Determination Of Arterial Input Function Research Articles

Kinetic modeling of the monoamine oxidase-B radioligand [18F]SMBT-1 in human brain with positron emission tomography.

This paper describes pharmacokinetic analyses of the monoamine-oxidase-B (MAO-B) radiotracer [18F](S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline ([18F]SMBT-1) for positron emission tomography (PET) brain imaging. Brain MAO-B expression is widespread, predominantly within astrocytes. Reactive astrogliosis in response to neurodegenerative disease pathology is associated with MAO-B overexpression. Fourteen elderly subjects (8 control, 5 mild cognitive impairment, 1 Alzheimer's disease) with amyloid ([11C]PiB) and tau ([18F]flortaucipir) imaging assessments underwent dynamic [18F]SMBT-1 PET imaging with arterial input function determination. [18F]SMBT-1 showed high brain uptake and a retention pattern consistent with the known MAO-B distribution. A two-tissue compartment (2TC) model where the K1/k2 ratio was fixed to a whole brain value best described [18F]SMBT-1 kinetics. The 2TC total volume of distribution (VT) was well identified and highly correlated (r2∼0.8) with post-mortem MAO-B indices. Cerebellar grey matter (CGM) showed the lowest mean VT of any region and is considered the optimal pseudo-reference region. Simplified analysis methods including reference tissue models, non-compartmental models, and standard uptake value ratios (SUVR) agreed with 2TC outcomes (r2 > 0.9) but with varying bias. We found the CGM-normalized 70-90 min SUVR to be highly correlated (r2 = 0.93) with the 2TC distribution volume ratio (DVR) with acceptable bias (∼10%), representing a practical alternative for [18F]SMBT-1 analyses.

Arterial input function for quantitative dynamic contrast-enhanced MRI to diagnose prostate cancer.

PURPOSE This study aims to analyze the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish between prostate cancer (PCa) and benign lesions in transition zone (TZ) and peripheral zone (PZ) using different methods for arterial input function (AIF) determination. Study endpoints are identification of a standard AIF method and optimal quantitative perfusion parameters for PCa detection. METHODS DCE image data of 50 consecutive patients with PCa who underwent multiparametric MRI were analyzed retrospectively with three different methods of AIF acquisition. First, a region of interest was manually defined in an artery (AIFm); second, an automated algorithm was used (AIFa); and third, a population-based AIF (AIFp) was applied. Values of quantitative parameters after Tofts (Ktrans, ve, and kep) in PCa, PZ, and TZ in the three different AIFs were analyzed. RESULTS Ktrans and kep were significantly higher in PCa than in benign tissue independent from the AIF method. Whereas in PZ, Ktrans and kep could differentiate PCa (P < .001), in TZ only kep using AIFpdemonstrated a significant difference (P = .039). The correlations of the perfusion parameters that resulted from AIFm and AIFa were higher than those that resulted from AIFp, and the absolute values of Ktrans, kep, and ve were significantly lower when using AIFp. The values of quantitative perfusion parameters for PCa were similar regardless of whether PCa was located in PZ or TZ. CONCLUSION Ktrans and kep were able to differentiate PCa from benign PZ independent of the AIF method. AIFaseems to be the most feasible method of AIF determination in clinical routine. For TZ, none of the quantitative perfusion parameters provided satisfying results.

Catheter-based Arterial Input Function Determination for Myocardial Perfusion Measurements

The quantification of myocardial perfusion with contrast agent (CA) tracers requires the precise knowledge of the arterial input function (AIF). In this study a method for MR-guided vascular interventions is evaluated that determines the AIF via an active tracking catheter during targeted CA injection. A phantom experiment with a dialysis filter was conducted to measure the AIF using an active catheter and a dynamic image series as reference. To compensate for dilution and coil sensitivity effects, correction methods were developed for the catheter-based AIF determination. From the dynamic MR measurements in the perfusion phantom quantitative perfusion maps were calculated by a deconvolution of the measured CA concentration with the AIF, and additional flow measurements were used to normalize the perfusion map. The signal-time-curves of the measured AIF using the catheter-based and imaging-based methods agree while the absolute values differ by a scaling factor of about 9. After normalization to the surrounding flow, both perfusion techniques are in excellent agreement. Catheter-based AIF measurements are feasible but require an additional normalization which can be determined from a flow measurement. The technique might enable faster perfusion measurements during cardiovascular interventions.

