Artificial intelligence in the diagnosis of necrotising enterocolitis in newborns.

Necrotising enterocolitis (NEC) is one of the most common diseases in neonates and predominantly affects premature or very-low-birth-weight infants. Diagnosis is difficult and needed in hours since the first symptom onset for the best therapeutic effects. Artificial intelligence (AI) may play a significant role in NEC diagnosis. A literature search on the use of AI in the diagnosis of NEC was performed. Four databases (PubMed, Embase, arXiv, and IEEE Xplore) were searched with the appropriate MeSH terms. The search yielded 118 publications that were reduced to 8 after screening and checking for eligibility. Of the eight, five used classic machine learning (ML), and three were on the topic of deep ML. Most publications showed promising results. However, no publications with evident clinical benefits were found. Datasets used for training and testing AI systems were small and typically came from a single institution. The potential of AI to improve the diagnosis of NEC is evident. The body of literature on this topic is scarce, and more research in this area is needed, especially with a focus on clinical utility. Cross-institutional data for the training and testing of AI algorithms are required to make progress in this area. IMPACT: Only a few publications on the use of AI in NEC diagnosis are available although they offer some evidence that AI may be helpful in NEC diagnosis. AI requires large, multicentre, and multimodal datasets of high quality for model training and testing. Published results in the literature are based on data from single institutions and, as such, have limited generalisability. Large multicentre studies evaluating broad datasets are needed to evaluate the true potential of AI in diagnosing NEC in a clinical setting.

Improved myocardial blood flow estimation with residual activity correction and motion correction in 18F-flurpiridaz PET myocardial perfusion imaging.

PURPOSE: We sought to evaluate the diagnostic performance for coronary artery disease (CAD) of myocardial blood flow (MBF) quantification with 18F-flurpiridaz PET using motion correction (MC) and residual activity correction (RAC). METHODS: In total, 231 patients undergoing same-day pharmacologic rest and stress 18F-flurpiridaz PET from Phase III Flurpiridaz trial (NCT01347710) were studied. Frame-by-frame MC was performed and RAC was accomplished by subtracting the rest residual counts from the dynamic stress polar maps. MBF and myocardial flow reserve (MFR) were derived with a two-compartment early kinetic model for the entire left ventricle (global), each coronary territory, and 17-segment. Global and minimal values of three territorial (minimal vessel) and segmental estimation (minimal segment) of stress MBF and MFR were evaluated in the prediction of CAD. MBF and MFR were evaluated with and without MC and RAC (1: no MC/no RAC, 2: no MC/RAC, 3: MC/RAC). RESULTS: The area-under the receiver operating characteristics curve (AUC [95% confidence interval]) of stress MBF with MC/RAC was higher for minimal segment (0.89 [0.85-0.94]) than for minimal vessel (0.86 [0.81-0.92], p = 0.03) or global estimation (0.81 [0.75-0.87], p < 0.0001). The AUC of MFR with MC/RAC was higher for minimal segment (0.87 [0.81-0.93]) than for minimal vessel (0.83 [0.76-0.90], p = 0.014) or global estimation (0.77 [0.69-0.84], p < 0.0001). The AUCs of minimal segment stress MBF and MFR with MC/RAC were higher compared to those with no MC/RAC (p < 0.001 for both) or no MC/no RAC (p < 0.0001 for both). CONCLUSIONS: Minimal segment MBF or MFR estimation with MC and RAC improves the diagnostic performance for obstructive CAD compared to global assessment.

Left atrial enlargement increases the risk of major adverse cardiac events independent of coronary vasodilator capacity.

PURPOSE: Longstanding uncontrolled atherogenic risk factors may contribute to left atrial (LA) hypertension, LA enlargement (LAE) and coronary vascular dysfunction. Together they may better identify risk of major adverse cardiac events (MACE). The aim of this study was to test the hypothesis that chronic LA hypertension as assessed by LAE modifies the relationship between coronary vascular function and MACE. METHODS: In 508 unselected subjects with a normal clinical (82)Rb PET/CT, ejection fraction ≥40 %, no prior coronary artery disease, valve disease or atrial fibrillation, LAE was determined based on LA volumes estimated from the hybrid perfusion and CT transmission scan images and indexed to body surface area. Absolute myocardial blood flow and global coronary flow reserve (CFR) were calculated. Subjects were systematically followed-up for the primary end-point - MACE - a composite of all-cause death, myocardial infarction, hospitalization for heart failure, stroke, coronary artery disease progression or revascularization. RESULTS: During a median follow-up of 862 days, 65 of the subjects experienced a composite event. Compared with subjects with normal LA size, subjects with LAE showed significantly lower CFR (2.25 ± 0.83 vs. 1.95 ± 0.80, p = 0.01). LAE independently and incrementally predicted MACE even after accounting for clinical risk factors, medication use, stress left ventricular ejection fraction, stress left ventricular end-diastolic volume index and CFR (chi-squared statistic increased from 30.9 to 48.3; p = 0.001). Among subjects with normal CFR, those with LAE had significantly worse event-free survival (risk adjusted HR 5.4, 95 % CI 2.3 - 12.8, p < 0.0001). CONCLUSION: LAE and reduced CFR are related but distinct cardiovascular adaptations to atherogenic risk factors. LAE is a risk marker for MACE independent of clinical factors and left ventricular volumes; individuals with LAE may be at risk of MACE despite normal coronary vascular function.

Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.

Vascular phenotypes in early hypertension.

The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-naïve primary hypertensive patients without co-morbidities (aged 18-55, n = 73), and matched normotensive controls (n = 79) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24 h ambulatory monitoring. Vascular phenotyping included SphygmoCor® measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT™-2000® provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing 'arterially stiffened', 'vaso-protected', and 'non-dipper' patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification.

BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery.

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise.

Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load.

The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version.

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

BACKGROUND: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraobserver variability because of factors, such as the experience of the operator, image quality, maternal characteristics, or fetal movements. Our study tested the hypothesis that a deep learning method can estimate fetal weight based on a video scan of the fetal abdomen and gestational age with similar performance to the full biometry-based estimations provided by clinical experts. OBJECTIVE: This study aimed to develop and test a deep learning method to automatically estimate fetal weight from fetal abdominal ultrasound video scans. STUDY DESIGN: A dataset of 900 routine fetal ultrasound examinations was used. Among those examinations, 800 retrospective ultrasound video scans of the fetal abdomen from 700 pregnant women between 15 6/7 and 41 0/7 weeks of gestation were used to train the deep learning model. After the training phase, the model was evaluated on an external prospectively acquired test set of 100 scans from 100 pregnant women between 16 2/7 and 38 0/7 weeks of gestation. The deep learning model was trained to directly estimate fetal weight from ultrasound video scans of the fetal abdomen. The deep learning estimations were compared with manual measurements on the test set made by 6 human readers with varying levels of expertise. Human readers used standard 3 measurements made on the standard planes of the head, abdomen, and femur and heuristic formula to estimate fetal weight. The Bland-Altman analysis, mean absolute percentage error, and intraclass correlation coefficient were used to evaluate the performance and robustness of the deep learning method and were compared with human readers. RESULTS: Bland-Altman analysis did not show systematic deviations between readers and deep learning. The mean and standard deviation of the mean absolute percentage error between 6 human readers and the deep learning approach was 3.75%±2.00%. Excluding junior readers (residents), the mean absolute percentage error between 4 experts and the deep learning approach was 2.59%±1.11%. The intraclass correlation coefficients reflected excellent reliability and varied between 0.9761 and 0.9865. CONCLUSION: This study reports the use of deep learning to estimate fetal weight using only ultrasound video of the fetal abdomen from fetal biometry scans. Our experiments demonstrated similar performance of human measurements and deep learning on prospectively acquired test data. Deep learning is a promising approach to directly estimate fetal weight using ultrasound video scans of the fetal abdomen.

Improved cardiac risk assessment with noninvasive measures of coronary flow reserve.

BACKGROUND: Impaired vasodilator function is an early manifestation of coronary artery disease and may precede angiographic stenosis. It is unknown whether noninvasive assessment of coronary vasodilator function in patients with suspected or known coronary artery disease carries incremental prognostic significance. METHODS AND RESULTS: A total of 2783 consecutive patients referred for rest/stress positron emission tomography were followed up for a median of 1.4 years (interquartile range, 0.7-3.2 years). The extent and severity of perfusion abnormalities were quantified by visual evaluation of myocardial perfusion images. Rest and stress myocardial blood flows were calculated with factor analysis and a 2-compartment kinetic model and were used to compute coronary flow reserve (coronary flow reserve equals stress divided by rest myocardial blood flow). The primary end point was cardiac death. Overall 3-year cardiac mortality was 8.0%. The lowest tertile of coronary flow reserve (<1.5) was associated with a 5.6-fold increase in the risk of cardiac death (95% confidence interval, 2.5-12.4; P<0.0001) compared with the highest tertile. Incorporation of coronary flow reserve into cardiac death risk assessment models resulted in an increase in the c index from 0.82 (95% confidence interval, 0.78-0.86) to 0.84 (95% confidence interval, 0.80-0.87; P=0.02) and in a net reclassification improvement of 0.098 (95% confidence interval, 0.025-0.180). Addition of coronary flow reserve resulted in correct reclassification of 34.8% of intermediate-risk patients (net reclassification improvement=0.487; 95% confidence interval, 0.262-0.731). Corresponding improvements in risk assessment for mortality from any cause were also demonstrated. CONCLUSION: Noninvasive quantitative assessment of coronary vasodilator function with positron emission tomography is a powerful, independent predictor of cardiac mortality in patients with known or suspected coronary artery disease and provides meaningful incremental risk stratification over clinical and gated myocardial perfusion imaging variables.

