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Dejene EM, Brenner W, Makowski MR, Kolbitsch C. Unified Bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver. Phys Med Biol 2023; 68:215018. [PMID: 37820640 DOI: 10.1088/1361-6560/ad0284] [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: 02/06/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023]
Abstract
Objective. Physiological parameter estimation is affected by intrinsic ambiguity in the data such as noise and model inaccuracies. The aim of this work is to provide a deep learning framework for accurate parameter and uncertainty estimates for DCE-MRI in the liver.Approach. Concentration time curves are simulated to train a Bayesian neural network (BNN). Training of the BNN involves minimization of a loss function that jointly minimizes the aleatoric and epistemic uncertainties. Uncertainty estimation is evaluated for different noise levels and for different out of distribution (OD) cases, i.e. where the data during inference differs strongly to the data during training. The accuracy of parameter estimates are compared to a nonlinear least squares (NLLS) fitting in numerical simulations andin vivodata of a patient suffering from hepatic tumor lesions.Main results. BNN achieved lower root-mean-squared-errors (RMSE) than the NLLS for the simulated data. RMSE of BNN was on overage of all noise levels lower by 33% ± 1.9% forktrans, 22% ± 6% forveand 89% ± 5% forvpthan the NLLS. The aleatoric uncertainties of the parameters increased with increasing noise level, whereas the epistemic uncertainty increased when a BNN was evaluated with OD data. For thein vivodata, more robust parameter estimations were obtained by the BNN than the NLLS fit. In addition, the differences between estimated parameters for healthy and tumor regions-of-interest were significant (p< 0.0001).Significance. The proposed framework allowed for accurate parameter estimates for quantitative DCE-MRI. In addition, the BNN provided uncertainty estimates which highlighted cases of high noise and in which the training data did not match the data during inference. This is important for clinical application because it would indicate cases in which the trained model is inadequate and additional training with an adapted training data set is required.
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Affiliation(s)
- Edengenet M Dejene
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Winfried Brenner
- Department of Nuclear Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Marcus R Makowski
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Munich, Germany
| | - Christoph Kolbitsch
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
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Roberts J, Kim SE, Kholmovski EG, Hitchcock Y, Richards TJ, Anzai Y. The arterial input function: Spatial dependence within the imaging volume and its influence on 3D quantitative dynamic contrast-enhanced MRI for head and neck cancer. Magn Reson Imaging 2023; 101:40-46. [PMID: 37030177 DOI: 10.1016/j.mri.2023.03.016] [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: 02/14/2023] [Accepted: 03/21/2023] [Indexed: 04/10/2023]
Abstract
PURPOSE To evaluate the dependence of the arterial input function (AIF) on the imaging z-axis and its effect on 3D DCE MRI pharmacokinetic parameters as mediated by the SPGR signal equation and Extended Tofts-Kermode model. THEORY For SPGR-based 3D DCE MRI acquisition of the head and neck, inflow effects within vessels violate the assumptions underlying the SPGR signal model. Errors in the SPGR-based AIF estimate propagate through the Extended Tofts-Kermode model to affect the output pharmacokinetic parameters. MATERIALS AND METHODS 3D DCE-MRI data were acquired for six newly diagnosed HNC patients in a prospective single arm cohort study. AIF were selected within the carotid arteries at each z-axis location. A region of interest (ROI) was placed in normal paravertebral muscle and the Extended Tofts-Kermode model solved for each pixel within the ROI for each AIF. Results were compared to those obtained with a published population average AIF. RESULTS Due to inflow effect, the AIF showed extreme variation in their temporal shapes. Ktrans was most sensitive to the initial bolus concentration and showed more variation over the muscle ROI with AIF taken from the upstream portion of the carotid. kep was less sensitive to the peak bolus concentration and showed less variation for AIF taken from the upstream portion of the carotid. CONCLUSION Inflow effects may introduce an unknown bias to SPGR-based 3D DCE pharmacokinetic parameters. Variation in the computed parameters depends on the selected AIF location. In the context of high flow, measurements may be limited to relative rather than absolute quantitative parameters.
