Norberto F. Ezquerra

Technical aspects of myocardial spect imaging with technetium-99m sestamibi

Most reports to date using single photon emission computed tomography (SPECT) with technetium-99m (Tc-99m) sestamibi have used acquisition parameters that were optimized for thallium-201. To fully utilize the superior imaging characteristics of Tc-99m sestamibi, there is a need to optimize the technical aspects of SPECT imaging for this agent. Performance can be enhanced through the careful selection of optimal radiopharmaceutical doses, imaging sequences, acquisition parameters, reconstruction filters, perfusion quantification methods and multidimensional methods for visualizing perfusion distribution. The current report describes theoretical considerations, phantom studies and preliminary patient results that have led to optimized protocols, developed at Emory University and Cedars-Sinai Medical Center, for same-day rest-stress studies, given existing instrumentation and recommended dose limits. The optimizations were designed to fit a low-dose-high-dose rest-stress same-day imaging protocol. A principal change in the acquisition parameters compared with previous Tc-99m sestamibi protocols is the use of a high-resolution collimator. The approach is being developed in both prone and supine positions. A new method for extracting a 3-dimensional myocardial count distribution has been developed that uses spherical coordinates to sample the apical region and cylindrical coordinates to sample the rest of the myocardium. New methods for visualizing the myocardial distribution in multiple dimensions are also described, with improved 2-dimensional, as well as 3- and 4-dimensional (3 dimensions plus time) displays. In the improved 2-dimensional display, distance-weighted and volume-weighted polar maps are used that appear to significantly improve the representation of defect location and defect extent, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Interactively deformable models for surgery simulation

A methodology that addresses important issues concerned with the underlying graphical models designed for surgical simulation, as well as issues related to the real-time interactivity with, and manipulation of, these models is presented. The specific application of interest is laparoscopic surgery, which is performed using endoscopes that present a video image of the organs to the clinicians. The surgeon then performs the surgery while looking at the video monitor. The particular focus is gall bladder surgery, which involves various gastrointestinal organs. The overall objective is to simulate this environment by creating realistic, manipulable models of these organs. The models are interactively manipulable and exhibit behavior both visually acceptable and physically accurate. The approach is based on the notion of active surfaces. The rationale, mathematical formalism, and visualization techniques encompassed by the methodology are described. Recent results obtained from applying these methods to the problem of endoscopic gall bladder surgery simulation are presented.<<ETX>>

Mining constrained association rules to predict heart disease

This work describes our experiences in discovering association rules in medical data to predict heart disease. We focus on two aspects of this work: mapping medical data to a transaction format suitable for mining association rules, and identifying useful constraints. Based on these aspects we introduce an improved algorithm to discover constrained association rules. We present an experimental section explaining several interesting discovered rules.

Visualization of multimodality cardiac imagery

The methods and results associated with a research program aimed at quantifying and visualizing the unified anatomic and physiologic information obtained from two complementary imaging modalities-structural information describing coronary vessel anatomy and functional information related to heart muscle physiology-are presented and discussed. The reconstruction, processing, and visualization of three-dimensional cardiovascular structure, including the procedures and results obtained from phantom and patient studies, are emphasized. The visualization methodology is designed to convey a significant amount of multimodality information in a single, meaningful display. The methodology is also designed to quantify the visualized information and to provide the information both visually and textually. Hence, both objective and subjective assessments of medical information are possible in complementary forms and in interactive fashions.<<ETX>>

Automated segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial, and structural constraints

The methods presented here have been developed to perform the automated segmentation of coronary arterial structure from cine sequences of biplanar x-ray angiograms. We introduce a methodology to impose an integrated set of constraints based on knowledge concerning the anatomical structure of the vascular system, temporal changes in position due to motion, and spatial coherence. Results are shown for data sets generated from both porcine and human studies.

Model-guided labeling of coronary structure

Assigning anatomic labels to coronary arteries in X-ray angiograms is an important task in medical imaging, motivated by the desire to standardize the assessment of coronary artery disease and to facilitate the three-dimensional (3-D) reconstruction and visualization of the coronary vasculature. However, automatic labeling poses a number of significant challenges, including the presence of noise, artifacts, competing structures, misleading visual cues, and other difficulties associated with a dynamic and inherently complex structure. The authors have developed a model-guided approach that addresses these challenges and automatically labels the vascular structure in coronary angiographic images. The approach consists of two models: (1) a symbolic model, represented through a directed acyclic graph, that captures vascular tree hierarchies and branch interrelationships and (2) a generalized 3-D model that captures spatial and geometric relationships. Importantly, the approach detects ambiguities (such as vessel overlaps) that may be found in a frame of a cine sequence, and resolves these ambiguities by considering the information derived from other (unambiguous) frames in the temporal sequence, employing dynamic programming methods to match the image features found in the different (ambiguous and unambiguous) frames. This paper presents this model-guided labeling algorithm and discusses the experimental results obtained from implementing and applying the resulting labeling system to a variety of clinical images. The results indicate the feasibility of achieving robust and consistently accurate image labeling through this model-guided, temporal disambiguation method.

Polyhedra generation from lattice points

This paper focuses on a method for generating polyhedra from a set of lattice points, such as three-dimensional (3D) medical computerized tomography images. The method is based on combinatorial topology [1] and algebraic properties of the 3D lattice space [2]. It is shown that the method can uniquely generate polyhedra from a subset of the lattice space independently of the choice of neighborhood. Furthermore, a practical algorithm is developed and experimental results using 3D medical imagery are presented.

A fast algorithm to cluster high dimensional basket data

Clustering is a data mining problem that has received significant attention by the database community. Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero. This is the case with basket data transactions containing items, that can be represented as sparse binary vectors with very high dimensionality. An experimental section shows performance, advantages and limitations of the proposed approach.

Automatic determination of LV orientation from SPECT data

Presents a new method to determine the orientation or pose of the left ventricle (LV) of the heart from cardiac SPECT (single photon emission computed tomography) data. This proposed approach offers an accurate, fast, and robust delineation of the LV long-axis. The location and shape of the generated long-axis can then be utilized to define automatically the tomographic slices for enhanced visualization and quantification of the clinical data. The methodology is broadly composed of two main steps: (1) volume segmentation of cardiac SPECT data; and (2) topological goniometry, a novel approach incorporating volume visualization and computer graphics ideas to determine the overall shape of 3-D objects. The outcome of the algorithm is a 3-D curve representing the overall pose of the LV long-axis. Experimental results on both phantom and clinical data (50 technetium-99m and 74 thallium-201) are presented. An interactive graphical interface to visualize the volume (3-D) data, the left ventricle, and its pose is an integral part of the overall methodology. This technique is completely data driven and expeditious, making it viable for routine clinical use.

Interactive, knowledge-guided visualization of 3D medical imagery

Abstract One of the most important and difficult decision-making tasks is that of interpreting three-dimensional (3D) medical imagery. This information-intensive task typically requires expediency, accuracy and reliability in both the visual presentation as well as in the interpretation of complex information. We present an approach that facilitates this task by using domain knowledge to assist in the interpretation and visualization of 3D cardiac imagery. The objective is to provide the clinician with a more efficient, reliable, comprehensive, and clinically useful manner with which to accurately interpret and display large amounts of complex information. These objectives are met by capturing and representing the visual reasoning process in a computational model, and providing the user with an intuitive graphical and textual representation of the data, as well as explanations, justifications, and interactive data visualizations. The approach is illustrated with cardiovascular 3D SPECT tomographic perfusion imagery, a technique aimed at diagnosing heart disease.