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Tseng CH, Jaspers J, Romero AM, Wielopolski P, Smits M, van Osch MJP, Vos F. Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI. NMR IN BIOMEDICINE 2024; 37:e5038. [PMID: 37712359 DOI: 10.1002/nbm.5038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 08/02/2023] [Accepted: 08/23/2023] [Indexed: 09/16/2023]
Abstract
The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measuredR 2 ∗ -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC-AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC-AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE-AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE-AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.
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Affiliation(s)
- Chih-Hsien Tseng
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
| | - Jaap Jaspers
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Alejandra Mendez Romero
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiotherapy, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Piotr Wielopolski
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Marion Smits
- Medical Delta, Delft, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Brain Tumour Center, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Matthias J P van Osch
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frans Vos
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
- Medical Delta, Delft, the Netherlands
- Holland Proton Therapy Center Consortium-Erasmus MC, Rotterdam, Holland Proton Therapy Centre, Delft, Leiden University Medical Center, Leiden and Delft University of Technology, Delft, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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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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3
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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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4
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Bourassa-Moreau B, Lebel R, Gilbert G, Mathieu D, Lepage M. Robust arterial input function surrogate measurement from the superior sagittal sinus complex signal for fast dynamic contrast-enhanced MRI in the brain. Magn Reson Med 2021; 86:3052-3066. [PMID: 34268824 DOI: 10.1002/mrm.28922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/06/2022]
Abstract
PURPOSE Accurately estimating the arterial input function for dynamic contrast-enhanced MRI is challenging. An arterial input function is typically determined from signal magnitude changes related to a contrast agent, often leading to underestimation of peak concentrations. Alternatively, signal phase recovers the accurate peak concentration for straight vessels but suffers from high noise. A recent method proposed to fit the signal in the complex plane by combining the advantages of the previous 2 methods. The purpose of this work is to refine this complex-based method to determine the venous output function (VOF), an arterial input function surrogate, from the superior sagittal sinus. METHODS We propose a state-of-the-art complex-based method that includes direct compensation for blood inflow and signal phase correction accounting for the curvature of the superior sagittal sinus, generally assumed collinear with B0 . We compared the magnitude-, phase-, and complex-based VOF determination methods against various simulated biases as well as for 29 brain metastases patients. RESULTS Angulation of the superior sagittal sinus relative to B0 varied widely within patients, and its effect on the signal phase caused an underestimation of peak concentrations of up to 65%. Correction significantly increased the VOF peak concentration for the phase- and complex-based VOFs in the cohort. The phase-based method recovered accurate peak concentrations but lacked precision in the tail of the VOF. Our complex-based VOF completely recovered the effect of inflow and resulted in a high-peak concentration with limited noise. CONCLUSION The new complex-based method resulted in high-quality VOF robust against superior sagittal sinus curvature and variations in patient positioning.
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Affiliation(s)
- Benoît Bourassa-Moreau
- Centre d'imagerie moléculaire de Sherbrooke, Département de médecine nucléaire et radiobiologie, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Réjean Lebel
- Centre d'imagerie moléculaire de Sherbrooke, Département de médecine nucléaire et radiobiologie, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, Ontario, Canada
| | - David Mathieu
- Service de neurochirurgie, Département de chirurgie, Université de Sherbrooke, Sherbrooke, Québec, Canada.,Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Centre intégré de santé et de services sociaux de l'Estrie, Sherbrooke, Québec, Canada
| | - Martin Lepage
- Centre d'imagerie moléculaire de Sherbrooke, Département de médecine nucléaire et radiobiologie, Université de Sherbrooke, Sherbrooke, Québec, Canada.,Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Centre intégré de santé et de services sociaux de l'Estrie, Sherbrooke, Québec, Canada
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5
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Gwilliam MN, Collins DJ, Leach MO, Orton MR. Quantifying MRI T1 relaxation in flowing blood: implications for arterial input function measurement in DCE-MRI. Br J Radiol 2021; 94:20191004. [PMID: 33507818 PMCID: PMC8011233 DOI: 10.1259/bjr.20191004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo. METHODS A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T1 were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo. RESULTS The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown. CONCLUSIONS The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity. ADVANCES IN KNOWLEDGE This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.
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Affiliation(s)
- Matthew N Gwilliam
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - David J Collins
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - Martin O Leach
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - Matthew R Orton
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
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6
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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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Foltz W, Driscoll B, Laurence Lee S, Nayak K, Nallapareddy N, Fatemi A, Ménard C, Coolens C, Chung C. Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements. ACTA ACUST UNITED AC 2020; 5:77-89. [PMID: 30854445 PMCID: PMC6403037 DOI: 10.18383/j.tom.2019.00001] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Accurate, patient-specific measurement of arterial input functions (AIF) may improve model-based analysis of vascular permeability. This study investigated factors affecting AIF measurements from magnetic resonance imaging (MRI) magnitude (AIFMAGN) and phase (AIFPHA) signals, and compared them against computed tomography (CT) (AIFCT), under controlled conditions relevant to clinical protocols using a multimodality flow phantom. The flow phantom was applied at flip angles of 20° and 30°, flow rates (3-7.5 mL/s), and peak bolus concentrations (0.5-10 mM), for in-plane and through-plane flow. Spatial 3D-FLASH signal and variable flip angle T1 profiles were measured to investigate in-flow and radiofrequency-related biases, and magnitude- and phase-derived Gd-DTPA concentrations were compared. MRI AIF performance was tested against AIFCT via Pearson correlation analysis. AIFMAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, and it grossly underestimated AIFCT peak concentrations. Conversion to Gd-DTPA concentration using T1 taken at the same orientation and flow rate as the dynamic contrast-enhanced acquisition improved AIFMAGN accuracy; yet, AIFMAGN metrics remained variable and significantly reduced from AIFCT at concentrations above 2.5 mM. AIFPHA performed equivalently within 1 mM to AIFCT across all tested conditions. AIFPHA, but not AIFMAGN, reported equivalent measurements to AIFCT across the range of tested conditions. AIFPHA showed superior robustness.
