Measurement Of Arterial Input Function Research Articles

Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements

DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.

Feasibility study of a SiPM-fiber detector for non-invasive measurement of arterial input function for preclinical and clinical positron emission tomography

Pharmacokinetic positron emission tomography (PET) studies rely on the measurement of the arterial input function (AIF), which represents the time-activity curve of the radiotracer concentration in the blood plasma. Traditionally, obtaining the AIF requires invasive procedures, such as arterial catheterization, which can be challenging, time-consuming, and associated with potential risks. Therefore, the development of non-invasive techniques for AIF measurement is highly desirable. This study presents a detector for the non-invasive measurement of the AIF in PET studies. The detector is based on the combination of scintillation fibers and silicon photomultipliers (SiPMs) which leads to a very compact and rugged device. The feasibility of the detector was assessed through Monte Carlo simulations conducted on mouse tail and human wrist anatomies studying relevant parameters such as energy spectrum, detector efficiency and minimum detectable activity (MDA). The simulations involved the use of 18F and 68Ga isotopes, which exhibit significantly different positron ranges. In addition, several prototypes were built in order to study the different components of the detector including the scintillation fiber, the coating of the fiber, the SiPMs, and the operating configuration. Finally, the simulations were compared with experimental measurements conducted using a tube filled with both 18F and 68Ga to validate the obtained results. The MDA achieved for both anatomies (approximately 1000 kBq/mL for mice and 1 kBq/mL for humans) falls below the peak radiotracer concentrations typically found in PET studies, affirming the feasibility of conducting non-invasive AIF measurements with the fiber detector. The sensitivity for measurements with a tube filled with 18F (68Ga) was 1.2 (2.07) cps/(kBq/mL), while for simulations, it was 2.81 (6.23) cps/(kBq/mL). Further studies are needed to validate these results in pharmacokinetic PET studies.

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.

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.

The ultra high sensitivity blood counter: a compact, MRI-compatible, radioactivity counter for pharmacokinetic studies in μl volumes

Quantification of physiological parameters in preclinical pharmacokinetic studies based on nuclear imaging requires the monitoring of arterial radioactivity over time, known as the arterial input function (AIF). Continuous derivation of the AIF in rodent models is very challenging because of the limited blood volume available for sampling. To address this challenge, an Ultra High Sensitivity Blood Counter (UHS-BC) was developed. The device detects beta particles in real-time using silicon photodiodes, custom low-noise electronics, and 3D-printed plastic cartridges to hold standard catheters. Two prototypes were built and characterized in two facilities. Sensitivities up to 39% for 18F and 58% for 11C-based positron emission tomography (PET) tracers were demonstrated. 99mTc and 125I based Single Photon Emission Computed Tomography (SPECT) tracers were detected with greater than 3% and 10% sensitivity, respectively, opening new applications in nuclear imaging and fundamental biology research. Measured energy spectra show all relevant peaks down to a minimum detectable energy of 20 keV. The UHS-BC was shown to be highly reliable, robust towards parasitic background radiation and electromagnetic interference in the PET or MRI environment. The UHS-BC provides reproducible results under various experimental conditions and was demonstrated to be stable over days of continuous operation. Animal experiments showed that the UHS-BC performs accurate AIF measurements using low detection volumes suitable for small animal models in PET, SPECT and PET/MRI investigations. This tool will help to reduce the time and number of animals required for pharmacokinetic studies, thus increasing the throughput of new drug development.

Towards robust glucose chemical exchange saturation transfer imaging in humans at 3T: Arterial input function measurements and the effects of infusion time.

Dynamic glucose-enhanced (DGE) magnetic resonance imaging (MRI) has shown potential for tumor imaging using D-glucose as a biodegradable contrast agent. The DGE signal change is small at 3T (around 1%) and accurate detection is hampered by motion. The intravenous D-glucose injection is associated with transient side effects that can indirectly generate subject movements. In this study, the aim was to study DGE arterial input functions (AIFs) in healthy volunteers at 3T for different scanning protocols, as a step towards making the glucose chemical exchange saturation transfer (glucoCEST) protocol more robust. Two different infusion durations (1.5 and 4.0min) and saturation frequency offsets (1.2 and 2.0ppm) were used. The effect of subject motion on the DGE signal was studied by using motion estimates retrieved from standard retrospective motion correction to create pseudo-DGE maps, where the apparent DGE signal changes were entirely caused by motion. Furthermore, the DGE AIFs were compared with venous blood glucose levels. A significant difference (p = 0.03) between arterial baseline and postinfusion DGE signal was found after D-glucose infusion. The results indicate that the measured DGE AIF signal change depends on both motion and blood glucose concentration change, emphasizing the need for sufficient motion correction in glucoCEST imaging. Finally, we conclude that a longer infusion duration (e.g. 3-4 min) should preferably be used in glucoCEST experiments, because it can minimize the glucose infusion side effects without negatively affecting the DGE signal change.

