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REVIEW 4 major objections 4 minor 37 references

Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Score-based diffusion models can synthesize full-dose FDG-PET from MRI alone with metabolic accuracy close to acquired scans in epilepsy patients.

desk verdict A useful feasibility study with an overreaching abstract; the clinical accuracy claim needs a reader study and better metrics, but the work deserves review. read the letter →

arxiv 2506.11297 v2 pith:NNZDZFIZ submitted 2025-06-12 eess.IV cs.LG

Jiaqi Wu , Jiahong Ouyang , Farshad Moradi , Mohammad Mehdi Khalighi , Greg Zaharchuk This is my paper · ORCID
classification eess.IVcs.LG
keywords score-basedgenerativemodelsdiffusionMRI-to-PETtranslationepilepsyFDG-PETimagesynthesisSUVRultralow-dosePET
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 asks whether deep learning can generate diagnostically usable full-dose FDG (fluorodeoxyglucose) brain PET images without any PET radiation, or with only 1% of the usual dose, for patients being evaluated for epilepsy. Using simultaneous PET/MRI from 52 subjects, it compares two score-based generative diffusion models (SGM-KD and SGM-VP) with a Transformer-U-Net baseline for three input combinations: T1-weighted MRI alone, T1w plus T2-FLAIR, and those MRIs plus 1% ultralow-dose PET. The authors report that SGM-KD synthesizing purely from T1w and T2-FLAIR achieves the lowest whole-brain SUVR error and highest SUVR reliability among the zero-dose models, and that adding 1% PET input improves all models to the point that they become interchangeable. The motivation is clinical: this patient population is young, and eliminating or reducing radiation while preserving metabolic quantitation would be a direct benefit.

What carries the argument

The load-bearing mechanism is conditional score-based diffusion: the forward process gradually corrupts the full-dose PET volume to Gaussian noise following a stochastic differential equation, and a neural network learns the score (the gradient of the log data density) conditioned on the MRI and/or 1% PET inputs; sampling reverses the SDE to generate the PET. Two variants are used: SGM-VP with a variance-preserving SDE and predictor-corrector sampling, and SGM-KD with the denoiser $D_\theta(x,\sigma,y)=c_\text{skip}(\sigma)x+c_\text{out}(\sigma)F_\theta(c_\text{in}(\sigma)x;c_\text{noise}(\sigma),y)$ and the Karras stochastic sampler. The clinical evaluation is carried by the Congruence Index and Congruency Mean Absolute Error, which score agreement of left-right SUVR asymmetry across eight paired regions of interest.

What would settle it

A blinded reader study in which epilepsy specialists mark the suspected focus on synthetic zero-dose PET and on the acquired full-dose PET would settle the clinical claim: if localization accuracy on the synthetic images does not agree with the acquired scan beyond chance, or if the Congruence Index disagrees with expert lateralization on the same cases, the central claim would be falsified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that conditional score-based diffusion models can synthesize full-dose FDG-PET from MRI alone accurately enough to reproduce standard metabolic quantification, and that all tested models produce accurate full-dose PET when supplied with T1w, T2-FLAIR, and 1% ultralow-dose PET. In the zero-dose task, SGM-KD with T1w and T2-FLAIR inputs had the best whole-brain voxel-wise SUVR accuracy (mean $\Delta$SUVR of $0.96\times10^{-2}$) and the highest SUVR ICC (0.84), while SGM-VP was best at reproducing hemispheric asymmetry as measured by the proposed Congruence Index and CMAE. The paper introduces these congruence metrics specifically because hemispheric metabolic asymmetry is central to epilepsy focus localization, and argues that standard image metrics such as SSIM and PSNR do not capture this. It also reports that including 1% PET turns the task into a denoising problem, with all models reaching Congruence Index values around 0.85-0.90 and TransUnet the fastest to sample.

Load-bearing premise

The claim that the synthetic PET images are accurate for epilepsy rests on the assumption that the Congruence Index, CMAE, and SUVR-based reliability metrics capture what clinicians need to localize epileptogenic zones, an assumption the paper explicitly leaves unvalidated because no reader study was performed.

