Kinetic Model Parameters Research Articles

A Practical In Silico Method for Predicting Compound Brain Concentration-Time Profiles: Combination of PK Modeling and Machine Learning.

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration-time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration-time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration-time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration-time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration-time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration-time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration-time profiles.

An Integrated Hydration and Property Evaluation Model for Coral Powder–Cement Binary Blends

With the rise in the marine industry and marine tourism, coral powder is increasingly used to make concrete for marine islands. This study proposes a three-parameter hydration model and a hydration kinetic model to predict the performance of coral powder concrete based on previous experimental data. The process of the proposed prediction model is as follows: 1. The input parameters of the three-parameter hydration model are calibrated for the first 7 days using the cumulative hydration heat per gram of cement. The maximum cumulative hydration heat (455.87 J/g cement) and the shape coefficient (−0.87) remain constant. In this study, the hydration rate coefficients for 0%, 10%, and 20% coral powder were 6.91, 6.19, and 5.55, respectively, showing decreases of 10.41% and 19.68% compared with the specimens without coral powder. 2. At 28 days, the cumulative heat release values per gram of cement for 0%, 10%, and 20% coral powder were 389.77, 395.69, and 401.62 J/g, showing increases of 1.52% and 3.04% for the specimens containing 10% and 20% coral powder, respectively. Meanwhile, the hydration degrees for 0%, 10%, and 20% coral powder were 0.855, 0.868, and 0.881, respectively, showing increases of 1.52% and 3.04%. Furthermore, the cumulative heat release values per gram of binder were 389.77, 356.12, and 321.29 J/g, showing decreases of 8.63% and 17.56% for specimens containing 10% and 20% coral powder, respectively. 3. Properties such as compressive strength, ultrasonic pulse velocity (UPV), and surface electrical resistivity were evaluated using the power function and the cumulative hydration heat per gram of binder. 4. At 28 days, the chemically bound water contents for samples with 0%, 10%, and 20% coral powder were 0.2402, 0.2197, and 0.1981 g/g binder, respectively. Moreover, the calcium hydroxide contents were 0.1848, 0.1690, and 0.1524 g/g binder, showing reductions of 8.53% and 17.52% in bound water and 8.54% and 17.53% in calcium hydroxide. 5. A hydration kinetic model is proposed, which can distinguish between the dilution effect and the nucleation effect of coral powder, unlike the three-parameter model, which cannot distinguish between the two effects. Furthermore, the input parameters of the hydration kinetic model remain unchanged for different mixtures, while the input parameters of the three-parameter model must be varied among mixtures. Parameter analysis of the hydration kinetic model indicated that a low water–binder ratio and a high coral powder substitution rate significantly improve the relative reaction level of cement.

Delineating acetaminophen biodegradation kinetics and metabolomics using bacterial community.

Acetaminophen [N-(4-hydroxyphenyl) acetamide, APAP]is an extensively and frequently consumed over-the-counter analgesic and antiphlogistic medication. It is being regarded as an emerging pollutant due to its continuous increment in the environment instigating inimical impacts on humans and the ecosystem. Considering its wide prevalence in the environment, there is an immense need of appropriate methods for the removal of APAP. The present study indulged screening and isolation of APAP degrading bacterial strains from pharmaceuticals-contaminated sites, followed by their molecular characterization via 16SrRNA sequencing. The phylogenetic analyses assigned the isolates to the genera Pseudomonas, Bacillus, Paracoccus, Agrobacterium, Brucella, Escherichia, and Enterobacter based on genetic relatedness. The efficacy of these strains in batch cultures tested through High-performance Liquid Chromatography (HPLC) revealed Paracoccus sp. and Enterobacter sp. as the most promising bacterial isolates degrading up to 88.96 and 85.92%, respectively of 300mg L-1 of APAP within 8days of incubation. Michaelis-Menten kinetics model parameters also elucidated the high degradation potential of these isolates. The major metabolites identified through FTIR and GC-MS analyses were 4-aminophenol, hydroquinone, and 3-hydroxy-2,4-hexadienedioic. Therefore, the outcomes of this comprehensive investigation will be of paramount significance in formulating strategies for the bioremediation of acetaminophen-contaminated sites through a natural augmentation process via native bacterial strains.