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Affiliation(s)
- Warren Foltz
- Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Brandon Driscoll
- Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada
| | | | - Krishna Nayak
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Naren Nallapareddy
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA
| | - Ali Fatemi
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Cynthia Ménard
- Department of Radiation Oncology, Centre Hospitalier Universite de Montreal, Montreal, Canada.,Department of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada; and
| | - Catherine Coolens
- Department of Medical Physics, Princess Margaret Cancer Center and University Health Network, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.,Department of Radiation Oncology, Centre Hospitalier Universite de Montreal, Montreal, Canada.,Department of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada; and
| | - Caroline Chung
- TECHNA Institute, University Health Network, Toronto, ON, Canada.,Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX
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8
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Ikoma Y, Kishimoto R, Tachibana Y, Omatsu T, Kasuya G, Makishima H, Higashi T, Obata T, Tsuji H. Reference region extraction by clustering for the pharmacokinetic analysis of dynamic contrast-enhanced MRI in prostate cancer. Magn Reson Imaging 2019; 66:185-192. [PMID: 31487532 DOI: 10.1016/j.mri.2019.08.034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 08/13/2019] [Accepted: 08/31/2019] [Indexed: 11/18/2022]
Abstract
PURPOSE Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures changes in the concentration of an administered contrast agent to quantitatively evaluate blood circulation in a tumor or normal tissues. This method uses a pharmacokinetic analysis based on the time course of a reference region, such as muscle, rather than arterial input function. However, it is difficult to manually define a homogeneous reference region. In the present study, we developed a method for automatic extraction of the reference region using a clustering algorithm based on a time course pattern for DCE-MRI studies of patients with prostate cancer. METHODS Two feature values related to the shape of the time course were extracted from the time course of all voxels in the DCE-MRI images. Each voxel value of T1-weighted images acquired before administration were also added as anatomical data. Using this three-dimensional feature vector, all voxels were segmented into five clusters by the Gaussian mixture model, and one of these clusters that included the gluteus muscle was selected as the reference region. RESULTS Each region of arterial vessel, muscle, and fat was segmented as a different cluster from the tumor and normal tissues in the prostate. In the extracted reference region, other tissue elements including scattered fat and blood vessels were removed from the muscle region. CONCLUSIONS Our proposed method can automatically extract the reference region using the clustering algorithm with three types of features based on the time course pattern and anatomical data. This method may be useful for evaluating tumor circulatory function in DCE-MRI studies.
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Affiliation(s)
- Yoko Ikoma
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Riwa Kishimoto
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Yasuhiko Tachibana
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Tokuhiko Omatsu
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Goro Kasuya
- Department of Charged Particle Therapy Research, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Hirokazu Makishima
- Department of Charged Particle Therapy Research, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Tatsuya Higashi
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
| | - Takayuki Obata
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan.
| | - Hiroshi Tsuji
- Department of Charged Particle Therapy Research, National Institute of Radiological Sciences, QST, 4-9-1 Anagawa, Inage-ku, Chiba 263-8555, Japan
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9
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Ahmed Z, Levesque IR. Pharmacokinetic modeling of dynamic contrast-enhanced MRI using a reference region and input function tail. Magn Reson Med 2019; 83:286-298. [PMID: 31393033 DOI: 10.1002/mrm.27913] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 06/18/2019] [Accepted: 06/18/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) requires an arterial input function (AIF) which is difficult to measure. We propose the reference region and input function tail (RRIFT) approach which uses a reference tissue and the washout portion of the AIF. METHODS RRIFT was evaluated in simulations with 100 parameter combinations at various temporal resolutions (5-30 s) and noise levels (σ = 0.01-0.05 mM). RRIFT was compared against the extended Tofts model (ETM) in 8 studies from patients with glioblastoma multiforme. Two versions of RRIFT were evaluated: one using measured patient-specific AIF tails, and another assuming a literature-based AIF tail. RESULTS RRIFT estimated the transfer constant K trans and interstitial volume v e with median errors within 20% across all simulations. RRIFT was more accurate and precise than the ETM at temporal resolutions slower than 10 s. The percentage error of K trans had a median and interquartile range of -9 ± 45% with the ETM and -2 ± 17% with RRIFT at a temporal resolution of 30 s under noiseless conditions. RRIFT was in excellent agreement with the ETM in vivo, with concordance correlation coefficients (CCC) of 0.95 for K trans , 0.96 for v e , and 0.73 for the plasma volume v p using a measured AIF tail. With the literature-based AIF tail, the CCC was 0.89 for K trans , 0.93 for v e and 0.78 for v p . CONCLUSIONS Quantitative DCE-MRI analysis using the input function tail and a reference tissue yields absolute kinetic parameters with the RRIFT method. This approach was viable in simulation and in vivo for temporal resolutions as low as 30 s.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, Canada.,Department of Physics, McGill University, Montreal, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, Canada.,Department of Physics, McGill University, Montreal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada.,Cancer Research Program, Research Institute of the McGill University Health Centre, Montreal, Canada
