Dynamic Contrast-enhanced CT of Intracranial Meningioma: Comparison of Distributed and Compartmental Tracer Kinetic Models—Initial Results

Comparison of tracer kinetic models in differentiating malignant from normal prostate tissue using dynamic contrast-enhanced MRI

Purpose

The aim of this study was to evaluate the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived kinetic parameters with high spatiotemporal resolution in discriminating malignant from normal prostate tissues.

Methods

Fifty patients with suspicious of malignant diseases in prostate were included in this study. Regions of interest (ROI) were manually delineated by experienced radiologists. Voxel-wise kinetic parameters were produced with the following tracer kinetic models (TKMs): Tofts model, extended Tofts model (ETM), Brix's conventional two-compartment model (Brix), adiabatic tissue homogeneity model (ATH), and distributed parameter model (DP). The initial area under the signal-time curve (IAUC) with an uptake integral approach was also included. Mann-Whitney U test and receiver operating characteristic (ROC) curves were used to evaluate the capability of distinguishing tumor lesions from normal tissues. A p-value of 0.05 or less is considered statistically significant. ROI based parameters correlation analysis between DP and ETM were performed.

Results

624 lesions and 269 normal tissue ROIs were obtained. Thirty parameters were derived from the six kinetic models. Except for PS from Brix, statistically significant differences between lesions and normal tissues (P<0.05) were observed in other parameters.Ve from DP, ATH and Brix and PS from ATH have AUC values less than 0.6 in the ROC analysis. MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, IAUC parameters and F from Brix have AUC values larger than 0.8. Ve and Vp from DP and ETM are correlated (r> 0.65). The correlation coefficient between Ktrans from ETM and PS from DP is 0.751.

Conclusion

MTT, Vp and PS from DP, Ktrans from ETM and Tofts, E and PS from ATH, F from Brix and IAUC parameters can be used to differentiate malignant lesions from normal tissues in the prostate.

Exercise-induced calf muscle hyperemia: Rapid mapping of magnetic resonance imaging using deep learning approach

Exercise-induced hyperemia in calf muscles was recently shown to be quantifiable with high-resolution magnetic resonance imaging (MRI). However, processing of the MRI data to obtain muscle-perfusion maps is time-consuming. This study proposes to substantially accelerate the mapping of muscle perfusion using a deep-learning method called artificial neural network (NN). Forty-eight MRI scans were acquired from 21 healthy subjects and patients with peripheral artery disease (PAD). For optimal training of NN, different training-data sets were compared, investigating the effect of data diversity and reference perfusion accuracy. Reference perfusion was estimated by tracer kinetic model fitting initialized with multiple values (multigrid model fitting). Result: The NN method was much faster than tracer kinetic model fitting. To generate a perfusion map of matrix 128 × 128 on a same computer, multigrid model fitting took about 80 min, single-grid or regular model fitting about 3 min, while the NN method took about 1 s. Compared to the reference values, NN trained with a diverse group gave estimates with mean absolute error (MAE) of 15.9 ml/min/100g and correlation coefficient (R) of 0.949, significantly more accurate than regular model fitting (MAE 22.3 ml/min/100g, R 0.889, p < .001). Conclusion: the NN method enables rapid perfusion mapping, and if properly trained, estimates perfusion with accuracy comparable to multigrid model fitting.

Angiogenesis in Hepatocellular Carcinoma; Pathophysiology, Targeted Therapy, and Role of Imaging

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, usually occurring on a background of liver cirrhosis. HCC is a highly vascular tumor in which angiogenesis plays a major role in tumor growth and spread. Tumor-induced angiogenesis is usually related to a complex interplay between multiple factors and pathways, with vascular endothelial growth factor being a major player in angiogenesis. In the past decade, understanding of tumor-induced angiogenesis has led to the emergence of novel anti-angiogenic therapies, which act by reducing neo-angiogenesis, and improving patient survival. Currently, Sorafenib and Lenvatinib are being used as the first-line treatment for advanced unresectable HCC. However, a disadvantage of these agents is the presence of numerous side effects. A major challenge in the management of HCC patients being treated with anti-angiogenic therapy is effective monitoring of treatment response, which decides whether to continue treatment or to seek second-line treatment. Several criteria can be used to assess response to treatment, such as quantitative perfusion on cross-sectional imaging and novel/emerging MRI techniques, including a host of known and emerging biomarkers and radiogenomics. This review addresses the pathophysiology of angiogenesis in HCC, accurate imaging assessment of angiogenesis, monitoring effects of anti-angiogenic therapy to guide future treatment and assessing prognosis.

