Arterial input functions determined from MR signal magnitude and phase for quantitative dynamic contrast-enhanced MRI in the human pelvis

Dynamic Contrast-Enhanced (DCE) MRI

The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.

Differentiation between glioblastoma and primary CNS lymphoma: application of DCE-MRI parameters based on arterial input function obtained from DSC-MRI

OBJECTIVE

This study aimed to evaluate whether arterial input functions (AIFs) obtained from dynamic susceptibility contrast (DSC)-MRI (AIF_DSC) improve the reliability and diagnostic accuracy of dynamic contrast-enhanced (DCE)-derived pharmacokinetic (PK) parameters for differentiating glioblastoma from primary CNS lymphoma (PCNSL) compared with AIFs derived from DCE-MRI (AIF_DCE).

METHODS

This retrospective study included 172 patients with glioblastoma (n = 147) and PCNSL (n = 25). All patients had undergone preoperative DSC- and DCE-MRI. The volume transfer constant (K^trans), volume of the vascular plasma space (v_p), and volume of the extravascular extracellular space (v_e) were acquired using AIF_DSC and AIF_DCE. The relative cerebral blood volume (rCBV) was obtained from DSC-MRI. Intraclass correlation coefficients (ICC) and ROC curves were used to assess the reliability and diagnostic accuracy of individual parameters.

RESULTS

The mean K^trans, v_p, and v_e values revealed better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.911 vs 0.355; v_p, 0.766 vs 0.503; v_e, 0.758 vs 0.657, respectively). For differentiating all glioblastomas from PCNSL, the mean rCBV (AUC = 0.856) was more accurate than the AIF_DSC-driven mean K^trans, which had the largest AUC (0.711) among the DCE-derived parameters (p = 0.02). However, for glioblastomas with low rCBV (≤ 75th percentile of PCNSL; n = 30), the AIF_DSC-driven mean K^trans and v_p were more accurate than rCBV (AUC: K^trans, 0.807 vs rCBV, 0.515, p = 0.004; v_p, 0.715 vs rCBV, p = 0.045).

CONCLUSION

DCE-derived PK parameters using the AIF_DSC showed improved reliability and diagnostic accuracy for differentiating glioblastoma with low rCBV from PCNSL.

KEY POINTS

• An accurate differential diagnosis of glioblastoma and PCNSL is crucial because of different therapeutic strategies. • In contrast to the rCBV from DSC-MRI, another perfusion imaging technique, the DCE parameters for the differential diagnosis have been limited because of the low reliability of AIFs from DCE-MRI. • When we analyzed DCE-MRI data using AIFs from DSC-MRI (AIF_DSC), AIF_DSC-driven DCE parameters showed improved reliability and better diagnostic accuracy than rCBV for differentiating glioblastoma with low rCBV from PCNSL.

Quantifying MRI T1 relaxation in flowing blood: implications for arterial input function measurement in DCE-MRI

OBJECTIVES

To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo.

METHODS

A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T₁ were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo.

RESULTS

The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown.

CONCLUSIONS

The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity.

ADVANCES IN KNOWLEDGE

This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.

Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements

Accurate, patient-specific measurement of arterial input functions (AIF) may improve model-based analysis of vascular permeability. This study investigated factors affecting AIF measurements from magnetic resonance imaging (MRI) magnitude (AIF_MAGN) and phase (AIF_PHA) signals, and compared them against computed tomography (CT) (AIF_CT), under controlled conditions relevant to clinical protocols using a multimodality flow phantom. The flow phantom was applied at flip angles of 20° and 30°, flow rates (3-7.5 mL/s), and peak bolus concentrations (0.5-10 mM), for in-plane and through-plane flow. Spatial 3D-FLASH signal and variable flip angle T1 profiles were measured to investigate in-flow and radiofrequency-related biases, and magnitude- and phase-derived Gd-DTPA concentrations were compared. MRI AIF performance was tested against AIF_CT via Pearson correlation analysis. AIF_MAGN was sensitive to imaging orientation, spatial location, flip angle, and flow rate, and it grossly underestimated AIF_CT peak concentrations. Conversion to Gd-DTPA concentration using T1 taken at the same orientation and flow rate as the dynamic contrast-enhanced acquisition improved AIF_MAGN accuracy; yet, AIF_MAGN metrics remained variable and significantly reduced from AIF_CT at concentrations above 2.5 mM. AIF_PHA performed equivalently within 1 mM to AIF_CT across all tested conditions. AIF_PHA, but not AIF_MAGN, reported equivalent measurements to AIF_CT across the range of tested conditions. AIF_PHA showed superior robustness.

