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REVIEW 2 major objections 5 minor 88 references

Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities

T0 review · 2 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read AI-guided PET reconstruction is becoming central to long-axial-field-of-view scanners, with deep learning promising faster, sharper, and multi-tracer images—if clinical validation catches up.

desk verdict Useful narrative review of AI in LAFOV PET with a concrete citation error in the one quantitative reconstruction claim; fix that and it's a solid field map. read the letter →

arxiv 2509.00304 v2 pith:EH3R2HMV submitted 2025-08-30 physics.med-ph

Movindu Dassanayake , Alejandro Lopez , Andrew Reader , Gary J.R. Cook , Clemens Mingels , Arman Rahmim , Robert Seifert , Ian Alberts
show 1 more author
Fereshteh Yousefirizi
This is my paper · ORCID
classification physics.med-ph
keywords long-axialfield-of-viewPETartificialintelligenceimagereconstructionmultiplexedimagingresolutionenhancementpositronrangecorrectiondeeplearning
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

Long-axial field-of-view (LAFOV) PET/CT scanners collect far more counts than standard scanners, but they do not automatically produce sharper images: their spatial resolution matches shorter scanners, and their larger data volumes make reconstruction slower. This review argues that artificial intelligence, particularly trained neural networks, is becoming the central tool for closing that gap. AI methods can reconstruct LAFOV data in seconds instead of minutes, recover resolution lost to positron range and other physical effects, and separate signals from multiple injected tracers in a single scan. The paper does not claim these methods are ready for routine clinical use; it stresses that generalization outside training data, tracer-specific behavior, and scalability to large 3D volumes remain open problems. A sympathetic reader would take the review's thesis as: AI is the bridge that turns LAFOV's sensitivity advantage into practical clinical gains.

What carries the argument

The key object is the trained neural network as a replacement for, or accelerator of, the PET reconstruction operator. FastPET's U-Net maps histo-images plus attenuation maps directly to OSEM-quality images; encoder-decoder networks map rebinned 2D sinograms to vendor reconstructions; unrolled networks fold iterative expectation-maximization steps into the network so that PET physics informs the learning; self-supervised deep image priors and model-informed diffusion methods work without paired training data. For multiplexed PET, kinetic-model-informed deep learning and deep image priors carry the separation of multiple tracer signals. What these mechanisms share is replacing a hand-built sy

What would settle it

Check reference [27] for the reported FastPET numbers; if they are absent, re-run FastPET on a held-out LAFOV patient cohort and compare reconstruction time and mean absolute difference against clinical OSEM. The review's central speed and quality claim is falsified if the time saving disappears or the image difference exceeds the reported roughly 2.3% on out-of-distribution data.

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Extended reading notes

Core claim

The review's core claim is that AI-guided reconstruction is no longer a niche standard-field-of-view experiment but a central enabling technology for LAFOV PET/CT. It organizes the field into direct methods (e.g., FastPET, an image-to-image network that maps histo-images and attenuation maps to OSEM-quality reconstructions), unrolled iterative methods that embed PET physics into the network, and self-supervised methods that need no paired training data; it extends the same taxonomy to multiplexed PET, where kinetic-model-informed networks and deep image priors separate simultaneous tracers. For resolution, it points to deep-learning positron range correction as a practical alternative to com

Load-bearing premise

The review's map of the field is only as trustworthy as its secondary reporting; for example, it attributes to reference [27] a FastPET result (7 minutes to 20 seconds, 2.3% average difference) that the cited source—a simulation tool—does not appear to contain, so if such misattributions are widespread, the claimed gains could be overstated.

Editorial extensions

If this is right

  • If direct AI reconstruction performs as reported on LAFOV scanners, reconstruction time drops from roughly 7 minutes to about 20 seconds, making dynamic multi-frame whole-body studies clinically feasible.
  • If AI resolution enhancement, especially positron range correction, works across radionuclides, isotopes such as 68Ga and 82Rb could be used without accepting their usual resolution penalty, widening tracer choice.
  • If multiplexed PET separation matures, multiple tracers can be injected and imaged in one session, reducing repeated CT radiation exposure and image-registration errors from sequential scans.
  • If learning-based methods are relied on, operators must expect failures when inputs fall outside training distributions, so validation and fallback to conventional OSEM reconstruction remain necessary.
  • LAFOV's higher sensitivity combined with AI reconstruction could enable ultra-low-dose scans, opening PET to healthy control cohorts for building physiological baselines.