A Comparative Study of Two-Compartment Exchange Models for Dynamic Contrast-Enhanced MRI in Characterizing Uterine Cervical Carcinoma

A variety of tracer kinetic methods have been employed to assess tumor angiogenesis. The Standard two-Compartment model (SC) used in cervix carcinoma was less frequent, and Adiabatic Approximation to the Tissue Homogeneity (AATH) and Distributed Parameter (DP) model are lacking. This study compares two-compartment exchange models (2CXM) (AATH, SC, and DP) for determining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in cervical cancer, with the aim of investigating the potential of various parameters derived from 2CXM for tumor diagnosis and exploring the possible relationship between these parameters in patients with cervix cancer. Parameters (tissue blood flow, F_p; tissue blood volume, V_p; interstitial volume, V_e; and vascular permeability, PS) for regions of interest (ROI) of cervix lesions and normal cervix tissue were estimated by AATH, SC, and DP models in 36 patients with cervix cancer and 17 healthy subjects. All parameters showed significant differences between lesions and normal tissue with a P value less than 0.05, except for PS from the AATH model, F_p from the SC model, and V_p from the DP model. Parameter V_e from the AATH model had the largest AUC (r = 0.85). Parameters F_p and V_p from SC and DP models and V_e and PS from AATH and DP models were highly correlated, respectively, (r > 0.8) in cervix lesions. Cervix cancer was found to have a very unusual microcirculation pattern, with over-growth of cancer cells but without evident development of angiogenesis. V_e has the best performance in identifying cervix cancer. Most physiological parameters derived from AATH, SC, and DP models are linearly correlated in cervix cancer.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

Models and methods for analyzing DCE-MRI: a review

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.

Patient characteristics influencing the variability of distributed parameter-based models in DCE-CT kinetic analysis

Kinetic parameter variability may be sensitive to kinetic model choice, kinetic model implementation or patient-specific effects. The purpose of this study was to assess their impact on the variability of dynamic contrast-enhanced computed tomography (DCE-CT) kinetic parameters. A total of 11 canine patients with sinonasal tumours received high signal-to-noise ratio, test-double retest DCE-CT scans. The variability for three distributed parameter (DP)-based models was assessed by analysis of variance. Mixed-effects modelling evaluated patient-specific effects. Inter-model variability (CV_inter ) was comparable to or lower than intra-model variability (CV_intra ) for blood flow (CV_inter :[4-28%], CV_intra :[28-31%]), fractional vascular volume (CV_inter :[3-17%], CV_intra :[16-19%]) and permeability-surface area product (CV_inter :[5-12%], CV_intra :[14-15%]). The kinetic models were significantly (P<0.05) impacted by patient characteristics for patient size, area underneath the curve of the artery and of the tumour. In conclusion, DP-based models demonstrated good agreement with similar differences between models and scans. However, high variability in the kinetic parameters and their sensitivity to patient size may limit certain quantitative applications.

Simulation study of the effect of golden-angle KWIC with generalized kinetic model analysis on diagnostic accuracy for lesion discrimination

PURPOSE

To quantitatively evaluate temporal blurring of dynamic contrast-enhanced MRI data generated using a k-space weighted image contrast (KWIC) image reconstruction technique with golden-angle view-ordering.

METHODS

K-space data were simulated using golden-angle view-ordering and reconstructed using a KWIC algorithm with a Fibonacci number of views enforced for each annulus in k-space. Temporal blurring was evaluated by comparing pharmacokinetic model parameters estimated from the simulated data with the true values. Diagnostic accuracy was quantified using receiver operator characteristic curves (ROC) and the area under the ROC curves (AUC).

RESULTS

Estimation errors of pharmacokinetic model parameters were dependent on the true curve type and the lesion size. For 10mm benign and malignant lesions, estimated AUC values using the true and estimate AIFs were consistent with the true AUC value. For 5mm benign and 20mm malignant lesions, estimated AUC values using the true and estimated AIFs were 0.906±0.020 and 0.905±0.021, respectively, as compared with the true AUC value of 0.896.

