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Collinot H, Balvay D, Autret G, Lagoutte I, Siauve N, Vaiman D, Salomon LJ. Dynamic contrast enhanced MRI demonstrate altered placental perfusion in the STOX1A preeclampsia mouse model. Placenta 2024; 158:69-77. [PMID: 39383640 DOI: 10.1016/j.placenta.2024.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 09/19/2024] [Accepted: 10/03/2024] [Indexed: 10/11/2024]
Abstract
INTRODUCTION Preeclampsia, a hypertensive disorder of pregnancy triggered by placental dysfunction, is reproduced in the murine STOX1A model, with hypertension, proteinuria, and abnormalities in umbilical and uterine Dopplers. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an innovative technique that provides insights into tissue perfusion. The present study aims at analyzing placental perfusion using DCE-MRI to further characterize placental defects in the STOX1A model. METHODS Two study groups were formed: the "TgSTOX13 pregnancy group" from mating TgSTOX13 genotype males with wild-type females, and the "wild-type pregnancy group" from mating wild-type males with wild-type females. Blood pressure, urinary albumin to creatinine ratio, and fetal weights were measured and compared between the groups, while perfusion parameters were analyzed using both conventional compartmental (1C) and free-time point-Hermite (FTPH) models in the DCE analysis. RESULTS Seventeen pregnant mice in the "TgSTOX13 pregnancy group" and thirteen in the "wild-type pregnant group" were included in the analysis. During late gestation, the TgSTOX13 pregnancy group exhibited higher blood pressure, elevated albumin/creatinine ratio, and decreased fetal weights compared to the wild-type pregnancy group. In the DCE analysis utilizing the 1C model, blood flow (Fb) was significantly reduced by approximately 31.8 % in the TgSTOX13 pregnancy group compared to the wild-type pregnancy group (p < 0.01), a finding corroborated by the FTPH model with a reduction estimated at 31.5 % (p < 0.01). DISCUSSION Our investigation successfully utilized DCE MRI to assess placental perfusion in a mouse model of preeclampsia, revealing a significant reduction of approximately 30 % in the preeclamptic mice, mirroring human pathophysiology.
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Affiliation(s)
- Hélène Collinot
- Maternité Port-Royal, AP-HP, APHP Centre, Université Paris Cité, FHU PREMA, Paris, France; Université Paris Cité, INSERM, U1016, CNRS, UMR 8104, Institut Cochin, Equipe "From Gamete To Birth", Paris, France.
| | - Daniel Balvay
- Université Paris Cité, Inserm, PARCC, U970, F-75015, Paris, France.
| | - Gwennhael Autret
- Université Paris Cité, Inserm, PARCC, U970, F-75015, Paris, France.
| | - Isabelle Lagoutte
- Université Paris Cité, INSERM, U1016, CNRS, UMR 8104, Institut Cochin, Plateforme d'Imagerie du Vivant, Paris, France.
| | - Nathalie Siauve
- Université Paris Cité, Inserm, PARCC, U970, F-75015, Paris, France.
| | - Daniel Vaiman
- Université Paris Cité, INSERM, U1016, CNRS, UMR 8104, Institut Cochin, Equipe "From Gamete To Birth", Paris, France.
| | - Laurent J Salomon
- Maternité, Obstétrique, Médecine, Chirurgie et Imagerie Fœtales, Hôpital Necker-Enfants malades, APHP, et Plateforme LUMIERE, URP7328, Université Paris Cité, Paris, France.