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Affiliation(s)
- John Roberts
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA..
| | - Seong-Eun Kim
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA
| | - Eugene G Kholmovski
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA.; Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ying Hitchcock
- Radiation Oncology, Huntsman Cancer Institute, SLC, UT, USA
| | | | - Yoshimi Anzai
- Dept. Radiology & Imaging Sciences, University of Utah, SLC, UT, USA
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Slot S, Lavini C, Zwezerijnen GJC, Boden BJH, Marcus JT, Huisman MC, Yaqub M, Barbé E, Wondergem MJ, Zijlstra JM, Zweegman S, Raijmakers PG. Characterizing the Bone Marrow Environment in Advanced-Stage Myelofibrosis during Ruxolitinib Treatment Using PET/CT and MRI: A Pilot Study. Tomography 2023; 9:459-474. [PMID: 36960997 PMCID: PMC10037592 DOI: 10.3390/tomography9020038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 02/24/2023] Open
Abstract
Current diagnostic criteria for myelofibrosis are largely based on bone marrow (BM) biopsy results. However, these have several limitations, including sampling errors. Explorative studies have indicated that imaging might form an alternative for the evaluation of disease activity, but the heterogeneity in BM abnormalities complicates the choice for the optimal technique. In our prospective diagnostic pilot study, we aimed to visualize all BM abnormalities in myelofibrosis before and during ruxolitinib treatment using both PET/CT and MRI. A random sample of patients was scheduled for examinations at baseline and after 6 and 18 months of treatment, including clinical and laboratory examinations, BM biopsies, MRI (T1-weighted, Dixon, dynamic contrast-enhanced (DCE)) and PET/CT ([15O]water, [18F]NaF)). At baseline, all patients showed low BM fat content (indicated by T1-weighted MRI and Dixon), increased BM blood flow (as measured by [15O]water PET/CT), and increased osteoblastic activity (reflected by increased skeletal [18F]NaF uptake). One patient died after the baseline evaluation. In the others, BM fat content increased to various degrees during treatment. Normalization of BM blood flow (as reflected by [15O]water PET/CT and DCE-MRI) occurred in one patient, who also showed the fastest clinical response. Vertebral [18F]NaF uptake remained stable in all patients. In evaluable cases, histopathological parameters were not accurately reflected by imaging results. A case of sampling error was suspected. We conclude that imaging results can provide information on functional processes and disease distribution throughout the BM. Differences in early treatment responses were especially reflected by T1-weighted MRI. Limitations in the gold standard hampered the evaluation of diagnostic accuracy.
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Affiliation(s)
- Stefanie Slot
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Cristina Lavini
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Bouke J H Boden
- Department of Radiology, Onze Lieve Vrouwe Gasthuis, Oosterpark 9, 1091 AC Amsterdam, The Netherlands
| | - J Tim Marcus
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Marc C Huisman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Ellis Barbé
- Department of Pathology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Mariëlle J Wondergem
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Sonja Zweegman
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Pieter G Raijmakers
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
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Dong W, Volk A, Djaroum M, Girot C, Balleyguier C, Lebon V, Garcia G, Ammari S, Temam S, Gorphe P, Wei L, Pitre-Champagnat S, Lassau N, Bidault F. Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer. J Magn Reson Imaging 2022. [PMID: 36269053 DOI: 10.1002/jmri.28486] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/04/2022] [Accepted: 10/06/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). PURPOSE To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. STUDY TYPE Prospective. POPULATION A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. ASSESSMENT Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. STATISTICAL TESTS Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. RESULTS Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). DATA CONCLUSION We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Wanxin Dong
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Andreas Volk
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Meriem Djaroum
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Charly Girot
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Corinne Balleyguier
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Vincent Lebon
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Gabriel Garcia
- Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Samy Ammari
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Stéphane Temam
- Department of Head and Neck Oncology, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Philippe Gorphe
- Department of Head and Neck Oncology, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - Lecong Wei
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Stéphanie Pitre-Champagnat
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France
| | - Nathalie Lassau
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
| | - François Bidault
- Multimodal Biomedical Imaging Laboratory (BioMaps), Paris-Saclay University, Inserm (UMR1281), CNRS (UMR9011), CEA, France.,Department of Medical Imaging, Gustave Roussy Cancer Campus, Paris-Saclay University, Villejuif, France
