{ "id": "epzfyzez2a", "slug": "dynamic-contrast-enhanced-mri", "title": "Dynamic contrast-enhanced MRI", "updated": "2026-10-15", "topic_path": [ { "id": "life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/topics/life" }, { "id": "life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health" }, { "id": "life.health.clinical", "label": "Clinical assessment and procedures", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical" }, { "id": "life.health.clinical.clinical-imaging", "label": "Medical imaging and radiography", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical.clinical-imaging" }, { "id": "life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "label": "Magnetic resonance imaging techniques", "api_url": "https://www.edgepedia.org/api/v1/topics/life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques" } ], "geo": [ { "id": "geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "label": "Non-geographic · 1946 to 2000: Magnetic resonance imaging techniques", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.life", "label": "Life and health", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life" }, { "id": "geo.nongeo.t1946.life.health", "label": "Human health and medicine", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health" }, { "id": "geo.nongeo.t1946.life.health.clinical", "label": "Clinical assessment and procedures", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical" }, { "id": "geo.nongeo.t1946.life.health.clinical.clinical-imaging", "label": "Medical imaging and radiography", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical.clinical-imaging" }, { "id": "geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "label": "Magnetic resonance imaging techniques", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques" } ] } ], "excerpt": "Dynamic contrast-enhanced MRI (DCE-MRI) is an MRI technique that rapidly images tissue before, during, and after gadolinium injection to quantify perfusion and vascular permeability, chiefly in oncology.", "snippet": "Dynamic contrast-enhanced MRI (DCE-MRI) is an MRI technique that rapidly images tissue before, during, and after gadolinium injection to quantify perfusion and vascular permeability, chiefly in oncology.", "node": "life.health.clinical.clinical-imaging.magnetic-resonance-imaging-techniques", "markdown": "# Dynamic contrast-enhanced MRI\n\nDynamic contrast-enhanced MRI (DCE-MRI) is a magnetic resonance imaging technique that acquires rapid images before, during, and after injection of a gadolinium contrast agent to measure tissue perfusion and vascular permeability through tracer kinetic modeling. It is used in oncology, where the volume transfer constant \\( K^{\\mathrm{trans}} \\) is the most common target, and in other settings that require quantification of capillary leak.[1](https://onlinelibrary.wiley.com/doi/10.1002/mrm.1910170208) • [2](https://link.springer.com/article/10.1007/s00330-025-12306-5)\n\n| Key fact | Detail |\n|---|---|\n| What it measures | Tissue perfusion and capillary permeability, via the T1-shortening kinetics of injected gadolinium; most applications target \\( K^{\\mathrm{trans}} \\) or the permeability-surface area product[2](https://link.springer.com/article/10.1007/s00330-025-12306-5) |\n| Standard parameters | \\( K^{\\mathrm{trans}} \\) (min⁻¹), \\( v_{e} \\) (extravascular extracellular volume fraction, 0–1), \\( k_{ep} \\) (min⁻¹), and \\( v_{p} \\) (fractional plasma volume)[3](https://doi.org/10.1002/%28sici%291522-2586%28199909%2910:3) |\n| Typical protocol | 3D spoiled gradient echo sequence, baseline T1 map, ≥5 baseline phases, 0.1 mmol/kg gadolinium at 2–4 mL/s with 20–30 mL saline flush, temporal resolution <10 s[4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf) |\n| Temporal resolution targets | <4 s for perfusion first-pass; 10–20 s acceptable for permeability with acquisitions longer than 3 