Gamma-variate Function Research Articles

The gamma-variate in contrast-enhanced imaging: a unified view and method from computed to electrical impedance tomography

Objective. Modern medical imaging plays a vital role in clinical practice, enabling non-invasive visualization of anatomical structures. Dynamic contrast enhancement (DCE) imaging is a technique that uses contrast agents to visualize blood flow dynamics in a time-resolved manner. It can be applied to different modalities, such as computed tomography (CT) and electrical impedance tomography (EIT). This study aims to develop a common theoretical and practical hemodynamic extraction basis for DCE modelling across modalities, based on the gamma-variate function. Approach. The study introduces a framework to generate time-intensity curves for multiple DCE imaging modalities from user-defined hemodynamic parameters. Thus, extensive datasets were simulated for both DCE-CT and EIT, representing different hemodynamic scenarios. Additionally, gamma-variate extensions to account for several physiological effects were detailed in a modality-agnostic manner, and three corresponding fitting strategies, namely nonlinear, linear, and a novel hybrid approach, were implemented and compared on the basis of accuracy of parameter estimation, first pass reconstruction, speed of computation, and failure rate. Main results. As a result, we found the linear method to be the most modality-dependent, exhibiting the greatest bias, variance and failure rates, although remaining the fastest alternative. The hybrid method at least matches the state-of-the-art nonlinear method’s accuracy, while improving its robustness and speed by 10 times. Significance. Our research suggests that the hybrid method may bring noteworthy accuracy and efficiency improvements in handling the high-dimensionality of DCE imaging in general, being a step towards real-time processing. Moreover, our generative model presents a potential asset to produce benchmarking and data augmentation datasets across modalities.

Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

ObjectivesTo investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients.MethodsThe study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board. The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores.ResultsPenumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p < 0.001) and negatively with the ASPECTS (r = − 0.43; p < 0.001). The CNN AIF estimated penumbra and core volume matching the patient symptoms, typically in patients with higher NIHSS (> 20) and lower ASPECT score (< 5). In group analysis, the median CBF < 20%, CBF < 30%, rCBF < 38%, Tmax > 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05).ConclusionsWith inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.Critical relevance statementWith CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.Graphic abstract

Effect of Cardiac Phase on Cardiac Output Index Derived from Dynamic CT Myocardial Perfusion Imaging.

Purpose: The aortic time-enhancement curve obtained from dynamic CT myocardial perfusion imaging can be used to derive the cardiac output (CO) index based on the indicator dilution principle. The objective of this study was to investigate the effect of cardiac phase at which CT myocardial perfusion imaging is triggered on the CO index measurement with this approach. Methods: Electrocardiogram (ECG) gated myocardial perfusion imaging was performed on farm pigs with consecutive cardiac axial scans using a large-coverage CT scanner (Revolution, GE Healthcare) after intravenous contrast administration. Multiple sets of dynamic contrast-enhanced (DCE) cardiac images were reconstructed retrospectively from 30% to 80% R-R intervals with a 5% phase increment. The time-enhancement curve sampled from above the aortic orifice in each DCE image set was fitted with a modified gamma variate function (MGVF). The fitted curve was then normalized to the baseline data point unaffected by the streak artifact emanating from the contrast solution in the right heart chamber. The Stewart–Hamilton equation was used to calculate the CO index based on the integral of the fitted normalized aortic curve, and the results were compared among different cardiac phases. Results: The aortic time-enhancement curves sampled at different cardiac phases were different from each other, especially in the baseline portion of the curve where the effect of streak artifact was prominent. After properly normalizing and denoising with a MGVF, the integrals of the aortic curve were minimally different among cardiac phases (0.228 ± 0.001 Hounsfield Unit × second). The corresponding mean CO index was 4.031 ± 0.028 L/min. There were no statistical differences in either the integral of the aortic curve or CO index among different cardiac phases (p > 0.05 for all phases).

Quantification and Classification of Contrast Enhanced Ultrasound Breast Cancer Data: A Preliminary Study.

This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R2 metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R2 of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, Gmean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.

A Novel Statistical Optimization Algorithm for Estimating Perfusion Curves in Susceptibility Contrast-Enhanced MRI.

