Pixel-wise statistical analysis of myocardial injury in STEMI patients with delayed enhancement MRI.

Objectives: Myocardial injury assessment from delayed enhancement magnetic resonance images is routinely limited to global descriptors such as size and transmurality. Statistical tools from computational anatomy can drastically improve this characterization, and refine the assessment of therapeutic procedures aiming at infarct size reduction. Based on these techniques, we propose a new characterization of myocardial injury up to the pixel resolution. We demonstrate it on the imaging data from the Minimalist Immediate Mechanical Intervention randomized clinical trial (MIMI: NCT01360242), which aimed at comparing immediate and delayed stenting in acute ST-Elevation Myocardial Infarction (STEMI) patients. Methods: We analyzed 123 patients from the MIMI trial (62 ± 12 years, 98 male, 65 immediate 58 delayed stenting). Early and late enhancement images were transported onto a common geometry using techniques inspired by statistical atlases, allowing pixel-wise comparisons across population subgroups. A practical visualization of lesion patterns against specific clinical and therapeutic characteristics was also proposed using state-of-the-art dimensionality reduction. Results: Infarct patterns were roughly comparable between the two treatments across the whole myocardium. Subtle but significant local differences were observed for the LCX and RCA territories with higher transmurality for delayed stenting at lateral and inferior/inferoseptal locations, respectively (15% and 23% of myocardial locations with a p-value <0.05, mainly in these regions). In contrast, global measurements were comparable for all territories (no statistically significant differences for all-except-one measurements before standardization / for all after standardization), although immediate stenting resulted in more subjects without reperfusion injury. Conclusion: Our approach substantially empowers the analysis of lesion patterns with standardized comparisons up to the pixel resolution, and may reveal subtle differences not accessible with global observations. On the MIMI trial data as illustrative case, it confirmed its general conclusions regarding the lack of benefit of delayed stenting, but revealed subgroups differences thanks to the standardized and finer analysis scale.

Additional prognostic value of echocardiographic follow-up in pulmonary hypertension-role of 3D right ventricular area strain.

AIMS: Outcomes in pulmonary hypertension (PH) are related to right ventricular (RV) function and remodelling. We hypothesized that changes in RV function and especially area strain (AS) could provide incremental prognostic information compared to the use of baseline data only. We therefore aimed to assess RV function changes between baseline and 6-month follow-up and evaluate their prognostic value for PH patients using 3D echocardiography. METHODS AND RESULTS: Ninety-five PH patients underwent a prospective longitudinal study including ESC/ERS guidelines prognostic assessment and 3D RV echocardiographic imaging at baseline and 6-month follow-up. Semi-automatic software tracked the RV along the cycle, and its output was post-processed to extract 3D deformation patterns. Over a median follow-up of 24.8 (22.1-25.7) months, 21 patients died from PH or were transplanted. Improvements in RV global AS were associated with stable or improving clinical condition as well as survival free from transplant (P < 0.001). The 3D deformation patterns confirmed that the most significant regional changes occurred within the septum. RV global AS change over 6-month by +3.5% identifies patients with a 3.7-fold increased risk of death or transplant. On multivariate COX analysis, changes in WHO class, BNP, and RV global AS were independent predictors of outcomes. Besides, the combination of these three parameters was of special interest to identify high-risk patients [HR 11.5 (1.55-86.06)]. CONCLUSION: Changes in RV function and especially changes in 3D RV AS are of prognostic importance. Our study underlines that assessing such changes from baseline to follow-up is of additional prognostic value for PH patients. CLINICAL TRIAL REGISTRATION: http://clinicaltrials.gov/ct2/show/NCT02799979.

Echocardiography Segmentation With Enforced Temporal Consistency.

Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints. Our method is a post-processing that takes as input segmented echocardiographic sequences produced by any state-of-the-art method and processes it in two steps to (i) identify spatio-temporal inconsistencies according to the overall dynamics of the cardiac sequence and (ii) correct the inconsistencies. The identification and correction of cardiac inconsistencies relies on a constrained autoencoder trained to learn a physiologically interpretable embedding of cardiac shapes, where we can both detect and fix anomalies. We tested our framework on 98 full-cycle sequences from the CAMUS dataset, which are available alongside this paper. Our temporal regularization method not only improves the accuracy of the segmentation across the whole sequences, but also enforces temporal and anatomical consistency.

