Eq. (15)-(16)] The definition of the GTP-image is unclear: the formula includes a seemingly gratuitous factor '1 × N_L', and the normalization by the sensitivity map S_v is introduced without explaining how S_v is computed. Please clarify.
[Section II-D and Algorithm 1] The intensity-matching coefficient C_IM is described as 'pre-calculated' but no formula or method for its determination is given. This is a free parameter in the method and should be specified for reproducibility.
[Algorithm 1] The index i is reused for the sampling step and for the LOR index in Step 5; although the text notes the distinction, the notation is confusing and should be changed (e.g., use k for LORs).
[Figure 6 caption] Typo: 'across across seven reconstructed NEMA phantom images'.
[Section II-B and III-D] The baseline 'Conditional-DDPM' is defined only as PET-DPC without posterior correction; this should be stated explicitly in the Methods, and the correction-free conditional model should be described as a separate baseline before being used in comparisons.
[References] Reference [29] for QuanTOF is a conference abstract; if the method is used as the training label and reference standard, please provide a fuller citation or a public implementation/algorithm description so that the reference results are reproducible.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; minor benchmark overlap via self-cited QuanTOF, plus an underived correction update.

full rationale

The claimed derivation chain is largely self-contained. The posterior correction in Algorithm 1 Step 5 is an MLEM-type multiplicative update driven by measured prompts y and the Poisson forward model (10); it does not use QuanTOF output at inference, so the physics correction is not defined in terms of the training target. Training on QuanTOF images and then evaluating PSNR/SSIM/NRMSE against QuanTOF on a held-out validation split creates a mild benchmark overlap, and QuanTOF is a self-cited method [29], but this is not a constructional circularity: the network is not fitted to the validation labels, and the phantom (known 4:1 ratio) and simulated brain (known 3.846 gray/white ratio) studies provide external checks on the quantitative claim. The paper does not actually recover the simulated 3.846 ratio (PET-DPC gives 1.868), and the connection between Eq. (14) and the Step 5 update is asserted without proof; these are correctness/rigor limitations, not circularity. Overall, the central derivation reduces to neither a same-data fit nor a self-citation chain.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central claim rests on the Poisson forward model, the DPS approximation, accurate scanner calibrations, and the choice of QuanTOF as training target. The most consequential free parameter is the intensity-matching coefficient C_IM, which is fitted to the data and directly influences the posterior correction.

free parameters (2)

C_IM (intensity matching coefficient)
Pre-calculated scalar that maps normalized diffusion output to physical activity; used in Step 5 of Algorithm 1. The paper does not specify how it is derived; it is calibrated to match injected dose and reconstructed activity, effectively fitting the correction to the data.
Number of posterior correction steps = 5
Chosen empirically; ablation study shows metrics improve from 1 to 5 steps, indicating this hyperparameter controls the accuracy-speed trade-off.

assumptions (4)

domain assumption PET coincidence data follow a Poisson distribution with mean y_bar_i = sum_j P_ij x_j + r_i + s_i (Eq. 10).
Standard PET physics, but the specific scatter and random means are not measured directly and must be estimated; the paper does not explain how these estimates are obtained for the correction step.
domain assumption The likelihood gradient can be approximated as grad_xt log p_t(y | x_t) ≈ grad_xt log p_t(y | x_hat_0) (Eq. 13, based on [28]).
This is a standard approximation in diffusion posterior sampling, but its validity for PET list-mode data with scatter and random is not verified in the paper.
domain assumption The GTP-image sensitivity normalization assumes an accurate sensitivity map S_v and normalization factors N_L (Eqs. 15-16).
The sensitivity map and normalization factors are scanner calibrations; their accuracy is assumed, and no uncertainty is propagated.
domain assumption The neural network trained on QuanTOF labels captures the mapping from GTP-image to activity distribution.
The training relies on QuanTOF reconstructions as ground truth; any bias in QuanTOF is inherited by the model.

invented entities (1)

GTP-image (Geometric TOF Probabilistic Image)
purpose: Input representation for the diffusion model that encodes TOF and geometric probabilities.
The GTP-image is a constructed histogram; it has no independent physical evidence and is only defined within the paper's pipeline.

