Input Function Research Articles

A prospective cohort study on the association between new falls and balancing ability among older adults over 80 years who are independent

ObjectiveThe purpose of this study was to prospectively investigate the relationship between new falls and the balancing ability of older adults aged ≥80 years who are independent and evaluate the validity of the assessment tools as a predictor of falls. MethodsWe enrolled a total of 160 participants (104 males and 56 females) aged 80 years or older. During the 12 months of observation, we investigated underlying diseases and drug use and performed a comprehensive geriatric assessment (including self-care ability, muscle strength, action ability, cognition, emotional state, and other aspects), as well as computerized dynamic posturography to assess balance and gait functions. We further analyzed the relationship between new falls and multiple internal risk factors. ResultsA total of 159 participants were included for statistical analysis, and there were 108 new falls among the 59 participants. Fall history and visual preference (PREF) scores on the sensory integration test showed a positive correlation with new falls. The composite equilibrium score (SOTcom), left total hip bone mineral density, left directional control, and end point deviation were all found to be negatively correlated with new falls (P < 0.05). The cut-off point of the timed “up and go” test (TUG) in predicting new falls in this cohort was >12.03 s, with a sensitivity of 78.0 %, a specificity of 51.5 %, and an AUC of 0.667 (P < 0.001, 95 % CI: 0.567–0.721). The cut-off point of SOTcom in predicting new falls was ≤52, with a sensitivity of 40.7 %, a specificity of 84.0 %, and an AUC of 0.606 (P = 0.028, 95 % CI: 0.525–0.682). ConclusionsThe decline of balance sensory input function (mainly vestibular and visual sense), skeletal muscle motor function, and related postural control ability constituted the main risk factors for new falls in older adults who were independent. The combined use of TUG and SOT was useful in further improving the accuracy of predicting new falls in this population and providing a direction for effective intervention and rehabilitation measures.

Ankylosing spondylitis PET imaging and quantifications via P2X7 receptor-targeting radioligand [18F]GSK1482160.

Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial spine; however, the quantitative detection of inflammation in AS remains a challenge in clinical settings. We aimed to investigate the feasibility of using a specific P2X7R-targeting 18F-labeled tracer [18F]GSK1482160 for positron emission tomography (PET) imaging and the quantification of AS. The radioligand [18F]GSK1482160 was obtained based on nucleophilic aliphatic substitution. Dynamic [18F]GSK1482160 and [18F]FDG micro-PET/CT imaging were performed on AS mice (n = 8) and age-matched controls (n = 8). Tracer kinetics modeling was performed using Logan's graphical arterial input function analysis to quantify the in vivo expression of P2X7R. The post-PET tissues were collected for hematoxylin-eosin(H&E), immunohistochemical (IHC), and immunofluorescence (IF) staining. [18F]GSK1482160 PET/CT imaging revealed that the specific binding in the ankle joint and sacroiliac joint (SIJ) of the AS at 8weeks group (BPNDankle-AS-8W (non-displaceable binding potential of the ankle) 3.931 ± 0.74; BPND SIJ-AS-8W (BPBD of the SIJ) 4.225 ± 0.84) were significantly higher than the controls at 8weeks group (BPNDankle-Ctr-8W 0.325 ± 0.15, BPNDSJJ-Ctr-8W 0.319 ± 0.17) respectively, and the AS at 14weeks group (BPNDankle-AS-14W 12.212 ± 2.25; BPNDSJJ-AS-14W 13.389 ± 3.60) were significantly higher than the controls at 14weeks group (BPNDankle-Ctr-14W 0.204 ± 0.16, BPNDSJJ-Ctr-14W 0.655 ± 0.35) respectively. The four groups had no significant difference in the [18F]FDG uptake of ankle and SIJ. IHC and IF staining revealed that the overexpression of P2X7R was colocalized with activated macrophages from the ankle synovium and spinal endplate in mice with AS, indicating that quantification of P2X7R may contribute to the understanding of the pathogenesis of inflammation in human AS. This study developed a novel P2X7R-targeting PET tracer [18F]GSK1482160 to detect the expression of P2X7R in AS mouse models and provided powerful non-invasive PET imaging and quantification for AS.

18F]FNDP PET neuroimaging test-retest repeatability and whole-body dosimetry in humans.

