Analysis Of Dynamic Contrast-enhanced Magnetic Resonance Imaging Data Research Articles

Exploring the potential of Physics-Informed Neural Networks to extract vascularization data from DCE-MRI in the presence of diffusion

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used to assess tissue vascularization, particularly in oncological applications. However, the most widely used pharmacokinetic (PK) models do not account for contrast agent (CA) diffusion between neighboring voxels, which can limit the accuracy of the results, especially in cases of heterogeneous tumors. To address this issue, previous works have proposed algorithms that incorporate diffusion phenomena into the formulation. However, these algorithms often face convergence problems due to the ill-posed nature of the problem.In this work, we present a new approach to fitting DCE-MRI data that incorporates CA diffusion by using Physics-Informed Neural Networks (PINNs). PINNs can be trained to fit measured data obtained from DCE-MRI while ensuring the mass conservation equation from the PK model. We compare the performance of PINNs to previous algorithms on different 1D cases inspired by previous works from literature. Results show that PINNs retrieve vascularization parameters more accurately from diffusion-corrected tracer-kinetic models. Furthermore, we demonstrate the robustness of PINNs compared to other traditional algorithms when faced with noisy or incomplete data. Overall, our results suggest that PINNs can be a valuable tool for improving the accuracy of DCE-MRI data analysis, particularly in cases where CA diffusion plays a significant role.

MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging \u2013 design, implementation and application on the example of DCE-MRI

BackgroundMany medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow.ResultsWe present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth.ConclusionsProviding a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.

Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (Ktrans) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.

ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies.

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising technique to characterize pathology and evaluate treatment response. However, analysis of DCE-MRI data is complex and benefits from concurrent analysis of multiple kinetic models and parameters. Few software tools are currently available that specifically focuses on DCE-MRI analysis with multiple kinetic models. Here, we developed ROCKETSHIP, an open-source, flexible and modular software for DCE-MRI analysis. ROCKETSHIP incorporates analyses with multiple kinetic models, including data-driven nested model analysis.ResultsROCKETSHIP was implemented using the MATLAB programming language. Robustness of the software to provide reliable fits using multiple kinetic models is demonstrated using simulated data. Simulations also demonstrate the utility of the data-driven nested model analysis. Applicability of ROCKETSHIP for both preclinical and clinical studies is shown using DCE-MRI studies of the human brain and a murine tumor model.ConclusionA DCE-MRI software suite was implemented and tested using simulations. Its applicability to both preclinical and clinical datasets is shown. ROCKETSHIP was designed to be easily accessible for the beginner, but flexible enough for changes or additions to be made by the advanced user as well. The availability of a flexible analysis tool will aid future studies using DCE-MRI.A public release of ROCKETSHIP is available at https://github.com/petmri/ROCKETSHIP.

Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of contrast leakage from the vascular tissue by using pharmacokinetic (PK) models. Such quantitative analysis of DCE-MRI data provides physiological parameters that are able to provide information of tumor pathophysiology and therapeutic outcome. Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue. In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initial promising results in the literature. To enhance the GCTT imaging biomarkers, we introduce a novel method for segmenting the tumor area into subregions according to their vascular heterogeneity characteristics. A cohort of 11 patients diagnosed with glioblastoma multiforme with known therapeutic outcome was used to assess the predictive value of both models in terms of correctly classifying responders and nonresponders based on only one DCE-MRI examination. The results indicate that GCTT model’s PK parameters perform better than those of ETM, while the segmentation of the tumor regions of interest based on vascular heterogeneity further enhances the discriminatory power of the GCTT model.

Automated localization of breast cancer in DCE-MRI.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly being used for the detection and diagnosis of breast cancer. Compared to mammography, DCE-MRI provides higher sensitivity, however its specificity is variable. Moreover, DCE-MRI data analysis is time consuming and depends on reader expertise. The aim of this work is to propose a novel automated breast cancer localization system for DCE-MRI. Such a system can be used to support radiologists in DCE-MRI analysis by marking suspicious areas. The proposed method initially corrects for motion artifacts and segments the breast. Subsequently, blob and relative enhancement voxel features are used to locate lesion candidates. Finally, a malignancy score for each lesion candidate is obtained using region-based morphological and kinetic features computed on the segmented lesion candidate. We performed experiments to compare the use of different classifiers in the region classification stage and to study the effect of motion correction in the presented system. The performance of the algorithm was assessed using free-response operating characteristic (FROC) analysis. For this purpose, a dataset of 209 DCE-MRI studies was collected. It is composed of 95 DCE-MRI studies with 105 breast cancers (55 mass-like and 50 non-mass-like malignant lesions) and 114 DCE-MRI studies from women participating in a screening program which were diagnosed to be normal. At 4 false positives per normal case, 89% of the breast cancers (91% and 86% for mass-like and non-mass-like malignant lesions, respectively) were correctly detected.