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Clemente A, Selva G, Berks M, Morrone F, Morrone AA, Aulisa MDC, Bliakharskaia E, De Nicola A, Tartaro A, Summers PE. Comparison of Early Contrast Enhancement Models in Ultrafast Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Prostate Cancer. Diagnostics (Basel) 2024; 14:870. [PMID: 38732285 PMCID: PMC11083228 DOI: 10.3390/diagnostics14090870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 05/13/2024] Open
Abstract
Tofts models have failed to produce reliable quantitative markers for prostate cancer. We examined the differences between prostate zones and lesion PI-RADS categories and grade group (GG) using regions of interest drawn in tumor and normal-appearing tissue for a two-compartment uptake (2CU) model (including plasma volume (vp), plasma flow (Fp), permeability surface area product (PS), plasma mean transit time (MTTp), capillary transit time (Tc), extraction fraction (E), and transfer constant (Ktrans)) and exponential (amplitude (A), arrival time (t0), and enhancement rate (α)), sigmoidal (amplitude (A0), center time relative to arrival time (A1 - T0), and slope (A2)), and empirical mathematical models, and time to peak (TTP) parameters fitted to high temporal resolution (1.695 s) DCE-MRI data. In 25 patients with 35 PI-RADS category 3 or higher tumors, we found Fp and α differed between peripheral and transition zones. Parameters Fp, MTTp, Tc, E, α, A1 - T0, and A2 and TTP all showed associations with PI-RADS categories and with GG in the PZ when normal-appearing regions were included in the non-cancer GG. PS and Ktrans were not associated with any PI-RADS category or GG. This pilot study suggests early enhancement parameters derived from ultrafast DCE-MRI may become markers of prostate cancer.
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Affiliation(s)
- Alfredo Clemente
- Radiology Unit, Centro Medicina Nucleare N1, “Centro Morrone”, 81100 Caserta, Italy; (A.C.); (G.S.)
| | - Guerino Selva
- Radiology Unit, Centro Medicina Nucleare N1, “Centro Morrone”, 81100 Caserta, Italy; (A.C.); (G.S.)
| | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, University of Manchester, Manchester M13 9PL, UK;
| | - Federica Morrone
- Radiology Unit, Centro Radiologico Vega, “Centro Morrone”, 81100 Caserta, Italy; (F.M.); (A.A.M.)
| | | | | | | | - Andrea De Nicola
- Radiology Unit, SS. Annunziata Hospital, ASL Lanciano Vasto Chieti, 66100 Chieti, Italy;
| | - Armando Tartaro
- Department of Clinical, Oral Sciences and Biotechnology, University “G. d’Annunzio”, 66100 Chieti, Italy;
- MRI Unit, Santissima Trinità Hospital, ASL Pescara, 65026 Popoli, Italy
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Hindel S. A Generalized Kinetic Model of Fractional Order Transport Dynamics with Transit Time Heterogeneity in Microvascular Space. Bull Math Biol 2024; 86:26. [PMID: 38300429 DOI: 10.1007/s11538-023-01255-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 12/30/2023] [Indexed: 02/02/2024]
Abstract
The aim of this study is to develop and validate a unifying kinetic model for microvascular transport by introducing an impulse response function that incorporates essential physiological parameters and integrates key features of existing models. This new methodology combines a one-compartment model of fractional order with a model that uses the gamma distribution to describe the distribution of capillary transit times. Central to this model are two primary parameters: [Formula: see text], representing the kurtosis of residue times, and [Formula: see text], signifying the width of the distribution of capillary transit times within a tissue voxel. To validate this proposed model, data from dynamic contrast-enhanced magnetic resonance imaging (DCI-MRI) were employed and the findings were compared with three existing models. Using the Akaike information criterion for model selection, the results demonstrate that the integrative model, especially at elevated blood flow rates, frequently offers superior fits in comparison to constrained models.
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Affiliation(s)
- Stefan Hindel
- Department of Radiation Therapy, Medical Physics Division, University Hospital Essen, Essen, North Rhine-Westphalia, Germany.
- Faculty of Physics, Technische Universität Kaiserslautern, Kaiserslautern, Rhineland-Palatinate, Germany.
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LoCastro E, Paudyal R, Konar AS, LaViolette PS, Akin O, Hatzoglou V, Goh AC, Bochner BH, Rosenberg J, Wong RJ, Lee NY, Schwartz LH, Shukla-Dave A. A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography 2023; 9:2052-2066. [PMID: 37987347 PMCID: PMC10661267 DOI: 10.3390/tomography9060161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.
