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Biomedical subjects

C E Metz

Publications and source records attributed to C E Metz.

At least 37 records · Page 2Linked to original sources
PubMed1996

Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.

PURPOSE: To evaluate the effect of a computer-aided diagnosis (CAD) scheme on radiologists' performance in the detection of lung nodules, and to examine a new method of receiver operating characteristic (ROC) analysis. MATERIALS AND METHODS: One hundred twenty radiographs (60 normal and 60 abnormal with lung nodules of varying subtlety) were used. Sixteen radiologists (two thoracic, six general, and eight residents) participated in an observer study in which they read both conventional radiographs and digitized radiographs. The radiologists' performance was evaluated with ROC analysis with two different methods (independent testing and sequential testing) and a continuous rating scale. RESULTS: Az (area under the best fit binormal ROC curve when it is plotted in the unit square) values obtained from ROC analysis with and without CAD output were 0.940 and 0.894, respectively, in the independent test and 0.948 and 0.906, respectively, in the sequential test. Findings with both methods indicated that the CAD scheme statistically significantly improved diagnostic accuracy, particularly for radiologists with less experience (P < .001). Reading time was not increased when CAD was used. CONCLUSION: The CAD scheme can assist radiologists in the detection of lung nodules on chest radiographs.

Adult↗
PubMed1996

A receiver operating characteristic partial area index for highly sensitive diagnostic tests.

PURPOSE: Area under a receiver operating characteristic (ROC) curve (Az) is widely used as an index of diagnostic performance. However, Az is not a meaningful summary of clinical diagnostic performance when high sensitivity must be maintained clinically. The authors developed a new ROC partial area index, which measures clinical diagnostic performance more meaningfully in such situations, to summarize an ROC curve in only a high-sensitivity region. MATERIALS AND METHODS: The mathematical formation of the partial area index was derived from the conventional binormal model. Statistical tests of apparent differences in this index were formulated analogous to that of Az. One common statistical test involving the partial area index was validated by computer simulations under realistic conditions. RESULTS: An example in mammography illustrates a situation in which the partial area index is more meaningful than Az in measuring clinical diagnostic performance. CONCLUSION: The partial area index can be used as a more meaningful alternative to the conventional Az index for highly sensitive diagnostic tests.

ROC Curve↗
PubMed1996

Diagnostic performance of a receptor-binding radiopharmacokinetic model.

UNLABELLED: The aim of this study was to determine which measurement obtained from a radiopharmacokinetic model of a receptor-binding radiotracer provides the highest diagnostic performance for the detection of diffuse hepatocellular disease. METHODS: Twenty-seven healthy subjects and 46 patients with diffuse hepatocellular disease were studied with the receptor-binding radiopharmaceutical, 99mTc-galactosyl-neoglycoalbumin. A radiopharmacokinetic model was used to produce estimates of receptor concentration [R]o, the scaled forward-binding rate constant Kb, hepatic plasma volume, Vh, extrahepatic plasma volume, Ve and hepatic plasma flow, F. Receiver operating characteristic analysis of each model estimate was conducted. RESULTS: Receptor concentration [R]o and the metrics [R]o/tbw and kb[R]o[R]o/tbw provided the best discrimination between healthy and diseased liver. The forward-binding rate constant kb and the metrics F/Ve and Vh/tbw provided no discrimination. CONCLUSION: Based on simplicity and higher measurement precision, [R]o was selected as the most accurate index of hepatic function.

Albumins↗
PubMed1995

Toward consensus on quantitative assessment of medical imaging systems.

Consensus has been developing over the past few decades on a number of measurements required for the laboratory assessment of medical imaging modalities. Nevertheless, understanding of the connection between these measurements and human observer performance in a broad range of tasks remains far from complete. Focusing primarily on projection radiography to provide concrete examples, this overview indicates areas in which consensus on methodology for physical image-quality measurement has been established. Concepts such as "noise equivalent quanta" (NEQ) and "detective quantum efficiency" (DQE) have been found useful for normalizing physical measurements on an absolute scale and for relating those measurements to the decision performance of a hypothetical "ideal observer" that effectively performs decision tasks from the image data. The connection between ideal observer performance and human performance, as determined by receiver operating characteristic (ROC) analysis, remains to be understood for many clinically relevant tasks.

