Biomedical subjects

John W Hoppin

Publications and source records attributed to John W Hoppin.

6 recordsLinked to original sources

PubMed2006

Comparing cardiac ejection fraction estimation algorithms without a gold standard.

RATIONALE AND OBJECTIVES: Imaging and estimation of left ventricular function have major diagnostic and prognostic importance in patients with coronary artery disease. It is vital that the method used to estimate cardiac ejection fraction (EF) allows the observer to best perform this task. To measure task-based performance, one must clearly define the task in question, the observer performing the task, and the patient population being imaged. In this report, the task is to accurately and precisely measure cardiac EF, and the observers are human-assisted computer algorithms that analyze the images and estimate cardiac EF. It is very difficult to measure the performance of an observer by using clinical data because estimation tasks typically lack a gold standard. A solution to this "no-gold-standard" problem recently was proposed, called regression without truth (RWT). MATERIALS AND METHODS: Results of three different software packages used to analyze gated, cardiac, and nuclear medicine images, each of which uses a different algorithm to estimate a patient's cardiac EF, are compared. The three methods are the Emory method, Quantitative Gated Single-Photon Emission Computed Tomographic method, and the Wackers-Liu Circumferential Quantification method. The same set of images is used as input to each of the three algorithms. Data were analyzed from the three different algorithms by using RWT to determine which produces the best estimates of cardiac EF in terms of accuracy and precision. RESULTS AND DISCUSSION: In performing this study, three different consistency checks were developed to ensure that the RWT method is working properly. The Emory method of estimating EF slightly outperformed the other two methods. In addition, the RWT method passed all three consistency checks, garnering confidence in the method and its application to clinical data.

Algorithms↗

PubMed2005

Noise characterization of block-iterative reconstruction algorithms: II. Monte Carlo simulations.

In Soares et al. (2000), the ensemble statistical properties of the rescaled block-iterative expectation-maximization (RBI-EM) reconstruction algorithm and rescaled block-iterative simultaneous multiplicative algebraic reconstruction technique (RBI-SMART) were derived. Included in this analysis were the special cases of RBI-EM, maximum-likelihood EM (ML-EM) and ordered-subset EM (OS-EM), and the special case of RBI-SMART, SMART. Explicit expressions were found for the ensemble mean, covariance matrix, and probability density function of RBI reconstructed images, as a function of iteration number. The theoretical formulations relied on one approximation, namely that the noise in the reconstructed image was small compared to the mean image. In this paper, we evaluate the predictions of the theory by using Monte Carlo methods to calculate the sample statistical properties of each algorithm and then compare the results with the theoretical formulations. In addition, the validity of the approximation will be justified.

Algorithms↗

PubMed2003

Experimental determination of object statistics from noisy images.

Modern imaging systems rely on complicated hardware and sophisticated image-processing methods to produce images. Owing to this complexity in the imaging chain, there are numerous variables in both the hardware and the software that need to be determined. We advocate a task-based approach to measuring and optimizing image quality in which one analyzes the ability of an observer to perform a task. Ideally, a task-based measure of image quality would account for all sources of variation in the imaging system, including object variability. Often, researchers ignore object variability even though it is known to have a large effect on task performance. The more accurate the statistical description of the objects, the more believable the task-based results will be. We have developed methods to fit statistical models of objects, using only noisy image data and a well-characterized imaging system. The results of these techniques could eventually be used to optimize both the hardware and the software components of imaging systems.

Artifacts↗

PubMed2003

Ideal-observer computation in medical imaging with use of Markov-chain Monte Carlo techniques.

The ideal observer sets an upper limit on the performance of an observer on a detection or classification task. The performance of the ideal observer can be used to optimize hardware components of imaging systems and also to determine another observer's relative performance in comparison with the best possible observer. The ideal observer employs complete knowledge of the statistics of the imaging system, including the noise and object variability. Thus computing the ideal observer for images (large-dimensional vectors) is burdensome without severely restricting the randomness in the imaging system, e.g., assuming a flat object. We present a method for computing the ideal-observer test statistic and performance by using Markov-chain Monte Carlo techniques when we have a well-characterized imaging system, knowledge of the noise statistics, and a stochastic object model. We demonstrate the method by comparing three different parallel-hole collimator imaging systems in simulation.

Diagnostic Imaging↗

PubMed2002

Estimation in medical imaging without a gold standard.

RATIONALE AND OBJECTIVES: In medical imaging, physicians often estimate a parameter of interest (eg, cardiac ejection fraction) for a patient to assist in establishing a diagnosis. Many different estimation methods may exist, but rarely can one be considered a gold standard. Therefore, evaluation and comparison of different estimation methods are difficult. The purpose of this study was to examine a method of evaluating different estimation methods without use of a gold standard. MATERIALS AND METHODS: This method is equivalent to fitting regression lines without the x axis. To use this method, multiple estimates of the clinical parameter of interest for each patient of a given population were needed. The authors assumed the statistical distribution for the true values of the clinical parameter of interest was a member of a given family of parameterized distributions. Furthermore, they assumed a statistical model relating the clinical parameter to the estimates of its value. Using these assumptions and observed data, they estimated the model parameters and the parameters characterizing the distribution of the clinical parameter. RESULTS: The authors applied the method to simulated cardiac ejection fraction data with varying numbers of patients, numbers of modalities, and levels of noise. They also tested the method on both linear and nonlinear models and characterized the performance of this method compared to that of conventional regression analysis by using x-axis information. Results indicate that the method follows trends similar to that of conventional regression analysis as patients and noise vary, although conventional regression analysis outperforms the method presented because it uses the gold standard which the authors assume is unavailable. CONCLUSION: The method accurately estimates model parameters. These estimates can be used to rank the systems for a given estimation task.

Diagnostic Imaging↗

PubMed2002

Objective comparison of quantitative imaging modalities without the use of a gold standard.

Imaging is often used for the purpose of estimating the value of some parameter of interest. For example, a cardiologist may measure the ejection fraction (EF) of the heart in order to know how much blood is being pumped out of the heart on each stroke. In clinical practice, however, it is difficult to evaluate an estimation method because the gold standard is not known, e.g., a cardiologist does not know the true EF of a patient. Thus, researchers have often evaluated an estimation method by plotting its results against the results of another (more accepted) estimation method, which amounts to using one set of estimates as the pseudogold standard. In this paper, we present a maximum-likelihood approach for evaluating and comparing different estimation methods without the use of a gold standard with specific emphasis on the problem of evaluating EF estimation methods. Results of numerous simulation studies will be presented and indicate that the method can precisely and accurately estimate the parameters of a regression line without a gold standard, i.e., without the x axis.

Analysis of Variance↗