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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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Reynolds HM, Tadimalla S, Wang YF, Montazerolghaem M, Sun Y, Williams S, Mitchell C, Finnegan ME, Murphy DG, Haworth A. Semi-quantitative and quantitative dynamic contrast-enhanced (DCE) MRI parameters as prostate cancer imaging biomarkers for biologically targeted radiation therapy. Cancer Imaging 2022; 22:71. [PMID: 36536464 PMCID: PMC9762110 DOI: 10.1186/s40644-022-00508-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Biologically targeted radiation therapy treatment planning requires voxel-wise characterisation of tumours. Dynamic contrast enhanced (DCE) DCE MRI has shown promise in defining voxel-level biological characteristics. In this study we consider the relative value of qualitative, semi-quantitative and quantitative assessment of DCE MRI compared with diffusion weighted imaging (DWI) and T2-weighted (T2w) imaging to detect prostate cancer at the voxel level. METHODS Seventy prostate cancer patients had multiparametric MRI prior to radical prostatectomy, including T2w, DWI and DCE MRI. Apparent Diffusion Coefficient (ADC) maps were computed from DWI, and semi-quantitative and quantitative parameters computed from DCE MRI. Tumour location and grade were validated with co-registered whole mount histology. Kolmogorov-Smirnov tests were applied to determine whether MRI parameters in tumour and benign voxels were significantly different. Cohen's d was computed to quantify the most promising biomarkers. The Parker and Weinmann Arterial Input Functions (AIF) were compared for their ability to best discriminate between tumour and benign tissue. Classifier models were used to determine whether DCE MRI parameters improved tumour detection versus ADC and T2w alone. RESULTS All MRI parameters had significantly different data distributions in tumour and benign voxels. For low grade tumours, semi-quantitative DCE MRI parameter time-to-peak (TTP) was the most discriminating and outperformed ADC. For high grade tumours, ADC was the most discriminating followed by DCE MRI parameters Ktrans, the initial rate of enhancement (IRE), then TTP. Quantitative parameters utilising the Parker AIF better distinguished tumour and benign voxel values than the Weinmann AIF. Classifier models including DCE parameters versus T2w and ADC alone, gave detection accuracies of 78% versus 58% for low grade tumours and 85% versus 72% for high grade tumours. CONCLUSIONS Incorporating DCE MRI parameters with DWI and T2w gives improved accuracy for tumour detection at a voxel level. DCE MRI parameters should be used to spatially characterise tumour biology for biologically targeted radiation therapy treatment planning.
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Affiliation(s)
- Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| | | | - Yu-Feng Wang
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | | | - Yu Sun
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Scott Williams
- Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Catherine Mitchell
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Declan G Murphy
- Division of Cancer Surgery, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Annette Haworth
- School of Physics, The University of Sydney, Sydney, NSW, Australia
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Tadimalla S, Wang W, Haworth A. Role of Functional MRI in Liver SBRT: Current Use and Future Directions. Cancers (Basel) 2022; 14:cancers14235860. [PMID: 36497342 PMCID: PMC9739660 DOI: 10.3390/cancers14235860] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/23/2022] [Accepted: 11/24/2022] [Indexed: 11/30/2022] Open
Abstract
Stereotactic body radiation therapy (SBRT) is an emerging treatment for liver cancers whereby large doses of radiation can be delivered precisely to target lesions in 3-5 fractions. The target dose is limited by the dose that can be safely delivered to the non-tumour liver, which depends on the baseline liver functional reserve. Current liver SBRT guidelines assume uniform liver function in the non-tumour liver. However, the assumption of uniform liver function is false in liver disease due to the presence of cirrhosis, damage due to previous chemo- or ablative therapies or irradiation, and fatty liver disease. Anatomical information from magnetic resonance imaging (MRI) is increasingly being used for SBRT planning. While its current use is limited to the identification of target location and size, functional MRI techniques also offer the ability to quantify and spatially map liver tissue microstructure and function. This review summarises and discusses the advantages offered by functional MRI methods for SBRT treatment planning and the potential for adaptive SBRT workflows.
