Lundström C, Persson A, Ross S, Ljung P, Lindholm S, Gyllensvärd F, Ynnerman A. State‐of‐the‐art of visualization in post‐mortem imaging. APMIS 2012; 120: 316–26. Autopsies constitute a valuable feedback to the healthcare chain to achieve improvements in quality of care and cost effectiveness. This review describes post‐mortem imaging, which has emerged as an important part of the pathology toolbox. A broad range of visualization aspects within post‐mortem imaging are covered. General state‐of‐the‐art overviews of the components in the visualization pipeline are complemented by in‐depth descriptions of methods developed by the authors and our collaborators. The forensic field is represented and related to, as it is spearheading much development in post‐mortem imaging. Other topics are workflow, imaging data acquisition, and visualization rendering technology. All in all, this review shows the mature state of visual analysis for a non‐ or minimal‐invasive investigation of the deceased patient.

Multiple-volume visualization is a growing field in medical imaging providing simultaneous exploration of volumes acquired from varying modalities. However, high complexity results in an increased strain on performance compared to single volume rendering as scenes may consist of volumes with arbitrary orientations and rendering is performed with varying sample densities. Expensive image order techniques such as depth peeling have previously been used to perform the necessary calculations. In this work we present a view-independent region based scene description for multi-volume pipelines. Using Binary Space Partitioning we are able to create a simple interface providing all required information for advanced multi-volume renderings while introducing a minimal overhead for scenes with few volumes. The modularity of our solution is demonstrated by the use of visual development and performance is documented with benchmarks and real-time simulations.

In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi-transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A-buffer, an enhanced framebuffer with additional per-pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume-geometry data based on the A-buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A-buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather, and medical visualization containing both, volumetric data and geometric structures.