Input Function Research Articles

Development of quantitative PET/MR imaging for measurements of hepatic portal vein input function: a phantom study

BackgroundAccurate pharmacokinetic modelling in PET necessitates measurements of an input function, which ideally is acquired non-invasively from image data. For hepatic pharmacokinetic modelling two input functions need to be considered, to account for the blood supply from the hepatic artery and portal vein. Image-derived measurements at the portal vein are challenging due to its small size and image artifacts caused by respiratory motion. In this work we seek to demonstrate, using phantom experiments, how a dedicated PET/MR protocol can tackle these challenges and potentially provide input function measurements of the portal vein in a clinical setup.MethodsA custom 3D printed PET/MR phantom was constructed to mimic the liver and portal vein. PET/MR acquisitions were made with emulated respiratory motion. The PET/MR imaging protocol consisted of high-resolution anatomical MR imaging of the portal vein, followed by a PET acquisition in parallel to a dedicated motion-tracking MR sequence. Motion tracking and deformation information were extracted from PET data and subsequently used in PET reconstruction to produce dynamic series of motion-free PET images. Anatomical MR images were used post PET reconstruction for partial volume correction of the input function measurements.ResultsReconstruction of dynamic PET data with motion-compensation provided nearly motion-free series of PET frame data, suitable for image derived input function measurements of the portal vein. After partial volume correction, the individual input function measurements were within a 16.1% error range from the true activity in the portal vein compartment at the time of PET acquisition.ConclusionThe proposed protocol demonstrates clinically feasible PET/MR imaging of the liver for pharmacokinetic studies with accurate quantification of the portal vein input function, including correction for respiratory motion and partial volume effects.

Machine learning for predictive AAC: Improving speech and gesture-based communication systems

Augmentative and alternative communication (AAC) systems play a crucial role in supporting individuals with severe communication disabilities by providing accessible means of expression and engagement. However, many conventional AAC devices rely on manual input or basic predictive functions, which can limit communication efficiency and responsiveness. The application of machine learning (ML) to AAC offers new opportunities to enhance these systems, enabling them to provide faster, more accurate, and contextually relevant communication assistance. Advances in ML, particularly in predictive text, speech recognition, and gesture interpretation, allow AAC systems to adapt more intuitively to user needs, predicting intent based on usage patterns and multimodal data, such as voice and gestures. Current research highlights the potential of ML to address key gaps in AAC technology by creating more responsive, personalized systems that align with individual user behaviours. This study proposes a novel ML framework designed to integrate these capabilities, promising improvements in communication speed, user autonomy, and accuracy. By addressing the challenges and limitations of traditional AAC devices, this research aims to advance accessible communication solutions that empower users and improve quality of life.

[11C]Metoclopramide PET can detect a seizure-induced up-regulation of cerebral P-glycoprotein in epilepsy patients

BackgroundP-glycoprotein (P-gp) is an efflux transporter which is abundantly expressed at the blood-brain barrier (BBB) and which has been implicated in the pathophysiology of various brain diseases. The radiolabelled antiemetic drug [11C]metoclopramide is a P-gp substrate for positron emission tomography (PET) imaging of P-gp function at the BBB. To assess whether [11C]metoclopramide can detect increased P-gp function in the human brain, we employed drug-resistant temporal lobe epilepsy (TLE) as a model disease with a well characterised, regional P-gp up-regulation at the BBB.MethodsEight patients with drug-resistant (DRE) TLE, 5 seizure-free patients with drug-sensitive (DSE) focal epilepsy, and 15 healthy subjects underwent brain PET imaging with [11C]metoclopramide on a fully-integrated PET/MRI system. Concurrent with PET, arterial blood sampling was performed to generate a metabolite-corrected arterial plasma input function for kinetic modelling. The choroid plexus was outmasked on the PET images to remove signal contamination from the neighbouring hippocampus. Using a brain atlas, 10 temporal lobe sub-regions were defined and analysed with a 1-tissue-2-rate constant compartmental model to estimate the rate constants for radiotracer transfer from plasma to brain (K1) and from brain to plasma (k2), and the total volume of distribution (VT = K1/k2).ResultsDRE patients but not DSE patients showed significantly higher k2 values and a trend towards lower VT values in several temporal lobe sub-regions located ipsilateral to the epileptic focus as compared to healthy subjects (k2: hippocampus: +34%, anterior temporal lobe, medial part: +28%, superior temporal gyrus, posterior part: +21%).Conclusions[11C]Metoclopramide PET can detect a seizure-induced P-gp up-regulation in the epileptic brain. The efflux rate constant k2 seems to be the most sensitive parameter to measure increased P-gp function with [11C]metoclopramide. Our study provides evidence that disease-induced alterations in P-gp expression at the BBB can lead to changes in the distribution of a central nervous system-active drug to the human brain, which could affect the efficacy and/or safety of drugs. [11C]Metoclopramide PET may be used to assess or predict the contribution of increased P-gp function to drug resistance and disease pathophysiology in various brain diseases.Trial registrationEudraCT 2019-003137-42. Registered 28 February 2020.

GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

Measurement variability of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify the blood-brain barrier (BBB) permeability-surface area product. Serial measurements can indicate changes in BBB health, of interest to the study of normal physiology, neurological disease, and the effect of therapeutics. We performed a scan-rescan study to inform both sample size calculation for future studies and an appropriate reference change value for patient care. The final dataset included 28 healthy individuals (mean age 53.0 years, 82% female) scanned twice with mean interval 9.9 weeks. DCE-MRI was performed at 3T using a 3D gradient echo sequence with whole brain coverage, T1 mapping using variable flip angles, and a 16-min dynamic sequence with a 3.2-s time resolution. Segmentation of white and grey matter (WM/GM) was performed using a 3D magnetization-prepared gradient echo image. The influx constant Ki was calculated using the Patlak method. The primary outcome was the within-subject coefficient of variation (CV) of Ki in both WM and GM. Ki values followed biological expectations in relation to known GM/WM differences in cerebral blood volume (CBV) and consequently vascular surface area. Subject-derived arterial input functions showed marked within-subject variability which were significantly reduced by using a venous input function (CV of area under the curve 46 vs. 12%, p < 0.001). Use of the venous input function significantly improved the CV of Ki in both WM (30 vs. 59%, p < 0.001) and GM (21 vs. 53%, p < 0.001). Further improvement was obtained using motion correction, scaling the venous input function by the artery, and using the median rather than the mean of individual voxel data. The final method gave CV of 27% and 17% in WM and GM, respectively. No further improvement was obtained by replacing the subject-derived input function by one standard population input function. CV of Ki was shown to be highly sensitive to dynamic sequence duration, with shorter measurement periods giving marked deterioration especially in WM. In conclusion, measurement variability of 3D brain DCE-MRI is sensitive to analysis method and a large precision improvement is obtained using a venous input function.

Efficient fuzzy simulations for estimating the failure credibility of engineering structures under fuzzy environment

Efficiently and accurately solving the failure credibility of a structural system plays a significant role in the engineering field under fuzzy environment. A more precise failure credibility can help assess the safety degree of the system, and then reduce the occurrence of security accidents. At present, the extended fuzzy first-order and second-moment method (EFFOSM) is effective to analyze failure credibility for some occasions. However, when it comes to complicated performance functions and fuzzy inputs, the accuracy of EFFOSM is greatly reduced and its efficiency also needs to be improved. To overcome the above shortcomings, this paper proposes two types of novel fuzzy simulation algorithms, namely uniform discretization algorithm (UDA) and bisection simulation algorithm (BSA). For the system involving frequently-encountered continuous and strictly monotone performance functions of regular LR fuzzy interval inputs, these two algorithms are designed to estimate failure credibility with higher efficiency and accuracy. Subsequently, with the aid of the linearization and regularization procedures in EFFOSM, the application of UDA and BSA is extended to non-monotone performance functions of irregular LR fuzzy intervals. To evaluate and verify the performance of the proposed two algorithms, their comparisons with EFFOSM are conducted through some numerical examples and practical problems. The results show that the proposed two algorithms outperform EFFOSM in terms of accuracy and efficiency, and also have wider application range for estimating the failure credibility of strictly monotone performance functions involving regular LR fuzzy interval inputs. Meanwhile, BSA is slightly better than UDA for the less runtime.

A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems.