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Affiliation(s)
- Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Correspondence:
| | - Wei Wang
- Crown Princess Mary Cancer Centre, Sydney West Radiation Oncology Network, Western Sydney Local Health District, Sydney, NSW 2145, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
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4
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Tadimalla S, Wilson DJ, Shelley D, Bainbridge G, Saysell M, Mendichovszky IA, Graves MJ, Guthrie JA, Waterton JC, Parker GJM, Sourbron SP. Bias, Repeatability and Reproducibility of Liver T 1 Mapping With Variable Flip Angles. J Magn Reson Imaging 2022; 56:1042-1052. [PMID: 35224803 PMCID: PMC9545852 DOI: 10.1002/jmri.28127] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/09/2022] [Accepted: 02/10/2022] [Indexed: 12/16/2022] Open
Abstract
Background Three‐dimensional variable flip angle (VFA) methods are commonly used for T1 mapping of the liver, but there is no data on the accuracy, repeatability, and reproducibility of this technique in this organ in a multivendor setting. Purpose To measure bias, repeatability, and reproducibility of VFA T1 mapping in the liver. Study Type Prospective observational. Population Eight healthy volunteers, four women, with no known liver disease. Field Strength/Sequence 1.5‐T and 3.0‐T; three‐dimensional steady‐state spoiled gradient echo with VFAs; Look‐Locker. Assessment Traveling volunteers were scanned twice each (30 minutes to 3 months apart) on six MRI scanners from three vendors (GE Healthcare, Philips Medical Systems, and Siemens Healthineers) at two field strengths. The maximum period between the first and last scans among all volunteers was 9 months. Volunteers were instructed to abstain from alcohol intake for at least 72 hours prior to each scan and avoid high cholesterol foods on the day of the scan. Statistical Tests Repeated measures ANOVA, Student t‐test, Levene's test of variances, and 95% significance level. The percent error relative to literature liver T1 in healthy volunteers was used to assess bias. The relative error (RE) due to intrascanner and interscanner variation in T1 measurements was used to assess repeatability and reproducibility. Results The 95% confidence interval (CI) on the mean bias and mean repeatability RE of VFA T1 in the healthy liver was 34 ± 6% and 10 ± 3%, respectively. The 95% CI on the mean reproducibility RE at 1.5 T and 3.0 T was 29 ± 7% and 25 ± 4%, respectively. Data Conclusion Bias, repeatability, and reproducibility of VFA T1 mapping in the liver in a multivendor setting are similar to those reported for breast, prostate, and brain. Level of Evidence 1 Technical Efficacy Stage 1
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Affiliation(s)
- Sirisha Tadimalla
- Institute of Medical Physics, University of Sydney, Sydney, Australia.,Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | | | | | | | | | | | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - John C Waterton
- Bioxydyn Ltd, Manchester, UK.,Centre for Imaging Sciences, Division of Informatics Imaging and Data Sciences, School of Health Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Geoffrey J M Parker
- Bioxydyn Ltd, Manchester, UK.,Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Steven P Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
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5
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Scotcher D, Melillo N, Tadimalla S, Darwich AS, Ziemian S, Ogungbenro K, Schütz G, Sourbron S, Galetin A. Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats. Mol Pharm 2021; 18:2997-3009. [PMID: 34283621 PMCID: PMC8397403 DOI: 10.1021/acs.molpharmaceut.1c00206] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
![]()
Physiologically based
pharmacokinetic (PBPK) models are increasingly
used in drug development to simulate changes in both systemic and
tissue exposures that arise as a result of changes in enzyme and/or
transporter activity. Verification of these model-based simulations
of tissue exposure is challenging in the case of transporter-mediated
drug–drug interactions (tDDI), in particular as these may lead
to differential effects on substrate exposure in plasma and tissues/organs
of interest. Gadoxetate, a promising magnetic resonance imaging (MRI)
contrast agent, is a substrate of organic-anion-transporting polypeptide
1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2).
In this study, we developed a gadoxetate PBPK model and explored the
use of liver-imaging data to achieve and refine in vitro–in
vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic
data. In addition, PBPK modeling was used to investigate gadoxetate
hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced
(DCE) MRI data of gadoxetate in rat blood, spleen, and liver were
used in this analysis. Gadoxetate in vitro uptake kinetic data were
generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte
uptake unbound Michaelis–Menten constant (Km,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total
uptake into hepatocytes. PBPK–IVIVE of these data (bottom-up
approach) captured reasonably systemic exposure, but underestimated
the in vivo gadoxetate DCE–MRI profiles and elimination from
the liver. Therefore, in vivo rat DCE–MRI liver data were subsequently
used to refine gadoxetate transporter kinetic parameters in the PBPK
model (top-down approach). Active uptake into the hepatocytes refined
by the liver-imaging data was one order of magnitude higher than the
one predicted by the IVIVE approach. Finally, the PBPK model was fitted
to the gadoxetate DCE–MRI data (blood, spleen, and liver) obtained
with and without coadministered rifampicin. Rifampicin was estimated
to inhibit active uptake transport of gadoxetate into the liver by
96%. The current analysis highlighted the importance of gadoxetate
liver data for PBPK model refinement, which was not feasible when
using the blood data alone, as is common in PBPK modeling applications.
The results of our study demonstrate the utility of organ-imaging
data in evaluating and refining PBPK transporter IVIVE to support
the subsequent model use for quantitative evaluation of hepatic tDDI.
