Highlights
- •Aims to support the clinical translation of quantitative MR imaging biomarkers.
- •Provides an overview of the current use and quality management of quantitative MRI.
- •Defines “translation ratio” between journal articles and clinical research studies.
- •Gives examples of successful academic qMR imaging biomarker translation.
- •Presents gap analysis between current practice and metrological ideal.
Abstract
Purpose
Methods
Results
Conclusions
Graphical abstract

Keywords
1. Introduction
Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019;49:e101–21. https://doi.org/10.1002/JMRI.26518.
- deSouza N.M.
- Achten E.
- Alberich-Bayarri A.
- Bamberg F.
- Boellaard R.
- Clément O.
- et al.
- McGee K.P.
- Hwang K.
- Sullivan D.C.
- Kurhanewicz J.
- Hu Y.
- Wang J.
- et al.

Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019;49:e101–21. https://doi.org/10.1002/JMRI.26518.
- McGee K.P.
- Hwang K.
- Sullivan D.C.
- Kurhanewicz J.
- Hu Y.
- Wang J.
- et al.
- Fedeli L.
- Belli G.
- Ciccarone A.
- Coniglio A.
- Esposito M.
- Giannelli M.
- et al.
- Fedeli L.
- Benelli M.
- Busoni S.
- Belli G.
- Ciccarone A.
- Coniglio A.
- et al.
2. Aim of the article
3. Literature and clinical research database search of quantitative MRI clinical research studies in the UK
3.1 Method
3.1.1 Literature search
3.1.2 Database search
- •the National Institute for Health Research Clinical Research Network (NIHR CRN) portfolio;
- •the ClinicalTrials.gov database;
- •the ISRCTN registry (originally this stood for International Standard Randomised Controlled Trial Number. The scope has now changed but the abbreviation remains).
3.2 Results
3.2.1 UK literature search
2011–2015 | 2016–2020 | % increase in number from 2011 to 2015 | |
---|---|---|---|
All qMR literature | |||
All journal articles (JA) | 1265 | 2235 | 77 % |
Clinical trials (CT) | 102 (8.1 % of JA) | 169 (7.6 % of JA) | 66 % |
Multicentre studies (MC) | 59 (4.7 % of JA) | 125 (5.6 % of JA) | 112 % |
Validation studies (VS) | 27 (2.1 % of JA) | 30 (1.3 % of JA) | 11 % |
Including quality management terms | |||
All journal articles (JA) | 312 (24.7 % of JA) | 620 (27.7 % of JA) | 99 % |
Clinical trials (CT) | 18 (17.6 % of all CT) | 27 (16.0 % of all CT) | 50 % |
Multicentre studies (MC) | 14 (23.7 % of all MC) | 43 (34.4 % of all MC) | 207 % |
Validation studies (VS) | 24 (88.9 % of all VS) | 27 (90.0 % of all VS) | 13 % |

3.2.2 Database search
3.3 Limitations
4. A survey of national clinical research MRI and quality management
4.1 Method
4.2 Results
4.2.1 Demographic
4.2.2 Scanners and ancillary equipment
4.2.3 QA/QC of the data acquisition


4.2.4 QA/QC of the acquired data and analysis

4.3 Limitations
5. Discussion
5.1 Linking the literature and database searches with the survey results
5.2 Standardisation, harmonisation and quality management of qMR data acquisition and analysis
- McGee K.P.
- Hwang K.
- Sullivan D.C.
- Kurhanewicz J.
- Hu Y.
- Wang J.
- et al.
- Alsop D.C.
- Detre J.A.
- Golay X.
- Günther M.
- Hendrikse J.
- Hernandez-Garcia L.
- et al.
- Barnes A.
- Alonzi R.
- Blackledge M.
- Charles-Edwards G.
- Collins D.J.
- Cook G.
- et al.
- Bernasconi A.
- Cendes F.
- Theodore W.H.
- Gill R.S.
- Koepp M.J.
- Hogan R.E.
- et al.
5.3 qMR imaging biomarker translation
5.3.1 Community initiatives
- Alberich-Bayarri A.
- Sourbron S.
- Golay X.
- deSouza N.
- Smits M.
- van der Lugt A.
- et al.
- Fedeli L.
- Belli G.
- Ciccarone A.
- Coniglio A.
- Esposito M.
- Giannelli M.
- et al.
5.3.2 qMR IBs on the translational path
- Wielema M.
- Dorrius M.D.
- Pijnappel R.M.
- De Bock G.H.