CONCLUSIONS

Although the investigated reconstruction algorithm does impose errors in pharmacokinetic model parameter estimation, they are not expected to significantly impact clinical studies of diagnostic accuracy.

A tracer-kinetic field theory for medical imaging

Dynamic imaging data are currently analyzed with a tracer-kinetic theory developed for individual time curves measured over whole organs. The assumption is that voxels represent isolated systems which all receive indicator through the same arterial inlet. This leads to well-known systematic errors, but also fails to exploit the spatial structure of the data. In this study, a more general theoretical framework is developed which makes full use of the specific structure of image data. The theory encodes the fact that voxels receive indicator from their immediate neighbors rather than from an upstream arterial input. This results in a tracer-kinetic field theory where the tissue parameters are functions of space which can be measured by analyzing the temporal and spatial patterns in the concentrations. The implications are evaluated through a number of field models for common tissue types. The key benefits of a tracer-kinetic field theory are that: 1) long-standing systematic errors can be corrected, specifically the issue of bolus dispersion and the contamination of large-vessel blood flow on tissue perfusion measurements; 2) additional tissue parameters can be measured that characterize convective or diffusive exchange between voxels; 3) the need to measure a separate arterial input function can be eliminated.

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)(-1), cardiac output = 3, 5, 8 L min(-1)). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that there is no particular advantage between quantitative estimation methods nor to performing dose reduction via tube current reduction compared to temporal sampling reduction. These data are important for optimizing implementation of cardiac dynamic CT in clinical practice and in prospective CT MBF trials.

Assessment of tumor blood flow distribution by dynamic contrast-enhanced CT

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.

Classic models for dynamic contrast-enhanced MRI

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.

Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer

Dynamic contrast-enhanced computed tomography (DCE-CT) and magnetic resonance imaging (DCE-MRI) are functional imaging techniques. They aim to characterise the microcirculation by applying the principles of tracer-kinetic analysis to concentration-time curves measured in individual image pixels. In this paper, we review the basic principles of DCE-MRI and DCE-CT, with a specific emphasis on the use of tracer-kinetic modeling. The aim is to provide an introduction to the field for a broader audience of pharmacokinetic modelers. In a first part, we first review the key aspects of data acquisition in DCE-CT and DCE-MRI, including a review of basic measurement strategies, a discussion on the relation between signal and concentration, and the problem of measuring reference data in arterial blood. In a second part, we define the four main parameters that can be measured with these techniques and review the most common tracer-kinetic models that are used in this field. We first discuss the models for the capillary bed and then define the most general four-parameter models used today: the two-compartment exchange model, the tissue-homogeneity model, the "adiabatic approximation to the tissue-homogeneity model" and the distributed-parameter model. In simpler tissue types or when the data quality is inadequate to resolve all the features of the more complex models, it is often necessary to resort to simpler models, which are special cases of the general models and hence have less parameters. We discuss the most common of these special cases, i.e. the uptake models, the extended Tofts model, and the one-compartment model. Models for two specific tissue types, liver and kidney, are discussed separately. We conclude with a review of practical aspects of DCE-CT and DCE-MRI data analysis, including the problem of identifying a suitable model for any given data set, and a brief discussion of the application of tracer-kinetic modeling in the context of drug development. Here, an important application of DCE techniques is the derivation of quantitative imaging biomarkers for the assessment of effects of targeted therapeutics on tumors.

Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability

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.

A comparative study of dynamic contrast-enhanced MRI parameters as biomarkers for anti-angiogenic drug therapy

The aim of the present study was to compare three tracer kinetics methods for the analysis of dynamic contrast-enhanced (DCE) MRI data, namely the generalized kinetics model, the distributed-parameter model and the initial area under the tumor tracer curve (IAUC) method, in a Phase I study of an anti-angiogenic drug ABT -869; and to explore their utility as biomarkers. Twenty-eight patients with a range of tumors formed the study population. DCE MRI performed at baseline and 2 weeks post-treatment was analyzed using all three methods, yielding percentage changes for various tracer kinetics parameters. Correlation analyzes were performed between these parameters and in relation to drug exposure. The association of these parameters with time-to-progression was examined using receiver-operating characteristic and Kaplan-Meier curves. Significant correlation with drug exposure was found for the following parameters: normalized IAUC (IAUC(norm)), fractional interstitial volume v(e), fractional intravascular volume v(1) and permeability PS. However, only v(e) and PS were effective in predicting late progression. A decrease in v(e) of more than 1.7% and a decrease in PS of more than 25.1% observed at 2 weeks post-treatment could be associated with late progression. All three tracer kinetics methods have biomarker potential for assessing the effects of anti-angiogenic therapy.