Reading between the lines

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

  • The review's emphasis on out-of-distribution failure suggests a concrete extension: a standardized hard-case benchmark of LAFOV scans with unusual body habitus, high noise, or novel tracers, used to compare direct, unrolled, and self-supervised methods.
  • If AI positron range correction matures, it could uncouple radiotracer choice from physics-driven resolution limits, possibly shifting clinical preference toward 68Ga-labelled agents over 18F for some oncology applications.
  • The 20-second reconstruction figure, if it holds, implies a workflow shift: reconstruction would no longer gate scan duration, enabling online quality assurance and potentially real-time adaptive acquisition.
  • A testable extension is to re-verify the review's quantitative claims, such as the 2.3% image difference, against the primary sources before using them in clinical or regulatory planning.
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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

2 major / 5 minor

Summary. This manuscript is a narrative review of AI-guided PET image reconstruction and multi-tracer imaging, with particular emphasis on long axial field-of-view (LAFOV) PET/CT. It surveys direct AI reconstruction methods, unrolled iterative approaches, self-supervised and diffusion-model methods, multiplexed PET tracer separation, and AI-based resolution enhancement (positron range correction, depth-of-interaction, motion). The paper concludes that AI methodologies are increasingly central to quantitative PET reconstruction and that clinical deployment of LAFOV-enabled AI techniques still requires validation and scalable solutions.

Significance. If the survey's attributions are accurate, the paper is a useful and timely map of a fast-moving field. Table 1 consolidates architecture, training-pair count, and LAFOV scalability considerations in one place, and the separation of reconstruction families (direct, unrolled, self-supervised, diffusion) is pedagogically valuable. The review also connects LAFOV-specific opportunities (histo-images, dynamic total-body frames, multiplexed PET) to existing SAFOV methods. However, the value of a secondary review depends heavily on citation accuracy; the one clearly identifiable misattribution is therefore a substantive concern rather than a cosmetic one.

major comments (2)
  1. [Deep learned PET reconstruction, p. 4] The sentence stating that FastPET was extended to the Biograph Vision Quadra 'achieving a reconstruction time reduction from 7 minutes to 20 seconds post-scan, with an average absolute image difference of only 2.3%' is cited to reference [27]. Reference [27] is Li et al., 'FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool' (Phys. Med. Biol. 2024). That paper describes a simulation tool and does not report FastPET-on-Quadra reconstruction timing or image-difference figures. This is the only concrete quantitative demonstration in the reconstruction section that AI can make LAFOV reconstruction clinically practical. The claim is unsupported as referenced and must be corrected by supplying the correct primary source or by removing the specific numbers.
  2. [AI Methods for Image Resolution Enhancement, p. 6] The section interleaves simulation-based studies and clinical or preclinical studies without consistently stating the evidence level. For example, the Deep-PRC positron range correction work (refs. [60]–[61]) is presented as recovering 18F-compatible resolution for 68Ga, but the underlying methodology is trained and validated on Monte Carlo simulations; the clinical-scanner extension is a conference contribution. Because the review's message that AI can enhance PET resolution is a central claim, the text should explicitly label which results are simulation-based and which are demonstrated on measured data. This will prevent over-reading of the current evidence and is essential for the review to serve as a trustworthy field map.
minor comments (5)
  1. [Section heading, p. 4] Typo: 'multiplexed imagin g' should read 'multiplexed imaging'.
  2. [Introduction to reconstruction section, p. 4] The phrase 'in the order of tens of 3D data/images' is ambiguous. Please specify whether this means tens of training subjects, tens of reconstructed volumes, or tens of patches/slices, and state the units in Table 1 consistently.
  3. [Table 1] The 'Training pairs used' column mixes different units (2D image pairs, 3D images, slices, subjects). Adding explicit units in each row or a footnote would improve comparability.
  4. [References] Several conference-proceedings entries are incomplete, e.g., ref. [44] lists only 'SPIE; 2023' without title or page numbers. Please complete the bibliographic details for all references.
  5. [Abstract / Clinical care points] The phrase 'has a spatial resolution of an equivalent scanner with a shorter axial field of view' is ambiguous; it likely means 'comparable to' rather than identical. Consider rewording for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: narrative review summarizing independent literature; the [27] citation mismatch is an accuracy concern, not a circular step.

full rationale

This manuscript is a narrative review, not a derivation or empirical study. It introduces no fitted parameters, no reconstruction equations, and no quantitative predictions that reduce to its inputs. Each section summarizes previously published work and supports claims with citations, including several self-citations by the authors (e.g., refs 6, 9, 21, 22, 25, 29, 41, 42, 45, 46). These self-citations are to peer-reviewed publications and are used as normal literature references, not as a self-referential chain that defines the review's conclusions into existence. The one notable problem in the text is the statement that FastPET was extended to Biograph Vision Quadra with a 7-minute-to-20-second reconstruction time and 2.3% average absolute image difference, cited to ref [27] (FAST-PET analytical simulator), which appears to be a citation mismatch. However, a citation mismatch is an accuracy/reporting error, not one of the specified circularity patterns: the claim is not equivalent by construction to its input, and no fitted parameter is renamed as a prediction. No step in the review's reasoning relies on a uniqueness theorem imported from the authors, and no ansatz is smuggled in via citation. The review is self-contained as a secondary source, so the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