Editorial extensions

If this is right

  • If the zero-dose SGM-KD result holds, FDG-PET-like metabolic quantification could be obtained from MRI alone, removing radiation exposure for epilepsy workup.
  • The finding that all models become interchangeable with 1% PET input suggests a 99% dose reduction could be clinically feasible with any of the three architectures.
  • Score-based diffusion models are the stronger choice for MRI-only synthesis, while the faster TransUnet becomes competitive once ultralow-dose PET is available.
  • The proposed Congruence Index and CMAE give a quantitative target for whether a synthetic PET preserves the hemispheric asymmetry pattern that matters for epilepsy diagnosis.

Reading between the lines

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

  • The paper leaves open whether the Congruence Index tracks expert judgment; a natural extension is a reader study comparing focus localization on synthetic versus acquired PET, and if the index predicts expert lateralization, the metrics could become a standard for PET synthesis evaluation.
  • Because the authors attribute some performance differences to limited dataset size, the ranking between SGM-VP and SGM-KD may shift with larger multi-center training cohorts; the 10-subject test set is too small to settle architecture superiority.
  • The 2D slice-based approach causes visible slice inconsistencies in coronal and sagittal views, so 3D or slice-consistent diffusion formulations are an obvious next step that could improve the zero-dose task more than adding MRI contrasts.
  • If the 1% PET result transfers from list-mode undersampling of full-dose data to true low-dose acquisitions, ultralow-dose reconstruction could replace full-dose imaging in routine PET/MRI epilepsy protocols.
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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 / 4 minor

Summary. The paper compares three deep learning models—SGM-KD, SGM-VP, and TransUnet—for synthesizing full-dose FDG brain PET from MRI alone or from MRI plus 1% ultralow-dose PET, using data from 52 subjects (40 train, 2 validation, 10 test) scanned with simultaneous PET/MRI. The authors report voxel-wise SUVR error, ICC, and two newly proposed epilepsy-specific metrics (Congruence Index and Congruence Mean Absolute Error) that quantify hemispheric asymmetry agreement. They conclude that diffusion models show strong potential for pure MRI-to-PET translation and that all three model types can synthesize full-dose FDG-PET accurately when MRI and ultralow-dose PET are available.

Significance. The study addresses a clinically relevant problem—reducing or eliminating radiation exposure in epilepsy FDG-PET—and has notable strengths: the use of list-mode data to simulate realistic 1% ultralow-dose PET, the inclusion of epilepsy-specific hemispheric-asymmetry metrics rather than generic image similarity metrics alone, and a head-to-head comparison of two score-based diffusion models with a transformer-based U-Net. If the quantitative claims were fully supported, the paper would provide a useful benchmark for MRI-to-PET synthesis in epilepsy. However, the central conclusion that the models synthesize full-dose PET 'accurately' in a clinical sense is not established by the presented evidence, because no reader study or diagnostic-endpoint evaluation is performed, the test set is small, and the proposed metrics remain unvalidated against clinically meaningful outcomes. The paper itself acknowledges in Section 5 that assessing whether the Congruence Index correlates with reader studies is future work, so the abstract and conclusion currently overstate the strength of the findings.