- Baltzer P.A.T.
- Oudkerk M.
- et al.


Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019;49:e101–21. https://doi.org/10.1002/JMRI.26518.
6. Gap analysis of qMR validation and quality management
- Alberich-Bayarri A.
- Sourbron S.
- Golay X.
- deSouza N.
- Smits M.
- van der Lugt A.
- et al.
Metrology and qMR imaging biomarkers. * Quantitative imaging is a form of measurement. The quantity of interest and, more importantly, our knowledge of it varies depending on how the image is formed. Any quantitatively estimated value of the quantity is the result of a measurement. The quantity intended to be measured is known as a measurand. MRI-based measurands include relaxation constants such as T1 and T2, and also measures of size, such as cortical or ventricular volume. Metrology as a field seeks to study measurements of all kinds and has some useful unifying principles which can be applied in any measurement context, including MRI. * It is common to talk about measurements in terms of their accuracy and precision. Precision refers to the spread of a set of values from similar measurements of the same quantity, and accuracy refers to the closeness of agreement of a measured value from a “true” value of the quantity. These terms are useful, but both implicitly assume the existence of some perfect, underlying true quantity value, sometimes referred to as a “ground truth”. The idea of a ground truth is intuitively appealing, it is not necessarily helpful in characterising a measurement. * In practice a measurement can be compared to one made by an alternate method which is known to be more accurate and precise (but perhaps is less practical or too expensive), but this measurement is not a “ground truth”. That measurement in turn can be assessed by comparing it to another non-perfect measurement. It is impossible, even in principle, for any measurement to be infinitely precise and completely accurate and hence eventually this chain of comparisons reaches a limit. As such, the “accuracy” of a measurement can never be truly known – the concept is not fully quantitative. * There are concepts which are fully quantitative and generic in metrology, however. Every measurement is made up of two elements: a numerical value and a unit, which acts as a reference to compare the measurement against. As such, we need firstly to know a numerical value, and the how (and how well) the measurement is calibrated to the reference unit. * The limitations of a measurement are captured in its uncertainty. Uncertainty expresses the limits of our knowledge of the quantity and is related to how the measurement is made. Uncertainty includes systematic effects, which introduce a consistent bias to measurements and random effects, which are stochastic but quantifiable. A typical measurement uncertainty will be quoted as a confidence interval, which gives a probability that the (unknown) “true value” is in a certain range. Note that it is distinct from the related idea of an error, which is the difference between the measured and true values. Since the true value is unknown, the error can never be known. Unlike error, uncertainty is quantifiable. * We also need to know how well the measurement is calibrated to the unit in question by comparing our measurement to other reference measurements. A particular measurement can be made of a standard object (a local standard), which is itself calibrated to another held at an accreditation laboratory (a secondary standard). The accreditation laboratory’s standard is then calibrated to a primary standard held by a National Measurement Institute (NMI) such as National Physical Laboratory (NPL) or National Institute of Standards and Technology (NIST) which is in turn calibrated to the definition of the units in question. This is the concept of traceability. * Via this chain of comparisons, and the associated calibration certificates, it is possible to trace (almost) any measurement back to a common set of agreed standards, allowing consistency and comparability between measurements made in different ways, in different places, using different procedures and equipment. * Notice that the focus here is not what is being measured, but how well it can be measured. In strict metrological terms, a measurement result consists of the value measured, and an associated uncertainty (range of values) and a confidence interval that measurement repeats would fall in the specified range. Once we have this the notion of a perfect ground truth becomes unnecessary. * Measurements themselves can be analysed using an uncertainty budget. Here, each step of the measurement is broken out and characterised by its own uncertainty, which are frequently simpler to estimate. The contribution