Lesion segmentation and identification of breast tumor on dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can show subtle lesion morphology, improve the display of lesion definitions, and objectively reflect the blood supply of breast tumors; it can also reflect different strengthening patterns of normal tissues and lesion areas after medical tracer injection. DCE-MRI has become an important basis for the clinical diagnosis of breast cancer. To DCE-MRI data acquired from several hospitals across multiple provinces, a series of in-silico computational methods were applied for lesion segmentation and identification of breast tumor in this paper. The image segmentation methods include Otsu segmentation of subtraction images, signal-interference-ratio segmentation method and an improved variational level set method, each has its own application scope. After that, the distribution of benign and malignant in lesion region is identified based on three-time-point theory. From the experiment, the analysis of DCE-MRI data of breast tumor can show the distribution of benign and malignant in lesion region, provide a great help for clinicians to diagnose breast cancer more expediently and lay a basis for medical diagnosis and treatment planning.

A Review Study on the Efficacy of Quantitative DCE-MRI in Breast Lesion Diagnosis

: Breast cancer is the most common cancer among women, for which a successful treatment depends in part, on early diagnosis. Accurate differentiation of lesions may greatly affect the outcome of treatment. In this regard, anatomical along with physiological MR imaging modalities, such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) which provide accurate, repeatable and noninvasive physiological biomarkers of tumor growth, have been increasingly used as secondary characterizing tools to ultrasonography (US) and mammography. The aim of this study is to present a thorough review on available scholarly works, especially where quantitative DCE-MRI has been employed for diagnostic decisions on breast cancer, concluding if the modality is either necessary for better sensitivity/specificity, or for a reliable surgery/treatment planning. US and mammography are the conventional screening modalities for breast cancer diagnosis. The overall sensitivity of mammography is 70%90%. However, this sensitivity is variable and can range from as high as 80%98% in women with fatty breast tissue to as low as 30%48% in women with dense breast tissue. Sonographic classification of benign and malignant tumors has a high sensitivity of as high as 98% but a low specificity of about 32%. The low specificity of mammography and US methods and the need for extracting biomarkers, has led to rising interest in employing physiological MR imaging methods for characterizing breast cancers. DCE-MR images can be analyzed in a semi-quantitative (non-model-abased) or quantitative (model-based) approach. The sensitivity and specificity of semi-quantitative DCE-MRI in detecting breast cancer reaches 88.1% and 67.7%, respectively. While it has been proved that DCE-MR imaging features could be useful to noninvasively distinguish highly aggressive breast carcinomas, they are not as useful in discriminating benign from malignant non-mass lesions (such as DCIS). On the other hand, the pharmacokinetic (or model-based) analysis of DCE-MRI can be useful in monitoring and predicting response to therapy, as the changes in tumor vascularization in responder patients precede changes in tumor size, and may better describe tumor response to therapy. Pharmacokinetic model analysis of DCE-MRI data provided a sensitivity and specificity of 100% and 78%, respectively. In comparison with semi-quantitative DCE-MRI, Quantitative DCE-MRI has significantly improved the sensitivity and specificity of breast cancer diagnosis. Other physiological MR imaging alternatives like DW-MRI and MRS have also been employed for breast cancer diagnosis. In a pooled analysis on clinical breast MRS gathered from five independent centers around the world, the sensitivity and specificity of breast MRS were 83% and 85%, respectively. In a study on 78 patients (110 lesions), the sensitivity and specificity of DWI in detection of malignant and benign lesions was reported at 86%. Although the sensitivity and specificity of imaging could be influenced by the selected values of ADC cut-off, the ADC-values may still be used to characterize breast lesions. The diagnosis of breast cancer is a very challenging issue, as early detection may allow for conservative surgery as an alternative to mastectomy. Although mammography and US allow earlier detection of cancers and have been shown to improve the survival of women with breast cancer, with a relatively high sensitivity in women older than 50 years, monitoring the breast cancer is limited by low specificity. DCE-MRI is a powerful imaging tool that has shown utility in delineating the extent of disease in patients with known breast cancer and in screening women at high risk for breast cancer. Semi-quantitative DCE-MRI has high sensitivity but only moderate specificity, because of an overlap between benign and malignant lesions, independent of breast density, tumor type, and menopausal status. Quantitative analysis of DCE-MRI is used by many researchers for detecting and evaluating breast diseases and is a means of increasing the specificity of breast MR examination. MRS of the breast has been proposed as an adjunct to the anatomical MRI examination to improve the specificity of distinguishing malignant from benign breast tumors, however, MRS alone is not suitable in lactating women. Nevertheless, many studies have reported that DWI-MRI may help to differentiate between benign and malignant lesions with good specificity. These findings suggest that incorporating MRS and DW-MRI may increase the overall specificity of quantitative analysis of breast DCE-MRI.

