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.

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.

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.

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.

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.

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.

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.

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.

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.

Digit ratio (2D:4D) in women and men with lung cancer

A prenatal sex steroid environment of high prenatal testosterone and low prenatal oestrogen inhibits lung development and may predispose individuals to be vulnerable to lung disease in later life. Therefore, the aim of this report was to investigate whether there is an association between right and left 2D:4D (biomarker of prenatal sex steroids exposure) and primary lung cancer in women and men. Also, we considered the relationship between right–left 2D:4D (Δ2D:4D, a negative correlate of high prenatal testosterone and low prenatal oestrogen) and the age of lung cancer diagnosis. The study included 109 patients (61 men) with lung cancer and 197 controls (78 men). In the study we found that: (i) women with lung cancer have lower 2D:4D compared to controls (the effect was independent of smoking), (ii) among women with cancer, age at diagnosis was positively related to 2D:4D, i.e. women with masculinized 2D:4D present earlier with the cancer than women with feminized 2D:4D, (iii) among men with lung cancer, those with the most aggressive form (small-cell lung cancer) had masculinized (low) Δ2D:4D compared to those with the less aggressive form (non-small cell lung cancer). The data suggests that masculinized right 2D:4D and Δ2D:4D are associated with a predisposition to lung cancer and/or the more aggressive forms of lung cancer.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

INTRODUCTION

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Resolution recovery for Compton camera using origin ensemble algorithm

PURPOSE

Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR.

METHODS

To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background.

RESULTS

Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration.

CONCLUSIONS

The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

Comment on 'Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: feasibility studies for range verification'

The origin ensemble (OE) algorithm is a new method used for image reconstruction from nuclear tomographic data. The main advantage of this algorithm is the ease of implementation for complex tomographic models and the sound statistical theory. In this comment, the author provides the basics of the statistical interpretation of OE and gives suggestions for the improvement of the algorithm in the application to prompt gamma imaging as described in Polf et al (2015 Phys. Med. Biol. 60 7085).

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.

Reconstruction of multiple-pinhole micro-SPECT data using origin ensembles

PURPOSE

The authors are currently developing a dual-resolution multiple-pinhole microSPECT imaging system based on three large NaI(Tl) gamma cameras. Two multiple-pinhole tungsten collimator tubes will be used sequentially for whole-body "scout" imaging of a mouse, followed by high-resolution (hi-res) imaging of an organ of interest, such as the heart or brain. Ideally, the whole-body image will be reconstructed in real time such that data need only be acquired until the area of interest can be visualized well-enough to determine positioning for the hi-res scan. The authors investigated the utility of the origin ensemble (OE) algorithm for online and offline reconstructions of the scout data. This algorithm operates directly in image space, and can provide estimates of image uncertainty, along with reconstructed images. Techniques for accelerating the OE reconstruction were also introduced and evaluated.

METHODS

System matrices were calculated for our 39-pinhole scout collimator design. SPECT projections were simulated for a range of count levels using the MOBY digital mouse phantom. Simulated data were used for a comparison of OE and maximum-likelihood expectation maximization (MLEM) reconstructions. The OE algorithm convergence was evaluated by calculating the total-image entropy and by measuring the counts in a volume-of-interest (VOI) containing the heart. Total-image entropy was also calculated for simulated MOBY data reconstructed using OE with various levels of parallelization.

RESULTS

For VOI measurements in the heart, liver, bladder, and soft-tissue, MLEM and OE reconstructed images agreed within 6%. Image entropy converged after ∼2000 iterations of OE, while the counts in the heart converged earlier at ∼200 iterations of OE. An accelerated version of OE completed 1000 iterations in <9 min for a 6.8M count data set, with some loss of image entropy performance, whereas the same dataset required ∼79 min to complete 1000 iterations of conventional OE. A combination of the two methods showed decreased reconstruction time and no loss of performance when compared to conventional OE alone.

CONCLUSIONS

OE-reconstructed images were found to be quantitatively and qualitatively similar to MLEM, yet OE also provided estimates of image uncertainty. Some acceleration of the reconstruction can be gained through the use of parallel computing. The OE algorithm is useful for reconstructing multiple-pinhole SPECT data and can be easily modified for real-time reconstruction.

