Interactive, in-browser cinematic volume rendering of medical images

Fan, Yubo; Thevenon, Gaspard; Chabat, Timothee; McCormick, Matthew; Li, Forrest; Birdsong, Tom; Martin, Ken; Lee, Yueh Z.; Aylward, Stephen

doi:10.1080/21681163.2022.2145239

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Even though visualization tools like VisIt [Chi12] or ParaView [AGL05] still use this simple model, the field is however gradually transitioning to unbiased rendering methods using free‐flight distance sampling to compute transmission estimates. This transition expresses itself in a number of recent papers on volume path tracing for scientific visualization [HMES20,MSG * 22,XTC * 22,ZWS * 22b].…”

Section: Related Workmentioning

confidence: 94%

Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh

Zellmann

Liu

et al. 2023

Computer Graphics Forum

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Davis 3 NVIDIA Figure 1: Overview of our method. Given a block-structured or octree-AMR data set (left) we first create the dual mesh (middle) and split that into the truly unstructured elements used to stitch the level boundaries (red) and those that are regular voxels (blue/white checkered). We then cluster voxels to become "gridlets". Right: gridlets colorized by their ID. We build a bounding volume hierarchy over the gridlets and the remaining unstructured elements. The result is a sampleable representation that generates the exact same result as sampling on the dual mesh directly, but with significantly lower memory overhead and higher sampling speed. On our largest data sets, we see memory savings of up to 3× compared to highly compressed state-of-the-art unstructured mesh representations.

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Section: Related Workmentioning

confidence: 94%

Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh

Zellmann

Liu

et al. 2023

Computer Graphics Forum

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“…In recent years, we have observed a gradual shift towards volumetric path tracing, also in the scientific visualization community [KPB12, WJA * 17, MHK * 19, HMES20, IGMM22, MSG * 22, ZWB * 22, XTC * ss, ZWS * 22b]. Instead of stepwise sampling the domain [ t min , t max ] using ray marching, volumetric path tracers compute free‐flight distances inside the domain.…”

Section: Background and Terminologymentioning

confidence: 99%

State‐of‐the‐art in Large‐Scale Volume Visualization Beyond Structured Data

Sarton,

Zellmann,

Demirci

et al. 2023

Computer Graphics Forum

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Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state‐of‐the‐art report, we review works focusing on large‐scale volume rendering beyond those typical structured and regular grid representations. We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out‐of‐core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever‐increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large‐scale volume rendering systems and also include a review of tools that support the various volume data types discussed.

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“…Particular items that have been considered in such studies have included factors such as data staging, rendering construction strategies, salient feature detection, highlighting, etc. (e.g., [2][3][4][5]). The work here considers a type of performance issue that has received somewhat less consideration-the energy consumption for computing certain volume features (i.e., descriptors) that have been used in the analyses and renderings of scientific datasets.…”

Section: Introductionmentioning

confidence: 99%

Achieving Better Energy Efficiency in Volume Analysis and Direct Volume Rendering Descriptor Computation

Hauenstein,

Newman

2024

Computers

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Approaches aimed at achieving improved energy efficiency for determination of descriptors—used in volumetric data analysis and one common mode of scientific visualisation—in one x86-class setting are described and evaluated. These approaches are evaluated against standard approaches for the computational setting. In all, six approaches for improved efficiency are considered. Four of them are computation-based. The other two are memory-based. The descriptors are classic gradient and curvature descriptors. In addition to their use in volume analyses, they are used in the classic ray-casting-based direct volume rendering (DVR), which is a particular application area of interest here. An ideal combination of the described approaches applied to gradient descriptor determination allowed them to to be computed with only 80% of the energy of a standard approach in the computational setting; energy efficiency was improved by a factor of 1.2. For curvature descriptor determination, the ideal combination of described approaches achieved a factor-of-two improvement in energy efficiency.

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Interactive, in-browser cinematic volume rendering of medical images

Cited by 7 publications

References 18 publications

Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh

Memory‐Efficient GPU Volume Path Tracing of AMR Data Using the Dual Mesh

State‐of‐the‐art in Large‐Scale Volume Visualization Beyond Structured Data

Achieving Better Energy Efficiency in Volume Analysis and Direct Volume Rendering Descriptor Computation

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