
@article{hofmann_neural_2020,
	title = {Neural Denoising for Path Tracing of Medical Volumetric Data},
	volume = {3},
	issn = {2577-6193},
	url = {https://dl.acm.org/doi/10.1145/3406181},
	doi = {10.1145/3406181},
	abstract = {In this paper, we transfer machine learning techniques previously applied to denoising surface-only Monte Carlo renderings to path-traced visualizations of medical volumetric data. In the domain of medical imaging, path-traced videos turned out to be an efficient means to visualize and understand internal structures, in particular for less experienced viewers such as students or patients. However, the computational demands for the rendering of high-quality path-traced videos are very high due to the large number of samples necessary for each pixel. To accelerate the process, we present a learning-based technique for denoising path-traced videos of volumetric data by increasing the sample count per pixel; both through spatial (integrating neighboring samples) and temporal filtering (reusing samples over time). Our approach uses a set of additional features and a loss function both specifically designed for the volumetric case. Furthermore, we present a novel network architecture tailored for our purpose, and introduce reprojection of samples to improve temporal stability and reuse samples over frames. As a result, we achieve good image quality even from severely undersampled input images, as visible in the teaser image.},
	pages = {1--18},
	number = {2},
	journaltitle = {Proceedings of the {ACM} on Computer Graphics and Interactive Techniques},
	shortjournal = {Proc. {ACM} Comput. Graph. Interact. Tech.},
	author = {Hofmann, Nikolai and Martschinke, Jana and Engel, Klaus and Stamminger, Marc},
	urldate = {2025-05-27},
	date = {2020-08-26},
	langid = {english},
	file = {Hofmann et al. - 2020 - Neural Denoising for Path Tracing of Medical Volum.pdf:/home/lucas/Zotero/storage/8YW842TQ/Hofmann et al. - 2020 - Neural Denoising for Path Tracing of Medical Volum.pdf:application/pdf},
}

@artwork{dilmen_english_2016,
	title = {English:  Magnetic resonance imaging of the cervical spine},
	rights = {Permission is granted to copy, distribute and/or modify this document under the terms of the {GNU} Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled {GNU} Free Documentation License.http://www.gnu.org/copyleft/fdl.{htmlGFDLGNU} Free Documentation Licensetruetrue},
	url = {https://commons.wikimedia.org/wiki/File:06_Cervical_MRI_scan_R_T1WFSE_G_T2WfrFSE_STIR_B.jpg},
	shorttitle = {English},
	author = {Dilmen, Nevit},
	urldate = {2025-05-26},
	date = {2016},
	file = {Wikimedia Snapshot:/home/lucas/Zotero/storage/N7FV7PW9/File06_Cervical_MRI_scan_R_T1WFSE_G_T2WfrFSE_STIR_B.html:text/html},
}

@artwork{anthonp_english_2011,
	title = {English:  {MRI} Scan showing herniation of a cervical disc at C6-C7},
	url = {https://commons.wikimedia.org/wiki/File:C6-C7-disc-herniation-cevical-mri-scan.jpg},
	shorttitle = {English},
	author = {{Anthonp}},
	urldate = {2025-05-26},
	date = {2011-12-07},
	file = {Wikimedia Snapshot:/home/lucas/Zotero/storage/53AY63TH/FileC6-C7-disc-herniation-cevical-mri-scan.html:text/html},
}

@software{kroes_thomaskroesexposure-render_2025,
	title = {{ThomasKroes}/exposure-render},
	url = {https://github.com/ThomasKroes/exposure-render},
	abstract = {Automatically exported from code.google.com/p/exposure-render},
	author = {Kroes, Thomas},
	urldate = {2025-05-27},
	date = {2025-03-12},
	note = {original-date: 2015-05-12T09:21:54Z},
}

@artwork{kroes_exposure-renderimagesmanixpng_nodate,
	title = {exposure-render/Images/manix.png at master · {ThomasKroes}/exposure-render},
	url = {https://github.com/ThomasKroes/exposure-render/blob/master/Images/manix.png},
	author = {Kroes, Thomas},
	urldate = {2025-05-27},
	file = {exposure-render/Images/manix.png at master · ThomasKroes/exposure-render:/home/lucas/Zotero/storage/596ES2H3/manix.html:text/html},
}

@article{ljung_state_2016,
	title = {State of the Art in Transfer Functions for Direct Volume Rendering},
	volume = {35},
	issn = {0167-7055, 1467-8659},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/cgf.12934},
	doi = {10.1111/cgf.12934},
	abstract = {A central topic in scientiﬁc visualization is the transfer function ({TF}) for volume rendering. The {TF} serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, {TFs} also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. {TFs} also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report ({STAR}) is to provide an overview of research into the various aspects of {TFs}, which lead to interpretation of the underlying data through the use of meaningful visual representations. The {STAR} classiﬁes {TF} research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The {STAR} concludes with some interesting research challenges that form the basis of an agenda for the development of next generation {TF} tools and methodologies.},
	pages = {669--691},
	number = {3},
	journaltitle = {Computer Graphics Forum},
	shortjournal = {Computer Graphics Forum},
	author = {Ljung, Patric and Krüger, Jens and Groller, Eduard and Hadwiger, Markus and Hansen, Charles D. and Ynnerman, Anders},
	urldate = {2025-06-01},
	date = {2016-06},
	langid = {english},
	file = {PDF:/home/lucas/Zotero/storage/M6TXUU78/Ljung et al. - 2016 - State of the Art in Transfer Functions for Direct Volume Rendering.pdf:application/pdf},
}