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Amini Farsani Z, Schmid VJ. Maximum Entropy Technique and Regularization Functional for Determining the Pharmacokinetic Parameters in DCE-MRI. J Digit Imaging 2022; 35:1176-1188. [PMID: 35618849 PMCID: PMC9582183 DOI: 10.1007/s10278-022-00646-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/15/2022] [Accepted: 04/21/2022] [Indexed: 10/31/2022] Open
Abstract
This paper aims to solve the arterial input function (AIF) determination in dynamic contrast-enhanced MRI (DCE-MRI), an important linear ill-posed inverse problem, using the maximum entropy technique (MET) and regularization functionals. In addition, estimating the pharmacokinetic parameters from a DCE-MR image investigations is an urgent need to obtain the precise information about the AIF-the concentration of the contrast agent on the left ventricular blood pool measured over time. For this reason, the main idea is to show how to find a unique solution of linear system of equations generally in the form of [Formula: see text] named an ill-conditioned linear system of equations after discretization of the integral equations, which appear in different tomographic image restoration and reconstruction issues. Here, a new algorithm is described to estimate an appropriate probability distribution function for AIF according to the MET and regularization functionals for the contrast agent concentration when applying Bayesian estimation approach to estimate two different pharmacokinetic parameters. Moreover, by using the proposed approach when analyzing simulated and real datasets of the breast tumors according to pharmacokinetic factors, it indicates that using Bayesian inference-that infer the uncertainties of the computed solutions, and specific knowledge of the noise and errors-combined with the regularization functional of the maximum entropy problem, improved the convergence behavior and led to more consistent morphological and functional statistics and results. Finally, in comparison to the proposed exponential distribution based on MET and Newton's method, or Weibull distribution via the MET and teaching-learning-based optimization (MET/TLBO) in the previous studies, the family of Gamma and Erlang distributions estimated by the new algorithm are more appropriate and robust AIFs.
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Affiliation(s)
- Zahra Amini Farsani
- Bayesian Imaging and Spatial Statistics Group, Institute of Statistics, Ludwig-Maximilian-Universität München, Ludwigstraße 33, 80539, Munich, Germany. .,Statistics Department, School of Science, Lorestan University, 68151-44316, Khorramabad, Iran.
| | - Volker J Schmid
- Bayesian Imaging and Spatial Statistics Group, Institute of Statistics, Ludwig-Maximilian-Universität München, Ludwigstraße 33, 80539, Munich, Germany
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Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI. Sci Rep 2022; 12:8737. [PMID: 35610281 PMCID: PMC9130284 DOI: 10.1038/s41598-022-12582-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/27/2022] [Indexed: 01/08/2023] Open
Abstract
Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOFSSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test-retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOFSSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOFSSS derived semi-quantitative parameters was good to excellent (ICC = 0.717-0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOFSSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.
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Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis. ENTROPY 2022; 24:e24020155. [PMID: 35205451 PMCID: PMC8871336 DOI: 10.3390/e24020155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 02/06/2023]
Abstract
Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.
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Bliesener Y, Lebel RM, Acharya J, Frayne R, Nayak KS. Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma. Radiology 2021; 300:410-420. [PMID: 34100683 PMCID: PMC8328086 DOI: 10.1148/radiol.2021203628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Background Advances in sub-Nyquist–sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility. Purpose To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma. Materials and Methods Two methods for sub-Nyquist–sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M0 maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (Ktrans) on five healthy tissue types. Results The images from 13 participants (mean age ± standard deviation, 61 years ± 10; nine women) with high-grade glioma were evaluated. In healthy tissues, the protocol achieved a coefficient of variation less than 57% for median Ktrans, if Ktrans was estimated consecutively. The maximum reproducibility coefficient for median Ktrans was estimated to be at 0.06 min–1 for large or low-enhancing tissues and to be as high as 0.48 min–1 in smaller or strongly enhancing tissues. Conclusion A fully automated, sparsely sampled DCE MRI reconstruction with patient-specific vascular input function offered high spatial and temporal resolution and whole-brain coverage; in healthy tissues, the protocol estimated median volume transfer constant with maximum reproducibility coefficient of 0.06 min–1 in large, low-enhancing tissue regions and maximum reproducibility coefficient of less than 0.48 min–1 in smaller or more strongly enhancing tissue regions. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Lenkinski in this issue.