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (CO_CMRI) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CO_DCE) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare CO_CMRI and CO_DCE. The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) CO_CMRI and CO_DCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation (r = 0.82, P < .01) and good agreement between CO_CMRI and CO_DCE. The CO_DCE is consistent with the reference standard CO_CMRI. This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.

Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients

Purpose

The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast‐enhanced (DCE)‐MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients.

Methods

Twenty‐two patients with biopsy‐proven prostate cancer underwent a 3T MRI exam twice. DCE‐MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIF_MAGN), phase (AIF_PHASE), and complex signal (AIF_COMPLEX). The Tofts model was applied to extract K^trans, k_ep and v_e. Repeatability of AIF curve characteristics and TK parameters was assessed with the within‐subject coefficient of variation (wCV).

Results

The wCV for peak height and full width at half maximum for AIF_COMPLEX (7% and 8%) indicated an improved repeatability compared to AIF_MAGN (12% and 12%) and AIF_PHASE (12% and 7%). This translated in lower wCV values for K^trans (11%) with AIF_COMPLEX in comparison to AIF_MAGN (24%) and AIF_PHASE (15%). For k_ep, the wCV was 16% with AIF_MAGN, 13% with AIF_PHASE, and 13% with AIF_COMPLEX.

Conclusion

Repeatability of AIF_PHASE and AIF_COMPLEX is higher than for AIF_MAGN, resulting in a better repeatability of TK parameters. Thus, use of either AIF_PHASE or AIF_COMPLEX improves the robustness of quantitative analysis of DCE‐MRI in prostate cancer.

Acceleration techniques and their impact on arterial input function sampling: Non-accelerated versus view-sharing and compressed sensing sequences

PURPOSE

The aim was to investigate the variation of the arterial input function (AIF) within and between various DCE MRI sequences.

MATERIAL AND METHODS

A dynamic flow-phantom and steady signal reference were scanned on a 3T MRI using fast low angle shot (FLASH) 2d, FLASH3d (parallel imaging factor (P) = P0, P2, P4), volumetric interpolated breath-hold examination (VIBE) (P = P0, P3, P2 × 2, P2 × 3, P3 × 2), golden-angle radial sparse parallel imaging (GRASP), and time-resolved imaging with stochastic trajectories (TWIST). Signal over time curves were normalized and quantitatively analyzed by full width half maximum (FWHM) measurements to assess variation within and between sequences.

RESULTS

The coefficient of variation (CV) for the steady signal reference ranged from 0.07-0.8%. The non-accelerated gradient echo FLASH2d, FLASH3d, and VIBE sequences showed low within sequence variation with 2.1%, 1.0%, and 1.6%. The maximum FWHM CV was 3.2% for parallel imaging acceleration (VIBE P2 × 3), 2.7% for GRASP and 9.1% for TWIST. The FWHM CV between sequences ranged from 8.5-14.4% for most non-accelerated/accelerated gradient echo sequences except 6.2% for FLASH3d P0 and 0.3% for FLASH3d P2; GRASP FWHM CV was 9.9% versus 28% for TWIST.

CONCLUSION

MRI acceleration techniques vary in reproducibility and quantification of the AIF. Incomplete coverage of the k-space with TWIST as a representative of view-sharing techniques showed the highest variation within sequences and might be less suited for reproducible quantification of the AIF.

Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging

Purpose To evaluate whether arterial input functions (AIFs) derived from dynamic susceptibility-contrast (DSC) magnetic resonance (MR) imaging, or AIF_DSC values, improve diagnostic accuracy and reliability of the pharmacokinetic (PK) parameters of dynamic contrast material-enhanced (DCE) MR imaging for differentiating high-grade from low-grade astrocytomas, compared with AIFs obtained from DCE MR imaging (AIF_DCE). Materials and Methods This retrospective study included 226 patients (138 men, 88 women; mean age, 52.27 years ± 15.17; range, 24-84 years) with pathologically confirmed astrocytomas (World Health Organization grade II = 21, III = 53, IV = 152; isocitrate dehydrogenase mutant, 11.95% [27 of 226]; 1p19q codeletion 0% [0 of 226]). All patients underwent both DSC and DCE MR imaging before surgery, and AIF_DSC and AIF_DCE were obtained from each image. Volume transfer constant (K^trans), volume of vascular plasma space (v_p), and volume of extravascular extracellular space (v_e) were processed by using postprocessing software with two AIFs. The diagnostic accuracies of individual parameters were compared by using receiver operating characteristic curve (ROC) analysis. Intraclass correlation coefficients (ICCs) and the Bland-Altman method were used to assess reliability. Results The AIF_DSC-driven mean K^trans and v_e were more accurate for differentiating high-grade from low-grade astrocytoma than those derived by using AIF_DCE (area under the ROC curve: mean K^trans, 0.796 vs 0.645, P = .038; mean v_e, 0.794 vs 0.658, P = .020). All three parameters had better ICCs with AIF_DSC than with AIF_DCE (K^trans, 0.737 vs 0.095; v_p, 0.848 vs 0.728; v_e, 0.875 vs 0.581, respectively). In AIF analysis, maximal signal intensity (0.837 vs 0.524) and wash-in slope (0.800 vs 0.432) demonstrated better ICCs with AIF_DSC than AIF_DCE. Conclusion AIF_DSC-driven DCE MR imaging PK parameters showed better diagnostic accuracy and reliability for differentiating high-grade from low-grade astrocytoma than those derived from AIF_DCE. ^© RSNA, 2017 Online supplemental material is available for this article.

Dynamic Contrast-Enhanced MRI to Study Atherosclerotic Plaque Microvasculature

Rupture of a vulnerable atherosclerotic plaque of the carotid artery is an important underlying cause of clinical ischemic events, such as stroke. Abundant microvasculature has been identified as an important aspect contributing to plaque vulnerability. Plaque microvasculature can be studied non-invasively with dynamic contrast-enhanced (DCE-)MRI in animals and patients. In recent years, several DCE-MRI studies have been published evaluating the association between microvasculature and other key features of plaque vulnerability (e.g., inflammation and intraplaque hemorrhage), as well as the effects of novel therapeutic interventions. The present paper reviews this literature, focusing on DCE-MRI methods of acquisition and analysis of atherosclerotic plaques, the current state and future potential of DCE-MRI in the evaluation of plaque microvasculature in clinical and preclinical settings.

Phase-based vascular input function: Improved quantitative DCE-MRI of atherosclerotic plaques

PURPOSE

Quantitative pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI can be used to assess atherosclerotic plaque microvasculature, which is an important marker of plaque vulnerability. Purpose of the present study was (1) to compare magnitude- versus phase-based vascular input functions (m-VIF vs ph-VIF) used in pharmacokinetic modeling and (2) to perform model calculations and flow phantom experiments to gain more insight into the differences between m-VIF and ph-VIF.

METHODS

Population averaged m-VIF and ph-VIFs were acquired from 11 patients with carotid plaques and used for pharmacokinetic analysis in another 17 patients. Simulations, using the Bloch equations and the MRI scan geometry, and flow phantom experiments were performed to determine the effect of local blood velocity on the magnitude and phase signal enhancement.

RESULTS

Simulations and flow phantom experiments revealed that flow within the lumen can lead to severe underestimation of m-VIF, while this is not the case for the ph-VIF. In line, the peak concentration of the m-VIF is significantly lower than ph-VIF (p < 0.001), in vivo. Quantitative model parameters for m- and ph-VIF differed in absolute values but were moderate to strongly correlated with each other [K(trans) Spearman's ρ > 0.93 (p < 0.001) and vp Spearman's ρ > 0.58 (p < 0.05)].

