Validity of perfusion parameters obtained using the modified Tofts model: a simulation study

Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma

Background and aims

Accurate differentiation of chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) is an area of unmet clinical need. In this study, a novel Multitasking dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) technique was used to quantitatively evaluate the microcirculation properties of pancreas in CP and PDAC and differentiate between them.

Methods

The Multitasking DCE technique was able to acquire one 3D image per second during the passage of MRI contrast agent, allowing the quantitative estimation of microcirculation properties of tissue, including blood flow F_p, plasma volume fraction v_p, transfer constant K^trans, and extravascular extracellular volume fraction v_e. Receiver operating characteristic (ROC) analysis was performed to differentiate the CP pancreas, PDAC pancreas, normal control pancreas, PDAC tumor, PDAC upstream, and PDAC downstream. ROCs from quantitative analysis and conventional analysis were compared.

Results

Fourteen PDAC patients, 8 CP patients and 20 healthy subjects were prospectively recruited. The combination of F_p, v_p, K^trans, and v_e can differentiate CP versus PDAC pancreas with good AUC (AUC [95% CI] = 0.821 [0.654 - 0.988]), CP versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), PDAC pancreas versus normal pancreas with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC tumor with excellent AUC (1.000 [1.000 - 1.000]), CP versus PDAC downstream with excellent AUC (0.917 [0.795 - 1.000]), and CP versus PDAC upstream with fair AUC (0.722 [0.465 - 0.980]). This quantitative analysis outperformed conventional analysis in differentiation of each pair.

Conclusion

Multitasking DCE MRI is a promising clinical tool that is capable of unbiased quantitative differentiation between CP from PDAC.

A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.

Incorporating cross-voxel exchange for the analysis of dynamic contrast-enhanced imaging data: pre-clinical results

Tumours exhibit abnormal interstitial structures and vasculature function often leading to impaired and heterogeneous drug delivery. The disproportionate spatial accumulation of a drug in the interstitium is determined by several microenvironmental properties (blood vessel distribution and permeability, gradients in the interstitial fluid pressure). Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the quantification of tracer perfusion based on contrast enhancement measured with non-invasive imaging techniques. An advanced cross-voxel exchange model (CVXM) was recently developed to provide a comprehensive description of tracer extravasation as well as advection and diffusion based on cross-voxel tracer kinetics (Sinnoet al2021). Transport parameters were derived from DCE-MRI of twenty TS-415 human cervical carcinoma xenografts by using CVXM. Tracer velocity flows were measured at the tumour periphery (mean 1.78-5.82μm.s^-1) pushing the contrast outward towards normal tissue. These elevated velocity measures and extravasation rates explain the heterogeneous distribution of tracer across the tumour and its accumulation at the periphery. Significant values for diffusivity were deduced across the tumours (mean 152-499μm².s^-1). CVXM resulted in generally smaller values for the extravasation parameterKext(mean 0.01-0.04 min^-1) and extravascular extracellular volume fractionve(mean 0.05-0.17) compared to the standard Tofts parameters, suggesting that Toft model underestimates the effects of inter-voxel exchange. The ratio of Tofts' extravasation parameters over CVXM's was significantly positively correlated to the cross-voxel diffusivity (P< 0.0001) and velocity (P= 0.0005). Tofts' increasedvemeasurements were explained using Sinnoet al(2021)'s theoretical work. Finally, a scan time of 15 min renders informative estimations of the transport parameters. However, a duration as low as 7.5 min is acceptable to recognize the spatial variation of transport parameters. The results demonstrate the potential of utilizing CVXM for determining metrics characterizing the exchange of tracer between the vasculature and the tumour tissue. Like for many earlier models, additional work is strongly recommended, in terms of validation, to develop more confidence in the results, motivating future laboratory work in this regard.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

Intravoxel Incoherent Motion Imaging on Sacroiliitis in Patients With Axial Spondyloarthritis: Correlation With Perfusion Characteristics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging

BACKGROUND

To prospectively explore the relationship between intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) parameters of sacroiliitis in patients with axial spondyloarthritis (axSpA).

METHODS

Patients with initially diagnosed axSpA prospectively underwent on 3.0 T MRI of sacroiliac joint (SIJ). The IVIM parameters (D, f, D ^*) were calculated using biexponential analysis. K ^trans, K _ep, V _e, and V _p from DCE-MRI were obtained in SIJ. The uni-variable and multi-variable linear regression analyses were used to evaluate the correlation between the parameters from these two imaging methods after controlling confounders, such as bone marrow edema (BME), age, agenda, scopes, and localization of lesions, and course of the disease. Then, their correlations were measured by calculating the Pearson's correlation coefficient (r).