Soluble epoxide hydrolase (sEH) is an enzyme that shapes immune signaling through its role in maintaining the homeostasis of polyunsaturated fatty acids and their related byproducts. [18F]FNDP is a radiotracer developed for use with positron emission tomography (PET) to image sEH, which has been applied to imaging sEH in the brains of healthy individuals. Here, we report the test-retest repeatability of [18F]FNDP brain PET binding and [18F]FNDP whole-body dosimetry in healthy individuals. Seven healthy adults (4 men, 3 women, ages 40.1 ± 4.6years) completed [18F]FNDP brain PET on two occasions within a period of 14days in a test-retest study design. [18F]FNDP regional total distribution volume (VT) values were derived from modeling time-activity data with a metabolite-corrected arterial input function. Test-retest variability, mean absolute deviation, and intraclass correlation coefficient (ICC) were investigated. Six other healthy adults (3 men, 3 women, ages 46.0 ± 7.0years) underwent [18F]FNDP PET/CT for whole-body dosimetry, which was acquired over 4.5h, starting immediately after radiotracer administration. Organ-absorbed doses and the effective dose were then estimated. The mean test-retest difference in regional VT (ΔVT) was 0.82 ± 5.17%. The mean absolute difference in regional VT was 4.01 ± 3.33%. The ICC across different brain regions ranged from 0.92 to 0.99. The organs with the greatest radiation-absorbed doses included the gallbladder (0.081 ± 0.024mSv/MBq), followed by liver (0.077 ± 0.018mSv/MBq) and kidneys (0.063 ± 0.006mSv/MBq). The effective dose was 0.020 ± 0.003mSv/MBq. These data support a favorable test-retest repeatability of [18F]FNDP brain PET regional VT. The radiation dose to humans from each [18F]FNDP PET scan is similar to that of other 18F-based PET radiotracers.

Bone Turnover in Patients with Chronic Kidney Disease Stage 5D and Healthy Controls - a Quantitative [18F]Fluoride PET Study.

Chronic kidney disease (CKD) is prevalent in the aging population and increases the risk of fracture 2-4 times. We compared optimized quantitative [18F]fluoride PET/CT methods to the reference standard with arterial input function (AIF) to identify a clinically accessible method for evaluation of bone turnover in patients with CKD. Ten patients on chronic hemodialysis treatment and ten control patients were recruited. A dynamic 60-min [18F]fluoride PET scan was obtained from the 5th lumbar vertebra to the proximal femur simultaneously with arterial blood sampling to achieve an AIF. Individual AIFs were time-shifted to compute a population curve (PDIF). Bone and vascular volumes-of-interest (VOIs) were drawn, and an image-derived-input-function (IDIF) was extracted. PDIF and IDIF were scaled to plasma. Bone turnover (Ki) was calculated with the AIF, PDIF, and IDIF and bone VOIs using a Gjedde-Patlak plot. Input methods were compared using correlations and precision errors. The calculated Ki from the five non-invasive methods all correlated to the Ki from the AIF method with the PDIF scaled to a single late plasma sample showing the highest correlations (r > 0.94), and the lowest precision error of 3-5%. Furthermore, the femoral bone VOI's correlated positively to p-PTH and showed significant differences between patients and controls. Dynamic 30 min [18F]fluoride PET/CT with a population based input curve scaled to a single venous plasma sample is a feasible and precise non-invasive diagnostic method for the assessment of bone turnover in patients with CKD. The method may potentially allow for earlier and more precise diagnosis and may be useful for assessment of treatment effects, which is crucial for development of future treatment strategies.

Transit time estimation of drying springs in Uttarakhand region using environmental tritium concentration

Many parts of the Uttarakhand state, situated in the hilly terrain of the Himalayan region of India are facing acute water crisis due to the drying up of the perennial springs which are the only source of potable water in those regions. Tritium (3H), the radioactive isotope of hydrogen (half-life of 12.32 years) and also a part of water molecule (in the form of HTO) acts as a very useful tracer in estimating the transit time of the hydrological systems. Tritium concentrations of three springs (S-1, S-2 and S-3) were monitored consecutively for three years (2017–2019) to better constrain the transit time estimation. The tritium concentrations of the springs are found to vary between 3.66 and 4.15 TU. All the springs show gradual decrease in tritium concentration with the passage of time indicating the diminishing percentage of freshly recharged modern water component. Among various lumped parameter models, the piston-flow model (PFM), exponential mixing model (EMM), exponential piston-flow model (EPM) and partial exponential mixing model (PEM) have been employed in this study. The historical record of weighted mean concentration of tritium in precipitation available for the Uttarakhand region is taken as input function in the modelling procedure. The application of various LPMs (PFM, EMM, EPM and PEM) indicates that the transit time of the S-1 spring ranges from 1.26 to 1.46 years whereas for S-2 spring, the transit time is found to vary from 5 months to 1.1 years. The MTT of S-3 spring ranges from 5 months to 11 months. The relatively short residence time of these springs indicates the actively recharged system. The estimation of accurate transit time is thus very crucial for understanding the renewability of the spring water systems.