Combined liver-intestinal and multivisceral transplantation for neuroendocrine tumors extending beyond the liver: A systematic literature review.

BACKGROUND: Neuroendocrine tumors (NET) have an increasing incidence and are characterized by an invasive and metastatic presentation, rendering a curative resection not always feasible. For some patients the only life-saving option would be a multivisceral transplantation (MvTx). This systematic review aims to summarize the reported experience on combined liver-intestinal and MvTx for NET according PRISMA-guidelines. METHODS: PubMed, EMBASE and Cochrane Controlled Trial Reports were searched until April 7, 2020. Structured data abstraction was performed, and methodological quality assessed. RESULTS: Fourteen single-center and three multicenter retrospective studies reported on 1 combined liver-intestinal and 38 MvTx for NET. Nine previously unreported MvTx were added to the analysis. This review found that: i) overall patient survival of 51.2% is attainable; ii) recurrence of 35% is similar to recurrence after liver transplantation for NET; and iii) NET with diffuse abdominal presentation, normally considered a contraindication, could actually benefit from radical resection and MvTx. Data on tailoring of immunosuppression and (neo-)adjuvant treatment are limited, and further studies are needed to optimize post-transplant management. CONCLUSIONS: Although results are encouraging, the reported MvTx experience for NET is limited and requires more detailed prospective multicenter studies and appropriate follow-up and reporting.

Value of 3D right ventricular function over 2D assessment in acute pulmonary embolism.

BACKGROUND: Pulmonary embolism (PE) is a common life-threatening disease, with mortality related to right ventricular (RV) dysfunction. AIMS: To investigate the value of 3D global and regional RV strain in patients with acute PE and at 1 month, as compared to a control population. METHODS AND RESULTS: We conducted a longitudinal case-control prospective study, including 24 consecutive intermediate-risk PE patients. All patients underwent 2D and 3D transthoracic echocardiography within 12 hours of PE diagnosis and 1 month after hospital discharge. A control group was recruited, consisting of healthy volunteers matched on age and sex with PE patients. 3D RV echocardiographic sequences were analyzed by commercial RV-specific software and output meshes were post-processed to extract regional deformation. 3D echocardiographic 1-month follow-up was available in 18 patients. During acute PE, area strain was substantially altered in the RV free wall and within the trabecular septum. PE patients initially had RV dysfunction as assessed by 2D and 3D parameters. At follow-up, 2D parameters were restored compared to the control group, contrary to 3D RV area and circumferential strains. The McConnell's sign was identified in 83% of patients and was associated with reduced apical and global RV area strain. CONCLUSIONS: Our 3D RV strain study demonstrates an incomplete recovery of 3D strain parameters 1 month after an episode of intermediate-risk acute PE despite restored 2D parameters. Further studies are required to assess the prognostic role and implications of this residual RV strain impairment after PE.

Multivisceral Transplantation for Diffuse Portomesenteric Thrombosis: Lessons Learned for Surgical Optimization.

Background: Multivisceral transplantation entails the en-bloc transplantation of stomach, duodenum, pancreas, liver and bowel following resection of the native organs. Diffuse portomesenteric thrombosis, defined as the complete occlusion of the portal system, can lead to life-threatening gastrointestinal bleeding, malnutrition and can be associated with liver and intestinal failure. Multivisceral transplantation is the only procedure that offers a definitive solution by completely replacing the portal system. However, this procedure is technically challenging in this setting. The aim of this study is to describe our experience, highlight the challenges and propose technical solutions. Materials and Methods: We performed a retrospective analysis of our cohort undergoing multivisceral transplantation for diffuse portomesenteric thrombosis at our institution from 2000 to 2020. Donor and recipient demographics and surgical strategies were reviewed in detail and posttransplant complications and survival were analyzed. Results: Five patients underwent MVTx. Median age was 47 years (23-62). All had diffuse portomesenteric thrombosis with life-threatening variceal bleeding. Major blood loss during exenteration was avoided by combining two techniques: embolization of the native organs followed by a novel, staged extraction. This prevented major perioperative blood loss [median intra-operative transfusion of 3 packed red blood cell units (0-5)]. Median CIT was 330 min (316-416). There was no perioperative death. One patient died due to invasive aspergillosis. Four others are alive and well with a median follow-up of 4.1 years (0.3-5.9). Conclusions: Multivisceral transplantation should be considered in patients with diffuse portomesenteric thrombosis that cannot be treated by any other means. We propose a standardized surgical approach to limit the operative risk and improve the outcome.