Dynamic susceptibility contrast-enhanced magnetic resonance imaging is an important tool for evaluating intravascular indicator dynamics, which in turn is valuable for understanding brain physiology and pathophysiology. This procedure usually involves fitting a gamma-variate function to observed concentration-time curves in order to eliminate undesired effects of recirculation and the leakage of contrast agents. Several conventional curve-fitting approaches are routinely applied. The nonlinear optimization methods typically are computationally expensive and require reliable initial values to guarantee success, whereas a logarithmic linear least-squares (LL-LS) method is more stable and efficient, and does not suffer from the initial-value problem, but it can show degraded performance, especially when a few data or outliers are present. In this paper, we demonstrate, that the original perfusion curve-fitting problem can be transformed into a gamma-distribution-fitting problem by treating the concentration-time curves as a random sample from a gamma distribution with time as the random variable. A robust maximum-likelihood estimation (MLE) algorithm can then be readily adopted to solve this problem. The performance of the proposed method is compared with the nonlinear Levenberg-Marquardt (L-M) method and the LL-LS method using both synthetic and real data. The results show that the performance of the proposed approach is far superior to those of the other two methods, while keeping the advantages of the LL-LS method, such as easy implementation, low computational load, and dispensing with the need to guess the initial values. We argue that the proposed method represents an attractive alternative option for assessing intravascular indicator dynamics in clinical applications. Moreover, we also provide valuable suggestions on how to select valid data points and set the initial values in the two traditional approaches (LL-LS and nonlinear L-M methods) to achieve more reliable estimations.

Manual Carotid Compression is a Viable Alternative for Reduction of Cerebral Microemboli.

Stroke is a devastating complication of cardiovascular surgeries, and the risk is particularly high for those requiring cardiopulmonary bypass (CPB). Embolic particles generated during the unclamping of the aortic cross-clamp may enter the cerebral circulation, lodging in small vessels. External manual compression of the carotid arteries is a non-invasive technique that has been proposed for cerebral protection during CPB procedures but is not widely deployed. The aim of this study is to assess the potential for cerebral emboli reduction with carotid compression using an in vitro model. Experiments were performed with a glass aortic arch model in a mock cardiovascular circuit. Small fluorescent particles were released into the circulation with and without carotid compression, and the particles visualized in the aortic midplane. The number of particles in the aorta and arch branch vessels were counted from the images before, during and following the release of carotid compression for durations of 10, 15 and 20s. A gamma variate function was fit to the data to describe the bolus dynamics. Carotid compression for 10 s reduces the number of embolic articles entering the carotid arteries by over 75%. A compression duration of 15-20s does not result in greater particle reduction than one of 10s. Brief compression of the common carotid arteries during cardiovascular interventions has the potential to dramatically reduce the number of cerebral emboli and should be investigated further.

Introducing a Linear Gamma Variate Fit to Measure Pulmonary Perfusion with Electrical Impedance Tomography

Abstract The indicator dilution method (IDM) is one approach to measure pulmonary perfusion using Electrical Impedance Tomography (EIT). To be able to calculate perfusion parameters and to increase robustnes, it is necessary to approximate and then to separate the components of the measured signals. The component referring to the passage of the injected bolus through the pixels can be modeled as a gamma variate function, its parameters are often determined using nonlinear optimization algorithms. In this paper, we introduce a linear approach that enables higher robustnes and faster computation, and compare the linear and nonlinear fitting approach on data of an animal study.

Blood flow quantification in dialysis access using digital subtraction angiography: A retrospective study