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Affiliation(s)
- Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Alvin C. Goh
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Bernard H. Bochner
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Jonathan Rosenberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Lawrence H. Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
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Zhou X, Fan X, Chatterjee A, Yousuf A, Antic T, Oto A, Karczmar GS. Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer. Phys Eng Sci Med 2023; 46:1215-1226. [PMID: 37432557 DOI: 10.1007/s13246-023-01289-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/14/2023] [Indexed: 07/12/2023]
Abstract
The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast ([Formula: see text] and [Formula: see text]) and one slow ([Formula: see text] and [Formula: see text]) exchanging compartment, compared with the standard Tofts model parameters (Ktrans and kep). On average, prostate cancer had significantly higher values (p < 0.01) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.001) between Ktrans and [Formula: see text] for cancer, but weak correlation (r = 0.28, p < 0.05) between kep and [Formula: see text]. Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast [Formula: see text] had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM is useful for quantitative analysis of prostate DCE-MRI data and provides new information in the diagnosis of prostate cancer.
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Affiliation(s)
- Xueyan Zhou
- School of Technology, Harbin University, Harbin, China.
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | | | - Ambereen Yousuf
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, 60637, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
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6
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Anyasodor AE, Nwose EU, Bwititi PT, Richards RS. Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria. Front Public Health 2022; 10:932631. [PMID: 35958851 PMCID: PMC9357922 DOI: 10.3389/fpubh.2022.932631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background The prevalence of diabetes mellitus (DM) is increasing globally, and this requires several approaches to screening. There are reports of alternative indices for prediction of DM, besides fasting blood glucose (FBG) level. This study, investigated the ability of combination of biochemical and anthropometric parameters and orodental disease indicators (ODIs) to generate models for DM prediction, using Akaike information criterion (AIC) to substantiate health economics of diabetes screening. Methods Four hundred and thirty-three subjects were enrolled in the study in Ndokwa communities, Delta State, Nigeria, and their glycaemic status was determined, using the CardioChek analyser® and previous data from the Prediabetes and Cardiovascular Complications Study were also used. The cost of screening for diabetes (NGN 300 = $0.72) in a not-for-profit organization/hospital was used as basis to calculate the health economics of number of individuals with DM in 1,000 participants. Data on the subjects' anthropometric, biochemical and ODI parameters were used to generate different models, using R statistical software (version 4.0.0). The different models were evaluated for their AIC values. Lowest AIC was considered as best model. Microsoft Excel software (version 2020) was used in preliminary analysis. Result The cost of identifying <2 new subjects with hyperglycemia, in 1,000 people was ≥NGN 300,000 ($ 716). A total of 4,125 models were generated. AIC modeling indicates FBG test as the best model (AIC = 4), and the least being combination of random blood sugar + waist circumference + hip circumference (AIC ≈ 34). Models containing ODI parameters had AIC values >34, hence considered as not recommendable. Conclusion The cost of general screening for diabetes in rural communities may appear high and burdensome in terms of health economics. However, the use of prediction models involving AIC is of value in terms of cost-benefit and cost-effectiveness to the healthcare consumers, which favors health economics.
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Affiliation(s)
- Anayochukwu Edward Anyasodor
- School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia
- *Correspondence: Anayochukwu Edward Anyasodor
| | - Ezekiel Uba Nwose
- School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia
- Department of Public and Community Health, Novena University, Kwale, Nigeria
| | | | - Ross Stuart Richards
- School of Dentistry and Medical Sciences, Charles Sturt University, Orange, NSW, Australia
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Berks M, Little RA, Watson Y, Cheung S, Datta A, O'Connor JPB, Scaramuzza D, Parker GJM. A model selection framework to quantify microvascular liver function in gadoxetate-enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma. Magn Reson Med 2021; 86:1829-1844. [PMID: 33973674 DOI: 10.1002/mrm.28798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/04/2021] [Accepted: 03/19/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI). METHODS Our framework includes a hierarchy of nested models, from which physiological parameters are derived in 2 regimes, corresponding to the active transport and free diffusion of gadoxetate. We use simulations to show the sensitivity of model selection and parameter estimation to temporal resolution, time-series duration, and noise. We apply the framework in 8 healthy volunteers (time-series duration up to 24 minutes) and 10 patients with hepatocellular carcinoma (6 minutes). RESULTS The active transport regime is preferred in 98.6% of voxels in volunteers, 82.1% of patients' non-tumorous liver, and 32.2% of tumor voxels. Interpatient variations correspond to known co-morbidities. Simulations suggest both datasets have sufficient temporal resolution and signal-to-noise ratio, while patient data would be improved by using a time-series duration of at least 12 minutes. CONCLUSIONS In patient data, gadoxetate exhibits different kinetics: (a) between liver and tumor regions and (b) within regions due to liver disease and/or tumor heterogeneity. Our generalized framework selects a physiological interpretation at each voxel, without preselecting a model for each region or duplicating time-consuming optimizations for models with identical functional forms.
