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Objective Task-based Evaluation of Quantitative Medical Imaging Methods: Emerging Frameworks and Future Directions

T0 review · 0 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This review argues that four emerging frameworks—virtual imaging trials, no-gold-standard evaluation, joint detection-quantification assessment, and multidimensional-parameter evaluation—together provide a practical route to objectively…

desk verdict A clear and honest review of four evaluation frameworks for quantitative imaging; the NGSE linearity caveat is real but already self-flagged. read the letter →

arxiv 2507.04591 v3 pith:SXH4G2SB submitted 2025-07-07 physics.med-ph cs.CVeess.IV

Yan Liu , Huitian Xia , Nancy A. Obuchowski , Richard Laforest , Arman Rahmim , Barry A. Siegel , Abhinav K. Jha This is my paper · ORCID
classification physics.med-phcs.CVeess.IV
keywords QuantitativeimagingTask-basedevaluationPositronemissiontomography(PET)VirtualtrialsNo-gold-standardJointdetectionandquantificationRadiomicsArtificialintelligence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This review paper argues that quantitative imaging (QI) methods—measures such as metabolic tumor volume, standardized uptake value ratios, or radiomic features extracted from PET—cannot move into clinical use without objective evaluation on clinically relevant tasks. It claims that this evaluation need can be met by four emerging frameworks that address complementary gaps: virtual imaging trials for cheap, safe, ground-truth-known evaluation; no-gold-standard evaluation for ranking methods on clinical data when truth is unavailable; joint detection-and-quantification evaluation for methods that must first find a lesion and then measure it; and clinical-decision-based evaluation for methods that output multidimensional parameters such as radiomics. A sympathetic reader would take the paper's contribution to be a coherent organization of scattered evaluation strategies into a practical roadmap, presented in the PET context but applicable beyond it.

What carries the argument

The load-bearing objects are the four frameworks themselves. Virtual imaging trials replace patients and scanners with digital anthropomorphic phantoms and Monte-Carlo or analytical PET simulators, providing known ground truth and figures of merit such as bias, repeatability, and ensemble mean squared error. No-gold-standard evaluation builds on regression-without-truth, which assumes measured value equals slope times true value plus bias plus zero-mean Gaussian noise, with true values drawn from a bounded parametric distribution, and uses maximum likelihood to estimate the noise-to-slope ratio (NSR) as the precision ranking metric. Joint detection and quantification evaluation uses estimation receiver operating characteristic (EROC) curves and the area under them (AEROC), computed from utility scores and false-positive fraction, with anthropomorphic and ideal observers performing the task. Multidimensional-parameter evaluation anchors on the clinical decision task, with study-type selection, representative test data, reference standards, and task-appropriate figures of merit such as AUC or Kaplan-Meier estimates.

What would settle it

A direct test would be to run the no-gold-standard framework on a clinical dataset for which ground truth is also available—for example, PET images with known lesion volumes from surgical pathology or from physical phantoms scanned on a real PET scanner—and compare the NSR-based ranking with rankings from bias, precision, and ensemble mean squared error computed against the known truth; disagreement would show that the linearity or bounded-distribution assumptions fail for that task. A second observation would come from comparing a virtual imaging trial's predicted ranking of two reconstruction or segmentation methods with the ranking from a prospective clinical reader study; inversion would indicate insufficient realism in the digital phantom or simulator.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that four existing but fragmented evaluation strategies can be assembled into a comprehensive structure for task-based evaluation of QI methods. The virtual imaging trial substitutes a digital patient population and a simulated scanner for real patients and hardware, giving access to ground truth at low cost. The no-gold-standard framework estimates, without any truth values, the linear relationship between measured and true values for each candidate method and ranks methods by noise-to-slope ratio, a precision-based figure of merit. The joint detection and quantification framework evaluates methods on the realistic two-step task of detecting a signal and estimating a parameter, summarized by the area under the estimation receiver operating characteristic curve. The multidimensional-parameter framework evaluates radiomics-style methods through their impact on diagnostic, prognostic, or predictive clinical decisions, following RELAINCE-style study design. The paper holds that together these cover evaluation in virtual and clinical settings, for unidimensional and multidimensional outputs, and with or without ground truth.

Load-bearing premise

The no-gold-standard ranking is only as sound as the assumptions that each method's measured values are linearly related to the true values, the true-value distribution has known bounds, and noise across methods is uncorrelated or correctly modeled; the virtual-imaging framework likewise assumes digital phantoms and simulated scanners faithfully reproduce clinical reality.