@article{kroes_exposure_2012,
	title = {Exposure Render: An Interactive Photo-Realistic Volume Rendering Framework},
	volume = {7},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0038586},
	doi = {10.1371/journal.pone.0038586},
	shorttitle = {Exposure Render},
	pages = {e38586},
	number = {7},
	journaltitle = {{PLoS} {ONE}},
	shortjournal = {{PLoS} {ONE}},
	author = {Kroes, Thomas and Post, Frits H. and Botha, Charl P.},
	editor = {Zuo, Xi-Nian},
	urldate = {2025-06-01},
	date = {2012-07-02},
	langid = {english},
	file = {Full Text PDF:/home/lucas/Zotero/storage/QU92LG7Q/Kroes et al. - 2012 - Exposure Render An Interactive Photo-Realistic Volume Rendering Framework.pdf:application/pdf},
}

@article{comaniciu_shaping_2016,
	title = {Shaping the future through innovations: From medical imaging to precision medicine},
	volume = {33},
	issn = {13618415},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841516300962},
	doi = {10.1016/j.media.2016.06.016},
	shorttitle = {Shaping the future through innovations},
	abstract = {Medical images constitute a source of information essential for disease diagnosis, treatment and followup. In addition, due to its patient-speciﬁc nature, imaging information represents a critical component required for advancing precision medicine into clinical practice. This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artiﬁcial agents for in-depth image understanding, support for minimally invasive procedures, and patient-speciﬁc computational models with enhanced predictive power. Throughout the manuscript we will analyze the capabilities of such technologies and extrapolate on their potential impact to advance the quality of medical care, while reducing its cost.},
	pages = {19--26},
	journaltitle = {Medical Image Analysis},
	shortjournal = {Medical Image Analysis},
	author = {Comaniciu, Dorin and Engel, Klaus and Georgescu, Bogdan and Mansi, Tommaso},
	urldate = {2025-06-01},
	date = {2016-10},
	langid = {english},
	file = {PDF:/home/lucas/Zotero/storage/GGC6NISB/Comaniciu et al. - 2016 - Shaping the future through innovations From medical imaging to precision medicine.pdf:application/pdf},
}

@article{kroes_visibility_nodate,
	title = {Visibility Sweeps for Joint-Hierarchical Importance Sampling of Direct Lighting for Stochastic Volume Rendering},
	abstract = {Physically-based light transport in heterogeneous volumetric data is computationally expensive because the rendering integral (particularly visibility) has to be stochastically solved. We present a visibility estimation method in concert with an importance-sampling technique for efﬁcient and unbiased stochastic volume rendering. Our solution relies on a joint strategy, which involves the environmental illumination and visibility inside of the volume. A major contribution of our method is a fast sweeping-plane algorithm to progressively estimate partial occlusions at discrete locations, where we store the result using an octahedral representation. We then rely on a quadtree-based hierarchy to perform a joint importance sampling. Our technique is unbiased, requires little precomputation, is highly parallelizable, and is applicable to a various volume data sets, dynamic transfer functions, and changing environmental lighting.},
	author = {Kroes, Thomas and Eisemann, Martin and Eisemann, Elmar},
	langid = {english},
	file = {PDF:/home/lucas/Zotero/storage/IEP693M5/Kroes et al. - Visibility Sweeps for Joint-Hierarchical Importance Sampling of Direct Lighting for Stochastic Volum.pdf:application/pdf},
}

@incollection{hofmann_efficient_2021,
	location = {Berkeley, {CA}},
	title = {Efficient Unbiased Volume Path Tracing on the {GPU}},
	isbn = {978-1-4842-7185-8},
	url = {https://doi.org/10.1007/978-1-4842-7185-8_43},
	abstract = {We present a set of optimizations that improve the performance of high-quality volumetric path tracing. We build upon unbiased volume sampling techniques, i.e., null-collision trackers [16, 8, 15], with voxel data stored in an {OpenVDB} [14, 13] tree. The presented optimizations achieve an overall 2* to 3* speedup when implemented on a modern {GPU}, with an approximately 6.5* reduction in memory footprint. The improvements primarily stem from a multi-level digital differential analyzer ({DDA}) [1, 11, 6] to step through a grid of precomputed bounds; a replacement of the top levels of the {OpenVDB} tree with a dense indirection texture, similar to virtual textures [3, 4, 17], while preserving some sparsity; and quantization of the voxel data, encoded using {GPU}-supported block compression. Finally, we examine the isolated effect of our optimizations, covering stochastic filtering, the use of dense indirection textures, compressed voxel data, and singleversus multi-level {DDAs}.},
	pages = {699--711},
	booktitle = {Ray Tracing Gems {II}: Next Generation Real-Time Rendering with {DXR}, Vulkan, and {OptiX}},
	publisher = {Apress},
	author = {Hofmann, Nikolai and Evans, Alex},
	editor = {Marrs, Adam and Shirley, Peter and Wald, Ingo},
	urldate = {2025-06-01},
	date = {2021},
	langid = {english},
	doi = {10.1007/978-1-4842-7185-8_43},
	file = {Full Text PDF:/home/lucas/Zotero/storage/9N4DDTKM/Hofmann and Evans - 2021 - Efficient Unbiased Volume Path Tracing on the GPU.pdf:application/pdf},
}

