Individual Arterial Input Function Research Articles

Bone Turnover in Patients with Chronic Kidney Disease Stage 5D and Healthy Controls - a Quantitative [18F]Fluoride PET Study.

Chronic kidney disease (CKD) is prevalent in the aging population and increases the risk of fracture 2-4 times. We compared optimized quantitative [18F]fluoride PET/CT methods to the reference standard with arterial input function (AIF) to identify a clinically accessible method for evaluation of bone turnover in patients with CKD. Ten patients on chronic hemodialysis treatment and ten control patients were recruited. A dynamic 60-min [18F]fluoride PET scan was obtained from the 5th lumbar vertebra to the proximal femur simultaneously with arterial blood sampling to achieve an AIF. Individual AIFs were time-shifted to compute a population curve (PDIF). Bone and vascular volumes-of-interest (VOIs) were drawn, and an image-derived-input-function (IDIF) was extracted. PDIF and IDIF were scaled to plasma. Bone turnover (Ki) was calculated with the AIF, PDIF, and IDIF and bone VOIs using a Gjedde-Patlak plot. Input methods were compared using correlations and precision errors. The calculated Ki from the five non-invasive methods all correlated to the Ki from the AIF method with the PDIF scaled to a single late plasma sample showing the highest correlations (r > 0.94), and the lowest precision error of 3-5%. Furthermore, the femoral bone VOI's correlated positively to p-PTH and showed significant differences between patients and controls. Dynamic 30 min [18F]fluoride PET/CT with a population based input curve scaled to a single venous plasma sample is a feasible and precise non-invasive diagnostic method for the assessment of bone turnover in patients with CKD. The method may potentially allow for earlier and more precise diagnosis and may be useful for assessment of treatment effects, which is crucial for development of future treatment strategies.

Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer.

We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known. Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment. Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining. Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

Patient-Adaptive Population-Based Modeling of Arterial Input Functions.

Kinetic modeling of dynamic PET data requires knowledge of tracer concentration in blood plasma, described by the arterial input function (AIF). Arterial blood sampling is the gold standard for AIF measurement, but is invasive and labour intensive. A number of methods have been proposed to accurately estimate the AIF directly from blood sampling and/or imaging data. Here we consider fitting a patient-adaptive mixture of historical population time course profiles to estimate individual AIFs. Travel time of a tracer atom from the injection site to the right ventricle of the heart is modeled as a realization from a Gamma distribution, and the time this atom spends in circulation before being sampled is represented by a subject-specific linear mixture of population profiles. These functions are estimated from independent population data. Individual AIFs are obtained by projection onto this basis of population profile components. The model incorporates knowledge of injection duration into the fit, allowing for varying injection protocols. Analyses of arterial sampling data from 18F-FDG, 15O-H2O and 18F-FLT clinical studies show that the proposed model can outperform reference techniques. The statistically significant gain achieved by using population data to train the basis components, instead of fitting these from the single individual sampling data, is measured on the FDG cohort. Kinetic analyses of simulated data demonstrate the reliability and potential benefit of this approach in estimating physiological parameters. These results are further supported by numerical simulations that demonstrate convergence and stability of the proposed technique under varying training population sizes and noise levels.

Measuring subtle Blood‐Brain Barrier permeability changes with reduced scan time in DCE‐MRI

AbstractBackgroundIncreasing evidence suggests the subtle changes of Blood‐Brain Barrier (BBB) permeability in normal aging and in Alzheimer’s disease using Dynamic Contrast‐Enhanced MRI (DCE‐MRI). However, measuring this subtle change poses great challenge for accurate measurement, resulting in inconsistent results among previous studies. Two major challenges are long scan times, as suggested by previous studies and selection of the arterial input function (AIF). In this study, we aim to estimate the capillary level input function (CIF) using a deep learning network to overcome these two challenges.MethodsHealthy volunteers (n= 8, ages: 21‐76) were recruited for DCE‐MRI scan for 28min. Golden‐angle RAdial Sampling Parallel (GRASP) sequence was used to obtain the dynamic images at ∼5s/frame. Individual AIF was sampled from the superior sagittal sinus of the brain (Fig.1a). FSL was used to segment the gray and white matters (Fig.1b). Each voxel was fitted using the graphical Patlak model (Fig.2a) to assess the vascular permeability‐surface area product (PS) for both 28‐min data and 10‐min truncated data. We used a 3x3 kernel sliding through the images (Fig.3) and feed each voxel’s dynamic as the input to our vision‐transformer. Training data were generated using individual AIFs with a mathematical model, consisting of two Gaussian and one exponential function, and used to simulate dynamic patches using the Extended Patlak model (Fig.2b).ResultWhen the 10‐min data are used, the conventional approach with AIF results in overestimation of PS when the scan‐time is reduced, while the network‐predicted CIF allows more accurate estimation, with refence to the results using the 28‐min data, as illustrated by an example in Figure 4. Figure 5 shows the regional permeability differences between young and old subjects, where the conventional approach with AIF does not show the difference, while the approach with CIF shows subtle increases in PS with aging.ConclusionOur proposed CIF‐based approach provides an appropriate input‐function for DCE analysis, allowing assessment of subtle permeability changes in the BBB.