Background and objectiveVascular access is the “lifeline” of end-stage renal disease patients, which is surgically constructed to remove blood-waste and return artificially filtered blood into circulation. The arteriovenous shunting causes an abrupt change in blood flow and results in increased fluidic stress, which predisposes to access stenosis and thrombosis. While access flow is crucial to evaluate interventional endpoint, application to measure flow using digital angiogram is not yet available. The goal of this study was to determine the feasibility of flow quantification in dialysis access using a software tool and to guide the design of an imaging protocol. Methods173 digital subtraction angiographic (DSA) images were retrospectively analyzed to evaluate access flow in a custom-programming environment. Four bolus transit time algorithms and a distance calculation method were assessed for flow computation. Gamma variate function was applied to remove secondary flow and intensity outliers in the bolus time-intensity curves and evaluated for enhancement in computational accuracy. The percent deviations of flow rates computed from dilution of iodinated radio-contrast material were compared with in situ catheter-based flow measurement. ResultsAmong the implemented bolus transit time algorithms, quantification error (mean ± standard error) of cross-correlation algorithm without and with gamma variate curve fitting was 35 ± 1% and 22 ± 1%, respectively. All other algorithms had quantification error >27%. The bias and limits of agreement of the cross-correlation algorithm with gamma variate curve fit was −94 ml/min and [−353, 165] mL/min, respectively. ConclusionsThe cross-correlation algorithm with gamma variate curve fit had the best accuracy and reproducibility for image-based blood flow computation. To further enhance accuracy, images may need to be acquired with a dedicated injection protocol with predetermined parameters such as the duration, rate and mode of bolus injection, and the acquisition frame rate.

Mapping cerebral perfusion from time-resolved contrast-enhanced MR angiographic data

A common method to acquire both perfusion and angiographic information is to have separate MRI scans for each information. In this study, we propose to achieve the goal by deriving perfusion parameters, specifically cerebral blood volume (CBV) and Tmax, from time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA). Both CE-MRA and DSC-MRI were performed on seven subjects with a diagnosed ischemic stroke. Concentration functions from CE-MRA were modeled using a modified gamma-variate function to appreciate the full first-pass transition of the tracer bolus. Perfusion parameters were calculated using concentration function derived from each imaging method and were compared to each other both visually and quantitatively by means of correlation studies. CBV and Tmax maps generally showed good agreement between the two methods. This study proved the concept of using time-resolved CE-MRA as both vascular imaging and tissue perfusion mapping while using a single injection of contrast agent, potentially reducing cost and improving patient safety and comfort.

TH-CD-207-01: BEST IN PHYSICS (IMAGING): Correction of the First-Pass Distortion in Arterial Input Function of DCE MRI for Perfusion Quantification

Purpose: To quantify perfusion in an abdominal organ (liver, spleen or kidney) from dynamic contrast-enhanced (DCE) MRI, an arterial input function(AIF) is commonly sampled in a volume of interest (VOI) on the descending aorta. However, the peak of the first pass of the AIF could be corrupted, due to the T2* effect of high contrast-concentration, particularly at 3T MR, and other factors. This study aimed to correct this distortion in the AIF for DCE MRI perfusion quantification of an abdominal organ. Methods: A post-processing correction method was developed to automatically recognize the distortion portion in the first pass of an AIF and fit it with a gamma-variate function. The fitting of the first pass was incorporated in pharmacokinetic(PK) modeling of the DCE curves from VOIs in both spleen and kidney. The optimal solution was determined by balancing between best fitting the PK model and matching the fitted first pass with the measured one. The corrected AIFs were applied to quantify hepatic perfusion from DCE MRI of 6 patients, which were acquired on a 3T scanner using a VIBE sequence. Results: In the DCE-MRI of 6 patients, the original AIFs had 20%±6% saturation in the first-pass peak compared with the corrected AIFs. Using the corrected AIFs to quantify hepatic perfusion yielded average differences of-36% (p=0.002) and 17% (p=0.01) in respective arterial perfusion and portal venous perfusion on VOIs of normal hepatic tissue, compared to those estimated using the original uncorrected AIFs. Conclusion: The proposed method corrects the distortion in the first pass of the AIF sampled in the aorta, and could improve DCE-MRI perfusion quantification in the liver and other abdominal organs. The resultant perfusion may assess tissue function and tumor response to radiation and provide tools to support physiologically adaptive RT. The work is supported in part by RO1CA132834 and PO1CA59827 The work is supported in part by NIH RO1CA132834 and PO1CA59827

Automatic differentiation of placental perfusion compartments by time-to-peak analysis in mice

