lecture 6b
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Exploration of Dynamic Medical
Volume Data
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Outline
1. Introduction
2. Medical Background
3. Basic Visualization Techniques
4. Advanced Visualization Techniques
5. Case Study: Tumor Perfusion
6. Case Study: Brain Perfusion
7. Summary
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Introduction
Static image data
Only provide a snapshot
Many aspects relevant for diagnostic decisions andtreatment planning cannot be judged by means of a
single snapshot
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Introduction
Dynamic image data
Might change over time
Acquired to assess blood flow (perfusion) andtissue kinetics by tracing the distribution of
contrast agents (CA) or other data changes
A special variant of dynamic data is functionalMRI where activation patterns after stimulation are
recorded
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Medical Background
Functional MRI Where activations of brain areas are imaged
Dynamic SPECT Where the temporal distribution of a radioactive
tracer is registered
Perfusion data Have a broad clinical relevance
Dynamic contrast enhanced (DCE) images areacquired to study these phenomena
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Medical Background
Parameters Peak enhancement (PE)
The maximum value (over all points in time)
Time To Peak (TTP) The point of time where peak enhancement occurs
Integral The area below the curve is computed
Mean Transit Time (MTT) MTT specifies the time where the area below the curve
is the same on the left and on the right
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Basic Visualization Techniques
Techniques to visualize volume data Cine-movies
Which step through all points in time for a selected slice
Subtraction images Which depict the intensity difference between two
selected points in time
Color-coded parameter maps for a selected slice A parameter map is a 2D display of a selected slice, in
which each pixel encodes the value of a selectedparameter
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Basic Visualization Techniques
Subtraction images to analyze cerebralperfusion
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Basic Visualization Techniques
Parametric images for slice 4 of a dynamicMRI sequence
TTP, MTT, and the integral are depicted as color-coded images
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Advanced Visualization Techniques
Drawbacks of basic visualization techniques
Basic techniques do not permit the integration ofseveral parameter maps in one image
Dynamic information cannot be integrated withmorphologic information that may be based on
another dataset with higher spatial resolution
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Advanced Visualization Techniques
Multiparameter visualization
The integrated visualization of several parametersin a suspicious region is desirable for various
diagnostic tasks
Combining Isolines and Color-coding
Exploration of Multiple Parameter Images withLenses
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Advanced Visualization Techniques
A gray scale MIP of the subtraction volume oftwo early points in time is combined with a
color-coded CVP (closest vessel projection)
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Advanced Visualization Techniques
Combining Isolines and Color-coding
The combination of isolines and colors isparticularly effective and can be easily interpreted
Isolines connect regions where the investigateddynamic parameter has a certain value
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Advanced Visualization Techniques
Principle of isoline generation with theMarching Squares approach
Isolines for isovalue 5 are computed by linearinterpolation along the grid cells
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Advanced Visualization Techniques
Ten isolines depict a dynamic parameterderived from MRI mammography
The data and the resulting isolines are smoothed
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Advanced Visualization Techniques
Exploration of Multiple Parameter Imageswith Lenses
Lenses might be employed to show differentinformation in the lens region
Lenses are useful for showing information relatingto one parameter in the context of a map of another
parameter
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Advanced Visualization Techniques
Magic Lenses for the exploration ofmultiparameter maps
The focus region inside the lens shows theparameter cerebral blood volume whereas the gray
values show the original MRI data
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Advanced Visualization Techniques
Exploration of MRI-mammography data witha lens
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Advanced Visualization Techniques
Integrating dynamic information andmorphology
It is useful to add spatial reference information inthe regions not containing dynamic information
The integration of dynamic and morphologicinformation can be carried out in 2D slice
visualizations or 3D renderings
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Advanced Visualization Techniques
The visualization of dynamic information isrestricted to the segmented tumor
The surrounding tissue is displayed asconventional volume rendering
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Case Study: Tumor Perfusion
Tumor perfusion
Perfusion imaging is carried out to evaluatewhether lesions regarded as suspicious in static