RESULTS

The study eventually enrolled 234 patients (178 men, 56 women; mean age, 28.51 ± 9.50 years) with axSpA. With controlling confounders, D was independently related to K ^trans (regression coefficient [b] = 27.593, p < 0.001), K _ep (b = -6.707, p = 0.021), and V _e (b = 131.074, p = 0.003), whereas f and D ^* had no independent correlation with the parameters from DCE MRI. The correlations above were exhibited with Pearson's correlation coefficients (r) (r = 0.662, -0.408, and 0.396, respectively, all p < 0.001).

CONCLUSION

There were independent correlations between D derived from IVIM DWI and K ^trans, K _ep, and V _e derived from DCE-MRI. The factors which affect their correlations mainly included BME, gender, and scopes of lesions.

Incorporating cross-voxel exchange into the analysis of dynamic contrast-enhanced imaging data: theory, simulations and experimental results

Predictions of tumour perfusion are key determinants of drug delivery and responsiveness to therapy. Pharmacokinetic models allow for the estimation of perfusion properties of tumour tissues but many assume no dispersion associated with tracer transport away from the capillaries and through the tissue. At the level of a voxel, this translates to assuming no cross-voxel tracer exchange, often leading to the misinterpretation of derived perfusion parameters. Tofts model (TM), a compartmental model widely used in oncology, also makes this assumption. A more realistic description is required to quantify kinetic properties of tracers, such as convection and diffusion. We propose a Cross-Voxel Exchange Model (CVXM) for analysing cross-voxel tracer kinetics.In silicodatasets quantifying the roles of convection and diffusion in tracer transport (which TM ignores) were employed to investigate the interpretation of Tofts' perfusion parameters compared to CVXM. TM returned inaccurate values ofKtransandvewhere diffusive and convective mechanisms are pronounced (up to 20% and 300% error respectively). A mathematical equation, developed in this work, predicts and gives the correct physiological interpretation of Tofts've.Finally, transport parameters were derived from dynamic contrast enhanced-magnetic resonance imaging of a TS-415 human cervical carcinoma xenograft by using TM and CVXM. The latter deduced lower values ofKtransandvecompared to TM (lower by up to 63% and 76% respectively). It also allowed the detection of a diffusive flux (mean diffusivity 155μm²s^-1) in the tumour tissue, as well as an increased convective flow at the periphery (mean velocity 2.3μm s^-1detected). The results serve as a proof of concept establishing the feasibility of using CVXM for accurately determining transport metrics that characterize the exchange of tracer between voxels. CVXM needs to be investigated further as its parameters can be linked to the tumour microenvironment properties (permeability, pressure…), potentially leading to enhanced personalized treatment planning.

Five-dimensional quantitative low-dose Multitasking dynamic contrast- enhanced MRI: Preliminary study on breast cancer

PURPOSE

To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T₁ mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm³ , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg).

METHODS

Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T₁ recovery dimension for dynamic T₁ quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T₁ maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standard-dose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups.

RESULTS

The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively).

CONCLUSION

The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE. The estimated kinetic parameters were capable of differentiating between normal breast tissue and benign and malignant tumors.

Test-Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts

Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest–averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm²/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test–retest coefficient-of-variation (CV_WS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CV_WS. Determination of T2 is also robust with a ∼3% CV_WS. Measurements of T1 and ADC are less robust with CV_WS values in the 6%–11% range. Preliminary DCE test–retest time-course determinations, as quantified by area under the curve and K^trans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%–40% CV_WS.

SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging

We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was ∼ 50 -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100 mL/min-100 g and a tube current-time product of 100 mAs (50% of nominal), the MBF estimates were 101 ± 12 , 94 ± 56 , and 54 ± 24    mL / min - 100    g for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( 103 ± 23    mL / min - 100    g ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics.

MRI evaluation of the effects of whole brain radiotherapy on breast cancer brain metastasis

PURPOSE

To assess early changes in brain metastasis in response to whole brain radiotherapy (WBRT) by longitudinal Magnetic Resonance Imaging (MRI).

MATERIALS AND METHODS

Using a 7T system, MRI examinations of brain metastases in a breast cancer MDA-MD231-Br mouse model were conducted before and 24 hours after 3 daily fractionations of 4 Gy WBRT. Besides anatomic MRI, diffusion-weighted (DW) MRI and dynamic contrast-enhanced (DCE) MRI were applied to study cytotoxic effect and blood-tumor-barrier (BTB) permeability change, respectively.