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Affiliation(s)
- Yannick Bliesener
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - R Marc Lebel
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Jay Acharya
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Richard Frayne
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Krishna S Nayak
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
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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] [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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Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development. Magn Reson Imaging 2021; 77:169-179. [PMID: 33388362 DOI: 10.1016/j.mri.2020.12.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/25/2020] [Accepted: 12/29/2020] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Quantitative MRI (qMRI) parameters have been increasingly used to develop predictive models to accurately monitor treatment response in prostate cancer after radiotherapy. To reliably detect changes in signal due to treatment response, predictive models require qMRI parameters with high repeatability and reproducibility. The purpose of this study was to measure qMRI parameter uncertainties in both commercial and in-house developed phantoms to guide the development of robust predictive models for monitoring treatment response. MATERIALS AND METHODS ADC, T1, and R2* values were acquired across three 3 T scanners with a prostate-specific qMRI protocol using the NIST/ISMRM system phantom, RSNA/NIST diffusion phantom, and an in-house phantom. A B1 field map was acquired to correct for flip angle inhomogeneity in T1 maps. All sequences were repeated in each scan to assess within-session repeatability. Weekly scans were acquired on one scanner for three months with the in-house phantom. Between-session repeatability was measured with test-retest scans 6-months apart on all scanners with all phantoms. Accuracy, defined as percentage deviation from reference value for ADC and T1, was evaluated using the system and diffusion phantoms. Repeatability and reproducibility coefficients of variation (%CV) were calculated for all qMRI parameters on all phantoms. RESULTS Overall, repeatability CV of ADC was <2.40%, reproducibility CV was <3.98%, and accuracy ranged between -8.0% to 2.7% across all scanners. Applying B1 correction on T1 measurements significantly improved the repeatability and reproducibility (p<0.05) but increased error in accuracy (p<0.001). Repeatability and reproducibility of R2* was <4.5% and <7.3% respectively in the system phantom across all scanners. CONCLUSION Repeatability, reproducibility, and accuracy in qMRI parameters from a prostate-specific protocol was estimated using both commercial and in-house phantoms. Results from this work will be used to identify robust qMRI parameters for use in the development of predictive models to longitudinally monitor treatment response for prostate cancer in current and future clinical trials.
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Besson FL, Fernandez B, Faure S, Mercier O, Seferian A, Mignard X, Mussot S, le Pechoux C, Caramella C, Botticella A, Levy A, Parent F, Bulifon S, Montani D, Mitilian D, Fadel E, Planchard D, Besse B, Ghigna-Bellinzoni MR, Comtat C, Lebon V, Durand E. 18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data. EJNMMI Res 2020; 10:88. [PMID: 32734484 PMCID: PMC7392998 DOI: 10.1186/s13550-020-00671-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.
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Affiliation(s)
- Florent L Besson
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France.
- Department of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, CHU Bicêtre, 94270, Le Kremlin-Bicêtre, France.
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France.
| | | | - Sylvain Faure
- Laboratoire de Mathématiques d'Orsay, CNRS, Université Paris-Saclay, 91405, Orsay, France
| | - Olaf Mercier
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Andrei Seferian
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Xavier Mignard
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
| | - Sacha Mussot
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Cecile le Pechoux
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Caroline Caramella
- Department of Radiology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Angela Botticella
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Florence Parent
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Sophie Bulifon
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - David Montani
- Service de Pneumologie, Centre de Référence de l'Hypertension Pulmonaire, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, 94270, Le Kremlin-Bicêtre, France
- Inserm UMR_S999, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Delphine Mitilian
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - Elie Fadel
- Department of Thoracic and Vascular Surgery and Heart-Lung Transplantation, Marie Lannelongue Hospital, 92350, Le Plessis Robinson, France
| | - David Planchard
- Department of Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | - Benjamin Besse
- Department of Oncology, Institut d'Oncologie Thoracique (IOT), Gustave Roussy, Université Paris Saclay, Villejuif, France
| | | | - Claude Comtat
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Vincent Lebon
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Emmanuel Durand
- Université Paris-Saclay, CEA, CNRS, Inserm, BioMAPs, 91401, Orsay, France
- Department of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, CHU Bicêtre, 94270, Le Kremlin-Bicêtre, France
- School of Medicine, Université Paris-Saclay, Le Kremlin-Bicêtre, France
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Koopman T, Martens RM, Lavini C, Yaqub M, Castelijns JA, Boellaard R, Marcus JT. Repeatability of arterial input functions and kinetic parameters in muscle obtained by dynamic contrast enhanced MR imaging of the head and neck. Magn Reson Imaging 2020; 68:1-8. [DOI: 10.1016/j.mri.2020.01.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 12/23/2019] [Accepted: 01/19/2020] [Indexed: 12/13/2022]
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Bliesener Y, Acharya J, Nayak KS. Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1712-1723. [PMID: 31794389 PMCID: PMC8887912 DOI: 10.1109/tmi.2019.2953901] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.
