Master's Thesis - Studierstube Augmented Reality Project - Graz ...

More documents

Recommendations

Info

5.3 Visualization nodes and subgraphs Figure 5.9: Increasing or decreasing the µ-value will result in a movement of the peak to the right or to the left. The σ-value determines the slope or the dilatation of the curve. The area under the curve must remain constant while modifying these parameters. The abscissa is defined by the measured slices. Consequently, with this curve, corresponding alpha values can be mapped to each anatomical image so that the transparency is decreasing from the focused slice outwards. velocity value in the given direction. The constant color enables the user to get a fast and clear overview of the currently segmented volume part’s flow conditions. Since no further parameter or seed regions need to be defined for this view, it was asserted to be the most intuitive usable flow-view of this work. Due to the described inconveniences with dynamic visualizations and in some sense unsuitable render nodes, the focus of the implementation turned subsequently to hardware accelerated options as described in the next section 5.3.3. However more ideas how to use a data flow driven scene graph library for cardiac MRI flow data are theoretically presented in [Kainz2006] and may be partially seen as future work. 5.3.3 GPGPU Visualizations As mentioned in the introduction to this section, all of the further described nodes can either be used in a new application with additionally defined vector fields or within the iMEDgine framework, utilizing the built-in interface node (SoViewParameter) as already described for flow and image data. 2D textures are interpolated bilinear on all hardware architectures per default whereas 3D-textures are interpolated trilinear only on architectures with plenty of resources. On other hardware a standard nearestneighbor texture lookup is performed. 5.3.3.1 Integration methods Section 2.1.1.2 in chapter 2 describes two common known integration methods. They can be anticipated here since all of the following nodes and their shader make use 100
5.3 Visualization nodes and subgraphs (a) A default view and adjustment of the slice renderer. (b) A 90 ◦ turned view of the default settings. (c) A high σ - more neighboring slices are shown with increasing transparency - and a lower µ - the brightest slice is placed in the lower part of the volume. (d) The same µ as (c) but a very small σ makes the slope very high, so only one nearly opaque slice is shown. Figure 5.10: Influence of the µ and σ values on a volumetric slice rendering node with Gauss distributed transparency values. 101
Page 1 and 2:
Master’s Thesis Quantitative Meas
Page 3 and 4:
I hereby certify that the work pres
Page 5 and 6:
Kurzfassung In dieser Arbeit werden
Page 7 and 8:
CONTENTS 4.2.2 Noise Suppression .
Page 9 and 10:
Chapter 1 Introduction Assessment o
Page 11 and 12:
Figure 1.1: This workflow diagram d
Page 13 and 14:
Chapter 2 Visualization Visualizati
Page 15 and 16:
2.1 Technical Visualization (a) (b)
Page 17 and 18:
2.1 Technical Visualization Next a
Page 19 and 20:
2.1 Technical Visualization Figure
Page 21 and 22:
2.1 Technical Visualization • ave
Page 23 and 24:
2.1 Technical Visualization spaced
Page 25 and 26:
Page 27 and 28:
2.1 Technical Visualization improve
Page 29 and 30:
2.1 Technical Visualization try to
Page 31 and 32:
2.1 Technical Visualization Visuali
Page 33 and 34:
Page 35 and 36:
2.1 Technical Visualization of N.
Page 37 and 38:
2.2 Advanced Graphics Processing ap
Page 39 and 40:
2.2 Advanced Graphics Processing pr
Page 41 and 42:
2.2 Advanced Graphics Processing te
Page 43 and 44:
Chapter 3 Magnetic Resonance Imagin
Page 45 and 46:
3.1 Magnetic Field and Magnetizatio
Page 47 and 48:
3.1 Magnetic Field and Magnetizatio
Page 49 and 50:
3.2 Excitation of Magnetized Matter
Page 51 and 52:
3.2 Excitation of Magnetized Matter
Page 53 and 54:
3.3 Signal localization 1. A gradie
Page 55 and 56:
3.3 Signal localization in one dire
Page 57 and 58: 3.3 Signal localization a commonly
Page 59 and 60: 3.4 Image Contrast (a) T1-contrast
Page 61 and 62: 3.5 Cine Cardiac Fast Low-Angle Sho
Page 71 and 72: 4.1 Requirements visualization of c
Page 73 and 74: 4.2 Data Preprocessing 4.2 Data Pre
Page 75 and 76: 4.2 Data Preprocessing and if ∆v
Page 77 and 78: 4.3 Intermediate File Format a simp
Page 79 and 80: 4.4 Visualization Framework impleme
Page 81 and 82: 4.4 Visualization Framework hardwar
Page 83 and 84: 4.4 Visualization Framework 3 void
Page 85 and 86: 4.4 Visualization Framework • fra
Page 87 and 88: 4.4 Visualization Framework Figure
Page 89 and 90: Chapter 5 Implementation This chapt
Page 91 and 92: 5.2 iMEDgine extensions Figure 5.1:
Page 93 and 94: 5.2 iMEDgine extensions which eases
Page 95 and 96: 5.2 iMEDgine extensions solution fu
Page 97 and 98: 5.2 iMEDgine extensions 23 cam_posi
Page 99 and 100: 5.2 iMEDgine extensions Figure 5.4:
Page 101 and 102: 5.2 iMEDgine extensions • SoSFStr
Page 103 and 104: 5.2 iMEDgine extensions 6 Z=" -238.
Page 105 and 106: 5.3 Visualization nodes and subgrap
Page 107: 5.3 Visualization nodes and subgrap
Page 121 and 122: Chapter 6 Experiments Several atten
Page 123 and 124: 6.3 Results 6.3 Results To complete
Page 125 and 126: 6.4 Scalability activity. The highe
Page 127 and 128: 6.4 Scalability (a) Stream lines fo
Page 129 and 130: 6.4 Scalability (a) Stream tubes ap
Page 131 and 132: 6.4 Scalability Figure 6.10: Compar
Page 133 and 134: Chapter 7 Future Work This chapter
Page 135 and 136: 7.3 Derived Magnitudes possibilitie
Page 137 and 138: 7.5 Clinical Evaluations (a) Stereo
Page 139 and 140: Beside the improvement of known spa
Page 141 and 142: Appendix A Intermediate File Format
Page 143 and 144: A.2 Data Files Offset Name Type Des
Page 149 and 150: This chapter supplements chapter 5
Page 151 and 152: 143 Figure B.3: An thinned out UML
Page 153 and 154: Figure B.5: This UML class diagram
Page 155 and 156: Glossary Notation bipolar gradient
Page 157 and 158: Glossary Notation Description MIP M
Page 159 and 160:
Glossary Notation Description shade
Page 161 and 162:
LIST OF FIGURES 3.5 The slice-profi
Page 163 and 164:
List of Tables 2.1 CPU - GPU analog
Page 165 and 166:
REFERENCES [Brill1994] M. Brill, W.
Page 167 and 168:
REFERENCES [iMedgine2006] T. Gross,
Page 169 and 170:
REFERENCES [Kniss2002a] Joe Kniss,
Page 171 and 172:
REFERENCES [Markl2003] Michael Mark
Page 173 and 174:
REFERENCES [Roth1982] Scott D. Roth
Page 175 and 176:
REFERENCES [Vivek1999] andAlexPang
Page 177 and 178:
INDEX flow data, 135 format definit
show all

Master's Thesis - Studierstube Augmented Reality Project - Graz ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?