GPU-accelerated nonparametric kinetic analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is often used to examine vascular function in malignant tumors and noninvasively monitor drug efficacy of antivascular therapies in clinical studies. However, complex numerical methods used to derive tumor physiological properties from DCE-MRI images can be time-consuming and computationally challenging. Recent advancement of computing technology in graphics processing unit (GPU) makes it possible to build an energy-efficient and high-power parallel computing platform for solving complex numerical problems. This study develops the first reported fast GPU-based method for nonparametric kinetic analysis of DCE-MRI data using clinical scans of glioblastoma patients treated with bevacizumab (Avastin®). In the method, contrast agent concentration-time profiles in arterial blood and tumor tissue are smoothed using a robust kernel-based regression algorithm in order to remove artifacts due to patient motion and then deconvolved to produce the impulse response function (IRF). The area under the curve (AUC) and mean residence time (MRT) of the IRF are calculated using statistical moment analysis, and two tumor physiological properties that relate to vascular permeability, volume transfer constant between blood plasma and extravascular extracellular space (Ktrans) and fractional interstitial volume (ve) are estimated using the approximations AUC/MRT and AUC. The most significant feature in this method is the use of GPU-computing to analyze data from more than 60,000 voxels in each DCE-MRI image in parallel fashion. All analysis steps have been automated in a single program script that requires only blood and tumor data as the sole input. The GPU-accelerated method produces Ktrans and ve estimates that are comparable to results from previous studies but reduces computational time by more than 80-fold compared to a previously reported central processing unit-based nonparametric method. Furthermore, it is at least several orders of magnitudes faster than standard parametric methods that perform compartmental modeling. This finding indicates that the GPU-based method can significantly shorten the computational times required to assess tumor physiology from DCE-MRI data in preclinical and clinical development of antivascular therapies.

Assessment of the interstitial fluid pressure of tumors by dynamic contrast-enhanced magnetic resonance imaging with contrast agents of different molecular weights

Background. Cancer patients showing highly elevated interstitial fluid pressure (IFP) in the primary tumor may benefit from particularly aggressive treatment. There is some evidence that gadolinium diethylene-triamine penta-acetic acid (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may be a useful non-invasive method for providing information on the IFP of tumors. The purpose of this preclinical study was to investigate whether any association between DCE-MRI-derived parametric images and tumor IFP can be strengthened by using MR contrast agents with higher molecular weights than that of Gd-DTPA. Material and methods. A-07 human melanoma xenografts were used as preclinical models of human cancer. Three contrast agents were compared: Gd-DTPA (0.55 kDa), P846 (3.5 kDa), and gadomelitol (6.5 kDa). A total of 46 tumors were subjected to DCE-MRI and subsequent measurement of IFP. Parametric images of Ktrans (the volume transfer constant of the contrast agent) and ve (the fractional distribution volume of the contrast agent) were produced by pharmacokinetic analysis of the DCE-MRI series. Results. Significant inverse correlations were found between median Ktrans and IFP for Gd-DTPA (p = 0.0076; R2 = 0.46; n = 14) and P846 (p = 0.0042; R2 = 0.45; n = 16), whereas there was no correlation between median Ktrans and IFP for gadomelitol (p > 0.05; n = 16). Significant correlation between median ve and IFP was not found for any of the contrast agents (p > 0.05 for Gd-DTPA, P846, and gadomelitol). Conclusion. Ktrans images, but not ve images, derived by pharmacokinetic analysis of DCE-MRI data for low-molecular-weight contrast agents may provide information on the IFP of tumors. Any association between Ktrans and IFP cannot be expected to be improved by using contrast agents with higher molecular weights than those of Gd-DTPA and P846.