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Cite this review

Pith. "Pith review of Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging." pith.science (2026). https://pith.science/paper/X6YUZH5K

@misc{pith2026250814364,
  author       = {Pith},
  title        = {Pith review of: Physics-Constrained Diffusion Reconstruction with Posterior Correction for Quantitative and Fast PET Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X6YUZH5K}},
  note         = {Machine review of arXiv:2508.14364}
}

read the original abstract

Deep learning-based reconstruction of positron emission tomography(PET) data has gained increasing attention in recent years. While these methods achieve fast reconstruction,concerns remain regarding quantitative accuracy and the presence of artifacts,stemming from limited model interpretability,data driven dependence, and overfitting risks.These challenges have hindered clinical adoption.To address them,we propose a conditional diffusion model with posterior physical correction (PET-DPC) for PET image reconstruction. An innovative normalization procedure generates the input Geometric TOF Probabilistic Image (GTP-image),while physical information is incorporated during the diffusion sampling process to perform posterior scatter,attenuation,and random corrections. The model was trained and validated on 300 brain and 50 whole-body PET datasets,a physical phantom,and 20 simulated brain datasets. PET-DPC produced reconstructions closely aligned with fully corrected OSEM images,outperforming end-to-end deep learning models in quantitative metrics and,in some cases, surpassing traditional iterative methods. The model also generalized well to out-of-distribution(OOD) data. Compared to iterative methods,PET-DPC reduced reconstruction time by 50% for brain scans and 85% for whole-body scans. Ablation studies confirmed the critical role of posterior correction in implementing scatter and attenuation corrections,enhancing reconstruction accuracy. Experiments with physical phantoms further demonstrated PET-DPC's ability to preserve background uniformity and accurately reproduce tumor-to-background intensity ratios. Overall,these results highlight PET-DPC as a promising approach for rapid, quantitatively accurate PET reconstruction,with strong potential to improve clinical imaging workflows.

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Works this paper leans on

35 extracted references · 33 canonical work pages

[1]

A combined PET/CT scanner for clinical oncology,

T. Beyer et al., "A combined PET/CT scanner for clinical oncology," Journal of nuclear medicine, vol. 41, no. 8, pp. 1369-1379, 2000

work page 2000
[2]

Recommendations on the use of 18F-FDG PET in oncology,

J. W. Fletcher et al., "Recommendations on the use of 18F-FDG PET in oncology," Journal of Nuclear Medicine, vol. 49, no. 3, pp. 480 -508, 2008

work page 2008
[3]

Clinical applications of PET in oncology,

E. M. Rohren, T. G. Turkington, and R. E. Coleman, "Clinical applications of PET in oncology," Radiology, vol. 231, no. 2, pp. 305 - 332, 2004

work page 2004
[4]

PET/CT: challenge for nuclear cardiology,

M. Schwaiger, S. Ziegler, and S. G. Nekolla, "PET/CT: challenge for nuclear cardiology," Journal of Nuclear Medicine, vol. 46, no. 10, pp. 1664-1678, 2005

work page 2005
[5]

A positron -emission transaxial tomograph for nuclear imaging (PETT),

M. M. Ter-Pogossian, M. E. Phelps, E. J. Hoffman, and N. A. Mullani, "A positron -emission transaxial tomograph for nuclear imaging (PETT)," Radiology, vol. 114, no. 1, pp. 89-98, 1975

work page 1975
[6]

Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care,

S. R. Cherry, T. Jones, J. S. Karp, J. Qi, W. W. Moses, and R. D. Badawi, "Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care," Journal of Nuclear Medicine, vol. 59, no. 1, pp. 3-12, 2018

work page 2018
[7]

Maximum likelihood reconstruction for emission tomography,

L. A. Shepp and Y. Vardi, "Maximum likelihood reconstruction for emission tomography," IEEE transactions on medical imaging, vol. 1, no. 2, pp. 113-122, 2007

work page 2007
[8]

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET,

S. Ahn et al. , "Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET," Physics in Medicine & Biology, vol. 60, no. 15, p. 5733, 2015

work page 2015

Show all 35 references

[9]

Oif-net: An optical flow registration -based pet/mr cross- modal interactive fusion network for low -count brain pet image denoising,

M. Fu et al., "Oif-net: An optical flow registration -based pet/mr cross- modal interactive fusion network for low -count brain pet image denoising," IEEE Transactions on Medical Imaging, vol. 43, no. 4, pp. 1554-1567, 2023

2023
[10]

Unified noise-aware network for low-count pet denoising with varying count levels,

H. Xie et al., "Unified noise-aware network for low-count pet denoising with varying count levels," IEEE transactions on radiation and plasma medical sciences, vol. 8, no. 4, pp. 366-378, 2023

2023
[11]

Spach Transformer: Spatial and channel -wise transformer based on local and global self -attentions for PET image denoising,

S.-I. Jang et al. , "Spach Transformer: Spatial and channel -wise transformer based on local and global self -attentions for PET image denoising," IEEE transactions on medical imaging, vol. 43, no. 6, pp. 2036-2049, 2023
[12]

2.5 D multi -view averaging diffusion model for 3D medical image translation: application to low-count PET reconstruction with CT -less attenuation correction,