IntroductionThe aim of this study was to develop an automatic differentiation of two perfusion compartments within the mouse placenta based on times of maximal contrast enhancement for a detailed and reproducible perfusion assessment. MethodsPlacentas (n = 17) from pregnant BALB/c mice (n = 10) were examined in vivo at 7T on gestation day 16.5. Coronal dual-echo 3D T1-weighted gradient-echo sequences were acquired after application of contrast agent for dynamic MRI. An adapted gamma variate function was fitted to the discrete concentration time curves to evaluate the effect of noise on perfusion and segmentation results. Time-to-peak maps based on fitted and discrete curves of each placenta were used to classify each voxel into the high- or low-blood flow compartment using k-means clustering. Perfusion analysis was performed using the steepest slope model and also applied to fitted and discrete curves. Results were compared to manually defined compartments from two independent observers using the Dice coefficient D. ResultsManually defined placental areas of high-flow and low-flow were similar to the automatic segmentation for discrete (D = 0.76/0.75; D = 0.76/0.79) and fitted (D = 0.80/0.80; D = 0.81/0.82) concentration time curves. Mean perfusion values of discrete and fitted curves ranged in the high-flow compartment from 134 to 142 ml/min/100 ml (discrete) vs. 138–143 ml/min/100 ml (fitted) and in the low-flow compartment from 91 to 94 ml/min/100 ml (discrete) vs. 74–82 ml/min/100 ml (fitted). DiscussionOur novel approach allows the automatic differentiation of perfusion compartments of the mouse placenta. The approach may overcome limitations of placental perfusion analyses caused by tissue heterogeneity and a potentially biased selection of regions of interest.

SU‐E‐QI‐13: Predictable Models for Radio‐Sensitizing Agent Kinetics: Application to Stereotactic Synchrotron Radiation Therapy

Purpose:Iodine‐enhanced radiotherapy is an innovative treatment combining the selective accumulation of an iodinated contrast agent in brain tumors with irradiations using monochromatic medium energy x‐rays. The radiation dose enhancement depends on the time course of iodine in the tumors. A prolonged CT scanning (∼30 min) is required to follow‐up iodine kinetics for recruited patients. This protocol could lead to substantial radiation dose to the patient. A novel method is proposed to reduce the acquisition time.Methods:12 patients received an intravenous bolus of iodinated contrast agent, followed by a steady‐state infusion to ensure stable intra‐tumoral amounts of iodine during the treatment. Absolute iodine concentrations (IC) were derived from 40 multi‐slice dynamic conventional CT images of the brain. The impulse response function (IRF) to the bolus was estimated using the adiabatic approximation of the Johnson and Wilson's model. The arterial input function (AIF) of the steady‐state infusion was fitted with several models: Gamma, Gamma with recirculation and hybrid. Estimated IC were calculated by convolving the IRF with the modeled AIF and were compared to the measured data.Results:The gamma variate function was not relevant to model the AIF due to high differences with the measured AIF. The hybrid and the gamma with recirculation models provided differences below 8% during the whole acquisition time. The absolute difference between the measured and the estimated IC was lower than 0.5 mg/ml, which corresponds to 5% of dose enhancement error.Conclusion:The proposed method allows a good estimation of the iodine time course with reduced scanning delays (3 instead of 30 min) and dose to the patient. The results suggest that the dose errors may stay within the radiotherapy standards.

Vascular Steal Explains Early Paradoxical Blood Oxygen Level-Dependent Cerebrovascular Response in Brain Regions with Delayed Arterial Transit Times