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Klawer EME, van Houdt PJ, Simonis FFJ, van den Berg CAT, Pos FJ, Heijmink SWTPJ, Isebaert S, Haustermans K, van der Heide UA. Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients. Magn Reson Med 2019; 81:3358-3369. [PMID: 30656738 PMCID: PMC6590420 DOI: 10.1002/mrm.27646] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 11/07/2018] [Accepted: 12/04/2018] [Indexed: 12/31/2022]
Abstract
Purpose The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast‐enhanced (DCE)‐MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients. Methods Twenty‐two patients with biopsy‐proven prostate cancer underwent a 3T MRI exam twice. DCE‐MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIFMAGN), phase (AIFPHASE), and complex signal (AIFCOMPLEX). The Tofts model was applied to extract Ktrans, kep and ve. Repeatability of AIF curve characteristics and TK parameters was assessed with the within‐subject coefficient of variation (wCV). Results The wCV for peak height and full width at half maximum for AIFCOMPLEX (7% and 8%) indicated an improved repeatability compared to AIFMAGN (12% and 12%) and AIFPHASE (12% and 7%). This translated in lower wCV values for Ktrans (11%) with AIFCOMPLEX in comparison to AIFMAGN (24%) and AIFPHASE (15%). For kep, the wCV was 16% with AIFMAGN, 13% with AIFPHASE, and 13% with AIFCOMPLEX. Conclusion Repeatability of AIFPHASE and AIFCOMPLEX is higher than for AIFMAGN, resulting in a better repeatability of TK parameters. Thus, use of either AIFPHASE or AIFCOMPLEX improves the robustness of quantitative analysis of DCE‐MRI in prostate cancer.
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Affiliation(s)
- Edzo M E Klawer
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petra J van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frank F J Simonis
- Department of Radiation Oncology, Imaging Division, University Medical Center, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiation Oncology, Imaging Division, University Medical Center, Utrecht, The Netherlands
| | - Floris J Pos
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Sofie Isebaert
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Karin Haustermans
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
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Evaluation of Dispersion MRI for Improved Prostate Cancer Diagnosis in a Multicenter Study. AJR Am J Roentgenol 2018; 211:W242-W251. [DOI: 10.2214/ajr.17.19215] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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16
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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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17
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Lavini C, Kramer G, Pieters-den Bos I, Hoekstra O, Marcus JT. MRI protocol optimization for quantitative DCE-MRI of the spine. Magn Reson Imaging 2017; 44:96-103. [PMID: 28867668 DOI: 10.1016/j.mri.2017.08.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 08/14/2017] [Accepted: 08/30/2017] [Indexed: 10/18/2022]
Abstract
PURPOSE In this study we systematically investigated different Dynamic Contrast Enhancement (DCE)-MRI protocols in the spine, with the goal of finding an optimal protocol that provides data suitable for quantitative pharmacokinetic modelling (PKM). MATERIALS AND METHODS In 13 patients referred for MRI of the spine, DCE-MRI of the spine was performed with 2D and 3D MRI protocols on a 3T Philips Ingenuity MR system. A standard bolus of contrast agent (Dotarem - 0.2ml/kg body weight) was injected intravenously at a speed of 3ml/s. Different techniques for acceleration and motion compensation were tested: parallel imaging, partial-Fourier imaging and flow compensation. The quality of the DCE MRI images was scored on the basis of SNR, motion artefacts due to flow and respiration, signal enhancement, quality of the T1 map and of the arterial input function, and quality of pharmacokinetic model fitting to the extended Tofts model. RESULTS Sagittal 3D sequences are to be preferred for PKM of the spine. Acceleration techniques were unsuccessful due to increased flow or motion artefacts. Motion compensating gradients failed to improve the DCE scans due to the longer echo time and the T2* decay which becomes more dominant and leads to signal loss, especially in the aorta. The quality scoring revealed that the best method was a conventional 3D gradient-echo acquisition without any acceleration or motion compensation technique. The priority in the choice of sequence parameters should be given to reducing echo time and keeping the dynamic temporal resolution below 5s. Increasing the number of acquisition, when possible, helps towards reducing flow artefacts. In our setting we achieved this with a sagittal 3D slab with 5 slices with a thickness of 4.5mm and two acquisitions. CONCLUSION The proposed DCE protocol, encompassing the spine and the descending aorta, produces a realistic arterial input function and dynamic data suitable for PKM.
