Purpose:
To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.
Methods:
DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA’s perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration–time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.
Results:
Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.
Conclusions:
Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1118/1.4898202

Models and methods for analyzing DCE-MRI: A review

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics).

Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and Interpretation

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion-weighted (DW) MRI), the extravascular, extracellular volume fraction (ve , from dynamic contrast-enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA-MB-231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n = 6), or saline (n = 7 for each cell line), and imaged using DW- and DCE-MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E-stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. For both BT474 and MDA-MB-231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p 0.05). While ADC correlates well with the histologically determined fraction of extracellular space, these data add to the growing body of literature that suggests that ve derived from DCE-MRI is not a reliable biomarker of extracellular space for a range of physiological conditions.

/pdf/correlation-of-tumor-characteristics-derived-from-dce-mri-3o5twxfdcs.pdf

Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer.

Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts-Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p ≪ 0.001). In poorly perfused (i.e., K(trans) ≤ 0.05 min(-1)) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low K(trans) values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.

/pdf/a-diffusion-compensated-model-for-the-analysis-of-dce-mri-4gu40iz06q.pdf

A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results.

Disclosed are methods for quantitatively predicting the severity of a tumor in a subject. In some embodiments, the methods further comprise selecting a course of therapy for the subject. In some embodiments, the tumor comprises is non-small cell lung cancer.

Quantitative predictors of tumor severity

Purpose: To describe and determine the reproducibility of a simplified model to quantitatively measure heterogeneous intralesion contrast agent diffusion in colorectal liver metastases. Materials and Methods: This HIPAA-compliant retrospective study received institutional review board approval, and written informed consent was obtained from 14 patients (mean age, 61 years ± 9 [standard deviation]; range, 41–78 years), including 10 men (mean age, 65 years ± 8; range, 47–78 years) and four women (mean age, 54 years ± 9; range, 41–59 years), with colorectal liver metastases. Magnetic resonance (MR) imaging was performed twice (first baseline MR image [B1] and second baseline MR image [B2]) in a single target lesion prior to therapy. Dynamic contrast material–enhanced MR imaging was performed by using a saturation-recovery fast gradient-echo sequence. A simplified contrast agent diffusion model was proposed, and a contrast agent diffusion coefficient (CDC) was calculated. The reproducibility of the CDC measur...

Colorectal liver metastases: contrast agent diffusion coefficient for quantification of contrast enhancement heterogeneity at MR imaging.

GuangJia,PhDCraigO’Dell,BSJohannesT.Heverhagen,MD,PhDXiangyuYang,BSJiachaoLiang,MSRichardV.Jacko,BSSteffenSammet,MD,PhDTheodorePellas,VMD,PhDPatriciaCole,MD,PhDMichaelV.Knopp,MD,PhDPurpose: To describe and determine the reproducibility of a simpli-ﬁed model to quantitatively measure heterogeneous in-tralesion contrast agent diffusion in colorectal liver metas-tases.Materials andMethods:This HIPAA-compliant retrospective study received insti-tutional review board approval, and written informed con-sent was obtained from 14 patients (mean age, 61 years 9 [standard deviation]; range, 41–78 years), including 10men (mean age, 65 years 8; range, 47–78 years) andfourwomen(meanage,54years 9;range,41–59years),withcolorectallivermetastases.Magneticresonance(MR)imaging was performed twice (ﬁrst baseline MR image[B

Colorectal Liver Metastases: ContrastAgentDiffusionCoefficient forQuantificationofContrast EnhancementHeterogeneityatMR

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Colorectal liver metastases: contrast agent diffusion coefficient for quantification of contrast enhancement heterogeneity at MR imaging.

Colorectal Liver Metastases: ContrastAgentDiffusionCoefficient forQuantificationofContrast EnhancementHeterogeneityatMR