Equivalent methods to analyse dynamic experiments in which the input function is noisy

Optimal Sampling Schedule for Both the Input and Output Functions Sampled Simultaneously

It has been suggested that the power law J = an, describing the relationship between numerical magnitude judgments and physical magnitudes, confounds a sensory or input function with an output function flawing to do with O’s use of numbers. Judged magnitudes of differences between stimuli offer some opportunity for separating these functions. We obtained magnitude judgments of differences between paired weights, as well as magnitude judgments of the weights making up the pairs. From the former we calculated simultaneously an input exponent and an output exponent, working upon Attneave’s assumption that both transformations are describable as power functions. The inferred input and output functions, in combination, closely predict the judgments of individual weights by the same Os. Although pooled data (geometric means of judgments) conform fairly well to a linear output function, individual data do not; i.e., individual Os deviate quite significantly fromlinearity and from one another in their use of numbers. Individual values of the inferred sensory exponent, k, show significantly better uniformity over Os than do values of the phenotypica! magnitude exponent previously found to describe interval judgments of weight.

In dynamic cardiac PET FDG studies the assessment of myocardial metabolic rate of glucose (MMRG) requires the knowledge of the blood input function (IF). IF can be obtained by manual or automatic blood sampling and cross calibrated with PET. These procedures are cumbersome, invasive and generate uncertainties. The IF is contaminated by spillover of radioactivity from the adjacent myocardium and this could cause important error in the estimated MMRG. In this study, we show that the IF can be extracted from the images in a rat heart study with 18F-fluorodeoxyglucose (18F-FDG) by means of Independent Component Analysis (ICA) based on Bayesian theory and Markov Chain Monte Carlo (MCMC) sampling method (BICA). Images of the heart from rats were acquired with the Sherbrooke small animal PET scanner. A region of interest (ROI) was drawn around the rat image and decomposed into blood and tissue using BICA. The Statistical study showed that there is a significant difference (p < 0.05) between MMRG obtained with IF extracted by BICA with respect to IF extracted from measured images corrupted with spillover.

A method has been developed to quantitate regional cerebral blood blow (rCBF) using iodine-123-labelled N-isopropyl-p-iodoamphetamine (IMP). This technique requires only two single-photon emission tomography (SPET) scans and one blood sample. Based on a two-compartment model, radioactivity concentrations in the brain for each scan time (early: t(e); delayed: td are described as: [formula: see text] respectively, where x denotes the convolution integral; Ca(t), the arterial input function; f, rCBF; and Vd, the regional distribution volume of IMP. Calculation of the ratio of the above two equations and a "table look-up" procedure yield a unique pair of rCBF and Vd for each region of interest (ROI). A standard input function has been generated by combining the input functions from 12 independent studies prior to this work to avoid frequent arterial blood sampling, and one blood sample is taken at 10 min following IMP administration for calibration of the standard arterial input function. This calibration time was determined such that the integration of the first 40 min of the calibrated, combined input function agreed best with those from 12 individual input functions (the difference was 5.3% on average). This method was applied to eight subjects (two normals and six patients with cerebral infarction), and yielded rCBF values which agreed well with those obtained by a positron emission tomography H2(15)O autoradiography method. This method was also found to provide rCBF values that were consistent with those obtained by the non-linear least squares fitting technique and those obtained by conventional microsphere model analysis. The optimum SPET scan times were found to be 40 and 180 min for the early and delayed scans, respectively. These scan times allow the use of a conventional rotating gamma camera for clinical purposes. Vd values ranged between 10 and 40 ml/g depending on the pathological condition, thereby suggesting the importance of measuring Vd for each ROI. In conclusion, optimization of the blood sampling time and the scanning time enabled quantitative measurement of rCBF with two SPET scans and one blood sample.