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Affiliation(s)
- Cristina Lavini
- Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, The Netherlands
| | - Gem Kramer
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Indra Pieters-den Bos
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Otto Hoekstra
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - J T Marcus
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
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T2*-Correction in Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Glioblastoma From a Half Dose of High-Relaxivity Contrast Agent. J Comput Assist Tomogr 2017; 41:916-921. [DOI: 10.1097/rct.0000000000000611] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Spanakis M, Kontopodis E, Van Cauter S, Sakkalis V, Marias K. Assessment of DCE-MRI parameters for brain tumors through implementation of physiologically-based pharmacokinetic model approaches for Gd-DOTA. J Pharmacokinet Pharmacodyn 2016; 43:529-47. [PMID: 27647272 DOI: 10.1007/s10928-016-9493-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 09/07/2016] [Indexed: 12/16/2022]
Abstract
Dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) is used for detailed characterization of pathology of lesions sites, such as brain tumors, by quantitative analysis of tracer's data through the use of pharmacokinetic (PK) models. A key component for PK models in DCE-MRI is the estimation of the concentration-time profile of the tracer in a nearby vessel, referred as Arterial Input Function (AIF). The aim of this work was to assess through full body physiologically-based pharmacokinetic (PBPK) model approaches the PK profile of gadoteric acid (Gd-DOTA) and explore potential application for parameter estimation in DCE-MRI based on PBPK-derived AIFs. The PBPK simulations were generated through Simcyp(®) platform and the predicted PK parameters for Gd-DOTA were compared with available clinical data regarding healthy volunteers and renal impairment patients. The assessment of DCE-MRI parameters was implemented by utilizing similar virtual profiles based on gender, age and weight to clinical profiles of patients diagnosed with glioblastoma multiforme. The PBPK-derived AIFs were then used to compute DCE-MRI parameters through the Extended Tofts Model and compared with the corresponding ones derived from image-based AIF computation. The comparison involved: (i) image measured AIF of patients vs AIF of in silico profile, and, (ii) population average AIF vs in silico mean AIFs. The results indicate that PBPK-derived AIFs allowed the estimation of comparable imaging biomarkers with those calculated from typical DCE-MRI image analysis. The incorporation of PBPK models and potential utilization of in silico profiles to real patient data, can provide new perspectives in DCE-MRI parameter estimation and data analysis.
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Affiliation(s)
- Marios Spanakis
- Institute of Computer Science, Computational BioMedicine Lab, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece.
| | - Eleftherios Kontopodis
- Institute of Computer Science, Computational BioMedicine Lab, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece
| | - Sophie Van Cauter
- Department of Radiology, University Hospitals Leuven, Louvain, Belgium
| | - Vangelis Sakkalis
- Institute of Computer Science, Computational BioMedicine Lab, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece
| | - Kostas Marias
- Institute of Computer Science, Computational BioMedicine Lab, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece
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Maijer KI, van der Leij C, de Hair MJH, Tas SW, Maas M, Gerlag DM, Tak PP, Lavini C. Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Pharmacokinetic Modeling: Initial Experience in Patients With Early Arthritis. Arthritis Rheumatol 2016; 68:587-96. [PMID: 26473331 DOI: 10.1002/art.39469] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 10/01/2015] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using pharmacokinetic modeling (PKM) provides quantitative measures that mirror microvessel integrity and can be used as an objective marker of the level of synovial inflammation. The aim of this study was to investigate the PKM parameters K(trans) , kep , and ve in a prospective cohort of disease-modifying antirheumatic drug (DMARD)-naive patients with early arthritis, and to validate the results by assessing their correlation with the number of synovial endothelial cells (ECs). METHODS Forty-seven patients with early arthritis (arthritis duration <1 year, DMARD naive; comprising 14 patients with rheumatoid arthritis, 22 with unclassified arthritis, 6 with spondyloarthritis [SpA], and 5 with other arthritides) were included. At baseline, DCE-MRI was performed on an inflamed knee joint of each patient. These images were used to calculate the K(trans) (volume transfer constant between the plasma and extracellular extravascular space [EES]), the kep (transfer constant between the EES and plasma), and the ve (fractional volume of the EES). Second, markers of disease activity were collected. Finally, vascularity was evaluated by immunohistochemical analysis of synovial tissue samples obtained from the inflamed knee joints, using antibodies to detect von Willebrand factor (vWF), a marker of ECs. RESULTS The 3 PKM parameters differed significantly between diagnostic groups at baseline, with the highest K(trans) value being observed in patients with SpA (median 0.050/minute, interquartile range [IQR] 0.041- 0.069). Furthermore, the K(trans) , kep , and ve values correlated significantly with markers of disease activity. Finally, the PKM parameters K(trans) and kep , but not ve , correlated significantly with synovial expression of vWF (r = 0.647, P = 0.004 for K(trans) ; r = 0.614, P = 0.007 for kep ; r = 0.398, P = 0.102 for ve ). CONCLUSION These results suggest that the K(trans) , kep , and ve can be used to detect synovial inflammation in patients with early arthritis, and these PKM parameters may be helpful in differential diagnosis. This approach may also be useful in translational research analyzing tissue microcirculation and angiogenesis.