Introduction: Blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) during manipulation of inhaled carbon dioxide (CO₂) can be used to measure cerebrovascular reactivity (CVR) and map regions of exhausted cerebrovascular reserve. These regions exhibit a reduced or negative BOLD response to inhaled CO₂. In this study, we sought to clarify the mechanism behind the negative BOLD response by investigating its time delay (TD). Dynamic susceptibility contrast (DSC) MRI with the injection of a contrast agent was used as the gold standard in order to provide measurement of the blood arrival time to which CVR TD could be compared. We hypothesize that if negative BOLD responses are the result of a steal phenomenon, they should be synchronized with positive BOLD responses from healthy brain tissue, even though the blood arrival time would be delayed. Methods: On a 3-tesla MRI system, BOLD CVR and DSC images were collected in a group of 19 patients with steno-occlusive cerebrovascular disease. For each patient, we generated a CVR magnitude map by regressing the BOLD signal with the end-tidal partial pressure of CO₂ (P_ETCO₂), and a CVR TD map by extracting the time of maximum cross-correlation between the BOLD signal and P_ETCO₂. In addition, a blood arrival time map was generated by fitting the DSC signal with a gamma variate function. ROI masks corresponding to varying degrees of reactivity were constructed. Within these masks, the mean CVR magnitude, CVR TD and DSC blood arrival time were extracted and averaged over the 19 patients. CVR magnitude and CVR TD were then plotted against DSC blood arrival time. Results: The results show that CVR magnitude is highly correlated to DSC blood arrival time. As expected, the most compromised tissues with the longest blood arrival time have the lowest (most negative) CVR magnitude. However, CVR TD shows a noncontinuous relationship with DSC blood arrival time. CVR TD is well correlated to DSC blood arrival time (p < 0.0001) for tissue of positive reactivity, but fails to maintain this trend for tissue of negative reactivity. Regions with negative reactivity have similar CVR TD than healthy regions. Conclusion: These results support the hypothesis that negative reactivity is the result of a steal phenomenon, lowering the BOLD signal as soon as healthier parts of the brain start to react and augment their blood flow. BOLD CVR MRI is capable of identifying this steal distribution, which has particular diagnostic significance as it represents an actual reduction in flow to already compromised tissue.

A model based algorithm for perfusion estimation in interventional C‐arm CT systems

Interventional C-arm CT imaging, today, plays an important role in the diagnosis and treatment of patients. The main part of the 3D imaging techniques, currently used in interventions, are morphological imaging techniques. So far, the ability for functional or perfusion imaging is limited, e.g., only static cerebral blood volume measurement [A. S. Ahmed, Y. Deuerling-Zheng, C. M. Strother, K. A. Pulfer, M. Zellerhoff, T. Redel, K. Royalty, D. Consigny, M. J. Lindstrom, and D. B. Niemann, "Impact of intra-arterial injection parameters on arterial, capillary, and venous time-concentration curves in a ca480 nine model," AJNR Am. J. Neuroradiol. 30, 1337-1341 (2009)] is available. The sample rate of current C-arm CT systems is not fast enough yet to measure dynamic parameters like cerebral blood flow using standard Feldkamp reconstruction. The authors propose a reconstruction algorithm that models the time-dependent attenuation values of each voxel using a gamma-variate function. The method can be divided into a segmentation-based initialization and an iterative optimization step. For the initialization, a threshold-based segmentation of vessel, tissue, and nondynamic structures (e.g., bone and air) is performed on the filtered backprojection (FBP) reconstructions. For each of these regions, homogeneous time-attenuation curves are estimated to initialize all the voxels within the region. The scaling-factor is then adjusted for each voxel using the attenuation values of the static reconstructions. The second part of the algorithm is an iterative optimization of the gamma-variate parameters of each voxel, based on a simultaneous algebraic reconstruction technique. Within each iteration, a Levenberg optimization is applied to minimize the backprojected errors. The algorithm is quantitatively evaluated with simulated forward projections as well as real C-arm CT projection data. In the phantom experiments, penumbra and infarct core could be segmented with an adjusted Rand index of up to 0.95 for a noise level of 10(5) photons. Perfusion CT data sets from three patients were used to compare the iterative reconstruction approach to the interpolated FBP reconstruction using different sweep times. In their experiments, a sweep time of 4 s using iterative reconstruction would be equivalent to that using interpolated FBP with a sweep time of around 1 s. The reconstruction results of the animal study are compared to a perfusion CT acquisition, sampled with 1 frame per second. A correlation coefficient of 0.75 between the original and the reconstructed CBF-maps could be reached with the iterative approach compared to 0.56 using the interpolated FBP reconstruction. In their experiments, the quality of dynamic perfusion measurements was improved using the proposed reconstruction algorithm compared to static reconstruction followed by interpolation. It could be used to increase the temporal resolution of current C-arm CT system without hardware modification to make them feasible for dynamic perfusion measurement. Furthermore, radiation dose could be reduced using their method to increase temporal resolution than using static reconstruction with a higher sampling frequency.