Measuring subtle Blood‐Brain Barrier permeability changes with reduced scan time in DCE‐MRI

AbstractBackgroundIncreasing evidence suggests the subtle changes of Blood‐Brain Barrier (BBB) permeability in normal aging and in Alzheimer’s disease using Dynamic Contrast‐Enhanced MRI (DCE‐MRI). However, measuring this subtle change poses great challenge for accurate measurement, resulting in inconsistent results among previous studies. Two major challenges are long scan times, as suggested by previous studies and selection of the arterial input function (AIF). In this study, we aim to estimate the capillary level input function (CIF) using a deep learning network to overcome these two challenges.MethodsHealthy volunteers (n= 8, ages: 21‐76) were recruited for DCE‐MRI scan for 28min. Golden‐angle RAdial Sampling Parallel (GRASP) sequence was used to obtain the dynamic images at ∼5s/frame. Individual AIF was sampled from the superior sagittal sinus of the brain (Fig.1a). FSL was used to segment the gray and white matters (Fig.1b). Each voxel was fitted using the graphical Patlak model (Fig.2a) to assess the vascular permeability‐surface area product (PS) for both 28‐min data and 10‐min truncated data. We used a 3x3 kernel sliding through the images (Fig.3) and feed each voxel’s dynamic as the input to our vision‐transformer. Training data were generated using individual AIFs with a mathematical model, consisting of two Gaussian and one exponential function, and used to simulate dynamic patches using the Extended Patlak model (Fig.2b).ResultWhen the 10‐min data are used, the conventional approach with AIF results in overestimation of PS when the scan‐time is reduced, while the network‐predicted CIF allows more accurate estimation, with refence to the results using the 28‐min data, as illustrated by an example in Figure 4. Figure 5 shows the regional permeability differences between young and old subjects, where the conventional approach with AIF does not show the difference, while the approach with CIF shows subtle increases in PS with aging.ConclusionOur proposed CIF‐based approach provides an appropriate input‐function for DCE analysis, allowing assessment of subtle permeability changes in the BBB.

Investigation of image-derived input functions for non-invasive quantification of myelin density using [11C]MeDAS PET

Multiple sclerosis (MS) is an inflammatory demyelinating disease. Current treatments are focussed on immune suppression to modulate pathogenic activity that causes myelin damage. New treatment strategies are needed to prevent demyelination and promote remyelination. Development of such myelin repair therapies require a sensitive and specific biomarker for efficacy evaluation. Recently, it has been shown that quantification of myelin density is possible using [11C]MeDAS PET. This method, however, requires arterial blood sampling to generate an arterial input function (AIF). As the invasive nature of arterial sampling will reduce clinical applicability, the purpose of this study was to assess whether an image-derived input function (IDIF) can be used as an alternative way to facilitate its routine clinical use.Six healthy controls and 11 MS patients underwent MRI and [11C]MeDAS PET with arterial blood sampling. The application of both population-based whole blood-to-plasma conversion and metabolite corrections were assessed for the AIF. Next, summed images of the early time frames (0–70 s) and the frame with the highest blood-brain contrast were used to generate IDIFs. IDIFs were created using either the hottest 2, 4, 6 or 12 voxels, or an isocontour of the hottest 10% voxels of the carotid artery. This was followed by blood-to-plasma conversion and metabolite correction of the IDIF.The application of a population-based metabolite correction of the AIF resulted in high correlations of tracer binding (Ki) within subjects, but variable bias across subjects. All IDIFs had a sharper and higher peak in the blood curves than the AIF, most likely due to dispersion during blood sampling. All IDIF methods resulted in similar high correlations within subjects (r = 0.95–0.98), but highly variable bias across subjects (mean slope=0.90–1.09).Therefore, both the use of population based blood-plasma and metabolite corrections and the generation of the image-derived whole-blood curve resulted in substantial bias in [11C]MeDAS PET quantification, due to high inter-subject variability. Consequently, when unbiased quantification of [11C]MeDAS PET data is required, individual AIF needs to be used.