of the uncertainty from each aspect of the measurement can then be combined into a single value for the entire process and allow a measurement to be characterised without knowing a ground truth. * Similarly, aspects of a measurement may themselves be made traceable. A good example of this is the timing reference used in MR scanners: pulse timing can be calibrated to a national or international timing reference so provide a reliable estimate of the uncertainty in pulse timings. Similar approaches can be applied to field strengths and gradients (via frequency). * Quantitative MRI is currently at a transition – we know how to make quantitative measurements, but even though the components of our scanners are well-calibrated there is not yet a metrological system in place to provide traceability to SI units and primary standards for the measurements we make from images. With an appropriate system in place to calibrate and benchmark scanners and acquisitions, the differences between measurements from different systems becomes comprehensible and quantitative comparisons become easier and more reliable. |
Quality management domain | Why this is important? | Ideal quality management activities | Examples of QAQC practice elicited from survey/literature | Gaps and challenges | ||
---|---|---|---|---|---|---|
GENERIC METROLOGY CONSIDERATIONS | ||||||
Accuracy (lack of bias) | If qMR is inaccurate or biased:
|
|
SEE FIGURE S13 and FIGURE 3/S14 |
| ||
Precision (treated as stochastic) | Repeatability | Same site, same scanner, same operator, same subject |
|
|
SEE BOX 1 |
|
Within site variation |
|
|
SEE FIGURE 3 |
| ||
Reproducibility | Different scanners across multiple sites |
|
| SEE BOX 1 |
| |
Linearity |
|
|
SEE FIGURE 4 |
| ||
QAQC OF DATA ACQUISITION | ||||||
Geometric | Dimensional |
|
|
SEE FIGURE 3/S15 |
| |
Gradient and B1 accuracy |
|
| ||||
SNR |
|
|
| |||
Motion |
|
|
SEE FIGURE S15 |
| ||
IB-specific quantification |
|
|
SEE FIGURE 3/S15 |
| ||
QAQC OF ACQUIRED DATA | ||||||
Imaging protocol compliance |
|
|
SEE FIGURE 5/S21 |
| ||
Data quality |
|
|
SEE FIGURE S25/S21 |
| ||
QAQC of data analysis | ||||||
Region-of-interest definition |
|
|
SEE FIGURE 5/S21 |
| ||
Fitting procedure and algorithm |
|
|
SEE FIGURE 5/S21 and FIGURE S12 |
| ||
SUSTAINABILITY AND TRANSPARENCY | ||||||
Evolution in MR hardware and software |
|
|
SEE FIGURE S17 and FIGURE S9 |
| ||
Data formats |
|
|
|
| ||
Data sharing |
|
|
SEE FIGURE S21 |
| ||
Code sharing and software sustainability |
|
|
SEE FIGURE S24 |
| ||
Open publishing and guidelines |
|
|
|
|
7. Conclusions
Declaration of Competing Interest
Acknowledgments
Appendix A. Supplementary data
- Supplementary data 1
- Supplementary data 2
References
Gulani V, Seiberlich N. Quantitative MRI: Rationale and Challenges 2020:xxxvii–li. https://doi.org/10.1016/B978-0-12-817057-1.00001-9.
Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019;49:e101–21. https://doi.org/10.1002/JMRI.26518.
- Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR). Insights.Imaging. 2019; 10https://doi.org/10.1186/s13244-019-0764-0
Food and Drug Administration. Clinical Trial Imaging Endpoint Process Standards Guidance for Industry Clinical/Medical Clinical Trial Imaging Endpoint Process Standards Guidance for Industry 2018.
- Recommendations towards standards for quantitative MRI (qMRI) and Outstanding Needs HHS Public Access.J Magn Reson Imaging. 2019; 49: e26-e39
- Quantitative magnetic resonance imaging biomarkers in oncological clinical trials: Current techniques and standardization challenges.Chronic Dis Transl Med. 2017; 3: 8-20https://doi.org/10.1016/j.cdtm.2017.02.002
- Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294.Med Phys. 2021; 48https://doi.org/10.1002/MP.14884
- Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom.PLoS ONE. 2021; 16: e0252966https://doi.org/10.1371/journal.pone.0252966
- Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom: Quantitative MRI Phantoms Review.Magn Reson Med. 2018; 79: 48-61
- New developments in MRI: System characterization, technical advances and radiotherapy applications.Phys Med. 2021; 90: 50-52https://doi.org/10.1016/j.ejmp.2021.09.001
ISO 9000: Quality management systems — Fundamentals and vocabulary https://www.iso.org/obp/ui/#iso:std:iso:9000:ed-4:v1:en (accessed February 1, 2022).
- QIN benchmarks for clinical translation of quantitative imaging tools.Benchmarks for Clinical Translation of Quantitative Imaging Tools. 2019; 5: 1-6
- The use of quantitative imaging in radiation oncology: A Quantitative Imaging Network (QIN) perspective radiation oncology.Int J Radiat Oncol Biol Phys. 2018; 102: 1219-1235
QIBA profiles http://qibawiki.rsna.org/index.php/Profiles (accessed February 1, 2022).