Validation of Bayesian analysis of compartmental kinetic models in medical imaging

INTRODUCTION

Kinetic compartmental analysis is frequently used to compute physiologically relevant quantitative values from time series of images. In this paper, a new approach based on Bayesian analysis to obtain information about these parameters is presented and validated.

MATERIALS AND METHODS

The closed-form of the posterior distribution of kinetic parameters is derived with a hierarchical prior to model the standard deviation of normally distributed noise. Markov chain Monte Carlo methods are used for numerical estimation of the posterior distribution. Computer simulations of the kinetics of F18-fluorodeoxyglucose (FDG) are used to demonstrate drawing statistical inferences about kinetic parameters and to validate the theory and implementation. Additionally, point estimates of kinetic parameters and covariance of those estimates are determined using the classical non-linear least squares approach.

RESULTS AND DISCUSSION

Posteriors obtained using methods proposed in this work are accurate as no significant deviation from the expected shape of the posterior was found (one-sided P>0.08). It is demonstrated that the results obtained by the standard non-linear least-square methods fail to provide accurate estimation of uncertainty for the same data set (P<0.0001).

CONCLUSIONS

The results of this work validate new methods for a computer simulations of FDG kinetics. Results show that in situations where the classical approach fails in accurate estimation of uncertainty, Bayesian estimation provides an accurate information about the uncertainties in the parameters. Although a particular example of FDG kinetics was used in the paper, the methods can be extended for different pharmaceuticals and imaging modalities.

Parental smoking during pregnancy shortens offspring's legs

One of the most severe detrimental environmental factors acting during pregnancy is foetal smoke exposure. The aim of this study was to assess the effect of maternal, paternal and parental smoking during pregnancy on relative leg length in 7- to 10-year-old children. The research conducted in the years 2001-2002 included 978 term-born children, 348 boys and 630 girls, at the age of 7-10 years. Information concerning the birth weight of a child was obtained from the health records of the women. Information about the mother's and the father's smoking habits during pregnancy and about the mothers' education level was obtained from a questionnaire. The influence of parental smoking on relative leg length, controlled for age, sex, birth weight and the mother's education, as a proxy measure of socioeconomic status, and controlled for an interaction between sex and birth weight, was assessed by an analysis of covariance, where relative leg length was the dependent variable, smoking and sex were the independent variables, and birth weight as well as the mother's education were the covariates. Three separate analyses were run for the three models of smoking habits during pregnancy: the mother's smoking, the father's smoking and both parents' smoking. Only both parents' smoking showed a significant effect on relative leg length of offspring. It is probable that foetal hypoxia caused by carbon monoxide contained in smoke decelerated the growth of the long bones of foetuses.

Sensitivity estimation in time-of-flight list-mode positron emission tomography

PURPOSE

An accurate quantification of the images in positron emission tomography (PET) requires knowing the actual sensitivity at each voxel, which represents the probability that a positron emitted in that voxel is finally detected as a coincidence of two gamma rays in a pair of detectors in the PET scanner. This sensitivity depends on the characteristics of the acquisition, as it is affected by the attenuation of the annihilation gamma rays in the body, and possible variations of the sensitivity of the scanner detectors. In this work, the authors propose a new approach to handle time-of-flight (TOF) list-mode PET data, which allows performing either or both, a self-attenuation correction, and self-normalization correction based on emission data only.

METHODS

The authors derive the theory using a fully Bayesian statistical model of complete data. The authors perform an initial evaluation of algorithms derived from that theory and proposed in this work using numerical 2D list-mode simulations with different TOF resolutions and total number of detected coincidences. Effects of randoms and scatter are not simulated.

RESULTS

The authors found that proposed algorithms successfully correct for unknown attenuation and scanner normalization for simulated 2D list-mode TOF-PET data.

CONCLUSIONS

A new method is presented that can be used for corrections for attenuation and normalization (sensitivity) using TOF list-mode data.

Limitations of Poisson statistics in describing radioactive decay

OBJECTIVES

The assumption that nuclear decays are governed by Poisson statistics is an approximation. This approximation becomes unjustified when data acquisition times longer than or even comparable with the half-lives of the radioisotope in the sample are considered. In this work, the limits of the Poisson-statistics approximation are investigated.

