Diderot

Abstract

Research scientists and medical professionals use imaging technology, such as computed tomography (CT) and magnetic resonance imaging (MRI) to measure a wide variety of biological and physical objects. The increasing sophistication of imaging technology creates demand for equally sophisticated computational techniques to analyze and visualize the image data. Analysis and visualization codes are often crafted for a specific experiment or set of images, thus imaging scientists need support for quickly developing codes that are reliable, robust, and efficient. In this paper, we present the design and implementation of Diderot, which is a parallel domain-specific language for biomedical image analysis and visualization. Diderot supports a high-level model of computation that is based on continuous tensor fields. These tensor fields are reconstructed from discrete image data using separable convolution kernels, but may also be defined by applying higher-order operations, such as differentiation (∇). Early experiments demonstrate that Diderot provides both a high-level concise notation for image analysis and visualization algorithms, as well as high sequential and parallel performance.