Assessing the impacts of past and ongoing deforestation on rainfall patterns in South America.

Despite recent advances in modeling forest-rainfall relationships, the current understanding of changes in observed rainfall patterns resulting from historical deforestation remains limited. To address this knowledge gap, we analyzed how 40 years of deforestation has altered rainfall patterns in South America as well as how current Amazonian forest cover sustains rainfall. First, we develop a spatiotemporal neural network model to simulate rainfall as a function of vegetation and climate inputs in South America; second, we assess the rainfall effects of observed deforestation in South America during the periods 1982-2020 and 2000-2020; third, we assess the potential rainfall changes in the Amazon biome under two deforestation scenarios. We find that, on average, cumulative deforestation in South America from 1982 to 2020 has reduced rainfall over the period 2016-2020 by 18% over deforested areas, and by 9% over non-deforested areas across South America. We also find that more recent deforestation, that is, from 2000 to 2020, has reduced rainfall over the period 2016-2020 by 10% over deforested areas and by 5% over non-deforested areas. Deforestation between 1982 and 2020 has led to a doubling in the area experiencing a minimum dry season of 4 months in the Amazon biome. Similarly, in the Cerrado region, there has been a corresponding doubling in the area with a minimum dry season of 7 months. These changes are compared to a hypothetical scenario where no deforestation occurred. Complete conversion of all Amazon forest land outside protected areas would reduce average annual rainfall in the Amazon by 36% and complete deforestation of all forest cover including protected areas would reduce average annual rainfall in the Amazon by 68%. Our findings emphasize the urgent need for effective conservation measures to safeguard both forest ecosystems and sustainable agricultural practices.

Enhancing Conditional Stalling to Boost Performance of Stream-Processing Logic With RAW Dependencies

Ambiguous read-after-Write (RAW) dependencies are omnipresent in multiple streaming applications, establishing hard to optimize bottlenecks. Considering actual input data, these may rarely be true dependencies. However, the increasingly used High-Level Synthesis (HLS) compilers must assume the worst-case scenario, as they rely on static optimizations. Conditional stalling is a simple yet impactful technique, useful even when conflicts are common. At the cost of a small area penalty, it allows improving (in some cases, by several times) the mean throughput of these systems. In this brief, we describe a high-frequency HLS implementation of the technique and examine its behavior as a function of input and architecture characteristics, with the goal of understanding when to use it and how to optimize throughput.

Spatiotemporal Learning of Dynamic Positron Emission Tomography Data Improves Diagnostic Accuracy in Breast Cancer

Positron emission tomography (PET) is a noninvasive imaging technology able to assess the metabolic or functional state of healthy and/or pathological tissues. In clinical practice, PET data are usually acquired statically and normalized for the evaluation of the standardized uptake value (SUV). In contrast, dynamic PET acquisitions provide information about radiotracer delivery to tissue, its interaction with the target and its physiological washout. The shape of the time activity curves (TACs) embeds tissue-specific biochemical properties. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function, and tracer-specific compartmental models to obtain a full quantitative analysis of PET data. This method’s primary disadvantage is the requirement for invasive arterial blood sample collection throughout the whole PET scan. In this study, we employ a variety of deep learning models to illustrate the diagnostic potential of dynamic PET acquisitions of varying lengths for discriminating breast cancer lesions in the absence of arterial blood sampling compared to static PET only. Our findings demonstrate that the use of TACs, even in the absence of arterial blood sampling and even when using only a share of all timeframes available, outperforms the discriminative ability of conventional SUV analysis.

Generalized neural closure models with interpretability

Improving the predictive capability and computational cost of dynamical models is often at the heart of augmenting computational physics with machine learning (ML). However, most learning results are limited in interpretability and generalization over different computational grid resolutions, initial and boundary conditions, domain geometries, and physical or problem-specific parameters. In the present study, we simultaneously address all these challenges by developing the novel and versatile methodology of unified neural partial delay differential equations. We augment existing/low-fidelity dynamical models directly in their partial differential equation (PDE) forms with both Markovian and non-Markovian neural network (NN) closure parameterizations. The melding of the existing models with NNs in the continuous spatiotemporal space followed by numerical discretization automatically allows for the desired generalizability. The Markovian term is designed to enable extraction of its analytical form and thus provides interpretability. The non-Markovian terms allow accounting for inherently missing time delays needed to represent the real world. Our flexible modeling framework provides full autonomy for the design of the unknown closure terms such as using any linear-, shallow-, or deep-NN architectures, selecting the span of the input function libraries, and using either or both Markovian and non-Markovian closure terms, all in accord with prior knowledge. We obtain adjoint PDEs in the continuous form, thus enabling direct implementation across differentiable and non-differentiable computational physics codes, different ML frameworks, and treatment of nonuniformly-spaced spatiotemporal training data. We demonstrate the new generalized neural closure models (gnCMs) framework using four sets of experiments based on advecting nonlinear waves, shocks, and ocean acidification models. Our learned gnCMs discover missing physics, find leading numerical error terms, discriminate among candidate functional forms in an interpretable fashion, achieve generalization, and compensate for the lack of complexity in simpler models. Finally, we analyze the computational advantages of our new framework.