RESULTS

Before treatment, high-resolution T2-weighted images revealed hyperintense multifocal lesions, many of which (∼50%) were not enhanced on T1-weighted contrast images, indicating intact BTB in the brain metastases. While no difference in the number of new lesions was observed, WBRT-treated tumors were significantly smaller than sham controls (p < .05). DW MRI detected significant increase in apparent diffusion coefficient (ADC) in WBRT tumors (p < .05), which correlated with elevated caspase 3 staining of apoptotic cells. Many lesions remained non-enhanced post WBRT. However, quantitative DCE MRI analysis showed significantly higher permeability parameter, K_trans, in WBRT than the sham group (p < .05), despite marked spatial heterogeneity.

CONCLUSIONS

MRI allowed non-invasive assessments of WBRT induced changes in BTB permeability, which may provide useful information for potential combination treatment.

The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was  ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1  >  RPM2  >  Fermi  >  SCM  >  LSVD  >  MUP [Formula: see text] other methods. In terms of bias, the models ranked best-to-worst are: SCM  >  RPM2  >  RPM1  >  PTU  >  LSVD [Formula: see text] other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.

A multiparametric analysis combining DCE-MRI- and IVIM -derived parameters to improve differentiation of parotid tumors: a pilot study

AIM

To evaluate dynamic contrast-enhanced (DCE)-MRI and diffusion weighted (DW)-MRI diagnostic value to differentiate Warthin tumors (WT) by pleomorphic adenomas (PA).

MATERIALS & METHODS

Seven WT and seven PA were examined. DCE- and  DW-MRI parameters were extracted from volumes of interest; volume of interest-based averages and standard deviations were calculated. Statistical analysis included: linear discriminant analysis, receiver operating characteristic curves, sensitivity and specificity.

RESULTS

No single feature was able to differentiate WT by PA (p > 0.05); linear discriminant analysis analysis showed that a combination of all features or combinations of feature pairs (namely: K^trans(std) & f(std), K^trans(std) & D(std), k_ep(std) & D(std), MRE(av) & TTP(av)) might achieve sensitivity (SENS), specificity (SPEC) = 100%, with a slight reduction after cross-validation analysis (SENS = 0.875; SPEC = 1).

CONCLUSION

Although preliminary and not conclusive, our results suggest that differentiation between WT and PA is possible through a multiparametric approach based on combination of DCE- and DW-MRI parameters.

SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification

There are several computational methods for estimating myocardial blood flow (MBF) using CT myocardial perfusion imaging (CT-MPI). Previous work has shown that model-based deconvolution methods are more accurate and precise than model-independent methods such as singular value decomposition and max-upslope. However, iterative optimization is computationally expensive and models are sensitive to image noise, thus limiting the utility of low x-ray dose acquisitions. We propose a new processing method, SLICR, which segments the myocardium into super-voxels using a modified simple linear iterative clustering (SLIC) algorithm and quantifies MBF via a robust physiologic model (RPM). We compared SLICR against voxel-wise SVD and voxel-wise model-based deconvolution methods (RPM, single-compartment and Johnson-Wilson). We used image data from a digital CT-MPI phantom to evaluate robustness of processing methods to noise at reduced x-ray dose. We validate SLICR in a porcine model with and without partial occlusion of the LAD coronary artery with known pressure-wire fractional flow reserve. SLICR was ~50 times faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show all clinically interesting features (e.g., a flow deficit in the endocardial wall). SLICR showed much better precision and accuracy than the other methods. For example, at simulated MBF=100 mL/min/100g and 100 mAs exposure (50% of nominal dose) in the digital simulator, MBF estimates were 101 ± 12 mL/min/100g, 160 ± 54 mL/min/100g, and 122 ± 99 mL/min/100g for SLICR, SVD, and Johnson-Wilson, respectively. SLICR even gave excellent results (103 ± 23 ml/min/100g) at 50 mAs, corresponding to 25% nominal dose.

Comparison of perfusion models for quantitative T1 weighted DCE-MRI of rectal cancer

In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T₁ weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and [Formula: see text] were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF_2CX = 68 ± 44 ml/100 ml/min, PF_2CU = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, [Formula: see text]:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and [Formula: see text] values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.

Estimating breast tumor blood flow during neoadjuvant chemotherapy using interleaved high temporal and high spatial resolution MRI

PURPOSE

To evaluate an interleaved MRI sampling strategy that acquires both high temporal resolution (HTR) dynamic contrast-enhanced (DCE) data for quantifying breast tumor blood flow (TBF) and high spatial resolution (HSR) DCE data for clinical reporting, following a single standard injection of contrast agent.

METHODS

A simulation study was used to evaluate the performance of the interleaved technique under different conditions. In a prospective clinical study, 18 patients with primary breast cancer, who were due to undergo neoadjuvant chemotherapy (NACT), were examined using interleaved HTR and HSR DCE-MRI at 1.5 Tesla. Tumor regions of interest were analyzed with a two-compartment tracer kinetic model. Paired parameters (n = 10) from the data acquired before and post-cycle 2 of NACT were compared using the nonparametric Wilcoxon signed-rank test.