Editorial extensions

If this is right

  • Promising QI methods can be screened in virtual imaging trials before committing to expensive clinical studies, and the same trials can supply the ground truth needed to check estimability and noise behavior.
  • Clinical datasets without any gold standard can still be used to rank candidate QI methods on precision, provided the linearity and distributional assumptions are checked and bootstrap confidence intervals are computed for the NSR differences.
  • Evaluation of PET methods that require lesion detection first can be summarized by AEROC, capturing both the detection and the quantification error in one number.
  • Radiomic and other multidimensional QI methods should be judged by their effect on the clinical decision, such as classification AUC or survival separation, rather than by per-feature accuracy alone.
  • When these frameworks are applied to AI-based methods, the resulting performance reports should follow the RELAINCE guidelines so claims are stated consistently.

Reading between the lines

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

  • The four frameworks could be assembled into a staged translational pipeline—VIT screening, NGSE ranking on clinical data, then JDQ and multidimensional validation—so that only methods that pass earlier gates proceed to more expensive evaluation; the paper outlines the parts but not this explicit workflow.
  • The NGSE linearity assumption could likely be relaxed to known monotonic nonlinear links if the true-value bounds remain available, but the paper does not develop this extension; testing it on simulated PET data would be straightforward.
  • If VITs are validated through VVUQ, in silico evidence could eventually support regulatory or reimbursement claims for QI methods, a consequence the paper points toward but leaves implicit.
  • A concrete testable extension is to apply the NGSE bootstrap-ranking procedure to deep-learning-based segmentation and quantification methods, where the linearity assumption is less obviously satisfied; where it fails, precision-only ranking would need replacement by a bias-aware figure of merit.
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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

0 major / 6 minor

Summary. This manuscript is a review article that outlines four emerging frameworks for objective task-based evaluation of quantitative imaging (QI) methods, with a focus on PET applications: (1) virtual imaging trials (VITs), (2) no-gold-standard evaluation (NGSE) of QI methods without ground truth, (3) evaluation of joint detection and quantification (JDQ) tasks, and (4) evaluation of QI methods that output multidimensional parameters such as radiomic features. For each framework, the paper describes the key components, provides illustrative figures, discusses figures of merit, and identifies areas of future research. The Discussion extends the scope to physical phantoms, human-in-the-loop AI algorithms, per-patient evaluation, and digital twins. The paper is explicitly based on previous literature and presents itself as a roadmap for clinical translation rather than a new methodological contribution.

Significance. The manuscript provides a useful, well-organized synthesis of emerging evaluation methodologies for quantitative medical imaging, particularly in the context of PET. It accurately represents the cited literature, including the underlying assumptions and limitations of each framework, such as the need for validation of virtual imaging trials and the linearity assumption in no-gold-standard evaluation. The paper also gives appropriate credit to prior work, including the authors' own contributions, and clearly distinguishes between what is established and what remains open. If adopted, these frameworks could help standardize evaluation practices and improve the reporting of QI method performance, which is timely given advances in long axial field-of-view PET and AI-based methods. The review is not a new methodological development, but it fulfills an important educational and catalytic role for the field.

minor comments (6)
  1. [III. Evaluating quantitative imaging methods without ground truth] The discussion under 'Check Linearity Between True and Measured Values' states that the linearity assumption can be verified through inter-method comparisons, realistic simulations, and phantom studies, but it does not specify what should be done if the linearity check fails; adding a brief statement about the consequences of violation would make the framework more complete.
  2. [III. Evaluating quantitative imaging methods without ground truth] The text notes that the noise-to-slope ratio (NSR) is a figure of merit based on precision, but it does not explicitly acknowledge that a method with favorable NSR may still have poor accuracy; a sentence recommending complementary evaluation of bias would be helpful for readers planning clinical translation.
  3. [I. Introduction] In the paragraph on recent advances in PET, there is a typo with a double comma after 'PET'; the sentence should read 'PET, including.'
  4. [III. Evaluating quantitative imaging methods without ground truth] The word 'summerized' in the sentence 'with key components summerized below' should be 'summarized.'
  5. [V. Evaluation of QI Methods for Quantifying Multi-dimensional Parameters] In the sentence 'Typical research studies using muti-dimensional parameters', 'muti-dimensional' should be 'multi-dimensional.'
  6. [References] In reference 31, 'Mont Carlo' should be 'Monte Carlo.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: this is a review article that explicitly synthesizes prior literature; the frameworks are presented, not derived, and no prediction reduces to a fitted input.

