Abstract
In this paper we introduce and investigate an adaptive direct volume rendering (DVR) method for real-time
visualization of cardiac 3D ultrasound. DVR is commonly used in cardiac ultrasound to visualize interfaces
between tissue and blood. However, this is particularly challenging with ultrasound images due to variability
of the signal within tissue as well as variability of noise signal within the blood pool. Standard DVR involves
a global mapping of sample values to opacity by an opacity transfer function (OTF). While a global OTF may
represent the interface correctly in one part of the image, it may result in tissue dropouts, or even artificial
interfaces within the blood pool in other parts of the image. In order to increase correctness of the rendered
image, the presented method utilizes blood pool statistics to do regional adjustments of the OTF. The regional
adaptive OTF was compared with a global OTF in a dataset of apical recordings from 18 subjects. For each
recording, three renderings from standard views (apical 4-chamber (A4C), inverted A4C (IA4C) and mitral
valve (MV)) were generated for both methods, and each rendering was tuned to the best visual appearance by
a physician echocardiographer. For each rendering we measured the mean absolute error (MAE) between the
rendering depth buffer and a validated left ventricular segmentation. The difference d in MAE between the global
and regional method was calculated and t-test results are reported with significant improvements for the regional
adaptive method (dA4C = 1.5 ± 0.3 mm, dIA4C = 2.5 ± 0.4 mm, dMV = 1.7 ± 0.2 mm, d.f. = 17, all p < 0.001).
This improvement by the regional adaptive method was confirmed through qualitative visual assessment by an
experienced physician echocardiographer who concluded that the regional adaptive method produced rendered
images with fewer tissue dropouts and less spurious structures inside the blood pool in the vast majority of the
renderings. The algorithm has been implemented on a GPU, running an average of 16 fps with a resolution of
512x512x100 samples (Nvidia GTX460).