An Efficient Framework for Accurate Arterial Input Selection in DSC-MRI of Glioma Brain Tumors

Automatic and accurate arterial input function (AIF) selection has an essential role for quantification of cerebral perfusion hemodynamic parameters using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI). The purpose of this study is to develop an optimal automatic method for arterial input function determination in DSC-MRI of glioma brain tumors by using a new preprocessing method.For this study, DSC-MR images of 43 patients with glioma brain tumors were retrieved retrospectively. Our proposed AIF selection framework consisted an effcient pre-processing step, through which non-arterial curves such as tumorous, tissue, noisy and partial-volume affected curves were excluded, followed by AIF selection through agglomerative hierarchical (AH) clustering method. The performance of automatic AIF clustering was compared with manual AIF selection performed by an experienced radiologist, based on curve shape parameters, i.e. maximum peak (MP), full-width-at-half-maximum (FWHM), M (=MP/ (TTP × FWHM)) and root mean square error (RMSE).Mean values of AIFs shape parameters were compared with those derived from manually selected AIFs by two-tailed paired t-test. The results showed statistically insignificant differences in MP, FWHM, and M parameters and lower RMSE, approving the resemblance of the selected AIF with the gold standard. The intraclass correlation coefficient and coefficients of variation percent showed a better agreement between manual AIF and our proposed AIF selection than previously proposed methods.The results of current work suggest that by using efficient preprocessing steps, the accuracy of automatic AIF selection could be improved and this method appears promising for efficient and accurate clinical applications.

Influence of arterial input function (AIF) on quantitative prostate dynamic contrast-enhanced (DCE) MRI and zonal prostate anatomy

PurposeReproducibility of quantitative perfusion analysis of DCE requires a standardized AIF acquisition. However, there are many different approaches for AIF assessment so that the absolute values of perfusion parameters may vary depending on the used method. This study analyzes the influence of the method of AIF determination on quantitative DCE-MRI. MethodsIn this retrospective, single-center, cohort study three different methods of AIF determination in 50 consecutive patients with multiparametric MRI of the prostate were conducted. As a reference, AIF was selected manually by defining a region of interest in an artery manually (AIFm). The second method (AIFa), based on an automated algorithm and the third, population-derived AIFp where then compared. Primary endpoint were differences in the performance of the perfusion parameters Ktrans, ve and kep regarding the AIF acquisition methods, secondary endpoints consisted of the evaluation of differences in the peripheral and transition zone of the prostate (PZ, TZ). ResultsIn all three methods, Ktrans, ve, and kep were significantly higher in PZ than in TZ with Ktrans showing least overlapping. There were no significant differences for Ktrans determined with AIFm and AIFa (0.3 ± 0.2 min−1 for PZ for both and 0.5 ± 0.3 min−1 for TZ in AIFm and 0.4 ± 0.3 min−1 in AIFa), while there were great differences between AIFa and AIFp and AIFm and AIFp (0.1 ± 0.03 min−1 for TZ and PZ in AIFp). Spearman test demonstrated good correlation of values for Ktrans and kep in all 3 methods (ρ ≥ 0.76). AIFa showed a success rate of 98% in finding the artery. ConclusionAIFa is a recommendable user-independent automatical method to determine quantitative perfusion parameters allowing an objective measurement and saving interactive time for the radiologist. AIFp may be applied as second alternative method.

Semiautomatic determination of arterial input function in DCE-MRI of the abdomen.

The goal of this study was to develop a semiautomatic segmentation technique of the abdominal aorta to determine the arterial input function (AIF). A total of 24 patients having therapy naïve abdominal cancers were imaged using DCE-MRI on a 3T MR scanner. DCE-MRI continued for 4.2 minutes with 2.1 seconds temporal resolution (120 acquisitions). Gadoteridol (0.1 mmol/kg) was infused intravenously at 30 seconds after starting DCE-MRI, and flushed with 20-ml saline (2 ml/s). Patients were instructed to hold breath after maximal inhalation, and repeat as needed to full inspiration. The location of the abdominal aorta was manually identified, but its segmentation and motion tracking were automatically implemented. AIFs determined in the aortic region with and without tracking motion were statistically compared. The aortic region was further segmented into multiple smaller regions, and the AIF change according to the size of the region of interest (ROI) was examined. The displacement of the abdominal aorta during DCE-MRI was 3.4±2.3 (mean±SD) mm. The root mean square error (RMSE) of AIF from the best fitting curve was 0.110±0.010 mM after motion correction, which was significantly smaller than that before motion correction (0.134±0.016 mM; p<0.001). The amplitude of AIF varied up to 15% according to the ROI size. However, when the radius of ROI was reduced more than 3 mm, the variation in AIF amplitude was less than 5%. Therefore the ROI having smaller radius than that of aorta will need to be used to determine a reliable AIF in abdominal DCE-MRI.