full rationale

The paper is a narrative review, not a derivation chain. Its abstract states 'based on previous literature, we outline four emerging frameworks' and the body repeatedly frames the content as a review of existing techniques rather than as new predictions. The NGSE discussion, the most heavily self-cited portion, restates assumptions from RWT/NGSE (linear relationship between true and measured values, known bounds, uncorrelated noise) and presents NSR as a figure of merit from prior publications; it does not fit a parameter to data and then rename that fit as a prediction. The paper is also explicit about a key limitation: NGSE validations 'rely on simulations due to the need for ground truth.' That stated dependence is the opposite of circularity, since it concedes the framework's clinical validation remains open. The self-citations in the NGSE and RELAINCE sections are normal for a review authored by the group that developed those methods, and the load-bearing references include independent prior work (Hoppin/Kupinski RWT, Clarkson EROC, XCAT phantoms, and multi-institutional guidelines). No equation in this paper is equal to its own input by construction, and no fitted value is presented as an independent prediction. The skeptical concern that the linearity assumption is clinically untested is a correctness/validity risk, not a circularity per the review rules.

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

The paper introduces no free parameters or invented entities. It relies on domain assumptions underlying the surveyed frameworks, particularly the linearity and distributional assumptions in NGSE and the realism assumption in VITs.

assumptions (3)
  • domain assumption Measured values are linearly related to true values for each QI method.
    Stated in Section III as a premise of the RWT/NGSE technique. If linearity fails for a specific method, the NSR-derived rankings may be invalid.
  • domain assumption The true values are sampled from a parametric distribution with known bounds.
    Required by the RWT technique to estimate linear-relationship parameters without ground truth, as described in Section III.
  • domain assumption Virtual imaging trials provide clinically realistic simulations of patient populations and imaging systems.
    The VIT framework's utility rests on realism of digital phantoms (e.g., XCAT) and scanner models. The paper itself notes that validation of this realism is an open research area.

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Cite this review

Pith. "Pith review of Objective Task-based Evaluation of Quantitative Medical Imaging Methods: Emerging Frameworks and Future Directions." pith.science (2026). https://pith.science/paper/SXH4G2SB

@misc{pith2026250704591,
  author       = {Pith},
  title        = {Pith review of: Objective Task-based Evaluation of Quantitative Medical Imaging Methods: Emerging Frameworks and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SXH4G2SB}},
  note         = {Machine review of arXiv:2507.04591}
}
read the original abstract

Quantitative imaging (QI) is demonstrating strong promise across multiple clinical applications. For clinical translation of QI methods, objective evaluation on clinically relevant tasks is essential. To address this need, multiple evaluation strategies are being developed. In this paper, based on previous literature, we outline four emerging frameworks to perform evaluation studies of QI methods. We first discuss the use of virtual imaging trials (VITs) to evaluate QI methods. Next, we outline a no-gold-standard evaluation framework to clinically evaluate QI methods without ground truth. Third, a framework to evaluate QI methods for joint detection and quantification tasks is outlined. Finally, we outline a framework to evaluate QI methods that output multi-dimensional parameters, such as radiomic features. We review these frameworks, discussing their utilities and limitations. Further, we examine future research areas in evaluation of QI methods. Given the recent advancements in PET, including long axial field-of-view scanners and the development of artificial-intelligence algorithms, we present these frameworks in the context of PET.

Figures

Figures reproduced from arXiv: 2507.04591 by the authors.

Figure 1
Figure 1. An illustration of the frameworks for evaluating QI methods outlined in this article. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. A block diagram that demonstrates the process of [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 5
Figure 5. A schematic demonstrating the process that an observer follows to decide on the tumor presence and then quantify the tumor when interpreting a PET image [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗

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Works this paper leans on

89 extracted references · 80 canonical work pages

  1. [1]

    Clinical utility of quantitative imaging

    Rosenkrantz AB, Mendiratta-Lala M, Bartholmai BJ, et al. Clinical utility of quantitative imaging. Acad Radiol. 2015;22(1):33-49

    2015

  2. [2]

    Quantitative imaging in cancer clinical trials

    Yankeelov TE, Mankoff DA, Schwartz LH, et al. Quantitative imaging in cancer clinical trials. Clin Cancer Res. 2016;22(2):284-290

    2016

  3. [3]

    Prognostic value of volumetric parameters of 18F -FDG PET in non-small-cell lung cancer: a meta-analysis

    Im HJ, Pak K, Cheon GJ, et al. Prognostic value of volumetric parameters of 18F -FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging. 2015;42(2):241-251

    2015

  4. [4]

    Predicting and monitoring cancer treatment response with diffusion-weighted MRI

    Thoeny HC, Ross BD. Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging. 2010;32(1):2-16

    2010

  5. [5]

    Strategies to Perform Joint Detection and Quantification Task : JDQ task s can be performed sequentially or simultaneously

    Quantification, as defined in the previous section, refers to the estimation of parameters such as MTV and TLG. Strategies to Perform Joint Detection and Quantification Task : JDQ task s can be performed sequentially or simultaneously. The sequential process typically involves first detection and then quantification, while the simultaneous process execute...