major comments (4)
  1. [Abstract and Conclusion (Section 5)] The claim that 'all 3 model types can synthesize full-dose FDG-PET accurately' is not supported by the evidence presented. Section 5 explicitly defers assessment of whether the Congruence Index 'correlates with findings from reader studies' to future work, and no reader study or diagnostic-localization endpoint is reported. Equations (15) and (16) quantify sign agreement and area-weighted error of hemispheric asymmetry, but they cannot establish that the synthesis preserves clinically meaningful focal hypometabolism: Eq. (15) counts an ROI as congruent whenever the sign of the asymmetry index agrees, regardless of magnitude, and Eq. (16) down-weights small ROIs such as HipAmy, which is often the epileptogenic focus in temporal lobe epilepsy. The conclusion should be tempered to state that the synthetic images match the acquired PET on quantitative similarity metrics, and that clinical utility remains to be demonstrated.
  2. [Tables 2 and 4] The 'best model' claims are not statistically robust. In Table 2, SGM-KD with T1w+T2-FLAIR has the highest SUVR ICC (0.84, 95% CI 0.70–0.92) and the lowest delta-SUVR mean in Table 4 (0.96, 95% CI 0.81–1.10), but these confidence intervals overlap substantially with those of several other model-input combinations, including SGM-KD with T1w alone (ICC 0.82, CI 0.67–0.91; delta-SUVR 1.04, CI 0.88–1.20) and TransUnet with T1w alone (delta-SUVR 1.16, CI 0.95–1.37). No formal pairwise testing or correction for multiple comparisons is reported, so the designation of a single best model is not justified by the data. The authors should either add appropriate statistical tests or soften the ranking language throughout the abstract and results.
  3. [Abstract and Results (Tables 2 and 3)] The statement that 'all models improve significantly' when 1% PET input is added is not supported by any significance test. The paper reports confidence intervals for CI, CMAE, and delta-SUVR, but it does not report paired comparisons between the zero-dose and ultralow-dose conditions, nor p-values or effect sizes for any metric. Given the overlapping intervals in Tables 2–4, the word 'significantly' should be removed or substantiated with an appropriate paired statistical analysis.
  4. [Section 2.4 and Tables 2–4] The test set consists of only 10 subjects, and the confidence intervals for the primary metrics are correspondingly wide. For example, the ICC intervals in Tables 2 and 3 span ranges such as 0.63–0.90 and 0.59–0.89, and the CI intervals for the zero-dose task include values as low as 0.51–0.71. With this sample size, the paper should avoid generalizing beyond the studied cohort and should explicitly discuss the uncertainty in the model rankings. A per-subject or per-ROI analysis, or bootstrap resampling, would help quantify the stability of the reported rankings.
minor comments (4)
  1. [Abstract] There are typographical and grammatical errors, including 'ultra-lowdose' (should be 'ultralow-dose') and 'SGMs holds' (should be 'SGMs hold').
  2. [Section 2.2] The inequality notation in the VPSDE description appears corrupted in the manuscript (e.g., '0 ¡ β1 ¡ β2 ¡ .... ¡ βT ¡ 1'), and several equations contain OCR-like artifacts. The authors should ensure that the mathematical notation renders correctly.
  3. [Tables 2 and 3] The table headers contain duplicated column names, such as 'SUVR ICC' appearing twice and 'Congruence Index' twice. The intended layout should be clarified, and unit labels (e.g., 'CMAE (x10^3)') should be placed consistently.
  4. [Section 2.6] The definition of Areamax in Eq. (16) is given in passing as 'usually the cerebral white matter'; this should be stated precisely and justified, since it directly affects the CMAE values and therefore the model rankings.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: synthetic PET is evaluated against external acquired full-dose PET; self-citations are background and non-load-bearing.

full rationale

The paper's derivation chain is: train conditional generative models (SGM-VP, SGM-KD, TransUnet) on paired MRI/1%PET and full-dose PET (Eqs. 9-10, 13), generate synthetic PET conditioned on inputs, and compare to separately acquired full-dose PET using SUVR ICC, delta-SUVR, CI (Eq. 15), and CMAE (Eq. 16). The evaluation target is external ground truth, not a function of model outputs or fitted parameters. No metric is constructed from the model's own outputs in a way that forces the stated ranking; Table 2 even shows metric disagreement (SGM-VP wins CI/CMAE, SGM-KD wins delta-SUVR/ICC), so the 'best model' statement is a selective reading rather than a construction artifact. The proposed CI and CMAE are unvalidated for clinical utility, and the paper explicitly defers reader-study correlation to future work (Section 5); this undermines the 'accurate synthesis' clinical claim, but that is a validation gap, not circularity. Similarly, Eq. (15)'s sign-only asymmetry matching and Eq. (16)'s area weighting may underweight small epileptogenic foci, but that affects sensitivity and clinical interpretation, not the logical independence of the evaluation. TransUnet [9] and prior PET methods [17,29,35] include overlapping authors, but they are cited only as architecture/method background, and each model's performance is measured independently here; no uniqueness theorem or circular ansatz is imported. The abstract/conclusion over-interpret similarity metrics as clinical accuracy, but the quantitative claims are self-contained against the external benchmark.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

No new physical entities are proposed. The central claim depends on trained neural network weights and on untested evaluation metrics, not on new forces or mediators.