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Affiliation(s)
- Michael Berks
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
| | - Ross A Little
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
| | - Yvonne Watson
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
| | - Sue Cheung
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
| | - Anubhav Datta
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
- The Christie NHS Foundation Trust, Manchester, UK
| | - James P B O'Connor
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
- The Christie NHS Foundation Trust, Manchester, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, UK
| | | | - Geoff J M Parker
- Division of Cancer Sciences, Quantitative Biomedical Imaging Laboratory, University of Manchester, Manchester, UK
- Bioxydyn Ltd, Manchester, UK
- Centre for Medical Image Computing, University College London, London, UK
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Paudyal R, Lu Y, Hatzoglou V, Moreira A, Stambuk HE, Oh JH, Cunanan KM, Nunez DA, Mazaheri Y, Gonen M, Ho A, Fagin JA, Wong RJ, Shaha A, Tuttle RM, Shukla-Dave A. Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers. NMR IN BIOMEDICINE 2020; 33:e4166. [PMID: 31680360 PMCID: PMC7687051 DOI: 10.1002/nbm.4166] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/04/2019] [Accepted: 07/17/2019] [Indexed: 06/10/2023]
Abstract
The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Andre Moreira
- Department of Pathology, NYU Langone Medical Center, New
York, USA
| | - Hilda E. Stambuk
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Kristen M. Cunanan
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - David Aramburu Nunez
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - Alan Ho
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - James A. Fagin
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Ashok Shaha
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - R. Michael Tuttle
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
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9
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Lee SH, Rimner A, Deasy JO, Hunt MA, Tyagi N. Dual-input tracer kinetic modeling of dynamic contrast-enhanced MRI in thoracic malignancies. J Appl Clin Med Phys 2019; 20:169-188. [PMID: 31602789 PMCID: PMC6839367 DOI: 10.1002/acm2.12740] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 09/09/2019] [Accepted: 09/11/2019] [Indexed: 12/29/2022] Open
Abstract
Pulmonary perfusion with dynamic contrast‐enhanced (DCE‐) MRI is typically assessed using a single‐input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual‐input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual‐input tracer kinetic models to DCE‐MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single‐input (pulmonary or systemic arterial input) tracer kinetic models for the voxel‐level analysis within the tumor with respect to goodness‐of‐fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE‐MRI prior to radiotherapy. DCE‐MRI data were analyzed using five different single‐ or dual‐input tracer kinetic models: Tofts‐Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability‐surface area product (PS), fractional interstitial volume (vI), and volume transfer constant (KTrans) were calculated for both single‐ and dual‐input models. The pulmonary arterial flow fraction (γ), pulmonary arterial blood flow (BFPA) and systemic arterial blood flow (BFA) were additionally calculated for only dual‐input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual‐input models yielded lower cAIC values than their corresponding single‐input models. The 2CX model was the best‐fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual‐input 2CX‐model‐derived BFA was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.
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Affiliation(s)
- Sang Ho Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Margie A Hunt
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Neelam Tyagi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Zöllner FG, Šerifović-Trbalić A, Kabelitz G, Kociński M, Materka A, Rogelj P. Image registration in dynamic renal MRI-current status and prospects. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 33:33-48. [PMID: 31598799 PMCID: PMC7210245 DOI: 10.1007/s10334-019-00782-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 09/16/2019] [Accepted: 09/25/2019] [Indexed: 12/26/2022]
Abstract
Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.