Evaluation and timing optimization of CT perfusion first pass analysis in comparison to maximum slope model in pancreatic adenocarcinoma

For implementation, performance evaluation and timing optimization of CT perfusion first pass analysis (FPA) by correlation with maximum slope model (MSM) in pancreatic adenocarcinoma, dynamic CT perfusion acquisitions of 34 time-points were performed in 16 pancreatic adenocarcinoma patients. Regions of interest were marked in both parenchyma and carcinoma. FPA, a low radiation exposure CT perfusion technique, was implemented. Blood flow (BF) perfusion maps were calculated using FPA and MSM. Pearson’s correlation between FPA and MSM was calculated at each evaluated time-point to determine optimum timing for FPA. Differences in BF between parenchyma and carcinoma were calculated. Average BF for MSM was 106.8 ± 41.5 ml/100 ml/min in parenchyma and 42.0 ± 24.8 ml/100 ml/min in carcinoma, respectively. For FPA, values ranged from 85.6 ± 37.5 ml/100 ml/min to 117.7 ± 44.5 ml/100 ml/min in parenchyma and from 27.3 ± 18.8 ml/100 ml/min to 39.5 ± 26.6 ml/100 ml/min in carcinoma, depending on acquisition timing. A significant difference (p value < 0.0001) between carcinoma and parenchyma was observed at all acquisition times based on FPA measurements. FPA shows high correlation with MSM (r > 0.90) and 94% reduction in the radiation dose compared to MSM. CT perfusion FPA, where the first scan is obtained after the arterial input function exceeds a threshold of 120 HU, followed by a second scan after 15.5–20.0 s, could be used as a potential imaging biomarker with low radiation exposure for diagnosing and evaluating pancreatic carcinoma in clinical practice, showing high correlation with MSM and the ability to differentiate between parenchyma and carcinoma.

Nitrogen Oxidation in a Multi-Pin Plasma System in the Presence and Absence of a Plasma/Liquid Interface

The recent energy crisis revealed that there is a strong need to replace hydrocarbon-fueled industrial nitrogen fixation processes by alternative, more sustainable methods. In light of this, plasma-based nitrogen fixation remains one of the most promising options, considering both theoretical and experimental aspects. Lately, plasma interacting with water has received considerable attention in nitrogen fixation applications as it can trigger a unique gas- and liquid-phase chemistry. Within this context, a critical exploration of plasma-assisted nitrogen fixation with or without water presence is of great interest with an emphasis on energy costs, particularly in plasma reactors which have potential for large-scale industrial application. In this work, the presence of water in a multi-pin plasma system on nitrogen oxidation is experimentally investigated by comparing two pulsed negative DC voltage plasmas in metal–metal and metal–liquid electrode configurations. The plasma setups are designed to create similar plasma properties, including plasma power and discharge regime in both configurations. The system energy cost is calculated, considering nitrogen-containing species generated in gas and liquid phases as measured by a gas analyzer, nitrate sensor, and a colorimetry method. The energy cost profile as a function of specific energy input showed a strong dependency on the plasma operational frequency and the gas flow rate, as a result of different plasma operation regimes and initiated reverse processes. More importantly, the presence of the plasma/liquid interface increased the energy cost up to 14 ± 8%. Overall, the results showed that the presence of water in the reaction zone has a negative impact on the nitrogen fixation process.

Whole-Brain Inputs to Major Descending Pathways of the Anterior Lateral Motor Cortex.