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.

On the scope and interpretation of the Tofts models for DCE-MRI

The Tofts model (TM) and extended Tofts model (ETM) have become a standard for the analysis of dynamic contrast-enhanced MRI. In this study, a mathematical analysis is used to identify exactly in which tissue types the Tofts models may be applied. The results show that the TM is accurate if and only if the tissue is weakly vascularised (small blood volume). The ETM is additionally accurate in highly perfused tissues (high blood flow). In tissues that are highly vascularised, or where tracer exchange is very fast or very slow, TM and ETM accurately fit the data but lead to a misinterpretation of the parameters. In tissue types with intermediate vascularity, perfusion and tracer exchange, neither model offers a good fit to the tissue concentrations. A good fit can be obtained with a measured input function by reducing the temporal resolution, but this does not improve the accuracy of the parameters. In conclusion, the Tofts models only produce reliable parameter values if the tissue is weakly vascularized (TM or ETM) or highly perfused (ETM). Without prior knowledge that at least one of these constraints is fulfilled, the physiological interpretation of the values produced by the Tofts models is unclear.

Tracer kinetic analysis of dynamic contrast-enhanced MRI and CT bladder cancer data: A preliminary comparison to assess the magnitude of water exchange effects

The purpose of this study was to determine the impact of water exchange on tracer kinetic parameter estimates derived from T(1)-weighted dynamic contrast-enhanced (DCE)-MRI data using a direct quantitative comparison with DCE-CT. Data were acquired from 12 patients with bladder cancer who underwent DCE-CT followed by DCE-MRI within a week. A two-compartment tracer kinetic model was fitted to the CT data, and two versions of the same model with modifications to account for the fast exchange and no exchange limits of water exchange were fitted to the MR data. The two-compartment tracer kinetic model provided estimates of the fractional plasma volume (v(p)), the extravascular extracellular space fraction (v(e)), plasma perfusion (F(p)), and the microvascular permeability surface area product. Our findings suggest that DCE-CT is an appropriate reference for DCE-MRI in bladder cancers as the only significant difference found between CT and MR parameter estimates were the no exchange limit estimates of v(p) (P = 0.002). These results suggest that although water exchange between the intracellular and extravascular-extracellular space has a negligible effect on DCE-MRI, vascular-extravascular-extracellular space water exchange may be more important.

Quantitative assessment of blood volume and permeability in cerebral mass lesions using dynamic contrast-enhanced computed tomography in the dog

RATIONALE AND OBJECTIVES

To evaluate cerebral blood volume (CBV) and permeability (PS) in spontaneously occurring cerebral neoplastic and non-neoplastic lesions in dogs using dynamic contrast-enhanced computed tomography (DCE-CT).

MATERIALS AND METHODS

Dogs presenting with spontaneous intracranial lesions (n = 16) underwent DCE-CT at the level of the lesion followed by a histologically confirmed diagnosis from a CT-guided stereotactic biopsy. Data post-processing was performed with commercially available CT software (GEMS Advantage Workstation 4.2). Symmetric regions of interest (ROIs) were drawn within the lesion and unaffected areas on the contralateral side. Values were compared between lesion types and ratios of lesion-to-normal brain were calculated.

RESULTS

Dogs with extra-axial lesions (n = 3 meningiomas) had marked elevation of CBV and PS compared to normal brain. All Grade III gliomas (n = 5) had mildly elevated CBV and markedly elevated PS values. All lower Grade II gliomas (n = 2) had minimal elevation in CBV and PS. Dogs with non-neoplastic intra-axial lesions (one each necrotizing, fungal, and lymphoplasmacytic encephalitis) had elevation of PS with normal to mildly elevated CBV. Lesion-to-normal brain ratios for PS separated extra- and intra-axial neoplasms and intra-axial inflammatory/degenerative lesions from each other.