Feasibility of Longitudinal Brain PET with Real-Time Arterial Input Function in Rats.

Preclinical dynamic brain PET studies remain hampered by the limitations related to the measurement of the arterial input function (AIF). In this regard, the use of an arterial-venous shunt is a promising method for the generation of real-time AIFs, but its application in longitudinal studies is still impeded by the cumbersome surgeries and high failure rates. We studied the feasibility and reproducibility of double arterial-venous shunt strategies for conducting longitudinal PET studies with real-time AIFs in rats. We studied the feasibility of double arterial-venous shunts in rats in the right/left inguinal region and evaluated inter-animal and intra-animal AIF reproducibilities. Image-derived input function (IDIF) was also obtained for comparison. Dynamic brain FDG PET studies were conducted to estimate kinetic constants and Cerebral Metabolic Rate of Glucose (CMRglc) obtained from standard 2-tissue compartment (2TCM) and Patlak analysis. We showed that longitudinal AIFs from double arterial-venous shunts can be obtained with very high success rate of the surgeries (88%). Our results provided highly reproducible AIF measurements with low inter-animal variabilities (11%) and intra-animal variabilities (5-10%) that were included into the kinetic models, such that longitudinal rate constants and CMRglc can be efficiently estimated without bias associated to the double shunt. Our results indicated that longitudinal IDIF can be also generated without bias along time but showing higher intra-animal uncertainties. We have demonstrated the feasibility and high reproducibility of conducting longitudinal AIF measurements and consequently accurate kinetic modeling using arterial shunt method.

Development of a non-radiometric method for measuring the arterial input function of a 11C-labeled PET radiotracer

Positron emission tomography (PET) uses radiotracers to quantify important biochemical parameters in human subjects. A radiotracer arterial input function (AIF) is often essential for converting brain PET data into robust output measures. For radiotracers labeled with carbon-11 (t1/2 = 20.4 min), AIF is routinely determined with radio-HPLC of blood sampled frequently during the PET experiment. There has been no alternative to this logistically demanding method, neither for regular use nor validation. A 11C-labeled tracer is always accompanied by a large excess of non-radioactive tracer known as carrier. In principle, AIF might be obtained by measuring the molar activity (Am; ratio of radioactivity to total mass; Bq/mol) of a radiotracer dose and the time-course of carrier concentration in plasma after radiotracer injection. Here, we implement this principle in a new method for determining AIF, as shown by using [11C]PBR28 as a representative tracer. The method uses liquid chromatography-tandem mass spectrometry for measuring radiotracer Am and then the carrier in plasma sampled regularly over the course of a PET experiment. Am and AIF were determined radiometrically for comparison. The new non-radiometric method is not constrained by the short half-life of carbon-11 and is an attractive alternative to conventional AIF measurement.

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.

Low-calorie sweeteners augment tissue-specific insulin sensitivity in a large animal model of obesity

Whether low-calorie sweeteners (LCS), such as sucralose and acesulfame K, can alter glucose metabolism is uncertain, particularly given the inconsistent observations relating to insulin resistance in recent human trials. We hypothesized that these discrepancies are accounted for by the surrogate tools used to evaluate insulin resistance and that PET 18FDG, given its capacity to quantify insulin sensitivity in individual organs, would be more sensitive in identifying changes in glucose metabolism. Accordingly, we performed a comprehensive evaluation of the effects of LCS on whole-body and organ-specific glucose uptake and insulin sensitivity in a large animal model of morbid obesity. Twenty mini-pigs with morbid obesity were fed an obesogenic diet enriched with LCS (sucralose 1mg/kg/day and acesulfame K 0.5mg/kg/day, LCS diet group), or without LCS (control group), for 3months. Glucose uptake and insulin sensitivity were determined for the duodenum, liver, skeletal muscle, adipose tissue and brain using dynamic PET 18FDG scanning together with direct measurement of arterial input function. Body composition was also measured using CT imaging and energy metabolism quantified with indirect calorimetry. The LCS diet increased subcutaneous abdominal fat by ≈ 20% without causing weight gain, and reduced insulin clearance by ≈ 40%, while whole-body glucose uptake and insulin sensitivity were unchanged. In contrast, glucose uptake in the duodenum, liver and brain increased by 57, 66 and 29% relative to the control diet group (P < 0.05 for all), while insulin sensitivity increased by 53, 55 and 28% (P < 0.05 for all), respectively. In the brain, glucose uptake increased significantly only in the frontal cortex, associated with improved metabolic connectivity towards the hippocampus and the amygdala. In miniature pigs, the combination of sucralose and acesulfame K is biologically active. While not affecting whole-body insulin resistance, it increases insulin sensitivity and glucose uptake in specific tissues, mimicking the effects of obesity in the adipose tissue and in the brain.