What does it mean for a complex system to "compute" or perform "computations"? Intuitively, we can understand complex "computation" as occurring when a system's state is a function of multiple inputs (potentially including its own past state). Here, we discuss how computational processes in complex systems can be generally studied using the concept of statistical synergy, which is information about an output that can only be learned when the joint state of all inputs is known. Building on prior work, we show that this approach naturally leads to a link between multivariate information theory and topics in causal inference, specifically, the phenomenon of causal colliders. We begin by showing how Berkson's paradox implies a higher-order, synergistic interaction between multidimensional inputs and outputs. We then discuss how causal structure learning can refine and orient analyses of synergies in empirical data, and when empirical synergies meaningfully reflect computation versus when they may be spurious. We end by proposing that this conceptual link between synergy, causal colliders, and computation can serve as a foundation on which to build a mathematically rich general theory of computation in complex systems.

Pharmacokinetic analysis and simplified uptake measures for tumour lesion [18F]F-AraG PET imaging in patients with non-small cell lung cancer.

The novel positron emission tomography (PET) imaging tracer, [18F]F-AraG, targets activated T-cells, offering a potential means to improve our understanding of immune-oncological processes. The aim of this study was to determine the optimal pharmacokinetic model to quantify tumour lesion [18F]F-AraG uptake in patients with non-small cell lung cancer (NSCLC), and to validate simplified measures at different time intervals against the pharmacokinetic uptake parameter. Ten patients with early-stage NSCLC and three patients with advanced NSCLC underwent a dynamic PET scan of minimal 60min. Venous and/or arterial blood sampling was obtained at maximum seven time points. Tumour lesion time activity curves and metabolite-corrected input functions were analysed using single-tissue reversible (1T2k), two-tissue irreversible (2T3k) and two-tissue reversible (2T4k) plasma input models. Simplified uptake measures, such as standardised uptake value (SUV) and tumour-to-blood (TBR) or tumour-to-plasma ratio (TPR), were evaluated for different time intervals. Whole-blood and plasma radioactivity concentrations showed rapid clearance of [18F]F-AraG. Metabolite analysis revealed a low rate of metabolism, at 70min p.i., on average, 79% (SD = 9.8%) of the total radioactivity found in blood corresponded to intact [18F]F-AraG. The time activity curves were best fitted by the 2T3k model. Strong positive correlations were found for SUV (body weight (BW), lean body mass (LBM) or body surface area (BSA) corrected), TBR and TPR for any time interval between 20 and 70min p.i. against the 2T3k-derived Ki. The correlation of TBR at 60-70min p.i. with 2T3K-derived Ki (r (df = 20) = 0.87, p < 0.01), was stronger than for SUVBW (r (df = 20) = 0.80, p < 0.01). Tumour lesion [18F]F-AraG uptake in patients with NSCLC is characterised by a 2T3k model. TBR and TPR show most potential for simplified quantification of tumour lesion [18F]F-AraG uptake in patients with NSCLC.

Ultrashort Oncologic Whole-Body [18F]FDG Patlak Imaging Using LAFOV PET.