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10
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Analytical validation of single-kidney glomerular filtration rate and split renal function as measured with magnetic resonance renography. Magn Reson Imaging 2019; 59:53-60. [PMID: 30849485 DOI: 10.1016/j.mri.2019.03.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/01/2019] [Accepted: 03/04/2019] [Indexed: 01/04/2023]
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11
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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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12
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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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13
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Georgiou L, Wilson DJ, Sharma N, Perren TJ, Buckley DL. A functional form for a representative individual arterial input function measured from a population using high temporal resolution DCE MRI. Magn Reson Med 2018; 81:1955-1963. [PMID: 30257053 DOI: 10.1002/mrm.27524] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 08/17/2018] [Accepted: 08/20/2018] [Indexed: 12/28/2022]
Abstract
PURPOSE To measure the arterial input function (AIF), an essential component of tracer kinetic analysis, in a population of patients using an optimized dynamic contrast-enhanced (DCE) imaging sequence and to estimate inter- and intrapatient variability. From these data, a representative AIF that may be used for realistic simulation studies can be extracted. METHODS Thirty-nine female patients were imaged on multiple visits before and during a course of neoadjuvant chemotherapy for breast cancer. A total of 97 T1 -weighted DCE studies were analyzed including bookend estimates of T1 and model-fitting to each individual AIF. Area under the curve and cardiac output were estimated from each first pass peak, and these data were used to assess inter- and intrapatient variability of the AIF. RESULTS Interpatient variability exceeded intrapatient variability of the AIF. There was no change in cardiac output as a function of MR visit (mean value 5.6 ± 1.1 L/min) but baseline blood T1 increased significantly following the start of chemotherapy (which was accompanied by a decrease in hematocrit). CONCLUSION The AIF in an individual patient can be measured reproducibly but the variability of AIFs between patients suggests that use of a population AIF will decrease the precision of tracer kinetic analysis performed in cross-patient comparison studies. A representative AIF is presented that is typical of the population but retains the characteristics of an individually measured AIF.
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Affiliation(s)
- Leonidas Georgiou
- Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Department of Medical Physics, German Oncology Center, Limassol, Cyprus
| | - Daniel J Wilson
- Department of Medical Physics and Engineering, Leeds Teaching Hospital NHS Trust, Leeds, United Kingdom
| | - Nisha Sharma
- Department of Radiology, Leeds Teaching Hospital NHS Trust, Leeds, United Kingdom
| | - Timothy J Perren
- Leeds Institute of Cancer and Pathology, St. James's University Hospital, Leeds, United Kingdom
| | - David L Buckley
- Biomedical Imaging, University of Leeds, Leeds, United Kingdom
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14
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Ahmed Z, Levesque IR. An extended reference region model for DCE-MRI that accounts for plasma volume. NMR IN BIOMEDICINE 2018; 31:e3924. [PMID: 29745982 DOI: 10.1002/nbm.3924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 02/20/2018] [Accepted: 02/27/2018] [Indexed: 06/08/2023]
Abstract
The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated Ktrans by over 10% at vp = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible vp where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, QC, Canada
- Department of Physics, McGill University, Montreal, QC, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, QC, Canada
- Department of Physics, McGill University, Montreal, QC, Canada
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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15
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Lecler A, Fournier L, Diard-Detoeuf C, Balvay D. Blood-Brain Barrier Leakage in Early Alzheimer Disease. Radiology 2018; 282:923-925. [PMID: 28218884 DOI: 10.1148/radiol.2017162578] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Augustin Lecler
- Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, 25 rue Manin, 75019 Paris, France
| | - Laure Fournier
- Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France †.,Department of Radiology, Université Paris Descartes Sorbonne Paris Cité, Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Paris, France ‡
| | | | - Daniel Balvay
- Cardiovascular Research Center-PARCC, Université Paris Descartes Sorbonne Paris Cité, UMR-S970, Paris, France †
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16