DCE-MRI protocol for constraining absolute pharmacokinetic modeling errors within specific accuracy limits.

To quantify the effects of DCE acquisition and pharmacokinetic modeling processing methodologies on the absolute accuracy and precision of derived pharmacokinetic (PK) parameter values using a novel anthropomorphic phantom test device in which "ground truth" values were known a priori. Ground truth arterial input function (AIF), tumor, and healthy tissue contrast agent concentration-time curves (CTCs) were established within the phantom and repeatedly measured on a 3T MRI scanner with varying temporal resolution (Tres =1.22-30.6s). Ground truth CTCs, Ktrans , ve , and kep values were directly compared to measured values as a function of Tres , with and without the application of voxel-wise flip-angle corrections applied to the data and PK modeling performed using linear and nonlinear forms of the standard Tofts model. Measurement of the AIF was strongly affected by the Tres used (AIF curve-shape feature errors: 3%-222% for Tres : 1.22-30.6s), which directly translated to errors in the derived Ktrans , ve , and kep values of 1%-24%, 2%-5%, and 1%-26% respectively across this Tres range (flip-angle correction applied). Further appreciable improvements in accuracy and precision arising from the use of flip angle corrections and nonlinear least squares fitting were quantified and used to identify optimal acquisition and analysis methodologies for which measurement errors could be constrained below threshold levels. This quantitative study provides insight into how errors in AIF measurement propagate to errors in PK parameter outputs. Absolute quantification of the accuracy and precision of MR-measured CTCs, and resultant PK parameter values, allowed for an optimal temporal resolution to be defined commensurate with maintaining Ktrans , ve , and kep measurement errors below 5% and 10% levels. An appreciable gain in PK parameter estimation accuracy at the analysis stage was also demonstrated using flip-angle corrections and a linear approach to PK model fitting.

Increased precision in the intravascular arterial input function with flow compensation.

In this study, we investigate the effects of pulsatile flow and inflow on dynamic susceptibility-contrast MRI intravascular arterial input function measurement in human brain arteries and measure how they are affected by first-order flow compensation. A dual-echo single-shot EPI sequence with alternating flow compensation gradients was used to acquire dynamic susceptibility-contrast images with electrocardiogram monitoring. The dynamic signal variations measured inside the middle cerebral and internal carotid arteries were associated to the pulsatile arterial blood velocities measured with a single-slice quantitative flow sequence throughout the cardiac cycle. Major inverse correlations between intravascular signal and blood velocity were found for the standard single-shot EPI sequence. Flow compensation reduces these correlated variations that contribute to signal physiological noise. This causes a significant twofold increase of intravascular SNR in the middle cerebral and the internal carotid arteries (2.3 ± 0.9, P = 0.03) and (2.0 ± 0.9, P = 0.04), respectively; and reduced phase SD for the internal carotid arteries (0.72 ± 0.14, P = 0.004). The correction proposed in this work translates into a quantitative arterial input function with reduced noise in the internal carotid arteries. The physiological noise added by pulsatile flow and inflow for intravascular arterial input function measurement in the brain arteries is significantly reduced by flow compensation.

Impact of the Arterial Input Function Recording Method on Kinetic Parameters in Small-Animal PET.