METHODS

The formalism for the statistics of radioactive decay based on binomial distribution is derived. The theoretical factor describing the deviation of variance of the number of decays predicated by the Poisson distribution from the true variance is defined and investigated for several commonly used radiotracers such as (18)F, (15)O, (82)Rb, (13)N, (99m)Tc, (123)I, and (201)Tl.

RESULTS

The variance of the number of decays estimated using the Poisson distribution is significantly different than the true variance for a 5-minute observation time of (11)C, (15)O, (13)N, and (82)Rb.

CONCLUSIONS

Durations of nuclear medicine studies often are relatively long; they may be even a few times longer than the half-lives of some short-lived radiotracers. Our study shows that in such situations the Poisson statistics is unsuitable and should not be applied to describe the statistics of the number of decays in radioactive samples. However, the above statement does not directly apply to counting statistics at the level of event detection. Low sensitivities of detectors which are used in imaging studies make the Poisson approximation near perfect.

Recovery and normalization of triple coincidences in PET

PURPOSE

Triple coincidences in positron emission tomography (PET) are events in which three γ-rays are detected simultaneously. These events, though potentially useful for enhancing the sensitivity of PET scanners, are discarded or processed without special consideration in current systems, because there is not a clear criterion for assigning them to a unique line-of-response (LOR). Methods proposed for recovering such events usually rely on the use of highly specialized detection systems, hampering general adoption, and/or are based on Compton-scatter kinematics and, consequently, are limited in accuracy by the energy resolution of standard PET detectors. In this work, the authors propose a simple and general solution for recovering triple coincidences, which does not require specialized detectors or additional energy resolution requirements.

METHODS

To recover triple coincidences, the authors' method distributes such events among their possible LORs using the relative proportions of double coincidences in these LORs. The authors show analytically that this assignment scheme represents the maximum-likelihood solution for the triple-coincidence distribution problem. The PET component of a preclinical PET/CT scanner was adapted to enable the acquisition and processing of triple coincidences. Since the efficiencies for detecting double and triple events were found to be different throughout the scanner field-of-view, a normalization procedure specific for triple coincidences was also developed. The effect of including triple coincidences using their method was compared against the cases of equally weighting the triples among their possible LORs and discarding all the triple events. The authors used as figures of merit for this comparison sensitivity, noise-equivalent count (NEC) rates and image quality calculated as described in the NEMA NU-4 protocol for the assessment of preclinical PET scanners.

RESULTS

The addition of triple-coincidence events with the authors' method increased peak NEC rates of the scanner by 26.6% and 32% for mouse- and rat-sized objects, respectively. This increase in NEC-rate performance was also reflected in the image-quality metrics. Images reconstructed using double and triple coincidences recovered using their method had better signal-to-noise ratio than those obtained using only double coincidences, while preserving spatial resolution and contrast. Distribution of triple coincidences using an equal-weighting scheme increased apparent system sensitivity but degraded image quality. The performance boost provided by the inclusion of triple coincidences using their method allowed to reduce the acquisition time of standard imaging procedures by up to ∼25%.

CONCLUSIONS

Recovering triple coincidences with the proposed method can effectively increase the sensitivity of current clinical and preclinical PET systems without compromising other parameters like spatial resolution or contrast.

Quantification of coronary flow reserve in patients with ischaemic and non-ischaemic cardiomyopathy and its association with clinical outcomes

AIMS

Patients with left ventricular systolic dysfunction frequently show abnormal coronary vascular function, even in the absence of overt coronary artery disease. Moreover, the severity of vascular dysfunction might be related to the aetiology of cardiomyopathy.We sought to determine the incremental value of assessing coronary vascular dysfunction among patients with ischaemic (ICM) and non-ischaemic (NICM) cardiomyopathy at risk for adverse cardiovascular outcomes.

METHODS AND RESULTS

Coronary flow reserve (CFR, stress/rest myocardial blood flow) was quantified in 510 consecutive patients with rest left ventricular ejection fraction (LVEF) ≤45% referred for rest/stress myocardial perfusion PET imaging. The primary end point was a composite of major adverse cardiovascular events (MACE) including cardiac death, heart failure hospitalization, late revascularization, and aborted sudden cardiac death.Median follow-up was 8.2 months. Cox proportional hazards model was used to adjust for clinical variables. The annualized MACE rate was 26.3%. Patients in the lowest two tertiles of CFR (CFR ≤ 1.65) experienced higher MACE rates than those in the highest tertile (32.6 vs. 15.5% per year, respectively, P = 0.004), irrespective of aetiology of cardiomyopathy.