Postoperative ileus after laparoscopic primary and incisional abdominal hernia repair with intraperitoneal mesh (DynaMesh®-IPOM versus Parietex™ Composite): a single institution experience.

PURPOSE: Laparoscopic primary or incisional abdominal hernia repair with intraperitoneal mesh placement is a well-accepted and safe technique. Evidence for complications however remains inconclusive, and little is known about the occurrence of postoperative ileus secondary to postoperative intra-abdominal adhesions with different types of IPOM meshes used. Therefore, we retrospectively compared the occurrence of postoperative ileus between two of the different meshes used in our center. METHODS: Three hundred seventy-five patients who underwent ventral hernia repair with intraperitoneal mesh placement, either with a DynaMesh®-IPOM (FEG Textiltechnik mbH, Aachen, Nordrhein-Westfalen, Germany) or a Parietex™ Composite mesh (Medtronic, Minneapolis, MN, USA), at the Heilig-Hart Hospital in Lier (Antwerp, Belgium) between 2012 and 2017 were retrospectively compared with regard to the occurrence of postoperative ileus until 6 weeks postoperatively. Baseline demographics and clinical data up to 6 weeks postoperatively of the patients in the two mesh groups are provided. RESULTS: The DynaMesh®-IPOM mesh group was associated with a significantly higher incidence of postoperative ileus compared with the Parietex™ Composite mesh group with a cutoff limit at postoperative day 1 (n = 17, 6.8% vs. n = 0, 0.0%; P = 0.003) and postoperative day 4 (n = 13, 5.2% vs. n = 0, 0.0%, P = 0.006), even with a mesh surface area of ≤ 300 cm2 and when both meshes were fixated with the same method of fixation (Securestrap™) with a cutoff limit for postoperative ileus at postoperative day 1 (n = 4, 7.7% vs. n = 0, 0.0%; P = 0.013) and postoperative day 4 (n = 3, 5.8% vs. n = 0, 0.0%, P = 0.040). Of the 17 patients with a postoperative ileus, 9 (52.9%) had a suspicion of adhesive small bowel obstruction on CT scan (P = 0.033) with definitive confirmation of small bowel adhesions with the DynaMesh®-IPOM mesh at laparoscopy in 2 patients. CONCLUSION: Our results confirm current literature available regarding postoperative ileus secondary to postoperative intra-abdominal adhesions with the DynaMesh®-IPOM mesh. However, further research with well-designed, multicenter randomized controlled studies to evaluate the use and related complications of these meshes is needed.

Right ventricular area strain from 3-dimensional echocardiography: Mechanistic insight of right ventricular dysfunction in pediatric pulmonary hypertension.

BACKGROUND: Right ventricular (RV) function is a major contributor to the outcome of pulmonary arterial hypertension (PAH). Adult studies demonstrated that regional and global changes in RV deformation are prognostic in PAH using 3-dimensional echocardiography (3DE). However, regional and global dynamic changes in RV mechanics have not been described in pediatric PAH. We compared 3DE RV regional and global deformation between pediatric patients who had associated PAH with congenital heart disease (APAH-CHD), pediatric patients who had idiopathic PAH (IPAH), and normal controls, and evaluated the clinical outcomes. METHODS: A total of 48 controls, 47 patients with APAH-CHD, and 45 patients with IPAH were evaluated. 3DE RV sequences were analyzed and post-processed to extract global and regional deformation (circumferential, longitudinal, and area strain). Statistical analyses compared the sub-groups on the basis of global and regional deformation, and outcome analysis was performed. RESULTS: Patients with PAH had significantl8y different global and regional deformation (p < 0.001) compared with controls. Patients with APAH-CHD and and those with IPAH significantly differed in global circumferential strain (p < 0.010), area strain (inlet septum, p = 0.041), and circumferential strain at the inlet septum (p < 0.019), apex free wall (p < 0.004), and inlet free wall (p < 0.004). Circumferential strain at the inlet free wall and circumferential, longitudinal, and area strain at the apex free wall were predictors of adverse events. CONCLUSIONS: RV regional and global strain differ between controls and pediatric patients with PAH. RV apical free-wall area strain provides insight into the mechanism of RV dysfunction in pediatric patients with PAH, with regional strain emerging as outcome predictors, suggesting that this novel measure may be considered as a future measure of RV function.