minutes[2](https://link.springer.com/article/10.1007/s00330-025-12306-5) |\n| Reproducibility | \\( K^{\\mathrm{trans}} \\) within-subject coefficient of variation about 11–24% across tumor types; \\( v_{p} \\) precision is poor, approaching 50% wCV[5](https://link.springer.com/article/10.1007/s00330-024-10970-7) |\n| Founding models | Larsson and colleagues (1990), Tofts and Kermode (1991), and Brix and colleagues (1991)[6](https://doi.org/10.1002/mrm.1910160111) • [1](https://onlinelibrary.wiley.com/doi/10.1002/mrm.1910170208) • [7](https://doi.org/10.1097/00004728-199107000-00018) |\n\n## How it works\n\nDCE-MRI is an indirect method: the signal change is driven by the T1-shortening effect of the contrast agent on water protons, not by direct detection of the agent.[8](https://ww-w.mriquestions.com/uploads/3/4/5/7/34572113/1-s2.0-s1064968923000788.pdf) Repeated T1-weighted images track how quickly gadolinium enters and leaves the tissue, and a tracer kinetic model converts these curves into physiological parameters.\n\nThe standard two-compartment framework describes exchange between blood plasma and the extravascular extracellular space (EES). In the unified Tofts formulation, with \\( C_{e} \\) denoting the EES contribution per unit tissue volume, the tissue concentration is \\( C_{t} = v_{b} \\cdot C_{b} + C_{e} \\), where \\( C_{e} \\) obeys \\( dC_{e}/dt = K^{\\mathrm{trans}} \\cdot C_{b} - k_{ep} \\cdot C_{e} \\) with \\( k_{ep} = K^{\\mathrm{trans}}/v_{e} \\); the commonly used Tofts model assumes a small vascular space and writes \\( C_{t} = C_{e} \\).[9](https://cds.ismrm.org/protected/09MProceedings/PDFfiles/Tues%20C37_06%20Su.pdf) The 1999 consensus paper standardized \\( K^{\\mathrm{trans}} \\) (min⁻¹), \\( v_{e} \\) (bounded 0 to 1), and \\( k_{ep} \\) (min⁻¹) as international standards.[3](https://doi.org/10.1002/%28sici%291522-2586%28199909%2910:3)\n\nThe meaning of \\( K^{\\mathrm{trans}} \\) depends on the regime: when permeability is high relative to flow (the Kety case), it approximates blood plasma flow per unit tissue volume; when permeability is low, it approximates the permeability surface area product per unit volume, \\( K^{\\mathrm{trans}} \\approx P \\cdot S \\).[3](https://doi.org/10.1002/%28sici%291522-2586%28199909%2910:3)\n\n## How it is done\n\nA quantitative study runs in this order:[2](https://link.springer.com/article/10.1007/s00330-025-12306-5) • [4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n\n1. **Sequence selection.** A 3D fast spoiled gradient recalled echo sequence is used, which provides high temporal sampling while maintaining acceptable SNR and spatial resolution.[10](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202) The QIBA DCEMRI-Q Profile V.2 (consensus version dated 2023-12-06), which supersedes the 2012 v1.0 profile and adds 3T-specific guidance on B1 correction and parallel imaging, should be consulted for flip-angle, bandwidth, and slice-thickness requirements, since these are field-strength dependent.[4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n2. **Baseline T1 mapping.** A baseline T1 map, plus a B1 map at 3 T and above, allows conversion of signal intensity to contrast agent concentration. With variable flip angle data, T1 is computed as \\( T_{1} = -\\mathrm{TR}/\\ln(s) \\) from a line fit to points \\( (\\mathrm{SI}/\\tan\\theta, \\mathrm{SI}/\\sin\\theta) \\).[2](https://link.springer.com/article/10.1007/s00330-025-12306-5) • [4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n3. **Dynamic acquisition.** At least 5 baseline phases are acquired before injection; the routine gadolinium dose is 0.1 mmol/kg injected at 2–4 mL/s followed by a 20–30 mL saline flush, with temporal resolution below 10 s and at least 5 minutes of post-injection data.[4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n4. **Arterial input function.