Biomedical subjects

M F McNitt-Gray

Publications and source records attributed to M F McNitt-Gray.

At least 19 recordsLinked to original sources

PubMed2005

A Monte Carlo based method to estimate radiation dose from multidetector CT (MDCT): cylindrical and anthropomorphic phantoms.

The purpose of this work was to extend the verification of Monte Carlo based methods for estimating radiation dose in computed tomography (CT) exams beyond a single CT scanner to a multidetector CT (MDCT) scanner, and from cylindrical CTDI phantom measurements to both cylindrical and physical anthropomorphic phantoms. Both cylindrical and physical anthropomorphic phantoms were scanned on an MDCT under the specified conditions. A pencil ionization chamber was used to record exposure for the cylindrical phantom, while MOSFET (metal oxide semiconductor field effect transistor) detectors were used to record exposure at the surface of the anthropomorphic phantom. Reference measurements were made in air at isocentre using the pencil ionization chamber under the specified conditions. Detailed Monte Carlo models were developed for the MDCT scanner to describe the x-ray source (spectra, bowtie filter, etc) and geometry factors (distance from focal spot to isocentre, source movement due to axial or helical scanning, etc). Models for the cylindrical (CTDI) phantoms were available from the previous work. For the anthropomorphic phantom, CT image data were used to create a detailed voxelized model of the phantom's geometry. Anthropomorphic phantom material compositions were provided by the manufacturer. A simulation of the physical scan was performed using the mathematical models of the scanner, phantom and specified scan parameters. Tallies were recorded at specific voxel locations corresponding to the MOSFET physical measurements. Simulations of air scans were performed to obtain normalization factors to convert results to absolute dose values. For the CTDI body (32 cm) phantom, measurements and simulation results agreed to within 3.5% across all conditions. For the anthropomorphic phantom, measured surface dose values from a contiguous axial scan showed significant variation and ranged from 8 mGy/100 mAs to 16 mGy/100 mAs. Results from helical scans of overlapping pitch (0.9375) and extended pitch (1.375) were also obtained. Comparisons between the MOSFET measurements and the absolute dose value derived from the Monte Carlo simulations demonstrate agreement in terms of absolute dose values as well as the spatially varying characteristics. This work demonstrates the ability to extend models from a single detector scanner using cylindrical phantoms to an MDCT scanner using both cylindrical and anthropomorphic phantoms. Future work will be extended to voxelized patient models of different sizes and to other MDCT scanners.

Body Burden↗

PubMed2003

A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient-specific models.

The purpose of this work is to develop and test a method to estimate the relative and absolute absorbed radiation dose from axial and spiral CT scans using a Monte Carlo approach. Initial testing was done in phantoms and preliminary results were obtained from a standard mathematical anthropomorphic model (MIRD V) and voxelized patient data. To accomplish this we have modified a general purpose Monte Carlo transport code (MCNP4B) to simulate the CT x-ray source and movement, and then to calculate absorbed radiation dose in desired objects. The movement of the source in either axial or spiral modes was modelled explicitly while the CT system components were modelled using published information about x-ray spectra as well as information provided by the manufacturer. Simulations were performed for single axial scans using the head and body computed tomography dose index (CTDI) polymethylmethacrylate phantoms at both central and peripheral positions for all available beam energies and slice thicknesses. For comparison, corresponding physical measurements of CTDI in phantom were made with an ion chamber. To obtain absolute dose values, simulations and measurements were performed in air at the scanner isocentre for each beam energy. To extend the verification, the CT scanner model was applied to the MIRD V model and compared with published results using similar technical factors. After verification of the model, the generalized source was simulated and applied to voxelized models of patient anatomy. The simulated and measured absolute dose data in phantom agreed to within 2% for the head phantom and within 4% for the body phantom at 120 and 140 kVp; this extends to 8% for the head and 9% for the body phantom across all available beam energies and positions. For the head phantom, the simulated and measured absolute dose data agree to within 2% across all slice thicknesses at 120 kVp. Our results in the MIRD phantom agree within 11% of all the different organ dose values published by the UK's ImPACT group for a scan using an equivalent scanner, kVp, collimation, pitch and mAs. The CT source model was shown to calculate both a relative and absolute radiation dose distribution throughout the entire volume in a patient-specific matrix geometry. Results of initial testing are promising and application to patient models was shown to be feasible.

Adult↗

PubMed2001

Patient-specific models for lung nodule detection and surveillance in CT images.