Biophysical Phenomena↗
PubMed1995

Determination of 3D imaging geometry and object configurations from two biplane views: an enhancement of the Metz-Fencil technique.

We present a new technique based on the method developed by Metz and Fencil for estimation of the 3D imaging geometry and 3D object configurations from biplane angiographic acquisitions. The new method employs the 3D configuration of points calculated by the Metz-Fencil technique as an initial estimate. A 3D Procrustes algorithm is employed to translate, rotate, and scale the configuration until it aligns optimally with the set of lines that connects a focal spot with the corresponding set of image points. This alignment procedure is applied independently for each view. The rotation and translation that relate the two aligned data sets are then determined by an additional 3D Procrustes calculation. These steps are applied iteratively. Evaluations were based on Monte Carlo simulation and phantom studies. With this new technique, the mean absolute errors in magnification, in the relative position of the points, and in the angles defining the rotation and translation matrices were approximately 3.0%, 1.5 mm, and 5 degrees and 3 degrees, respectively, for rms input errors in the image data up to 2.0 pixels (0.7 mm). Errors in the results can be as small as 0.5%, 0.16 mm, 0.6 degrees, and 0.3 degrees, respectively, if input image-data error is 0.035 mm. The improvement of the Metz-Fencil technique described here may provide a basis for precise estimation of the biplane imaging geometry and the 3D positions of vessel bifurcation points.

Algorithms↗
PubMed1994

Reduction of false positives in computerized detection of lung nodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme.

A computer-aided diagnosis (CAD) scheme is being developed to identify image regions considered suspicious for lung nodules in chest radiographs to assist radiologists in making correct diagnoses. Automated classifiers--an artificial neural network, discriminant analysis, and a rule-based scheme--are used to reduce the number of false-positive detections of the CAD scheme. The CAD scheme first detects nodule candidates from chest radiographs based on a difference image technique. Nine image features characterizing nodules are extracted automatically for each of the nodule candidates. The extracted image features are then used as input data to the classifiers for distinguishing actual nodules from the false-positive detections. The performances of the classifiers are evaluated by receiver-operating characteristic analysis. On the basis of the database of 30 normal and 30 abnormal chest images, the neural network achieves an AZ value (area under the receiver-operating-characteristic curve) of 0.79 in detecting lung nodules, as tested by the round-robin method. The neural network, after being trained with a training database, is able to eliminate more than 83% of the false-positive detections reported by the CAD scheme. Moreover, the combination of the trained neural network and a rule-based scheme eliminates 96% of the false-positive detections of the CAD scheme.

Diagnosis, Computer-Assisted↗
PubMed1994

Effect of case selection on the performance of computer-aided detection schemes.

The choice of clinical cases used to train and test a computer-aided diagnosis (CAD) scheme can affect the test results (i.e., error rate). In this study, we deliberately modified the components of our testing database to study the effects of this modification on measured performance. Using a computerized scheme for the automated detection of breast masses from mammograms, it was found that the sensitivity of the scheme ranged between 26% and 100% (at a false positive rate of 1.0 per image) depending on the cases used to test the scheme. Even a 20% change in the cases comprising the database can reduce the measured sensitivity by 15%-25%. Because of the strong dependence of measured performance on the testing database, it is difficult to estimate reliably the accuracy of a CAD scheme. Furthermore, it is questionable to compare different CAD schemes when different cases are used for testing. Sharing databases, creating a common database, or using a quantitative measure to characterize databases are possible solutions to this problem. However, none of these solutions exists or is practiced at present. Therefore, as a short-term solution, it is recommended that the method used for selecting cases, and histograms or mean and standard deviations of relevant image features be reported whenever performance data are presented.

Breast Neoplasms↗
PubMed1994

The magnetization transfer effect in cerebral infarction.

PURPOSE: To estimate the age of cerebral infarcts using magnetization transfer. METHODS: Twelve patients with radiographically and clinically documented cortical and subcortical cerebral infarctions underwent MR imaging on a 0.1T magnet. Magnetization transfer contrast images were generated by application of off-resonance pulses to every other repetition time on intermediate-weighted images. The magnetization transfer effect was calculated by obtaining an intensity value in the region of interest within the infarcted area. RESULTS: The data show a pattern in which the magnetization transfer effect decreases as the chronicity of the infarct increases. Infarcts less than 1 week old had an average magnetization transfer effect of 0.35. Those more than 1 week and less than 1 month old average 0.30. Infarcts more than 1 month and specifically those more than 1 year old averaged 0.16 or less. CONCLUSION: Magnetization transfer offers the potential to estimate the age of cerebral infarcts.