** The AIF, the contrast concentration in feeding blood, is measured directly (the gold standard is arterial blood sampling), taken from a population-averaged function, measured subject-specifically, derived from a reference tissue, or estimated jointly with the kinetic parameters.[10](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202)\n5. **Model fitting.** \\( K^{\\mathrm{trans}} \\) and \\( k_{ep} \\) are estimated voxel-wise from the Tofts model using Levenberg-Marquardt or Minpack-1 curve fitting, with delay correction of the vascular input function to match tumor arrival time per voxel.[4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n\n## Origin\n\nDCE-MRI was developed in the late 1980s, when hardware limitations imposed significant constraints on data quality.[11](https://beta.iopscience.iop.org/article/10.1088/0031-9155/57/2/R1) The graphical Patlak analysis, a precursor method for evaluating blood-to-brain transfer constants from multiple-time uptake data, was published by Clifford S. Patlak, Ronald G. Blasberg, and Joseph D. Fenstermacher in 1983 in the Journal of Cerebral Blood Flow & [Metabolism](https://www.edgepedia.org/metabolism).[12](https://doi.org/10.1038/jcbfm.1983.1) The founding pharmacokinetic models followed within two years: H. B. W. Larsson and colleagues quantified blood-brain barrier defect with gadolinium-DTPA in multiple sclerosis and brain tumors in 1990 in Magnetic Resonance in Medicine,[6](https://doi.org/10.1002/mrm.1910160111) Paul S. Tofts and Allan G. Kermode published their fundamental-concepts paper on measuring barrier permeability and leakage space in 1991, also in Magnetic Resonance in Medicine,[1](https://onlinelibrary.wiley.com/doi/10.1002/mrm.1910170208) and Gunnar Brix and colleagues published the Brix model for CNS Gd-DTPA imaging in 1991 in the Journal of Computer Assisted Tomography.[7](https://doi.org/10.1097/00004728-199107000-00018) A multi-author consensus paper standardized the quantities and mapped the earlier group-specific notations onto them.[3](https://doi.org/10.1002/%28sici%291522-2586%28199909%2910:3)\n\n## Variants\n\nParametric models divide into compartmental models (Larsson, Brix, Tofts and Kermode, extended Tofts-Kermode, two-compartment exchange, Patlak) and spatially distributed models (distributed parameter, tissue homogeneity, and the adiabatic approximation of the tissue homogeneity model, AATH).[10](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202)\n\n**Tofts versus extended Tofts.** The original Tofts-Kermode model depends on two parameters, \\( K^{\\mathrm{trans}} \\) and \\( v_{e} \\), and assumes weakly vascularized tissue (\\( v_{p} = 0 \\)); the extended model adds the intravascular term \\( v_{p} \\cdot C_{p}(t) \\) for applications such as tumors.[10](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202) QIBA recommends the extended Tofts model for brain tumors.[13](https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1380793/full)\n\n**Patlak and advanced models.** Patlak analysis is the graphical precursor method for blood-to-brain transfer constants.[12](https://doi.org/10.1038/jcbfm.1983.1) Tofts-family models use the single parameter \\( K^{\\mathrm{trans}} \\) to describe both blood flow and vessel wall permeability, whereas tissue homogeneity and distributed parameter models can estimate blood flow and permeability-surface area product separately under their assumptions; the Brix model is a separate pharmacokinetic model with its own parameterization.[13](https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1380793/full) Supporting methods include reference-tissue input functions introduced by David A. Kovar, Marta Lewis, and Gregory S. Karczmar in 1998,[14](https://doi.org/10.1002/jmri.1880080519) a reference-region model without an AIF published by Thomas E. Yankeelov and colleagues in 2005,[15](https://doi.org/10.1016/j.mri.2005.02.013) the population-averaged high-temporal-resolution AIF functional form published by Geoff J.M. Parker and colleagues in 2006,[16](https://doi.org/10.1002/mrm.21066) Bayesian pharmacokinetic estimation published by Volker J. Schmid and colleagues in 2006,[17](https://doi.org/10.1109/tmi.2006.884210) and computationally efficient sums-of-exponentials vascular input function models published by Matthew R. Orton and colleagues in 2008.