CONCLUSION

Impaired coronary vascular function, as assessed by reduced CFR by PET imaging, is common in patients with both ischaemic and non-ischaemic cardiomyopathy and is associated with MACE.

Time-of-flight PET image reconstruction using origin ensembles

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Comparison and prognostic validation of multiple methods of quantification of myocardial blood flow with 82Rb PET

The quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) using PET with (82)Rb in patients with known or suspected coronary artery disease has been demonstrated to have substantial prognostic and diagnostic value. However, multiple methods for estimation of an image-derived input function and several models for the nonlinear first-pass extraction of (82)Rb by myocardium have been used. We sought to compare the differences in these methods and models and their impact on prognostic assessment in a large clinical dataset.

METHODS

Consecutive patients (n = 2,783) underwent clinically indicated rest-stress myocardial perfusion PET with (82)Rb. The input function was derived using a region of interest (ROI) semiautomatically placed in the region of the mitral valve, factor analysis, and a hybrid method that creates an ROI from factor analysis. We used 5 commonly used extraction models for (82)Rb to estimate MBF and MFR. Pearson correlations, bias, and Cohen κ were computed for the various measures. The relationship between MFR/stress MBF and annual rate of cardiac mortality was estimated with spline fits using Poisson regression. Finally, incremental value was assessed with the net reclassification improvement using Cox proportional hazards regression.

RESULTS

Correlations between MFR or stress MBF measures made with the same input function derivation method were generally high, regardless of extraction model used (Pearson r > 0.90). However, correlations between measures derived with the ROI method and other methods were only moderate (Pearson r = 0.42-0.62). Importantly, substantial biases were seen for most combinations. We saw that the relationship between cardiac mortality and stress MBF was variable depending on the input function method and extraction model, whereas the relationship between MFR and risk was highly consistent. Net reclassification improvement was comparable for most methods and models for MFR but was highly variable for stress MBF.

CONCLUSION

Although both stress MBF and MFR can improve prognostic assessment, MFR is substantially more consistent, regardless of choice of input function derivation method and extraction model used.

Bayesian Analysis of a One Compartment Kinetic Model Used in Medical Imaging

Kinetic models are used extensively in science, engineering, and medicine. Mathematically, they are a set of coupled differential equations including a source function, otherwise known as an input function. We investigate whether parametric modeling of a noisy input function offers any benefit over the non-parametric input function in estimating kinetic parameters. Our analysis includes four formulations of Bayesian posteriors of model parameters where noise is taken into account in the likelihood functions. Posteriors are determined numerically with a Markov chain Monte Carlo simulation. We compare point estimates derived from the posteriors to a weighted non-linear least squares estimate. Results imply that parametric modeling of the input function does not improve the accuracy of model parameters, even with perfect knowledge of the functional form. Posteriors are validated using an unconventional utilization of the chi square test. We demonstrate that if the noise in the input function is not taken into account, the resulting posteriors are incorrect.

Prenatal factors associated with the neonatal line thickness in human deciduous incisors

The neonatal line (NNL) is used to distinguish developmental events observed in enamel which occurred before and after birth. However, there are few studies reporting relationship between the characteristics of the NNL and factors affecting prenatal conditions. The aim of the study was to determine prenatal factors that may influence the NNL thickness in human deciduous teeth. The material consisted of longitudinal ground sections of 60 modern human deciduous incisors obtained from full-term healthy children with reported birth histories and prenatal factors. All teeth were sectioned in the labio-lingual plane using diamond blade (Buechler IsoMet 1000). Final specimens were observed using scanning electron microscopy at magnifications 320×. For each tooth, linear measurements of the NNL thickness were taken on its labial surface at the three levels from the cemento-enamel junction. The difference in the neonatal line thickness between tooth types and between males and females was statistically significant. A multiple regression analyses confirmed influence of two variables on the NNL thickness standardised on tooth type and the children's sex (z-score values). These variables are the taking of an antispasmodic medicine by the mother during pregnancy and the season of the child's birth. These two variables together explain nearly 17% of the variability of the NNL. Children of mothers taking a spasmolytic medicine during pregnancy were characterised by a thinner NNL compared with children whose mothers did not take such medication. Children born in summer and spring had a thinner NNL than children born in winter. These results indicate that the prenatal environment significantly contributes to the thickness of the NNL influencing the pace of reaching the post-delivery homeostasis by the newborn's organism.