free parameters (3)
  • Sampling steps for SGM-KD = 50-150 steps
    Chosen by the authors as a trade-off between quality and speed; affects synthetic image quality and metrics.
  • Sampling steps for SGM-VP = 200-300 steps
    Chosen by authors; much slower and may affect comparability of model results.
  • Input slice window = 3 slices (center slice predicted)
    Authors state larger numbers of surrounding slices were explored but showed limited improvement; this is a hand-selected architecture choice.
assumptions (5)
  • standard math Score-based generative modeling theory (forward and backward SDE, score matching) is correct and applicable to 2D slices
    Invoked in Section 2.1 via [15,16]; not re-derived in this paper.
  • domain assumption The acquired full-dose PET is a valid ground truth for synthesis
    Used throughout evaluation (SUVR, ICC, CI, CMAE); assumes no motion, registration error, or reconstruction artifacts.
  • domain assumption 1% dose PET created by selecting 1 event per 100 from list-mode faithfully represents true ultralow-dose PET
    Section 2.4; the paper cites [35] for concordance but does not validate on this dataset.
  • ad hoc to paper The proposed Congruence Index and CMAE are clinically meaningful for epilepsy
    Equations (15)-(16) are introduced here; the paper says in Section 5 that whether CI correlates with reader studies remains to be assessed.
  • domain assumption Freesurfer Destrieux parcellation and ROI grouping provide anatomically correct SUVR regions
    Section 2.6; no manual QC or alternative parcellation comparison reported.

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Cite this review

Pith. "Pith review of Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients." pith.science (2026). https://pith.science/paper/NNZDZFIZ

@misc{pith2026250611297,
  author       = {Pith},
  title        = {Pith review of: Score-based Generative Diffusion Models to Synthesize Full-dose FDG Brain PET from MRI in Epilepsy Patients},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NNZDZFIZ}},
  note         = {Machine review of arXiv:2506.11297}
}
read the original abstract

Fluorodeoxyglucose (FDG) PET to evaluate patients with epilepsy is one of the most common applications for simultaneous PET/MRI, given the need to image both brain structure and metabolism, but is suboptimal due to the radiation dose in this young population. Little work has been done synthesizing diagnostic quality PET images from MRI data or MRI data with ultralow-dose PET using advanced generative AI methods, such as diffusion models, with attention to clinical evaluations tailored for the epilepsy population. Here we compared the performance of diffusion- and non-diffusion-based deep learning models for the MRI-to-PET image translation task for epilepsy imaging using simultaneous PET/MRI in 52 subjects (40 train/2 validate/10 hold-out test). We tested three different models: 2 score-based generative diffusion models (SGM-Karras Diffusion [SGM-KD] and SGM-variance preserving [SGM-VP]) and a Transformer-Unet. We report results on standard image processing metrics as well as clinically relevant metrics, including congruency measures (Congruence Index and Congruency Mean Absolute Error) that assess hemispheric metabolic asymmetry, which is a key part of the clinical analysis of these images. The SGM-KD produced the best qualitative and quantitative results when synthesizing PET purely from T1w and T2 FLAIR images with the least mean absolute error in whole-brain specific uptake value ratio (SUVR) and highest intraclass correlation coefficient. When 1% low-dose PET images are included in the inputs, all models improve significantly and are interchangeable for quantitative performance and visual quality. In summary, SGMs hold great potential for pure MRI-to-PET translation, while all 3 model types can synthesize full-dose FDG-PET accurately using MRI and ultralow-dose PET.

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Reference graph

Works this paper leans on

37 extracted references · 23 canonical work pages

  1. [1]

    Chapter 51 - Neuroimaging of epilepsy

    Cendes F, Theodore WH, Brinkmann BH, Sulc V, Cascino GD. Chapter 51 - Neuroimaging of epilepsy. In: Masdeu JC, Gonz´ alez RG, editors. Neuroimag- ing Part II. vol. 136 of Handbook of Clinical Neurology. Elsevier; 2016. p. 985–1014. Available from: https://www.sciencedirect.com/science/article/pii/ B978044453486600051X

    work page 2016

  2. [2]

    The Role of SPECT and PET in Epilepsy

    Ponisio MR, Zempel JM, Day BK, Eisenman LN, Miller-Thomas MM, Smyth MD, et al. The Role of SPECT and PET in Epilepsy. American Journal of Roentgenology. 2021;216(3):759–768. PMID: 33474983. https://doi.org/10.2214/ AJR.20.23336

    work page 2021

  3. [3]

    Diagnostic accuracy for the epilepto- genic zone detection in focal epilepsy could be higher in FDG-PET/MRI than in FDG-PET/CT