  6. [6]

    The clinical value of myocardial blood flow measurement

    Camici PG, Rimoldi OE. The clinical value of myocardial blood flow measurement. J Nucl Med . 2009;50(7):1076-1087

    2009

  7. [7]

    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. Phys Med Biol . 2013;58(20):7391-7418

    2013

  8. [8]

    The impact of PET and SPECT on dosimetry for targeted radionuclide therapy

    Flux G, Bardies M, Monsieurs M, Savolainen S, Strand SE, Lassmann M. The impact of PET and SPECT on dosimetry for targeted radionuclide therapy. Z Med Phys. 2006;16(1):47-59

    2006

Show all 89 references
  1. [9]

    Radiomics: the bridge between medical imaging and personalized medicine

    Lambin P , Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762

    2017

  2. [10]

    Objective task -based evaluation of artificial intelligence -based medical imaging methods: framework, strategies, and role of the physician

    Jha AK, Myers KJ, Obuchowski NA, et al. Objective task -based evaluation of artificial intelligence -based medical imaging methods: framework, strategies, and role of the physician. PET Clin. 2021;16(4):493-511

    2021

  3. [11]

    Total-body imaging: transforming the role of positron emission tomography

    Cherry SR, Badawi RD, Karp JS, Moses WW, Price P , Jones T. Total-body imaging: transforming the role of positron emission tomography. Sci Transl Med. 2017;9(381):eaaf6169

    2017

  4. [12]

    Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

    Arabi H, Zaidi H. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy. European J Hybrid Imaging. 2020;4(1):17

    2020

  5. [13]

    Virtual clinical trials in medical imaging: a review

    Abadi E, Segars WP , Tsui BMW, et al. Virtual clinical trials in medical imaging: a review. J Med Imag . 2020;7(04):1

    2020

  6. [14]

    Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

    Badano A, Graff CG, Badal A, et al. Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial. JAMA Netw Open. 2018;1(7):e185474

    2018

  7. [15]

    4D XCAT phantom for multimodality imaging research

    Segars WP , Sturgeon G, Mendonca S, Grimes J, Tsui BMW. 4D XCAT phantom for multimodality imaging research. Med Phys. 2010;37(9):4902-4915

    2010

  8. [16]

    Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization

    Segars WP , Bond J, Frush J, et al. Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization. Medical Physics. 2013;40(4):043701

    2013

  9. [17]

    The development of a population of 4D pediatric XCAT phantoms for imaging research and optimization

    Segars WP , Norris H, Sturgeon GM, et al. The development of a population of 4D pediatric XCAT phantoms for imaging research and optimization. Medical Physics. 2015;42(8):4719-4726

    2015

  10. [18]

    A Bayesian approach to tissue -fraction estimation for oncological PET segmentation

    Liu Z, Mhlanga JC, Laforest R, Derenoncourt PR, Siegel BA, Jha AK. A Bayesian approach to tissue -fraction estimation for oncological PET segmentation. Phys Med Biol. 2021;66(12):124002

    2021

  11. [19]

    A physics -guided modular deep -learning based automated framework for tumor segmentation in PET

    Leung KH, Marashdeh W, Wray R, et al. A physics -guided modular deep -learning based automated framework for tumor segmentation in PET. Phys Med Biol. 2020;65(24):245032

    2020

  12. [20]

    GATE: a simulation toolkit for PET and SPECT

    Jan S, Santin G, Strul D, et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol. 2004;49(19):4543- 4561

    2004

  13. [21]

    Monte Carlo simulation and scatter correction of the GE Advance PET scanner with SimSET and Geant4

    Barret O, Carpenter TA, Clark JC, Ansorge RE, Fryer TD. Monte Carlo simulation and scatter correction of the GE Advance PET scanner with SimSET and Geant4. Phys Med Biol. 2005;50(20):4823-4840

    2005

  14. [22]

    SMART (SiMulAtion and ReconsTruction) PET: an efficient PET simulation-reconstruction tool

    Pfaehler E, De Jong JR, Dierckx RAJO, Van Velden FHP , Boellaard R. SMART (SiMulAtion and ReconsTruction) PET: an efficient PET simulation-reconstruction tool. EJNMMI Phys. 2018;5(1):16