PurposeDigital transformations are altering service models and care delivery methods in healthcare. Artificial Intelligence (AI) represents the next wave of transformation in healthcare. This study aims to understand patient perceptions of AI and its impact on value co-creation.Design/methodology/approachA conceptual model was developed to investigate how value co-creation operant resources (digital self-efficacy and relational service quality) impact value co-creation engagement (shared decision-making) and value co-creation outcomes (anticipatory AI value co-creation and intention to adopt AI). Data were collected from 332 respondents and analyzed using structural equation modeling.FindingsThe results indicate that the value co-creation process for AI technologies is a function of inputs, experiences and AI outputs. Operant resources were found to be positively associated with shared decision-making. However, not all operant resources directly and positively impacted AI outcomes. The indirect and positive mediated relationships through shared decision-making to AI outcomes suggest an interactive AI value co-creation process.Research limitations/implicationsAI technologies are still in early stages of consumer adoption in healthcare. Future research is warranted that investigates the validity of the model through maturing service life cycles.Practical implicationsCustomer perceptions of new digital innovations are formed in the context of previous digital experiences. Marketers need to understand how customers view their current non-AI technologies. Strong engagement and perceived value of current technologies will help ease customers into the usage of AI technologies.Originality/valueThis study investigates the unique stages of the value co-creation process for AI technologies in healthcare. The results demonstrate that the value co-creation process is a function of inputs, tech-enabled experiences and AI outputs.

Fluorine-18 3'-deoxy-3'-fluorothymidine (18FLT) is a tissue proliferation marker which has been suggested as a new tumour-specific imaging tracer in positron emission tomography (PET). The objectives of this study were to investigate the pharmacokinetics of 18FLT in patients with colorectal cancer, defining methodologies for the quantitative analysis of the in vivo 18FLT uptake and subsequently assessing the accuracy of semi-quantitative measures. Dynamic acquisitions over a single field of view of interest identified by computed tomography were carried out for up to 60 min following injection of 18FLT (360 +/- 25 MBq). Dynamic arterial blood sampling was carried out in order to provide a blood input function. Simultaneous venous samples were also taken in order to investigate their potential utilisation in deriving a hybrid input function. Arterial and venous blood samples at 5, 15, 30, 60 and 90 min p.i. were used for metabolite analysis. Eleven patients with primary and/or metastatic colorectal cancer were studied on a lesion by lesion basis (n = 21). All acquired images were reconstructed using ordered subsets expectation maximisation and segmented attenuation correction. Time-activity curves were derived by image region of interest (ROI) analysis and image-based input functions were obtained using abdominal or thoracic aorta ROIs. Standardised uptake values (SUVs) were calculated to provide semi-quantitative indices of uptake, while non-linear regression (NLR) methodology in association with a three-compartment model and Patlak analysis were carried out to derive the net influx constant Ki. The metabolite analysis revealed two radioactive metabolites, with the parent compound representing approximately 80% of the total radioactivity in the 30-min plasma sample. In the case of NLR, better fits were obtained with a 3k model (i.e. k4 = 0) for both lesion and bone marrow time-activity curves. For the same lesions, a high correlation was observed between the Ki derived from either Patlak analysis or NLR(3k) and the corresponding SUVs. Our results also suggest that the quantitative behaviour of 18FLT in vivo (up to 60 min p.i.) may be characterised using a 3k model or Patlak analysis in combination with image-derived input functions. The good correlation found between the SUVs (at 60 min) and Ki values supports the use of semi-quantitative indices to assess the proliferation rate of colorectal cancer lesions in vivo with 18FLT.

BackgroundAccurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling.MethodsDynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35–65, 40–65, 45–65, 50–65, and 55–65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed.ResultsThere was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55–65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55–65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB.ConclusionWe demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