Perspective on automated predictive kinetics using estimates derived from large datasets

AbstractA longstanding project of the chemical kinetics community is to predict reaction rates and the behavior of reacting systems, even for systems where there are no experimental data. Many important reacting systems (atmosphere, combustion, pyrolysis, partial oxidations) involve a large number of reactions occurring simultaneously, and reaction intermediates that have never been observed, making this goal even more challenging. Improvements in our ability to compute rate coefficients and other important parameters accurately from first principles, and improvements in automated kinetic modeling software, have partially overcome many challenges. Indeed, in some cases quite complicated kinetic models have been constructed which accurately predicted the results of independent experiments. However, the process of constructing the models, and deciding which reactions to measure or compute ab initio, relies on accurate estimates (and indeed most of the numerical rate parameters in most large kinetic models are estimates.) Machine‐learned models trained on large datasets can improve the accuracy of these estimates, and allow a better integration of quantum chemistry and experimental data. The need for continued development of shared (perhaps open‐source) software and databases, and some directions for improvement, are highlighted. As we model more complicated systems, many of the weaknesses of the traditional ways of doing chemical kinetic modeling, and of testing kinetic models, have been exposed, identifying several challenges for future research by the community.

Migration Rules and Mechanisms of Nano-Biochar in Soil Columns under Various Transport Conditions.

Compared to traditional biochar (BC), nano-biochar (NBC) boasts superior physicochemical properties, promising extensive applications in agriculture, ecological environments, and beyond. Due to its strong adsorption and migration properties, NBC may carry nutrients or pollutants to deeper soil layers or even groundwater, causing serious environmental risks. Nevertheless, the migration rules and mechanisms of NBC in soil are still unclear. Therefore, this study employed soil column migration experiments to systematically explore the migration rules and mechanisms of NBC under various flow rates, initial soil water contents, soil depths, and soil textures. The results showed that regulated by smaller particle size differences and greater surface charges, NBC exhibited a stronger migration ability compared with traditional BC. As the soil texture transitioned from fine to coarse, the migration capability of NBC significantly improved, driven by both pore structure and interaction forces as described by the DLVO theory. The migration ability of NBC was also greatly boosted as the soil transitioned from saturated to unsaturated conditions, primarily because of preferential flow. When the flow rate increased from 70% KS to 100% KS and 130% KS, the migration ability of NBC also increased accordingly, as changes in injection flow rates altered the velocity distribution of pore water. NBC in 25 cm soil columns was more prone to shallow retention compared with 10 cm soil columns, resulting in weaker overall migration ability. In addition, through fitting of the two-site kinetic model and related parameters, the penetration curves of NBC under various variable conditions were effectively characterized. These findings could offer valuable insights for NBC's future efficient, rational, and sustainable utilization, facilitating the evaluation and mitigation of its potential environmental risks.

Porous sodium alginate/poly (acrylic acid) composites cross-linked with FeCl3 for acid black 1 dye removal from aqueous solution

This study developed a polymeric composite, sodium alginate (SA)/poly (acrylic acid) (PAA) cross-linked with FeCl3 (SA/PAA-Fe), for its function as adsorbent to eliminate acid black 1 dye (AB1) from an aqueous solution. The structural analysis of SA/PAA-Fe revealed a porous structure. To comprehensively evaluate the adsorption of AB1 onto SA/PAA-Fe and gain insight into its mechanism, we examined the effects of solution’s pH, initial dye concentration, adsorbent dosage, contact time, and adsorption temperature. The best adsorption conditions for achieving a highly effective removal percentage of 95.43 % were a pH of 8.9, contact period of 90 min, and a temperature of 45 °C. Pseudo-second-order and Langmuir models were fitted for the isotherm and kinetic model parameters, respectively. Thermodynamic analyses strongly suggested that the adsorption process occurred was endothermic and spontaneous. The adsorption of AB1 was primarily observed on the solid surface, as confirmed by the SEM-EDS and FTIR measurements.