CONCLUSIONS

Low-grade gliomas do not consistently demonstrate elevated vascular parameters, whereas Grade III gliomas and non-neoplastic intra-axial lesions have elevated PS. Ratios between such lesions and normal brain may prove useful for differentiating types of lesions. These findings resemble those previously reported in similar lesions in people indicating that the dog may act as a good model for intracranial masses for the study of lesion angiogenesis and response to therapy.

A novel estimation method for physiological parameters in dynamic contrast-enhanced MRI: application of a distributed parameter model using Fourier-domain calculations

Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.

Phase I and biomarker study of ABT-869, a multiple receptor tyrosine kinase inhibitor, in patients with refractory solid malignancies

PURPOSE

To determine the safety and tolerability of ABT-869 at escalating doses and its effects on biomarkers relevant for antiangiogenic activity in patients with solid malignancies.

PATIENTS AND METHODS

Patients with solid malignancies refractory to or for which no standard effective therapy exists were enrolled onto escalating-dose cohorts and treated with oral ABT-869 once daily continuously.

RESULTS

Thirty-three patients were studied at doses of 10 mg/d, 0.1 mg/kg/d, 0.25 mg/kg/d, and 0.3 mg/kg/d. Dose-limiting toxicities in the first cycle (21 days) included grade 3 fatigue in a patient at 10 mg/d, grade 3 proteinuria and grade 3 hypertension in two separate patients at 0.25 mg/kg/d, and grade 3 hypertension and grade 3 proteinuria in two separate patients at 0.3 mg/kg/d, which was the maximum-tolerated dose. Other significant treatment-related adverse events included asthenia, hand and foot blisters, and myalgia. Oral clearance of ABT-869 was linear, with a mean of 2.7 +/- 1.2 L/h and half-life of 18.4 +/- 5.7 hours, with no evidence of drug accumulation at day 15. Two patients with lung cancer and one patient with colon cancer achieved partial response. Stable disease for more than four cycles was observed in 16 patients (48%). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) showed dose-dependent reduced tumor vascular permeability that correlated with drug exposure. By day 15 of treatment, circulating endothelial cells were significantly reduced (P = .007), whereas plasma vascular endothelial growth factor was increased (P = .004).

CONCLUSION

ABT-869 by continuous once-daily dosing was tolerable at doses </= 0.25 mg/kg/d. Biomarker evidence of antiangiogenic activity and DCE-MRI evidence of tumor antiangiogenesis were observed together with promising clinical activity.

Utilization of dynamic CT perfusion in the study of intracranial meningiomas and their surrounding tissue

OBJECTIVE

To use CT perfusion (CTP) to assess levels of ischemia in brain areas around intracranial meningiomas.

MATERIALS AND METHODS

Fifteen patients with intracranial meningiomas were analysed preoperatively with CTP study. The cerebral blood flow (CBF), cerebral blood volume (CBV) and time to peak (TTP) were measured in the edema, peritumoral edema and in the normal areas of brain.

RESULTS

The peritumoral edema measured a mean CBF of 17.36 ml/min/100 ml (median=15.8) and the mean CBF value in the whole edema was 93.86 ml/min/100 ml (median=79.9). The mean CBV measured in the peritumoral edema was 2.7 (median=2.3) and the measured mean CBV of edema was 15 (median=13.2). In the region of the peritumoral edema, the mean CBF and CBV were lower than in the edema bed. Normal brain remote from the edema measured less CBF (mean=28.36 ml/min/100 ml, median=29.7) and CBV (mean=4.1, median=3.8) than the edema. Six patients were noted to have CBF of less than 15 ml/min/100 ml in the perilesional edema. The measured mean CBF and CBV in the normal area of brain were higher than in the peritumoral edema, while the TTP was greater in the perilesional edema (mean=11, median=10.4) when compared with areas of normal brain (mean=9.9, median=9.5) with statistically significant p values.

CONCLUSION

The values obtained elucidate the fact that perilesional edematous areas are ischemic. By subset analysis, it may be possible to identify those areas with recoverable tissue from non-recoverable tissue.