- Quality assurance multicenter comparison of different MR scanners for quantitative diffusion-weighted imaging.J Magn Reson Imaging. 2016; 43: 213-219
- Dependence of apparent diffusion coefficient measurement on diffusion gradient direction and spatial position – A quality assurance intercomparison study of forty-four scanners for quantitative diffusion-weighted imaging.Phys Med. 2018; 55: 135-141
- On the dependence of quantitative diffusion-weighted imaging on scanner system characteristics and acquisition parameters: A large multicenter and multiparametric phantom study with unsupervised clustering analysis.Phys Med. 2021; 85: 98-106
- Imaging biomarker roadmap for cancer studies.Nat Rev Clin Oncol. 2017; 14: 169-186
National Library of Medicine. 2020 MeSH Pubtypes https://www.nlm.nih.gov/mesh/pubtypes.html (accessed July 14, 2021).
- CRS report for congress the helium-3 shortage.Supply, demand, and options for Congress. 2010;
NHS. The NHS Long Term Plan 2019. https://www.longtermplan.nhs.uk/wp-content/uploads/2019/08/nhs-long-term-plan-version-1.2.pdf (accessed February 1, 2022).
NHS. Diagnostic imaging network implementation guide 2021. https://www.england.nhs.uk/wp-content/uploads/2021/04/B0030-Implementation-guide.pdf (accessed February 1, 2022).
NHS. Diagnostic imaging network capital equipment planning guide 2021. https://www.england.nhs.uk/wp-content/uploads/2021/04/B0030-Capital-equipment-planning-guide-April-2021.pdf (accessed February 1, 2022).
- Moving beyond quality control in diagnostic radiology and the role of the clinically qualified medical physicist.Phys Med. 2017; 41: 104-108
- Reproducibility and the future of MRI research.Magn Reson Med. 2019; 82: 1981-1983https://doi.org/10.1002/mrm.27939
MR Together https://mritogether.github.io/ (accessed February 1, 2022).
Prostate cancer: diagnosis and management NICE guideline 2019. www.nice.org.uk/guidance/ng131 (accessed January 24, 2022).
- Recommended implementation of arterial spin-labeled Perfusion mri for clinical applications: A consensus of the ISMRM Perfusion Study group and the European consortium for ASL in dementia.Magn Reson Med. 2015; 73: 102-116
- Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives.Eur Radiol. 2018; 28: 1118-1131
- UK quantitative WB-dWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer.Br J Radiol. 2018; 91: 20170577
- Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in highgrade gliomas.Neuro Oncol. 2020; 22: 1262-1275
- Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.Eur Radiol. 2020; 30: 1436-1450
- Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations.Neoplasia. 2009; 11: 102-125
- 4D cardiovascular magnetic resonance consensus statement.J Cardiovasc Magn Reson. 2015; 17: 72https://doi.org/10.1186/s12968-015-0174-5
- Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis—clinical implementation in the diagnostic process.Nat Rev | Neurol. 2015; 11: 471-482
- Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the international league against epilepsy neuroimaging task force.Epilepsia. 2019; https://doi.org/10.1111/epi.15612
- Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI.Magn Reson Mater Physics, Biol Med. 2020; 33: 177-195
- Consensus-based technical recommendations for clinical translation of renal ASL MRI.Magn Reson Mater Physics, Biol Med. 2020; 33: 141-161
- Technical recommendations for clinical translation of renal MRI: a consensus project of the Cooperation in Science and Technology Action PARENCHIMA.Magn Reson Mater Physics, Biol Med. 2020; 33: 131-140
- Consensus-based technical recommendations for clinical translation of renal T1 and T2 mapping MRI.Biol Med. 2020; 33: 163-176
- Quantitative magnetic resonance imaging phantoms: A review and the need for a system phantom.Magn Reson Med. 2018; 79: 48-61
- Quality assurance of quantitative cardiac T1-mapping in multicenter clinical trials - A T1 phantom program from the hypertrophic cardiomyopathy registry (HCMR) study.Int J Cardiol. 2021; 330: 251-258
Karakuzu A, Biswas L, Cohen-Adad J, Stikov N. Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. BioRxiv 2022;1:2021.12.27.474259.
Software Sustainability Institute https://www.software.ac.uk/ (accessed February 18, 2022).
Github https://github.com/ (accessed February 18, 2022).
Gitlab https://gitlab.com/ (accessed February 18, 2022).
Docker Hub https://hub.docker.com/ (accessed February 23, 2022).
Singularity https://sylabs.io/singularity (accessed February 27, 2022).
Open Science Initiative for Perfusion Imaging https://www.osipi.org/ (accessed February 10, 2022).
ISMRM MR-Hub https://ismrm.github.io/mrhub/ (accessed February 10, 2022).