    Kikuchi K, Togao O, Yamashita K, et al. Diagnostic accuracy for the epilepto- genic zone detection in focal epilepsy could be higher in FDG-PET/MRI than in FDG-PET/CT. European Radiology. 2021;31:2915–2922. https://doi.org/10. 1007/s00330-020-07389-1

    work page 2021

  4. [4]

    Addressing Global Inequities in Positron Emission Tomography-Computed Tomography (PET-CT) for Cancer Management: A Statistical Model to Guide Strategic Planning

    Gallach M, Lette MM, Abdel-Wahab M, Giammarile F, Pellet O, Paez D. Addressing Global Inequities in Positron Emission Tomography-Computed Tomography (PET-CT) for Cancer Management: A Statistical Model to Guide Strategic Planning. Medical Science Monitor. 2020;26:e926544. https://doi.org/ 10.12659/MSM.926544

    work page doi:10.12659/msm.926544 2020

  5. [5]

    Whole-Body PET/CT Scanning: Estimation of Radiation Dose and Cancer Risk

    Huang B, Law MWM, Khong PL. Whole-Body PET/CT Scanning: Estimation of Radiation Dose and Cancer Risk. Radiology. 2009;251(1):166–174. PMID: 19251940. https://doi.org/10.1148/radiol.2511081300

    work page doi:10.1148/radiol.2511081300 2009

  6. [6]

    Deep auto-context convolutional neural networks for standard-dose PET image estimation from low- dose PET/MRI

    Xiang L, Qiao Y, Nie D, An L, Lin W, Wang Q, et al. Deep auto-context convolutional neural networks for standard-dose PET image estimation from low- dose PET/MRI. Neurocomputing. 2017;267:406–416. https://doi.org/10.1016/j. neucom.2017.06.048

    doi:10.1016/j 2017

  7. [7]

    MedGAN: Medical image translation using GANs

    Armanious K, Jiang C, Fischer M, K¨ ustner T, Hepp T, Nikolaou K, et al. MedGAN: Medical image translation using GANs. Computerized Medical Imag- ing and Graphics. 2020 Jan;79:101684. https://doi.org/10.1016/j.compmedimag. 2019.101684

    arXiv 2020

  8. [8]

    Swin transformer-based GAN for multi-modal medical image translation

    Yan S, Wang C, Chen W, Lyu J. Swin transformer-based GAN for multi-modal medical image translation. Frontiers in Oncology. 2022;12:942511. https://doi. org/10.3389/fonc.2022.942511

    work page arXiv 2022

Show all 37 references
  1. [9]

    Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms

    Ouyang J, Chen KT, Duarte Armindo R, Davidzon GA, Hawk KE, Moradi F, et al. Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms. Journal of Magnetic Resonance Imaging. 2024;59(3):1010–1020. https://doi.org/10.1002/jmri.28837. ...

    2024 doi

  2. [10]

    Available from: https: //arxiv.org/abs/2102.04306

    Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, et al.: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. Available from: https: //arxiv.org/abs/2102.04306

    arXiv

  3. [11]

    Available from: https://arxiv.org/abs/2405.14802

    Jiang H, Imran M, Ma L, Zhang T, Zhou Y, Liang M, et al.: Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation. Available from: https://arxiv.org/abs/2405.14802

    arXiv

  4. [12]

    Available from: https://arxiv.org/abs/2209.12104

    Lyu Q, Wang G.: Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models. Available from: https://arxiv.org/abs/2209.12104

    arXiv

  5. [13]

    Score- Based Generative Models for PET Image Reconstruction

    Singh IR, Denker A, Barbano R, Kereta Z, Jin B, Thielemans K, et al. Score- Based Generative Models for PET Image Reconstruction. Machine Learning for Biomedical Imaging. 2024 Jan;2(Generative Models):547–585. https://doi.org/ 10.59275/j.melba.2024-5d51

    2024 doi

  6. [14]

    PET image denoising based on denoising diffusion probabilistic model

    Gong K, Johnson K, El Fakhri G, et al. PET image denoising based on denoising diffusion probabilistic model. European Journal of Nuclear Medicine and Molec- ular Imaging. 2024;51:358–368. https://doi.org/10.1007/s00259-023-06417-8

    2024 doi

  7. [15]

    Available from: https://arxiv.org/abs/2011.13456

    Song Y, Sohl-Dickstein J, Kingma DP, Kumar A, Ermon S, Poole B.: Score-Based Generative Modeling through Stochastic Differential Equations. Available from: https://arxiv.org/abs/2011.13456