Multivoxel MR Spectroscopic Imaging – Distinguishing Intracranial Tumours from Non-neoplastic Disease

Introduction: Multi-voxel MR spectroscopic imaging (MRSI) provides chemical metabolite information that can supplement conventional MR imaging in the study of intracranial neoplasia. Our purpose was to use a robust semi-automated spectroscopic analysis to distinguish intracranial tumours from non-neoplastic disease. Materials and Methods: Twenty intracranial tumours and 15 patients with non-neoplastic disease confirmed on histological examination or serial neuroimaging were studied with 2-dimensional MRSI using point-resolved spectroscopic (PRESS) imaging localisation. Using semi-automated post-processing software, spectra were analysed for peak heights of choline (Cho), creatine (Cr), N-acetyl aspartate (NAA), lactate (Lac) and lipid (Lip). Normalised Cho (nCho) ratios, computed by dividing maximum Cho in the lesion by the normal-appearing brain, were compared between intracranial tumours and non-neoplastic disease. Results: Meningiomas displayed homogenously elevated Cho. Malignant tumours, especially large glioblastoma multiforme, displayed inhomogeneity of metabolites within the tumour. All tumours had elevation of nCho >1 (mean 1.91 ± 0.65), and non-neoplastic diseases had tumour nCho <1 (mean 0.91 ± 0.46), which was significantly lower (P <0.05). Two patients with non-neoplastic lesions, one with subacute cerebral infarction and the other with cryptococcoma, had elevated Cho compared to normal tissue (false positive rate 13%). Conclusion: Using semi-automated MRSI method, a simplified normalised Cho algorithm provides a method to distinguish intracranial tumours from non-neoplastic disease. Key words: Brain neoplasm, Magnetic resonance imaging, Nuclear magnetic resonance, Spectroscopy

CT coronary angiography: quantitative assessment of myocardial perfusion using test bolus data-initial experience

The aim of this study is to quantify myocardial perfusion during coronary CT angiography using data from a modified timing test-bolus acquisition. Institutional review board approval and informed consent were obtained. Nineteen patients with suspected coronary artery disease underwent combined coronary CT angiography and cardiac (82)Rubidium-PET perfusion. Prior to the CT angiogram a retrospectively ECG-gated dynamic test bolus was obtained following 25 mls of IV contrast medium injected at 5 ml/s. Images were acquired every 1.5 s for 30 s using 4 x 1.25-mm slices at 120 kV, 35 mAs. Regions of interest were drawn to delineate the myocardium and aorta on the resulting transaxial images. Time density curves were created and perfusion calculated using two simple approaches: maximum-slope method and peak method. In patients with normal PET myocardial perfusion, the mean (SD) resting myocardial perfusion estimated by CT using the maximum-slope method was 0.89 (+/-0.27) ml/min/g and 0.93 (+/-0.21) ml/min/g at end-systole and end-diastole, respectively, and 0.69 (+/-0.11) ml/min/g and 0.79 (+/-0.19) at end-systole and end-diastole, respectively, for the peak method. Thus quantification of myocardial perfusion from a routine coronary CT angiography test bolus is possible. CT-derived myocardial perfusion values are consistent with published values derived from other techniques.

Optimization of perfusion CT protocol for imaging of extracranial head and neck tumors

The in vivo assessment of physiological processes associated with microcirculation in the head and neck tissue by means of perfusion computed tomography is widely used in the management of patients with head and neck tumors. However, there is no systematic consideration of the total acquisition duration and placement of the scans. A simulation study for optimizing perfusion studies of extracranial head and neck tumors, with considerations of reducing radiation dose while maintaining accuracy of the perfusion parameters, is demonstrated here. The suggested that dual-phase optimized protocols may provide reliable estimations of the permeability surface area product as well as blood flow and volume without additional radiation burden and serious patient discomfort. These optimized protocols can potentially be useful in the clinical setting of examining patients with extracranial head and neck tumors.