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Affiliation(s)
- Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | | | - Gordian Kabelitz
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Marek Kociński
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Andrzej Materka
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Peter Rogelj
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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11
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Elkin R, Nadeem S, LoCastro E, Paudyal R, Hatzoglou V, Lee NY, Shukla-Dave A, Deasy JO, Tannenbaum A. Optimal mass transport kinetic modeling for head and neck DCE-MRI: Initial analysis. Magn Reson Med 2019; 82:2314-2325. [PMID: 31273818 DOI: 10.1002/mrm.27897] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/13/2019] [Accepted: 06/14/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. METHOD Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( Φ F ) and backward flux ( Φ B ). OMT-derived Φ F was compared with the volume transfer constant for CA, K trans , derived from the Extended Tofts Model (ETM). RESULTS The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min - 1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min - 1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( Φ F ) is comparable with the ETM-derived K trans . CONCLUSIONS This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.
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Affiliation(s)
- Rena Elkin
- Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Nancy Y Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Allen Tannenbaum
- Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York
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12
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Lecler A, Balvay D, Cuenod C, Marais L, Zmuda M, Sadik J, Galatoire O, Farah E, El Methni J, Zuber K, Bergès O, Savatovsky J, Fournier L. Quality‐based pharmacokinetic model selection on DCE‐MRI for characterizing orbital lesions. J Magn Reson Imaging 2019; 50:1514-1525. [DOI: 10.1002/jmri.26747] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/15/2019] [Accepted: 03/18/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Augustin Lecler
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Daniel Balvay
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Charles‐André Cuenod
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
- Radiology Department, Hôpital Européen Georges PompidouUniversité Paris Descartes Sorbonne Paris Cité, Assistance Publique‐Hôpitaux de Paris Paris France
| | - Louise Marais
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Mathieu Zmuda
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Jean‐Claude Sadik
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Olivier Galatoire
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Edgar Farah
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Jonathan El Methni
- MAP5, UMR CNRS 8145Université Paris Descartes Sorbonne Paris Cité France
| | - Kevin Zuber
- Department of Clinical ResearchFoundation Adolphe de Rothschild Hospital Paris France
| | - Olivier Bergès
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Julien Savatovsky
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Laure Fournier
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
- Radiology Department, Hôpital Européen Georges PompidouUniversité Paris Descartes Sorbonne Paris Cité, Assistance Publique‐Hôpitaux de Paris Paris France
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Debus C, Floca R, Nörenberg D, Abdollahi A, Ingrisch M. Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI. ACTA ACUST UNITED AC 2017; 62:9322-9340. [DOI: 10.1088/1361-6560/aa8989] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Gaa T, Neumann W, Sudarski S, Attenberger UI, Schönberg SO, Schad LR, Zöllner FG. Comparison of perfusion models for quantitative T1 weighted DCE-MRI of rectal cancer. Sci Rep 2017; 7:12036. [PMID: 28931946 PMCID: PMC5607266 DOI: 10.1038/s41598-017-12194-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Accepted: 09/05/2017] [Indexed: 12/17/2022] Open
Abstract
In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T1 weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and [Formula: see text] were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF2CX = 68 ± 44 ml/100 ml/min, PF2CU = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, [Formula: see text]:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and [Formula: see text] values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.
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Affiliation(s)
- Tanja Gaa
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
| | - Wiebke Neumann
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Sonja Sudarski
- Institute of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Ulrike I Attenberger
- Institute of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Stefan O Schönberg
- Institute of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
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Zöllner FG, Daab M, Sourbron SP, Schad LR, Schoenberg SO, Weisser G. An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited. BMC Med Imaging 2016; 16:7. [PMID: 26767969 PMCID: PMC4712457 DOI: 10.1186/s12880-016-0109-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 01/06/2016] [Indexed: 12/11/2022] Open
Abstract
Background Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology. Results Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters. Conclusion We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.
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Affiliation(s)
- Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | - Markus Daab
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | | | - Lothar R Schad
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | - Stefan O Schoenberg
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
| | - Gerald Weisser
- Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
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Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel'farb G, Ouseph R, Dwyer AC. Models and methods for analyzing DCE-MRI: a review. Med Phys 2015; 41:124301. [PMID: 25471985 DOI: 10.1118/1.4898202] [Citation(s) in RCA: 199] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.