This is a review; the central claim rests on faithful representation of prior work, not on new derivations. No free parameters or entities are introduced.

assumptions (1)
  • domain assumption The cited literature summarizes the state of AI PET reconstruction accurately and the authors' descriptions are faithful to those sources.
    A review's claims are only as strong as the accuracy of its secondary reporting; the FastPET/Quadra performance claim is attributed to a reference that appears unrelated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities." pith.science (2026). https://pith.science/paper/EH3R2HMV

@misc{pith2026250900304,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EH3R2HMV}},
  note         = {Machine review of arXiv:2509.00304}
}
read the original abstract

LAFOV PET/CT has the potential to unlock new applications such as ultra-low dose PET/CT imaging, multiplexed imaging, for biomarker development and for faster AI-driven reconstruction, but further work is required before these can be deployed in clinical routine. LAFOV PET/CT has unrivalled sensitivity but has a spatial resolution of an equivalent scanner with a shorter axial field of view. AI approaches are increasingly explored as potential avenues to enhance image resolution.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

88 extracted references · 79 canonical work pages

  1. [27]

    FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool

    Li S, Hamdi M, Dutta K, Fraum TJ, Luo J, Laforest R, et al. FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool. Physics in Medicine & Biology. 2024;69(16):165020

    work page 2024

  2. [60]

    Deep-learning based positron range correction of PET images

    Herraiz JL, Bembibre A, López-Montes A. Deep-learning based positron range correction of PET images. Applied sciences. 2020;11(1):266

    work page 2020

  3. [61]

    Deep-PRC: A Positron Range Correction Tool for preclinical and clinical PET/CT images

    Encina-Baranda N, Lopez -Rodriguez J, Lopez -Montes A, Ibañez P, Zheng Y , Cabello J, et al., editors. Deep-PRC: A Positron Range Correction Tool for preclinical and clinical PET/CT images. 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD); 2024: IEEE

    work page 2024

  4. [1]

    PET/CT imaging in cancer: Current applications and future directions

    Farwell MD, Pryma DA, Mankoff DA. PET/CT imaging in cancer: Current applications and future directions. Cancer. 2014;120(22):3433-45

    2014

  5. [2]

    FDG PET/CT –based Response Assessment in Malignancies

    Parihar AS, Dehdashti F, Wahl RL. FDG PET/CT –based Response Assessment in Malignancies. RadioGraphics. 2023;43(4):e220122

    2023

  6. [3]

    PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy

    Fletcher J, Kinahan P. PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy. NIH Public Access. 2010;31(6):496-505

    2010

  7. [4]

    Repeatability of the maximum standard uptake value (SUVmax) in FDG PET

    Lindholm H, Staaf J, Jacobsson H, Brolin F, Hatherly R, Sânchez -Crespo A. Repeatability of the maximum standard uptake value (SUVmax) in FDG PET. Molecular imaging and radionuclide therapy. 2014;23(1):16

    2014

  8. [5]

    Performance Evaluation of the uEXPLORER Total-Body PET/CT Scanner Based on NEMA NU 2-2018 with Additional Tests to Characterize PET Scanners with a Long Axial Field of View

    Spencer BA, Berg E, Schmall JP , Omidvari N, Leung EK, Abdelhafez YG, et al. Performance Evaluation of the uEXPLORER Total-Body PET/CT Scanner Based on NEMA NU 2-2018 with Additional Tests to Characterize PET Scanners with a Long Axial Field of View. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2021;62(6):861-70

    2018

Show all 88 references
  1. [6]

    Clinical performance of long axial field of view PET/CT: a head -to-head intra-individual comparison of the Biograph Vision Quadra with the Biograph Vision PET/CT

    Alberts I, Hünermund JN, Prenosil G, Mingels C, Bohn KP, Viscione M, et al. Clinical performance of long axial field of view PET/CT: a head -to-head intra-individual comparison of the Biograph Vision Quadra with the Biograph Vision PET/CT. European journal of nuclear medicine ...