Methods to shorten [18F]FDG Patlak PET imaging procedures ranging from 65-90 to 20-30 min after injection, using a population-averaged input function (PIF) scaled to patient-specific image-derived input function (IDIF) values, were recently evaluated. The aim of the present study was to explore the feasibility of ultrashort 10-min [18F]FDG Patlak imaging at 55-65 min after injection using a PIF combined with direct Patlak reconstructions to provide reliable quantitative accuracy of lung tumor uptake, compared with a full-duration 65-min acquisition using an IDIF. Methods: Patients underwent a 65-min dynamic PET acquisition on a long-axial-field-of-view (LAFOV) Biograph Vision Quadra PET/CT scanner. Subsequently, direct Patlak reconstructions and image-based (with reconstructed dynamic images) Patlak analyses were performed using both the IDIF (time to relative kinetic equilibrium between blood and tissue concentration (t*) = 30 min) and a scaled PIF at 30-60 min after injection. Next, direct Patlak reconstructions were performed on the system console using only the last 10 min of the acquisition, that is, from 55 to 65 min after injection, and a scaled PIF using maximum crystal ring difference settings of both 85 and 322. Tumor lesion and healthy-tissue uptake was quantified and compared between the differently obtained parametric images to assess quantitative accuracy. Results: Good agreement was obtained between direct- and image-based Patlak analyses using the IDIF (t* = 30 min) and scaled PIF at 30-60 min after injection, performed using the different approaches, with no more than 8.8% deviation in tumor influx rate value (Ki ) (mean difference ranging from -0.0022 to 0.0018 mL/[min × g]). When direct Patlak reconstruction was performed on the system console, excellent agreement was found between the use of a scaled PIF at 30-60 min after injection versus 55-65 min after injection, with 2.4% deviation in tumor Ki (median difference, -0.0018 mL/[min × g]; range, -0.0047 to 0.0036 mL/[min × g]). For different maximum crystal ring difference settings using the scan time interval of 55-65 min after injection, only a 0.5% difference (median difference, 0.0000 mL/[min × g]; range, -0.0004 to 0.0013 mL/[min × g]) in tumor Ki was found. Conclusion: Ultrashort whole-body [18F]FDG Patlak imaging is feasible on an LAFOV Biograph Vision Quadra PET/CT system without loss of quantitative accuracy to assess lung tumor uptake compared with a full-duration 65-min acquisition. The ultrashort 10-min direct Patlak reconstruction with PIF allows for its implementation in clinical practice.

Design of a Computational Intelligence System for Detection of Multiple Sclerosis with Visual Evoked Potentials

In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized.This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%.

Adaptive Neural Control for Hysteretic Nonlinear Systems With Hysteresis Neural Direct Inverse Compensator and Its Application.

Aiming at high precision control for a class of hysteretic nonlinear systems, a new hysteresis direct inverse compensator-based adaptive output feedback control scheme is designed in this article. First, a novel long short-term memory neural network (LSTMNN)-based hysteresis inverse compensator is established to compensate the asymmetric hysteresis nonlinearity, where the LSTMNN is used as the prediction mechanism for model operator weights, rather than the overall mapping of hysteresis input and output. Second, by designing the modified high-gain K-Filter states observer and the error transformed function, the unmeasurable states are estimated with arbitrarily small estimation error and the prespecified tracking performance is achieved. Lastly, the biconical dielectric elastomer actuator (DEA) motion platform is constructed. Then, the effectiveness of the proposed LSTMNN-based hysteresis inverse compensator and control scheme are verified on the experimental platform. The experimental results illustrate the effectiveness and advantages of proposed control scheme.

Nurses’ Experiences with the Use of Electronic Nursing Record: A Qualitative Study

Background: The electronic nursing record has contributed to improving nursing practice and ensuring nursing quality, but it is still incomplete in supporting nursing workflow. The purpose of this study is to explore the experience of using the electronic nursing record by nurses working in frontline care for trauma, emergency, and infectious disease-exposed patients.Methods: The study employed a qualitative descriptive design and qualitative content analysis. In-depth interviews were conducted with 10 nurses working in emergency, trauma, and critical care units in the tertiary hospital.Results: The nurses’ experiences with the use of the electronic nursing record emerged into three subjects (improvements to electronic nursing record implementation, benefits of electronic nursing record implementation, electronic nursing record user factors), five categories (information finding and sharing function, information input function, facilitating efficient communication, ensuring record quality, need for education), and eight subcategories.Conclusions: Electronic nursing record supports the visualization of special information such as emergency and infection, and has the advantage of facilitating communication between medical staff. Both support better clinical decision-making. However, system instability and low nursing friendliness delay nursing work in the process of inputting, confirming, and sharing information. Copy-andpaste practices, poor quality of records, and insufficient electronic nursing record education were identified as factors to be changed on the user side. Improvement of nursing-friendly interfaces, optimization of nursing workflows, and participation of nurses in the improvement process are necessary. We should also focus on developing electronic nursing record education programs.

Assessment of dual time point protocols to produce parametric Ki images in FDG PET/CT: A virtual clinical study.