RESULTS

Simulations demonstrated that TBF was reliably estimated using the proposed strategy. The region of interest analysis revealed significant changes in TBF (0.81-0.43 mL/min/mL; P = 0.002) following two cycles of NACT. The HSR data were reported in the normal way and enabled the assessment of tumor volume, which decreased by 53% following NACT (P = 0.065).

CONCLUSIONS

TBF can be measured reliably using the proposed strategy without compromising a standard clinical protocol. Furthermore, in our feasibility study, TBF decreased significantly following NACT, whereas capillary permeability surface-area product did not. Magn Reson Med 79:317-326, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal Muscle

The purpose of this study was to assess the accuracy of fractional blood volume (v_b) estimates in low-perfused and low-vascularized tissue using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The results of different MRI methods were compared with histology to evaluate the accuracy of these methods under clinical conditions. v_b was estimated by DCE-MRI using a 3D gradient echo sequence with k-space undersampling in five muscle groups in the hind leg of 9 female pigs. Two gadolinium-based contrast agents (CA) were used: a rapidly extravasating, extracellular, gadolinium-based, low-molecular-weight contrast agent (LMCA, gadoterate meglumine) and an extracellular, gadolinium-based, albumin-binding, slowly extravasating blood pool contrast agent (BPCA, gadofosveset trisodium). LMCA data were evaluated using the extended Tofts model (ETM) and the two-compartment exchange model (2CXM). The images acquired with administration of the BPCA were used to evaluate the accuracy of v_b estimation with a bolus deconvolution technique (BD) and a method we call equilibrium MRI (EqMRI). The latter calculates the ratio of the magnitude of the relaxation rate change in the tissue curve at an approximate equilibrium state to the height of the same area of the arterial input function (AIF). Immunohistochemical staining with isolectin was used to label endothelium. A light microscope was used to estimate the fractional vascular area by relating the vascular region to the total tissue region (immunohistochemical vessel staining, IHVS). In addition, the percentage fraction of vascular volume was determined by multiplying the microvascular density (MVD) with the average estimated capillary lumen, π(d2)2, where d = 8μm is the assumed capillary diameter (microvascular density estimation, MVDE). Except for ETM values, highly significant correlations were found between most of the MRI methods investigated. In the cranial thigh, for example, the v_b medians (interquartile range, IQRs) of IHVS, MVDE, BD, EqMRI, 2CXM and ETM were v_b = 0.7(0.3)%, 1.1(0.4)%, 1.1(0.4)%, 1.4(0.3)%, 1.2(1.8)% and 0.1(0.2)%, respectively. Variances, expressed by the difference between third and first quartiles (IQR) were highest for the 2CXM for all muscle groups. High correlations between the values in four muscle groups—medial, cranial, lateral thigh and lower leg - estimated with MRI and histology were found between BD and EqMRI, MVDE and 2CXM and IHVS and ETM. Except for the ETM, no significant differences between the v_b medians of all MRI methods were revealed with the Wilcoxon rank sum test. The same holds for all muscle regions using the 2CXM and MVDE. Except for cranial thigh muscle, no significant difference was found between EqMRI and MVDE. And except for the cranial thigh and the lower leg muscle, there was also no significant difference between the v_b medians of BD and MVDE. Overall, there was good v_b agreement between histology and the BPCA MRI methods and the 2CXM LMCA approach with the exception of the ETM method. Although LMCA models have the advantage of providing excellent curve fits and can in principle determine more physiological parameters than BPCA methods, they yield more inaccurate results.

Optimization of DCE-MRI protocol for the assessment of patients with brain tumors

The interstitium-to-plasma rate constant (k_ep), extracted from dynamic contrast enhancement (DCE-MRI) MRI data, seems to have an important role in the assessment of patients with brain tumors. This parameter is affected by the slow behavior of the system, and thus is expected to be highly dependent on acquisition duration. The aim of this study was to optimize the scan duration and protocol of DCE-MRI for accurate estimation of the k_ep parameter in patients with high grade brain tumors. The effects of DCE-MRI scan duration and protocol design (continuous vs integrated scanning) on the estimated pharmacokinetic (PK) parameters and on model selection, were studied using both simulated and patient data. Scan duration varied, up to 60min for simulated data, and up to 25min in 25 MRI scans obtained from patients with high grade brain tumors, with continuous and integrated scanning protocols. Converging results were obtained from simulated and real data. Significant effect of scan duration was detected on k_ep. Scan duration of 9min, with integrated protocol in which the data are acquired continuously for 5min, and additional volumes at 7 and 9min, was sufficient for accurate estimation of even low k_ep values, with an average error of 3%. Over-estimation of the PK parameters was detected for scan duration <12min, being more pronounced at low k_ep values (<0.1min^-1). For the model selection maps, significantly lower percentage of the full extended-Tofts-model (ETM) was selected in patients at scan duration of 5min compared to >12min. An integrated protocol of 9min is suggested as optimal for clinical use in patients with high grade brain tumors. Lower acquisition time may result in over-estimation of k_ep when using ETM, and therefore care should be taken using model selection.