    2021

  2. [7]

    Total -Body PET: Maximizing Sensitivity to Create New Opportunities for Clinical Research and Patient Care

    Cherry SR, Jones T, Karp JS, Qi J, Moses WW, Badawi RD. Total -Body PET: Maximizing Sensitivity to Create New Opportunities for Clinical Research and Patient Care. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2018;59(1):3-12

    2018

  3. [8]

    Impact of the new ultra-high sensitivity mode in a long axial field-of-view PET/CT

    Mingels C, Weidner S, Sari H, Buesser D, Zeimpekis K, Shi K, et al. Impact of the new ultra-high sensitivity mode in a long axial field-of-view PET/CT. Annals of nuclear medicine. 2023

    2023

  4. [9]

    Long -axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients

    Alberts I, Sari H, Mingels C, Afshar -Oromieh A, Pyka T, Shi K, et al. Long -axial field-of-view PET/CT: perspectives and review of a revolutionary development in nuclear medicine based on clinical experience in over 7000 patients. Cancer Imaging. 2023;23(1):28

    2023

  5. [10]

    Application of the long axial field -of-view PET/CT with low -dose [18F] FDG in melanoma

    Sachpekidis C, Pan L, Kopp -Schneider A, Weru V , Hassel JC, Dimitrakopoulou -Strauss A. Application of the long axial field -of-view PET/CT with low -dose [18F] FDG in melanoma. European Journal of Nuclear Medicine and Molecular Imaging. 2023;50(4):1158-67

    2023

  6. [11]

    Combined [68 Ga]Ga-PSMA-11 and low -dose 2 -[18F]FDG PET/CT using a long -axial field of view scanner for patients referred for [177Lu] -PSMA-radioligand therapy

    Alberts I, Schepers R, Zeimpekis K, Sari H, Rominger A, Afshar -Oromieh A. Combined [68 Ga]Ga-PSMA-11 and low -dose 2 -[18F]FDG PET/CT using a long -axial field of view scanner for patients referred for [177Lu] -PSMA-radioligand therapy. European journal of nu clear medicine a...

    2022

  7. [12]

    Long axial field -of-view PET for ultra -low- dose imaging of non-Hodgkin lymphoma during pregnancy

    van Sluis J, Bellido M, Glaudemans AW, Slart RH. Long axial field -of-view PET for ultra -low- dose imaging of non-Hodgkin lymphoma during pregnancy. Diagnostics. 2022;13(1):28

    2022

  8. [13]

    Identifying the individual metabolic abnormities from a systemic perspective using whole -body PET imaging

    Sun T, Wang Z, Wu Y , Gu F, Li X, Bai Y , et al. Identifying the individual metabolic abnormities from a systemic perspective using whole -body PET imaging. European Journal of Nuclear Medicine and Molecular Imaging. 2022;49(8):2994-3004

    2022

  9. [14]

    Dynamic whole-body PET parametric imaging: I

    Karakatsanis NA, Lodge MA, Tahari AK, Zhou Y , Wahl RL, Rahmim A. Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application. Physics in Medicine & Biology. 2013;58(20):7391

    2013

  10. [15]

    Dynamic whole-body PET parametric imaging: II

    Karakatsanis NA, Lodge MA, Zhou Y , Wahl RL, Rahmim A. Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation. Physics in Medicine & Biology. 2013;58(20):7419

    2013

  11. [16]

    A 18F -MPPF PET normative database of 5 -HT1A receptor binding in men and women over aging

    Costes N, Merlet I, Ostrowsky K, Faillenot I, Lavenne F, Zimmer L, et al. A 18F -MPPF PET normative database of 5 -HT1A receptor binding in men and women over aging. Journal of Nuclear Medicine. 2005;46(12):1980-9

    2005

  12. [17]

    Normative database of the serotonergic system in healthy subjects using multi-tracer PET

    Savli M, Bauer A, Mitterhauser M, Ding YS, Hahn A, Kroll T, et al. Normative database of the serotonergic system in healthy subjects using multi-tracer PET. Neuroimage. 2012;63(1):447-59. 9

    2012

  13. [18]

    Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives

    Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives. European journal of nuclear medicine and molecular imaging. 2...

    2021

  14. [19]

    Quantitation of dynamic total -body PET imaging: recent developments and future perspectives

    Gu F, Wu Q. Quantitation of dynamic total -body PET imaging: recent developments and future perspectives. European Journal of Nuclear Medicine and Molecular Imaging. 2023;50(12):3538-57

    2023

  15. [20]

    Towards enhanced PET quantification in clinical oncology

    Zaidi H, Karakatsanis N. Towards enhanced PET quantification in clinical oncology. The British journal of radiology. 2017;91(1081):20170508

    2017

  16. [21]

    AI for PET image reconstruction

    Reader AJ, Pan B. AI for PET image reconstruction. The British journal of radiology. 2023;96(1150):20230292

    2023

  17. [22]

    Deep Learning for PET Image Reconstruction

    Reader AJ, Corda G, Mehranian A, Costa-Luis Cd, Ellis S, Schnabel JA. Deep Learning for PET Image Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021;5(1):1-25

    2021

  18. [23]

    Deep learning-based PET image denoising and reconstruction: a review

    Hashimoto F, Onishi Y , Ote K, Tashima H, Reader AJ, Yamaya T. Deep learning-based PET image denoising and reconstruction: a review. Radiol Phys Technol. 2024;17(1):24-46