Ferriscan - Resonance Health https://www.resonancehealth.com/products/ferriscan-mri-measurement-of-liver-iron-concentration.html (accessed February 18, 2022).
LiverMultiScan - Perspectum https://perspectum.com/products/livermultiscan (accessed February 18, 2022).
IDEAL IQ - GE Healthcare https://www.gehealthcare.co.uk/en/products/magnetic-resonance-imaging/mr-applications/ideal-iq---body (accessed February 18, 2022).
mDIXON Quant - Philips https://www.philips.co.uk/healthcare/product/HCNMRB462/mdixon-quant-mr-clinical-application (accessed February 18, 2022).
LiverLab - Siemens Healthineers https://www.siemens-healthineers.com/en-uk/magnetic-resonance-imaging/options-and-upgrades/clinical-applications/liver-lab (accessed February 18, 2022).
FDA Biomarker Qualification Program https://www.fda.gov/drugs/drug-development-tool-ddt-qualification-programs/biomarker-qualification-program (accessed February 18, 2022).
- The Quantitative Imaging Network: A decade of achievement.Tomography. 2019; 5: A8
ECOG-ACRIN Imaging Core Lab https://ecog-acrin.org/research-cores/imaging-core-laboratory/ (accessed February 1, 2022).
EIBALL https://www.myesr.org/research/european-imaging-biomarkers-alliance-eiball (accessed February 18, 2022).
- ESR Statement on the Validation of Imaging Biomarkers.Eur Soc Radiol. 2020; 11https://doi.org/10.1186/s13244-020-00872-9
EIBALL Biomarker Inventory https://www.myesr.org/research/biomarkers-inventory (accessed February 23, 2022).
- Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group.Magn Reson Med. 2022; 87: 1184-1206
- Report on a multicenter fMRI quality assurance protocol.J Magn Reson Imaging. 2006; 23: 827-839https://doi.org/10.1002/JMRI.20583
- A straightforward multiparametric quality control protocol for proton magnetic resonance spectroscopy: Validation and comparison of various 1.5 T and 3 T clinical scanner systems.Phys Med. 2018; 54: 49-55
The Osteoarthritis Initiative (OAI) https://nda.nih.gov/oai/ (accessed February 18, 2022).
Alzheimer’s Disease Neuroimaging Initiative (ADNI) http://adni.loni.usc.edu/ (accessed February 18, 2022).
IMI/IHI projects https://www.imi.europa.eu/about-imi/innovative-health-initiative (accessed February 18, 2022).
- Introduction to the National Cancer Imaging Translational Accelerator (NCITA): a UK-wide infrastructure for multicentre clinical translation of cancer imaging biomarkers.Br J Cancer. 2021; 125: 1462-1465
- Diffusion-weighted MRI in the body: Applications and challenges in oncology.Am J Roentgenol. 2007; 188: 1622-1635https://doi.org/10.2214/AJR.06.1403
- Diffusion-weighted magnetic resonance imaging and its application to cancer.Cancer Imaging. 2006; 6: 135-143https://doi.org/10.1102/1470-7330.2006.0021
- Apparent diffusion coefficient from magnetic resonance imaging as a biomarker in oncology drug development.Eur J Cancer. 2012; 48: 425-431https://doi.org/10.1016/j.ejca.2011.11.034
- Whole-body MRI: a practical guide for imaging patients with malignant bone disease.Clin Radiol. 2021; 76: 715-727https://doi.org/10.1016/j.crad.2021.04.001
- Diffusion-weighted MRI for predicting treatment response in patients with nasopharyngeal carcinoma: a systematic review and meta-analysis.Sci Rep. 2021; 11
- Diagnostic accuracy of apparent diffusion coefficient (ADC) value in differentiating malignant from benign solid liver lesions: a systematic review and meta-analysis.Br J Radiol. 2021; 94: 20210059
- Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis.Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis. 2020; 15: e0232856
- Imaging brain microstructure with diffusion MRI: practicality and applications.NMR Biomed. 2019; 32: e3841
- Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.Magn Reson Med. 2021; 86: 2987-3011
- The sensitivity of diffusion MRI to microstructural properties and experimental factors.J Neurosci Methods. 2021; 347: 108951
- The role of tissue microstructure and water exchange in biophysical modelling of diffusion in white matter.Magn Reson Mater Phy. 2013; 26: 345-370https://doi.org/10.1007/s10334-013-0371-x
- Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies.Nat Rev Clin Oncol. 2012; 9: 167-177https://doi.org/10.1038/nrclinonc.2012.2
- Rapid improvement in carotid adventitial angiogenesis and plaque neovascularization after rosuvastatin therapy in statin treatment–naïve subjects.J Clin Lipidol. 2019; 13: 847-853https://doi.org/10.1016/j.jacl.2019.07.008
- Repeatability and response to therapy of dynamic contrast-enhanced magnetic resonance imaging biomarkers in rheumatoid arthritis in a large multicentre trial setting.Eur Radiol. 2017; 27: 3662-3668
- Variability in Quantitative DCE-MRI: Sources and Solutions.J Nat Sci. 2018; 4: 1-16
- Tracer kinetic modelling in MRI: Estimating perfusion and capillary permeability.Phys Med Biol. 2012; 57: R1-R33
- Models and methods for analyzing DCE-MRI: A review.Med Phys. 2014; 41: 124301
- Semipermeable Hollow Fiber Phantoms for Development and Validation of Perfusion-Sensitive MR Methods and Signal Models.Concepts Magn Reson Part B Magn Reson Eng. 2011; 39B: 149-158https://doi.org/10.1002/cmr.b
Barboriak Lab; Duke Univeristy. QIBA digital reference object https://sites.duke.edu/dblab/qibacontent/.