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Background

Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology.

Results

Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters.

Conclusion

We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.

Monitoring Vascular Disrupting Therapy in a Rabbit Liver Tumor Model: Relationship between Tumor Perfusion Parameters at IVIM Diffusion-weighted MR Imaging and Those at Dynamic Contrast-enhanced MR Imaging

PURPOSE

To investigate whether perfusion-related intravoxel incoherent motion (IVIM) diffusion-weighted (DW) magnetic resonance (MR) imaging parameters correlate with dynamic contrast material-enhanced MR imaging parameters in between-subject and/or within-subject longitudinal settings for monitoring the therapeutic effects of a vascular disrupting agent (VDA) (CKD-516) in rabbit VX2 liver tumors.

MATERIALS AND METHODS

With institutional Animal Care and Use Committee approval, 21 VX2 liver tumor-bearing rabbits (treated, n = 15; control, n = 6) underwent IVIM DW imaging with 12 b values (0-800 sec/mm(2)) and dynamic contrast-enhanced MR imaging performed before (baseline) CKD-516 administration and 4 hours, 24 hours, and 7 days after administration. Perfusion-related IVIM DW imaging parameters of the tumors, including the pseudodiffusion coefficient (D*) and perfusion fraction (f), as well as dynamic contrast-enhanced MR imaging parameters, including the volume transfer coefficient (K(trans)) and initial area under the gadolinium concentration-time curve until 60 seconds (iAUC), were measured. IVIM DW imaging parameters were correlated with dynamic contrast-enhanced MR imaging parameters by using Pearson correlation analysis between subjects at each given time and by using a linear mixed model for within-subject longitudinal data.

RESULTS

In the treated group, D*, f, K(trans), and iAUC significantly decreased (-40.7% to -26.3%) at 4-hour follow-up compared with these values in the control group (-6.9% to +5.9%) (P < .05). For longitudinal monitoring of CKD-516 treatment, D* and f showed significant positive correlations with K(trans) and iAUC (P = .004 and P = .02; P < .001 and P = .006, respectively), while no significant correlations were observed between IVIM DW imaging and dynamic contrast-enhanced MR imaging parameters between subjects at any given time (P > .05).

CONCLUSION

In a rabbit tumor model, perfusion parameters serially quantified with IVIM DW imaging can be used as alternatives to dynamic contrast-enhanced MR imaging parameters in reflecting the dynamic changes in tumor perfusion during the within-subject longitudinal monitoring of VDA treatment.

Feasibility study of high-resolution DCE-MRI for glomerular filtration rate (GFR) measurement in a routine clinical modal

Dynamic contrast enhanced (DCE) MR renography has been identified as an interesting tool to determine single-kidney GFR. However, a fundamental issue for the applicability of MR-based estimate of single-kidney GFR is selecting a balance between spatial and temporal resolution of DCE-MRI data. The purpose is to assess the feasibility of GFR estimate from high-resolution (HR) dynamic contrast-enhanced (DCE) MRI in a routine clinical modal. Standard MR renography (2.4s/phase, total 4min; 4-ml Gd) and five-phase, HR-based imaging protocol (0, 30, 70, 120, and 240s; 0.05mmol/kg Gd) were prospectively performed in twelve volunteers who were scheduled for routine renal MRI. Data were plotted with Patlak, two-compartment modified Tofts model (2CTM), and two-compartment filtration model (2CFM) for GFR estimate. During all the measurements, only the signal intensities in the aorta and whole kidney parenchyma were considered. Standard 2CFM and 2CTM produced lower residuals over the fitted interval than HR-based measures (p<0.05); and HR-bases 2CFM and 2CTM did not reflect significant correlation to standard values. Standard Patlak plots with 0-240s data points produced significantly lower GFR and higher residuals than that plots with 0-120s data points (p<0.05). HR-based Patlak plots with 0, 30, 70, and 120s data points significantly correlated with reference values (Pearson ρ=0.97, p<0.01), and produced a 33.2% underestimation of reference value, which was better than that plots with 0, 30, 70, 120, and 240s data points (ρ=0.92, p<0.01; 58.6% underestimation of reference value). It concludes that it is feasible to estimate GFR with HR-based DCE-MRI and appreciate kinetic model. Patlak plots from 0, 30, 70, and 120s data points is better than plots from 0, 30, 70, 120, and 240s data points.

Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties

PURPOSE

To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models.

METHODS

By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined.

RESULTS

Fast injections are beneficial for K(trans) precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average K(trans) bias of 40% for a 30% overestimation throughout all models.

CONCLUSION

This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology.

Dynamic contrast-enhanced MRI in mouse tumors at 11.7 T: comparison of three contrast agents with different molecular weights to assess the early effects of combretastatin A4

Dynamic contrast-enhanced (DCE)-MRI is useful to assess the early effects of drugs acting on tumor vasculature, namely anti-angiogenic and vascular disrupting agents. Ultra-high-field MRI allows higher-resolution scanning for DCE-MRI while maintaining an adequate signal-to-noise ratio. However, increases in susceptibility effects, combined with decreases in longitudinal relaxivity of gadolinium-based contrast agents (GdCAs), make DCE-MRI more challenging at high field. The aim of this work was to explore the feasibility of using DCE-MRI at 11.7 T to assess the tumor hemodynamics of mice. Three GdCAs possessing different molecular weights (gadoterate: 560 Da, 0.29 mmol Gd/kg; p846: 3.5 kDa, 0.10 mmol Gd/kg; and p792: 6.47 kDa, 0.15 mmol Gd/kg) were compared to see the influence of the molecular weight in the highlight of the biologic effects induced by combretastatin A4 (CA4). Mice bearing transplantable liver tumor (TLT) hepatocarcinoma were divided into two groups (n = 5-6 per group and per GdCA): a treated group receiving 100 mg/kg CA4, and a control group receiving vehicle. The mice were imaged at 11.7 T with a T1 -weighted FLASH sequence 2 h after the treatment. Individual arterial input functions (AIFs) were computed using phase imaging. These AIFs were used in the Extended Tofts Model to determine K(trans) and vp values. A separate immunohistochemistry study was performed to assess the vascular perfusion and the vascular density. Phase imaging was used successfully to measure the AIF for the three GdCAs. In control groups, an inverse relationship between the molecular weight of the GdCA and K(trans) and vp values was observed. K(trans) was significantly decreased in the treated group compared with the control group for each GdCA. DCE-MRI at 11.7 T is feasible to assess tumor hemodynamics in mice. With K(trans) , the three GdCAs were able to track the early vascular effects induced by CA4 treatment.

Accurate determination of blood-brain barrier permeability using dynamic contrast-enhanced T1-weighted MRI: a simulation and in vivo study on healthy subjects and multiple sclerosis patients

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used to estimate permeability in situations with subtle blood-brain barrier (BBB) leakage. However, the method's ability to differentiate such low values from zero is unknown, and no consensus exists on optimal selection of total measurement duration, temporal resolution, and modeling approach under varying physiologic circumstances. To estimate accuracy and precision of the DCE-MRI method we generated simulated data using a two-compartment model and progressively down-sampled and truncated the data to mimic low temporal resolution and short total measurement duration. Model fit was performed with the Patlak, the extended Tofts, and the Tikhonov two-compartment (Tik-2CM) models. Overall, 17 healthy controls were scanned to obtain in vivo data. Long total measurement duration (15 minutes) and high temporal resolution (1.25 seconds) greatly improved accuracy and precision for all three models, enabling us to differentiate values of permeability as low as 0.1 ml/100 g/min from zero. The Patlak model yielded highest accuracy and precision for permeability values <0.3 ml/100 g/min, but for higher values the Tik-2CM performed best. Our results emphasize the importance of optimal parameter setup and model selection when characterizing low BBB permeability.

DCE-MRI in hepatocellular carcinoma-clinical and therapeutic image biomarker

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables tumor vascular physiology to be assessed. Within the tumor tissue, contrast agents (gadolinium chelates) extravasate from intravascular into the extravascular extracellular space (EES), which results in a signal increase on T1-weighted MRI. The rate of contrast agents extravasation to EES in the tumor tissue is determined by vessel leakiness and blood flow. Thus, the signal measured on DCE-MRI represents a combination of permeability and perfusion. The semi-quantitative analysis is based on the calculation of heuristic parameters that can be extracted from signal intensity-time curves. These enhancing curves can also be deconvoluted by mathematical modeling to extract quantitative parameters that may reflect tumor perfusion, vascular volume, vessel permeability and angiogenesis. Because hepatocellular carcinoma (HCC) is a hypervascular tumor, many emerging therapies focused on the inhibition of angiogenesis. DCE-MRI combined with a pharmacokinetic model allows us to produce highly reproducible and reliable parametric maps of quantitative parameters in HCC. Successful therapies change quantitative parameters of DCE-MRI, which may be used as early indicators of tumor response to anti-angiogenesis agents that modulate tumor vasculature. In the setting of clinical trials, DCE-MRI may provide relevant clinical information on the pharmacodynamic and biologic effects of novel drugs, monitor treatment response and predict survival outcome in HCC patients.