Improving the estimation of subtle blood-brain barrier permeability changes in aging using a deep learning approach

Background: Increasing evidence suggests detecting the subtle changes in blood-brain barrier (BBB) permeability in normal aging and in Alzheimer’s disease by using dynamic contrast-enhanced MRI (DCE-MRI) (Figure 1).1,2 However, measuring these subtle changes poses a great challenge for accurate measurement, resulting in inconsistent results among previous studies.1,2 Two major challenges are long scan times, as suggested by other studies, and the selection of the arterial input function (AIF). In this study, we aim to estimate the capillary level input function (CIF) using a deep learning network to overcome these two challenges.&#x0D; Methods: Healthy volunteers (n=12, age 21-78) were recruited for DCE-MRI scan for 28 min. Golden-angle radial sampling parallel (GRASP) sequence was used to obtain the dynamic images at ~5s/frame. Individual AIF was sampled from the superior sagittal sinus of the brain. FSL3 was used to segment the gray and white matter. Each voxel was fitted using the graphical Patlak model4 to assess the vascular permeability-surface area product (PS) for both 28-min data and 10-min truncated data. We used a 3x3 kernel sliding through the images, and feed each voxel’s dynamic as the input to our vision-transformer.5 Training data were generated using individual AIFs with a mathematical model and used to simulate dynamic patches using the extended Patlak model.4&#x0D; Results: The conventional approach with AIF results in the majority of voxels exhibiting negative PS, regardless of scan time. This is not physiologically valid, as this indicates the contrast agent extravasates into the vessel. However, the proposed approach with the network-predicted CIF results in most voxels in positive PS, even with a scan-time of 10 min. The estimated PS levels are in good accordance with the previous studies.1 Due to the limited sample size, we could not find the difference in BBB permeability between young and old groups.&#x0D; Conclusions: Our approach showed promising quantification of subtle permeability. The results in this study suggest that our proposed CIF-based approach provides an appropriate input function for DCE analysis, allowing assessment of subtle permeability changes in the BBB even with a reduced scan time of 10 min. Future studies will include larger cohorts to investigate the BBB permeability changes in normal aging.

Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer.

Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP). To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (Ktrans , ve , kep , and vp ), for HNC and muscle. Prospective. A total of 34 HNCpatients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins. Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios. Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant. Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean Ktrans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8). We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies. 2 TECHNICAL EFFICACY: Stage 1.

Differentiation of endometrial adenocarcinoma from adenocarcinoma of cervix using kinetic parameters derived from DCE-MRI

PurposeThis prospective study aimed to investigate the value of kinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating uterine endometrioid adenocarcinoma (EAC) from adenocarcinoma of cervix (AdC). MethodsSeventy-five newly diagnosed patients with distinctive pathology underwent DCE-MRI. Observers independently calculated the tumor diameters and DCE-MRI parameters using both population and individual-based arterial input function (AIF). Inter-observer consistency was evaluated, and a comparative analysis between EAC (n = 47) and AdC (n = 28) was performed. Regression analysis was used to select parameters that best distinguished EAC from AdC, and to generate predictive models. Receiver operating characteristic curve (ROC) was applied to calculate the diagnostic efficiency of single parameter and the predictive models. ResultsInter-observer consistency was excellent (intra-class correlation [ICC] = 0.902−0.981), especially when calculated via population AIF with relatively higher ICC and smaller SD on Bland-Altman plot. Tumor diameters were not correlated with tumor types. All the DCE-MRI parameters were lower in EAC compared to AdC, except Kep by population AIF and TTP by both sets of AIFs. The statistical parameters were Ve, Maxslop, and Maxconc by population AIF, and Maxslop and Ktrans by individual AIF included in the predictive models, respectively. The two predictive models with combined parameters showed improved diagnostic efficiency in differentiating these two diseases compared with a single parameter. ConclusionDCE-MRI can quantitatively evaluate the perfusion difference between EAC and AdC, thus improving the identification of uterine adenocarcinoma with uncertain biopsy pathology.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

A functional form for a representative individual arterial input function measured from a population using high temporal resolution DCE MRI.