    2024

  19. [24]

    A review on AI in PET imaging

    Matsubara K, Ibaraki M, Nemoto M, Watabe H, Kimura Y . A review on AI in PET imaging. Annals of nuclear medicine. 2022;36(2):133-43

    2022

  20. [25]

    Self -supervised and supervised deep learning for PET image reconstruction

    Reader AJ. Self -supervised and supervised deep learning for PET image reconstruction. AIP Conference Proceedings. 2024;3061(1)

    2024

  21. [26]

    FastPET: Near Real -Time Reconstruction of PET Histo -Image Data Using a Neural Network

    Whiteley W, Panin V , Zhou C, Cabello J, Bharkhada D, Gregor J. FastPET: Near Real -Time Reconstruction of PET Histo -Image Data Using a Neural Network. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021;5(1):65-77

    2021

  22. [28]

    An encoder -decoder network for direct image reconstruction on sinograms of a long axial field of view PET

    Ma R, Hu J, Sari H, Xue S, Mingels C, Viscione M, et al. An encoder -decoder network for direct image reconstruction on sinograms of a long axial field of view PET. European journal of nuclear medicine and molecular imaging. 2022;49(13):4464-77

    2022

  23. [29]

    Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

    Matej S, Daube -Witherspoon ME, Karp JS. Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework. Phys Med Biol. 2016;61(9):3365-86

    2016

  24. [30]

    PET image reconstruction with deep progressive learning

    Lv Y , Xi C. PET image reconstruction with deep progressive learning. Phys Med Biol. 2021;66(10)

    2021

  25. [31]

    Diffusion models for medical image reconstruction

    Webber G, Reader AJ. Diffusion models for medical image reconstruction. BJR|Artificial Intelligence. 2024;1(1)

    2024

  26. [32]

    Image reconstruction by domain -transform manifold learning

    Zhu B, Liu JZ, Cauley SF, Rosen BR, Rosen MS. Image reconstruction by domain -transform manifold learning. Nature. 2018;555(7697):487-92

    2018

  27. [33]

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

    Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. DeepPET: A deep encoder -decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019;54:253- 62

    2019

  28. [34]

    Simultaneous quantitative imaging of two PET radiotracers via the detection of positron-electron annihilation and prompt gamma emissions

    Pratt EC, Lopez -Montes A, V olpe A, Crowley MJ, Carter LM, Mittal V , et al. Simultaneous quantitative imaging of two PET radiotracers via the detection of positron-electron annihilation and prompt gamma emissions. Nat Biomed Eng. 2023;7(8):1028-39

    2023

  29. [35]

    Effective dose estimation for oncological and neurological PET/CT procedures

    Martí-Climent JM, Prieto E, Morán V , Sancho L, Rodríguez-Fraile M, Arbizu J, et al. Effective dose estimation for oncological and neurological PET/CT procedures. EJNMMI Res. 2017;7(1):37

    2017

  30. [36]

    An investigation of a double -tracer technique for positron computerized tomography

    Huang SC, Carson RE, Hoffman EJ, Kuhl DE, Phelps ME. An investigation of a double -tracer technique for positron computerized tomography. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 1982;23(9):816-22

    1982

  31. [37]

    Simultaneous dual-tracer PET imaging of the rat brain and its application in the study of cerebral ischemia

    Figueiras FP, Jiménez X, Pareto D, Gómez V , Llop J, Herance R, et al. Simultaneous dual-tracer PET imaging of the rat brain and its application in the study of cerebral ischemia. Molecular imaging and biology. 2011;13(3):500-10

    2011

  32. [38]

    Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology

    Pantel AR, Viswanath V , Muzi M, Doot RK, Mankoff DA. Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2022;63(3):342-52. 10

    2022

  33. [39]

    Mathematical Models for FDG Kinetics in Cancer: A Review

    Sommariva S, Caviglia G, Sambuceti G, Piana M. Mathematical Models for FDG Kinetics in Cancer: A Review. Metabolites. 2021;11(8)

    2021

  34. [40]

    Temporal information-guided dynamic dual-tracer PET signal separation network

    Tong J, Wang C, Liu H. Temporal information-guided dynamic dual-tracer PET signal separation network. Medical physics. 2022;49(7):4585-98

    2022

  35. [41]

    Dual -Tracer PET Image Separation by Deep Learning: A Simulation Study

    Pan B, Marsden PK, Reader AJ. Dual -Tracer PET Image Separation by Deep Learning: A Simulation Study. Applied Sciences. 2023;13(7):4089

    2023

  36. [42]

    Deep learned triple -tracer multiplexed PET myocardial image separation

    Pan B, Marsden PK, Reader AJ. Deep learned triple -tracer multiplexed PET myocardial image separation. Front Nucl Med. 2024;4:1379647