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Affiliation(s)
- Fahmi Khalifa
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Tarek El-Diasty
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Georgy Gimel'farb
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Rosemary Ouseph
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
| | - Amy C Dwyer
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
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17
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Heye AK, Thrippleton MJ, Armitage PA, Valdés Hernández MDC, Makin SD, Glatz A, Sakka E, Wardlaw JM. Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability. Neuroimage 2015; 125:446-455. [PMID: 26477653 PMCID: PMC4692516 DOI: 10.1016/j.neuroimage.2015.10.018] [Citation(s) in RCA: 110] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 09/17/2015] [Accepted: 10/07/2015] [Indexed: 01/07/2023] Open
Abstract
There is evidence that subtle breakdown of the blood–brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) combined with tracer kinetic modelling is widely used for assessing permeability and perfusion in brain tumours and body tissues where contrast agents readily accumulate in the extracellular space. However, in diseases where leakage is subtle, the optimal approach for measuring BBB integrity is likely to differ since the magnitude and rate of enhancement caused by leakage are extremely low; several methods have been reported in the literature, yielding a wide range of parameters even in healthy subjects. We hypothesised that the Patlak model is a suitable approach for measuring low-level BBB permeability with low temporal resolution and high spatial resolution and brain coverage, and that normal levels of scanner instability would influence permeability measurements. DCE-MRI was performed in a cohort of mild stroke patients (n = 201) with a range of cerebral small vessel disease severity. We fitted these data to a set of nested tracer kinetic models, ranking their performance according to the Akaike information criterion. To assess the influence of scanner drift, we scanned 15 healthy volunteers that underwent a “sham” DCE-MRI procedure without administration of contrast agent. Numerical simulations were performed to investigate model validity and the effect of scanner drift. The Patlak model was found to be most appropriate for fitting low-permeability data, and the simulations showed vp and KTrans estimates to be reasonably robust to the model assumptions. However, signal drift (measured at approximately 0.1% per minute and comparable to literature reports in other settings) led to systematic errors in calculated tracer kinetic parameters, particularly at low permeabilities. Our findings justify the growing use of the Patlak model in low-permeability states, which has the potential to provide valuable information regarding BBB integrity in a range of diseases. However, absolute values of the resulting tracer kinetic parameters should be interpreted with extreme caution, and the size and influence of signal drift should be measured where possible. We performed DCE-MRI in 201 patients with a range of small vessel disease severity. We tested tracer kinetic model performance via simulations and statistical analysis. The Patlak model was optimal for assessing leakage in normal tissues and lesions. Scanner drift leads to substantial errors in measured tracer kinetic parameters. DCE-MRI measurements of subtle leakage should be interpreted with caution.
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Affiliation(s)
- Anna K Heye
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
| | - Michael J Thrippleton
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
| | - Paul A Armitage
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK; Department of Cardiovascular Science, University of Sheffield, Medical School, Beech Hill Road, Sheffield S10 2RX UK.
| | - Maria Del C Valdés Hernández
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
| | - Stephen D Makin
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
| | - Andreas Glatz
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
| | - Eleni Sakka
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
| | - Joanna M Wardlaw
- Neuroimaging Sciences, University of Edinburgh, Chancellors Building, 49 Little France Crescent, Edinburgh EH16 4SB, UK.
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Rukat T, Walker-Samuel S, Reinsberg SA. Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties. Magn Reson Med 2015; 73:1979-87. [PMID: 25052296 DOI: 10.1002/mrm.25319] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Revised: 05/13/2014] [Accepted: 05/23/2014] [Indexed: 11/08/2022]
Abstract
PURPOSE To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models. METHODS By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined. RESULTS Fast injections are beneficial for K(trans) precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average K(trans) bias of 40% for a 30% overestimation throughout all models. CONCLUSION This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology.
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Affiliation(s)
- Tammo Rukat
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada; Department of Physics, Humboldt University, Berlin, Germany
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Veksler R, Shelef I, Friedman A. Blood-brain barrier imaging in human neuropathologies. Arch Med Res 2014; 45:646-52. [PMID: 25453223 DOI: 10.1016/j.arcmed.2014.11.016] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Accepted: 11/20/2014] [Indexed: 01/22/2023]
Abstract
The blood-brain barrier (BBB) is essential for normal function of the brain, and its role in many brain pathologies has been the focus of numerous studies during the last decades. Dysfunction of the BBB is not only being shown in numerous brain diseases, but animal studies have indicated that it plays a direct key role in the genesis of neurovascular dysfunction and associated neurodegeneration. As such evidence accumulates, the need for robust and clinically applicable methods for minimally invasive assessment of BBB integrity is becoming urgent. This review provides an introduction to BBB imaging methods in the clinical scenario. First, imaging modalities are reviewed, with a focus on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We then proceed to review image analysis methods, including quantitative and semi-quantitative methods. The advantages and limitations of each approach are discussed, and future directions and questions are highlighted.