An optimization-free Fisher information driven approach for online design of experiments

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system.

Corrosion inhibition of two tetracycline‐based expired antibiotics as eco‐friendly inhibitors for mild steel in 1M HCl solution

AbstractTwo expired Tetracycline‐based antibiotics (Oxytetracycline and Tetracycline) were utilized as eco‐friendly corrosion inhibitors for mild steel in 1M HCl solution, employing weight loss analysis, electrochemical measurements, scanning electron microscopy, and so on. The effect and mechanism were discussed using thermodynamic modeling, quantum chemical calculation, molecular dynamics simulation, and so on. The corrosion inhibition rate (η) increases with higher concentration and temperature, leading to reduced heat absorption and increased order degree. The of Oxytetracycline and Tetracycline was 96.14% and 97.92% at 323.15 K of 2 × 10−4 mol L−1, respectively. The key factors affecting η in the kinetic model parameters vary at lower concentrations. Both of them belong to cathodic corrosion inhibitors and undergo Langmuir spontaneous adsorption processes involving both physisorption and chemisorption. The disparity in energy levels between the highest occupied molecular orbital and lowest unoccupied molecular orbital, global hardness, electronegativity, molecular polarizability, dipole moment, and transfer electron number are key factors that enhance a corrosion inhibitor's ability to adsorb chloride ions.

Valorization of wild thyme (Thymus serpyllum L.) herbal dust by supercritical fluid extraction – Experiments and modeling

The aim of this study was to develop five empirical and three mass-transfer based models for fitting the T. serpyllum SFE process. Additionally, the determination of the effect of SFE parameters (pressure, temperature, CO2 flow rate and mean particle size) on kinetic curves and model parameters was investigated. Among the selected models, all empirical and mass-transfer models provided adequate fit with experimental data. However, mass-transfer model VIII has proved to fit the experimental results accordingly and has shown that the most influential parameters were pressure and CO2 flow rate. The pressure and CO2 flow rate exhibited positive effect, while the temperature had rather negative impact on Y. In addition, the initial slopes of Model VIII were calculated in order to increase the efficiency and reduce unnecessary process costs.

Dual-cycle-based lumped kinetic model for methanol-to-aromatics (MTA) reaction over H-ZSM-5 zeolites of different Si/Al ratio

In this work we developed a 7-lump kinetic model for the methanol-to-aromatics (MTA) reaction based on the dual-cycle mechanism, with consideration of deactivation of the catalyst by coke formation and applicable to H-ZSM-5 zeolite-based catalysts of different acidity. The model developed is suitable to be implemented at an industrial scale, as the intrinsic kinetic aspect fits the experimental data in a wide range of operating conditions. Moreover, the model is validated for two catalysts (prepared from two H-ZSM-5 zeolites of Si/Al ratio = 15 and 40), and considers a combined formation of coke from both unreacted oxygenates and reaction products (specifically, aromatics and light olefins). The final model comprises 10 reaction steps, and considers the effect of water co-feeding. The kinetic parameters of best fitting are obtained in the simultaneous fitting of the zero time and the deactivation kinetics. We compared the kinetic parameters of the best-fitting model for the two catalysts and related the differences obtained between both sets of parameters to catalyst properties.

Quantitative functional imaging with CT perfusion: technical considerations, kinetic modeling, and applications

CT perfusion (CTP)-derived quantitative maps of hemodynamic parameters have found important clinical applications in stroke, cancer, and cardiovascular disease. Blood flow, blood volume, transit time, and other perfusion parameters are sensitive markers of pathophysiology with impaired perfusion. This review summarizes the basic principles of CTP including image acquisition, tracer kinetic modeling, deconvolution algorithms, and diagnostic interpretation. The focus is on practical and theoretical considerations for accurate quantitative parametric imaging. Recommended CTP scan parameters to maintain CT number accuracy and optimize radiation dose versus image noise are first reviewed. Tracer kinetic models, which describe how injected contrast material is distributed between blood and the tissue microenvironment by perfusion and bidirectional passive exchange, are then derived. Deconvolution algorithms to solve for hemodynamic parameters of kinetic models are discussed and their quantitative accuracy benchmarked. The applications and diagnostic interpretation of CTP in stroke, cancer, and cardiovascular disease are summarized. Finally, we conclude with a discussion of future directions for CTP research, including radiation dose reduction, new opportunities with novel CT hardware, and emerging diagnostic applications.