Three-dimensional right ventricular shape and strain in congenital heart disease patients with right ventricular chronic volume loading.

AIMS: Right ventricular (RV) function assessment is crucial in congenital heart disease patients, especially in atrial septal defect (ASD) and repaired Tetralogy of Fallot (TOF) patients with pulmonary regurgitation (PR). In this study, we aimed to analyse both 3D RV shape and deformation to better characterize RV function in ASD and TOF-PR. METHODS AND RESULTS: We prospectively included 110 patients (≥16 years old) into this case-control study: 27 ASD patients, 28 with TOF, and 55 sex- and age-matched healthy controls. Endocardial tracking was performed on 3D transthoracic RV echocardiographic sequences and output RV meshes were post-processed to extract local curvature and deformation. Differences in shape and deformation patterns between subgroups were quantified both globally and locally. Curvature highlights differences in RV shape between controls and patients while ASD and TOF-PR patients are similar. Conversely, strain highlights differences between controls and TOF-PR patients while ASD and controls are similar [global area strain: -31.5 ± 5.8% (controls), -34.1 ± 7.9% (ASD), -24.8 ± 5.7% (TOF-PR), P < 0.001, similar significance for longitudinal and circumferential strains]. The regional and local analysis highlighted differences in particular in the RV free wall and the apical septum. CONCLUSION: Chronic RV volume loading results in similar RV shape remodelling in both ASD and TOF patients while strain analysis demonstrated that RV strain is only reduced in the TOF group. This suggests a fundamentally different RV remodelling process between both conditions.

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.

Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.

Variability in the Assessment of Myocardial Strain Patterns: Implications for Adequate Interpretation.

Variability in global and regional peak strain has been thoroughly studied, but variability in spatiotemporal myocardial strain patterns has not been studied as well. This study reports on such variability and its implications for adequate disease interpretation. Forty in-training operators, distributed on 20 workstations, analyzed six cases with representative deformation patterns with commercial speckle tracking. Inter-operator differences were quantified through the variability in myocardial delineations, spatiotemporal longitudinal strain patterns and peak longitudinal strain. Intra-operator differences were assessed similarly using 10 repeated measurements from a single clinician expert. Delineations varied mainly along the lateral wall and at the valve level. Peak longitudinal strain variability was low to moderate. The spatiotemporal strain patterns were consistent despite high variability at the apex and near the valve. The results indicate that relevant pattern assessment is possible despite heterogeneous experience with speckle tracking and that careful interpretation of pattern abnormalities should be recommended before a more systematic quantitative analysis.

Machine Learning Approaches for Myocardial Motion and Deformation Analysis.

Information about myocardial motion and deformation is key to differentiate normal and abnormal conditions. With the advent of approaches relying on data rather than pre-conceived models, machine learning could either improve the robustness of motion quantification or reveal patterns of motion and deformation (rather than single parameters) that differentiate pathologies. We review machine learning strategies for extracting motion-related descriptors and analyzing such features among populations, keeping in mind constraints specific to the cardiac application.

Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy.

AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT). METHODS AND RESULTS: We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT-D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K-means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT-D on the primary outcome (all-cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT-D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19-0.64; P = 0.0005 and HR 0.36; 95% CI 0.19-0.68; P = 0.001] than observed in the other groups (interaction P = 0.02). CONCLUSIONS: Our results serve as a proof-of-concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies.

Right Ventricular Function Evolution With Pregnancy in Repaired Tetralogy of Fallot.