The purpose of this work is to develop patient-specific models for automatically detecting lung nodules in computed tomography (CT) images. It is motivated by significant developments in CT scanner technology and the burden that lung cancer screening and surveillance imposes on radiologists. We propose a new method that uses a patient's baseline image data to assist in the segmentation of subsequent images so that changes in size and/or shape of nodules can be measured automatically. The system uses a generic, a priori model to detect candidate nodules on the baseline scan of a previously unseen patient. A user then confirms or rejects nodule candidates to establish baseline results. For analysis of follow-up scans of that particular patient, a patient-specific model is derived from these baseline results. This model describes expected features (location, volume and shape) of previously segmented nodules so that the system can relocalize them automatically on follow-up. On the baseline scans of 17 subjects, a radiologist identified a total of 36 nodules, of which 31 (86%) were detected automatically by the system with an average of 11 false positives (FPs) per case. In follow-up scans 27 of the 31 nodules were still present and, using patient-specific models, 22 (81%) were correctly relocalized by the system. The system automatically detected 16 out of a possible 20 (80%) of new nodules on follow-up scans with ten FPs per case.

Automation↗

PubMed2000

Knowledge-based segmentation of thoracic computed tomography images for assessment of split lung function.

The assessment of differential left and right lung function is important for patients under consideration for lung resection procedures such as single lung transplantation. We developed an automated, knowledge-based segmentation algorithm for purposes of deriving functional information from dynamic computed tomography (CT) image data. Median lung attenuation (HU) and area measurements were automatically calculated for each lung from thoracic CT images acquired during a forced expiratory maneuver as indicators of the amount and rate of airflow. The accuracy of these derived measures from fully automated segmentation was validated against those from segmentation using manual editing by an expert observer. A total of 1313 axial images were analyzed from 49 patients. The images were segmented using our knowledge-based system that identifies the chest wall, mediastinum, trachea, large airways and lung parenchyma on CT images. The key components of the system are an anatomical model, an inference engine and image processing routines, and segmentation involves matching objects extracted from the image to anatomical objects described in the model. The segmentation results from all images were inspected by the expert observer. Manual editing was required to correct 183 (13.94%) of the images, and the sensitivity, specificity, and accuracy of the knowledge-based segmentation were greater than 98.55% in classifying pixels as lung or nonlung. There was no significant difference between median lung attenuation or area values from automated and edited segmentations (p > 0.70). Using the knowledge-based segmentation method we can automatically derive indirect quantitative measures of single lung function that cannot be obtained using conventional pulmonary function tests.

Algorithms↗

PubMed1999

A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results.

The purpose of this research is to characterize solitary pulmonary nodules as benign or malignant based on quantitative measures extracted from high resolution CT (HRCT) images. High resolution CT images of 31 patients with solitary pulmonary nodules and definitive diagnoses were obtained. The diagnoses of these 31 cases (14 benign and 17 malignant) were determined from either radiologic follow-up or pathological specimens. Software tools were developed to perform the classification task. On the HRCT images, solitary nodules were identified using semiautomated contouring techniques. From the resulting contours, several quantitative measures were extracted related to each nodule's size, shape, attenuation, distribution of attenuation, and texture. A stepwise discriminant analysis was performed to determine which combination of measures were best able to discriminate between the benign and malignant nodules. A linear discriminant analysis was then performed using selected features to evaluate the ability of these features to predict the classification for each nodule. A jackknifed procedure was performed to provide a less biased estimate of the linear discriminator's performance. The preliminary discriminant analysis identified two different texture measures--correlation and difference entropy--as the top features in discriminating between benign and malignant nodules. The linear discriminant analysis using these features correctly classified 28/31 cases (90.3%) of the training set. A less biased estimate, using jackknifed training and testing, yielded the same results (90.3% correct). The preliminary results of this approach are very promising in characterizing solitary nodules using quantitative measures extracted from HRCT images. Future work involves including contrast enhancement and three-dimensional measures extracted from volumetric CT scans, as well as the use of several pattern classifiers.

Biophysical Phenomena↗

PubMed1999

Cardiac electron-beam CT in children undergoing surgical repair for pulmonary atresia.