Accelerating the kinetics of curing reaction of SBR/BR blend by silicon carbide via modification of thermal diffusivity

In this work, the accelerating effect of nano-silicon carbide (SiC) as a high thermal conductive filler on the kinetics of curing reaction of styrene-butadiene rubber/butadiene rubber (SBR/BR) as a compound for tire tread was investigated. The oscillating disk rheometer (ODR) showed that the SiC filler reduced the scorch and optimum curing times by ∼10%. The SiC amount of 5 phr caused an unparalleled reduction of ∼50% in the time difference between the scorch and curing times. The calculated parameters of the autocatalytic kinetic model of Kamal–Sourour using ODR results indicated that SiC accelerated the curing reaction by ∼1.25 times. To find the main mechanism of action of SiC in the improvement of vulcanization kinetic of SBR/BR-SiC, thermal diffusivity of the compound was detected in the thermalization process using a special setup during the curing and also the dethermalization process after that. The results exhibited that the SiC particles led to enhanced heat transfer rate into the compound during the vulcanization by ∼30% and into the cured composite by ∼50%, particularly at 5 phr of SiC. Moreover, fitting the heat transfer equation on the dethermalization data showed that the amount of 5 phr SiC in the recipe enhanced the thermal diffusivity by ∼97%, which can prevent the tire explosion while deriving due to the increment of the air temperature within the tire. The SiC filler can save significant energy in the manufacturing process of the tire tread, which has been neglected so far.

Kinetics of phosphorous sorption to biochar-amended soils

Biochar has been investigated as a potential soil amendment for increasing P sorption to soils. Several studies of shown that coating biochar with Fe oxides can increase the amount of P sorbed to the biochar, yet little is known about the kinetics of P sorption to soils amended with Fe-coated biochar. In this study, the kinetics of P sorption are measured in four soils with contrasting surface properties and textures. In addition, a wood-based biochar, both unmodified (BC) and modified by chemical precipitation of Fe oxides (BCFe), was added to these four soils at a rate of 5% (w/w). P sorption to each soil with and without the unmodified or Fe-coated biochar was measured at incubation times ranging from 1 to 314 h. The data were fit using five different kinetic models to determine if the addition of the BC or BCFe significantly affected the amount of P sorption and the kinetic behavior of P sorption to the biochar-amended soils. Results showed that amending with BC had minimal impact on P sorption to the four soils, whereas the impact of the BCFe on P sorption varied depending on soil. In the low P sorbing soil, the BCFe nearly doubled the amount of P sorbed whereas in the high P sorbing soil, addition of the BCFe resulted in less-than-expected increases in P sorption. For each biochar and soil treatment, the same kinetic model provided the best fit to the observed sorption over time. In two soils, the kinetic model parameters were significantly different following the addition of the BC whereas the model parameters for all four soils were significantly different following addition of BCFe. This study provides new insights into P sorption kinetics to biochar-amended soils.

A Review of Computational Approach for S-system-based Modeling of Gene Regulatory Network.

Inference of gene regulatory network (GRN) from time series microarray data remains as a fascinating task for computer science researchers to understand the complex biological process that occurred inside a cell. Among the different popular models to infer GRN, S-system is considered as one of the promising non-linear mathematical tools to model the dynamics of gene expressions, as well as to infer the GRN. S-system is based on biochemical system theory and power law formalism. By observing the value of kinetic parameters of S-system model, it is possible to extract the regulatory relationships among genes. In this review, several existing intelligent methods that were already proposed for inference of S-system-based GRN are explained. It is observed that finding out the most suitable and efficient optimization technique for the accurate inference of all kinds of networks, i.e., in-silico, in-vivo, etc., with less computational complexity is still an open research problem to all. This paper may help the beginners or researchers who want to continue their research in the field of computational biology and bioinformatics.