    2011 arXiv

  8. [16]

    Available from: https://arxiv.org/abs/2206.00364

    Karras T, Aittala M, Aila T, Laine S.: Elucidating the Design Space of Diffusion- Based Generative Models. Available from: https://arxiv.org/abs/2206.00364

    arXiv

  9. [17]

    Ultra–Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs

    Chen KT, Gong E, de Carvalho Macruz FB, Xu J, Boumis A, Khalighi M, et al. Ultra–Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs. Radiology. 2020;296(3):E195. https://doi.org/ 10.1148/radiol.2020202527

    2020 doi

  10. [18]

    A sulcal depth-based anatomical parcel- lation of the cerebral cortex

    Destrieux C, Fischl B, Dale A, Halgren E. A sulcal depth-based anatomical parcel- lation of the cerebral cortex. NeuroImage. 2009;47:S151. Organization for Human Brain Mapping 2009 Annual Meeting. https://doi.org/10.1016/S1053-8119(09) 71561-7

    2009 doi

  11. [19]

    Altered hemispheric symmetry found in left-sided mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE/HS) but not found in right-sided MTLE/HS

    Lu J, Li W, He H, Feng F, Jin Z, Wu L. Altered hemispheric symmetry found in left-sided mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE/HS) but not found in right-sided MTLE/HS. Magnetic Resonance Imaging. 2013;31(1):53–59. https://doi.org/10.1016/j.mri.2012.06.030

    2013 doi

  12. [20]

    Useful- ness of extent analysis for statistical parametric mapping with asymmetry index using inter-ictal FDG-PET in mesial temporal lobe epilepsy

    Soma T, Momose T, Takahashi M, Koyama K, Kawai K, Murase K, et al. Useful- ness of extent analysis for statistical parametric mapping with asymmetry index using inter-ictal FDG-PET in mesial temporal lobe epilepsy. Annals of Nuclear Medicine. 2012;26:319–326. https://doi.org/1...

    2012 doi

  13. [21]

    Voxel-Based Analysis of Asymmetry Index Maps Increases the Specificity of 18F-MPPF PET Abnormalities for Localizing the Epileptogenic Zone in Temporal Lobe Epilepsies

    Didelot A, Maugui` ere F, Redout´ e J, Bouvard S, Lothe A, Reilhac A, et al. Voxel-Based Analysis of Asymmetry Index Maps Increases the Specificity of 18F-MPPF PET Abnormalities for Localizing the Epileptogenic Zone in Temporal Lobe Epilepsies. Journal of Nuclear Medicine. 201...

    2010 doi

  14. [22]

    Enhancing the Diagnostic Utility of ASL Imaging in Tempo- ral Lobe Epilepsy through FlowGAN: An ASL to PET Image Translation Framework

    Lucas A, Vadali C, Mouchtaris S, Arnold TC, Gugger JJ, Kulick-Soper C, et al. Enhancing the Diagnostic Utility of ASL Imaging in Tempo- ral Lobe Epilepsy through FlowGAN: An ASL to PET Image Translation Framework. medRxiv. 2024;https://doi.org/10.1101/2024.05.28.24308027. http...

    2024 doi

  15. [23]

    A guideline of selecting and reporting intraclass correlation coefficients for reliability research

    Too TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine. 2016;15:155–

    2016

  16. [24]

    Deep learning based imaging data completion for improved brain disease diagnosis

    Li R, Zhang W, Suk HI, Wang L, Li J, Shen D, et al. Deep learning based imaging data completion for improved brain disease diagnosis. Med Image Comput Comput Assist Interv. 2014;17(Pt 3):305–312. https://doi.org/10.1007/ 978-3-319-10443-0 39

    2014

  17. [25]

    A zero-dose synthetic baseline for the personalized analysis of [ 18F]FDG- PET: Application in Alzheimer’s disease

    Hinge C, Henriksen OM, Lindberg U, Hasselbalch SG, Højgaard L, Law I, et al. A zero-dose synthetic baseline for the personalized analysis of [ 18F]FDG- PET: Application in Alzheimer’s disease. Frontiers in Neuroscience. 2022 Nov 24;16:1053783. https://doi.org/10.3389/fnins.202...

