Modeling the effect of intra-voxel diffusion of contrast agent on the quantitative analysis of dynamic contrast enhanced magnetic resonance imaging

Current status in spatiotemporal analysis of contrast-based perfusion MRI

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety-Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, K^trans (volume transfer constant) and v_e (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the K^trans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

Quantitative perfusion parameters of benign inflammatory breast pathologies: A descriptive study

PURPOSE

With this study, we evaluated the perfusion magnetic resonance imaging (MRI) features of benign inflammatory breast lesions for the first time and compared their K_trans, K_ep, V_e values and contrast kinetic curves to benign masses and invasive ductal carcinoma (IDC).

MATERIALS AND METHODS

Perfusion MRIs of the benign masses (n = 42), inflammatory lesions (n = 25), and IDCs (n = 16) were evaluated retrospectively in terms of K_trans, K_ep, V_e values and contrast kinetic curves and compared by the Kruskal-Wallis, Mann-Whitney U, chi-square tests statistically. Cronbach α test was used to measure intraobserver and interobserver reliability.

RESULTS

Mean K_trans values were 0.052 for benign masses, 0.086 for inflammatory lesions and 0.101 for IDC (p < 0.001). Mean K_ep values were 0.241 for benign masses, 0.435 for inflammatory lesions and 0.530 for IDC (p < 0.001). Mean V_e values were 0.476 for benign masses, 0.318 for inflammatory lesions and 0.310 for IDC (p = 0.067). For inflammatory and IDC lesions, K_trans and K_ep values were found to be higher and V_e values were lower than benign masses (p = 0.001 for K_trans, p = 0.001 for K_ep, p = 0.045 for V_e). There were excellent or good intra-interobserver reliabilities. For the kinetic curve pattern, most of the benign lesions showed progressive (81%), inflammatory lesions progressive (64%) and IDC lesions plateau (75%) patterns (p < 0.001).

CONCLUSIONS

On T1 perfusion MRI, similar to IDC lesions, inflammatory lesions demonstrate higher K_trans and K_ep and lower V_e values than benign masses. Quantitative perfusion parameters are not helpful in differentiating them from IDC lesions.

The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains

PURPOSE

Quantitative evaluation of dynamic contrast enhanced MRI (DCE-MRI) allows for estimating perfusion, vessel permeability, and tissue volume fractions by fitting signal intensity curves to pharmacokinetic models. These compart mental models assume rapid equilibration of contrast agent within each voxel. However, there is increasing evidence that this assumption is violated for small molecular weight gadolinium chelates. To evaluate the error introduced by this invalid assumption, we simulated DCE-MRI experiments with volume fractions computed from entire histological tumor cross-sections obtained from murine studies.

METHODS

A 2D finite element model of a diffusion-compensated Tofts-Kety model was developed to simulate dynamic T₁ signal intensity data. Digitized histology slices were segmented into vascular (v_p ), cellular and extravascular extracellular (v_e ) volume fractions. Within this domain, K^trans (the volume transfer constant) was assigned values from 0 to 0.5 min^-1 . A representative signal enhancement curve was then calculated for each imaging voxel and the resulting simulated DCE-MRI data analyzed by the extended Tofts-Kety model.

RESULTS

Results indicated parameterization errors of -19.1% ± 10.6% in K^trans , -4.92% ± 3.86% in v_e , and 79.5% ± 16.8% in v_p for use of Gd-DTPA over 4 tumor domains.

CONCLUSION

These results indicate a need for revising the standard model of DCE-MRI to incorporate a correction for slow diffusion of contrast agent. Magn Reson Med 80:330-340, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A continuous-infusion dynamic MRI model at 3.0 Tesla for the serial quantitative evaluation of microvascular proliferation in an animal model of glioblastoma multiforme

PURPOSE

To develop a continuous-infusion dynamic MRI technique to characterize tumor-associated microvascular proliferation (MVP) in a rat brain model of glioblastoma multiforme.

METHODS

The proposed model assumes effects due to tumor-associated MVP (eg, vascular permeability, K^trans ; intravascular plasma fraction, v_p ) cannot be individually separated and solves for a single parameter (k^vasc ) that quantifies the T₁ -weighted contrast enhancement from dynamic images acquired during continuous contrast agent (CA) infusion. Untreated C6 tumor-bearing animals (N = 6) were serially imaged on postoperative days (PODs) 14 and 18 with a 3 Tesla clinical scanner utilizing a dynamic spatial and temporal resolution of 0.38 × 0.38 × 1.5 mm³ and 3.47 s, respectively.

RESULTS

An association was present between PODs 14 and 18 for median tumor k^vasc (Pearson's r = 0.94, P = 0.0052) and CA concentration ([CA], derived from pre- and postcontrast R₁ maps; r = 0.94, P = 0.0054). On POD 18, there was a voxel-based association between k^vasc and [CA] within each tumor (0.45 < r < 0.82, P < 0.001). However, voxel-based subregions demonstrated a reduced association between k^vasc and [CA] (N = 5; -0.08 < r < 0.22, P > 0.05) or an inverse association (N = 1; r = -0.28, P = 0.001), indicating differences between locations of vascular permeability and subsequent CA pooling in tumors.

CONCLUSION

The continuous-infusion method may provide a quantitative measure for characterizing and monitoring tumor-associated MVP. Magn Reson Med 78:1824-1838, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

MR Imaging Biomarkers in Oncology Clinical Trials

The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.

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

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 < 0.01, respectively). Median ve calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between ve and extracellular space was calculated (p = 0.02), while MDA-MB-231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and ve with either whole tumor analysis or central slice analysis (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.