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Affiliation(s)
- Karen I Maijer
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Maria J H de Hair
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Sander W Tas
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Mario Maas
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Daniëlle M Gerlag
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Paul P Tak
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Cristina Lavini
- Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
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Turco S, Wijkstra H, Mischi M. Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review. IEEE Rev Biomed Eng 2016; 9:121-47. [PMID: 27337725 DOI: 10.1109/rbme.2016.2583541] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.
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Sahoo P, Gupta PK, Awasthi A, Pandey CM, Patir R, Vaishya S, Saha I, Gupta RK. Comparison of actual with default hematocrit value in dynamic contrast enhanced MR perfusion quantification in grading of human glioma. Magn Reson Imaging 2016; 34:1071-7. [PMID: 27211259 DOI: 10.1016/j.mri.2016.05.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 05/11/2016] [Indexed: 01/07/2023]
Abstract
PURPOSE Dynamic contrast enhanced (DCE) MRI is used to grade and to monitor the progression of glioma while on treatment. Usually, a fixed hematocrit (Hct) value for adults is assumed to be ~45%; however, it is actually known for individual variations. Purpose of this study was to investigate the effect of measured Hct values in glioma grading using DCE-MRI. MATERIALS AND METHODS Fifty glioma patients were included in this study. Kinetic and hemodynamic parameters were estimated for each patient using assumed as well as measured Hct values. To look the changes in Hct value over time, Hct was measured multiple times from 10 of these glioma patients who were on treatment. Simulation was done to look for the effect of extreme variations of Hct values on perfusion metrics. The data was compared to look for significant differences in the perfusion metrics derived from assumed and measured Hct values. RESULTS The measured Hct value in patients was found to be (40.4±4.28)%. The sensitivity and specificity of DCE-MRI parameters in glioma grading were not significantly influenced by using measured vis-a-vis assumed Hct values. The serial Hct values from 10 patients who were on treatment showed a fluctuation of 15-20% over time. The simulated data showed linear influence of Hct values on kinetic parameters. The tumor grading was altered on altering the Hct values in borderline cases. CONCLUSION Hct values influence the hemodynamic and kinetic metrics linearly and may affect glioma grading. However, perfusion metrics values might change significantly with large change in Hct values, especially in patients who are on chemotherapy necessitating its use in the DCE model.
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Affiliation(s)
- Prativa Sahoo
- Philips Health Systems, Philips India Ltd, Bangalore, India
| | - Pradeep K Gupta
- Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, India
| | - Ashish Awasthi
- Biostatistics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Chandra M Pandey
- Biostatistics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India
| | - Rana Patir
- Department of Neurosurgery, Fortis Memorial Research Institute, Gurgaon, India
| | - Sandeep Vaishya
- Department of Neurosurgery, Fortis Memorial Research Institute, Gurgaon, India
| | - Indrajit Saha
- Philips Health Systems, Philips India Ltd, Gurgaon, India
| | - Rakesh K Gupta
- Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurgaon, India.