    2018

  15. [23]

    ASIM: An Analytic PET Simulator

    Elston BF, Comtat C, Harrison RL, Kinahan P . ASIM: An Analytic PET Simulator . In: Monte Carlo Calculations in Nuclear Medicine. 2nd ed. CRC Press; 2012

    2012

  16. [24]

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

    Li S, Hamdi M, Dutta K, et al. FAST (fast analytical simulator of tracer) -PET: an accurate and efficient PET analytical simulation tool. Phys Med Biol. 2024;69(16):165020

    2024

  17. [25]

    STIR: software for tomographic image reconstruction release 2

    Thielemans K, Tsoumpas C, Mustafovic S, et al. STIR: software for tomographic image reconstruction release 2. Phys Med Biol. 2012;57(4):867-883

    2012

  18. [26]

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

    Merlin T, Stute S, Benoit D, et al. CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction. Phys Med Biol. 2018;63(18):185005

    2018

  19. [27]

    PyTomography: A python library for medical image reconstruction

    Polson LA, Fedrigo R, Li C, et al. PyTomography: A python library for medical image reconstruction. SoftwareX. 2025;29:102020

    2025

  20. [28]

    A Maximum Likelihood Method for Region -of-Interest Evaluation in Emission Tomography

    Carson RE. A Maximum Likelihood Method for Region -of-Interest Evaluation in Emission Tomography. J Comp Assist Tomography. 1986;10(4):654-663

    1986

  21. [29]

    WIN -PDQ: a Wiener -estimator-based projection -domain quantitative SPECT method that accounts for intra -regional uptake heterogeneity

    Li Z, Benabdallah N, Thorek DLJ, Jha AK. WIN -PDQ: a Wiener -estimator-based projection -domain quantitative SPECT method that accounts for intra -regional uptake heterogeneity. Proc SPIE Med Imag . 2024;12925:1292512

    2024

  22. [30]

    Estimating random signal parameters from noisy images with nuisance parameters: linear and scanning-linear methods

    Whitaker MK, Clarkson E, Barrett HH. Estimating random signal parameters from noisy images with nuisance parameters: linear and scanning-linear methods. Opt Express. 2008;16(11):8150

    2008

  23. [31]

    Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks

    Kang SK, Kim D, Shin SA, Kim YK, Choi H, Lee JS. Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks. J Nucl Med. 2023;64(4):659-666

    2023

  24. [32]

    Development of a GPU-Based Mont Carlo Simulation Tool for PET

    Lai Y , Zhong Y, Chalise A, et al. Development of a GPU-Based Mont Carlo Simulation Tool for PET. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE; 2019:1-3

    2019

  25. [33]

    MCGPU -PET: An open-source real-time Monte Carlo PET simulator

    Herraiz JL, Lopez-Montes A, Badal A. MCGPU -PET: An open-source real-time Monte Carlo PET simulator . Comput Phys Commun. 2024;296:109008

    2024

  26. [34]

    Observer -study-based approaches to quantitatively evaluate the realism of synthetic medical images

    Liu Z, Wolfe S, Yu Z, et al. Observer -study-based approaches to quantitatively evaluate the realism of synthetic medical images. Phys Med Biol. 2023;68(7):074001

    2023

  27. [35]

    Observer study-based evaluation of TGAN architecture used to generate oncological PET images

    Fedrigo R, Yousefirizi F, Liu Z, et al. Observer study-based evaluation of TGAN architecture used to generate oncological PET images. Proc SPIE Med Imag. 2023;12467:124670S

    2023

  28. [36]

    How much realism is needed?

    Badano A. “How much realism is needed?” — the wrong question in silico imagers have been asking. Med Phys. 2017;44(5):1607-1609

    2017

  29. [37]

    A Framework for In Silico Clinical Trials for Medical Devices Using Concepts From Model Verification, Validation, and Uncertainty Quantification (VVUQ)

    Bodner J, Kaul V. A Framework for In Silico Clinical Trials for Medical Devices Using Concepts From Model Verification, Validation, and Uncertainty Quantification (VVUQ). In: ASME 2021 Verification and Validation Symposium. American Society of Mechanical Engineers; 2021:V001T02A001

    2021

  30. [38]

    40-Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices

    V&V. 40-Assessing Credibility of Computational Modeling through Verification and Validation: Application to Medical Devices. American Society of Mechanical Engineers; 2018

    2018

  31. [39]

    Toward widespread use of virtual trials in medical imaging innovation and regulatory science