EM reconstruction of dual isotope PET using staggered injections and prompt gamma positron emitters

PURPOSE

The aim of dual isotope positron emission tomography (DIPET) is to create two separate images of two coinjected PET radiotracers. DIPET shortens the duration of the study, reduces patient discomfort, and produces perfectly coregistered images compared to the case when two radiotracers would be imaged independently (sequential PET studies). Reconstruction of data from such simultaneous acquisition of two PET radiotracers is difficult because positron decay of any isotope creates only 511 keV photons; therefore, the isotopes cannot be differentiated based on the detected energy.

METHODS

Recently, the authors have proposed a DIPET technique that uses a combination of radiotracer A which is a pure positron emitter (such as(18)F or (11)C) and radiotracer B in which positron decay is accompanied by the emission of a high-energy (HE) prompt gamma (such as (38)K or (60)Cu). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons allow the authors to identify the lines-of-response (LORs) of isotope B. These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm. In this work the authors propose a new version of the DIPET EM-based reconstruction algorithm that allows the authors to include an additional, independent estimate of radiotracer A distribution which may be obtained if radioisotopes are administered using a staggered injections method. In this work the method is tested on simple simulations of static PET acquisitions.

RESULTS

The authors' experiments performed using Monte-Carlo simulations with static acquisitions demonstrate that the combined method provides better results (crosstalk errors decrease by up to 50%) than the positron-gamma DIPET method or staggered injections alone.

CONCLUSIONS

The authors demonstrate that the authors' new EM algorithm which combines information from triple coincidences with prompt gammas and staggered injections improves the accuracy of DIPET reconstructions for static acquisitions so they reach almost the benchmark level calculated for perfectly separated tracers.

Dual-tracer PET using generalized factor analysis of dynamic sequences

PURPOSE

With single-photon emission computed tomography, simultaneous imaging of two physiological processes relies on discrimination of the energy of the emitted gamma rays, whereas the application of dual-tracer imaging to positron emission tomography (PET) imaging has been limited by the characteristic 511-keV emissions.

PROCEDURES

To address this limitation, we developed a novel approach based on generalized factor analysis of dynamic sequences (GFADS) that exploits spatio-temporal differences between radiotracers and applied it to near-simultaneous imaging of 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG) (brain metabolism) and (11)C-raclopride (D2) with simulated human data and experimental rhesus monkey data. We show theoretically and verify by simulation and measurement that GFADS can separate FDG and raclopride measurements that are made nearly simultaneously.

RESULTS

The theoretical development shows that GFADS can decompose the studies at several levels: (1) It decomposes the FDG and raclopride study so that they can be analyzed as though they were obtained separately. (2) If additional physiologic/anatomic constraints can be imposed, further decomposition is possible. (3) For the example of raclopride, specific and nonspecific binding can be determined on a pixel-by-pixel basis. We found good agreement between the estimated GFADS factors and the simulated ground truth time activity curves (TACs), and between the GFADS factor images and the corresponding ground truth activity distributions with errors less than 7.3 ± 1.3 %. Biases in estimation of specific D2 binding and relative metabolism activity were within 5.9 ± 3.6 % compared to the ground truth values. We also evaluated our approach in simultaneous dual-isotope brain PET studies in a rhesus monkey and obtained accuracy of better than 6 % in a mid-striatal volume, for striatal activity estimation.

CONCLUSIONS

Dynamic image sequences acquired following near-simultaneous injection of two PET radiopharmaceuticals can be separated into components based on the differences in the kinetics, provided their kinetic behaviors are distinct.

Evaluation of imaging systems using the posterior variance of emission counts

We investigate an approach to evaluation of emission-tomography (ET) imaging systems used for region-of-interest (ROI) estimation tasks. In the evaluation we employ the concept of "emission counts" (EC), which are the number of events per voxel emitted during a scan. We use the reduction in posterior variance of ROI EC, compared to the prior ROI EC variance, as the metric of primary interest, which we call the "posterior variance reduction index" (PVRI). Systems that achieve a higher PVRI are considered superior to systems with lower PVRI. The approach is independent of the reconstruction method and is applicable to all photon-limited data types including list-mode data. We analyzed this approach using a model of 2-D tomography, and compared our results to the classical theory of tomographic sampling. We found that performance evaluations using the PVRI index were consistent with the classical theory. System evaluation based on EC posterior variance is an intuitively appealing and physically meaningful method that is useful for evaluation of system performance in ROI quantitation tasks.