To measure the arterial input function (AIF), an essential component of tracer kinetic analysis, in a population of patients using an optimized dynamic contrast-enhanced (DCE) imaging sequence and to estimate inter- and intrapatient variability. From these data, a representative AIF that may be used for realistic simulation studies can be extracted. Thirty-nine female patients were imaged on multiple visits before and during a course of neoadjuvant chemotherapy for breast cancer. A total of 97 T1 -weighted DCE studies were analyzed including bookend estimates of T1 and model-fitting to each individual AIF. Area under the curve and cardiac output were estimated from each first pass peak, and these data were used to assess inter- and intrapatient variability of the AIF. Interpatient variability exceeded intrapatient variability of the AIF. There was no change in cardiac output as a function of MR visit (mean value 5.6 ± 1.1 L/min) but baseline blood T1 increased significantly following the start of chemotherapy (which was accompanied by a decrease in hematocrit). The AIF in an individual patient can be measured reproducibly but the variability of AIFs between patients suggests that use of a population AIF will decrease the precision of tracer kinetic analysis performed in cross-patient comparison studies. A representative AIF is presented that is typical of the population but retains the characteristics of an individually measured AIF.

Modification of population based arterial input function to incorporate individual variation

This technical note describes how to modify a population-based arterial input function to incorporate variation among the individuals. In DCE-MRI, an arterial input function (AIF) is often distorted by pulsated inflow effect and noise. A population-based AIF (pAIF) has high signal-to-noise ratio (SNR), but cannot incorporate the individual variation. AIF variation is mainly induced by variation in cardiac output and blood volume of the individuals, which can be detected by the full width at half maximum (FWHM) during the first passage and the amplitude of AIF, respectively. Thus pAIF scaled in time and amplitude fitting to the individual AIF may serve as a high SNR AIF incorporating the individual variation. The proposed method was validated using DCE-MRI images of 18 prostate cancer patients. Root mean square error (RMSE) of pAIF from individual AIFs was 0.88±0.48mM (mean±SD), but it was reduced to 0.25±0.11mM after pAIF modification using the proposed method (p<0.0001).

Estimation of arterial input by a noninvasive image derived method in brain H215O PET study: confirmation of arterial location using MR angiography

A noninvasive method to estimate input function directly from H215O brain PET data for measurement of cerebral blood flow (CBF) was proposed in this study. The image derived input function (IDIF) method extracted the time-activity curves (TAC) of the major cerebral arteries at the skull base from the dynamic PET data. The extracted primordial IDIF showed almost the same radioactivity as the arterial input function (AIF) from sampled blood at the plateau part in the later phase, but significantly lower radioactivity in the initial arterial phase compared with that of AIF-TAC. To correct the initial part of the IDIF, a dispersion function was applied and two constants for the correction were determined by fitting with the individual AIF in 15 patients with unilateral arterial stenoocclusive lesions. The area under the curves (AUC) from the two input functions showed good agreement with the mean AUCIDIF/AUCAIF ratio of 0.92 ± 0.09. The final products of CBF and arterial-to-capillary vascular volume (V0) obtained from the IDIF and AIF showed no difference, and had with high correlation coefficients.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge.

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T1, were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Impact of arterial input function selection on the accuracy of dynamic contrast-enhanced MRI quantitative analysis for the diagnosis of clinically significant prostate cancer.

Using a limited temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) dataset to assess the impact of the arterial input function (AIF) choice on the transfer constant (K(trans) ) to distinguish prostate carcinoma (PCa) from benign tissue. Thirty-eight patients with clinically important peripheral PCa (≥0.5 cc) were retrospectively studied. These patients underwent 1.5T multiparametric prostate MR with PCa and benign regions of interest (ROIs) selected using a visual registration with morphometric reconstruction obtained from radical prostatectomy. Using three pharmacokinetic (PK) analysis software programs, the mean K(trans) of ROIs was computed using three AIFs: an individual AIF (Ind-AIF) and two literature population average AIFs of Weinmann (W-AIF) and of Fritz-Hansen (FH-AIF). A pairwise comparison of the area under the receiver operating characteristic curves (AUROCC) obtained with different AIFs was performed. AUROCCs obtained with W-AIF (ranging from 0.801 to 0.843) were significantly higher than FH-AIF (ranging from 0.698 to 0.780, 0.002 ≤ P ≤ 0.045) and similar to or higher than Ind-AIF (ranging from 0.591 to 0.839, 0.014 ≤ P ≤ 0.9). Ind-AIF and FH-AIF provided similar AUROCC (0.34 ≤ P ≤ 0.81). The pairwise correlation of K(trans) values was moderate to very strong when comparing W-AIF with FH-AIF (the Spearman's correlation coefficients [SCCs] ranged from 0.55 to 0.93) and very weak to moderate when comparing W-AIF with Ind-AIF (the SCCs ranged from 0.018 to 0.59) or FH-AIF with Ind-AIF (the SCCs ranged from 0.30 to 0.51). W-AIF yielded a higher performance than FH-AIF and a similar or higher performance than Ind-AIF in distinguishing PCa from benign tissue.

