Scintigraphic diagnosis of coronary artery disease: myocardial bull's-eye images contain the important information

Stent Maps--Comparative Visualization for the Prediction of Adverse Events of Transcatheter Aortic Valve Implantations

Transcatheter aortic valve implantation (TAVI) is a minimally-invasive method for the treatment of aortic valve stenosis in patients with high surgical risk. Despite the success of TAVI, side effects such as paravalvular leakages can occur postoperatively. The goal of this project is to quantitatively analyze the co-occurrence of this complication and several potential risk factors such as stent shape after implantation, implantation height, amount and distribution of calcifications, and contact forces between stent and surrounding structure. In this paper, we present a two-dimensional visualization (stent maps), which allows (1) to comprehensively display all these aspects from CT data and mechanical simulation results and (2) to compare different datasets to identify patterns that are typical for adverse effects. The area of a stent map represents the surface area of the implanted stent - virtually straightened and uncoiled. Several properties of interest, like radial forces or stent compression, are displayed in this stent map in a heatmap-like fashion. Important anatomical landmarks and calcifications are plotted to show their spatial relation to the stent and possible correlations with the color-coded parameters. To provide comparability, the maps of different patient datasets are spatially adjusted according to a corresponding anatomical landmark. Also, stent maps summarizing the characteristics of different populations (e.g. with or without side effects) can be generated. Up to this point several interesting patterns have been observed with our technique, which remained hidden when examining the raw CT data or 3D visualizations of the same data. One example are obvious radial force maxima between the right and non-coronary valve leaflet occurring mainly in cases without leakages. These observations confirm the usefulness of our approach and give starting points for new hypotheses and further analyses. Because of its reduced dimensionality, the stent map data is an appropriate input for statistical group evaluation and machine learning methods.

Clinical decision support systems in myocardial perfusion imaging

Diagnostic imaging is becoming more complicated, physicians are also required to master an ever-expanding knowledge base and take into account an ever increasing amount of patient-specific clinical information while the time available to master this knowledge base, assemble the relevant clinical data, and apply it to specific tasks is steadily shrinking. Compounding these problems, there is an ever increasing number of aging "Baby Boomers" who are becoming patients coupled with a declining number of cardiac diagnosticians experienced in interpreting these studies. Hence, it is crucial that decision support tools be developed and implemented to assist physicians in interpreting studies at a faster rate and at the highest level of up-to-date expertise. Such tools will minimize subjectivity and intra- and inter-observer variation in image interpretation, help achieve a standardized high level of performance, and reduce healthcare costs. Presently, there are many decision support systems and approaches being developed and implemented to provide greater automation and to further objectify and standardize analysis, display, integration, interpretation, and reporting of myocardial perfusion SPECT and PET studies. This review focuses on these systems and approaches.

Computer-assisted diagnosis in renal nuclear medicine: rationale, methodology, and interpretative criteria for diuretic renography

The goal of artificial intelligence, expert systems, decision support systems, and computer-assisted diagnosis (CAD) in imaging is the development and implementation of software to assist in the detection and evaluation of abnormalities, to alert physicians to cognitive biases, to reduce intraobserver and interobserver variability, and to facilitate the interpretation of studies at a faster rate and with a higher level of accuracy. These developments are needed to meet the challenges resulting from a rapid increase in the volume of diagnostic imaging studies coupled with a concurrent increase in the number and complexity of images in each patient data. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. Errors are even more likely when physicians interpret low-volume studies such as technetium-99m-mercaptoacetyltriglycine diuretic scans where imagers may have had limited training or experience. Decision support systems include neural networks, case-based reasoning, expert systems, and statistical systems. iRENEX (renal expert) is an expert system for diuretic renography that uses a set of rules obtained from human experts to analyze a knowledge base of both clinical parameters and quantitative parameters derived from the renogram. Initial studies have shown that the interpretations provided by iRENEX are comparable to the interpretations of a panel of experts. iRENEX provides immediate patient-specific feedback at the time of scan interpretation, can be queried to provide the reasons for its conclusions, and can be used as an educational tool to teach trainees to better interpret renal scans. It also has the capacity to populate a structured reporting module and generate a clear and concise impression based on the elements contained in the report; adherence to the procedural and data entry components of the structured reporting module ensures and documents procedural competency. Finally, although the focus is CAD applied to diuretic renography, this review offers a window into the rationale, methodology, and broader applications of computer-assisted diagnosis in medical imaging.