- Evaluation of dynamic contrast-enhanced MRI biomarkers for stratified cancer medicine: How do permeability and perfusion vary between human tumours?.Magn Reson Imaging. 2018; 46: 98-105
- Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer.Radiother Oncol. 2020; 153: 114-121
- Diffusion coefficient measurement using a temperature controlled fluid for quality control in multi-center studies.J Magn Reson Imaging. 2011; 34: 983-987
- Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study.AJNR Am J Neuroradiol. 2017; 38: 537-545
- Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements.Magn Reson Med. 2014; 71: 1312-1323https://doi.org/10.1002/mrm.24773
- Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom.J Magn Reson Imaging. 2013; 37: 1238-1246
- Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the American college of radiology imaging network 6698 breast cancer trial.J Magn Reson Imaging. 2015; 42: 908-919
- Extracranial soft-tissue tumors: repeatability of apparent diffusion coefficient estimates from diffusion-weighted MR imaging.Radiology. 2017; 284: 88-99
- Diffusion-weighted (DW) MRI in Lung Cancers: ADC Test-retest Repeatability Europe PMC Funders Group.Eur Radiol. 2017; 27: 4552-4562
- Diffusion-weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility, repeatability, and diagnostic accuracy.NMR Biomed. 2016; 29: 1445-1453
- Repeatability and reproducibility of ADC measurements: a prospective multicenter whole-body-MRI study.Eur Radiol. 2021; 31: 4514-4527
- Diffusion-weighted MR imaging of upper abdominal organs: Field strength and intervendor variability of apparent diffusion coefficients.Radiology. 2014; 270: 454-463
European Medicine Agency Qualification https://www.ema.europa.eu/en/human-regulatory/research-development/scientific-advice-protocol-assistance/qualification-novel-methodologies-medicine-development-0 (accessed February 1, 2022).
NHS NICE. Medical Technologies Evaluation Programme https://www.nice.org.uk/process/pmg33/resources/medical-technologies-evaluation-programme-methods-guide-pdf-72286774205893 (accessed February 10, 2022).
NHS NICE. Diagnostic Assessment Programme https://www.nice.org.uk/Media/Default/About/what-we-do/NICE-guidance/NICE-diagnostics-guidance/Diagnostics-assessment-programme-manual.pdf (accessed February 10, 2022).
NHS NICE. Technology Appraisal Guidance https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/nice-technology-appraisal-guidance.
NHS. Transforming imaging services in England: a national strategy for imaging networks 2019:55. https://www.england.nhs.uk/transforming-imaging-services-in-england/ (accessed February 1, 2022).
NCITA MR Core Lab ncita.org.uk/mr-core-lab.
- Clinical quantitative MRI and the need for metrology.Br J Radiol. 2021; 94: 20201215
- Metrology Standards for Quantitative Imaging Biomarkers.Radiology. 2015; 277: 813-825
- Consensus statement Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.Nat Med. 2020; 26: 1364-1374
Article info
Publication history
Identification
Copyright
User license
Creative Commons Attribution (CC BY 4.0) |
Permitted
- Read, print & download
- Redistribute or republish the final article
- Text & data mine
- Translate the article
- Reuse portions or extracts from the article in other works
- Sell or re-use for commercial purposes
Elsevier's open access license policy