This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP-Ki) scan protocol involving two 3-min/bed routine static PET scans and, subsequently, assessed DTP-Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0-93min dynamic PET data. Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N=360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP-Ki images were generated by performing standard linear indirect Patlak analysis with t* -min (Patlakt* = 12) using a scaled population-based input function (sPBIF) model on 66 combinations of early and late 3-min/bed static whole-body PET reconstructed images. All DTP-Ki images were evaluated against respective DTP-Ki images estimated with Patlakt* = 12 and 0-93min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0-93min iIFs and tissue time activity curves from all reconstructed WB passes 12-93min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP-Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2-min early and late frames. Moreover, the performance of DTP-Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients. High correlations(>0.9) were observed between DTP-Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP-Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p<0.05 using the two-tailed t-test by Williams) with the highest correlation attained for the 50-53-min early + 90-93-min late scan time frames (optimal DTP protocol). Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3-min/bed routine static scan using a population-based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP-Ki and gold-standard Ki images that are significantly larger than those between RI and gold-standard Ki.

Assessment of image-derived input functions from small vessels for patlak parametric imaging using total-body PET/CT.

The image-derived input function (IDIF) from the descending aorta has demonstrated performance comparable to arterial blood sampling while avoiding its invasive nature in parametric imaging. However, in conventional PET, large vessels may not always be within the imaging field of view (FOV). This study aims to evaluate the efficacy of dynamic parametric Ki imaging using image-derived input functions (IDIFs) extracted from various arteries, facilitated by total-body PET/CT. Twenty-three participants underwent a 60-minute total-body [18F]FDG PET scan. Data from each subject were used to reconstruct both total-body PET images and short-axis field-of-view PET images at different bed positions, each with a 25cm axial field-of-view (AFOV). Partial volume correction (PVC) was performed using the blurred Van Cittert iterative deconvolution. IDIFs extracted from the descending aorta, carotid artery, abdominal aorta, and iliac artery were employed for Patlak analysis. The resulting Ki images were compared using quantification indicators and subjective assessment. Linear regression analysis was conducted to examine the correlation of Ki values among IDIFs in normal organ and lesion regions of interest (ROIs). High similarities were observed in Ki images derived from the IDIFs from the descending aorta and other arteries, with a median structural similarity index measure (SSIM) above 0.98 and a median peak signal-to-noise ratio (PSNR) above 37dB. Linear regression analysis revealed strong correlations in Ki values (r² > 0.88) between the descending aorta and the three alternative vessels, with slopes of the linear fits close to 1. No significant difference in lesion detectability among IDIFs was found, as assessed visually and using metrics such as tumor-to-background ratio (TBR) and contrast-to-noise ratio (CNR) (P < 0.05). IDIFs from smaller vessels can reliably reconstruct parametric Ki images without compromising lesion detectability, providing clinically relevant information.

Generalized Lipschitz Tracing of Implicit Surfaces

AbstractWe present a versatile and robust framework to render implicit surfaces defined by black‐box functions that only provide function value queries. We assume that the input function is locally Lipschitz continuous; however, we presume no prior knowledge of its Lipschitz constants. Our pre‐processing step generates a discrete acceleration structure, a Lipschitz field, that provides data to infer local and directional Lipschitz upper bounds. These bounds are used to compute safe step sizes along rays during rendering. The Lipschitz field is constructed by generating local polynomial approximations to the input function, then bounding the derivatives of the approximating polynomials. The accuracy of the approximation is controlled by the polynomial degree and the granularity of the spatial resolution used during fitting, which is independent from the resolution of the Lipschitz field. We demonstrate that our process can be implemented in a massively parallel way, enabling straightforward integration into interactive and real‐time modelling workflows. Since the construction only requires function value evaluations, the input surface may be represented either procedurally or as an arbitrarily filtered grid of function samples. We query the original implicit representation upon ray trace, as such, we preserve the geometric and topological details of the input as long as the Lipschitz field supplies conservative estimates. We demonstrate our method on both procedural and discrete implicit surfaces and compare its exact and approximate variants.

Homotopy trust-region method for phase-field approximations in perimeter-regularized binary optimal control

We consider optimal control problems that have binary-valued control input functions and a perimeter regularization. We develop and analyze a trust-region algorithm that solves a sequence of subproblems in which the regularization term and the binarity constraint are relaxed by a non-convex energy functional. We show how the parameter that controls the distinctiveness of the resulting phase field can be coupled to the trust-region radius updates and be driven to zero over the course of the iterations in order to obtain convergence to points that satisfy a first-order optimality condition of the limit problem under suitable regularity assumptions. Finally, we highlight and discuss the assumptions and restrictions of our approach and provide the first computational results for a motivating application in the field of control of acoustic waves in dissipative media.