Quantification of Antiangiogenic and Antivascular Drug Activity by Kinetic Analysis of DCE-MRI Data

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to quantify the response of tumors to vascular targeting agents. Tumor response is frequently assessed using mathematical models that describe the distribution of contrast agents over time as a function of fundamental characteristics of vascular physiology. Generally, mathematical models of biological systems are abstractions that attempt to retain fundamental physiologic characteristics, thereby allowing for multiple potential modeling approaches and structures. Various DCE-MRI modeling techniques are discussed in this article.

Fundamentals of tracer kinetics for dynamic contrast‐enhanced MRI

Tracer kinetic methods employed for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) share common roots with earlier tracer studies involving arterial-venous sampling and other dynamic imaging modalities. This article reviews the essential foundation concepts and principles in tracer kinetics that are relevant to DCE MRI, including the notions of impulse response and convolution, which are central to the analysis of DCE MRI data. We further examine the formulation and solutions of various compartmental models frequently used in the literature. Topics of recent interest in the processing of DCE MRI data, such as the account of water exchange and the use of reference tissue methods to obviate the measurement of an arterial input, are also discussed. Although the primary focus of this review is on the tracer models and methods for T(1) -weighted DCE MRI, some of these concepts and methods are also applicable for analysis of dynamic susceptibility contrast-enhanced MRI data.

Sorafenib (S) with preoperative chemoradiotherapy for extremity soft tissue sarcomas (STS) and evaluation by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

TPS335 Background: We previously reported on the treatment of AJCC stage III extremity STS with 3 cycles of preoperative epirubicin/ifosfamide (E/I) and hypofractionated radiation (RT) followed by 3 cycles of E/I after resection (Ryan et al., Cancer 2008). Phase II studies have shown single-agent activity of S in advanced sarcoma. We hypothesize that the addition of S to our chemoradiotherapy regimen is safe and will improve outcomes by potentiating the effects on the local tumor and enhancing the effects on micrometastatic disease. DCE-MRI is a functional imaging modality that can detect changes in blood flow and/or vessel wall permeability within a tumor. The “Shutter-Speed” Model (SSM) (Li et al., Magn Reson Med 2005) is a unique method of quantitative DCE-MRI data analysis that is more accurate in estimating pharmacokinetic parameters such as Ktrans and ve compared to the conventional Tofts' model (Tofts et al., J Magn Reson Imaging 1999). These biomarkers may be useful in early assessment of activity...

Quantitative Analysis of Dynamic Contrast Enhanced MRI for Assessment of Bowel Inflammation in Crohn's Disease: Pilot Study

The aim of this study was to evaluate the feasibility of quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data in the detection of bowel inflammation in patients with Crohn's disease. Eleven patients who underwent magnetic resonance enterography for known or suspected Crohn's disease and had colonoscopy or surgery within 4 weeks of imaging were included in this study. DCE-MRI data were acquired using a 1.5-T scanner with temporal resolution of 5 to 12 seconds for approximately 5 to 7 minutes. A two-compartment pharmacokinetic model was used to analyze the data to obtain the volume transfer constant (K(trans)) and the extravascular extracellular space (v(e)). Additionally, semiquantitative parameters were derived using an empirical mathematical model to fit the contrast concentration curves. Endoscopic, surgical, and pathologic results were compared to the MRI data. Receiver-operating characteristic analysis was performed to compare the diagnostic accuracy of the parameters in the task of distinguishing normal tissue from inflammation. A total of 51 bowel segments (19 with inflammation, 32 normal) were included in the analyses. Inflamed bowel segments had faster K(trans) values, larger v(e) values, increased contrast uptake, larger initial areas under the contrast concentration curve, and steeper initial enhancement slopes than normal bowel segments (P < .05). The areas under the receiver-operating characteristic curve for these parameters ranged from 0.70 to 0.86. These preliminary results demonstrate that the quantitative analysis of DCE-MRI data is possible for the assessment of bowel inflammation in patients with Crohn's disease. Future studies need be performed on larger numbers of patients to correlate the severity and type of inflammation with kinetic parameters.

Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE‐MRI

To develop a methodology for improved estimation of bolus arrival time (BAT) and arterial input function (AIF) which are prerequisites for tracer kinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and to verify the applicability of the same in the case of intracranial lesions (brain tumor and tuberculoma). A continuous piecewise linear (PL) model (with BAT as one of the free parameters) is proposed for concentration time curve C(t) in T(1)-weighted DCE-MRI. The resulting improved procedure suggested for automatic extraction of AIF is compared with earlier methods. The accuracy of BAT and other estimated parameters is tested over simulated as well as experimental data. The proposed PL model provides a good approximation of C(t) trends of interest and fit parameters show their significance in a better understanding and classification of different tissues. BAT was correctly estimated. The automatic and robust estimation of AIF obtained using the proposed methodology also corrects for partial volume effects. The accuracy of tracer kinetic analysis is improved and the proposed methodology also reduces the time complexity of the computations. The PL model parameters along with AIF measured by the proposed procedure can be used for an improved tracer kinetic analysis of DCE-MRI data.

Assessment of extravascular extracellular space fraction in human melanoma xenografts by DCE-MRI and kinetic modeling

Tumor aggressiveness and response to therapy are influenced by the extravascular extracellular space fraction (EESF) of the malignant tissue. The EESF may, therefore, be an important prognostic parameter for cancer patients. The aim of this study was to investigate whether gadopentetate dimeglumine (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to assess the EESF of tumors. Amelanotic human melanoma xenografts (A-07, R-18) were used as preclinical models of human cancer. Images of E.F (E is the initial extraction fraction of Gd-DTPA and F is perfusion) and lambda (the partition coefficient of Gd-DTPA) were obtained by Kety analysis of DCE-MRI data. Our study was based on the hypothesis that lambda is governed by the EESF and is not influenced significantly by microvascular density (MVD) or blood perfusion. To test this hypothesis, we searched for correlations between lambda and E.F, MVD or EESF by comparing lambda images with E.F images, histological preparations from the imaged tissue and the radial heterogeneity in EESF obtained by invasive imaging. Positive correlations were found between lambda and EESF. Thus, median lambda was larger in A-07 tumors than in R-18 tumors by a factor of 4.2 (P<.00001), consistent with the histological observation that EESF is approximately fourfold larger in A-07 tumors than in R-18 tumors. The radial heterogeneity in lambda in A-07 and R-18 tumors was almost identical to the radial heterogeneity in EESF. Moreover, lambda was larger in tissue regions with high EESF than in tissue regions with low EESF in A-07 tumors (P=.048). On the other hand, significant correlations between lambda and MVD or E.F could not be detected. Consequently, Kety analysis of Gd-DTPA-based DCE-MRI series of xenografted tumors provides lambda images that primarily reflect the EESF of the tissue.

Magnetic Resonance Macromolecular Agents for Monitoring Tumor Microvessels and Angiogenesis Inhibition

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using macromolecular contrast media enables assessments of the tumor vasculature based on the differential distribution of the contrast agent within normal and pathologic tissues. Quantitative assays of both morphologic and functional properties can provide useful diagnostic insight into tissue angiogenesis. The use of MRI enhanced with macromolecular agents for the characterization of tumor microvessels has been experimentally demonstrated in a range of malignant tumor types. Kinetic analysis of DCE-MRI data can be used to estimate microvascular permeability and tumor blood volume. By measuring these functional tumor properties, an accurate, noninvasive, and quantitative description of the microcirculation of individual tumors can be acquired, improving the specificity of imaging examinations for cancer diagnosis and for treatment and follow up. The noninvasive MRI assessment of tumor angiogenesis can be applied in the diagnostic differentiation between benign and malignant tumors and can also provide means for in vivo monitoring of antitumor therapy. In this review, the potential clinical applications and limitations of various macromolecular contrast agents applied for evaluations of tumor angiogenesis, with and without drug interventions, are discussed.