@software{lavik_mlavik1unityvolumerendering_2025,
	title = {mlavik1/{UnityVolumeRendering}},
	rights = {{MIT}},
	url = {https://github.com/mlavik1/UnityVolumeRendering},
	abstract = {Volume rendering, implemented in Unity3D.  Want to support the project? Donate a small sum to Save The Children(https://www.savethechildren.net/) or another charity, and send me a message, and I will be greatly motivated!},
	author = {Lavik, Matias},
	urldate = {2025-06-01},
	date = {2025-05-30},
	note = {original-date: 2019-02-11T16:16:35Z},
	keywords = {computer-graphics, dicom, raymarching, shaders, transfer-functions, unity, unity3d, volume-rendering},
}

@article{breen_3d_2022,
	title = {3D Cinematic Volume-Rendering Technique of Traumatic Spine Injuries: A Powerful Tool for Radiology Education},
	volume = {12},
	issn = {2637-8329},
	url = {https://www.ingentaconnect.com/content/10.3174/ng.2100069},
	doi = {10.3174/ng.2100069},
	shorttitle = {3D Cinematic Volume-Rendering Technique of Traumatic Spine Injuries},
	abstract = {Spine trauma represents a common indication for imaging in the emergency department. Multidetector {CT} imaging, with its advantage of multiplanar reconstructions and fast image acquisition, is the technique of choice in the initial assessment of a patient with polytrauma. While many simple spinal fractures are adequately evaluated on 2D multiplanar reconstruction images, more complex, potentially displaced fracture patterns can be more fully highlighted on 3D reconstructions. Compared with conventional volume-rendering techniques, cinematic rendering uses a complex illumination model to create a more photorealistic representation of the fracture patterns, images that are certain to excite both trainees and clinicians. This review demonstrates the educational value and clinical utility of 3D cinematic rendering images in understanding complex spinal column injuries.},
	pages = {203--209},
	number = {4},
	journaltitle = {Neurographics},
	shortjournal = {neurograph},
	author = {Breen, M.S. and Jawhar, O.M. and Lustrin, E.S. and Young, M.G.},
	urldate = {2025-06-01},
	date = {2022-10-01},
	langid = {english},
	file = {PDF:/home/lucas/Zotero/storage/EZGA9RPC/Breen et al. - 2022 - 3D Cinematic Volume-Rendering Technique of Traumatic Spine Injuries A Powerful Tool for Radiology E.pdf:application/pdf},
}

@article{giesse_volumetric_nodate,
	title = {Volumetric Path Tracing inside the Unity Game Engine},
	author = {Giesse, Simon},
	langid = {english},
	file = {PDF:/home/lucas/Zotero/storage/5493NKKY/Giesse - Volumetric Path Tracing inside the Unity Game Engine.pdf:application/pdf},
}

@software{payton_pjbomb2truetrace-unity-pathtracer_2025,
	title = {Pjbomb2/{TrueTrace}-Unity-Pathtracer},
	rights = {{MIT}},
	url = {https://github.com/Pjbomb2/TrueTrace-Unity-Pathtracer},
	abstract = {A High Performance Compute Shader Based Mesh Pathtracer in Unity3d without {RT} Cores},
	author = {Payton},
	urldate = {2025-06-02},
	date = {2025-06-02},
	note = {original-date: 2022-02-20T06:33:01Z},
	keywords = {unity, unity3d, bindless, bvh, compute-shader, cwbvh, denoising, disney-bsdf, global-illumination, gpu, hlsl, nee, oidn, panorama, path-tracing, raytracing, realtime, refit, restir-gi, rtx},
}

@software{hofmann_nihofmvolren_2025,
	title = {nihofm/volren},
	rights = {{MIT}},
	url = {https://github.com/nihofm/volren},
	abstract = {Simple {OpenGL}-based renderer for volumetric (medical) data},
	author = {Hofmann, Nikolai},
	urldate = {2025-06-12},
	date = {2025-06-04},
	note = {original-date: 2023-03-17T00:23:49Z},
}