CONCLUSIONS

m-VIF is strongly influenced by local blood velocity, which leads to underestimation of the contrast medium concentration. Therefore, it is advised to use ph-VIF for DCE-MRI analysis of carotid plaques for accurate quantification.

Dynamic contrast-enhanced MRI in mice at high field: estimation of the arterial input function can be achieved by phase imaging

PURPOSE

Quantitative dynamic contrast-enhanced MRI requires an accurate arterial input function (AIF). At high field, increased susceptibility effects and decreased longitudinal relaxivity of contrast agents lead to predominant T2* effects in blood vessels, producing a dip in signal during passage of the contrast agent bolus. This study determined phase-derived AIFs in mice at 11.7 T.

METHODS

AIFs were measured in aorta/vena cava for five FBV/N mice and in iliac arteries/veins for five NMRI mice with a fast low angle shot sequence, simultaneously with tumor imaging (temporal resolution: 1.19 s). Gadoterate was injected into the tail vein as a bolus (0.286 mmol Gd/kg). An in vitro study was also performed to calculate the relationship between ΔΦ and gadolinium concentration.

RESULTS

The phantom system confirmed the linear relationship between measured ΔΦ and gadolinium concentration. In vivo, a dip in arterial magnitude signal made it impossible to quantify the AIF. With phase imaging, a clear quantifiable bolus peak was obtained; peak measured concentration in plasma was 4.9 ± 0.9 mM for FBV/N mice and 8.0 ± 0.6 mM for NMRI mice, close to the expected concentration of 6.8 mM.

CONCLUSION

Phase imaging seems to be an appropriate means to measure the AIF of mice at high field.

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.

Clinical imaging of tumor angiogenesis

Angiogenesis is an integral part of tumor growth and invasion. This has led to the emergence of several antiangiogenic therapies and stimulated efforts to accurately evaluate the extent of angiogenesis before and in response to anticancer treatment. The most commonly used approach has been the assessment of new vessel formation in histological samples. However, it is becoming apparent that this is insufficient for a full understanding of tumor physiology and for in vivo guidance of cancer management. Imaging has the potential to provide noninvasive and repeatable assessment of the angiogenic process. Imaging approaches use a variety of modalities and are aimed at either assessment of the functional integrity of tumor vasculature or assessment of its molecular status. This review summarizes the aims and methods of clinical tumor angiogenesis imaging, including present technologies and ones that will be developed within the next 5-10 years.

Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas

BACKGROUND AND PURPOSE

The accuracy of tumor plasma volume and K(trans) estimates obtained with DCE MR imaging may have inaccuracies introduced by a poor estimation of the VIF. In this study, we evaluated the diagnostic accuracy of a novel technique by using a phase-derived VIF and "bookend" T1 measurements in the preoperative grading of patients with suspected gliomas.

MATERIALS AND METHODS

This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. Both magnitude and phase images were acquired during DCE MR imaging for estimates of K(trans)_φ and V(p_)φ (calculated from a phase-derived VIF and bookend T1 measurements) as well as K(trans)_SI and V(p_)SI (calculated from a magnitude-derived VIF without T1 measurements).

RESULTS

Median K(trans)_φ values were 0.0041 minutes(-1) (95 CI, 0.00062-0.033), 0.031 minutes(-1) (0.011-0.150), and 0.088 minutes(-1) (0.069-0.110) for grade II, III, and IV gliomas, respectively (P ≤ .05 for each). Median V(p_)φ values were 0.64 mL/100 g (0.06-1.40), 0.98 mL/100 g (0.34-2.20), and 2.16 mL/100 g (1.8-3.1) with P = .15 between grade II and III gliomas and P = .015 between grade III and IV gliomas. In differentiating low-grade from high-grade gliomas, AUCs for K(trans)_φ, V(p_φ), K(trans)_SI, and V(p_)SI were 0.87 (0.73-1), 0.84 (0.69-0.98), 0.81 (0.59-1), and 0.84 (0.66-0.91). The differences between the AUCs were not statistically significant.