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van Schie JJN, Lavini C, van Vliet LJ, Kramer G, Pieters-van den Bos I, Marcus JT, Stoker J, Vos FM. Estimating the arterial input function from dynamic contrast-enhanced MRI data with compensation for flow enhancement (II): Applications in spine diagnostics and assessment of crohn's disease. J Magn Reson Imaging 2017; 47:1197-1204. [PMID: 29193469 DOI: 10.1002/jmri.25905] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 10/16/2017] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Pharmacokinetic (PK) models can describe microvascular density and integrity. An essential component of PK models is the arterial input function (AIF) representing the time-dependent concentration of contrast agent (CA) in the blood plasma supplied to a tissue. PURPOSE/HYPOTHESIS To evaluate a novel method for subject-specific AIF estimation that takes inflow effects into account. STUDY TYPE Retrospective study. SUBJECTS Thirteen clinical patients referred for spine-related complaints; 21 patients from a study into luminal Crohn's disease with known Crohn's Disease Endoscopic Index of Severity (CDEIS). FIELD STRENGTH/SEQUENCE Dynamic fast spoiled gradient echo (FSPGR) at 3T. ASSESSMENT A population-averaged AIF, AIFs derived from distally placed regions of interest (ROIs), and the new AIF method were applied. Tofts' PK model parameters (including vp and Ktrans ) obtained with the three AIFs were compared. In the Crohn's patients Ktrans was correlated to CDEIS. STATISTICAL TESTS The median values of the PK model parameters from the three methods were compared using a Mann-Whitney U-test. The associated variances were statistically assessed by the Brown-Forsythe test. Spearman's rank correlation coefficient was computed to test the correlation of Ktrans to CDEIS. RESULTS The median vp was significantly larger when using the distal ROI approach, compared to the two other methods (P < 0.05 for both comparisons, in both applications). Also, the variances in vp were significantly larger with the ROI approach (P < 0.05 for all comparisons). In the Crohn's disease study, the estimated Ktrans parameter correlated better with the CDEIS (r = 0.733, P < 0.001) when the proposed AIF was used, compared to AIFs from the distal ROI method (r = 0.429, P = 0.067) or the population-averaged AIF (r = 0.567, P = 0.011). DATA CONCLUSION The proposed method yielded realistic PK model parameters and improved the correlation of the Ktrans parameter with CDEIS, compared to existing approaches. LEVEL OF EVIDENCE 3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2018;47:1197-1204.
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Affiliation(s)
- Jeroen J N van Schie
- Quantitative Imaging Group, Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Cristina Lavini
- Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, The Netherlands
| | - Lucas J van Vliet
- Quantitative Imaging Group, Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Gem Kramer
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Indra Pieters-van den Bos
- 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
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, The Netherlands
| | - Frans M Vos
- Quantitative Imaging Group, Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.,Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, The Netherlands
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17
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van Schie JJN, Lavini C, van Vliet LJ, Vos FM. Estimating the arterial input function from dynamic contrast-enhanced MRI data with compensation for flow enhancement (I): Theory, method, and phantom experiments. J Magn Reson Imaging 2017; 47:1190-1196. [PMID: 29193415 DOI: 10.1002/jmri.25906] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 10/03/2017] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The arterial input function (AIF) represents the time-dependent arterial contrast agent (CA) concentration that is used in pharmacokinetic modeling. PURPOSE To develop a novel method for estimating the AIF from dynamic contrast-enhanced (DCE-) MRI data, while compensating for flow enhancement. STUDY TYPE Signal simulation and phantom measurements. PHANTOM MODEL Time-intensity curves (TICs) were simulated for different numbers of excitation pulses modeling flow effects. A phantom experiment was performed in which a solution (without CA) was passed through a straight tube, at constant flow velocity. FIELD STRENGTH/SEQUENCE Dynamic fast spoiled gradient echo (FSPGRs) at 3T MRI, both in the simulations and in the phantom experiment. TICs were generated for a duration of 373 seconds and sampled at intervals of 1.247 seconds (300 timepoints). ASSESSMENT The proposed method first estimates the number of pulses that spins have received, and then uses this knowledge to accurately estimate the CA concentration. STATISTICAL TESTS The difference between the median of the estimated number of pulses and the true value was determined, as well as the interquartile range (IQR) of the estimations. The estimated CA concentrations were evaluated in the same way. The estimated number of pulses was also used to calculate flow velocity. RESULTS The difference between the median estimated and reference number of pulses varied from -0.005 to -1.371 (corresponding IQRs: 0.853 and 48.377) at true values of 10 and 180 pulses, respectively. The difference between the median estimated CA concentration and the reference value varied from -0.00015 to 0.00306 mmol/L (corresponding IQRs: 0.01989 and 1.51013 mmol/L) at true values of 0.5 and 8.0 mmol/l, respectively, at an intermediate value of 100 pulses. The estimated flow velocities in the phantom were within 10% of the reference value. DATA CONCLUSION The proposed method accurately corrects the MRI signal affected by the inflow effect. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1190-1196.
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Affiliation(s)
| | - Cristina Lavini
- Department of Radiology and Nuclear Medicine, Academic Medical Center Amsterdam, The Netherlands
| | - Lucas J van Vliet
- Quantitative Imaging Group, University of Technology Delft, The Netherlands
| | - Frans M Vos
- Quantitative Imaging Group, University of Technology Delft, The Netherlands.,Department of Radiology and Nuclear Medicine, Academic Medical Center Amsterdam, The Netherlands
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18
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Taxt T, Reed RK, Pavlin T, Rygh CB, Andersen E, Jiřík R. Semi-parametric arterial input functions for quantitative dynamic contrast enhanced magnetic resonance imaging in mice. Magn Reson Imaging 2017; 46:10-20. [PMID: 29066294 DOI: 10.1016/j.mri.2017.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Revised: 09/15/2017] [Accepted: 10/17/2017] [Indexed: 01/23/2023]
Abstract
OBJECTIVE An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.
