Abstract
This study describes an acceleration method that can perform efficient maximum intensity projection (MIP) visualization, which is essential for medical imaging systems. The proposed method is based on shear-warp volume rendering and produces rendering images using trilinear interpolation in real time without a GPU. This study includes two acceleration methods. First, we propose a high-speed interpolation method using AVX2, which is a single instruction, multiple data system of modern CPUs. Trilinear interpolation can be performed rapidly using the AVX2 instructions by taking advantage of the fact that each interpolation weight is the same while using the shear-warp volume rendering. Second, we propose a method for efficiently accessing the memory, focusing on the fact that changing the order of the comparison operations does not affect the image quality in MIP. We propose a new method for changing the repetition and memory access orders so that large volume data can be read sequentially, and image data can be accessed repeatedly. Moreover, we investigate the effectiveness of aligned memory access. The experiment demonstrates significant improvements compared to existing methods. As a result, volume data composed of more than 500 images used in clinical practice can be rendered in real time using trilinear interpolation. In this study, high-quality MIP volume rendering is possible in real-time with only CPU. Since this study does not go through a complicated pre-processing process, it can be easily applied to existing medical imaging systems.
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References
Agulleiro JI, Fernandez JJ (2015) Tomo3D 2.0–exploitation of advanced vector extensions (AVX) for 3D reconstruction. J Struct Biol 189:147–152
Amiri H, Shahbahrami A (2020) SIMD programming using Intel vector extensions. J Parallel Distrib Comput 135:83–100
Belina S, Cuk V, Klapan I (2009) Virtual endoscopy and 3D volume rendering in the management of frontal sinus fractures. Coll Antropol 33:43–51
Chen K, Duan Y, Yan L, Sun J, Guo Z (2012) Efficient SIMD optimization of HEVC encoder over X86 processors. In Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), pp 1–4
Dachille F, Kreeger K, Chen B, Bitter I, Kaufman A (1998) High-quality volume rendering using texture mapping hardware. SIGGRAPH/Eurographics workshop on graphics Hardware’98, pp 69–76
Engel K, Kraus M, Ertl T (2001) High-quality pre-integrated volume rendering using hardware-accelerated pixel shading. In proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on graphics hardware, pp 9-16
Fang L, Wang Y, Qiu B, Qian Y (2002) Fast maximum intensity projection algorithm using shear warp factorization and reduced resampling. Magn Reson Med Off J Int Soc Magn Reson Med 47:696–700
Hofmann J, Treibig J, Hager G, Wellein G (2014) Performance engineering for a medical imaging application on the Intel Xeon phi accelerator. In architecture of computing systems (ARCS), 2014 workshop proceedings, pp 1–8
Knoll A, Thelen S, Wald I, Hansen C D, Hagen H, Papka ME (2011) Full-resolution interactive CPU volume rendering with coherent BVH traversal. In 2011 IEEE Pacific visualization symposium, pp 3–10
Kye H, Lee SH, Lee J (2018) CPU-based real-time maximum intensity projection via fast matrix transposition using parallelization operations with AVX instruction set. Multimed Tools Appl 77(12):15971–15994
Lacroute P, Levoy M (1994) Fast volume rendering using a shear-warp factorization of the viewing transformation. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques. pp 451–458
Levoy M (1988) Display of surfaces from volume data. IEEE Comput Graph Appl 8(3):29–37
Meißner M, Grimm S, Straßer W, Packer J, Latimer D (2001) Parallel volume rendering on a single-chip SIMD architecture. In proceedings IEEE 2001 symposium on parallel and large-data visualization and graphics, pp 107–157
Mroz L, Hauser H, Gröller E (2000) Interactive high-quality maximum intensity projection. Comput Graphics Forum 19(3):341–350
Mroz L, König A, Gröller E (1999) Real-time maximum intensity projection. In: data Visualization'99, springer, pp 135–144
Rezk-Salama C, Engel K, Bauer M, Greiner G, Ertl T (2000) Interactive volume rendering on standard PC graphics hardware using multi-textures and multi-stagerasterization. Proceedings of SIGGRAPH/Eurographics Workshop on Graphics Hardware’00, pp 109–118
Sabella P (1988) A Rendering algorithm for visualizing 3D scalar fields. ACM SIGGRAPH 1988 Proceedings of the 15th Annual Conference on Computer Graphics and Interactive Techniques, pp 51–58
Schreiner S, Galloway RL Jr (1993) A fast maximum-intensity projection algorithm for generating magnetic resonance angiograms. IEEE Trans Med Imaging 12(1):50–57
Schulze JP, Kraus M, Lang U, Ertl T (2003) Integrating pre-integration into the shear-warp algorithm. In Proceedings of the 2003 Eurographics/IEEE TVCG Workshop on Volume graphics, pp 109–118
Shahbahrami A, Juurlink B, Vassiliadis S (2006) Performance impact of misaligned accesses in SIMD extensions. Proceedings of the 17th annual workshop on circuits, systems and signal processing (ProRISC 2006), pp 334–342
Sweeney J, Mueller K (2002) Shear-warp deluxe: the shear-warp algorithm revisited. Eurographics - IEEE TCVG symposium on visualization, pp 95–104
Treibig J, Hager G, Hofmann HG, Hornegger J, Wellein G (2013) Pushing the limits for medical image reconstruction on recent standard multicore processors. Int J High Perform Comput Appl 27:162–177
Wald I, Johnson GP, Amstutz J, Brownlee C, Knoll A, Jeffers J, Navrátil P (2016) Ospray-a CPU ray tracing framework for scientific visualization. IEEE Trans Vis Comput Graph 23(1):931–940
Zekri AS (2014) Enhancing the matrix transpose operation using Intel AVX instruction set extension. Int J Comput Sci Inf Technol (IJCSIT) 6(3):67–78
Zhao K, Sakamoto N, Koyamada K (2014) Fused visualization for large-scale time-varying volume data with adaptive particle-based rendering. AsiaSim 2014, 14th international conference on systems simulation, pp 228–242
Acknowledgements
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1048637, Sehee Lee). This research was financially supported by Hansung University (Heewon Kye).
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This research was financially supported by Hansung University.
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Lee, S., Kye, H. Efficient MIP volume rendering via fast SIMD interpolation and memory access reordering. Multimed Tools Appl 82, 10515–10534 (2023). https://doi.org/10.1007/s11042-022-13732-z
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DOI: https://doi.org/10.1007/s11042-022-13732-z