    Abadi E, Barufaldi B, Lago M, et al. Toward widespread use of virtual trials in medical imaging innovation and regulatory science. Medical Physics. 2024;51(12):9394-9404

    2024

  32. [40]

    Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons

    Obuchowski NA, Reeves AP , Huang EP , et al. Quantitative imaging biomarkers: a review of statistical methods for computer algorithm comparisons. Stat Methods Med Res. 2015;24(1):68-106

    2015

  33. [41]

    Objective comparison of quantitative imaging modalities without the use of a gold standard

    Hoppin JW, Kupinski MA, Kastis GA, Clarkson E, Barrett HH. Objective comparison of quantitative imaging modalities without the use of a gold standard. IEEE Trans Med Imaging. 2002;21(5)

    2002

  34. [42]

    Estimation in medical imaging without a gold standard

    Kupinski MA, Hoppin JW, Clarkson E, Barrett HH, Kastis GA. Estimation in medical imaging without a gold standard. Acad Radiol. 2002;9(3):290-297

    2002

  35. [43]

    Comparing cardiac ejection fraction estimation algorithms without a gold standard

    Kupinski MA, Hoppin JW, Krasnow J, et al. Comparing cardiac ejection fraction estimation algorithms without a gold standard. Acad Radiol. 2006;13(3):329-337

    2006

  36. [44]

    Nonsupervised ranking of different segmentation approaches: application to the estimation of the left ventricular ejection fraction from cardiac cine MRI sequences

    Lebenberg J, Buvat I, Lalande A, et al. Nonsupervised ranking of different segmentation approaches: application to the estimation of the left ventricular ejection fraction from cardiac cine MRI sequences. IEEE Trans Med Imaging. 2012;31(8):1651-1660

    2012

  37. [45]

    A no -gold-standard technique for objective assessment of quantitative nuclear - medicine imaging methods

    Jha AK, Caffo B, Frey EC. A no -gold-standard technique for objective assessment of quantitative nuclear - medicine imaging methods. Phys Med Biol. 2016;61(7):2780-2800

    2016

  38. [46]

    Task -based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard

    Jha AK, Kupinski MA, Rodríguez JJ, Stephen RM, Stopeck AT. Task -based evaluation of segmentation algorithms for diffusion-weighted MRI without using a gold standard. Phys Med Biol. 2012;57(13):4425

    2012

  39. [47]

    No-gold-standard evaluation of quantitative imaging methods in the presence of correlated noise

    Liu Z, Li Z, Mhlanga JC, Siegel BA, Jha AK. No-gold-standard evaluation of quantitative imaging methods in the presence of correlated noise. Proc SPIE Med Imag. 2022;12035:12035M

    2022

  40. [48]

    A no -gold-standard technique for objective evaluation of quantitative nuclear -medicine imaging methods in the presence of correlated noise

    Liu J, Liu Z, Moon HS, Mhlanga J, Jha A. A no -gold-standard technique for objective evaluation of quantitative nuclear -medicine imaging methods in the presence of correlated noise. J Nucl Med . 2020;61(supplement 1):523-523

    2020

  41. [49]

    No-gold-standard evaluation of quantitative SPECT methods for alpha-particle radiopharmaceutical therapy

    Liu Y , Liu Z, Li Z, Thorek D, Siegel B, Jha A. No-gold-standard evaluation of quantitative SPECT methods for alpha-particle radiopharmaceutical therapy. J Nucl Med. 2023;64(supplement 1):P1331-P1331

    2023

  42. [50]

    Practical no -gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography

    Jha AK, Mena E, Caffo B, et al. Practical no -gold-standard evaluation framework for quantitative imaging methods: application to lesion segmentation in positron emission tomography. J Med Imag . 2017;4(1):011011

    2017

  43. [51]

    Incorporating prior information in a no -gold-standard technique to assess quantitative SPECT reconstruction methods

    Jha AK, Frey EC. Incorporating prior information in a no -gold-standard technique to assess quantitative SPECT reconstruction methods. International Meeting on Fully 3D reconstruction in Radiology and Nuclear Medicine. Published online 2015:47-51

    2015

  44. [52]

    Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks

    Clarkson E. Estimation receiver operating characteristic curve and ideal observers for combined detection/estimation tasks. J Opt Soc Am A. 2007;24(12):B91

    2007

  45. [53]

    Shannon information for joint estimation/detection tasks and complex imaging systems

    Clarkson E, Cushing JB. Shannon information for joint estimation/detection tasks and complex imaging systems. J Opt Soc Am A. 2016;33(3):286

    2016

  46. [54]

    Human - and model -observer performance in ramp -spectrum noise: effects of regularization and object variability