Computed tomography assessment of cerebral perfusion using a distributed parameter tracer kinetics model: validation with H(2)((15))O positron emission tomography measurements and initial clinical experience in patients with acute stroke

We describe a distributed parameter (DP) model for tracer kinetic analysis in brain and validate the derived perfusion values with positron emission tomography (PET) scans. The proposed model is applied on actual clinical cases of hemispheric stroke. Nine patients with experienced transient ischaemic attack or minor stroke and a stenosis of the internal carotid artery were referred for computed tomography (CT) and PET imaging. The applicability of the DP model in clinical practice was tested in seven patients with acute stroke who received a baseline perfusion CT study and a noncontrast follow-up CT study after 2.4+/-1.8 days. The mean blood flow (F) value for all patients with carotid stenosis in the pooled data (54 regions of interest (ROIs)) was 37.9+/-11.2 mL/min per 100 g in perfusion CT and 35.6+/-9.8 mL/min per 100 g in perfusion PET imaging [r=0.77 (P=0.00)]. Regression analysis of the pooled ROIs for every patient revealed significant correlation between F values in seven patients [r=0.50 to 0.79 (r(2)-values ranged from 0.45 to 0.79), (0.01 < or = P < or = 0.05)]. Parametric maps that corresponded to all physiologic parameters were generated for every perfusion CT in the patients with acute stroke using the DP model. The ischaemic area was better delineated in F, intravascular blood volume and lag time (t(lag)) maps. The correlation coefficient comparing the visually outlined regions of abnormality between the t(lag) parametric map and the follow-up CT scans was 0.81 (P=0.003). In conclusion, DP physiological model using more realistic pharmacokinetics is feasible in dynamic contrast-enhanced CT of the brain in patients with acute and chronic cerebrovascular disease.

Dynamic contrast-enhanced CT of head and neck tumors: perfusion measurements using a distributed-parameter tracer kinetic model. Initial results and comparison with deconvolution-based analysis

The objective of this work was to evaluate the feasibility of a two-compartment distributed-parameter (DP) tracer kinetic model to generate functional images of several physiologic parameters from dynamic contrast-enhanced CT data obtained of patients with extracranial head and neck tumors and to compare the DP functional images to those obtained by deconvolution-based DCE-CT data analysis. We performed post-processing of DCE-CT studies, obtained from 15 patients with benign and malignant head and neck cancer. We introduced a DP model of the impulse residue function for a capillary-tissue exchange unit, which accounts for the processes of convective transport and capillary-tissue exchange. The calculated parametric maps represented blood flow (F), intravascular blood volume (v(1)), extravascular extracellular blood volume (v(2)), vascular transit time (t(1)), permeability-surface area product (PS), transfer ratios k(12) and k(21), and the fraction of extracted tracer (E). Based on the same regions of interest (ROI) analysis, we calculated the tumor blood flow (BF), blood volume (BV) and mean transit time (MTT) by using a modified deconvolution-based analysis taking into account the extravasation of the contrast agent for PS imaging. We compared the corresponding values by using Bland-Altman plot analysis. We outlined 73 ROIs including tumor sites, lymph nodes and normal tissue. The Bland-Altman plot analysis revealed that the two methods showed an accepted degree of agreement for blood flow, and, thus, can be used interchangeably for measuring this parameter. Slightly worse agreement was observed between v(1) in the DP model and BV but even here the two tracer kinetic analyses can be used interchangeably. Under consideration of whether both techniques may be used interchangeably was the case of t(1) and MTT, as well as for measurements of the PS values. The application of the proposed DP model is feasible in the clinical routine and it can be used interchangeably for measuring blood flow and vascular volume with the commercially available reference standard of the deconvolution-based approach. The lack of substantial agreement between the measurements of vascular transit time and permeability-surface area product may be attributed to the different tracer kinetic principles employed by both models and the detailed capillary tissue exchange physiological modeling of the DP technique.

Delineation and segmentation of cerebral tumors by mapping blood-brain barrier disruption with dynamic contrast-enhanced CT and tracer kinetics modeling-a feasibility study