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Wang H, Su Z, Ye H, Xu X, Sun Z, Li L, Duan F, Song Y, Lambrou T, Ma L. Reproducibility of Dynamic Contrast-Enhanced MRI in Renal Cell Carcinoma: A Prospective Analysis on Intra- and Interobserver and Scan-Rescan Performance of Pharmacokinetic Parameters. Medicine (Baltimore) 2015; 94:e1529. [PMID: 26376399 PMCID: PMC4635813 DOI: 10.1097/md.0000000000001529] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The objective of this study was to investigate the intra- and interobserver as well as scan-rescan reproducibility of quantitative parameters of renal cell carcinomas (RCCs) with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 21 patients with clear cell RCCs (17 men, 4 woman; age 37-69 years, mean age 54.6 years, mean size, 5.0 ± 2.2 cm) were prospectively recruited from September 2012 to November 2012. Patients underwent paired DCE-MRI studies on a 3.0 T MR system with an interval of 48 to 72 hours. The extended-Tofts model and population-based arterial input function were used to calculate kinetic parameters. Three observers defined the 2-dimensional whole-tumor region of interest at the slice with the maximum diameter of the RCC. Intraobserver and scan-rescan differences were assessed using paired t tests, whereas interobserver differences using two-way analysis of variance. Intra- and interobserver reproducibility and scan-rescan reproducibility were evaluated using within-subject coefficient of variation (wCoV) and intraclass correlation coefficient (ICC). There were no significant intra-, interobserver, or scan-rescan differences in parameters (all P > 0.05). All ICCs for intra- and interobserver agreements were >0.75 (P < 0.05), whereas the scan-rescan agreement was moderate to good; V(e) (0.764, 95% confidence interval [CI]: 0.378-0.925) and K(ep) (0.906, 95% CI: 0.710-0.972) had higher ICC than K(trans) (0.686; 95% CI: 0.212-0.898) and V(p) (0.657; 95% CI: 0.164-0.888). In intra- and interobserver variability analyses, all parameters except V(p) had low wCoV values. K(trans) and V(e) had slightly lower intraobserver wCoV (1.2% and 0.9%) compared with K(ep) (3.7%), whereas all 3 of these parameters had similar interobserver wCoV values (2.5%, 3.1%, and 2.9%, respectively). Regarding scan-rescan variability, K(trans) and K(ep) showed slightly higher variation (15.6% and 15.4%) than V(e) (10.1%). V(p) had the largest wCoV in all variability analyses (all >30%). DCE-MRI demonstrated good intra- and interobserver reproducibility and moderate to good scan-rescan performance in the assessment of RCC using K(trans), K(ep), and V(e) as parameters under noncontinuous scanning mode. V(p) showed poor reproducibility, and thus may not be suitable for this scanning protocol.
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Affiliation(s)
- Haiyi Wang
- From the Department of Radiology (HW, HY, LL, LM), Chinese PLA General Hospital, Beijing, China; Advanced Application (ZS), GE Healthcare, Beijing, China; Advanced Application (XX), GE Healthcare, Shanghai, China; Department of Radiology (ZS), Zhangjiakou First Hospital, Zhangjiakou City, Hebei Province, China; Medical Imaging Center (FD), Jiayuguan Jiugang Hospital, Jiayuguan City, Gansu Province, China; Department of Radiology (YS), General Hospital of Pingdingshan Coal Group, Pingdingshan City, Henan Province, China; and Lincoln School of Computer Science (TL), University of Lincon, Lincon, UK
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24
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Filice S, Crisi G. Dynamic Contrast-Enhanced Perfusion MRI of High Grade Brain Gliomas Obtained with Arterial or Venous Waveform Input Function. J Neuroimaging 2015; 26:124-9. [PMID: 25923172 DOI: 10.1111/jon.12254] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Accepted: 03/26/2015] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE The aim of this study was to evaluate the differences in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion estimates of high-grade brain gliomas (HGG) due to the use of an input function (IF) obtained respectively from arterial (AIF) and venous (VIF) approaches by two different commercially available software applications. METHODS This prospective study includes 20 patients with pathologically confirmed diagnosis of high-grade gliomas. The data source was processed by using two DCE dedicated commercial packages, both based on the extended Toft model, but the first customized to obtain input function from arterial measurement and the second from sagittal sinus sampling. The quantitative parametric perfusion maps estimated from the two software packages were compared by means of a region of interest (ROI) analysis. The resulting input functions from venous and arterial data were also compared. RESULTS No significant difference has been found between the perfusion parameters obtained with the two different software packages (P-value < .05). The comparison of the VIFs and AIFs obtained by the two packages showed no statistical differences. CONCLUSIONS Direct comparison of DCE-MRI measurements with IF generated by means of arterial or venous waveform led to no statistical difference in quantitative metrics for evaluating HGG. However, additional research involving DCE-MRI acquisition protocols and post-processing would be beneficial to further substantiate the effectiveness of venous approach as the IF method compared with arterial-based IF measurement.
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Affiliation(s)
- Silvano Filice
- Department of Medical Physics and the Department of Neuroradiology, University Hospital of Parma, Parma, Italy
| | - Girolamo Crisi
- Department of Medical Physics and the Department of Neuroradiology, University Hospital of Parma, Parma, Italy
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