Parametric color coding of digital subtraction angiography.

BACKGROUND AND PURPOSE: Color has been shown to facilitate both visual search and recognition tasks. It was our purpose to examine the impact of a color-coding algorithm on the interpretation of 2D-DSA acquisitions by experienced and inexperienced observers. MATERIALS AND METHODS: Twenty-six 2D-DSA acquisitions obtained as part of routine clinical care from subjects with a variety of cerebrovascular disease processes were selected from an internal data base so as to include a variety of disease states (aneurysms, AVMs, fistulas, stenosis, occlusions, dissections, and tumors). Three experienced and 3 less experienced observers were each shown the acquisitions on a prerelease version of a commercially available double-monitor workstation (XWP, Siemens Healthcare). Acquisitions were presented first as a subtracted image series and then as a single composite color-coded image of the entire acquisition. Observers were then asked a series of questions designed to assess the value of the color-coded images for the following purposes: 1) to enhance their ability to make a diagnosis, 2) to have confidence in their diagnosis, 3) to plan a treatment, and 4) to judge the effect of a treatment. The results were analyzed by using 1-sample Wilcoxon tests. RESULTS: Color-coded images enhanced the ease of evaluating treatment success in >40% of cases (P < .0001). They also had a statistically significant impact on treatment planning, making planning easier in >20% of the cases (P = .0069). In >20% of the examples, color-coding made diagnosis and treatment planning easier for all readers (P < .0001). Color-coding also increased the confidence of diagnosis compared with the use of DSA alone (P = .056). The impact of this was greater for the naïve readers than for the expert readers. CONCLUSIONS: At no additional cost in x-ray dose or contrast medium, color-coding of DSA enhanced the conspicuity of findings on DSA images. It was particularly useful in situations in which there was a complex flow pattern and in evaluation of pre- and posttreatment acquisitions. Its full potential remains to be defined.

Nonlinear mixed effects models for repeated measures data.

We propose a general, nonlinear mixed effects model for repeated measures data and define estimators for its parameters. The proposed estimators are a natural combination of least squares estimators for nonlinear fixed effects models and maximum likelihood (or restricted maximum likelihood) estimators for linear mixed effects models. We implement Newton-Raphson estimation using previously developed computational methods for nonlinear fixed effects models and for linear mixed effects models. Two examples are presented and the connections between this work and recent work on generalized linear mixed effects models are discussed.