Dynamic contrast-enhanced (DCE) imaging is a promising approach for in vivo assessment of tissue microcirculation. Twenty patients with clinical and routine computed tomography (CT) evidence of intracerebral neoplasm were examined with DCE-CT imaging. Using a distributed-parameter model for tracer kinetics modeling of DCE-CT data, voxel-level maps of cerebral blood flow (F), intravascular blood volume (vi) and intravascular mean transit time (t1) were generated. Permeability-surface area product (PS), extravascular extracellular blood volume (ve) and extraction ratio (E) maps were also calculated to reveal pathologic locations of tracer extravasation, which are indicative of disruptions in the blood-brain barrier (BBB). All maps were visually assessed for quality of tumor delineation and measurement of tumor extent by two radiologists. Kappa (kappa) coefficients and their 95% confidence intervals (CI) were calculated to determine the interobserver agreement for each DCE-CT map. There was a substantial agreement for the tumor delineation quality in the F, ve and t1 maps. The agreement for the quality of the tumor delineation was excellent for the vi, PS and E maps. Concerning the measurement of tumor extent, excellent and nearly excellent agreement was achieved only for E and PS maps, respectively. According to these results, we performed a segmentation of the cerebral tumors on the base of the E maps. The interobserver agreement for the tumor extent quantification based on manual segmentation of tumor in the E maps vs. the computer-assisted segmentation was excellent (kappa = 0.96, CI: 0.93-0.99). The interobserver agreement for the tumor extent quantification based on computer segmentation in the mean images and the E maps was substantial (kappa = 0.52, CI: 0.42-0.59). This study illustrates the diagnostic usefulness of parametric maps associated with BBB disruption on a physiology-based approach and highlights the feasibility for automatic segmentation of cerebral tumors.

Functional Imaging: Dynamic Contrast-Enhanced CT using a Distributed-Parameter Physiologic Model for Accessing Stroke and Intracranial Tumor

Functional imaging has the potential to be a practical and widely-available method of studying the pathphysiology of disease using modern CT and MRI technologies. With the high temporal resolution achievable by these technologies, a two-compartment distributed-parameter model, which more accurate represents the tracer concentration within the vascular space, was applied on two patients' data with intracranial tumor and stroke. The parametric maps successfully generated were more informative than the current commercial software packages and the commonly used lumped-parameter compartmental models.

Cerebral perfusion mapping using a robust and efficient method for deconvolution analysis of dynamic contrast-enhanced images

Dynamic contrast-enhanced (DCE) imaging using MRI or CT is emerging as a promising tool for diagnostic imaging of cerebral disorders and the monitoring of tumor response to treatment. In this study, we present a robust and efficient deconvolution method based on a linearized model of the impulse residue function, which allows for the mapping of functional cerebral parameters such as cerebral blood flow, volume, mean transit time, and permeability. Monte Carlo simulation studies were performed to study the accuracy and stability of the proposed method, before applying it to clinical study cases of patients with cerebral tumors imaged using DCE CT. Functional parameter maps generated using the proposed method revealed the locations of the cerebral tumors and were found to be of sufficiently good clarity for marked regional differences in tissue vascularity and permeability to be assessed. In particular, tumor visualization and delineation were found to be better on the parameter maps that were indicative of the breakdown of the blood-brain barrier.

Experimental study of multi-slice spiral CT perfusion imaging in VX2 soft-tissue tumor of rabbits

An experimental animal model of malignant soft-tissue tumor was established to investigate the applied value of multi-slice spiral CT perfusion imaging preliminarily. Ten New Zealand white rabbits which were implanted with VX2 tumor in either proximal thigh were subjected to CT plain scan and perfusion scan two weeks later respectively, then the original perfusion images were transmitted to AW4.0 Workstation. The functional maps and perfusion parameters including blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface (PS) were computed and analyzed. All the values of BF, BV and PS in VX2 soft-tissue tumors were obviously higher while the MTT-values were lower than those in the normal muscular tissues significantly. It was suggested that multi-slice spiral CT perfusion imaging is an accurate, convenient and relatively safe functional imaging technique, and can give a quantitative assessment to angiogenesis and blood perfusion of soft-tissue tumors.

A distributed parameter model of cerebral blood-tissue exchange with account of capillary transit time distribution

Quantitative estimates of physiological parameters associated with cerebral blood flow can be derived from the analysis of dynamic contrast-enhanced (DCE) images, using an appropriate model of the underlying tissue impulse residue function. The theoretical formulation of a distributed parameter model of tissue microcirculation, which accounts for the effects of capillary permeability and transit time distribution, is presented here. This model considers a statistical distribution of capillary-tissue units, each described by a distributed parameter model that accounts for convective transport within the capillary and transcapillary axial diffusion. Monte Carlo simulations were performed to study the confidence of the parameter estimates, and the model was used to analyze DCE CT images of patient study cases with metastatic cerebral tumors. The tumors were found to yield significantly higher estimates than normal tissues for the parameters associated with the extravasation of tracer and for the standard deviation of capillary transit times. The proposed model can be used with DCE imaging to study the microcirculatory characteristics of cerebral tumors.