Deep learning fetal ultrasound video model match human observers in biometric measurements.

Objective.This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos.Approach.We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated.Main results.We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model.Significance.We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development.

The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.

Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day.

UNLABELLED: The technology revolution in image acquisition, instrumentation, and methods has resulted in vast data sets that far outstrip the human observers' ability to view, digest, and interpret modern medical images by using traditional methods. This may require a paradigm shift in the radiologic interpretation process. As human observers, radiologists must search for, detect, and interpret targets. Potential interventions should be based on an understanding of human perceptual and attentional abilities and limitations. New technologies and tools already in use in other fields can be adapted to the health care environment to improve medical image analysis, visualization, and navigation through large data sets. This historical psychophysical and technical review touches on a broad range of disciplines but focuses mainly on the analysis, visualization, and navigation of image data performed during the interpretive process. Advanced postprocessing, including three-dimensional image display, multimodality image fusion, quantitative measures, and incorporation of innovative human-machine interfaces, will likely be the future. Successful new paradigms will integrate image and nonimage data, incorporate workflow considerations, and be informed by evidence-based practices. This overview is meant to heighten the awareness of the complexities and limitations of how radiologists interact with images, particularly the large image sets generated today. Also addressed is how human-machine interface and informatics technologies could combine to transform the interpretation process in the future to achieve safer and better quality care for patients and a more efficient and effective work environment for radiologists. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11091276/-/DC1.

Fast image reconstruction for Compton camera using stochastic origin ensemble approach.

PURPOSE: Compton camera has been proposed as a potential imaging tool in astronomy, industry, homeland security, and medical diagnostics. Due to the inherent geometrical complexity of Compton camera data, image reconstruction of distributed sources can be ineffective and/or time-consuming when using standard techniques such as filtered backprojection or maximum likelihood-expectation maximization (ML-EM). In this article, the authors demonstrate a fast reconstruction of Compton camera data using a novel stochastic origin ensembles (SOE) approach based on Markov chains. METHODS: During image reconstruction, the origins of the measured events are randomly assigned to locations on conical surfaces, which are the Compton camera analogs of lines-of-responses in PET. Therefore, the image is defined as an ensemble of origin locations of all possible event origins. During the course of reconstruction, the origins of events are stochastically moved and the acceptance of the new event origin is determined by the predefined acceptance probability, which is proportional to the change in event density. For example, if the event density at the new location is higher than in the previous location, the new position is always accepted. After several iterations, the reconstructed distribution of origins converges to a quasistationary state which can be voxelized and displayed. RESULTS: Comparison with the list-mode ML-EM reveals that the postfiltered SOE algorithm has similar performance in terms of image quality while clearly outperforming ML-EM in relation to reconstruction time. CONCLUSIONS: In this study, the authors have implemented and tested a new image reconstruction algorithm for the Compton camera based on the stochastic origin ensembles with Markov chains. The algorithm uses list-mode data, is parallelizable, and can be used for any Compton camera geometry. SOE algorithm clearly outperforms list-mode ML-EM for simple Compton camera geometry in terms of reconstruction time. The difference in computational time will be much larger when full Compton camera system model, including resolution recovery, is implemented and realistic Compton camera geometries are used. It was also shown in this article that while correctly reconstructing the relative distribution of the activity in the object, the SOE algorithm tends to underestimate the intensity values and increase variance in the images; improvements to the SOE reconstruction algorithm will be considered in future work.

Artificial Intelligence in PET: An Industry Perspective.

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks.

This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. A total of 10 300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a ten-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We, thus, demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

Reproducibility and accuracy of quantitative myocardial blood flow assessment with (82)Rb PET: comparison with (13)N-ammonia PET.