Decision support systems in diuresis renography

The volume of diagnostic imaging studies performed in the United States is rapidly increasing resulting from an increase in the number of patients as well as an increase in the volume of studies per patient. Concurrently, the number and complexity of images in each patient data set are also increasing. Nuclear medicine physicians and radiologists are required to master an ever-expanding knowledge base whereas the hours available to master this knowledge base and apply it to specific tasks are steadily shrinking. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. The problem is particularly acute for low-volume studies such as MAG3 diuresis renography where many imagers may have had limited training or experience. To address this problem, renal decision support systems (DSS) are being developed to assist physicians evaluate suspected obstruction in patients referred for diuresis renography. Categories of DSS include neural networks, case-based reasoning, expert systems and statistical systems; RENEX and CART are examples of renal DSS currently in development. RENEX (renal expert) uses a set of rules obtained from human experts to analyze a knowledge base of expanded quantitative parameters obtained from diuresis MAG3 scintigraphy whereas CART (classification and regression tree analysis) is a statistical method that grows and prunes a decision tree based on an analysis of these quantitative parameters in a training data set. RENEX can be queried to provide the reasons for its conclusions. Initial data show that the interpretations provided by RENEX and CART are comparable to the interpretations of a panel of experts blinded to clinical information. This project should serve as a benchmark for the scientific comparison and collaboration of these 2 fields of medical decision-making. Moreover, we anticipate that these DSS will better define the essential interpretative criteria, foster standardized interpretation, teach trainees to better interpret renal scans, enhance diagnostic accuracy and provide a methodology applicable to other diagnostic problems in radiology and medicine.

Holistic polar map for integrated evaluation of cardiac imaging results

Polar map display (PM) is a comprehensive interpretation of the left ventricle. This is a non-rigid registration of the left ventricle originally for the visual and quantitative analysis of tomographic myocardial perfusion scintigrams. In this scheme the maximal-count circumferential profiles of well-defined short- and long-axis planes are plotted to a map showing the distribution of the perfusion tracer onto a two-dimensional polar representation. The usual coronary artery distribution is often indicated on the PMs of SPECT studies by referring to the regions of the three main coronary branches, nevertheless, the individual variations may differ extensively. We set out to develop an Access (Microsoft)-based computer program that permits an integrated evaluation of the imaging results (coronary angiography, echocardiography and SPECT) on patients with coronary artery disease. This semi-quantitative registration of the coronary tree to a PM focused on the relation between the supplying coronary branches and the myocardial regions of the 16-segment left ventricular evaluating model. All the recorded anatomical and functional data were related to these 16 left ventricular segments, which allowed the direct comparison and holistic synthesis of the results. Two projections were taken into consideration for generation of the coronary PM: from the right anterior oblique projections, the left anterior descendent (LAD)/right coronary artery (RCA) border was assessed through the comparison of the left and right coronary angiograms. The terminations of the visually detected end-arteries showed the separation of the myocardial beds supplied by the two branches. The border of the myocardial beds on the polar map was determined on the "vertical axis" of the local coordinate system. The RCA/ left circumflex (LCx) separation can be determined from the left anterior oblique view. In this projection, the left ventricular septal edge was delineated by the LAD, while the LCx indicated the lateral epicardial surface. The individual coronary artery circulation was typified from among 12 variations in the Holistic Coronary Care program. With this determination of the individual coronary circulation, the lesion-associated segments are generated automatically by the software. The lesion-associated regions are defined as the myocardial bed of a diseased artery distal to the lesion. The PMs generated from the coronary angiographic results were compared with those of 99Tc-labelled MIBI single photon emission computed tomography (SPECT) in order to test the accuracy of the localizing method. The overlap between the segments associated with the coronary lesion and the stress perfusion defects (<80% relative MIBI activity during stress tests) was analyzed in 10 patients with (sub)total coronary occlusion after myocardial infarction. The distributions of the segments with stress perfusion defects on MIBI SPECT gave positive and negative predictive values of coronary occlusion of 0.94 and 0.8, respectively. According to the 16-segment wall motion analysis by echocardiography, the positive and negative predictive values of coronary occlusion for wall motion abnormality were 0.82 and 0.76, respectively. While the distal part of the subtended region usually demonstrated a higher degree perfusion abnormality than the proximal part, the high positive predictive value proved that, during the stress condition, the perfusion defect could be detected in practically all the subtended regions. The low negative predictive value of the coronary lesion for the wall motion abnormality was associated with the remodeling of the entire left ventricle.

