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3D ultrasound imaging is quickly gaining widespread clinical acceptance as a visualization tool that allows clinicians to obtain unique views not available with traditional 2D ultrasound imaging and an accurate understanding of patient anatomy. The ability to acquire, manipulate and interact with the 3D data in real time is an important feature of 3D ultrasound imaging. Volume rendering is often used to transform the 3D volume into 2D images for visualization. Unlike computed tomography (CT) and magnetic resonance imaging (MRI), volume rendering of 3D ultrasound data creates noisy images in which surfaces cannot be readily discerned due to speckles and low signal-to-noise ratio. The degrading effect of speckles is especially severe when gradient shading is performed to add depth cues to the image. Several researchers have reported that smoothing the pre-rendered volume with a 3D convolution kernel, such as 5x5x5, can significantly improve the image quality, but at the cost of decreased resolution.
In this paper, we have analyzed the reasons for the improvement in image quality with 3D filtering and determined that the improvement is due to two effects. The filtering reduces speckles in the volume data, which leads to (1) more accurate gradient computation and better shading and (2) decreased noise during compositing. We have found that applying a moderate-size smoothing kernel (e.g., 7x7x7) to the volume data before gradient computation combined with some smoothing of the volume data (e.g., with a 3x3x3 lowpass filter) before compositing yielded images with good depth perception and no appreciable loss in resolution. Providing the clinician with the flexibility to control both of these effects (i.e., shading and compositing) independently could improve the visualization of the 3D ultrasound data. Introducing this flexibility into the ultrasound machine requires 3D filtering to be performed twice on the volume data, once before gradient computation and again before compositing. 3D filtering of an ultrasound volume containing millions of voxels requires a large amount of computation, and doing it twice decreases the number of frames that can be visualized per second. To address this, we have developed several techniques to make computation efficient. For example, we have used the moving average method to filter a 128x128x128 volume with a 3x3x3 boxcar kernel in 17 ms on a single MAP processor running at 400 MHz. The same methods reduced the computing time on a Pentium 4 running at 3 GHz from 110 ms to 62 ms. We believe that our proposed method can improve 3D ultrasound visualization without sacrificing resolution and incurring an excessive computing time.
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