    Abbey CK, Barrett HH. Human - and model -observer performance in ramp -spectrum noise: effects of regularization and object variability. J Opt Soc Am A. 2001;18(3):473

    2001

  47. [55]

    Addition of a channel mechanism to the ideal -observer model

    Myers KJ, Barrett HH. Addition of a channel mechanism to the ideal -observer model. J Opt Soc Am A . 1987;4(12):2447

    1987

  48. [56]

    Foundations of Image Science

    Barrett HH, Myers KJ. Foundations of Image Science. John Wiley & Sons; 2004

    2004

  49. [57]

    Clinical impact of time -of-flight and point response modeling in PET reconstructions: a lesion detection study

    Schaefferkoetter J, Casey M, Townsend D, El Fakhri G. Clinical impact of time -of-flight and point response modeling in PET reconstructions: a lesion detection study. Phys Med Biol. 2013;58(5):1465-1478

    2013

  50. [58]

    Model Observers in Medical Imaging Research

    He X, Park S. Model Observers in Medical Imaging Research. Theranostics. 2013;3(10):774-786

    2013

  51. [59]

    A Perceptually Relevant Channelized Joint Observer (PCJO) for the Detection -Localization of Parametric Signals

    Lu Zhang, Cavaro -Menard C, Le Callet P , Tanguy JY. A Perceptually Relevant Channelized Joint Observer (PCJO) for the Detection -Localization of Parametric Signals. IEEE Trans Med Imaging . 2012;31(10):1875- 1888

    2012

  52. [60]

    Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size

    Zhang L, Goossens B, Cavaro-Ménard C, Callet PL, Ge D. Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size. J Opt Soc Am A. 2013;30(11):2422

    2013

  53. [61]

    Combination of detection and estimation tasks using channelized scanning linear observer for CT imaging systems

    Tseng HW, Fan J, Kupinski MA. Combination of detection and estimation tasks using channelized scanning linear observer for CT imaging systems. Proc SPIE Med Imag. 2015;9416:94160H

    2015

  54. [62]

    A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov -Chain Monte Carlo Methods

    Li K, Zhou W, Li H, Anastasio MA. A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov -Chain Monte Carlo Methods. IEEE Trans Med Imaging. 2022;41(5):1114-1124

    2022

  55. [63]

    Optimal Joint Detection and Estimation That Maximizes ROC -Type Curves

    Wunderlich A, Goossens B, Abbey CK. Optimal Joint Detection and Estimation That Maximizes ROC -Type Curves. IEEE Trans Med Imaging. 2016;35(9):2164-2173

    2016

  56. [64]

    Noise properties of the EM algorithm

    Barrett HH, Wilson DW, Tsui BMW. Noise properties of the EM algorithm. I. Theory. Phys Med Biol . 1994;39(5):833

    1994

  57. [65]

    Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis

    Huang S ying, Franc BL, Harnish RJ, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer. 2018;4(1):1-13

    2018

  58. [66]

    A framework for evaluating the technical performance of multiparameter quantitative imaging biomarkers (mp-QIBs)

    Obuchowski NA, Huang E, deSouza NM, et al. A framework for evaluating the technical performance of multiparameter quantitative imaging biomarkers (mp-QIBs). Acad Radiol. 2023;30(2):147-158

    2023

  59. [67]

    The effect of SUV discretization in quantitative FDG -PET Radiomics: the need for standardized methodology in tumor texture analysis

    Leijenaar RTH, Nalbantov G, Carvalho S, et al. The effect of SUV discretization in quantitative FDG -PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015;5(1):11075

    2015

  60. [68]

    Robustness of radiomic features in [11C]Choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization

    Lu L, Lv W, Jiang J, et al. Robustness of radiomic features in [11C]Choline and [18F]FDG PET/CT imaging of nasopharyngeal carcinoma: impact of segmentation and discretization. Mol Imaging Biol. 2016;18(6):935- 945

    2016

  61. [69]

    Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

    Nyflot MJ, Yang F, Byrd D, Bowen SR, Sandison GA, Kinahan PE. Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards. J Med Imag. 2015;2(4):041002

    2015

  62. [70]

    Joint EANM/SNMMI guideline on radiomics in nuclear medicine: jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council

    Hatt M, Krizsan AK, Rahmim A, et al. Joint EANM/SNMMI guideline on radiomics in nuclear medicine: jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging. 2023;50(2):352-375

    2023

  63. [71]

    Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE guidelines)

    Jha AK, Bradshaw TJ, Buvat I, et al. Nuclear medicine and artificial intelligence: best practices for evaluation (the RELAINCE guidelines). J Nucl Med. 2022;63(9):1288-1299