    2024

  37. [43]

    Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning

    Zeng F, Fang J, Muhashi A, Liu H. Direct reconstruction for simultaneous dual-tracer PET imaging based on multi-task learning. EJNMMI Res. 2023;13(1):7

    2023

  38. [44]

    Direct reconstruction and separation for triple -tracer PET imaging based on three-dimensional encoder-decoder network: SPIE; 2023

    Wang C, Fang J, Liu H. Direct reconstruction and separation for triple -tracer PET imaging based on three-dimensional encoder-decoder network: SPIE; 2023

    2023

  39. [45]

    Kinetic model-informed deep learning for multiplexed PET image separation

    Pan B, Marsden PK, Reader AJ. Kinetic model-informed deep learning for multiplexed PET image separation. EJNMMI Phys. 2024;11(1):56

    2024

  40. [46]

    Self -supervised parametric map estimation for multiplexed PET with a deep image prior

    Pan B, Marsden PK, Reader AJ. Self -supervised parametric map estimation for multiplexed PET with a deep image prior. Phys Med Biol. 2025;70(4)

    2025

  41. [47]

    Fundamental limits of spatial resolution in PET

    Moses WW. Fundamental limits of spatial resolution in PET. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2011;648:S236-S40

    2011

  42. [48]

    Performance characteristics of the Biograph Vision Quadra PET/CT system with a long axial field of view using the NEMA NU 2-2018 standard

    Prenosil GA, Sari H, Fürstner M, Afshar -Oromieh A, Shi K, Rominger A, et al. Performance characteristics of the Biograph Vision Quadra PET/CT system with a long axial field of view using the NEMA NU 2-2018 standard. Journal of nuclear medicine. 2022;63(3):476-84

    2018

  43. [49]

    Performance evaluation of the uEXPLORER total -body PET/CT scanner based on NEMA NU 2 -2018 with additional tests to characterize PET scanners with a long axial field of view

    Spencer BA, Berg E, Schmall JP , Omidvari N, Leung EK, Abdelhafez YG, et al. Performance evaluation of the uEXPLORER total -body PET/CT scanner based on NEMA NU 2 -2018 with additional tests to characterize PET scanners with a long axial field of view. Journ al of Nuclear Medi...

    2018

  44. [50]

    Performance characteristics of a new -generation digital bismuth germanium oxide PET/CT system, Omni Legend 32, according to NEMA NU 2-2018 standards

    Yamagishi S, Miwa K, Kamitaki S, Anraku K, Sato S, Yamao T, et al. Performance characteristics of a new -generation digital bismuth germanium oxide PET/CT system, Omni Legend 32, according to NEMA NU 2-2018 standards. Journal of Nuclear Medicine. 2023;64(12):1990-7

    2018

  45. [51]

    Standardization of preclinical PET/CT imaging to improve quantitative accuracy, precision, and reproducibility: a multicenter study

    McDougald W, Vanhove C, Lehnert A, Lewellen B, Wright J, Mingarelli M, et al. Standardization of preclinical PET/CT imaging to improve quantitative accuracy, precision, and reproducibility: a multicenter study. Journal of Nuclear Medicine. 2020;61(3):461-8

    2020

  46. [52]

    Physics of pure and non -pure positron emitters for PET: a review and a discussion

    Conti M, Eriksson L. Physics of pure and non -pure positron emitters for PET: a review and a discussion. EJNMMI physics. 2016;3:1-17

    2016

  47. [53]

    Tissue-dependent and spatially-variant positron range correction in 3D PET

    Cal-Gonzalez J, Perez-Liva M, Herraiz JL, Vaquero JJ, Desco M, Udias JM. Tissue-dependent and spatially-variant positron range correction in 3D PET. IEEE transactions on medical imaging. 2015;34(11):2394-403

    2015

  48. [54]

    Positron range corrections and denoising techniques for gallium-68 PET imaging: a literature review

    Gavriilidis P, Koole M, Annunziata S, Mottaghy FM, Wierts R. Positron range corrections and denoising techniques for gallium-68 PET imaging: a literature review. Diagnostics. 2022;12(10):2335

    2022

  49. [55]

    Monte Carlo simulations of the GE Signa PET/MR for different radioisotopes

    Caribé PR, Vandenberghe S, Diogo A, Pérez-Benito D, Efthimiou N, Thyssen C, et al. Monte Carlo simulations of the GE Signa PET/MR for different radioisotopes. Frontiers in Physiology. 2020;11:525575

    2020

  50. [56]

    Effect of the positron range of 18 F, 68 Ga and 124 I on PET/CT in lung -equivalent materials