CT perfusion to measure venous outflow in acute ischemic stroke in patients with a large vessel occlusion

BackgroundRobust venous outflow (VO) profiles, measured by degree of venous opacification on pre-thrombectomy CT angiography (CTA) studies, are strongly correlated with favorable outcomes in patients with large vessel occlusion acute...

CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors

ObjectivesCT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methodsIn this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. ResultsIn the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. ConclusionExperimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net.

Distinct codes for environment structure and symmetry in postrhinal and retrosplenial cortices

Complex sensory information arrives in the brain from an animal’s first-person (‘egocentric’) perspective. However, animals can efficiently navigate as if referencing map-like (‘allocentric’) representations. The postrhinal (POR) and retrosplenial (RSC) cortices are thought to mediate between sensory input and internal maps, combining egocentric representations of physical cues with allocentric head direction (HD) information. Here we show that neurons in the POR and RSC of female Long-Evans rats are tuned to distinct but complementary aspects of local space. Egocentric bearing (EB) cells recorded in square and L-shaped environments reveal that RSC cells encode local geometric features, while POR cells encode a more global account of boundary geometry. Additionally, POR HD cells can incorporate egocentric information to fire in two opposite directions with two oppositely placed identical visual landmarks, while only a subset of RSC HD cells possess this property. Entorhinal grid and HD cells exhibit consistently allocentric spatial firing properties. These results reveal significant regional differences in the neural encoding of spatial reference frames.

Impact of different parametric Patlak imaging approaches and comparison with a 2-tissue compartment pharmacokinetic model with a long axial field-of-view (LAFOV) PET/CT in oncological patients.

The recently introduced Long-Axial-Field-of-View (LAFOV) PET-CT scanners allow for the first-time whole-body dynamic- and parametric imaging. Primary aim of this study was the comparison of direct and indirect Patlak imaging as well as the comparison of different time frames for Patlak calculation with the LAFOV PET-CT in oncological patients. Secondary aims of the study were lesion detectability and comparison of Patlak analysis with a two-tissue-compartment model (2TCM). 50 oncological patients with 346 tumor lesions were enrolled in the study. All patients underwent [18F]FDG PET/CT (skull to upper thigh). Here, the Image-Derived-Input-Function) (IDIF) from the descending aorta was used as the exclusive input function. Four sets of images have been reviewed visually and evaluated quantitatively using the target-to-background (TBR) and contrast-to-noise ratio (CNR): short-time (30min)-direct (STD) Patlak Ki, short-time (30min)-indirect (STI) Patlak Ki, long-time (59.25min)-indirect (LTI) Patlak Ki, and 50-60min SUV (sumSUV). VOI-based 2TCM was used for the evaluation of tumor lesions and normal tissues and compared with the results of Patlak model. No significant differences were observed between the four approaches regarding the number of tumor lesions. However, we found three discordant results: a true positive liver lesion in all Patlak Ki images, a false positive liver lesion delineated only in LTI Ki which was a hemangioma according to MRI and a true negative example in a patient with an atelectasis next to a lung tumor. STD, STI and LTI Ki images had superior TBR in comparison with sumSUV images (2.9-, 3.3- and 4.3-fold higher respectively). TBR of LTI Ki were significantly higher than STD Ki. VOI-based k3 showed a 21-fold higher TBR than sumSUV. Parameters of different models vary in their differential capability between tumor lesions and normal tissue like Patlak Ki which was better in normal lung and 2TCM k3 which was better in normal liver. 2TCM Ki revealed the highest correlation (r = 0.95) with the LTI Patlak Ki in tumor lesions group and demonstrated the highest correlation with the STD Patlak Ki in all tissues group and normal tissues group (r = 0.93 and r = 0.74 respectively). Dynamic [18F]-FDG with the new LAFOV PET/CT scanner produces Patlak Ki images with better lesion contrast than SUV images, but does not increase the lesion detection rate. The time window used for Patlak imaging plays a more important role than the direct or indirect method. A combination of different models, like Patlak and 2TCM may be helpful in parametric imaging to obtain the best TBR in the whole body in future.