PURPOSE: To study whether electron-beam computed tomography (CT) is as accurate as conventional angiocardiography for the characterization of the true pulmonary arteries and the aortopulmonary collateral vessels in children undergoing surgical correction for pulmonary atresia. MATERIALS AND METHODS: Twenty-three children with pulmonary atresia underwent 48 cardiac-triggered dynamic contrast material-enhanced electron-beam CT studies. Correlation was made with surgical findings in all patients and with 34 cineangiocardiograms. Data from reconstructed electron-beam CT images and cineangiocardiograms were reviewed for the presence, caliber, and origin of true pulmonary arteries and aortopulmonary collateral vessels; for stenosis; for thrombosis; and for the patency of vascular conduits and shunts. RESULTS: Electron-beam CT was more sensitive than angiography in the identification of hypoplastic and/or nonconfluent branch pulmonary arteries, coronary anomalies, conduit and shunt thrombosis, and other postoperative complications, but it was less sensitive in the demonstration of stenoses at collateral vascular origins and anastomoses. Overall test parameters for electron-beam CT and angiography to characterize pulmonary vascularity were similar (sensitivity, 0.94 vs 0.90; specificity, 0.99 for both; accuracy, 0.97 vs 0.95). Three-dimensional reconstructions, although they were helpful in conveying electron-beam CT findings to referring cardiologists and surgeons, did not add diagnostic information to that displayed on images of the transverse sections. CONCLUSION: Electron-beam CT complements conventional diagnostic angiocardiography in preoperative evaluation, especially in the detection of hypoplastic pulmonary arteries. It is well suited for postoperative shunt surveillance.

Adolescent↗

PubMed1998

Airway hyperreactivity: assessment with helical thin-section CT.

PURPOSE: To determine the accuracy of helical computed tomography (CT) for assessing reversible changes in bronchial size and air trapping due to airway hyperreactivity. MATERIALS AND METHODS: Spirometry and helical CT were performed in 15 patients with mild asthma and six healthy control subjects before and after bronchial provocation with methacholine chloride and after reversal of provocation with albuterol. CT was performed at suspended functional residual capacity and at residual volume in two lung regions (above and below the carina). Bronchial area and lung attenuation measurements were compared. RESULTS: At baseline, lung attenuation frequency distribution curves were similar between the control and asthma groups. After methacholine, control subjects showed a decrease of less than 10% in the forced expiratory volume at 1 second (FEV1) and no significant differences in lung attenuation curves. Patients with asthma showed a 20%-36% decrease in FEV1, with significant decreases in the median and lowest 10th percentile regions of the attenuation curves and in the cross-sectional area of small (< 5-mm2) airways (P < .001 for all comparisons). After albuterol, control subjects showed no change in spirometric measurements, lung attenuation, or bronchial size, whereas all such parameters returned to baseline levels in patients with asthma. CONCLUSION: Functional helical CT can accurately demonstrate reversible airflow obstruction resulting from airway hyperreactivity.

Adolescent↗

PubMed1998

The evolution of an integrated timeline for oncology patient healthcare.

The introduction of computers in the medical environment has contributed to the proliferation of medical data, often making it difficult to consolidate information on a single patient. In patients with complex medical problems, such as oncology patients, the lack of data integration can negatively impact on patient care. This paper presents an infrastructure for the creation of an integrated multimedia timeline that automatically combines patient information from distributed hospital information sources, and creates a visual summary of pertinent events in a patient's medical history. In this prototype, we focus on oncology patients under treatment for advanced cancers.

Database Management Systems↗

PubMed1997

Coronary artery calcium: alternate methods for accurate and reproducible quantitation.

RATIONALE AND OBJECTIVES: The aim of this study was to determine a more precise and accurate method of quantitating coronary artery calcium (CAC) detected with electron-beam computed tomography (CT) in patients with low CAC scores. MATERIALS AND METHODS: Two 40-section, 3-mm-collimation, electrocardiographically gated electron-beam CT examinations of the heart were performed in each patient. Fifty patients with average scores between 2 and 100, as determined with the conventional scoring algorithm, were selected. The modified conventional scoring algorithm was compared with two techniques: calculated calcium volume and approximated calcium mass. RESULTS: The percentage difference between scans ranged from 37.2% for the conventional scoring method to 28.2% and 28.4% for volume- and mass-based methods, respectively. Increasing lesion size thresholds does not improve quantitative precision and reduces accuracy in patients with small amounts of CAC. CONCLUSION: Quantification methods based on calcification volume or mass decrease score variation compared with the conventional scoring method, and increased size threshold does not improve accuracy.

Adult↗

PubMed1997

Method for segmenting chest CT image data using an anatomical model: preliminary results.