    2022

  18. [26]

    MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-Modal Alzheimer’s Classification

    Sikka A, Peri SV, Bathula DR. MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-Modal Alzheimer’s Classification. In: Gooya A, Goksel O, Oguz I, Burgos N, editors. Proceedings of SASHIMI 2018. vol. 11037 of Lecture Notes in Computer Science. Springer, Cham; 2018. ...

    2018 doi

  19. [27]

    Synthesizing Missing PET from MRI with Cycle-Consistent Generative Adversarial Networks for Alzheimer’s Disease Diagnosis

    Pan Y, Liu M, Lian C, Zhou T, Xia Y, Shen D. Synthesizing Missing PET from MRI with Cycle-Consistent Generative Adversarial Networks for Alzheimer’s Disease Diagnosis. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-L´ opez C, Fichtinger G, editors. Proceedings of MICCAI 20...

    2018 doi

  20. [28]

    Deep learning only by normal brain PET identify unheralded brain anomalies

    Choi H, Ha S, Kang H, Lee H, Lee DSADNI. Deep learning only by normal brain PET identify unheralded brain anomalies. EBioMedicine. 2019 May;43:447–453. Epub 2019 Apr 16. https://doi.org/10.1016/j.ebiom.2019.04.022

    2019 doi

  21. [29]

    Ultra-low dose PET reconstruction using generative adversarial network with feature mapping and task-specific perceptual loss

    Ouyang J, Chen KT, Gong E, Pauly J, Zaharchuk G. Ultra-low dose PET reconstruction using generative adversarial network with feature mapping and task-specific perceptual loss. Medical Physics. 2019;46:3555–3564. 20

    2019

  22. [30]

    MPGAN: Multi Pareto Gener- ative Adversarial Network for the denoising and quantitative analysis of low-dose PET images of human brain

    Fu Y, Dong S, Huang Y, Niu M, Ni C, Yu L, et al. MPGAN: Multi Pareto Gener- ative Adversarial Network for the denoising and quantitative analysis of low-dose PET images of human brain. Medical Image Analysis. 2024 December;98:103306. Epub 2024 Aug 17. https://doi.org/10.1016/j...

    2024

  23. [31]

    Realization of high-end PET devices that assist conventional PET devices in improving image quality via diffusion modeling

    Zhang Q, Zhou C, Zhang X, Fan W, Zheng H, Liang D, et al. Realization of high-end PET devices that assist conventional PET devices in improving image quality via diffusion modeling. EJNMMI Physics. 2024 December 18;11(1):103. https://doi.org/10.1186/s40658-024-00706-3

    2024 doi

  24. [32]

    Pseudo-normal PET Synthesis with Generative Adversarial Networks for Local- ising Hypometabolism in Epilepsies

    Yaakub SN, McGinnity CJ, Clough JR, Kerfoot E, Girard N, Guedj E, et al. Pseudo-normal PET Synthesis with Generative Adversarial Networks for Local- ising Hypometabolism in Epilepsies. In: Simulation and Synthesis in Medical Imaging. Springer International Publishing; 2019. p. 42–51

    2019

  25. [33]

    Available from: https://arxiv.org/abs/2303.01469

    Song Y, Dhariwal P, Chen M, Sutskever I.: Consistency Models. Available from: https://arxiv.org/abs/2303.01469

    arXiv

  26. [34]

    Available from: https://arxiv.org/abs/2206.00927

    Lu C, Zhou Y, Bao F, Chen J, Li C, Zhu J.: DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps. Available from: https://arxiv.org/abs/2206.00927

    arXiv

  27. [35]

    True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

    Chen KT, Toueg TN, Koran MEI, et al. True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation. European Journal of Nuclear Medicine and Molecular Imaging. 2021;48:2416–2425. https://doi.org/ 10.1007/s00259-020-05151-9

    2021 doi

  28. [36]

    Slice-Consistent 3D Volumetric Brain CT-to- MRI Translation with 2D Brownian Bridge Diffusion Model

    Choo K, Jun Y, Yun M, Hwang SJ. Slice-Consistent 3D Volumetric Brain CT-to- MRI Translation with 2D Brownian Bridge Diffusion Model. In: Linguraru MG, Dou Q, Feragen A, Giannarou S, Glocker B, Lekadir K, et al., editors. Medical Image Computing and Computer Assisted Interventi...

    2024

  29. [163]

    https://doi.org/10.1016/j.jcm.2016.02.012

    2016 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.