SU-D-303-02: Impact of Arterial Input Function Selection and T10 Correction On DCE-MRI Tumour Response Prediction Using Compared to Volumetric DCE CT

Purpose: To evaluate the impact of individualized magnitude and phase signal arterial input function (AIF) measurements as well as voxel-based pre-contrast T10 relaxation on tumour perfusion metrics from DCE-MRI compared to DCE-CT using a common 4D temporal dynamic analysis (TDA) method. Methods: Nine patients with 13 brain metastases underwent volumetric DCE-CT (Toshiba, Aquilion ONE) and DCE-MRI (IMRIS 3T Verio) at baseline then 7 and 21 days post-radiosurgery. Voxel-based whole brain TDA was performed on all data using in-house software producing kinetic parameters AUC, Ktrans, Kep, and Vb (using the Modified Tofts model). AIF susceptibility was investigated in DCE-CT by selecting the AIF (from internal carotid artery) or VIF (from Sagittal Sinus). In DCE-MRI an individual Magnitude or Phase-based AIF (Sagittal Sinus) was compared to population-based AIF together with susceptibility to voxel-based T10 maps instead of a constant of 2400 msec. Absolute DCE-MRI values were compared to DCE-CT by Pearson correlation. Results: No significant difference in median Ktrans (0.048 +/−0.03 s-1) or AUC (2785.5 +/−1143.6 HU.s) was found between individual AIF and VIF-based DCE CT analyses. Using individual Magnitude VIF or Phase-based AIF for DCE-MRI (T10 2400 ms) resulted in higher Ktrans values (0.181 +/−0.11 vs. 0.121 +/−0.099 s-1). This is likely resulting from the smaller AIF peak since the population AIF (which more closely resembles CT) correlates better to DCE-CT metrics. Using voxel-based T10 maps caused further statistically-significant increase in Ktrans and AUC (p<0.0006) that could be contributed to a lower median T10 value (1572 +/−594, n=41). Conclusion: This preliminary data highlights the stability of DCE-CT calculations as well as susceptibility of DCE-MRI Ktrans measurements to various imaging factors, including AIF selection and T10 values used for modeling. Efforts to improve voxel-based T10 map calculations are being explored to further explain discrepancies between analysis methods. Brain Tumor Foundation of Canada and Ontario Institute of Cancer Research

Noninvasive quantification of cerebral metabolic rate for glucose in rats using (18)F-FDG PET and standard input function.

Measurement of arterial input function (AIF) for quantitative positron emission tomography (PET) studies is technically challenging. The present study aimed to develop a method based on a standard arterial input function (SIF) to estimate input function without blood sampling. We performed 18F-fluolodeoxyglucose studies accompanied by continuous blood sampling for measurement of AIF in 11 rats. Standard arterial input function was calculated by averaging AIFs from eight anesthetized rats, after normalization with body mass (BM) and injected dose (ID). Then, the individual input function was estimated using two types of SIF: (1) SIF calibrated by the individual's BM and ID (estimated individual input function, EIFNS) and (2) SIF calibrated by a single blood sampling as proposed previously (EIF1S). No significant differences in area under the curve (AUC) or cerebral metabolic rate for glucose (CMRGlc) were found across the AIF-, EIFNS-, and EIF1S-based methods using repeated measures analysis of variance. In the correlation analysis, AUC or CMRGlc derived from EIFNS was highly correlated with those derived from AIF and EIF1S. Preliminary comparison between AIF and EIFNS in three awake rats supported an idea that the method might be applicable to behaving animals. The present study suggests that EIFNS method might serve as a noninvasive substitute for individual AIF measurement.