Velocity Tracking–A Novel Method for Quantitative Analysis of Longitudinal Myocardial Function

Doppler tissue imaging is a method for quantitative analysis of longitudinal myocardial velocity. Commercially available ultrasound systems can only present velocity information using a color Doppler-based overlapping continuous color scale. The analysis is time-consuming and does not allow for simultaneous analysis in different projections. We have developed a new method, velocity tracking, using a stepwise color coding of the regional longitudinal myocardial velocity. The velocity data from 3 apical projections are presented as static and dynamic bull's-eye plots to give a 3-dimensional understanding of the function of the left ventricle. The static bull's-eye plot can display peak systolic velocity, late diastolic tissue velocity, or the sum of peak systolic velocity and early diastolic tissue velocity. Conversely, the dynamic bull's-eye plot displays how the myocardial velocities change over one heart cycle. Velocity tracking allows for a fast, simple, and intuitive visual analysis of the regional longitudinal contraction pattern of the left ventricle with a great potential to identify characteristic pathologic patterns.

Scandinavian test of artificial neural network for classification of myocardial perfusion images

Artificial neural networks are systems of elementary computing units capable of learning from examples. They have been applied to automated interpretation of myocardial perfusion images and have been shown to perform even better than experienced physicians. It has been shown that physicians interpreting myocardial perfusion images benefit from the advice of such networks. These networks have been developed and validated in the same hospital. However, widespread use of neural networks will only take place if the networks can maintain a high accuracy in other hospitals, i.e. hospitals using different gamma cameras, different acquisition techniques, different study protocols, etc. The purpose of this study was to develop a neural network in one hospital and test it in another. An artificial neural network was trained to detect coronary artery disease using myocardial perfusion scintigrams from 135 patients at a Swedish hospital. Thereafter, this network was tested using scintigrams from 68 patients at a Danish hospital and compared to six criteria based on expert physician analysis and quantitative analysis by the CEqual program. The sensitivity of the network was significantly higher than that of one of the physician criteria (0. 92 versus 0.71) and two of the CEqual-based criteria (0.94 versus 0. 63 and 0.96 versus 0.65) compared at equal specificities. It was concluded that an artificial neural network can maintain high accuracy in a hospital other than the one where it was developed.

Myocardial SPET: artificial neural networks describe extent and severity of perfusion defects

Artificial neural networks are computer programs that learn from examples. They have been successfully used to detect coronary artery disease from myocardial perfusion images. The purpose of the present study was to develop neural networks that could classify myocardial scintigrams regarding reversibility, localization, severity and extent of perfusion defects. Rest/exercise technetium-99m sestamibi scintigrams from 338 patients were studied. The classifications of two experts were employed as the gold standard. Artificial neural networks were trained to classify both reversible (ischaemia) and non-reversible (infarct) defects in three vascular territories, corresponding to the main coronary arteries. The extent (small or large) and severity (mild or severe) of the defects were described by the networks. After the training process, separate test sets were used to compare the neural networks with one of the experts who reclassified the scintigrams two months later. The neural networks made correct classifications in 71% of the test cases and the human expert in 70% (P=0.10). It was concluded that artificial neural networks can be trained to make clinical interpretations of myocardial perfusion scintigrams. The results indicate that networks can assist physicians in achieving correct interpretations and thereby improve the diagnostic accuracy of medical imaging.