    Kemerink GJ, Visser MG, Franssen R, Beijer E, Zamburlini M, Halders SG, et al. Effect of the positron range of 18 F, 68 Ga and 124 I on PET/CT in lung -equivalent materials. European journal of nuclear medicine and molecular imaging. 2011;38:940-8

    2011

  51. [57]

    Positron range in combination with point-spread-function correction: an evaluation of different implementations for [124I]-PET imaging

    Kertész H, Conti M, Panin V , Cabello J, Bharkhada D, Beyer T, et al. Positron range in combination with point-spread-function correction: an evaluation of different implementations for [124I]-PET imaging. EJNMMI physics. 2022;9(1):56

    2022

  52. [58]

    An investigation of 68 Ga positron range correction through de-blurring: A simulation study

    Rukiah A, Meikle SR, Gillam JE, Kench PL, editors. An investigation of 68 Ga positron range correction through de-blurring: A simulation study. 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC); 2018: IEEE. 11

    2018

  53. [59]

    Synthesized image reconstruction for post-reconstruction resolution recovery

    Vass L, Reader AJ. Synthesized image reconstruction for post-reconstruction resolution recovery. IEEE Transactions on Radiation and Plasma Medical Sciences. 2023;7(5):473-82

    2023

  54. [62]

    Compensating Positron Range Effects of Ga -68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study

    Yang C-C. Compensating Positron Range Effects of Ga -68 in Preclinical PET Imaging by Using Convolutional Neural Network: A Monte Carlo Simulation Study. Diagnostics. 2021;11(12):2275

    2021

  55. [63]

    Self-supervised Positron Range Correction for Dynamic Rubidium-82 Cardiac PET Imaging

    Xie H, Velo A, Guo X, Zhou B, Chen X, Tsai Y-J, et al. Self-supervised Positron Range Correction for Dynamic Rubidium-82 Cardiac PET Imaging. Society of Nuclear Medicine; 2023

    2023

  56. [64]

    Dual-Input Dynamic Convolution for Positron Range Correction in PET Image Reconstruction

    Mellak Y , Bousse A, Merlin T, Émond É, Visvikis D. Dual-Input Dynamic Convolution for Positron Range Correction in PET Image Reconstruction. arXiv preprint arXiv:250300587. 2025

    2025

  57. [65]

    Sub‐2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single‐ended readout using convolutional neural networks

    LaBella A, Cao X, Zeng X, Zhao W, Goldan AH. Sub‐2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single‐ended readout using convolutional neural networks. Medical physics. 2021;48(3):1019-25

    2021

  58. [66]

    Depth of interaction estimation in a preclinical PET scanner equipped with monolithic crystals coupled to SiPMs using a deep neural network

    Sanaat A, Zaidi H. Depth of interaction estimation in a preclinical PET scanner equipped with monolithic crystals coupled to SiPMs using a deep neural network. Applied Sciences. 2020;10(14):4753

    2020

  59. [67]

    Motion correction of respiratory-gated PET images using deep learning based image registration framework

    Li T, Zhang M, Qi W, Asma E, Qi J. Motion correction of respiratory-gated PET images using deep learning based image registration framework. Physics in Medicine & Biology. 2020;65(15):155003

    2020

  60. [68]

    Deep learning -aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment

    Lu Y , Kang F, Zhang D, Li Y , Liu H, Sun C, et al. Deep learning -aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment. European Journal of Nuclear Medicine and Molecular Imaging. ...

    2024

  61. [69]

    Supervised deep learning for head motion correction in PET

    Zeng T, Zhang J, Revilla E, Lieffrig EV , Fang X, Lu Y , et al., editors. Supervised deep learning for head motion correction in PET. International Conference on Medical Image Computing and Computer - Assisted Intervention; 2022: Springer

    2022

  62. [70]

    Deep Residual-in-Residual Model-Based PET Image Super-Resolution with Motion Blur

    Tian X, Chen S, Wang Y , Han D, Lin Y , Zhao J, et al. Deep Residual-in-Residual Model-Based PET Image Super-Resolution with Motion Blur. Electronics. 2024;13(13):2582

    2024

  63. [71]

    Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation

    Karakatsanis NA, Tsoumpas C, Zaidi H. Quantitative PET image reconstruction employing nested expectation-maximization deconvolution for motion compensation. Computerized medical imaging and graphics. 2017;60:11-21

    2017

  64. [72]

    Deep learning -based image reconstruction and post -processing methods in positron emission tomography for low -dose imaging and resolution enhancement

    Pain CD, Egan GF, Chen Z. Deep learning -based image reconstruction and post -processing methods in positron emission tomography for low -dose imaging and resolution enhancement. European Journal of Nuclear Medicine and Molecular Imaging. 2022;49(9):3098-118

    2022

  65. [73]