Renal perfusion in acute kidney injury with DCE-MRI: deconvolution analysis versus two-compartment filtration model

PURPOSE

To investigate the results of different pharmacokinetic models of a quantitative analysis of renal blood flow (RBF) in acute kidney injury using deconvolution analysis and a two-compartment renal filtration model.

MATERIALS AND METHODS

MRI data of ten male Lewis rats were analyzed retrospectively. Six animals were subjected to unilateral acute kidney injury and underwent perfusion imaging by dynamic contrast-enhanced MRI (DCE-MRI). Renal blood flow was estimated from regions-of-interest depicting the cortex in the DCE-MRI perfusion maps. The perfusion models were compared by a paired t-test and Bland-Altman plots.

RESULTS

No significant difference was found between the two compartment model and the deconvolution analysis (P=0.2807). Differences between healthy and diseased kidney in the AKI model were significant for both methods (P<0.05). A Bland-Altman plot showed no systematic errors, and values were equally distributed around the mean difference between the methods lying within the range of 1.96 standard deviations.

CONCLUSION

Both quantification strategies could detect the kidneys that were impaired by AKI. When just aiming at RBF as a marker, a deconvolution analysis can provide similar values as the 2CFM. If functional parameters beyond RBF like glomerular filtration rate are needed, the 2CFM should be employed.

Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhanced MRI using time series analysis

PURPOSE

To accurately segment invasive ductal carcinomas (IDCs) from dynamic contrast-enhanced MRI (DCE-MRI) using time series analysis based on linear dynamic system (LDS) modeling.

MATERIALS AND METHODS

Quantitative segmentation methods based on black-box modeling and pharmacokinetic modeling are highly dependent on imaging pulse sequence, timing of bolus injection, arterial input function, imaging noise, and fitting algorithms. We modeled the underlying dynamics of the tumor by an LDS and used the system parameters to segment the carcinoma on the DCE-MRI. Twenty-four patients with biopsy-proven IDCs were analyzed. The lesions segmented by the algorithm were compared with an expert radiologist's segmentation and the output of a commercial software, CADstream. The results are quantified in terms of the accuracy and sensitivity of detecting the lesion and the amount of overlap, measured in terms of the Dice similarity coefficient (DSC).

RESULTS

The segmentation algorithm detected the tumor with 90% accuracy and 100% sensitivity when compared with the radiologist's segmentation and 82.1% accuracy and 100% sensitivity when compared with the CADstream output. The overlap of the algorithm output with the radiologist's segmentation and CADstream output, computed in terms of the DSC was 0.77 and 0.72, respectively. The algorithm also shows robust stability to imaging noise. Simulated imaging noise with zero mean and standard deviation equal to 25% of the base signal intensity was added to the DCE-MRI series. The amount of overlap between the tumor maps generated by the LDS-based algorithm from the noisy and original DCE-MRI was DSC = 0.95.

CONCLUSION

The time-series analysis based segmentation algorithm provides high accuracy and sensitivity in delineating the regions of enhanced perfusion corresponding to tumor from DCE-MRI.

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.

Modeling DCE-MRI at low temporal resolution: a case study on rheumatoid arthritis

PURPOSE

To identify the optimal tracer-kinetic modeling strategy for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data acquired at low temporal resolution.

MATERIALS AND METHODS

DCE-MRI was performed on 13 patients with rheumatoid arthritis of the hand before and after anti-tumor necrosis factor alpha (TNFα) therapy, using a 3D sequence with a temporal resolution of 13 seconds, imaging for 4 minutes postcontrast injection. Concentration-time curves were extracted from regions of interest (ROIs) in enhancing synovium and fitted to the 3-parameter modified Tofts model (MT) and the 4-parameter two-compartment exchange model (2CXM). To assist the interpretation of the data, the same analysis was applied to simulated data with similar characteristics.

RESULTS

Both models fitted the data closely, and showed similar therapy effects. The MT plasma volume was significantly lower than with 2CXM, but the differences in permeability and interstitial volume were not significant. 2CXM was less precise than MT, with larger standard deviations relative to the mean in most parameters. The additional perfusion parameter determined with 2CXM did not provide a statistically significant trend due to low precision.

CONCLUSION

The standard MT model is the optimal modeling strategy at low temporal resolution. Advanced models improve the accuracy and generate an additional parameter, but these benefits are offset by low precision.