CONCLUSIONS

K(trans)_φ and V(p_)φ are parameters that can help in differentiating low-grade from high-grade gliomas.

Pharmacokinetic analysis based on dynamic contrast-enhanced MRI for evaluating tumor response to preoperative therapy for oral cancer

PURPOSE

To evaluate whether a pharmacokinetic analysis is useful for monitoring the response of oral cancer to chemoradiotherapy (CRT).

MATERIALS AND METHODS

Twenty-nine patients were included. They underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before and after CRT. The DCE-MRI data were analyzed using a Tofts and Kermode (TK) model. The histological evaluation of the effects of CRT was performed according to Ohboshi and Shimosato's classification.

RESULTS

None of the pre-CRT parameters were significantly different between the responders and nonresponders. The post-CRT volume of the extravascular extracellular space (EES) per unit volume of tissue (v(e) ) of responders (0.397 ± 0.080) was higher than that of nonresponders (0.281 ± 0.076) (P = 0.01). The change of the v(e) between the pre- and post-CRT of the responders (0.154 ± 0.093) was larger than that of the nonresponders (0.033 ± 0.073) (P = 0.001). Therefore, the increase in the v(e) strongly suggested a good tumor response to CRT, which reflected an increase of the EES secondary to the destruction of the cancer nest. The changes in the volume transfer constant (K(trans) ) were significantly different between the responders and nonresponders (P = 0.018).

CONCLUSION

Both the increase of the v(e) and the elevation of permeability (K(trans) ) were indicative of a good tumor response to CRT. The pharmacokinetic analysis had potential for monitoring the histopathological response to CRT.

Correction of arterial input function in dynamic contrast-enhanced MRI of the liver

PURPOSE

To develop a postprocessing method to correct saturation of arterial input function (AIF) in T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantification of hepatic perfusion.

MATERIALS AND METHODS

The saturated AIF is corrected by parameterizing the first pass of the AIF as a smooth function with a single peak and minimizing a least-squares error in fitting the liver DCE-MRI data to a dual-input single-compartment model. Sensitivities of the method to the degree of saturation in the AIF first-pass peak and the image contrast-to-noise ratio were assessed. The method was also evaluated by correlating portal venous perfusion with an independent overall liver function measurement.

RESULTS

The proposed method corrects the distorted AIF with a saturation ratio up to 0.45. The corrected AIF improved hepatic arterial perfusion by -23.4% and portal venous perfusion by 26.9% in a study of 12 patients with liver cancers. The correlation between the mean voxelwise portal venous perfusion and overall liver function measurement was improved by using the corrected AIFs (R(2) = 0.67) compared with the saturated AIFs (R(2) = 0.39).

CONCLUSION

The method is robust for correcting AIF distortion and has the potential to improve quantification of hepatic perfusion for assessment of liver tissue response to treatment in patients with hepatic cancers.

Blind estimation of the arterial input function in dynamic contrast-enhanced MRI using purity maximization

Uncertainty in arterial input function (AIF) estimation is one of the major errors in the quantification of dynamic contrast-enhanced MRI. A blind source separation algorithm was proposed to determine the AIF by selecting the voxel time course with maximum purity, which represents a minimal contamination from partial volume effects. Simulations were performed to assess the partial volume effect on the purity of AIF, the estimation accuracy of the AIF, and the influence of purity on the derived kinetic parameters. In vivo data were acquired from six patients with hypopharyngeal cancer and eight rats with brain tumor. Results showed that in simulation the AIF with the highest purity is closest to the true AIF. In patients, the manually selection had reduced purity, which could lead to underestimations of K(trans) and V(e) and an overestimation of V(p) when compared with those obtained by the proposed blind source separation algorithm. The derived kinetic parameters in the tumor were more susceptible to the changes in purity when compared with those in the muscle. The animal experiment demonstrated good reproducibility in blind source separation-AIF derived parameters. In conclusion, the blind source separation method is feasible and reproducible to identify the voxel with the tracer concentration time course closest to the true AIF.

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.