Acute Disease↗
PubMed1994

Magnetization transfer: a potential method to determine the age of multiple sclerosis lesions.

PURPOSE: To determine whether magnetization transfer contrast can differentiate acute from chronic lesions in multiple sclerosis. METHODS: Thirteen patients with multiple sclerosis and eight healthy patients were studied with MR using a 0.1-T system. Relatively T2-weighted spin-echo images were obtained without and with magnetization transfer contrast. The magnetization transfer effect of multiple sclerosis lesions was calculated and compared with the ages of the lesions. The magnetization transfer effect of normal-appearing white matter in patients with multiple sclerosis was calculated and compared with the magnetization transfer effect of white matter in healthy volunteers. Statistical analysis was performed. RESULTS: White matter in the healthy volunteers had values from 0.40 to 0.45. Normal-appearing white matter in the patients with multiple sclerosis had magnetization transfer effect values ranging from 0.41 to 0.45. Multiple sclerosis plaques of less than 1 year's duration had magnetization transfer effect values ranging from 0.05 to 0.26; older plaques had values from 0.25 to 0.41. The difference in the distributions of these values for acute and chronic multiple sclerosis plaques is statistically significant. CONCLUSION: Current imaging modalities do not differentiate acute multiple sclerosis lesions from chronic ones. Our data on magnetization transfer show a statistically significant difference in magnetization transfer effect values between lesions of less than 1 year's duration and older lesions. The different values may correspond to the histologic changes of multiple sclerosis plaques over time. Magnetization transfer may be a reliable method for determining the age of multiple sclerosis lesions.

Acute Disease↗
PubMed1994

An evaluation of lesion detectability with cone-beam, fanbeam and parallel-beam collimation in SPECT by continuous ROC study.

UNLABELLED: To evaluate lesion detectability for clinical evaluation of cone-beam (CB), fanbeam (FB) and parallel-beam (PB) collimator sensitivity, experimentally acquired phantom data were used to assess the advantage of CB collimation over conventional collimation. METHODS: Lesion detectability with CB, FB and PB collimation in SPECT was compared using a three-dimensional brain phantom and continuous receiver operating characteristic (CROC) analysis. A simulated cold lesion was located near the posterior portion of the thalamus. High count density scans of this phantom were acquired with CB, FB and PB collimators with similar resolution. These projections were scaled to count levels which reflected the measured sensitivities of the three collimators. Computer-generated Poisson noise was added to the projections to produce uncorrelated data sets. Images were reconstructed using a filtered backprojection algorithm. All reconstructions used a Hann filter with multiplicative attenuation correction. Each of seven trained observers viewed 288 sets of images and indicated the certainty of perceiving a cold lesion at a specified location by a rating of 0-100. Each image set contained four adjacent slices centered on the lesion to minimize partial volume effects. The program LABROC4 was used to fit CROC curves to individual observers' ratings. A t-test for paired data was performed on the individual areas. RESULTS: The average areas (standard deviations) under CROC curves for CB, FB and PB were 0.89 (0.03), 0.83 (0.05) and 0.76 (0.04), respectively. The differences of the areas were statistically significant with all two-tailed p values < 0.02. CONCLUSION: These results demonstrate that cold lesions in the posterior portion of the thalamus are best detected by images obtained using CB followed by FB and PB collimation.

Brain↗
PubMed1993

Simulation studies of data classification by artificial neural networks: potential applications in medical imaging and decision making.

Artificial neural networks are being investigated in the field of medical imaging as a means to facilitate pattern recognition and patient classification. In the work reported here, the effects of internal structure and the nature of input data on the performance of neural networks were investigated systematically using computer-simulated data. Network performance was evaluated quantitatively by means of receiver operating characteristic analysis and compared with the performance of an ideal statistical decision maker. We found that the relatively simple neural networks investigated in this study can perform at the level of an ideal decision maker. These simple networks were also found to learn accurately even when the training data are extremely unbalanced with respect to the prevalence of actually positive cases and to differentiate input data patterns by recognizing their unique characteristics.