Quantification of Myocardial Perfusion Reserve Using Dynamic SPECT Imaging in Humans: A Feasibility Study

Myocardial perfusion imaging (MPI) is well established in the diagnosis and workup of patients with known or suspected coronary artery disease (CAD); however, it can underestimate the extent of obstructive CAD. Quantification of myocardial perfusion reserve with PET can assist in the diagnosis of multivessel CAD. We evaluated the feasibility of dynamic tomographic SPECT imaging and quantification of a retention index to describe global and regional myocardial perfusion reserve using a dedicated solid-state cardiac camera.

METHODS

Ninety-five consecutive patients (64 men and 31 women; median age, 67 y) underwent dynamic SPECT imaging with (99m)Tc-sestamibi at rest and at peak vasodilator stress, followed by standard gated MPI. The dynamic images were reconstructed into 60-70 frames, 3-6 s/frame, using ordered-subsets expectation maximization with 4 iterations and 32 subsets. Factor analysis was used to estimate blood-pool time-activity curves, used as input functions in a 2-compartment kinetic model. K1 values ((99m)Tc-sestamibi uptake) were calculated for the stress and rest images, and K2 values ((99m)Tc-sestamibi washout) were set to zero. Myocardial perfusion reserve (MPR) index was calculated as the ratio of the stress and rest K1 values. Standard MPI was evaluated semiquantitatively, and total perfusion deficit (TPD) of at least 5% was defined as abnormal.

RESULTS

Global MPR index was higher in patients with normal MPI (n = 51) than in patients with abnormal MPI (1.61 [interquartile range (IQR), 1.33-2.03] vs. 1.27 [IQR, 1.12-1.61], P = 0.0002). By multivariable regression analysis, global MPR index was associated with global stress TPD, age, and smoking. Regional MPR index was associated with the same variables and with regional stress TPD. Sixteen patients undergoing invasive coronary angiography had 20 vessels with stenosis of at least 50%. The MPR index was 1.11 (IQR, 1.01-1.21) versus 1.30 (IQR, 1.12-1.67) in territories supplied by obstructed and nonobstructed arteries, respectively (P = 0.02). MPR index showed a stepwise reduction with increasing extent of obstructive CAD (P = 0.02).

CONCLUSION

Dynamic tomographic imaging and quantification of a retention index describing global and regional perfusion reserve are feasible using a solid-state camera. Preliminary results show that the MPR index is lower in patients with perfusion defects and in regions supplied by obstructed coronary arteries. Further studies are needed to establish the clinical role of this technique as an aid to semiquantitative analysis of MPI.

Practical implementation of tetrahedral mesh reconstruction in emission tomography

This paper presents a practical implementation of image reconstruction on tetrahedral meshes optimized for emission computed tomography with parallel beam geometry. Tetrahedral mesh built on a point cloud is a convenient image representation method, intrinsically three-dimensional and with a multi-level resolution property. Image intensities are defined at the mesh nodes and linearly interpolated inside each tetrahedron. For the given mesh geometry, the intensities can be computed directly from tomographic projections using iterative reconstruction algorithms with a system matrix calculated using an exact analytical formula. The mesh geometry is optimized for a specific patient using a two stage process. First, a noisy image is reconstructed on a finely-spaced uniform cloud. Then, the geometry of the representation is adaptively transformed through boundary-preserving node motion and elimination. Nodes are removed in constant intensity regions, merged along the boundaries, and moved in the direction of the mean local intensity gradient in order to provide higher node density in the boundary regions. Attenuation correction and detector geometric response are included in the system matrix. Once the mesh geometry is optimized, it is used to generate the final system matrix for ML-EM reconstruction of node intensities and for visualization of the reconstructed images. In dynamic PET or SPECT imaging, the system matrix generation procedure is performed using a quasi-static sinogram, generated by summing projection data from multiple time frames. This system matrix is then used to reconstruct the individual time frame projections. Performance of the new method is evaluated by reconstructing simulated projections of the NCAT phantom and the method is then applied to dynamic SPECT phantom and patient studies and to a dynamic microPET rat study. Tetrahedral mesh-based images are compared to the standard voxel-based reconstruction for both high and low signal-to-noise ratio projection datasets. The results demonstrate that the reconstructed images represented as tetrahedral meshes based on point clouds offer image quality comparable to that achievable using a standard voxel grid while allowing substantial reduction in the number of unknown intensities to be reconstructed and reducing the noise.