Determination of an Optimal Pharmacokinetic Model of 18F-FET for Quantitative Applications in Rat Brain Tumors.

O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) is a radiolabeled artificial amino acid used in PET for tumor delineation and grading. The present study compares different kinetic models to determine which are more appropriate for 18F-FET in rats. Methods: Rats were implanted with F98 glioblastoma cells in the right hemisphere and scanned 9-15 d later. PET data were acquired during 50 min after a 1-min bolus of 18F-FET. Arterial blood samples were drawn for arterial input function determination. Two compartmental pharmacokinetic models were tested: the 2-tissue model and the 1-tissue model. Their performance at fitting concentration curves from regions of interest was evaluated using the Akaike information criterion, F test, and residual plots. Graphical models were assessed qualitatively. Results: Metrics indicated that the 2-tissue model was superior to the 1-tissue model for the current dataset. The 2-tissue model allowed adequate decoupling of 18F-FET perfusion and internalization by cells in the different regions of interest. Of the 2 graphical models tested, the Patlak plot provided adequate results for the tumor and brain, whereas the Logan plot was appropriate for muscles. Conclusion: The 2-tissue-compartment model is appropriate to quantify the perfusion and internalization of 18F-FET by cells in various tissues of the rat, whereas graphical models provide a global measure of uptake.

Improving Perfusion Measurement in DSC-MR Imaging with Multiecho Information for Arterial Input Function Determination.

Clinical measurements of cerebral perfusion have been increasingly performed with multiecho dynamic susceptibility contrast-MR imaging techniques due to their ability to remove confounding T1 effects of contrast agent extravasation from perfusion quantification. However, to this point, the extra information provided by multiecho techniques has not been used to improve the process of estimating the arterial input function, which is critical to accurate perfusion quantification. The purpose of this study is to investigate methods by which multiecho DSC-MRI data can be used to automatically avoid voxels whose signal decreases to the level of noise when calculating the arterial input function. Here we compare postprocessing strategies for clinical multiecho DSC-MR imaging data to test whether arterial input function measures could be improved by automatically identifying and removing voxels exhibiting signal attenuation (truncation) artifacts. In a clinical pediatric population, we found that the Pearson correlation coefficient between ΔR2* time-series calculated from each TE individually was a valuable criterion for automated estimation of the arterial input function, resulting in higher peak arterial input function values while maintaining smooth and reliable arterial input function shapes. This work is the first to demonstrate that multiecho information may be useful in clinically important automatic arterial input function estimation because it can be used to improve automatic selection of voxels from which the arterial input function should be measured.

Automated Determination of Arterial Input Function for Dynamic Susceptibility Contrast MRI from Regions around Arteries Using Independent Component Analysis

Purpose. Quantitative cerebral blood flow (CBF) measurement using dynamic susceptibility contrast- (DSC-) MRI requires accurate estimation of the arterial input function (AIF). The present work utilized the independent component analysis (ICA) method to determine the AIF in the regions adjacent to the middle cerebral artery (MCA) by the alleviated confounding of partial volume effect. Materials and Methods. A series of spin-echo EPI MR scans were performed in 10 normal subjects. All subjects received 0.2 mmol/kg Gd-DTPA contrast agent. AIFs were calculated by two methods: (1) the region of interest (ROI) selected manually and (2) weighted average of each component selected by ICA (weighted-ICA). The singular value decomposition (SVD) method was then employed to deconvolve the AIF from the tissue concentration time curve to obtain quantitative CBF values. Results. The CBF values calculated by the weighted-ICA method were 41.1 ± 4.9 and 22.1 ± 2.3 mL/100 g/min for cortical gray matter (GM) and deep white matter (WM) regions, respectively. The CBF values obtained based on the manual ROIs were 53.6 ± 12.0 and 27.9 ± 5.9 mL/100 g/min for the same two regions, respectively. Conclusion. The weighted-ICA method allowed semiautomatic and straightforward extraction of the ROI adjacent to MCA. Through eliminating the partial volume effect to minimum, the CBF thus determined may reflect more accurate physical characteristics of the T2⁎ signal changes induced by the contrast agent.