Combined diffusion-weighted, blood oxygen level-dependent, and dynamic contrast-enhanced MRI for characterization and differentiation of renal cell carcinoma

PURPOSE

To investigate a multiparametric magnetic resonance imaging (MRI) approach comprising diffusion-weighted imaging (DWI), blood oxygen-dependent (BOLD), and dynamic contrast-enhanced (DCE) MRI for characterization and differentiation of primary renal cell carcinoma (RCC).

MATERIAL AND METHODS

Fourteen patients with clear-cell carcinoma and four patients with papillary RCC were examined with DWI, BOLD MRI, and DCE MRI at 1.5T. The apparent diffusion coefficient (ADC) was calculated with a monoexponential decay. The spin-dephasing rate R2* was derived from parametric R2* maps. DCE-MRI was analyzed using a two-compartment exchange model allowing separation of perfusion (plasma flow [FP] and plasma volume [VP]), permeability (permeability surface area product [PS]), and extravascular extracellular volume (VE). Statistical analysis was performed with Wilcoxon signed-rank test, Pearson's correlation coefficient, and receiver operating characteristic curve analysis.

RESULTS

Clear-cell RCC showed higher ADC and lower R2* compared to papillary subtypes, but differences were not significant. FP of clear-cell subtypes was significantly higher than in papillary RCC. Perfusion parameters showed moderate but significant inverse correlation with R2*. VE showed moderate inverse correlation with ADC. Fp and Vp showed best sensitivity for histological differentiation.

CONCLUSION

Multiparametric MRI comprising DWI, BOLD, and DCE MRI is feasible for assessment of primary RCC. BOLD moderately correlates to DCE MRI-derived perfusion. ADC shows moderate correlation to the extracellular volume, but does not correlate to tumor oxygenation or perfusion. In this preliminary study DCE-MRI appeared superior to BOLD and DWI for histological differentiation.

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.

Sampling requirements in DCE-MRI based analysis of high grade gliomas: simulations and clinical results

PURPOSE

To investigate the effect of variations in temporal resolution and total measurement times on the estimations of kinetic parameters derived from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas (HGGs).

MATERIALS AND METHODS

DCE-MRI with high temporal resolution (dynamic sampling time (T(s)) = 2.1 s and 3.4 s) and total sampling time (T(acq)) of 5.2 min was acquired in 101 examinations from 15 patients. Using the modified Tofts model K(trans), k(ep) v(e) and v(p) were estimated. The effects of increasing T(s) and reducing T(acq) on the estimated kinetic parameters were estimated through down-sampling and data truncation, and the results were compared with numerical simulations.

RESULTS

There was an overall dependence of all four kinetic parameters on T(s) and T(acq). Increasing T(s) resulted in under-estimation of K(trans) and over-estimation of V(p), whereas k(ep) and V(e) varied in a less predictable manner. Reducing T(acq) resulted in over-estimation of K(trans) and k(ep) and under-estimation of v(p) and v(e). Increasing T(s) and reducing T(acq) resulted in increased relative error for all four parameters.

CONCLUSION

Estimated K(trans), K(ep), and V(e) in HGGs were within 15% of the high sampling rate reference values for T(s) <20 s. Increasing T(s) and reducing T(acq) leads to reduced precision of the estimated values.

Assessing liver function using dynamic Gd-EOB-DTPA-enhanced MRI with a standard 5-phase imaging protocol

PURPOSE

To evaluate liver function obtained by tracer-kinetic modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data acquired with a routine gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced protocol.

MATERIALS AND METHODS

Data were acquired from 25 cases of nonchronic liver disease and 94 cases of cirrhosis. DCE-MRI was performed with a dose of 0.025 mmol/kg Gd-EOB-DTPA injected at 2 mL/sec. A 3D breath-hold sequence acquired 5 volumes of 72 slices each: precontrast, double arterial phase, portal phase, and 4-minute postcontrast. Regions of interest (ROIs) were selected semiautomatically in the aorta, portal vein, and whole liver on a middle slice. A constrained dual-inlet two-compartment uptake model was fitted to the ROI curves, producing three parameters: intracellular uptake rate (UR), extracellular volume (Ve), and arterial flow fraction (AFF).

RESULTS

Median UR dropped from 4.46 10(-2) min(-1) in the noncirrhosis to 3.20 in Child-Pugh A (P = 0.001), and again to 1.92 in Child-Pugh B (P < 0.0001). Median Ve dropped from 6.64 mL 100 mL(-1) in the noncirrhosis to 5.80 in Child-Pugh A (P = 0.01). Other combinations of Ve and AFF changes were not significant for any group.

CONCLUSION

UR obtained from tracer kinetic analysis of a routine DCE-MRI has the potential to become a novel index of liver function.

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.