Biomedical subjects

D R Aberle

Publications and source records attributed to D R Aberle.

61 records · Page 4Linked to original sources

PubMedDate not supplied

A unified timeline model and user interface for multimedia medical databases.

A multimedia medical database model and prototype is described for supporting a timeline-based presentation of information. The database links image and text data in a way that permits users to look at medical information in a single unified view. Various visualization programs permit the user to view data in various ways, including full image views, graphs, and tables. Our technology is applied for proof-of-concept to two areas: thoracic oncology and thermal tumor ablation therapy of the brain. This effort is part of the multidisciplinary KMeD project in collaboration with medical research and clinical treatment projects at UCLA.

Data Display↗

PubMedDate not supplied

The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography.

In this project, patients with a solitary pulmonary nodule, were imaged using high resolution computed tomography. Quantitative measures of texture were extracted from these images using co-occurrence matrices. These matrices were formed with different combinations of gray level quantization, distance between pixels and angles. The derived measures were input to a linear discriminant classifier to predict the classification (benign or malignant) of each nodule. Using a relative quantization scheme with eight levels, four features yielded an area under the ROC curve (Az) of 0.992; 93.8% (30/32) of cases were correctly classified when training and testing on the same cases; while 90.6% (29/32) were correctly classified when jackknifing was used.

Diagnosis, Computer-Assisted↗

PubMedDate not supplied

Development and testing of image-processing methods for the quantitative assessment of airway hyperresponsiveness from high-resolution CT images.

PURPOSE: Our goal was to develop a protocol and image-processing methods to quantitate both bronchial and lung attenuation changes in patients imaged with helical high-resolution CT (HRCT). METHOD: Human subjects underwent helical HRCT at two suspended breath-hold conditions, functional residual capacity and residual volume, at baseline and following methacholine-induced bronchoprovocation. A semiautomated contouring program was used to define anatomically like bronchi and axial lung sections from the different physiologic sequences, from which automated measurements of area, shape, and attenuation were made. Because the gray level threshold for contouring directly affects the measured area of an anatomic structure, two types of evaluation studies were performed. These included in vivo measurements using baseline parameters of human subjects as the standard of reference and in vitro measurements of a CT phantom designed to simulate the air-soft tissue interfaces of bronchi. RESULTS: Phantom tests showed that the minimum difference between actual and measured areas of holes occurred at a threshold of -500 HU. The smallest diameter holes were most sensitive to changes in threshold value. However, although absolute area measurements of both simulated and human bronchi varied with threshold level, the percent changes in airway areas between baseline and bronchoprovocation sequences were relatively stable at any given threshold. CONCLUSION: These image-processing tools provide reproducible measurements of area as well as attenuation characteristics of pulmonary structures and may offer insights into the practical use of functional imaging in evaluating conditions of airflow obstruction.

Bronchial Hyperreactivity↗

PubMedDate not supplied

Automated measurement of single and total lung volume from CT.

PURPOSE: The goal of this work was to develop an automated method for calculating single (SLV) and total (TLV) lung volumes from CT images. METHOD: Patients underwent volumetric CT scanning through the entire chest in a single breath-hold, as well as pulmonary function tests. An automated, knowledge-based system was developed to segment the lungs in the CT images. Image-processing routines were used to extract sets of voxels from the image data that were identified by matching them to anatomical objects defined in a model. SLV and TLV were calculated by summing included voxels. RESULTS: For 43 patients analyzed, TLV from CT and total lung capacity from body plethysmography were strongly correlated (r = 0.90). On average, the CT-derived volume of the left lung accounted for 47.2% of the total. CONCLUSION: A knowledge-based approach to segmentation of the lungs in CT can be used to automatically estimate SLV and TLV.

Artificial Intelligence↗

PubMedDate not supplied

Problem-oriented prefetching for an integrated clinical imaging workstation.

Prefetching methods have traditionally been used to restore archived images from picture archiving and communication systems to diagnostic imaging workstations prior to anticipated need, facilitating timely comparison of historical studies and patient management. The authors describe a problem-oriented prefetching scheme, detailing 1) a mechanism supporting selection of patients for prefetching via characterizations of clinical problems, using multiple data sources (picture archiving and communication systems, hospital information systems, and radiology information systems), classifying patients into cohorts on the basis of their medical conditions (e.g., lung cancer); and 2) prefetching of multimedia data (imaging, laboratory, and medical reports) from clinical databases to enable the viewing of an integrated patient record. Preliminary evaluation of the prefetching algorithm using classic information retrieval measures showed that the system had high recall (100 percent), correctly identifying and retrieving data for all patients belonging to a target cohort, but low precision (50 percent). A key finding during testing was that the recall of the system was increased through the use of multiple data sources (compared with one data source), because of better patient descriptors. Medical problems and patient cohorts were more specifically defined by combining information from heterogeneous databases.

Algorithms↗

PubMedDate not supplied

Hyperad: augmenting and visualizing free text radiology reports.

Hyperad is an automated computer system designed to extract key concepts from thoracic radiology reports and give physicians access to a large database containing the reports and key concepts. The concepts are extracted from textual documents with natural language processing techniques, then stored with the original documents in the database, which can be queried in terms of findings or associated attributes from an intuitive and easily accessible interface. The extracted concepts are represented both textually in a coded hypertext format and graphically on a coronal cross-sectional anatomy atlas, an idealized graphical model of human anatomy. To facilitate implementation, the communication protocols and standards of the World Wide Web (Web) were adopted. The reports and associated forms are encoded in standard hypertext markup language, which makes it possible to use hypermedia links to navigate the Hyperad database with any graphical Web browser. In the future, Hyperad may prove useful for other applications.

Computer Systems↗

PubMedDate not supplied

Pitfalls in the diagnosis of thoracic aortic dissection at CT angiography.

Two hundred seventy-five computed tomographic (CT) angiograms of the thoracic aorta were obtained over a period of approximately 4 years in patients with suspected or known aortic dissection. In all cases, unenhanced images were initially obtained, followed by contrast material-enhanced images. A variety of pitfalls were encountered that mimicked aortic dissection. These pitfalls were attributable to technical factors (eg, improper timing of contrast material administration relative to image acquisition); streak artifacts generated by high-attenuation material, high-contrast interfaces, or cardiac motion; periaortic structures (eg, aortic arch branches, mediastinal veins, pericardial recess, thymus, atelectasis, pleural thickening or effusion adjacent to the aorta); aortic wall motion and normal aortic sinuses; aortic variations such as congenital ductus diverticulum and acquired aortic aneurysm with thrombus; and penetrating atherosclerotic ulcer. Although several of these pitfalls are easy to recognize and therefore unlikely to present a diagnostic problem, others are potentially confusing. Familiarity with these common pitfalls, coupled with a knowledge of normal intrathoracic anatomy, will facilitate recognition of true aortic dissection and help avoid misdiagnosis at thoracic aortic CT angiography.

Aortic Dissection↗