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Affiliation(s)
- Torfinn Taxt
- Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Dept. of Radiology, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway
| | - Rolf K Reed
- Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Centre for Cancer Biomarkers (CCBIO), University of Bergen, Jonas Lies vei 87, Bergen N-5021, Norway
| | - Tina Pavlin
- Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway; Dept. of Radiology, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway
| | - Cecilie Brekke Rygh
- Dept. of Biomedicine, University of Bergen, Jonas Lies vei 91, Bergen N-5020, Norway
| | - Erling Andersen
- Dept. of Clinical Engineering, Haukeland University Hospital, Jonas Lies vei 83, Bergen N-5020, Norway
| | - Radovan Jiřík
- Czech Academy of Sciences, Inst. of Scientific Instruments, Královopolská 147, Brno 61264, Czech Republic.
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19
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Ahmed Z, Levesque IR. Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches. Magn Reson Med 2016; 78:1547-1557. [PMID: 27797110 DOI: 10.1002/mrm.26530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 09/25/2016] [Accepted: 10/06/2016] [Indexed: 12/23/2022]
Abstract
PURPOSE Reference region models (RRMs) can quantify tumor perfusion in dynamic contrast-enhanced MRI without an arterial input function. Inspection of the RRM reveals that one of the free parameters in the fit is uniquely linked to the reference region and is common to all voxels. A two-step approach is proposed that takes this constraint into account. METHODS Three constrained RRM (CRRM) approaches were devised and evaluated. Simulations were performed to compare their accuracy and precision over a range of noise and temporal resolutions. The CRRM was also applied on a virtual phantom that simulates different perfusion values. In vivo evaluation was performed on data from breast cancer and soft tissue sarcoma. RESULTS In simulations, the CRRM consistently improved precision and had better accuracy at low signal-to-noise ratio (SNR). In virtual phantom, the CRRMs were able to fit voxels that had similar kinetics to the reference tissue, whereas the unconstrained models failed to accurately fit these voxels. In the in vivo data, the constrained approaches produced parameter maps that had less variability and were in better agreement with the Tofts model. CONCLUSION These findings indicate that the two-step fitting approach of the CRRM can reduce the variability of perfusion estimates for quantifying perfusion with dynamic contrast-enhanced (DCE) MRI. Magn Reson Med 78:1547-1557, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, QC, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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20
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Ter Voert EEGW, Heijmen L, Punt CJA, de Wilt JHW, van Laarhoven HWM, Heerschap A. Reduced respiratory motion artifacts using structural similarity in fast 2D dynamic contrast enhanced MRI of liver lesions. NMR IN BIOMEDICINE 2016; 29:1526-1535. [PMID: 27598946 DOI: 10.1002/nbm.3606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 07/21/2016] [Accepted: 07/25/2016] [Indexed: 06/06/2023]
Abstract
The purpose of this work was to improve dynamic contrast enhanced MRI (DCE-MRI) of liver lesions by removing motion corrupted images as identified by a structural similarity (SSIM) algorithm, and to assess the effect of this correction on the pharmacokinetic parameter Ktrans using automatically determined arterial input functions (AIFs). Fifteen patients with colorectal liver metastases were measured twice with a T1 weighted multislice 2D FLASH sequence for DCE-MRI (time resolution 1.2 s). AIFs were automatically derived from contrast inflow in the aorta of each patient. Thereafter, SSIM identified motion corrupted images of the liver were removed from the DCE dataset. From this corrected data set Ktrans and its reproducibility were determined. Using the SSIM algorithm a median fraction of 46% (range 37-50%) of the liver images in DCE time series was labeled as motion distorted. Rejection of these images resulted in a significantly lower median Ktrans (p < 0.05) and lower coefficient of repeatability of Ktrans in liver metastases compared with an analysis without correction. SSIM correction improves the reproducibility of the DCE-MRI parameter Ktrans in liver metastasis and reduces contamination of Ktrans values of lesions by that of surrounding normal liver tissue.
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Affiliation(s)
- Edwin E G W Ter Voert
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Linda Heijmen
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Cornelis J A Punt
- Department of Medical Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Johannes H W de Wilt
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Medical Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Arend Heerschap
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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21
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van Hoof RHM, Hermeling E, Truijman MTB, van Oostenbrugge RJ, Daemen JWH, van der Geest RJ, van Orshoven NP, Schreuder AH, Backes WH, Daemen MJAP, Wildberger JE, Kooi ME. Phase-based vascular input function: Improved quantitative DCE-MRI of atherosclerotic plaques. Med Phys 2016; 42:4619-28. [PMID: 26233189 DOI: 10.1118/1.4924949] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Quantitative pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI can be used to assess atherosclerotic plaque microvasculature, which is an important marker of plaque vulnerability. Purpose of the present study was (1) to compare magnitude- versus phase-based vascular input functions (m-VIF vs ph-VIF) used in pharmacokinetic modeling and (2) to perform model calculations and flow phantom experiments to gain more insight into the differences between m-VIF and ph-VIF. METHODS Population averaged m-VIF and ph-VIFs were acquired from 11 patients with carotid plaques and used for pharmacokinetic analysis in another 17 patients. Simulations, using the Bloch equations and the MRI scan geometry, and flow phantom experiments were performed to determine the effect of local blood velocity on the magnitude and phase signal enhancement. RESULTS Simulations and flow phantom experiments revealed that flow within the lumen can lead to severe underestimation of m-VIF, while this is not the case for the ph-VIF. In line, the peak concentration of the m-VIF is significantly lower than ph-VIF (p < 0.001), in vivo. Quantitative model parameters for m- and ph-VIF differed in absolute values but were moderate to strongly correlated with each other [K(trans) Spearman's ρ > 0.93 (p < 0.001) and vp Spearman's ρ > 0.58 (p < 0.05)]. CONCLUSIONS m-VIF is strongly influenced by local blood velocity, which leads to underestimation of the contrast medium concentration. Therefore, it is advised to use ph-VIF for DCE-MRI analysis of carotid plaques for accurate quantification.