Data analysis in emission tomography using emission-count posteriors

A novel approach to the analysis of emission tomography data using the posterior probability of the number of emissions per voxel (emission count) conditioned on acquired tomographic data is explored. The posterior is derived from the prior and the Poisson likelihood of the emission-count data by marginalizing voxel activities. Based on emission-count posteriors, examples of Bayesian analysis including estimation and classification tasks in emission tomography are provided. The application of the method to computer simulations of 2D tomography is demonstrated. In particular, the minimum-mean-square-error point estimator of the emission count is demonstrated. The process of finding this estimator can be considered as a tomographic image reconstruction technique since the estimates of the number of emissions per voxel divided by voxel sensitivities and acquisition time are the estimates of the voxel activities. As an example of a classification task, a hypothesis stating that some region of interest (ROI) emitted at least or at most r-times the number of events in some other ROI is tested. The ROIs are specified by the user. The analysis described in this work provides new quantitative statistical measures that can be used in decision making in diagnostic imaging using emission tomography.

Investigation of dynamic SPECT measurements of the arterial input function in human subjects using simulation, phantom and human studies

Computer simulations, a phantom study and a human study were performed to determine whether a slowly rotating single-photon computed emission tomography (SPECT) system could provide accurate arterial input functions for quantification of myocardial perfusion imaging using kinetic models. The errors induced by data inconsistency associated with imaging with slow camera rotation during tracer injection were evaluated with an approach called SPECT/P (dynamic SPECT from positron emission tomography (PET)) and SPECT/D (dynamic SPECT from database of SPECT phantom projections). SPECT/P simulated SPECT-like dynamic projections using reprojections of reconstructed dynamic (94)Tc-methoxyisobutylisonitrile ((94)Tc-MIBI) PET images acquired in three human subjects (1 min infusion). This approach was used to evaluate the accuracy of estimating myocardial wash-in rate parameters K(1) for rotation speeds providing 180° of projection data every 27 or 54 s. Blood input and myocardium tissue time-activity curves (TACs) were estimated using spatiotemporal splines. These were fit to a one-compartment perfusion model to obtain wash-in rate parameters K(1). For the second method (SPECT/D), an anthropomorphic cardiac torso phantom was used to create real SPECT dynamic projection data of a tracer distribution derived from (94)Tc-MIBI PET scans in the blood pool, myocardium, liver and background. This method introduced attenuation, collimation and scatter into the modeling of dynamic SPECT projections. Both approaches were used to evaluate the accuracy of estimating myocardial wash-in parameters for rotation speeds providing 180° of projection data every 27 and 54 s. Dynamic cardiac SPECT was also performed in a human subject at rest using a hybrid SPECT/CT scanner. Dynamic measurements of (99m)Tc-tetrofosmin in the myocardium were obtained using an infusion time of 2 min. Blood input, myocardium tissue and liver TACs were estimated using the same spatiotemporal splines. The spatiotemporal maximum-likelihood expectation-maximization (4D ML-EM) reconstructions gave more accurate reconstructions than did standard frame-by-frame static 3D ML-EM reconstructions. The SPECT/P results showed that 4D ML-EM reconstruction gave higher and more accurate estimates of K(1) than did 3D ML-EM, yielding anywhere from a 44% underestimation to 24% overestimation for the three patients. The SPECT/D results showed that 4D ML-EM reconstruction gave an overestimation of 28% and 3D ML-EM gave an underestimation of 1% for K(1). For the patient study the 4D ML-EM reconstruction provided continuous images as a function of time of the concentration in both ventricular cavities and myocardium during the 2 min infusion. It is demonstrated that a 2 min infusion with a two-headed SPECT system rotating 180° every 54 s can produce measurements of blood pool and myocardial TACs, though the SPECT simulation studies showed that one must sample at least every 30 s to capture a 1 min infusion input function.