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Affiliation(s)
- Daniel Scotcher
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester M13 9PL, U.K
| | - Nicola Melillo
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester M13 9PL, U.K
| | - Sirisha Tadimalla
- Division of Medical Physics, University of Leeds, Leeds LS2 9JT, U.K
| | - Adam S Darwich
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester M13 9PL, U.K
| | - Sabina Ziemian
- MR & CT Contrast Media Research, Bayer AG, Berlin 13342, Germany
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester M13 9PL, U.K
| | - Gunnar Schütz
- MR & CT Contrast Media Research, Bayer AG, Berlin 13342, Germany
| | - Steven Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield S10 2TN, U.K
| | - Aleksandra Galetin
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester M13 9PL, U.K
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6
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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7
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Scotcher D, Tadimalla S, Darwich A, Ziemian S, Ogungbenro K, Schütz G, Sourbron S, Galetin A. P243 - Physiologically-based pharmacokinetic modelling of transporter-mediated hepatic disposition using the imaging biomarker gadoxetate. Drug Metab Pharmacokinet 2020. [DOI: 10.1016/j.dmpk.2020.04.244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Waterton JC, Hines CDG, Hockings PD, Laitinen I, Ziemian S, Campbell S, Gottschalk M, Green C, Haase M, Hassemer K, Juretschke HP, Koehler S, Lloyd W, Luo Y, Mahmutovic Persson I, O'Connor JPB, Olsson LE, Pindoria K, Schneider JE, Sourbron S, Steinmann D, Strobel K, Tadimalla S, Teh I, Veltien A, Zhang X, Schütz G. Repeatability and reproducibility of longitudinal relaxation rate in 12 small-animal MRI systems. Magn Reson Imaging 2019; 59:121-129. [PMID: 30872166 PMCID: PMC6477178 DOI: 10.1016/j.mri.2019.03.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 01/29/2019] [Accepted: 03/08/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Many translational MR biomarkers derive from measurements of the water proton longitudinal relaxation rate R1, but evidence for between-site reproducibility of R1 in small-animal MRI is lacking. OBJECTIVE To assess R1 repeatability and multi-site reproducibility in phantoms for preclinical MRI. METHODS R1 was measured by saturation recovery in 2% agarose phantoms with five nickel chloride concentrations in 12 magnets at 5 field strengths in 11 centres on two different occasions within 1-13 days. R1 was analysed in three different regions of interest, giving 360 measurements in total. Root-mean-square repeatability and reproducibility coefficients of variation (CoV) were calculated. Propagation of reproducibility errors into 21 translational MR measurements and biomarkers was estimated. Relaxivities were calculated. Dynamic signal stability was also measured. RESULTS CoV for day-to-day repeatability (N = 180 regions of interest) was 2.34% and for between-centre reproducibility (N = 9 centres) was 1.43%. Mostly, these do not propagate to biologically significant between-centre error, although a few R1-based MR biomarkers were found to be quite sensitive even to such small errors in R1, notably in myocardial fibrosis, in white matter, and in oxygen-enhanced MRI. The relaxivity of aqueous Ni2+ in 2% agarose varied between 0.66 s-1 mM-1 at 3 T and 0.94 s-1 mM-1 at 11.7T. INTERPRETATION While several factors affect the reproducibility of R1-based MR biomarkers measured preclinically, between-centre propagation of errors arising from intrinsic equipment irreproducibility should in most cases be small. However, in a few specific cases exceptional efforts might be required to ensure R1-reproducibility.
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Affiliation(s)
- John C Waterton
- Bioxydyn Ltd, Manchester Science Park, Rutherford House, Pencroft Way, MANCHESTER M15 6SZ, United Kingdom; Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, MANCHESTER M13 9PL, United Kingdom.
| | | | - Paul D Hockings
- Antaros Medical, BioVenture Hub, 43183 Mölndal, Sweden; MedTech West, Chalmers University of Technology, Gothenburg, Sweden.
| | - Iina Laitinen
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
| | - Sabina Ziemian
- Bayer AG, Research and Development, Pharmaceuticals, MR and CT Contrast Media Research, Müllerstraße 178, D-13353 Berlin, Germany.
| | - Simon Campbell
- In-Vivo Bioimaging UK, RD Platform Technology & Science, GSK Medicines Research Centre, Gunnels Wood Road, STEVENAGE, Hertfordshire, SG1 2NY, United Kingdom.
| | - Michael Gottschalk
- Lund University BioImaging Center, Klinikgatan 32, SE-222-42 Lund, Sweden.
| | - Claudia Green
- Bayer AG, Research and Development, Pharmaceuticals, MR and CT Contrast Media Research, Müllerstraße 178, D-13353 Berlin, Germany.