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Affiliation(s)
- Ronel Veksler
- Departments of Physiology and Cell Biology, Brain and Cognitive Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Ilan Shelef
- Department of Medical Imaging, Soroka University Medical Center and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Alon Friedman
- Departments of Physiology and Cell Biology, Brain and Cognitive Sciences, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Department of Medical Neuroscience, Faculty of Medicine, Dalhousie University, Halifax, Canada.
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Ewing JR, Bagher-Ebadian H. Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications. NMR IN BIOMEDICINE 2013; 26:1028-41. [PMID: 23881857 PMCID: PMC3752406 DOI: 10.1002/nbm.2996] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2012] [Revised: 05/15/2013] [Accepted: 06/11/2013] [Indexed: 05/22/2023]
Abstract
A review of the selection of models in dynamic contrast-enhanced MRI (DCE-MRI) is conducted, with emphasis on the balance between the bias and variance required to produce stable and accurate estimates of vascular parameters. The vascular parameters considered as a first-order model are the forward volume transfer constant K(trans) , the plasma volume fraction vp and the interstitial volume fraction ve . To illustrate the critical issues in model selection, a data-driven selection of models in an animal model of cerebral glioma is followed. Systematic errors and extended models are considered. Studies with nested and non-nested pharmacokinetic models are reviewed; models considering water exchange are considered.
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Affiliation(s)
- James R Ewing
- Department of Neurology, Henry Ford Health System, Detroit, MI, USA.
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Ingrisch M, Sourbron S. Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer. J Pharmacokinet Pharmacodyn 2013; 40:281-300. [PMID: 23563847 DOI: 10.1007/s10928-013-9315-3] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Accepted: 03/22/2013] [Indexed: 12/19/2022]
Abstract
Dynamic contrast-enhanced computed tomography (DCE-CT) and magnetic resonance imaging (DCE-MRI) are functional imaging techniques. They aim to characterise the microcirculation by applying the principles of tracer-kinetic analysis to concentration-time curves measured in individual image pixels. In this paper, we review the basic principles of DCE-MRI and DCE-CT, with a specific emphasis on the use of tracer-kinetic modeling. The aim is to provide an introduction to the field for a broader audience of pharmacokinetic modelers. In a first part, we first review the key aspects of data acquisition in DCE-CT and DCE-MRI, including a review of basic measurement strategies, a discussion on the relation between signal and concentration, and the problem of measuring reference data in arterial blood. In a second part, we define the four main parameters that can be measured with these techniques and review the most common tracer-kinetic models that are used in this field. We first discuss the models for the capillary bed and then define the most general four-parameter models used today: the two-compartment exchange model, the tissue-homogeneity model, the "adiabatic approximation to the tissue-homogeneity model" and the distributed-parameter model. In simpler tissue types or when the data quality is inadequate to resolve all the features of the more complex models, it is often necessary to resort to simpler models, which are special cases of the general models and hence have less parameters. We discuss the most common of these special cases, i.e. the uptake models, the extended Tofts model, and the one-compartment model. Models for two specific tissue types, liver and kidney, are discussed separately. We conclude with a review of practical aspects of DCE-CT and DCE-MRI data analysis, including the problem of identifying a suitable model for any given data set, and a brief discussion of the application of tracer-kinetic modeling in the context of drug development. Here, an important application of DCE techniques is the derivation of quantitative imaging biomarkers for the assessment of effects of targeted therapeutics on tumors.
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Affiliation(s)
- Michael Ingrisch
- Institute for Clinical Radiology, Ludwig-Maximilians University Hospital Munich, Marchioninistr. 15, 81377, Munich, Germany.
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