We present an automated, knowledge-based method for segmenting chest computed tomography (CT) datasets. Anatomical knowledge including expected volume, shape, relative position, and X-ray attenuation of organs provides feature constraints that guide the segmentation process. Knowledge is represented at a high level using an explicit anatomical model. The model is stored in a frame-based semantic network and anatomical variability is incorporated using fuzzy sets. A blackboard architecture permits the data representation and processing algorithms in the model domain to be independent of those in the image domain. Knowledge-constrained segmentation routines extract contiguous three-dimensional (3-D) sets of voxels, and their feature-space representations are posted on the blackboard. An inference engine uses fuzzy logic to match image to model objects based on the feature constraints. Strict separation of model and image domains allows for systematic extension of the knowledge base. In preliminary experiments, the method has been applied to a small number of thoracic CT datasets. Based on subjective visual assessment by experienced thoracic radiologists, basic anatomic structures such as the lungs, central tracheobronchial tree, chest wall, and mediastinum were successfully segmented. To demonstrate the extensibility of the system, knowledge was added to represent the more complex anatomy of lung lesions in contact with vessels or the chest wall. Visual inspection of these segmented lesions was also favorable. These preliminary results suggest that use of expert knowledge provides an increased level of automation compared with low-level segmentation techniques. Moreover, the knowledge-based approach may better discriminate between structures of similar attenuation and anatomic contiguity. Further validation is required.

Computer Simulation↗

PubMed1996

Integrated multimedia timeline of medical images and data for thoracic oncology patients.

A prototype multimedia medical database has been developed to provide image and textual data for thoracic oncology patients undergoing treatment of advanced malignancies. The database integrates image data from the hospital picture archiving and communication system with textual reports from the radiology information system, alphanumeric data contained in the hospital information system, and other electronic medical data. The database presents information in a timeline format and also contains visualization programs that permit the user to view and annotate radiographic measurements in tabular or graphic form. The database provides an efficient and intuitive display of the changing status of oncology patients. The ability to integrate, manage, and access relevant multimedia information may substantially enhance communication among distributed multidisciplinary health care providers and may ensure greater consistency and completeness of patient-related data.

Databases, Factual↗

PubMed1993

An automatic method for enhancing the display of different tissue densities in digital chest radiographs.

Digital chest radiographs are often too bright and/or lack contrast when viewed on a video display. This often occurs in radiographs taken of patients with dense lungs, or when incorrect x-ray exposure techniques or inappropriate image preprocessing operations are performed (eg, by the computed radiography system or laser scanner). This article describes a method to automatically provide brightness and contrast adjustments to selectively enhance either soft or dense tissues. This method reduces viewer interaction and improves displayed image quality. The algorithm analyzes the gray-level histogram of a chest radiograph and determines the breakpoints that separate the region outside the patient (background), the radiographically soft tissues, and the radiographically dense tissues. From these breakpoints, a series of piecewise linear look-up tables (LUTs) is generated to selectively enhance either the soft tissues or the dense tissues. This is performed by: (1) varying the contrast in the patient background to achieve the desired overall brightness, (2) selectively increasing the contrast of the tissue region of interest, and (3) reducing or maintaining the contrast of the remaining region. The resulting LUTs are applied to the original image via video display.

Algorithms↗

PubMed1992

Image preprocessing for a picture archiving and communication system.

OBJECTIVES AND RATIONALE: In a picture archiving and communication system (PACS), images are acquired from multiple modalities and displayed on an electronic workstation. Each modality has different image characteristics. This variability must be addressed before the image is displayed. METHODS: The authors developed methods to automatically process magnetic resonance (MR), computed tomographic (CT), and computed radiography (CR) images before display and subjectively evaluated their effectiveness. RESULTS: Unwanted background successfully was automatically removed from 89.5% of 615 CR images. Of 803 chest, abdomen, and hand images 93% were automatically rotated to the correct orientation. CONCLUSIONS: Automated preprocessing of PACS images can be performed successfully, improving speed and convenience for the radiologist interpreting images at an electronic workstation.

Image Processing, Computer-Assisted↗

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

Knowledge-based segmentation of pediatric kidneys in CT for measurement of parenchymal volume.

PURPOSE: The purpose of this work was to develop an automated method for segmenting pediatric kidneys in helical CT images and measuring their volume. METHOD: An automated system was developed to segment the kidneys. Parametric features of anatomic structures were used to guide segmentation and labeling of image regions. Kidney volumes were calculated by summing included voxels. For validation, the kidney volumes of four swine were calculated using our approach and compared with the "true" volumes measured after harvesting the kidneys. Automated volume calculations were also performed in a cohort of nine children. RESULTS: The mean difference between the calculated and measured values in the swine kidneys was 1.38 ml. For the pediatric cases, calculated volumes ranged from 41.7 to 252.1 ml/kidney, and the mean ratio of right to left kidney volume was 0.96. CONCLUSION: These results demonstrate the accuracy of a volumetric technique that may in the future provide an objective assessment of renal damage.

Artificial Intelligence↗