UNLABELLED: (82)Rb cardiac PET allows the assessment of myocardial perfusion with a column generator in clinics that lack a cyclotron. There is evidence that the quantitation of myocardial blood flow (MBF) and coronary flow reserve (CFR) with dynamic (82)Rb PET is feasible. The objectives of this study were to determine the accuracy and reproducibility of MBF estimates from dynamic (82)Rb PET by using our methodology for generalized factor analysis (generalized factor analysis of dynamic sequences [GFADS]) and compartment analysis. METHODS: Reproducibility was evaluated in 22 subjects undergoing dynamic rest and dipyridamole stress (82)Rb PET studies at a 2-wk interval. The inter- and intraobserver variability of MBF quantitation with dynamic (82)Rb PET was assessed with 4 repeated estimations by each of 4 observers. Accuracy was evaluated in 20 subjects undergoing dynamic rest and dipyridamole stress PET studies with (82)Rb and (13)N-ammonia, respectively. The left ventricular and right ventricular blood pool and left ventricular tissue time-activity curves were estimated by GFADS. MBF was estimated by fitting the blood pool and tissue time-activity curves to a 2-compartment kinetic model for (82)Rb and to a 3-compartment model for (13)N-ammonia. CFR was estimated as the ratio of peak MBF to baseline MBF. RESULTS: The reproducibility of the MBF estimates in repeated (82)Rb studies was very good at rest and during peak stress (R(2)= 0.935), as was the reproducibility of the CFR estimates (R(2) = 0.841). The slope of the correlation line was very close to one for the estimation of MBF (0.986) and CFR (0.960) in repeated (82)Rb studies. The intraobserver reliability was less than 3% for the estimation of MBF at rest and during peak stress as well as for the estimation of CFR. The interobserver reliabilities were 0.950 at rest and 0.975 at peak stress. The correlation between myocardial flow estimates obtained at rest and those obtained during peak stress in (82)Rb and (13)N-ammonia studies was very good (R(2) = 0.857). Bland-Altman plots comparing CFR estimated with (82)Rb and CFR estimated with (13)N-ammonia revealed an underestimation of CFR with (82)Rb compared with (13)N-ammonia; the underestimation was within +/-1.96 SD. CONCLUSION: MBF quantitation with GFADS and dynamic (82)Rb PET demonstrated excellent reproducibility as well as intra- and interobserver reliability. The accuracy of the absolute quantitation of MBF with factor and compartment analyses and dynamic (82)Rb PET was very good, compared with that achieved with (13)N-ammonia, for MBF of up to 2.5 mL/g/min.

Feasibility of measurement of pancreatic perfusion parameters with single-compartment kinetic model applied to dynamic contrast-enhanced CT images.

PURPOSE: To examine the feasibility of measuring pancreatic perfusion parameters by using a single-compartment kinetic model applied to contrast material-enhanced computed tomographic (CT) images. MATERIALS AND METHODS: This study received institutional review board approval and was HIPAA compliant. Informed consent was waived. Eight subjects (four men, four women; median age, 40 years; range, 35-57 years), all potential renal donors with no pancreatic pathologic abnormalities, underwent abdominal CT imaging, which resulted in 30 10-mm-thick sections obtained at a single level. Imaging was a direct result of bolus timing employed for standard renal donor protocol; no additional imaging beyond what was clinically warranted was performed. Images were obtained every 3 seconds; scanning was initiated at the onset of contrast material administration. Region-of-interest measurements were obtained for the pancreatic body and the aorta to generate time-enhancement curves (TECs). A one-compartment model was applied by using the aortic and pancreatic TECs as the input and output functions, respectively. Pancreatic volumetric blood flow F(V), volume of distribution V(D), and blood transit time tau were determined. Modeled pancreatic TECs were generated and were compared with actual TECs for wellness of fit. RESULTS: Pancreatic F(V) values from the single-compartment model ranged from 0.961 to 6.405 min(-1) (mean, 3.560 min(-1) +/- 1.900 [standard deviation]). Volume of distribution V(D) ranged from 1.491 to 3.080 (mean, 2.383 +/- 0.638), while values of tau ranged from -3.090 to 6.436 seconds (mean, 0.481 second +/- 3.000). Modeled pancreatic TECs closely matched true pancreatic TECs for each subject, with R(2) values ranging from 0.840 to 0.959. CONCLUSION: A simple one-compartment kinetic model can be applied to contrast-enhanced images of normal pancreas to yield accurate pancreatic TECs, which attest to the perfusion parameters obtained. In addition to yielding volumetric blood flow similar to that of other models of tissue perfusion, two additional pancreatic perfusion parameters can be obtained. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/2492080026/DC1.