Quantitative relationship between the extent and morphology of coronary atherosclerotic plaque and downstream myocardial perfusion

OBJECTIVES

The purpose of this study was to quantify the effects of coronary atherosclerosis morphology and extent on myocardial flow reserve (MFR).

BACKGROUND

Although the relationship between coronary stenosis and myocardial perfusion is well established, little is known about the contribution of other anatomic descriptors of atherosclerosis burden to this relationship.

METHODS

We evaluated the relationship between atherosclerosis plaque burden, morphology, and composition and regional MFR (MFR(regional)) in 73 consecutive patients undergoing Rubidium-82 positron emission tomography and coronary computed tomography angiography for the evaluation of known or suspected coronary artery disease.

RESULTS

Atherosclerosis was seen in 51 of 73 patients and in 107 of 209 assessable coronary arteries. On a per-vessel basis, the percentage diameter stenosis (p = 0.02) or summed stenosis score (p = 0.002), integrating stenoses in series, was the best predictor of MFR(regional). Importantly, MFR(regional) varied widely within each coronary stenosis category, even in vessels with nonobstructive plaques (n = 169), 38% of which had abnormal MFR(regional) (<2.0). Total plaque length, composition, and remodeling index were not associated with lower MFR. On a per-patient basis, the modified Duke CAD (coronary artery disease) index (p = 0.04) and the number of segments with mixed plaque (p = 0.01) were the best predictors of low MFR(global).

CONCLUSIONS

Computed tomography angiography descriptors of atherosclerosis had only a modest effect on downstream MFR. On a per-patient basis, the extent and severity of atherosclerosis as assessed by the modified Duke CAD index and the number of coronary segments with mixed plaque were associated with decreased MFR.

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.

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

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.

Reconstruction of emission tomography data using origin ensembles

A new statistical reconstruction method based on origin ensembles (OE) for emission tomography (ET) is examined. Using a probability density function (pdf) derived from first principles, an ensemble expectation of numbers of detected event origins per voxel is determined. These numbers divided by sensitivities of voxels and acquisition time provide OE estimates of the voxel activities. The OE expectations are shown to be the same as expectations calculated using the complete-data space. The properties of the OE estimate are examined. It is shown that OE estimate approximates maximum likelihood (ML) estimate for conditions usually achieved in practical applications in emission tomography. Three numerical experiments with increasing complexity are used to validate theoretical findings and demonstrate similarities of ML and OE estimates. Recommendations for achieving improved accuracy and speed of OE reconstructions are provided.

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.

Dynamic single photon emission computed tomography--basic principles and cardiac applications

The very nature of nuclear medicine, the visual representation of injected radiopharmaceuticals, implies imaging of dynamic processes such as the uptake and wash-out of radiotracers from body organs. For years, nuclear medicine has been touted as the modality of choice for evaluating function in health and disease. This evaluation is greatly enhanced using single photon emission computed tomography (SPECT), which permits three-dimensional (3D) visualization of tracer distributions in the body. However, to fully realize the potential of the technique requires the imaging of in vivo dynamic processes of flow and metabolism. Tissue motion and deformation must also be addressed. Absolute quantification of these dynamic processes in the body has the potential to improve diagnosis. This paper presents a review of advancements toward the realization of the potential of dynamic SPECT imaging and a brief history of the development of the instrumentation. A major portion of the paper is devoted to the review of special data processing methods that have been developed for extracting kinetics from dynamic cardiac SPECT data acquired using rotating detector heads that move as radiopharmaceuticals exchange between biological compartments. Recent developments in multi-resolution spatiotemporal methods enable one to estimate kinetic parameters of compartment models of dynamic processes using data acquired from a single camera head with slow gantry rotation. The estimation of kinetic parameters directly from projection measurements improves bias and variance over the conventional method of first reconstructing 3D dynamic images, generating time-activity curves from selected regions of interest and then estimating the kinetic parameters from the generated time-activity curves. Although the potential applications of SPECT for imaging dynamic processes have not been fully realized in the clinic, it is hoped that this review illuminates the potential of SPECT for dynamic imaging, especially in light of new developments that enable measurement of dynamic processes directly from projection measurements.