T. Chen et al. , "2.5 D multi -view averaging diffusion model for 3D medical image translation: application to low-count PET reconstruction with CT -less attenuation correction," IEEE Transactions on Medical Imaging, 2025

2025
[13]

Evaluation of deep learning -based scatter correction on a long -axial field-of-view PET scanner,

B. Laurent, A. Bousse, T. Merlin, A. Rominger, K. Shi, and D. Visvikis, "Evaluation of deep learning -based scatter correction on a long -axial field-of-view PET scanner," European Journal of Nuclear Medicine and Molecular Imaging, vol. 52, no. 7, pp. 2563-2576, 2025

2025
[14]

Artificial intelligence on FDG PET images identifies mild cognitive impairment patients with neurodegenerative disease,

J. Prats -Climent et al. , "Artificial intelligence on FDG PET images identifies mild cognitive impairment patients with neurodegenerative disease," Journal of medical systems, vol. 46, no. 8, p. 52, 2022

2022
[15]

Image reconstruction by domain-transform manifold learning,

B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, "Image reconstruction by domain-transform manifold learning," Nature, vol. 555, no. 7697, pp. 487-492, 2018

2018
[16]

DeepPET: A deep encoder –decoder network for directly solving the PET image reconstruction inverse problem,

I. Hä ggströ m, C. R. Schmidtlein, G. Campanella, and T. J. Fuchs, "DeepPET: A deep encoder –decoder network for directly solving the PET image reconstruction inverse problem," Medical image analysis, vol. 54, pp. 253-262, 2019

2019
[17]

FastPET: near real-time reconstruction of PET histo-image data using a neural network,

W. Whiteley, V. Panin, C. Zhou, J. Cabello, D. Bharkhada, and J. Gregor, "FastPET: near real-time reconstruction of PET histo-image data using a neural network," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 5, no. 1, pp. 65-77, 2020

2020
[18]

Anatomically guided pet image reconstruction using conditional weakly -supervised multi -task learning integrating self -attention,

B. Yang, K. Gong, H. Liu, Q. Li, and W. Zhu, "Anatomically guided pet image reconstruction using conditional weakly -supervised multi -task learning integrating self -attention," IEEE Transactions on Medical Imaging, vol. 43, no. 6, pp. 2098-2112, 2024
[19]

PET image reconstruction using deep image prior,

K. Gong, C. Catana, J. Qi, and Q. Li, "PET image reconstruction using deep image prior," IEEE transactions on medical imaging, vol. 38, no. 7, pp. 1655-1665, 2018

2018
[20]

Deep image prior,

D. Ulyanov, A. Vedaldi, and V. Lempitsky, "Deep image prior," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 9446-9454

2018
[21]

PET image reconstruction incorporating deep image prior and a forward projection model,

F. Hashimoto, K. Ote, and Y. Onishi, "PET image reconstruction incorporating deep image prior and a forward projection model," IEEE Transactions on Radiation and Plasma Medical Sciences, vol. 6, no. 8, pp. 841-846, 2022

2022
[22]

Deep kernel representation for image reconstruction in PET,

S. Li and G. Wang, "Deep kernel representation for image reconstruction in PET," IEEE transactions on medical imaging, vol. 41, no. 11, pp. 3029-3038, 2022

2022
[23]

Neural KEM: A kernel method with deep coefficient prior for PET image reconstruction,

S. Li, K. Gong, R. D. Badawi, E. J. Kim, J. Qi, and G. Wang, "Neural KEM: A kernel method with deep coefficient prior for PET image reconstruction," IEEE transactions on medical imaging, vol. 42, no. 3, pp. 785-796, 2022

2022
[24]

Denoising diffusion probabilistic models,

J. Ho, A. Jain, and P. Abbeel, "Denoising diffusion probabilistic models," Advances in neural information processing systems, vol. 33, pp. 6840-6851, 2020

2020
[25]

Score-based generative modeling through stochastic differential equations,

Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, "Score-based generative modeling through stochastic differential equations," arXiv preprint arXiv:2011.13456, 2020

2011 arXiv
[26]

PET image denoising based on denoising diffusion probabilistic model,

K. Gong, K. Johnson, G. El Fakhri, Q. Li, and T. Pan, "PET image denoising based on denoising diffusion probabilistic model," European Journal of Nuclear Medicine and Molecular Imaging, vol. 51, no. 2, pp. 358-368, 2024

2024
[27]

Full‐dose whole‐body PET synthesis from low‐dose PET using high ‐efficiency denoising diffusion probabilistic model: PET consistency model,