The anterior lateral motor cortex (ALM) is critical to subsequent correct movements and plays a vital role in predicting specific future movements. Different descending pathways of the ALM are preferentially involved in different roles in movements. However, the circuit function mechanisms of these different pathways may be concealed in the anatomy circuit. Clarifying the anatomy inputs of these pathways should provide some helpful information for elucidating these function mechanisms. Here, we used a retrograde trans-synaptic rabies virus to systematically generate, analyze and compare whole-brain maps of inputs to the thalamus (TH)-, medulla oblongata (Med)-, superior colliculus (SC)-, and pontine nucleus (Pons)-projecting ALM neurons in C57BL/6J mice. Fifty-nine separate regions from nine major brain areas projecting to the descending pathways of the ALM were identified. Brain-wide quantitative analyses revealed identical whole-brain input patterns between these descending pathways. Most inputs to the pathways originated from the ipsilateral side of the brain, with most innervations provided by the cortex and TH. The contralateral side of the brain also sent sparse projections, but these were rare, emanating only from the cortex and cerebellum. Nevertheless, the inputs received by TH-, Med-, SC- and Pons-projecting ALM neurons had different weights, potentially laying an anatomical foundation for understanding the diverse functions of well-defined descending pathways of the ALM. Our findings provide anatomical information to help elucidate the precise connections and diverse functions of the ALM.

Modeling and optimization of osmotic dehydration of wax apple slices using adaptive neuro-fuzzy inference system

The effect of ultrasound on osmotic dehydration of wax apple was investigated using the Fuzzy inference system, and the process parameters of ultrasonic assisted osmotic dehydration were optimized. The effect of ultrasonication temperature (XUT; 25 - 45 °C), sonication time (XIt; 140 - 220 min), sugar concentration (XSC; 30 - 70%), and sample to solvent ratio (XSS; 1:6 - 1:14) was studied. The responses such as water loss, solid gain, and color change, were investigated by modeling the ultrasonic assisted osmotic dehydration of wax apple using an adaptive neural fuzzy interface (ANFIS). Three ANFIS models were developed for the responses with Gaussian and linear membership functions as the input and output membership functions, respectively. The developed ANFIS model had R2 value more than 0.985 and an RMSE value less than 0.04, indicating a higher predictive capability. The ANFIS output was considered as input for the genetic algorithm (GA) optimization, with the objective of achieving maximal water loss while minimizing solid gain and color change. The integrated ANFIS-GA optimized the process parameters with a fitness value of 2.8. The optimum values of ultrasonic assisted osmotic dehydration process parameters were ultrasonication temperature of 38.6 °C, sonication time of 186 min, sugar concentration of 62%, and solvent to sample ratio of 11.7 w/v. The predicted values of the response water loss, solid gain, and color change at the optimum condition were observed to be 40.698, 3.097, and 10.916, respectively.

Synaptic density in aging mice measured by [18F]SynVesT-1 PET

Synaptic alterations in certain brain structures are related to cognitive decline in neurodegeneration and in aging. Synaptic loss in many neurodegenerative diseases can be visualized by positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A). However, the use of SV2A PET for studying synaptic changes during aging is not particularly explored. Thus, in the present study, PET ligand [18F]SynVesT-1, which binds to SV2A, was used to investigate synaptic density at different ages in healthy mice.Wild type C57BL/6 mice divided into three age groups (4–5 months (n = 7), 12–14 months (n = 11), 17–19 months (n = 7)) were PET scanned with [18F]SynVesT-1. Brain retention of [18F]SynVesT-1 expressed as the volume of distribution (VIDIF) was calculated using an image-derived input function. Estimates of VIDIF were derived using either a one-tissue compartment model (1TCM), a two-tissue compartment model (2TCM), or the Logan plot with blood input to find the best-fit model for [18F]SynVesT-1. After the PET scans, tissue sections were immunostained for the detection of SV2A and neuronal markers.We found that [18F]SynVesT-1 data acquired 60 min post intravenously injection and analyzed with 1TCM described the brain pharmacokinetics of the radioligand in mice well. [18F]SynVesT-1 brain retention was lower in the oldest group of mice, indicating a decrease in synaptic density in this age group. However, no gradual age-dependent decrease in synaptic density at a region-specific level was observed. Immunostaining indicated that SV2A expression and neuron numbers were similar across all three age groups. In general, these data obtained in healthy aging mice are consistent with previous findings in humans where synaptic density appeared stable during aging up to a certain age, after which a small decrease is observed.