Computer Simulation↗
PubMed1993

Evaluation of an enhanced digital film-duplication system by receiver operating characteristic analysis.

RATIONALE AND OBJECTIVES: Over- or underexposure often results in poor quality radiographs. Using receiver operating characteristic analysis, the authors investigated the potential utility of a system that corrects densities in such images to improve the accuracy of lung nodule detection. METHODS: Chest radiographs were digitized by a laser film digitizer. Simulated nodules were superimposed on 36 improperly exposed chest radiographs. All images, with and without nodules, were then processed by a nonlinear density-correction technique. Hard-copy images were produced by a laser film printer. Eight radiologists evaluated the corrected and uncorrected images for the presence or absence of lung nodules using a continuous (0-100) confidence-rating scale. RESULT: The detectability of lung nodules improved significantly in the retrocardiac/retrodiaphragmatic regions of underexposed images and in the peripheral lung region of overexposed images. CONCLUSIONS: This system may increase diagnostic accuracy in detection of lung nodules in improperly exposed images.

Confidence Intervals↗
PubMed1993

Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.

The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.

Female↗
PubMed1993

Results of a clinical receiver operating characteristic study comparing filtered backprojection and maximum likelihood estimator images in FDG PET studies.

The results of a receiver operator characteristic (ROC) study comparing maximum likelihood estimator (MLE) reconstructions of human FDG PET brain scan data to filtered backprojection reconstructions of the same data are reported. The purpose of the study was to determine whether MLE reconstructions would result in higher detectability of small focal lesions introduced artificially into otherwise normal scan data. One physician assisted in defining the location and intensity of the lesions and five physicians read the final images. Data from 90 datasets were used for the study. Of those, 42 were left in their original "normal" condition and 48 were modified by added lesions. All datasets were reconstructed by the two methods and submitted to the five physicians for evaluation. The results show an increase in the area under the ROC curve from approximately 0.65 for filtered backprojection to approximately 0.71 for the maximum likelihood reconstructions for four of the five observers with good statistical significance.

Brain↗
PubMed1992

The use of continuous and discrete confidence judgments in receiver operating characteristic studies of diagnostic imaging techniques.

Virtually all receiver operating characteristic (ROC) studies of medical imaging methods have involved data collected on a discrete confidence-rating scale, though in principle ROC studies can be conducted with either discrete or continuous scales. Continuous scales provide potential advantages in medical applications of ROC analysis, but the possible dependence of ROC results on the kind of scale used in an observer-performance experiment has not been investigated empirically. The authors conducted a multi-observer ROC study in which radiologists read abdominal computed tomography (CT) examination results displayed on a workstation with a high-resolution video monitor. Ninety-five examinations were evaluated by five radiologists who reported their confidence concerning the presence of abdominal masses on a conventional five-category discrete scale and, in a separate reading session, on a continuous subjective-probability scale. ROC analysis showed no statistically significant difference between the detection accuracies (Az) achieved with the two scales by any reader. Accuracies averaged over all readers were identical with the two scales. The results of this study suggest that discrete or continuous scales often can be used interchangeably in image-evaluation studies. However, the authors recommend continuous scales for routine use in radiologic ROC studies because of their potential advantages in some situations.

Confidence Intervals↗
PubMed1992

An evaluation of maximum likelihood-expectation maximization reconstruction for SPECT by ROC analysis.

A ROC study was performed in order to evaluate whether the maximum likelihood expectation maximization (ML-EM) reconstruction algorithm improves diagnostic performance compared to the conventional filtered backprojection method in SPECT. Several implementations of the algorithm were tested including 25 and 50 iteration stopping points, with and without nonuniform attenuation compensation, and with and without Metz filtering. Filtered backprojection was with Metz filter and without attenuation compensation. The test data were computer simulated to model cardiac 201Tl SPECT. The data incorporated the effects of nonuniform attenuation, distance-dependent collimator response, and scatter. Patient CT images provided realistic anatomy and attenuation information for the data simulation. Four observers each viewed 120 images for each of the reconstruction methods. Lesion detectability with ML-EM increased with Metz filtering and decreased with nonuniform attenuation compensation. The best MIL-EM implementation, 50 iterations with Metz filtering and without attenuation compensation, was not statistically better than filtered backprojection.

Algorithms↗