S. Pan et al., "Full‐dose whole‐body PET synthesis from low‐dose PET using high ‐efficiency denoising diffusion probabilistic model: PET consistency model," Medical Physics, vol. 51, no. 8, pp. 5468-5478, 2024

2024
[28]

Diffusion posterior sampling for general noisy inverse problems,

H. Chung, J. Kim, M. T. Mccann, M. L. Klasky, and J. C. Ye, "Diffusion posterior sampling for general noisy inverse problems," arXiv preprint arXiv:2209.14687, 2022

2022 arXiv
[29]

QUANTOF: multi -GPU-based list -mode fully quantitative TOF PET image reconstruction,

Z. Yuan et al. , "QUANTOF: multi -GPU-based list -mode fully quantitative TOF PET image reconstruction," in 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 2024: IEEE, pp. 1-1

2024
[30]

A new improved version of the realistic digital brain phantom,

B. Aubert-Broche, A. C. Evans, and L. Collins, "A new improved version of the realistic digital brain phantom," NeuroImage, vol. 32, no. 1, pp. 138-145, 2006

2006
[31]

Twenty new digital brain phantoms for creation of validation image data bases,

B. Aubert-Broche, M. Griffin, G. B. Pike, A. C. Evans, and D. L. Collins, "Twenty new digital brain phantoms for creation of validation image data bases," IEEE transactions on medical imaging, vol. 25, no. 11, pp. 1410- 1416, 2006

2006
[32]

Artificial neural network enhanced Bayesian PET image reconstruction,

B. Yang, L. Ying, and J. Tang, "Artificial neural network enhanced Bayesian PET image reconstruction," IEEE transactions on medical imaging, vol. 37, no. 6, pp. 1297-1309, 2018

2018
[33]

GATE: a simulation toolkit for PET and SPECT,

S. Jan et al., "GATE: a simulation toolkit for PET and SPECT," Physics in Medicine & Biology, vol. 49, no. 19, p. 4543, 2004

2004
[34]

The OpenGATE ecosystem for Monte Carlo simulation in medical physics,

D. Sarrut et al., "The OpenGATE ecosystem for Monte Carlo simulation in medical physics," Physics in Medicine & Biology, vol. 67, no. 18, p. 184001, 2022

2022
[35]

The generalized contrast-to-noise ratio: A formal definition for lesion detectability,

A. Rodriguez-Molares et al., "The generalized contrast-to-noise ratio: A formal definition for lesion detectability," IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 67, no. 4, pp. 745- 759, 2019

2019

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

A combined PET/CT scanner for clinical oncology,

T. Beyer et al., "A combined PET/CT scanner for clinical oncology," Journal of nuclear medicine, vol. 41, no. 8, pp. 1369-1379, 2000

work page 2000

[2] [2]

Recommendations on the use of 18F-FDG PET in oncology,

J. W. Fletcher et al., "Recommendations on the use of 18F-FDG PET in oncology," Journal of Nuclear Medicine, vol. 49, no. 3, pp. 480 -508, 2008

work page 2008

[3] [3]

Clinical applications of PET in oncology,

E. M. Rohren, T. G. Turkington, and R. E. Coleman, "Clinical applications of PET in oncology," Radiology, vol. 231, no. 2, pp. 305 - 332, 2004

work page 2004

[4] [4]

PET/CT: challenge for nuclear cardiology,

M. Schwaiger, S. Ziegler, and S. G. Nekolla, "PET/CT: challenge for nuclear cardiology," Journal of Nuclear Medicine, vol. 46, no. 10, pp. 1664-1678, 2005

work page 2005

[5] [5]

A positron -emission transaxial tomograph for nuclear imaging (PETT),

M. M. Ter-Pogossian, M. E. Phelps, E. J. Hoffman, and N. A. Mullani, "A positron -emission transaxial tomograph for nuclear imaging (PETT)," Radiology, vol. 114, no. 1, pp. 89-98, 1975

work page 1975

[6] [6]

Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care,

S. R. Cherry, T. Jones, J. S. Karp, J. Qi, W. W. Moses, and R. D. Badawi, "Total-body PET: maximizing sensitivity to create new opportunities for clinical research and patient care," Journal of Nuclear Medicine, vol. 59, no. 1, pp. 3-12, 2018

work page 2018

[7] [7]

Maximum likelihood reconstruction for emission tomography,

L. A. Shepp and Y. Vardi, "Maximum likelihood reconstruction for emission tomography," IEEE transactions on medical imaging, vol. 1, no. 2, pp. 113-122, 2007

work page 2007

[8] [8]

Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET,

S. Ahn et al. , "Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET," Physics in Medicine & Biology, vol. 60, no. 15, p. 5733, 2015

work page 2015