    PET image super -resolution using generative adversarial networks

    Song T -A, Chowdhury SR, Yang F, Dutta J. PET image super -resolution using generative adversarial networks. Neural Networks. 2020;125:83-91

    2020

  66. [74]

    Pseudo-MRI-guided PET image reconstruction method based on a diffusion probabilistic model

    Gan W, Xie H, von Gall C, Platsch G, Jurkiewicz MT, Andrade A, et al. Pseudo-MRI-guided PET image reconstruction method based on a diffusion probabilistic model. IEEE Transactions on Radiation and Plasma Medical Sciences. 2025

    2025

  67. [75]

    Generative Super-Resolution PET Imaging with Fourier Diffusion Models

    Tivnan M, Li Q. Generative Super-Resolution PET Imaging with Fourier Diffusion Models. arXiv preprint arXiv:250215055. 2025

    2025

  68. [76]

    DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network

    Hu Z, Xue H, Zhang Q, Gao J, Zhang N, Zou S, et al. DPIR-Net: Direct PET Image Reconstruction Based on the Wasserstein Generative Adversarial Network. IEEE Transactions on Radiation and Plasma Medical Sciences. 2021;5(1):35-43

    2021

  69. [77]

    EMnet: an unrolled deep neural network for PET image reconstruction: SPIE; 2019

    Gong K, Wu D, Kim K, Yang J, El Fakhri G, Seo Y , et al. EMnet: an unrolled deep neural network for PET image reconstruction: SPIE; 2019

    2019

  70. [78]

    MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction: SPIE; 2019

    Gong K, Wu D, Kim K, Yang J, Sun T, El Fakhri G, et al. MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction: SPIE; 2019

    2019

  71. [79]

    Model-Based Deep Learning PET Image Reconstruction Using Forward- Backward Splitting Expectation-Maximization

    Mehranian A, Reader AJ. Model-Based Deep Learning PET Image Reconstruction Using Forward- Backward Splitting Expectation-Maximization. IEEE Trans Radiat Plasma Med Sci. 2020;5(1):54-64. 12

    2020

  72. [80]

    Improved Low-Count Quantitative PET Reconstruction With an Iterative Neural Network

    Lim H, Chun IY , Dewaraja YK, Fessler JA. Improved Low-Count Quantitative PET Reconstruction With an Iterative Neural Network. IEEE transactions on medical imaging. 2020;39(11):3512-22

    2020

  73. [81]

    TransEM: Residual Swin -Transformer Based Regularized PET Image Reconstruction2022; Cham: Springer Nature Switzerland

    Hu R, Liu H, editors. TransEM: Residual Swin -Transformer Based Regularized PET Image Reconstruction2022; Cham: Springer Nature Switzerland

  74. [82]

    Deep Kernel Representation for Image Reconstruction in PET

    Li S, Wang G. Deep Kernel Representation for Image Reconstruction in PET. IEEE transactions on medical imaging. 2022;41(11):3029-38

    2022

  75. [83]

    Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction

    Li S, Gong K, Badawi RD, Kim EJ, Qi J, Wang G. Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction. IEEE transactions on medical imaging. 2023;42(3):785 - 96

    2023

  76. [84]

    PET Image Reconstruction Using Deep Image Prior

    Gong K, Catana C, Qi J, Li Q. PET Image Reconstruction Using Deep Image Prior. IEEE transactions on medical imaging. 2019;38(7):1655-65

    2019

  77. [85]

    Direct Reconstruction of Linear Parametric Images From Dynamic PET Using Nonlocal Deep Image Prior

    Gong K, Catana C, Qi J, Li Q. Direct Reconstruction of Linear Parametric Images From Dynamic PET Using Nonlocal Deep Image Prior. IEEE transactions on medical imaging. 2022;41(3):680-9

    2022

  78. [86]

    Unsupervised Bayesian PET Reconstruction

    Shen C, Xia W, Ye H, Hou M, Chen H, Liu Y , et al. Unsupervised Bayesian PET Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences. 2023;7(2):175-90

    2023

  79. [87]

    Score -Based Generative Models for PET Image Reconstruction

    Singh IR, Denker A, Barbano R, Kereta v, Jin B, Thielemans K, et al. Score -Based Generative Models for PET Image Reconstruction. ArXiv. 2023;abs/2308.14190

    2023 arXiv

  80. [88]

    Likelihood-Scheduled Score- Based Generative Modeling for Fully 3D PET Image Reconstruction

    Webber G, Mizuno Y , Howes OD, Hammers A, King AP, Reader AJ. Likelihood-Scheduled Score- Based Generative Modeling for Fully 3D PET Image Reconstruction. ArXiv. 2024;abs/2412.04339. 13 TABLES AND FIGURES Table 1: Comparison of AI-enabled PET reconstruction methods. Some infor...

    2024 arXiv

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