| | - Michael Haase
- In-Vivo Bioimaging UK, RD Platform Technology & Science, GSK Medicines Research Centre, Gunnels Wood Road, STEVENAGE, Hertfordshire, SG1 2NY, United Kingdom.
| | - Katja Hassemer
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
| | - Hans-Paul Juretschke
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany
| | - Sascha Koehler
- Bruker BioSpin MRI GmbH, Rudolf-Plank-Straße 23, D-76275 Ettlingen, Germany.
| | - William Lloyd
- Centre for Imaging Sciences, Division of Informatics Imaging & Data Sciences, School of Health Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, MANCHESTER M13 9PL, United Kingdom.
| | - Yanping Luo
- iSAT Discovery, Abbvie, 1 North Waukegan Road, North Chicago, IL, 60064-1802, United States of America.
| | - Irma Mahmutovic Persson
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Skåne University Hospital, SE-205 02 Malmö, Sweden.
| | - James P B O'Connor
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology Medicine & Health, University of Manchester, Manchester Academic Health Sciences Centre, MANCHESTER M20 4BX, United Kingdom. james.o'
| | - Lars E Olsson
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Skåne University Hospital, SE-205 02 Malmö, Sweden.
| | - Kashmira Pindoria
- In-Vivo Bioimaging UK, RD Platform Technology & Science, GSK Medicines Research Centre, Gunnels Wood Road, STEVENAGE, Hertfordshire, SG1 2NY, United Kingdom.
| | - Jurgen E Schneider
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - Steven Sourbron
- Leeds Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, LIGHT Labs, Clarendon Way, LEEDS LS2 9JT, United Kingdom.
| | - Denise Steinmann
- Sanofi-Aventis Deutschland GmbH, R&D TIM - Bioimaging Germany, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
| | - Klaus Strobel
- Bruker BioSpin MRI GmbH, Rudolf-Plank-Straße 23, D-76275 Ettlingen, Germany.
| | - Sirisha Tadimalla
- Leeds Imaging Biomarkers Group, Department of Biomedical Imaging Sciences, University of Leeds, LIGHT Labs, Clarendon Way, LEEDS LS2 9JT, United Kingdom.
| | - Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, United Kingdom.
| | - Andor Veltien
- Radboud university medical center, Radiology (766), P.O.Box 9101, 6500, HB, Nijmegen, the Netherlands.
| | - Xiaomeng Zhang
- iSAT Discovery, Abbvie, 1 North Waukegan Road, North Chicago, IL, 60064-1802, United States of America.
| | - Gunnar Schütz
- Bayer AG, Research and Development, Pharmaceuticals, MR and CT Contrast Media Research, Müllerstraße 178, D-13353 Berlin, Germany.
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Tadimalla S, Tourell MC, Knott R, Momot KI. Assessment of collagen fiber orientation dispersion in articular cartilage by small-angle X-ray scattering and diffusion tensor imaging: Preliminary results. Magn Reson Imaging 2018; 48:115-121. [DOI: 10.1016/j.mri.2017.12.032] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 12/29/2017] [Indexed: 12/23/2022]
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10
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Tadimalla S, Momot KI. Effect of partial H2O-D2O replacement on the anisotropy of transverse proton spin relaxation in bovine articular cartilage. PLoS One 2014; 9:e115288. [PMID: 25545955 PMCID: PMC4278899 DOI: 10.1371/journal.pone.0115288] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 11/21/2014] [Indexed: 11/28/2022] Open
Abstract
Anisotropy of transverse proton spin relaxation in collagen-rich tissues like cartilage and tendon is a well-known phenomenon that manifests itself as the "magic-angle" effect in magnetic resonance images of these tissues. It is usually attributed to the non-zero averaging of intra-molecular dipolar interactions in water molecules bound to oriented collagen fibers. One way to manipulate the contributions of these interactions to spin relaxation is by partially replacing the water in the cartilage sample with deuterium oxide. It is known that dipolar interactions in deuterated solutions are weaker, resulting in a decrease in proton relaxation rates. In this work, we investigate the effects of deuteration on the longitudinal and the isotropic and anisotropic contributions to transverse relaxation of water protons in bovine articular cartilage. We demonstrate that the anisotropy of transverse proton spin relaxation in articular cartilage is independent of the degree of deuteration, bringing into question some of the assumptions currently held over the origins of relaxation anisotropy in oriented tissues.
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Affiliation(s)
- Sirisha Tadimalla
- School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, Queensland, Australia
- Institute of Health and Biomedical Innovation, Kelvin Grove, Queensland, Australia
| | - Konstantin I. Momot
- School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, Queensland, Australia
- Institute of Health and Biomedical Innovation, Kelvin Grove, Queensland, Australia
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