images are likely to represent a cancer
Perfusion images support diagnosis of tumordiseases and therapy monitoring
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Case Study: Tumor Perfusion
Typical parameters for DCE MRImammography
Matrix: 512 512
Slice distance: 2 mm
Number of slices: 6080
Temporal resolution: 11.5 min (510measurements)
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Case Study: Tumor Perfusion
Computer support
Software solutions are challenging, due to thedynamic nature of DCE MRI mammography
Data processing, in particular motion correction, ismore challenging than brain perfusion imaging
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Case Study: Tumor Perfusion
Visualization techniques
Maximum Intensity Projection
Conventionally used for gray scale volume data inwhich the interesting structures have a small volume-
filling factor
Closest Vessel Projection
Developed to add depth information to MIP images
Dedicated to the visualization of vascular structures
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Case Study: Tumor Perfusion
Left: a malignant breast tumor visualized usinga MIP. Right: the same data visualized using a
CVP
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Case Study: Tumor Perfusion
Left: a lesion is represented as an area ofbright yellow. Right: the graph of the selected
voxel is shown
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Case Study: Brain Perfusion
Brain perfusion
Ischemic stroke is among the leading causes ofdeath in all western countries
The identification of tissue at risk (ischemicpenumbra) is crucial before considering any
patient treatment
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Case Study: Brain Perfusion
Typical parameters for contrast-enhanced MRIperfusion
Matrix: 128 128
Slice distance: 7 mm
Number of slices: 1015
Temporal resolution: 12 seconds (4080measurements)
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Case Study: Brain Perfusion
CT Imaging
CT perfusion studies only acquire one slice
To reduce image noise, a large slice thickness (10mm) is employed
Perfusion maps
Brain perfusion maps can be quantified in terms ofabsolute blood flow and blood volume
Derived from CT and MRI data
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Case Study: Brain Perfusion
Visualization techniques
The symmetry of the brain is the basis fordiagnostic evaluation of static and dynamic images
Magic Lenses
Synchronization of ROIs
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Case Study: Brain Perfusion
Enhancement curves are simultaneouslyderived for the symmetric regions in both
hemispheres
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Case Study: Brain Perfusion
Synchronized lenses in both hemispheres ofthe brain support the comparison between the
symmetric regions
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Summary
Dynamic image data have a great potential forenhancing diagnosis and therapy monitoring
for important diseases
The acquisition of appropriate data and theirinterpretation require long term experience
Focus on the role of visualization to support afast and unambiguous interpretation of such
data
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Clipping, Cutting, and
Virtual Resection
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Outline
1. Clipping
2. Virtual Resection
3. Virtual Resection with a Deformable Cutting
Plane
4. Cutting Medical Volume Data
5. Summary
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Part 2 start
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Clipping
A fundamental interaction technique forexploring medical volume data
It is used to restrict the visualization to sub-volumes
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Clipping
The tumor is demonstrated by tilting the clipplane vertically
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Clipping
Implementation of clipping
For volume rendering, each voxel affected by theclipping plane are discarded completely
For surface rendering, each triangle is tested todetermine whether it should be drawn or not
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Clipping
Volume and surface rendering with a clippingplane for exploring spatial relations in a CT
head dataset
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Clipping
Selective clipping
A special variant of clipping
Used to emphasize structures (those not affectedby clipping) while presenting contextual
information (structures which are partially visible
due to clipping)
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Clipping
Selective clipping
Left: the brain and the ventricles are renderedcompletely. Right: the vertical symmetry is used
for selective clipping of a CT head dataset
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Clipping
Selective clipping with boolean textures
An elegant and efficient way to accomplishselective clipping is the use of Boolean textures
Boolean textures are constructed by implicitfunction, such as quadrics
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Clipping
Box clipping
Combine six clipping planes to define a sub-volume
Useful for exploring a region in detail, for example,an aneurysm or the region around a tumor
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Clipping
Box clipping for the analysis of an intracranialaneurysm
A detailed view of the region of interest iscombined with an overview rendering
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Clipping