Hybrid-modeling for PTFE polymerization reaction with deep learning-based reaction rate model

Abstract The tetrafluoroethylene (TFE) polymerization process is an essential industrial process to produce polytetrafluoroethylene (PTFE), which is extensively utilized in aerospace and medical domains. A precise mechanism model is a prerequisite for comprehensively understanding this process. However, significant uncertainties in the kinetic model parameters may hinder attaining an optimal reaction rate. This study proposes a hybrid polymerization reaction model that integrates process mechanism modeling and data-driven modeling to address this challenge. In the hybrid modeling approach, the mechanism model for the polymerization reaction is developed based on reaction kinetic and thermodynamic assumptions. Additionally, a long short-term memory (LSTM) neural network is employed to predict the reaction rate for chain initiation by leveraging temporal relationships derived from archived measurements. The proposed methodology is implemented using a PTFE polymer reactor system, and experimental comparisons affirm its superior performance and effectiveness.

Optimization for Kinetic Model of Oxidative Desulfurization of Sour Naphtha Over a Natural Base Zeolite Catalyst in a Three Phase Oscillatory Baffled Reactor

The oxidative desulfurization (ODS) of Iraqi sour naphtha was studied in a novel design of a heterogeneous catalytic reactor. Molecular oxygen was used as an oxidizing agent and a clay-based zeolite was used as a catalyst. The present work of ODS was conducted in a three-phase oscillatory baffled reactor under different operating conditions; temperature =25, 35, 45, and 55°C, residence times (1 – 14 min), Reynold number of oscillation (175, 235, 315), and net flow Reynolds number (25, 50, 75). A zeolitic base ODS catalyst was prepared and applied as a heterogeneous catalyst in the OBR unit. The aim of this study is to obtain the ODS kinetic parameters and concentration profile of the sulfur compounds in the naphtha cut. The model developed was based on the properties of the feedstock, characteristics of the catalyst, and operation conditions inside the OBR according to the experimental observations. To obtain the kinetic model parameters mass transfer and flow conditions were applied in the model network. The equations were employed in gPROMS software to simulate the experimental results of sulfur concentration remaining after completion of the ODS process. The set of equations was successful in simulating the experimental results with a 5% absolute error. The predicted data were used for optimizing the kinetic parameters to get the ODS kinetic parameters based on minimizing sulfur concentration.

Kinetics of glycerol and polyglycerol maleates production: Raw materials for partially bio-based concrete admixtures

In this work, the self-catalyzed kinetics to produce glycerol, diglycerol, and polyglycerol-4 maleate esters were experimentally determined from 60 ºC to 80 °C. These esters can be used as intermediates in the production of partially bio-based admixtures that can replace petroleum-derived polycarboxylate ethers, which are traditionally used as concrete superplasticizers. The obtained experimental data were used to adjust the kinetic model parameters for the three reactions, including the effect of temperature. The model adequately predicted the behavior of the three proposed reactions, which was statistically validated using the Fisher-Snedecor test. The activation energies of glycerol, diglycerol, and polyglycerol-4 maleate were 29.5, 85.5, and 68.9 kJ/mol, respectively, with frequency constants of 7.93 × 102, 2.02 × 1011, and 4.48 × 108 min−1. The highest activation energy was obtained for the esterification of maleic anhydride (MAH) with diglycerol, indicating that this reaction is more temperature dependent. The kinetic constants at 70 °C were 2.56 × 10−2 min−1, 1.92 × 10−2 min−1, and 1.46 × 10−2 min−1 for the esterification of MAH with glycerol, diglycerol, and polyglycerol respectively, showing a greater reaction rate for the esterification with glycerol.