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Affiliation(s)
- R H M van Hoof
- Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands
| | - E Hermeling
- Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands
| | - M T B Truijman
- Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands; CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands; and Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands
| | - R J van Oostenbrugge
- Department of Neurology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands
| | - J W H Daemen
- Department of Surgery, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands
| | - R J van der Geest
- Department of Radiology, Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - N P van Orshoven
- Department of Neurology, Orbis Medical Center, Sittard 6130 MB, The Netherlands
| | - A H Schreuder
- Department of Neurology, Atrium Medical Center, Heerlen 6401 CX, The Netherlands
| | - W H Backes
- Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands
| | - M J A P Daemen
- Department of Pathology, Academic Medical Center, Amsterdam 1100 DD, The Netherlands
| | - J E Wildberger
- Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands
| | - M E Kooi
- Department of Radiology, Maastricht University Medical Center, Maastricht 6202 AZ, The Netherlands and CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht 6200 MD, The Netherlands
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22
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Simonis FF, Sbrizzi A, Beld E, Lagendijk JJ, van den Berg CA. Improving the arterial input function in dynamic contrast enhanced MRI by fitting the signal in the complex plane. Magn Reson Med 2015; 76:1236-45. [DOI: 10.1002/mrm.26023] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Revised: 09/29/2015] [Accepted: 09/30/2015] [Indexed: 01/14/2023]
Affiliation(s)
- Frank F.J. Simonis
- Department of Radiotherapy; Imaging Division, University Medical Center Utrecht; Utrecht the Netherlands
| | - Alessandro Sbrizzi
- Department of Radiology; University Medical Center Utrecht; Utrecht the Netherlands
| | - Ellis Beld
- Department of Radiotherapy; Imaging Division, University Medical Center Utrecht; Utrecht the Netherlands
| | - Jan J.W. Lagendijk
- Department of Radiotherapy; Imaging Division, University Medical Center Utrecht; Utrecht the Netherlands
| | - Cornelis A.T. van den Berg
- Department of Radiotherapy; Imaging Division, University Medical Center Utrecht; Utrecht the Netherlands
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23
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Dickie BR, Banerji A, Kershaw LE, McPartlin A, Choudhury A, West CM, Rose CJ. Improved accuracy and precision of tracer kinetic parameters by joint fitting to variable flip angle and dynamic contrast enhanced MRI data. Magn Reson Med 2015; 76:1270-81. [PMID: 26480291 DOI: 10.1002/mrm.26013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 09/18/2015] [Accepted: 09/18/2015] [Indexed: 12/23/2022]
Abstract
PURPOSE To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors. THEORY Quantitative DCE-MRI requires an estimate of precontrast T1 , which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters. METHODS Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions. RESULTS In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%). CONCLUSION Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Ben R Dickie
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK. .,Institute of Cancer Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
| | - Anita Banerji
- Centre for Imaging Sciences, Institute of Population Health, Centre for Imaging Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Lucy E Kershaw
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK.,Institute of Cancer Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Andrew McPartlin
- Institute of Cancer Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.,Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Ananya Choudhury
- Institute of Cancer Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.,Department of Clinical Oncology, The Christie NHS Foundation Trust, Manchester, UK
| | - Catharine M West
- Institute of Cancer Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Chris J Rose
- Centre for Imaging Sciences, Institute of Population Health, Centre for Imaging Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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24
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Broadbent DA, Biglands JD, Ripley DP, Higgins DM, Greenwood JP, Plein S, Buckley DL. Sensitivity of quantitative myocardial dynamic contrast-enhanced MRI to saturation pulse efficiency, noise and t1 measurement error: Comparison of nonlinearity correction methods. Magn Reson Med 2015; 75:1290-300. [PMID: 25946025 DOI: 10.1002/mrm.25726] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 03/19/2015] [Accepted: 03/20/2015] [Indexed: 12/12/2022]
Abstract
PURPOSE To compare methods designed to minimize or correct signal nonlinearity in quantitative myocardial dynamic contrast-enhanced (DCE) MRI. METHODS DCE-MRI studies were simulated and data acquired in eight volunteers. Signal nonlinearity was corrected using either a dual-bolus approach or model-based correction using proton-density weighted imaging (conventional or dual-sequence acquisition) or T1 data (native or bookend). Scanning of healthy and infarcted myocardium at 3 T was simulated, including noise, saturation imperfection and T1 measurement error. Data were analyzed using model-based deconvolution with a one-compartment (mono-exponential) model. RESULTS Substantial variation between methods was demonstrated in volunteers. In simulations the dual-bolus method proved stable for realistic levels of saturation efficiency but demonstrated bias due to residual nonlinearity. Model-based methods performed ideally in the absence of confounding error sources and were generally robust to noise or saturation imperfection, except for native T1 based correction which was highly sensitive to the latter. All methods demonstrated large variation in accuracy above an over-saturation level where baseline signal was nulled. For the dual-sequence approach this caused substantial bias at the saturation efficiencies observed in volunteers. CONCLUSION The choice of nonlinearity correction method in myocardial DCE-MRI impacts on accuracy and precision of estimated parameters, particularly in the presence of nonideal saturation.