Kinetic modelling in PET requires the arterial input function (AIF), defined as the time-activity curve (TAC) in plasma. This measure is challenging to obtain in mice due to low blood volumes, resulting in a reliance on image-based methods for AIF derivation. We present a comparison of PET- and MR-based region-of-interest (ROI) analysis to obtain image-derived AIFs from the left ventricle (LV) of a mouse model. ROI-based partial volume correction (PVC) was performed to improve quantification. MRI and dynamic PET images were obtained from a recent study investigating treatment effects in 12 mice following myocardial infarction [1], where half the mice received a new treatment and half did not. Prospectively gated MRI (4.7T Bruker BioSpec, FLASH TR/TE 400/3ms, spatial resolution 140μm in 1mm slices) were acquired prior to PET acquisition (approx. 25MBq 18F-FDG bolus, 45 minute emission listmode acquisition reconstructed with 3DRP in four cardiac frames) on a split-magnet PET camera [2]. Images were co-registered using SPMMouse [3] (see Figure ​Figure11). Figure 1 Mouse heart MR (left) and fused with 18F-FDG static PET (right). Arrow indicates infarcted region. AIF extraction AIFs were obtained by taking mean time courses from LV Lumen ROIs, shown in Figure ​Figure2.2. The regional geometric transfer matrix (GTM) method was applied for PVC [4], using ROIs drawn on either the co-registered MR images or directly onto the last dynamic frame PET images. ROIs covered LV lumen, myocardium, lungs/body and background. Patlak [5] analysis was performed to evaluate glucose metabolism. Figure 2 AIFs and TACs derived for single subject using (A) MR ROIs, (B) PET ROIs with PVC and (C) MR ROIs with PVC. Insets detail first 500s Uncorrected AIFs and myocardial TACs produced by manual ROI delineation displayed contamination with myocardial signal. AIFs and myocardial curves became distinguishable if GTM PVC was applied. Only MR-based PVC produced significant differences (p<0.05) in Ki values between the treated and untreated groups (see Table ​Table11). Table 1 GTM-based PVC gives best results in mice when ROIs are based on MRI data, due to its high-resolution and excellent soft-tissue contrast.

Chapter 48 - Simultaneous Extraction of Physiological and Input Function Parameters from PET Measurements

Anatomic and functional aspects of biologic processes are studied with multi-modality imaging. PET can perform this in-vivo and almost real-time. The dynamics and kinetics of new drugs, their optimal dose and individual response need to be determined. In human cancer, non-responders to therapy need to be separated from responders, and patients surveyed to detect recurrence. Objective: We explored the potential of Factor Analysis (FA) by extracting vessels and tissues from the dynamic images. With thresholding techniques a volume-of-interest (VOI) is delineated and regional blood clearance (input function) and tissue uptake (output function) measured. Kinetic Modeling (KM) estimates the rate constants between compartments representing the underlying biologic pathways. Methods: Tracers used were: <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> C-acetate, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> N-ammonia, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-fluoride, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FDOPA, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FLT and <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FDG. Dynamic studies were acquired with high temporal sampling during the first 2 minutes, decreasing over the next phase to 5 min frames. Total image acquisition varied from 20–75 min dependent on tracer and organ investigated. Breast and prostate cancer, brain tumor, myocardial perfusion, and osteoporosis were evaluated. Results: FA was successful in all cases and produced clear parametric images. Accurate input functions could be derived for all situations. Tissue or tumor uptake can be low and images noisy, yielding sub-optimal output functions. Comparison of the FA input function to plasma blood samples in breast cancer showed excellent concordance. KM provided rate constants that did not reveal significant differences between input curves from blood samples vs FA derived. KM may help elucidating the metabolic pathways, e.g. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FDOPA revealed a different mechanism for tumor than striatum. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> C-acetate kinetics in prostate cancer differ completely from those in myocardium. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FLT uptake reflects cell proliferation and shows the greatest decrease within 1 week after treatment in responders. Conclusion:Our procedure is automated and virtually user independent; this processing tool facilitates PET as a reproducible quantification method in routine clinical applications.

To investigate regional airways obstruction in patients with cystic fibrosis (CF) with quantitative analysis of dynamic hyperpolarized (HP) (3)He MRI. Dynamic radial projection MRI of HP (3)He gas was used to study respiratory dynamics in a group of eight children with CF. Signal kinetics in a total of seven regions of interest (ROIs; three in each lung, and one in the trachea) were compared with the results of spirometric pulmonary function tests (PFTs). The tracheal signal intensity was used as a form of "input function" to normalize for input flow effects. A pattern of low flow rate in the upper lobes was observed. When the flow measurements from the peripheral ROIs were averaged to obtain an index of flow in the peripheral lung, a good correlation was found (P = 3.74 x 10(-5)) with the forced expired volume in one second (FEV1). These results suggest that a quantitative measurement of localized airways obstruction in the early stages of CF may be obtained from dynamic (3)He MRI by using the slope of the signal rise as a measure of air flow into the peripheral lung. This study also demonstrates that children can cooperate well with the (3)He MRI technique.