This case illustrates the evolution of right ventricular (RV) 3-dimensional (3D) area strain during pregnancy in a patient with repaired Tetralogy of Fallot. The report highlights impairment in RV function with pregnancy, suggesting the importance of prepregnancy RV systolic function assessment, especially using 3D echocardiography.

3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation.

We propose a method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of the U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. The 3-D consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from the U.K. Biobank. It is then tested on the 756 different cases from the U.K. Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, and RVSC with 16 cases). Results comparable or even better than the state of the art in terms of distance measures are achieved. They also emphasize the assets of our method, namely, enhanced spatial consistency (currently neither considered nor achieved by the state of the art), and the generalization ability to unseen cases even from other databases.

Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures differences between HFpEF and healthy subjects. METHODS AND RESULTS: One hundred fifty-six subjects aged >60 years (72 HFpEF+33 healthy for the initial analyses; 24 hypertensive+27 breathless for independent evaluation) underwent stress echocardiography, in the MEDIA study (Metabolic Road to Diastolic Heart Failure). Left ventricular long-axis myocardial velocity patterns were analyzed using an unsupervised ML algorithm that orders subjects according to their similarity, allowing exploration of the main trends in velocity patterns. ML identified a continuum from health to disease, including a transition zone associated to an uncertain diagnosis. Clinical validation was performed (1) to characterize the main trends in the patterns for each zone, which corresponded to known characteristics and new features of HFpEF; the ML-diagnostic zones differed for age, body mass index, 6-minute walk distance, B-type natriuretic peptide, and left ventricular mass index (P<0.05) and (2) to evaluate the consistency of the proposed groupings against diagnosis by current clinical criteria; correlation with diagnosis was good (κ, 72.6%; 95% confidence interval, 58.1-87.0); ML identified 6% of healthy controls as HFpEF. Blinded reinterpretation of imaging from subjects with discordant clinical and ML diagnoses revealed abnormalities not included in diagnostic criteria. The algorithm was applied independently to another 51 subjects, classifying 33% of hypertensive and 67% of breathless controls as mild-HFpEF. CONCLUSIONS: The analysis of left ventricular long-axis function on exercise by interpretable ML may improve the diagnosis and understanding of HFpEF.

Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.

Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes left ventricular remodeling after MI is an open research question. A better descriptor of remodeling is expected to provide a more accurate evaluation of disease status in MI patients. We therefore designed a challenge to test shape characterization in MI given a set of three-dimensional left ventricular surface points. The training set comprised 100 MI patients, and 100 asymptomatic volunteers (AV). The challenge was initiated in 2015 at the Statistical Atlases and Computational Models of the Heart workshop, in conjunction with the MICCAI conference. The training set with labels was provided to participants, who were asked to submit the likelihood of MI from a different (validation) set of 200 cases (100 AV and 100 MI). Sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were used as the outcome measures. The goals of this challenge were to (1) establish a common dataset for evaluating statistical shape modeling algorithms in MI, and (2) test whether statistical shape modeling provides additional information characterizing MI patients over standard clinical measures. Eleven groups with a wide variety of classification and feature extraction approaches participated in this challenge. All methods achieved excellent classification results with accuracy ranges from 0.83 to 0.98. The areas under the receiver operating characteristic curves were all above 0.90. Four methods showed significantly higher performance than standard clinical measures. The dataset and software for evaluation are available from the Cardiac Atlas Project website1.

Model-Based Generation of Large Databases of Cardiac Images: Synthesis of Pathological Cine MR Sequences From Real Healthy Cases.

Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis, and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database with large amounts of synthetic cases requires further investigation. We build upon these works and propose a revised scheme for synthesizing pathological cardiac sequences from real healthy sequences. Our new pipeline notably involves a much easier registration problem to reduce potential artifacts, and takes advantage of mesh correspondences to generate new data from a given case without additional registration. The output sequences are thoroughly examined in terms of quality and usability on a given application: the assessment of myocardial viability, via the generation of 465 synthetic cine MR sequences (15 healthy and 450 with pathological tissue viability [random location, extent, and grade, up to myocardial infarct]). We demonstrate that: 1) our methodology improves the state-of-the-art algorithms in terms of realism and accuracy of the simulated images and 2) our methodology is well-suited for the generation of large databases at small computational cost.