Technical Note: Clinical translation of the Rapid-Steady-State-T1 MRI method for direct cerebral blood volume quantification.

In preclinical studies, the Rapid-Steady-State-T1 (RSST1) MRI method has advantages over conventional MRI methods for blood volume fraction (BVf) mapping, since after contrast agent administration, the BVf is directly quantifiable from the signal amplitude corresponding to the vascular equilibrium magnetization. This study focuses on its clinical implementation and feasibility. Following sequence implementation on clinical Philips Achieva scanners, the RSST1-method is assessed at 1.5 and 3 T in the follow-up examination of neurooncological patients receiving 0.1-0.2 mmol/kg Gd-DOTA to determine the threshold dose needed for cerebral BVf quantification. Confounding effects on BVf quantification such as transendothelial water exchange, transverse relaxation, and contrast agent extravasation are evaluated. For a dose≥0.13 mmol/kg at 1.5 T and ≥0.16 mmol/kg at 3 T, the RSST1-signal time course in macrovessels and brain tissue with Gd-DOTA impermeable vasculature reaches a steady state at maximum amplitude for about 8 s. In macrovessels, a BVf of 100% was obtained validating cerebral microvascular BVf quantification (3.5%-4.5% in gray matter and 1.5%-2.0% in white matter). In tumor tissue, a continuously increasing signal is detected, necessitating signal modeling for tumor BVf calculation. Using approved doses of Gd-DOTA, the steady state RSST1-signal in brain tissue is reached during the first pass and corresponds to the BVf. The first-pass duration is sufficient to allow accurate BVf quantification. The RSST1-method is appropriate for serial clinical studies since it allows fast and straightforward BVf quantification without arterial input function determination. This quantitative MRI method is particularly useful to assess the efficacy of antiangiogenic agents.

A model‐based reconstruction technique for quantitative myocardial perfusion imaging

To reduce saturation effects in the arterial input function (AIF) estimation of quantitative myocardial first-pass saturation recovery perfusion imaging by employing a model-based reconstruction. Imaging was performed with a saturation recovery prepared radial FLASH sequence. A model-based reconstruction was applied for reconstruction. By exploiting prior knowledge about the relaxation process, an image series with different saturation recovery times was reconstructed. By evaluating images with an effective saturation time of approximately 3 ms, saturation effects in the AIF determination were reduced. In a volunteer study, this approach was compared with a standard prebolus technique. In comparison to the low-dose injection of a prebolus acquisition, saturation effects were further reduced in the AIFs determined using the model-based approach. These effects, which were clearly visible for all six volunteers, were reflected in a statistically significant difference of up to 20% in the absolute perfusion values. The application of model-based reconstruction algorithms in quantitative myocardial perfusion imaging promises a significant improvement of the AIF determination. In addition to greatly reducing saturation effects that occur even for the prebolus methods, only a single bolus has to be applied. Magn Reson Med 76:880-887, 2016. © 2015 Wiley Periodicals, Inc.

Automatic and fast determination of arterial input function on the middle cerebral artery in perfusion weighted image of patients with acute stroke

Automatic and fast determination of arterial input function on the middle cerebral artery in perfusion weighted image of patients with acute stroke

Evaluating the Feasibility of an Agglomerative Hierarchy Clustering Algorithm for the Automatic Detection of the Arterial Input Function Using DSC-MRI

During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically.

Rapid-Steady-State-T1 signal modeling during contrast agent extravasation: toward tumor blood volume quantification without requiring the arterial input function.

This study demonstrates how to quantify the tumor blood volume fraction (BVf) using the dynamic Rapid-Steady-State-T1 (RSST1 )-MRI method despite contrast agent (CA) leakage and without arterial input function (AIF) determination. For vasculature impermeable to CAs, the BVf is directly quantified from the RSST1 signal amplitude. In case of CA extravasation, we propose a two-compartment model to describe the dynamic RSST1 signal increase. We applied the mathematical model in a pilot-study on a RG2-glioma model to compare extravasation of two Gd-based CAs. The BVf quantification using the mathematical model in a C6-glioma model (n = 8) with the clinical CA Gd-DOTA was validated using a ΔR2 *-steady-state MRI method with an USPIO and by immunohistochemical staining of perfused vessels labeled with Hoechst-33342 dye in the same rats. BVf in tumor and in healthy brain tissues (0.034 ± 0.005 and 0.026 ± 0.004, respectively) derived from the dynamic RSST1 signal were confirmed by ΔR2 *-steady-state MRI (0.036 ± 0.003 and 0.027 ± 0.002, respectively, correlation coefficient rS = 0.74) and by histology (0.036 ± 0.003 and 0.025 ± 0.004 respectively, rS = 0.87). Straightforward tumor BVf quantification without AIF determination is demonstrated in presence of CA leakage. The method will facilitate angiogenesis assessment in longitudinal neuro-oncologic studies in particular when monitoring the response to antiangiogenic therapies.