[18](https://doi.org/10.1088/0031-9155/53/5/005)\n\n**Deep-learning estimation.** DCE-Qnet, a 7-layer neural network trained on simulated signals from the extended Tofts model with the Parker AIF, estimates \\( K^{\\mathrm{trans}} \\), \\( v_{p} \\), \\( v_{e} \\), tissue T1, proton density, and bolus arrival time from a single acquisition, outperforming nonlinear least-squares fitting in a phantom and eliminating the separate T1 scan for a reduction of 10 minutes per scan.[19](https://pubmed.ncbi.nlm.nih.gov/38827459/) An earlier LSTM-based approach for pharmacokinetic parameter estimation was published by Jiaren Zou, James M. Balter, and Yue Cao in 2020 in Medical Physics.[20](https://doi.org/10.1002/mp.14222)\n\n## Applications\n\n**Prostate.** Prostate DCE-MRI uses serial 3D T1-weighted fast spoiled gradient-echo acquisitions every few seconds for 5–10 minutes after a 2–4 mL/s gadolinium injection with a 20 mL saline flush; a European consensus recommends an optimal temporal resolution of 5 seconds with a maximum of 15 seconds.[21](https://www.ajronline.org/doi/10.2214/AJR.12.8510?doi=10.2214%2FAJR.12.8510)\n\n**Breast.** Clinical breast protocols emphasize spatial resolution over temporal resolution, acquiring one high-quality post-contrast set within 2 minutes of injection, with percent enhancement measured as \\( [S(t) - S_{0}]/S_{0} \\times 100\\% \\).[9](https://cds.ismrm.org/protected/09MProceedings/PDFfiles/Tues%20C37_06%20Su.pdf)\n\n**Brain and other tumors.** DCE-MRI-based blood-brain barrier permeability predicts hemorrhage risk after stroke treatment, and the method has prognostic value in locally advanced cervical cancer, where well-perfused tumors have better prognosis.[2](https://link.springer.com/article/10.1007/s00330-025-12306-5) In a 92-patient soft-tissue tumor study at 3.0 T (VIBE sequence, 8 s temporal resolution, 40 scans, 0.1 mmol/kg [gadoteridol](https://www.edgepedia.org/gadoteridol)), \\( K^{\\mathrm{trans}} \\) showed the best diagnostic performance among all tracer-kinetic parameters for distinguishing benign from malignant lesions.[22](https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-024-00710-x) Distributed-parameter model advantages over the Tofts model have been demonstrated in assessing IDH mutation status in glioma.[13](https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1380793/full)\n\n**Reproducibility.** Temporal resolution requirements depend on the target: perfusion measurement needs resolution below 4 s to capture the bolus first pass, while permeability can be measured at 10–20 s with acquisitions longer than 3 minutes.[2](https://link.springer.com/article/10.1007/s00330-025-12306-5) [Reproducibility](https://www.edgepedia.org/reproducibility) of \\( K^{\\mathrm{trans}} \\) is moderate and consistent across sites: a lung cancer study found a within-subject coefficient of variation of 16.45%, comparable to literature values of 11.0% and 20.1% in prostate, 15.6% in renal cancer, 20.3% in gynecological pelvic tumors, and 15.4% and 24% in colorectal liver metastases.[5](https://link.springer.com/article/10.1007/s00330-024-10970-7) The QIBA profile claims \\( K^{\\mathrm{trans}} \\) measurable with a 20% within-subject coefficient of variation for solid tumors at least 2 cm in diameter at 1.5 T.[4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n\n## Limitations and alternatives\n\n**AIF determination.** Accurate AIF measurement is difficult because of flow artifacts, inflow and nonlinear effects at high contrast concentrations, and partial volume effects; a subject-specific AIF requires a large feeding artery in the field of view, which may not exist for small lesions such as breast cancer.