Gaze visualization has been used to understand the results from gaze tracking studies in a wide range of fields. In the medical field, diagnoses of medical images have been studied with gaze tracking technology to understand how radiologists read medical images. While prior work were mainly based on diagnosis with a single image, recent work focused on diagnosis with consecutive cross-sectional medical images acquired from preoperative computed tomography (CT) or magnetic resonance imaging (MRI). In the diagnosis, radiologists scroll through a stack of images to get a 3D cognition of organs and lesions. Thus, it is important to understand radiologists' gaze patterns three dimensionally across such contiguous cross-sectional images. However, little has been done to visualize more complicated gaze patterns from the contiguous cross-sectional medical images. To address this problem, we present an interactive 3D gaze visualization tool, GazeVis, where InfoVis and SciVis techniques are harmonized to show the abstract gaze data along with a realistic 3D rendering of the visual stimuli (i.e., organs and lesions). We present case studies with 12 radiologists who use GazeVis to investigate gaze patterns of their colleagues with different levels of expertise, providing empirical evidences about the competence of our gaze visualization system.

Volume ray-casting with a higher order reconstruction filter and/or a higher sampling rate has been adopted in direct volume rendering frameworks to provide a smooth reconstruction of the volume scalar and/or to reduce artifacts when the combined frequency of the volume and transfer function is high. While it enables high-quality volume rendering, it cannot support interactive rendering due to its high computational cost. In this paper, we propose a fast high-quality volume ray-casting algorithm which effectively increases the sampling rate. While a ray traverses the volume, intensity values are uniformly reconstructed using a high-order convolution filter. Additional samplings, referred to as virtual samplings, are carried out within a ray segment from a cubic spline curve interpolating those uniformly reconstructed intensities. These virtual samplings are performed by evaluating the polynomial function of the cubic spline curve via simple arithmetic operations. The min max blocks are refined accordingly for accurate empty space skipping in the proposed method. Experimental results demonstrate that the proposed algorithm, also exploiting fast cubic texture filtering supported by programmable GPUs, offers renderings as good as a conventional ray-casting algorithm using high-order reconstruction filtering at the same sampling rate, while delivering 2.5x to 3.3x rendering speed-up.