Quantitative dynamic contrast‐enhanced MRI for mouse models using automatic detection of the arterial input function

Dynamic contrast-enhanced MRI (DCE-MRI) is widely accepted for the evaluation of cancer. DCE-MRI, a noninvasive measurement of microvessel permeability, blood volume and blood flow, is extremely useful for understanding disease mechanisms and monitoring therapeutic responses in preclinical research. For the accurate quantification of pharmacokinetic parameters using DCE-MRI, determination of the arterial input function (AIF) from a large arterial vessel near the tumor is required. However, a manual determination of AIF in mouse MR images is often difficult because of the small spatial dimensions or the location of the tumor. In this study, we propose an algorithm for the automatic detection of AIF from mouse DCE-MR images using Kendall's coefficient of concordance. The proposed method was tested with computer simulations and then applied to tumor-bearing mice (n = 8). Results from computer simulations showed that the proposed algorithm is capable of categorizing simulated AIF signals according to their noise levels. We found that the resulting pharmacokinetic parameters computed from our method were comparable with those from the manual determination of AIF, with acceptable differences in K(trans) (5.14 ± 3.60%), v(e) (6.02 ± 3.22%), v(p) (5.10 ± 7.05%) and k(ep) (5.38 ± 4.72%). The results of the current study suggest the usefulness of an automatically defined AIF using Kendall's coefficient of concordance for quantitative DCE-MRI in mouse models for cancer evaluation.

Use of PONS as a normalizing region for [c-11]pib-pet scans: Effect on subject classification

Background: Negligible levels of fibrillar Abeta deposits in the cerebellum (CER) in sporadic Alzheimer’s disease (AD) supports the use of CER as reference for normalizing regional [C]PiB retention measures. A CER reference, however, may not be ideal for all groups, particularly eoFAD or Down subjects, where significant CER Abeta deposition is a common autopsy finding. For this reason, we examined the suitability of pons (PON) as a reference region.Methods: [C]PiB PETwas performed in 190 subjects (93 Control, 51 MCI, 46 AD) and 46 of these (21 Control, 14, 11 AD) underwent fully quantitative imaging involving 90 min dynamic scanning, arterial input function determination, and estimation of Logan DVR outcomes (ART90). Regional SUVR50-70 retentionmeasureswere determined for all using CER (SUVRCER) and PON (SUVRPON) as reference. For the quantitative subjects, results of the SUVRmethods were compared to ART90. Iterative outlier and k-means clustering approaches were used to classify subjects as PiB positive or negative. Results: CER:plasma ratios reached a plateau of w6 at 30 min, while PON:plasma plateau was slower and more transient, peaking at w12 at 50 min. Across cortical areas, SUVRCER was less biased and more highly correlated with ART90 than SUVRPON. However, both provided similar Cohen’s effect sizes for the distinction of AD and PIB negative Control groups. This is attributable to lower variance in SUVRPON outcomes despite a compressed dynamic range. Using the iterative outlier method, SUVRPON identified significantly more PIB + Cons than SUVRCER (38/93 vs. 27/93), while the overall rate of discordance between SUVRCER and SUVRPON was 12% (23/190). When available, visual interpretation agreed with theSUVRCER classification in 89% of the discordant cases. Use of a k-means clustering algorithm resulted in less discordance between SUVRPON and SUVRCER classification (7%), but classified fewer Control subjects as PiB + compared to the iterative outlier method using both SUVRCER and SUVRPON outcomes (17/ 93 and 21/93, respectively). Conclusions: SUVRPON effectively discriminates amyloid negative controls fromAD subjects andmay be useful when cerebellar data is unavailable or contraindicated. However, the transient kinetics underlying SUVRPON may lead to less reliable subject classification at the PiB + /-interface, relative to SUVRCER.