Using Machine Learning With Partial Dependence Analysis to Investigate Coupling Between Soil Moisture and Near‐Surface Temperature

AbstractSoil moisture (SM) influences near‐surface air temperature by partitioning downwelling radiation into latent and sensible heat fluxes, through which dry soils generally lead to higher temperatures. The strength of this coupled soil moisture‐temperature (SM‐T) relationship is not spatially uniform, and numerous methods have been developed to assess SM‐T coupling strength across the globe. These methods tend to involve either idealized climate‐model experiments or linear statistical methods which cannot fully capture nonlinear SM‐T coupling. In this study, we propose a nonlinear machine‐learning (ML)‐based approach for analyzing SM‐T coupling and apply this method to various mid‐latitude regions using historical reanalysis datasets. We first train convolutional neural networks (CNNs) to predict daily maximum near‐surface air temperature (TMAX) given daily SM and geopotential height fields. We then use partial dependence analysis to isolate the average sensitivity of each CNN's TMAX prediction to the SM input under daily atmospheric conditions. The resulting SM‐T relationships broadly agree with previous assessments of SM‐T coupling strength. Over many regions, we find nonlinear relationships between the CNN's TMAX prediction and the SM input map. These nonlinearities suggest that the coupled interactions governing SM‐T relationships vary under different SM conditions, but these variations are regionally dependent. We also apply this method to test the influence of SM memory on SM‐T coupling and find that our results are consistent with previous studies. Although our study focuses specifically on local SM‐T coupling, our ML‐based method can be extended to investigate other coupled interactions within the climate system using observed or model‐derived datasets.

Quantification of myocardial blood flow using stress cardiac magnetic resonance for the detection of coronary artery disease

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): GE Healthcare. Background Myocardial blood flow (MBF) analysis using stress cardiac magnetic resonance (CMR) has been shown to detect obstructive coronary artery disease (CAD); however, evaluation of its diagnostic performance has primarily been limited to single-center studies. AQUA-MBF (Assessment of QUAntitative MBF using stress CMR) is an international study with the goal of assessing the diagnostic performance of stress MBF for the detection of CAD. In this study, we aim to determine how stress MBF assessment compared against visual analysis (VA) of stress CMR images for the detection of CAD. Methods. 144 individuals (89 (62%) men, age 62±16 years, 97 (68%) hypertension, 54 (38%) diabetes, 92 (64%) hyperlipidemia) from 9 centers who underwent dual sequence stress CMR (1.5T or 3.0T GE Healthcare) and also had either a coronary computed tomography angiography (CTA, n=31), invasive coronary angiogram (ICA, n=95), or low pre-test probability for CAD (n=18) were included. CAD was defined as the presence of: (1) a stenosis ≥50% in the left main coronary artery or ≥70% in the 1 major vessel based on ICA or CTA or (2) an invasive fractional flow reserve (FFR) ≤ 0.8. Absence of obstructive coronary disease (NOCAD) was defined as a no history of myocardial infarction and stenosis &amp;lt;50% by ICA or CTA, 50–70% stenosis with FFR&amp;gt;0.8, or a young individual with no cardiac risk factors. Myocardial perfusion imaging was performed during first pass perfusion of a gadolinium-based contrast agent following the administration of adenosine or regadenoson with a low-resolution image acquired to assess the arterial input function and 2–3 short axis slices acquired to assess myocardial perfusion. VA was performed by 2 experienced cardiologists who assigned a grade of 1–5 based on the probability a study was abnormal. Stress MBF values were determined for each of the 16 myocardial segments using Fermi deconvolution (CircleCVI). The global stress MBF was calculated as the average value of two segments with the lowest values from each of the three coronary artery territories. Unpaired t-test was used to compare stress MBF values between CAD vs NOCAD. Receiver-operating characteristics curves were used to determine diagnostic performance. Results. 60 patients had CAD (20: 1-vessel, 26: 2-vessel, and 14: 3-vessel) (Figure 1A) while 84 had NOCAD (Figure 1B). The global stress MBF in CAD was lower than in NOCAD (1.63±0.52ml/g/min vs 2.41±0.68ml/g/min, p&amp;lt;0.0001) (Figure 2A). Stress MBF had a higher AUC than VA (reader 1 – 0.83 vs 0.73, p = 0.07: reader 2–0.83 vs 0.71, p = 0.02). The optimal stress MBF cut-off value for detecting CAD was 2.05ml/g/min (Figure 2B). Conclusions. In this multicenter study, we show that global stress MBF reported as a single value can identify patients with CAD at least as accurately as VA performed by physicians experienced in the interpretation of stress CMR images.

DSA-Based 2D Perfusion Measurements in Delayed Cerebral Ischemia to Estimate the Clinical Outcome in Patients with Aneurysmal Subarachnoid Hemorrhage: A Technical Feasibility Study.