By the spleen, we calculated a time-delay correction of the input function for quantitation of hepatic blood flow with oxygen-15 water and dynamic positron emission tomography. The time delay (deltat) between the sample site and the spleen was calculated based on nonlinear multiple regression analysis when splenic blood flow was determined. Then hepatic blood flow was quantified by a method using the input function and incorporating deltat, which was assumed to be equal to the time delay between the sample site and the liver. Then hepatic arterial and portal blood flows were estimated separately as well as the delay time for passage within the organs of the portal circulation. The mean coefficient of variation and the mean sum of squares of errors decreased to about 70% when total hepatic blood flow was calculated from the results for regions of interest in three slices of the same liver segment. We concluded that using the spleen for time-delay correction of the input function for measuring hepatic blood flow by this method gave satisfactory results.

The relationships between tissue tracer concentrations, length of time of tissue contact with film, and the darkness of resulting autoradiographic images were explored. Operational equations were then developed relating ranges of film darkening to the ranges of tracer concentration contained within the sections. These equations were solved and used to define ranges of optical density that are optimal for precise determination of tracer concentration using digital image analyzers. The solutions indicated that optimal optical densities are a function of the range of tracer concentrations in the sections. For autoradiograms of typical cerebral blood flow and metabolism tracers, exposure should be controlled to produce images that are significantly less dark than what is generally considered pleasing to the eye.

Quantitative kinetic modeling requires an input function. A noninvasive image-derived input function (IDIF) can be obtained from dynamic PET images. However, a robust IDIF location (e.g., aorta) may be far from a tissue of interest, particularly in total-body PET, introducing a time delay between the IDIF and the tissue. The standard practice of joint estimation (JE) of delay, along with model fitting, is computationally expensive. To improve the efficiency of delay correction for total-body PET parametric imaging, this study investigated the use of pulse timing methods to estimate and correct for delay. Methods: Simulation studies were performed with a range of delay values, frame lengths, and noise levels to test the tolerance of 2 pulse timing methods-leading edge (LE) and constant fraction discrimination and their thresholds. The methods were then applied to data from 21 subjects (14 healthy volunteers, 7 cancer patients) who underwent a 60-min dynamic total-body 18F-FDG PET acquisition. Region-of-interest kinetic analysis was performed and parametric images were generated to compare LE and JE methods of delay correction, as well as no delay correction. Results: Simulations demonstrated that a 10% LE threshold resulted in biases and SDs at tolerable levels for all noise levels tested, with 2-s frames. Pooled region-of-interest-based results (n = 154) showed strong agreement between LE (10% threshold) and JE methods in estimating delay (Pearson r = 0.96, P < 0.001) and the kinetic parameters vb (r = 0.96, P < 0.001), Ki (r = 1.00, P < 0.001), and K1 (r = 0.97, P < 0.001). When tissues with minimal delay were excluded from pooled analyses, there were reductions in vb (69.4%) and K1 (4.8%) when delay correction was not performed. Similar results were obtained for parametric images; additionally, lesion Ki contrast was improved overall with LE and JE delay correction compared with no delay correction and Patlak analysis. Conclusion: This study demonstrated the importance of delay correction in total-body PET. LE delay correction can be an efficient surrogate for JE, requiring a fraction of the computational time and allowing for rapid delay correction across more than 106 voxels in total-body PET datasets.

The intelligent experimental data processing system is designed and implemented by using Matlab. This system has functions of data input and output, intelligent processing, etc. Its processing functions cover most of the items in physical experiments. Strengthen ideological and political education, optimize the pattern of education to build a platform for all employees, improve the effectiveness of the whole process of education, and achieve the goal of all-round education. Theoretically, it gives a comprehensive explanation of “Three Comprehensive Education”, and according to the new requirements and new background of the Party and the government for the establishment of morality in colleges and universities in the new era, combines theory and practice, and sorts out the basic concept of “Three Comprehensive Education”. Explain the inner connection between “Three Complete Education” and Lide S huren.