    2022

  64. [72]

    AI in medical physics: guidelines for publication

    El Naqa I, Boone JM, Benedict SH, et al. AI in medical physics: guidelines for publication. Med Phys . 2021;48(9):4711-4714

    2021

  65. [73]

    MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins

    He D, Wang X, Fu C, et al. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging. 2021;21(1):46

    2021

  66. [74]

    Use of expert panels to define the reference standard in diagnostic research: a systematic review of published methods and reporting

    Bertens LCM, Broekhuizen BDL, Naaktgeboren CA, et al. Use of expert panels to define the reference standard in diagnostic research: a systematic review of published methods and reporting. PLoS Med . 2013;10(10):e1001531

    2013

  67. [75]

    PET parametric imaging: past, present, and future

    Wang G, Rahmim A, Gunn RN. PET parametric imaging: past, present, and future. IEEE Trans Radiat Plasma Med Sci. 2020;4(6):663-675

    2020

  68. [76]

    Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound

    Filippou V, Tsoumpas C. Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound. Med Phys. 2018;45(9):e740-e760

    2018

  69. [77]

    Abdo -Man: a 3D -printed anthropomorphic phantom for validating quantitative SIRT

    Gear JI, Cummings C, Craig AJ, et al. Abdo -Man: a 3D -printed anthropomorphic phantom for validating quantitative SIRT. EJNMMI Phys. 2016;3(1):17

    2016

  70. [78]

    Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling

    Black DG, Yazdi YO, Wong J, et al. Design of an anthropomorphic PET phantom with elastic lungs and respiration modeling. Med Phys. 2021;48(8):4205-4217

    2021

  71. [79]

    Anthropomorphic thorax phantom for cardio - respiratory motion simulation in tomographic imaging

    Bolwin K, Czekalla B, Frohwein LJ, Büther F, Schäfers KP . Anthropomorphic thorax phantom for cardio - respiratory motion simulation in tomographic imaging. Phys Med Biol. 2018;63(3):035009

    2018

  72. [80]

    PET textural features stability and pattern discrimination power for radiomics analysis: An “ad-hoc” phantoms study

    Presotto L, Bettinardi V, De Bernardi E, et al. PET textural features stability and pattern discrimination power for radiomics analysis: An “ad-hoc” phantoms study. Physica Medica. 2018;50:66-74

    2018

  73. [81]

    Phantom and methodology for comparison of small lesion detectability in PET

    Wollenweber SD, Alessio AM, Kinahan PE. Phantom and methodology for comparison of small lesion detectability in PET. Med Phys. 2023;50(5):2998-3007

    2023

  74. [82]

    A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET Images

    Forgacs A, Pall Jonsson H, Dahlbom M, et al. A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET Images. Chen K, ed. PLoS ONE. 2016;11(10):e0164113

    2016

  75. [83]

    Human-in-the- loop machine learning: a state of the art

    Mosqueira-Rey E, Hernández-Pereira E, Alonso-Ríos D, Bobes-Bascarán J, Fernández-Leal Á. Human-in-the- loop machine learning: a state of the art. Artif Intell Rev. 2023;56(4):3005-3054

    2023

  76. [84]

    Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis

    University of California, San Francisco, Adler-Milstein J, Aggarwal N, et al. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspectives. 2022;22(9)

    2022

  77. [85]

    Adaptive SPECT

    Barrett HH, Furenlid LR, Freed M, et al. Adaptive SPECT. IEEE Trans Med Imaging. 2008;27(6):775-788

    2008

  78. [86]

    Statistics for radiology research

    Obuchowski N, Subhas N, Polster J. Statistics for radiology research. Semin Musculoskelet Radiol . 2017;21(01):023-031

    2017

  79. [87]

    Testing for interchangeability of imaging tests

    Obuchowski NA, Subhas N, Schoenhagen P . Testing for interchangeability of imaging tests. Acad Radiol. 2014;21(11):1483-1489

    2014

  80. [88]

    Digital twins for predictive oncology will be a paradigm shift for precision cancer care

    Hernandez-Boussard T, Macklin P , Greenspan EJ, et al. Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat Med. 2021;27(12):2065-2066

    2021

  81. [89]

    Integrating mechanism -based modeling with biomedical imaging to build practical digital twins for clinical oncology

    Wu C, Lorenzo G, Hormuth DA, et al. Integrating mechanism -based modeling with biomedical imaging to build practical digital twins for clinical oncology. Biophys Rev. 2022;3(2):021304

    2022

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.