(1) Background: To predict clinical outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) and delayed cerebral ischemia (DCI) by assessment of the cerebral perfusion using a 2D perfusion angiography (2DPA) time-contrast agent (CA) concentration model. (2) Methods: Digital subtraction angiography (DSA) data sets of n = 26 subjects were acquired and post-processed focusing on changes in contrast density using a time-concentration model at three time points: (i) initial presentation with SAH (T0); (ii) vasospasm-associated acute clinical impairment (T1); and (iii) directly after endovascular treatment (T2) of SAH-associated large vessel vasospasm (LVV), which resulted in n = 78 data sets. Maximum slope (MS in SI/ms), time-to-peak (TTP in ms), and maximum amplitude of a CA bolus (dSI) were measured in brain parenchyma using regions of interest (ROIs). First, acquired parameters were standardized to the arterial input function (AIF) and then statistically analyzed as mean values. Additionally, data were clustered into two subsets consisting of patients with regredient or with stable/progredient symptoms (or Doppler signals) after endovascular treatment (n = 10 vs. n = 16). (3) Results: Perfusion parameters (MS, TTP, and dSI) differed significantly between T0 and T1 (p = 0.003 each). Significant changes between T1 and T2 were only detectable for MS (0.041 ± 0.016 vs. 0.059 ± 0.026; p = 0.011) in patients with regredient symptoms at T2 (0.04 ± 0.012 vs. 0.066 ± 0.031; p = 0.004). For dSI, there were significant differences between T0 and T2 (5095.8 ± 2541.9 vs. 3012.3 ± 968.3; p = 0.001), especially for those with stable symptoms at T2 (5685.4 ± 2967.2 vs. 3102.8 ± 1033.2; p = 0.02). Multiple linear regression analysis revealed that a) the difference in MS between T1 and T2 and b) patient's age (R = 0.6; R2 = 0.34; p = 0.009) strongly predict the modified Rankin Scale (mRS) at discharge. (4) Conclusions: 2DPA allows the direct measurement of treatment effects in SAH associated DCI and may be used to predict outcomes in these critically ill patients.

Automated quantitative myocardial perfusion cardiac magnetic resonance with artificial intelligence-based arterial input function correction versus fractional flow reserve

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Wellcome Trust. Background Quantitative myocardial perfusion cardiac magnetic resonance (QP-CMR) is gaining importance in clinical practice due to the increasing availability of automated processing software. However, a significant obstacle in the widespread deployment of QP-CMR is the accurate estimation of the arterial input function (AIF), as this is precluded by the non-linear relationship between observed MR signal and the concentration of contrast agent. Modern solutions favour a dual-saturation acquisition sequence, acquiring specific low-resolution images with a short saturation time to minimise the signal saturation of the AIF. However, this approach is limited by the availability of the pulse sequence. Recently, the AI-AIF has been proposed (1), which uses an artificial intelligence (AI)-based correction of the AIF, without the need for a dual-sequence or dual-bolus acquisition. Purpose In this work, we compare QP-CMR values, quantified with the AI-AIF versus the invasive reference standard: fractional flow reserve (FFR), to identify diagnostically significant coronary artery disease (CAD), as defined by FFR &amp;lt; 0.80. Methods This study enrolled 18 patients (mean age 65 (± 11), 8 female) who underwent stress perfusion CMR (Philips Achieva 3T) and invasive FFR assessment of at least one coronary artery. Perfusion was quantified in a fully-automated manner with motion compensation (2) and AI-based segmentation and processing (3), using a Fermi function-constrained deconvolution. Fully automated pixelwise perfusion maps were generated, and further subdivided into 16 myocardial segments. Segments were assigned to their respective perfusion territory (4) and the QP value for a territory is given as the mean of its two lowest segments. Receiver operating characteristic (ROC) analysis was performed to identify cut-off thresholds and report the diagnostic accuracy of the proposed approach. Results The overall mean QP-CMR value was 2.29 ml/min/g (± 0.32). Figure 1 shows QP maps and FFR values for 3 example patients. The mean QP-CMR value was 2.09 ml/min/g (± 0.36) in patients with significant CAD and 2.95 ml/min/g (± 0.48) in patients without significant CAD. In total, 28 vessels underwent FFR assessment with 12 vessels in 10 patients being positive for significant CAD (FFR &amp;lt; 0.80). The mean QP-CMR value in FFR-positive coronary territories was 1.76 ml/min/g (± 0.37). The ROC curves are shown in Figure 2 on a per-vessel (left) and per-patient (right) level. The optimal QP threshold on a per-vessel level is 2.11 ml/min/g and on a per-patient level is 1.96 ml/min/g. Conclusion QP-CMR is feasible with the AI-AIF and does not require dual-sequence acquisition or a dual contrast injection. The QP values derived after the AI-based correction of the AIF show good agreement with FFR assessment of epicardial coronary disease. However, due to the limited sample size, further work is required to confirm these findings.