Assessment of fraction of radiobiologically hypoxic cells in human melanoma xenografts by dynamic contrast‐enhanced MRI

A noninvasive method for assessment of the extent of hypoxia in experimental and human tumors is highly needed. In this study, the potential usefulness of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was investigated, using gadopentetate dimeglumine (Gd-DTPA) as contrast agent and A-07 human melanoma xenografts as tumor model. DCE-MRI was performed at a voxel size of 0.3 x 0.6 x 2.0 mm3 with spoiled gradient-recalled sequences. Images of E . F (E is the initial extraction fraction of Gd-DTPA and F is perfusion) and lambda (the partition coefficient of Gd-DTPA, which is proportional to extracellular volume fraction) were obtained by Kety analysis of DCE-MRI data. The study was based on the hypothesis that hypoxic tissue would have low E . F (i.e., poor oxygen supply) and/or low lambda (i.e., high cell density and, hence, high oxygen consumption rate). Twenty-two tumors were first subjected to DCE-MRI and then to measurement of fraction of hypoxic cells, using a radiobiological assay. E . F was found to be strongly correlated to fraction of hypoxic cells (P < 0.000001), whereas significant correlation between lambda and fraction of hypoxic cells could not be detected. It is thus possible that E . F may be a useful parameter for the extent of hypoxia in experimental and human tumors with physiologic properties similar to those of A-07 tumors. This possibility warrants further studies involving experimental tumors of several lines, as well as human tumors.

Visual exploratory analysis of DCE-MRI data in breast cancer by dimensional data reduction: A comparative study

One trend in modern medical imaging is the growing signal dimension in new multi-modal or multivariate imaging approaches. To analyze such high dimensional data, new approaches need to be proposed and evaluated. The scope of this study is to investigate the potential of three different algorithms for dimensional data reduction for the visual exploration of biomedical signals arising from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) applied to breast cancer detection. The algorithms employed are the established Principal Component Analysis (PCA) and Self-Organizing Maps (SOM), and the recently proposed Locally Linear Embedding (LLE). The experimental dataset comprises the time-series associated with the voxels of six benign and six malignant breast tumors. In order to visually explore the dataset, the multi-dimensional signal space of all the time-series is projected into a two-dimensional space by PCA, SOM and LLE, respectively. We show how the visualization of the respective projected spaces with customized colors can allow the user to discover hidden regularities in the data, in particular with regard to the differentiation between benign and malignant lesions. The performances of the three algorithms are quantitatively compared, while discussing their advantages and drawbacks.

Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumor perfusion, microvascular vessel wall permeability and extravascular-extracellular volume fraction. Analysis of DCE-MRI data is usually based on indicator dilution theory that requires knowledge of the concentration of the contrast agent in the blood plasma, the arterial input function (AIF). A method is presented that compares the tissues of interest (TOI) curve shape to that of a reference region (RR), thereby eliminating the need for direct AIF measurement. By assigning literature values for Ktrans (the blood perfusion-vessel permeability product) and v(e) (extravascular-extracellular volume fraction) in a reference tissue, it is possible to extract the Ktrans and v(e) values for a TOI without knowledge of the AIF. The operational RR equation for DCE-MRI analysis is derived, and its sensitivity to noise and incorrect assignment of the RR parameters is tested via simulations. The method is robust at noise levels of 10%, returning accurate (+/-20% in the worst case) and precise (+/-15% in the worst case) values. Errors in the TOI Ktrans and v(e) values scale approximately linearly with the errors in the assigned RR Ktrans and v(e) values. The methodology is then applied to a Lewis Lung Carcinoma mouse tumor model. A slowly enhancing TOI yielded Ktrans=0.039+/-0.002 min-1 and v(e)=0.46+/-0.01, while a rapidly enhancing region yielded Ktrans=0.35+/-0.05 min-1 and v(e)=0.31+/-0.01. Parametric Ktrans and v(e) mappings manifested a tumor periphery with elevated Ktrans (>0.30 min-1) and v(e) (>0.30) values. The main advantage of the RR approach is that it allows for quantitative assessment of tissue properties without having to obtain high temporal resolution images to characterize an AIF. This allows for acquiring images with higher spatial resolution and/or SNR, and therefore, increased ability to probe tissue heterogeneity.