Scalable Web-based Volume Rendering for Large-scale Biomedical Data Sets

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: July 2023
• Date (Start): October 2022
• Date (End): July 2023
• TU Wien Library:
• Second Supervisor: Johanna Beyer
• Diploma Examination: 27. September 2023
• Open Access: yes
• First Supervisor: Eduard Gröller
• Pages: 99
• Keywords: volume rendering, ray-guided rendering, large-scale data, out-of-core rendering, multi-resolution data, multi-channel volumes, web-based visualization

Abstract

Recent advances in imaging modalities produce large-scale volumetric data sets with a large number of channels. Interactive visualization of such data sets requires out-of-core direct volume rendering (DVR) methods such as octrees or page-table hierarchies. For this reason, data sets are both down-sampled into a multi-resolution hierarchy and divided into smaller bricks, in order to stream only those parts of the volume contributing to the rendered image to the GPU. Furthermore, rendering multiple channels requires careful optimization because the high computational cost of DVR grows with the number of channels to render. A common optimization in DVR is empty-space skipping where fully translucent regions in the volume are not sampled to reduce the number of loop iterations and texture look-ups during rendering. Previous out-of-core DVR methods are designed for single-channel volumes and are only suitable for multi-channel volumes to a limited extent. In octree-based methods, accessing cached volume data requires traversing the tree for each sample and channel. Furthermore, in previous approaches, the spatial subdivision of the octree is intrinsically coupled to the down-sampling ratio and bricking granularity in the data set. This leads to suboptimal cache utilization and makes fine-grained empty-space skipping costly. Page-table hierarchies, on the other hand, allow access to any cached brick from any resolution without traversing a tree structure. However, their support for empty-space skipping is also tied to the bricking granularity in the data set and is thus limited. We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. We enable flexible mixed-resolution out-of-core multi- volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks in each resolution level. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2023.106203

BibTeX

@mastersthesis{herzberger-2023-swv,
  title =      "Scalable Web-based Volume Rendering for Large-scale
               Biomedical Data Sets",
  author =     "Lukas Herzberger",
  year =       "2023",
  abstract =   "Recent advances in imaging modalities produce large-scale
               volumetric data sets with a large number of channels.
               Interactive visualization of such data sets requires
               out-of-core direct volume rendering (DVR) methods such as
               octrees or page-table hierarchies. For this reason, data
               sets are both down-sampled into a multi-resolution hierarchy
               and divided into smaller bricks, in order to stream only
               those parts of the volume contributing to the rendered image
               to the GPU. Furthermore, rendering multiple channels
               requires careful optimization because the high computational
               cost of DVR grows with the number of channels to render. A
               common optimization in DVR is empty-space skipping where
               fully translucent regions in the volume are not sampled to
               reduce the number of loop iterations and texture look-ups
               during rendering. Previous out-of-core DVR methods are
               designed for single-channel volumes and are only suitable
               for multi-channel volumes to a limited extent. In
               octree-based methods, accessing cached volume data requires
               traversing the tree for each sample and channel.
               Furthermore, in previous approaches, the spatial subdivision
               of the octree is intrinsically coupled to the down-sampling
               ratio and bricking granularity in the data set. This leads
               to suboptimal cache utilization and makes fine-grained
               empty-space skipping costly. Page-table hierarchies, on the
               other hand, allow access to any cached brick from any
               resolution without traversing a tree structure. However,
               their support for empty-space skipping is also tied to the
               bricking granularity in the data set and is thus limited. We
               present a hybrid multi-volume rendering approach based on a
               novel Residency Octree that combines the advantages of
               out-of-core volume rendering using page tables with those of
               standard octrees. We enable flexible mixed-resolution
               out-of-core multi- volume rendering by decoupling the cache
               residency of multi-resolution data from a
               resolution-independent spatial subdivision determined by the
               tree. Instead of one-to-one node-to-brick correspondences,
               each residency octree node is mapped to a set of bricks in
               each resolution level. This makes it possible to efficiently
               and adaptively choose and mix resolutions, adapt sampling
               rates, and compensate for cache misses. At the same time,
               residency octrees support fine-grained empty-space skipping,
               independent of the data subdivision used for caching.
               Finally, to facilitate collaboration and outreach, and to
               eliminate local data storage, our implementation is a
               web-based, pure client-side renderer using WebGPU and
               WebAssembly. Our method is faster than prior approaches and
               efficient for many data channels with a flexible and
               adaptive choice of data resolution.",
  month =      jul,
  pages =      "99",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "volume rendering, ray-guided rendering, large-scale data,
               out-of-core rendering, multi-resolution data, multi-channel
               volumes, web-based visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2023/herzberger-2023-swv/",
}