Comparison of the diagnostic accuracy of dynamic CT perfusion and cardiac CMR with quantitative perfusion: a subanalysis of ADVANTAGE II study

Abstract Funding Acknowledgements Type of funding sources: None. Background The ADVANTAGE II study has evaluated the performance of dynamic myocardial perfusion assessed by CT (CTP) in stented patient using ICA and in particular invasive coronary physiology (FFR and IMR) as gold-standard. Recently, quantitative myocardial blood flow (MBF) analysis using stress cardiac magnetic resonance (CMR) has been showed to detect obstructive coronary artery disease (CAD) and coronary microvascular dysfunction (CMD). In a substudy of ADVANTAGE II we aimed to evaluate the performance of quantitative stress CMR in comparison to CTP. Methods We present data from 39 subjects with clinically indicated ICA who underwent both vasodilator stress CMR using 1.5T MR scanner (General Electric) and dynamic stress myocardial CTP using a last generation scanner characterized by a 16-cm Z-axis coverage. In CMR Quantitative myocardial perfusion imaging was performed using the dual sequence technique (i.e. low-resolution slice for arterial input function and 2–3 short axis slices for myocardial perfusion). Resting MBF (rMBF), Stress MBF (sMBF), and myocardial perfusion reserve (MPR) were measured using Fermi deconvolution (Circle Cvi42). Quantitative perfusion results were displayed on color-encoded pixel-wise maps and on a 16-segment plot with segmental values listed. Results When FFR was used as gold standard the diagnostic accuracy of CTP was higher than those of CCTA (84.7% vs. 61.5%, p&amp;lt;0.001). When IMR was used as gold standard, diagnostic accuracy of CTP was higher than CCTA (82.9% vs. 59.3%, p&amp;lt;0.01, respectively). When the presence of at least one abnormal physiological parameter among FFR and IMR was used as gold standard, the diagnostic accuracy of CTP was higher than that of CCTA (90.8%vs 68%). Quantitative stress CMR using 2.2 ml/min/g as cut-off has a sensitivity of 83.3%, 77.7% and 100% vs QCA, FFR and IMR, respectively with higher performance of dynamic CTP in comparison to quantitative stress CMR. Conclusions In this substudy of ADVANTAGE II, we showed that global sMBF in MRI can accurately identify patients with CAD. Dynamic CTP seems to offer higher value of diagnostic performance in comparison to quantitative CMR that seems to be very promising, but more data are needed.

Intravenous and oral whole body ketone dosimetry, biodistribution, metabolite correction and kinetics studied by (R)-[1-11C]β-hydroxybutyrate ([11C]OHB) PET in healthy humans

BackgroundKetones are increasingly recognized as an important and possibly oxygen sparing source of energy in vital organs such as the heart, the brain and the kidneys. Drug treatments, dietary regimens and oral ketone drinks designed to deliver ketones for organ and tissue energy production have therefore gained popularity. However, whether ingested ketones are taken up by various extra-cerebral tissues and to what extent is still largely unexplored. It was therefore the aim of this study to use positron emission tomography (PET) to explore the whole body dosimetry, biodistribution and kinetics of the ketone tracer (R)-[1-11C]β-hydroxybutyrate ([11C]OHB). Six healthy subjects (3 women and 3 men) underwent dynamic PET studies after both intravenous (90 min) and oral (120 min) administration of [11C]OHB. Dosimetry estimates of [11C]OHB was calculated using OLINDA/EXM software, biodistribution was assessed visually and [11C]OHB tissue kinetics were obtained using an arterial input function and tissue time-activity curves.ResultsRadiation dosimetry yielded effective doses of 3.28 upmuSv/MBq (intravenous administration) and 12.51 upmuSv/MBq (oral administration). Intravenous administration of [11C]OHB resulted in avid radiotracer uptake in the heart, liver, and kidneys, whereas lesser uptake was observed in the salivary glands, pancreas, skeletal muscle and red marrow. Only minimal uptake was noted in the brain. Oral ingestion of the tracer resulted in rapid radiotracer appearance in the blood and radiotracer uptake in the heart, liver and kidneys. In general, [11C]OHB tissue kinetics after intravenous administration were best described by a reversible 2-tissue compartmental model.ConclusionThe PET radiotracer [11C]OHB shows promising potential in providing imaging data on ketone uptake in various physiologically relevant tissues. As a result, it may serve as a safe and non-invasive imaging tool for exploring ketone metabolism in organs and tissues of both patients and healthy individuals.Trial registrationClinical trials, NCT0523812, Registered February 10th 2022, https://clinicaltrials.gov/ct2/show/NCT05232812?cond=NCT05232812&draw=2&rank=1.