Investigation on the applicability of parameters identification of marine diesel engine SCR kinetic model based on Discrete-Grid algorithm

Selective catalytic reduction (SCR) technology is the most widely used denitrification method in industry. However, the calibration of its kinetic model is very problematic due to the large number of parameters involved and the nonlinear characteristics, especially for the undesired reactions such as formation-decomposition of ammonium nitrate (AN), ammonium sulfate (AS), ammonium bisulfate (ABS), etc. In this paper, the synthetic gas test bench and the SCR system of WD10 marine diesel engine burning high-sulfur fuel oil (HSFO) and low-sulfur fuel oil (LSFO) are taken as the research objects. Firstly, based on the experimental data obtained from the synthetic gas test bench, the pre-exponential multiplier and activation energy of each reaction were identified using the Discrete-Grid algorithm. In this work, considering that the production of N2O, SO3, AN, AS, and ABS in the undesired reactions is very low, not on the same order of magnitude as that of NO and NH3, it is easy to overlook their reaction characteristics in the optimization process. We have adopted an approach that amplifies the error between the predicted concentration and the observed concentration of the species to solve this problem. Secondly, a detailed and complete SCR kinetic model was constructed. Finally, with the parameter identification results obtained from the synthesis gas bench as initial values, all the kinetic parameters of the WD10 marine engine SCR model was calibrated simultaneously using the Discrete-Grid algorithm. The results showed that the SCR kinetic model derived from the synthetic gas test bench was suitable for the real marine engine SCR system when the range of temperature and space velocity (SV) of the synthetic gas bench test is similar to that of the marine SCR system.

Modeling of an unseeded reactive crystallization process using multiobjective optimization

Modeling of reactive crystallization processes is considered to be a complex task where the reaction and crystallization processes occuring together presents additional challenges. A simplified approach is shown in this work to model such reactive crystallization processes using limited number of experiments covering the design space. In the current application, a case study is demonstrated using unseeded semi-batch reactive crystallization of salicylic acid. Multiobjective optimization (MOO) approach is used to determine the kinetic model parameters considering surrogate objectives related to solution concentration and mean size of crystals using the experimental data collected. The proposed first principles model is in good agreement with the experimental data and thus can be further applied for model predictions to determine optimal control strategies.

A systematic evaluation of five different image-derived input functions for the clinical implementation of 18F-NaF bone PET/CT in patients with chronic kidney disease-mineral and bone disorder.

The aim of this study was to investigate the impact of the use of varying input parameters on resulting bone plasma clearance (Ki ) and other kinetic modelling parameters in a group of patients with chronic kidney disease-mineral and bone disorder (CKD-MBD). Raw PET/CT data and blood data were systematically analyzed using five different VOIs for the input functions in the left ventricle and in the thoracic aorta. Standardized VOIs were placed in four thoracic vertebrae and the results pooled and averaged. The basic image-derived input functions (IDIFs) were corrected for partial volume effect and spill-over and modified by substitution of the terminal image exponential with the corresponding plasma-exponentials derived from blood samples. Ki was then calculated using both a non-linear regression (NLR) analysis and a graphical Patlak analysis and compared. Our original results were reproducible with an inter-observer difference of approximately 6%. The correction factors varied with the VOI volumes from 0.73 ± 0.17 for the largest LV-VOI (48.7 ± 25.3 cm3) to 0.99 ± 0.10 for the AO-VOI (3.4 ± 1.2 cm3). The mean NLR-Ki results varied between 0.0378 ± 0.0112 and 0.0432 ± 0.0095 ml/min ml-1 with a fixed vB and 0.0408 ± 0.0111 and 0.045 ± 0.0102 ml/min ml-1 with a free-fitted vB. The corresponding Patl-Ki -results varied between 0.0302 ± 0.0071 and 0.0325 ± 0.0070 ml/min ml-1, having lesser differences and variances. The input functions with least variance and mean differences compared with NLR results were derived from the left ventricle with a VOI volume of 19.2 ± 11.3 cm3 corrected for PVE and Bg with a mean Ki -difference: 0.0097 ± 0.0370 ml/min ml-1 and 95% confidence limits (-0.023 to 0.004). Our results indicated that a VOI with a volume of approximately 20 cm3 with a correction factor of 0.83 ± 0.13 results in Patlak results with the least variance and difference compared with the NLR results. The use of free-fitted vB in the NLR analysis showed the most robust results in all input series. The Patlak results were in comparison generally lower than the NLR results (-17.3% to -23.4%) but very robust across the various input series and with results comparable to previously published data and are therefore recommended for future analysis.