Evaluation of Tumor Angiogenesis in a Mouse PC-3 Prostate Cancer Model Using Dynamic Contrast-Enhanced Sonography

The purpose of this study was to evaluate tumor angiogenesis in a mouse xenograft model injected with human PC-3 prostate cancer cells using contrast-enhanced sonography. Sixteen nude mice were injected with human prostate cancer cells on the back or the flank. Contrast-enhanced sonography was performed with a 5- to 12-MHz broadband linear transducer after a 500-μL bolus injection of a sonographic contrast agent composed of lipid shells and sulfur hexafluoride. Contrast-enhanced sonograms were obtained by the pulse inversion coded harmonic technique with a low mechanical index of 0.07. A region of interest was drawn to encompass the tumor, and time-intensity curves were acquired. After fitting the curve by a gamma variate function, the maximum intensity, area under the curve for up to 50 seconds, time to peak, shape parameter, and scale parameter were derived. The tumor volume, percentage of vascular endothelial growth factor expression, and CD31-positive vessel count (microvessel density) were correlated with the parameters derived from the time-intensity curve. The maximum intensity was positively correlated with the microvessel density with statistical significance (r = 0.552; P = .03). The percentage of vascular endothelial growth factor expression did not have any correlation with the parameters from the curve. Contrast-enhanced sonography can reflect tumor vascularity in a prostate cancer animal model. Sonography of tumor angiogenesis may permit functional assessment of the tumor vasculature and provide an imaging biomarker for tumor responses to antiangiogenic therapies.

Clinical application of pharmacokinetic analysis as a biomarker of solitary pulmonary nodules: Dynamic contrast‐enhanced MR imaging

The purpose of this study is to evaluate perfusion indices and pharmacokinetic parameters in solitary pulmonary nodules (SPNs). Thirty patients of 34 enrolled with SPNs (15-30 mm) were evaluated in this study. T1 and T2-weighted structural images and 2D turbo FLASH perfusion images were acquired with shallow free breathing. B-spline nonrigid image registration and optimization by χ² test against pharmacokinetic model curve were performed on dynamic contrast-enhanced MRI. This allowed voxel-by-voxel calculation of k(ep) , the rate constant for tracer transport to and from plasma and the extravascular extracellular space. Mean transit time, time-to-peak, initial slope, and maximum enhancement (E(max) ) were calculated from time-intensity curves fitted to a gamma variate function. After blinded data analysis, correlation with tissue histology from surgical resection or biopsy samples was performed. Histologic evaluation revealed 25 malignant and five benign SPNs. All benign SPNs had k(ep) < 1.0 min⁻¹. Nineteen of 25 (76%) malignant SPNs showed k(ep) > 1.0 min⁻¹. Sensitivity to diagnose malignant SPNs at a cutoff of k(ep) = 1.0 min⁻¹ was 76%, specificity was 100%, positive predictive value was 100%, negative predictive value was 45%, and accuracy was 80%. Of all indices studied, k(ep) was the most significant in differentiating malignant from benign SPNs.

An improved model for describing the contrast bolus in perfusion MRI

Quantification of perfusion measurements using dynamic, susceptibility-weighted contrast-enhanced (DSC) MRI depends on estimating the size and shape of the tracer bolus. Typically, the bolus is described as a gamma variate function (GV) fitted to the bolus portion of tracer concentration time curve (CTC). However, the last point to fit is arbitrary which can lead to considerable variation in the fitted curve in the presence of noise. In this technical note, we present a model which takes into account recirculation explicitly and fits robustly to the entire CTC in the presence of noise. Signal data measurements from ten DSC MRI patients were fitted with our new model and a GV function using four different methods of estimating the end of the bolus. Estimates of the area under the curves (AUC) and first moments (FMs) of the bolus were compared at different noise levels. The new model gave errors similar to or smaller than those of the most effective methods for fitting a GV. The single compartment recirculation (SCR) model is the most robust fitting technique with respect to noise both for bias and variability.

