Medical Visualization Bernhard Preim Visualization Group EMail preimisg

Medical Visualization Bernhard Preim, Visualization Group E-Mail: preim@isg. cs. uni-magdeburg. de Phone: 58512 Benjamin Köhler E-Mail: ben. koehler@isg. cs. uni-magdeburg. de Medical Visualization: Basics, Algorithms, Applications Phone: 52759 Friday, 9 -11, 29 -426 Bernhard Preim 1

Organization Audience: Elective subject: CVM 1 -2, Elective subject: CV-Bachelor (as of semester 5) Computer Science: Elective subject: Computer Science 2/3 Master CV Credit Points: 6 Information: ISG website: Visualization: Lectures: Med. CV or: http: //www. vismd. de/doku. php? id=teaching: medicalvisualization User: medvis_student, password: medvis_lecture Exam: Tutorial: für CV mit AF Medizin, Teil der Medizinprüfung for all other students: oral exam (20 or 30 min. in Sept. ) M. Sc. Benjamin Köhler, VIS website Bernhard Preim 2

Tutorials • Me. Vis. Lab (www. mevislab. de) Bernhard Preim 3

Introduction Motivation • Imaging technique senable detailed views into the human body • Most created data require integration Goals: • Better understanding of anatomy • More precise and objectifiable diagnoses • Controlling the course of diseases • Visualization as basis for discussions • Visualization for the planning of therapeutic interventions • Surgery planning as prerequisite for computer-assisted surgery (navigation and control of surgery robots) Bernhard Preim 4

Introduction • Advanced visualizations and interaction to train surgical interventions Bernhard Preim 5

Introduction • Visualization of anatomy and biomechanical simulations for surgery planning in orthopedics. • Simulations are used to predict the range of motion after surgery. Bernhard Preim 6

Conventional diagnosis and therapy planning • Costly revision of all images • The use of imaging techniques is restricted to easily manageable and analyzable versions • Very costly analysis of image series • Quantitative data are neglected/are very imprecise Bernhard Preim 7

Introduction Medical Visualization: Visualization • of data from imaging procedures (attenuation of X-rays or sound waves, distribution of contrast agent), • of simulations of biological processes (e. g. flexibility of objects, gas exchange in the lung, …) • Mainly: 3 D and 4 D data • Goals: better diagnostics Bernhard Preim 8

Medical Visualization: Basics, Algorithms, Applications Bernhard Preim 9

Introduction Requirements: • Basic knowledge in 3 D computer graphics (projections, transformations, rendering) and visualization (visual perception, data structures, basic algorithms) • a certain interet in medical applications Learning goals: • Medicine as an example for applied Computer Science • Concentration on user requirements, data features and specific workflows • Interaction techniques for data exploration • Knowledge of Computer Graphics procedures • Embedding of these procedures into usable software systems Bernhard Preim 10

Introduction • Medical Visualization Approach Reference: Hastreiter [2002] Bernhard Preim 11

Introduction • Medical Visualization Approach Reference: Hastreiter [2002] Bernhard Preim 12

Introduction General tasks: • Depiction of patient-individual structures • Depiction of tree-like structures (vessels, nerves) • Highlighting in medical visualizations (e. g. , show a tumor in its spatial context) • Measurement in medical 3 D visualizations • Virtual resection Bernhard Preim 13

Sample applications • Computer tomography of the lung (250 layers, 1024 x 1024 pixels each) Bernhard Preim 14

Sample applications • Anatomy training, interaction with/labeling of volume data (Voxel. Man, Uni Hamburg) Bernhard Preim 15

Application areas • 3 D radiation treatment planning of a brain tumor Bernhard Preim 16

Application areas • 3 D radiation treatment planning of a brain tumor Bernhard Preim 17

Application areas Surgical Planning Unit, University of Leipzig Bernhard Preim 18

Application areas Medical Visualization Table, SECTRA Medical Systems, Linköping/Sweden. Scenario: Virtual autopsy. Patient (in original size) lying on the table to be analyzed. Bernhard Preim 19

Application areas Planning of neck surgeries Bernhard Preim 20

Application areas • Assessment of infiltrations Bernhard Preim 21

Application areas • Sequences of simple visualizations for assessing infiltrations • Assessment of infiltrations (thyroid cartilages) Bernhard Preim 22

Application areas Implant placement during middle ear surgeries (© M. Gessat, ICCAS Leipzig) Bernhard Preim 23

Structure of the Lecture • Basics • Image processing and analysis for medical visualization (segmentation, skeletonization, quantification) (2 lectures) • Medical visualization in radiology (1 lecture) • Volume Visualization • Indirect volume visualization (1 lecture) • Direct volume visualization, algorithms for display and lighting (3 lectures), • Interaction techniques (TF, measurement, virtual resection) (2 lectures) • Special techniques and application areas • Vessel visualization, • highlighting techniques, illustrative images • Virtual endoscopy • Computer-assisted surgery (1 lecture each) Bernhard Preim 15

Literature • www. medvisbook. de Bernhard Preim 25

Image Analysis for Visualization • Image processing and image analysis: • segmentation, skeletonization, quantification Bernhard Preim 26

Volume Visualization Bernhard Preim 27

Volume Visualization Reference: Rezk-Salama, Phd thesis, 2002 Bernhard Preim 28

Volume Visualization Projection techniques, incorporation of depth cues Bernhard Preim 29

Interaction Techniques • Edge enhancement through multi-dimensional transfer functions (TFs) Bernhard Preim 30

Interaction Techniques Focus-context-rendering with two transfer functions. Gradient-magnitude used for edge-enhancing rendering. Bernhard Preim 31

Interaction Techniques Bernhard Preim 32

Vessel Visualization Bernhard Preim 33

Vessel Visualization • Comparison and assessment of methods • Quantitative and qualitative validation • Reference: Oeltze/Preim (2004) Bernhard Preim 34

Vessel Visualization Bernhard Preim 35

Illustrative Display and Highlighting Techniques • Hatchings along the main curvature direction (From: Dong et al. [2003]) • Combination of laminar illustration, silhouettes and feature lines (From: Tietjen [2005]) Bernhard Preim 36

Illustrative Display and Highlighting Techniques (From: Tietjen, Pfisterer, Smart. Graphics, 2008) Bernhard Preim 37

Illustrative Display and Highlighting Techniques Streamline-based generation of feature lines (From: Lawonn et al. , Euro. Vis, 2013) Bernhard Preim 38

Computer-Assisted Surgery • Planning of a jaw surgery, Reference: Zachow [2004] • → http: //www. zib. de/visual/projects/casgallery. en. html Bernhard Preim 39

Computer-Assisted Surgery Bernhard Preim 40

Videos • • Aneurysm Vessel. Seg 3 d Lung. Thin. Slab. MIP Segmented 3 d (liver tumor surgery planning) Draw. Resection/Modify. Resection. Plane Applicator 2 d/3 d Bernhard Preim 41