The goal of this study was to validate the use of an MR-compatible blood sampler (BS) with a detector system based on a lutetium oxyorthosilicate scintillator and avalanche photodiodes for small-animal PET. Methods: Five rats underwent a 60-min 18F-FDG study. For each animal, the arterial input function (AIF) was derived from the BS recording, from manual sampling (MS), and from the PET image. These AIFs were applied for kinetic modeling of the striatum using the irreversible 2-tissue-compartment model. The MS-based technique with a dispersion correction served as a reference approach, and the kinetic parameters that were estimated with the BS- and the image-derived AIFs were compared with the reference values. Additionally, the effect of applying a population-based activity ratio for plasma to whole blood (p/wb) and the dispersion correction was assessed. Results: The K1, k2, and k3 values estimated with the reference approach were 0.174 ± 0.037 mL/min/cm3, 0.342 ± 0.080 1/min, and 0.048 ± 0.009 1/min, respectively. The corresponding parameters obtained with the BS- and image-derived AIFs deviated from these values by 0.6%-18.8% and 16.7%-47.9%, respectively. To compensate for the error in the BS-based technique, data from one MS collected at the end of the experiment were combined with the data from the first 10 min of the BS recording. This approach reduced the deviation in the kinetic parameters to 1.8%-6.3%. Using p/wb led to a 1.7%-8.3% difference from the reference parameters. The sensitivity of the BS was 23%, the energy resolution for the 511-keV photopeak was 19%, and the timing resolution was 11.2 ns. Conclusion: Online recording of the blood activity level with the BS allows precise measurement of AIF, without loss of blood volume. Combining the BS data with one MS is the most accurate approach for the data analysis. The high sensitivity of the device may allow application of lower radioactivity doses.

Arterial input function in a dedicated slice for cerebral perfusion measurements in humans.

We aimed to modify our previously published method for arterial input function measurements for evaluation of cerebral perfusion (dynamic susceptibility contrast MRI) such that it can be applied in humans in a clinical setting. Similarly to our previous work, a conventional measurement sequence for dynamic susceptibility contrast MRI is extended with an additional measurement slice at the neck. Measurement parameters at this slice were optimized for the blood signal (short echo time, background suppression, magnitude and phase images). Phase-based evaluation of the signal in the carotid arteries is used to obtain quantitative arterial input functions. In all pilot measurements, quantitative arterial input functions were obtained. The resulting absolute perfusion parameters agree well with literature values (gray and white matter mean values of 46 and 24 mL/100g/min, respectively, for cerebral blood flow and 3.0% and 1.6%, respectively, for cerebral blood volume). The proposed method has the potential to quantify arterial input functions in the carotid arteries from a direct measurement without any additional normalization.

Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification

BackgroundQuantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium.MethodsA dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress.ResultsFor phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 ± 1.2 for stress and 4.4 ± 1.2 mmol/L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 ± 6.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. The estimated perfusion flow was 3.9 ± 0.38 and 1.03 ± 0.19 ml/min/g using the BTEX model and 3.4 ± 0.39 and 0.95 ± 0.16 using a Fermi model, for stress and rest, respectively.ConclusionsA dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.

Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (Ktrans) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.

Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues

Measurements of arterial input function (AIF) can have large systematic errors at standard contrast agent doses in dynamic contrast enhanced MRI (DCE-MRI). We compared measured AIFs from low dose (AIFLD) and standard dose (AIFSD) contrast agent injections, as well as the AIF derived from a muscle reference tissue and artery (AIFref). Twenty-two prostate cancer patients underwent DCE-MRI. Data were acquired on a 3T scanner using an mDixon sequence. Gadobenate dimeglumine was injected twice, at doses of 0.015 and 0.085mmol/kg. Directly measured AIFs were fitted with empirical mathematical models (EMMs) and compared to the AIF derived from a muscle reference tissue (AIFref). EMMs accurately fitted the AIFs. The 1st and 2nd pass peaks were visualized in AIFLD, but not in AIFSD, thus the peak and shape of AIFSD could not be accurately measured directly. The average scaling factor between AIFSD and AIFLD in the washout phase was only 56% of the contrast dose ratio (~6:1). The shape and magnitude of AIFref closely approximated that of AIFLD after empirically determined dose-dependent normalization. This suggests that AIFref may be a good approximation of the local AIF.

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.

Toward noninvasive quantification of brain radioligand binding by combining electronic health records and dynamic PET imaging data.

Quantitative analysis of positron emission tomography (PET) brain imaging data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, measurement error, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but at least one arterial blood sample is necessary. In this study, we describe a noninvasive SIME (nSIME) approach that utilizes a pharmacokinetic input function model and constraints derived from machine learning applied to an electronic health record database consisting of "long tail" data (digital records, paper charts, and handwritten notes) that were collected ancillary to the PET studies. We evaluated the performance of nSIME on 95 [(11)C]DASB PET scans that had measured AIFs. The results indicate that nSIME is a promising alternative to invasive AIF measurement. The general framework presented here may be expanded to other metabolized radioligands, potentially enabling quantitative analysis of PET studies without blood sampling. A glossary of technical abbreviations is provided at the end of this paper.