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Affiliation(s)
- David A Broadbent
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.,Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - John D Biglands
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Department of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom.,Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - David P Ripley
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | | | - John P Greenwood
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - Sven Plein
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
| | - David L Buckley
- Division of Biomedical Imaging, University of Leeds, Leeds, United Kingdom.,Multidisciplinary Cardiovascular Research Centre, University of Leeds, Leeds, United Kingdom
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25
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Brynolfsson P, Yu J, Wirestam R, Karlsson M, Garpebring A. Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling. Magn Reson Med 2014; 74:1156-64. [DOI: 10.1002/mrm.25490] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Revised: 09/10/2014] [Accepted: 09/17/2014] [Indexed: 02/04/2023]
Affiliation(s)
| | - Jun Yu
- Department of Mathematics and Mathematical Statistics; Umeå University; Umeå Sweden
| | - Ronnie Wirestam
- Department of Medical Radiation Physics; Lund University; Lund Sweden
| | - Mikael Karlsson
- Department of Radiation Physics; Umeå University; Umeå Sweden
| | - Anders Garpebring
- Department of Radiation Physics; Umeå University; Umeå Sweden
- CJ Gorter Center for High Field MRI; Leiden University Medical Center; Leiden Netherlands
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26
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Simulating the effect of input errors on the accuracy of Tofts' pharmacokinetic model parameters. Magn Reson Imaging 2014; 33:222-35. [PMID: 25308097 DOI: 10.1016/j.mri.2014.10.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Revised: 09/26/2014] [Accepted: 10/05/2014] [Indexed: 01/19/2023]
Abstract
Pharmacokinetic modeling in Dynamic Contrast Enhanced (DCE)-MRI is an elegant and useful method that provides valuable insight into angiogenesis in cancer and inflammatory diseases. Despite its widespread use, the reliability of the model results is still questioned, as many factors hamper the calculation of the model's parameters, resulting in the poor reproducibility and accuracy of the method. Pharmacokinetic modeling relies on the knowledge of inputs such as the Arterial Input Function (AIF) and of the tissue contrast agent concentration, both of which are difficult to accurately measure. Any errors in the measurement of either of the inputs propagate into the calculated pharmacokinetic model parameters (PMPs), and the significance of the effect depends on the source of the measurement error. In this work we systematically investigate the effect of the incorrect estimation of the parameters describing the inputs of the model on the calculated PMPs when using Tofts' model. Furthermore, we analyze the dependence of these errors on the native values of the PMPs. We show that errors on the measurement of the native T1 as well as errors on the parameters describing the initial peak of the AIF have the largest impact on the calculated PMPs. The parameter whose error has the least effect is the one describing the slow decay of the AIF. The effect of input parameter (IP) errors on the calculated PMPs is found to be dependent on the native set of PMPs: this is particularly true for the errors in the flip angle, and for the errors in parameters describing the initial AIF peak. Conversely the effect of T1 and AIF scaling errors on the calculated PMPs is only slightly dependent on the native PMPs.
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27
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Cutajar M, Thomas DL, Hales PW, Banks T, Clark CA, Gordon I. Comparison of ASL and DCE MRI for the non-invasive measurement of renal blood flow: quantification and reproducibility. Eur Radiol 2014; 24:1300-8. [PMID: 24599625 DOI: 10.1007/s00330-014-3130-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 02/06/2014] [Accepted: 02/13/2014] [Indexed: 12/27/2022]
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28
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Skinner JT, Yankeelov TE, Peterson TE, Does MD. Comparison of dynamic contrast-enhanced MRI and quantitative SPECT in a rat glioma model. CONTRAST MEDIA & MOLECULAR IMAGING 2013; 7:494-500. [PMID: 22991315 DOI: 10.1002/cmmi.1479] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Pharmacokinetic modeling of dynamic contrast-enhanced (DCE) MRI data provides measures of the extracellular-extravascular volume fraction (v(e) ) and the volume transfer constant (K(trans) ) in a given tissue. These parameter estimates may be biased, however, by confounding issues such as contrast agent and tissue water dynamics, or assumptions of vascularization and perfusion made by the commonly used model. In contrast to MRI, radiotracer imaging with SPECT is insensitive to water dynamics. A quantitative dual-isotope SPECT technique was developed to obtain an estimate of v(e) in a rat glioma model for comparison with the corresponding estimates obtained using DCE-MRI with a vascular input function and reference region model. Both DCE-MRI methods produced consistently larger estimates of v(e) in comparison to the SPECT estimates, and several experimental sources were postulated to contribute to these differences.