Reproducibility of the gadolinium concentration measurements and of the fitting parameters of the vascular input function in the superior sagittal sinus in a patient population

It is widely recognised that the measurement of the arterial input function (AIF) is a key issue and a major source of errors in the pharmacokinetic modelling of dynamic, contrast-enhanced magnetic resonance imaging (DCE-MRI) data, and the modality of the AIF determination is still a matter of debate. In this study we addressed the problem of the intrinsic variability of the AIF within the imaged volume of a DCE-MRI scan by systematically investigating the change in the concentration of contrast agent over time and the fit parameters of the derived vascular input function (VIF) obtained from the superior sagittal sinus (SSS) of a patient population that was scanned longitudinally during treatment for high grade glioma. From a total of 82 scanning sessions, we compared the results obtained with three different DCE-MRI protocols and between two different fitting functions. We applied a correction algorithm to the measured concentration-time curves to minimize the effect of the low temporal resolution on the VIF, and investigated the effect of this algorithm on the reproducibility. Finally, where possible, we compared the signal obtained in the SSS to the signal obtained in the middle cerebral artery. We found a good intrapatient reproducibility of both the measured gadolinium concentrations and VIF parameters, and that the variation of the parameters due to slice location within a patient was significantly lower than the intra patient variation. Intrapatient, interscan differences were significantly less marked than inter-patient differences showing a good intraclass correlation coefficient. We did encounter a MRI protocol dependence of the VIF fitting parameters. The correction algorithm significantly improved the reproducibility of the fitting parameters. These results support the idea that the use of a patient specific measured AIF, not necessarily averaged over a large volume, offers a significant benefit with respect to an external AIF or a measured cohort average AIF.

A model-constrained Monte Carlo method for blind arterial input function estimation in dynamic contrast-enhanced MRI: II. In vivo results

Accurate quantification of pharmacokinetic model parameters in tracer kinetic imaging experiments requires correspondingly accurate determination of the arterial input function (AIF). Despite significant effort expended on methods of directly measuring patient-specific AIFs in modalities as diverse as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), dynamic positron emission tomography (PET), and perfusion computed tomography (CT), fundamental and technical difficulties have made consistent and reliable achievement of that goal elusive. Here, we validate a new algorithm for AIF determination, the Monte Carlo blind estimation (MCBE) method (which is described in detail and characterized by extensive simulations in a companion paper), by comparing AIFs measured in DCE-MRI studies of eight brain tumor patients with results of blind estimation. Blind AIFs calculated with the MCBE method using a pool of concentration–time curves from a region of normal brain tissue were found to be quite similar to the measured AIFs, with statistically significant decreases in fit residuals observed in six of eight patients. Biases between the blind and measured pharmacokinetic parameters were the dominant source of error. Averaged over all eight patients, the mean biases were +7% in K trans, 0% in kep, −11% in vp and +10% in ve. Corresponding uncertainties (median absolute deviation from the best fit line) were 0.0043 min−1 in K trans, 0.0491 min−1 in kep, 0.29% in vp and 0.45% in ve. The use of a published population-averaged AIF resulted in larger mean biases in three of the four parameters (−23% in K trans, −22% in kep, −63% in vp), with the bias in ve unchanged, and led to larger uncertainties in all four parameters (0.0083 min−1 in K trans, 0.1038 min−1 in kep, 0.31% in vp and 0.95% in ve). When blind AIFs were calculated from a region of tumor tissue, statistically significant decreases in fit residuals were observed in all eight patients despite larger deviations of these blind AIFs from the measured AIFs. The observed decrease in root-mean-square fit residuals between the normal brain and tumor tissue blind AIFs suggests that the local blood supply in tumors is measurably different from that in normal brain tissue and that the proposed method is able to discriminate between the two. We have shown the feasibility of applying the MCBE algorithm to DCE-MRI data acquired in brain, finding generally good agreement with measured AIFs and decreased biases and uncertainties relative to the use of a population-averaged AIF. These results demonstrate that the MCBE algorithm is a useful alternative to direct AIF measurement in cases where acquisition of high-quality arterial input function data is difficult or impossible.

Comment on "Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype

In our comment some essential issues concerning determination of arterial input function (AIF), cardiac and respiratory related motion artifacts, contrast agent application and compartmental model fitting done by Cao et al., 2009 are discussed.

Response to Letter Regarding Article: “Developing DCE-CT to Quantify Intra-Tumor Heterogeneity in Breast Tumors With Differing Angiogenic Phenotype”

In this response letter, technical issues raised by the comments concerning determination of arterial input function, cardiac and respiratory related motion artifacts, contrast agent application, and compartmental model fitting in our work are addressed in detail, and a discussion on identifying causes of uncertainty in physiological measurement and how to minimize their contribution is presented.