Real time computation and temporal coherence of opacity transfer functions for direct volume rendering of ultrasound data

Petersch, Bernhard; Hadwiger, Markus; Hauser, Helwig; Hönigmann, Dieter

doi:10.1016/j.compmedimag.2004.09.013

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…Second, we do temporal smoothing over successive frames to reduce the per-frame adaptivity and potential flickering. 27 The smoothing has been implemented as averaging filters on the framebuffer containing regional statistics. All regional adaptive renderings and statistics have been produced without any regularization in this paper.…”

Section: Spatial and Temporal Regularizationmentioning

confidence: 99%

Adaptive volume rendering of cardiac 3D ultrasound images: utilizing blood pool statistics

Åsen

Steen²,

Kiss

et al. 2012

SPIE Proceedings

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In this paper we introduce and investigate an adaptive direct volume rendering (DVR) method for real-time visualization of cardiac 3D ultrasound. DVR is commonly used in cardiac ultrasound to visualize interfaces between tissue and blood. However, this is particularly challenging with ultrasound images due to variability of the signal within tissue as well as variability of noise signal within the blood pool. Standard DVR involves a global mapping of sample values to opacity by an opacity transfer function (OTF). While a global OTF may represent the interface correctly in one part of the image, it may result in tissue dropouts, or even artificial interfaces within the blood pool in other parts of the image. In order to increase correctness of the rendered image, the presented method utilizes blood pool statistics to do regional adjustments of the OTF. The regional adaptive OTF was compared with a global OTF in a dataset of apical recordings from 18 subjects. For each recording, three renderings from standard views (apical 4-chamber (A4C), inverted A4C (IA4C) and mitral valve (MV)) were generated for both methods, and each rendering was tuned to the best visual appearance by a physician echocardiographer. For each rendering we measured the mean absolute error (MAE) between the rendering depth buffer and a validated left ventricular segmentation. The difference d in MAE between the global and regional method was calculated and t-test results are reported with significant improvements for the regional adaptive method (d A4C = 1.5 ± 0.3 mm,d IA4C = 2.5 ± 0.4 mm,d MV = 1.7 ± 0.2 mm, d.f. = 17, all p < 0.001). This improvement by the regional adaptive method was confirmed through qualitative visual assessment by an experienced physician echocardiographer who concluded that the regional adaptive method produced rendered images with fewer tissue dropouts and less spurious structures inside the blood pool in the vast majority of the renderings. The algorithm has been implemented on a GPU, running an average of 16 fps with a resolution of 512x512x100 samples (Nvidia GTX460).

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Section: Spatial and Temporal Regularizationmentioning

confidence: 99%

Adaptive volume rendering of cardiac 3D ultrasound images: utilizing blood pool statistics

Åsen

Steen²,

Kiss

et al. 2012

SPIE Proceedings

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“…Voxel intensities prior to and at the detected interface steer the extent of modification of an initial, parabolic OTF in a multiplicative way. In a subsequent publication the authors assess the temporal coherence of the tube core method and conclude that it is sufficiently efficient and robust for online-computation of OTFs for an entire sequence of acquired volumes, if smoothing in the temporal domain is employed [54].…”

Section: Transfer Function Designmentioning

confidence: 99%

The Ultrasound Visualization Pipeline

Birkeland

Šoltészová

Hönigmann

et al. 2014

Mathematics and Visualization

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Note: Also to appear in the Dagstuhl 2012 SciVis book by Springer. Please cite this paper with its arXiv citation information. Abstract Ultrasound is one of the most frequently used imaging modality in medicine. The high spatial resolution, its interactive nature and non-invasiveness makes it the first choice in many examinations. Image interpretation is one of ultrasound's main challenges. Much training is required to obtain a confident skill level in ultrasound-based diagnostics. State-of-the-art graphics techniques is needed to provide meaningful visualizations of ultrasound in real-time. In this paper we present the process-pipeline for ultrasound visualization, including an overview of the tasks performed in the specific steps. To provide an insight into the trends of ultrasound visualization research, we have selected a set of significant publications and divided them into a technique-based taxonomy covering the topics pre-processing, segmentation, registration, rendering and augmented reality. For the different technique types we discuss the difference between ultrasound-based techniques and techniques for other modalities.

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“…Kniss et al provided methods to allow the user to manipulate transfer functions in higher dimensional data space in order to locate surfaces and features [KKH01]. Petersch et al performed real time opacity adjustment for visualization of ultrasound imagery by searching for interfaces, taking point‐of‐view into consideration [PHHH05].…”

Section: Related Workmentioning

confidence: 99%

Semi‐Automatic Time‐Series Transfer Functions via Temporal Clustering and Sequencing

Woodring

Shen

2009

Computer Graphics Forum

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When creating transfer functions for time-varying data, it is not clear what range of values to use for classification, as data value ranges and distributions change over time. In order to generate time-varying transfer functions, we search the data for classes that have similar behavior over time, assuming that data points that behave similarly belong to the same feature. We utilize a method we call temporal clustering and sequencing to find dynamic features in value space and create a corresponding transfer function. First, clustering finds groups of data points that have the same value space activity over time. Then, sequencing derives a progression of clusters over time, creating chains that follow value distribution changes. Finally, the cluster sequences are used to create transfer functions, as sequences describe the value range distributions over time in a data set.

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Real time computation and temporal coherence of opacity transfer functions for direct volume rendering of ultrasound data

Cited by 14 publications

References 29 publications

Adaptive volume rendering of cardiac 3D ultrasound images: utilizing blood pool statistics

Adaptive volume rendering of cardiac 3D ultrasound images: utilizing blood pool statistics

The Ultrasound Visualization Pipeline

Semi‐Automatic Time‐Series Transfer Functions via Temporal Clustering and Sequencing

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