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Affiliation(s)
- Jack T Skinner
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232-2310, USA
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29
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Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR IN BIOMEDICINE 2013; 26:1004-1027. [PMID: 23674304 DOI: 10.1002/nbm.2940] [Citation(s) in RCA: 272] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2012] [Revised: 02/12/2013] [Accepted: 02/12/2013] [Indexed: 06/02/2023]
Abstract
Dynamic contrast-enhanced MRI (DCE-MRI) is a functional MRI method where T1 -weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer-kinetic modelling. In the brain, DCE-MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood-brain barrier (BBB) permeability-surface area product (PS) and the volume of the interstitium (ve ). These parameters can be combined to form others such as the volume-transfer constant K(trans) , the extraction fraction E and the contrast-agent mean transit times through the intra- and extravascular spaces. A first generation of tracer-kinetic models for DCE-MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE-MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model-free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model-based and model-free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs.
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30
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Moroz J, Wong CL, Yung AC, Kozlowski P, Reinsberg SA. Rapid measurement of arterial input function in mouse tail from projection phases. Magn Reson Med 2013; 71:238-45. [DOI: 10.1002/mrm.24660] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Revised: 12/04/2012] [Accepted: 01/05/2013] [Indexed: 11/11/2022]
Affiliation(s)
- Jennifer Moroz
- Department of Physics and Astronomy; University of British Columbia; Vancouver Canada
| | - Clayton L. Wong
- Department of Physics; Simon Fraser University; Burnaby Canada
| | - Andrew C. Yung
- University of British Columbia MRI Research Centre; Vancouver Canada
| | - Piotr Kozlowski
- University of British Columbia MRI Research Centre; Vancouver Canada
| | - Stefan A. Reinsberg
- Department of Physics and Astronomy; University of British Columbia; Vancouver Canada
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31
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Wirestam R. Using contrast agents to obtain maps of regional perfusion and capillary wall permeability. ACTA ACUST UNITED AC 2012. [DOI: 10.2217/iim.12.24] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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32
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Garpebring A, Brynolfsson P, Yu J, Wirestam R, Johansson A, Asklund T, Karlsson M. Uncertainty estimation in dynamic contrast-enhanced MRI. Magn Reson Med 2012; 69:992-1002. [DOI: 10.1002/mrm.24328] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Revised: 03/27/2012] [Accepted: 04/18/2012] [Indexed: 12/21/2022]
Affiliation(s)
- Anders Garpebring
- Division of Radiation Physics; Department of Radiation Sciences; Umeå University; Umeå; Sweden
| | - Patrik Brynolfsson
- Division of Radiation Physics; Department of Radiation Sciences; Umeå University; Umeå; Sweden
| | - Jun Yu
- Centre of Biostochastics; Swedish University of Agricultural Sciences; Umeå; Sweden
| | - Ronnie Wirestam
- Department of Medical Radiation Physics; Lund University; Lund; Sweden
| | - Adam Johansson
- Division of Radiation Physics; Department of Radiation Sciences; Umeå University; Umeå; Sweden
| | - Thomas Asklund
- Division of Oncology; Department of Radiation Sciences; Umeå University; Umeå; Sweden
| | - Mikael Karlsson
- Division of Radiation Physics; Department of Radiation Sciences; Umeå University; Umeå; Sweden
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33
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Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 2011; 57:R1-33. [PMID: 22173205 DOI: 10.1088/0031-9155/57/2/r1] [Citation(s) in RCA: 243] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter K(trans) that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.
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Affiliation(s)
- S P Sourbron
- Division of Medical Physics, University of Leeds, Leeds, West Yorkshire, UK
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Phase-based arterial input functions in humans applied to dynamic contrast-enhanced MRI: potential usefulness and limitations. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2011; 24:233-45. [PMID: 21626278 DOI: 10.1007/s10334-011-0257-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2011] [Revised: 04/08/2011] [Accepted: 05/03/2011] [Indexed: 10/18/2022]
Abstract
OBJECT Phase-based arterial input functions (AIFs) provide a promising alternative to standard magnitude-based AIFs, for example, because inflow effects are avoided. The usefulness of phase-based AIFs in clinical dynamic contrast-enhanced MRI (DCE-MRI) was investigated, and relevant pitfalls and sources of uncertainty were identified. MATERIALS AND METHODS AIFs were registered from eight human subjects on, in total, 21 occasions. AIF quality was evaluated by comparing AIFs from right and left internal carotid arteries and by assessing the reliability of blood plasma volume estimates. RESULTS Phase-based AIFs yielded an average bolus peak of 3.9 mM and a residual concentration of 0.37 mM after 3 min, (0.033 mmol/kg contrast agent injection). The average blood plasma volume was 2.7% when using the AIF peak in the estimation, but was significantly different (p < 0.0001) and less physiologically reasonable when based on the AIF tail concentration. Motion-induced phase shifts and accumulation of contrast agent in background tissue regions were identified as main sources of uncertainty. CONCLUSION Phase-based AIFs are a feasible alternative to magnitude AIFs, but sources of errors exist, making quantification difficult, especially of the AIF tail. Improvement of the technique is feasible and also required for the phase-based AIF approach to reach its full potential.
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