[10](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202) Reference-tissue and reference-region methods, which avoid an AIF, are one alternative.[14](https://doi.org/10.1002/jmri.1880080519) • [15](https://doi.org/10.1016/j.mri.2005.02.013)\n\n**T1 and flip-angle error.** Uncertainties in T1 map values and applied flip angle have been reported to cause errors of 88% in \\( K^{\\mathrm{trans}} \\) and 73% in \\( v_{e} \\); QIBA notes that variation in \\( R_{1} \\) greater than 15% from the true value may severely affect quantification reliability.[23](https://www.nature.com/articles/s41598-017-11554-w) • [4](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf) A 7-fold reduction in temporal resolution has been reported to decrease \\( K^{\\mathrm{trans}} \\) by up to 48%.[23](https://www.nature.com/articles/s41598-017-11554-w)\n\n**Model complexity and standardization.** The Tofts model combined with the Parker AIF requires 12 free parameters in total, making voxel-wise fitting prone to local minima.[10](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202) Absolute values depend on the analysis pipeline: in a xenograft study, median \\( K^{\\mathrm{trans}} \\) ranged from 0.09 to 0.18 min⁻¹ across analytical methods, and wCVs for \\( K^{\\mathrm{trans}} \\) ranged from 11.6% to 41.9%.[24](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0130168) A multi-institutional comparison of eleven published algorithms found less than 3% average error on noise-free digital reference objects, but with noise only 87% of \\( K^{\\mathrm{trans}} \\) algorithm-object combinations fell in the correct order.[23](https://www.nature.com/articles/s41598-017-11554-w) Different sequences, AIF choices, and post-processing algorithms make published studies difficult to compare,[21](https://www.ajronline.org/doi/10.2214/AJR.12.8510?doi=10.2214%2FAJR.12.8510) and adoption of quantitative DCE-MRI is hindered by variability from acquisition and analysis choices, vendor protocol differences, and incomplete, non-standardized reporting.[2](https://link.springer.com/article/10.1007/s00330-025-12306-5)\n\n## References\n\n1. [Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts (Tofts & Kermode, Magn Reson Med 1991)](https://onlinelibrary.wiley.com/doi/10.1002/mrm.1910170208)\n2. [ESR Essentials: Perfusion MRI, practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology](https://link.springer.com/article/10.1007/s00330-025-12306-5)\n3. [(sici)1522 2586(199909)10:3 (doi.org)](https://doi.org/10.1002/%28sici%291522-2586%28199909%2910:3)\n4. [RSNA QIBA Profile: DCE MRI Quantification v1.0](https://qibawiki.rsna.org/images/2/24/DCEMRI_Quantification_Profile_v1.0-Implementation-09MAY2012.pdf)\n5. [Measuring repeatability of DCE-MRI biomarkers improves evaluation of biological response to radiotherapy in lung cancer (European Radiology, 2024)](https://link.springer.com/article/10.1007/s00330-024-10970-7)\n6. [H. B. W. Larsson and colleagues (1990). Quantitation of blood‐brain barrier defect by magnetic resonance imaging and gadolinium‐DTPA in patients with multiple sclerosis and brain tumors. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.1910160111)\n7. [Gunnar Brix and colleagues (1991). Pharmacokinetic Parameters in CNS Gd-DTPA Enhanced MR Imaging. Journal of Computer Assisted Tomography.](https://doi.org/10.1097/00004728-199107000-00018)\n8. [Dynamic Contrast-Enhanced (DCE) MRI (Journal of Magnetic Resonance Imaging, 2023, PDF copy hosted on mriquestions.com)](https://ww-w.mriquestions.com/uploads/3/4/5/7/34572113/1-s2.0-s1064968923000788.pdf)\n9. [Analyzing DCE-MRI (ISMRM 2009 educational session)](https://cds.ismrm.org/protected/09MProceedings/PDFfiles/Tues%20C37_06%20Su.pdf)\n10. [Models and methods for analyzing DCE-MRI: A review (Khalifa et al., Medical Physics 2014)](https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4898202)\n11. [Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability (Sourbron & Buckley, Phys Med Biol 2012)](https://beta.iopscience.iop.org/article/10.1088/0031-9155/57/2/R1)\n12. [Clifford S. Patlak, Ronald G. Blasberg, Joseph D. Fenstermacher (1983). Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data. Journal of Cerebral Blood Flow & Metabolism.](https://doi.org/10.1038/jcbfm.1983.1)\n13. [Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging (Frontiers in Oncology, 2024)](https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1380793/full)\n14. [David A. Kovar, Marta Lewis, Gregory S. Karczmar (1998). A new method for imaging perfusion and contrast extraction fraction: Input functions derived from reference tissues. Journal of Magnetic Resonance Imaging.](https://doi.org/10.1002/jmri.1880080519)\n15. [Thomas E. Yankeelov and colleagues (2005). Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model. Magnetic Resonance Imaging.](https://doi.org/10.1016/j.mri.2005.02.013)\n16. [Geoff J.M. Parker and colleagues (2006). Experimentally‐derived functional form for a population‐averaged high‐temporal‐resolution arterial input function for dynamic contrast‐enhanced MRI. Magnetic Resonance in Medicine.](https://doi.org/10.1002/mrm.21066)\n17. [Volker J. Schmid and colleagues (2006). Bayesian Methods for Pharmacokinetic Models in Dynamic Contrast-Enhanced Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging.](https://doi.org/10.1109/tmi.2006.884210)\n18. [Matthew R Orton and colleagues (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology.](https://doi.org/10.1088/0031-9155/53/5/005)\n19. [DCE-Qnet: Deep Network Quantification of Dynamic Contrast Enhanced (DCE) MRI (PubMed abstract record)](https://pubmed.ncbi.nlm.nih.gov/38827459/)\n20. [Jiaren Zou, James M. Balter, Yue Cao (2020). Estimation of pharmacokinetic parameters from DCE‐MRI by extracting long and short time‐dependent features using an LSTM network. Medical Physics.](https://doi.org/10.1002/mp.14222)\n21. [Overview of Dynamic Contrast-Enhanced MRI in Prostate Cancer Diagnosis and Management (AJR)](https://www.ajronline.org/doi/10.2214/AJR.12.8510?doi=10.2214%2FAJR.12.8510)\n22. [Applying dynamic contrast-enhanced MRI tracer kinetic models to differentiate benign and malignant soft tissue tumors (Cancer Imaging, 2024)](https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-024-00710-x)\n23. [A Multi-Institutional Comparison of Dynamic Contrast-Enhanced MRI Parameter Calculations (Scientific Reports)](https://www.nature.com/articles/s41598-017-11554-w)\n24. [Dependence of DCE-MRI biomarker values on analysis algorithm (PLOS One)](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0130168)\n\n---\n*Topic: Encyclopedia › Life and health › Human health and medicine › Clinical assessment and procedures › Medical imaging and radiography › Magnetic resonance imaging techniques*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/dynamic-contrast-enhanced-mri", "markdown_url": "https://www.edgepedia.org/dynamic-contrast-enhanced-mri.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Dynamic contrast-enhanced MRI\", Edgepedia (EdgeChat), https://www.edgepedia.org/dynamic-contrast-enhanced-mri. Edgepedia Community License 1.0.", "credit_md": "\"[Dynamic contrast-enhanced MRI](https://www.edgepedia.org/dynamic-contrast-enhanced-mri)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/dynamic-contrast-enhanced-mri](https://www.edgepedia.org/dynamic-contrast-enhanced-mri). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Dynamic contrast-enhanced MRI\", Edgepedia (EdgeChat), https://www.edgepedia.org/dynamic-contrast-enhanced-mri. Edgepedia Community License 1.0.", "speakable": "Dynamic contrast-enhanced MRI is an MRI technique that rapidly images tissue before, during, and after gadolinium injection to quantify perfusion and vascular permeability, chiefly in oncology." }