Radial multigradient-echo DCE-MRI for 3DKtransmapping with individual arterial input function measurement in mouse tumor models

The purpose of this study was to provide proof of concept for a new three-dimensional (3D) radial dynamic contrast enhanced MRI acquisition technique, called "Radial Entire Tumor with Individual Arterial input function dynamic contrast-enhanced MRI" (RETIA dynamic contrast-enhanced MRI), which allows for the simultaneous measurement of an arterial input function in the mouse heart at 2 s temporal resolution and coverage of the whole tumor. Alternating 2D and 3D projections contribute to the 2D heart image or 3D tumor data with a 3-cm field of view. Sixty-four 2D images of the heart are obtained during acquisition of each 3D tumor dataset. In a pilot study, global K(trans) and ve values were measured in four mice, in a respiratory motion-animated subcutaneously implanted breast tumor model. This technique is expected to be most useful for the characterization of microvasculature in motion-animated orthotopic tumors.

Assessment of the effect of haematocrit‐dependent arterial input functions on the accuracy of pharmacokinetic parameters in dynamic contrast‐enhanced MRI

The detection and prognosis of prostate cancer in its early stages are critically important. It is therefore essential to improve the existing dynamic contrast-enhanced MRI (DCE MRI) techniques commonly used for the assessment of the tumour vascular environment. The goal of this study was to describe a method for the estimation of the arterial input function (AIF) in DCE MRI by measuring R(2) * values in the femoral artery of patients with early-stage prostate cancer. The calculation of contrast agent concentrations was based on calibration curves determined in whole blood samples for a range of normal haematocrit (HCT) values (HCT = 0.35-0.525). Individual AIFs corrected for HCT were compared with individual AIFs calibrated with a mean whole blood [R(2)*-Gd-DTPA-BMA] [Gd-DTPA-BMA, gadolinium diethylenetriaminepentaacetate-bis(methylamide) (gadodiamide)] curve at an assumed HCT = 0.44, as well as a population AIF at an assumed HCT = 0.45. The area under the curve of the first-pass bolus ranged between 0.6 min mM at HCT = 0.53 and 1.3 min mM at HCT = 0.39. Significant differences in magnitude at peak contrast agent concentrations (HCT = 0.36, [Gd-DTPA-BMA](max) = 9 ± 0.4 mM; HCT = 0.46, [Gd-DTPA-BMA](max) = 4.0 ± 0.2 mM) were found. Using model-based simulations, the accuracy of the kinetic parameters estimated using individual AIFs corrected for HCT demonstrated that, for the use of individual calibration curves with HCT values differing by more than 10%, K(trans) and k(ep) values were largely underestimated (up to 60% difference for K(trans)). Moreover, blood volume estimates were severely underestimated. Estimates of kinetic parameters in early-stage prostate cancer patients demonstrated that the efflux rate constant (k(ep)) was influenced significantly by the definition of AIF. Regardless of whether an individually calibrated AIF or a population AIF (average of all individually calibrated AIFs) was used, pixel-by-pixel mapping of k(ep) and v(b) in the prostate gland appeared to be more sensitive than with the usual biexponential approach.

Measuring perfusion and permeability in renal cell carcinoma with dynamic contrast‐enhanced MRI: A pilot study

To retrospectively assess an improved quantitative methodology with separate assessment of perfusion and permeability for characterization of primary renal cell carcinoma (RCC) and monitoring antiangiogenic treatment. Fifteen RCC patients before surgery, 6 RCC patients before and after neoadjuvant antiangiogenic therapy, and 15 patients without renal disease underwent dynamic contrast-enhanced (DCE)-MRI of the kidney with integrated retrospective respiratory triggering and an individual arterial input function. Tracer kinetic analysis was performed with a two-compartment-filtration-model for the kidney data and a two-compartment-exchange-model for the tumor data, providing four independent parameters: the perfusion-parameters plasma flow (F(P)) and plasma volume (V(P)), and the permeability-parameters extraction flow (F(E)) and extravascular-extracellular volume (V(E)). In tumors F(P) and F(E) were significantly lower than in normal kidneys. Tracer kinetic analysis displayed hemodynamic alteration caused by vessel infiltration or necrosis. Papillary RCC could be differentiated from clear-cell variants by a distinct perfusion pattern. In antiangiogenically treated RCC V(E) was not significantly decreased, while the perfusion parameters V(P) and F(P) were significantly diminished. DCE-MRI with integrated motion compensation enables evaluation of primary RCC and detects distinct perfusion patterns. Quantification with a two-compartment-exchange-model produces a separate perfusion- and permeability characterization and may become a diagnostic tool to monitor antiangiogenic treatment.