A constrained independent component analysis technique for artery–vein separation of two-photon laser scanning microscopy images of the cerebral microvasculature

Understanding brain hemodynamics as well as the coupling between microvascular hemodynamics and neural activity is important in pathophysiology of cerebral microvasculature. When local increases in neuronal activity occur, the blood volume changes in the surrounding brain vasculature. Dynamic contrast enhanced imaging (DCE) is a powerful technique that quantifies these changes in the blood flow by repeatedly imaging the vasculature over time. Separating artery, vein and capillaries in the images and extracting their intensity-time curves from the DCE image sequence is an important first step in understanding vascular function. A constrained independent component analysis (ICA) technique is developed to analyze the two photon laser scanning microscopy (2PLSM) images of rat brain microvasculature, where a bolus of fluorescent dye is administered to the vascular system as the contrast agent. A priori information inferred from the gamma variate model of cerebral microvasculature is incorporated with the data driven technique in temporal and spatial domains using two constraints. The constraints are: no independent component (IC) is allowed to have negative contribution in forming the images (positivity constraint) and the component curves follow a gamma variate function (model fitting constraint). Experimental and simulation studies are conducted to demonstrate the improved performance of the proposed constrained ICA (CICA) technique over the most commonly used classical ICA algorithm (fast-ICA) in providing physiologically meaningful ICs and its ability to separate the model following factors from other factors are shown. The efficiency of CICA in handling noise is compared to model based techniques. Its capability in providing improved separation between artery, vein and capillaries compared to the other two techniques is also demonstrated.

SU-E-I-180: Simulation of Arterial First Pass and Recirculation Input Functions Using a Series of Related Gamma Variate Function

Purpose: Any tracer kinetic analysis requires an arterial input function (AIF) when the analysis is performed for quantitative measurements of concerned variables such as blood flow (BF) or blood volume (BV) as in the case of dynamic CT, PET or MR perfusion studies. The AIF is often measured at the middle cerebral artery (MCA) or only the first pass AIF is modeled using a single gamma variate function. However, the recirculating input, which is sometimes disregarded for the computational convenience or sometime modeled using too simplistic function (e.g. straight line), has not well been formulated. Here, a full AIF including recirculating input is modeled using a series of gamma variate functions that have related parameters, and the feasibility was tested by a computer simulation. Methods: The simulation was performed using an in‐house program developed in MATLAB. The shape of a gamma variate function was defined using two parameters (&#945;,&#61538;, reference). In addition, recirculation time (T) and fraction (f), i.e. the first arrival time and fraction after leaving the brain, were included in the simulation. A single recirculation was described as a related gamma variate function (reference), and the total recirculation was described as the sum of all related gamma variate functions. Results: The arterial input function, including the first pass and recirculating inputs, can be modeled using the sum of a series of related gamma variate functions. Conclusions: The arterial input function in its complete form can be modeled using a series of related gamma variate functions. From this modeling, the AIF can be described as its complete form, which might benefit the quantitative measurements of BF, BV or mean transit time (MTT) in those dynamic contrast based perfusion imaging technique such as in CT, PET or MRI.

Early time points perfusion imaging: Theoretical analysis of correction factors for relative cerebral blood flow estimation given local arterial input function

If local arterial input function (AIF) could be identified, we present a theoretical approach to generate a correction factor based on local AIF for the estimation of relative cerebral blood flow (rCBF) under the framework of early time points perfusion imaging (ET). If C(t), the contrast agent bolus concentration signal time course, is used for rCBF estimation in ET, the correction factor for C(t) is the integral of its local AIF. The recipe to apply the correction factor is to divide C(t) by the integral of its local AIF to obtain the correct rCBF. By similar analysis, the correction factor for the maximum derivative (MD1) of C(t) is the maximum signal of AIF and the correction factor for the maximum second derivative (MD2) of C(t) is the maximum derivative of AIF. In the specific case of using normalized gamma-variate function as a model for AIF, the correction factor for C(t) (but not for MD1) at the time to reach the maximum derivative is relatively insensitive to the shape of the local AIF.