Local volume rendering for the evaluation ofthe surrounding of a tumor in CT thorax data
The tumor is visualized as an isosurface whereasthe vascular structures around it are rendered as
direct volume rendering
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Virtual Resection
Resection refers to the removal of tissueduring a surgical intervention
Virtual resection is a core function of manyintervention planning systems
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Virtual Resection
Requirements of virtual resection functions
The user must be able to specify a virtual resectionintuitively and precisely
The Modification must be supported to changevirtual resections
Virtual resections should be visualizedimmediately, with high quality
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Virtual Resection
Specification of virtual resections by erasing
Use scalable 3D shapes as erasers to remove thetouched tissue
Boolean operations on voxel values are used todecide which subset of voxels should be drawn
the visual quality is limited by the resolution of theunderlying voxel grid
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Virtual Resection
Left: a resection area specified by erasingliver tissue with a sphere. Right: the result of
the virtual resection is displayed in a 2D view
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Virtual Resection
Specification of virtual resections by drawingon slices
Inspired by the communication between surgeonsand radiologists discussing a resection
The resection is marked by drawing on the sliceswith a pen or mouse
This process is time-consuming if the entireresection volume should be specified
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Virtual Resection
Virtual liver resection by drawing on the slices
The virtually resected and the remaining portion ofthe liver are separated to support the evaluation of
the shape of virtual resections
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Virtual Resection with a Deformable
Cutting Plane
Based on a surface representation of an organ,usually achieved with explicit segmentation
The user draws lines on the (3D) surface of anorgan to initialize the cutting plane
The plane is deformed locally to fit the linesdrawn by the user
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Virtual Resection with a Deformable
Cutting Plane
Defining cutting plane boundaries
The user employs a 2D pointing device andcontrols the movement of a cursor on a 3D surface
This control is accomplished by casting a ray fromthe viewpoint through the 2D point to the 3D
position on the surface
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Virtual Resection with a Deformable
Cutting Plane
Definition of the cut path
The Euclidean distance represents the shortestdistance between successive points
The geodesic shortest path connects points on the3D surface with a path on that surface
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Virtual Resection with a Deformable
Cutting Plane
Euclidean (left) versus geodesic distance(right) between surface points
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Virtual Resection with a Deformable
Cutting Plane
Cut boundary specification specified with apen on a digitizer tablet, which is shown
enlarged in the right image
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Virtual Resection with a Deformable
Cutting Plane
Cut boundary specification with a tactile inputdevice
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Virtual Resection with a Deformable
Cutting Plane
Generation of the initial cutting plane
Determine the oriented bounding box of the linesdrawn by the user
Determine the orientation and extent of the cuttingplane
Set the center of the cutting plane
Project the point-set into the cutting plane
Calculate displacements
Smoothing
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Virtual Resection with a Deformable
Cutting Plane
Definition of the plane E based on the (dashed)lines P drawn by the user
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Virtual Resection with a Deformable
Cutting Plane
Modification of virtual resections
The resection can be refined by translating gridpoints
The user can define the sphere of influence as wellas the amplitude of the deformation
There is also a facility to translate the whole mesh
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Virtual Resection with a Deformable
Cutting Plane
Left: fine-tuning of the plane with respect toblood vessels. Right: the initial cutting plane is
translated with a sphere of influence
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Virtual Resection with a Deformable
Cutting Plane
Based on the two lines drawn on the objectsurface, an initial resection has been specified
that might be refined by the user
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Virtual Resection with a Deformable
Cutting Plane
The result of a virtual resection by means of adeformable mesh
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Virtual Resection with a Deformable
Cutting Plane
Transformation of the resection boundary to aresection plane
Flat projection
A planar projection of the boundary is deformed torepresent the points specified by a user
Minimal surfaces
They are constructed to exactly match the givenboundary
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Virtual Resection with a Deformable
Cutting Plane
A flat surface (left) as approximation of thegiven boundary compared to a minimal surface
(right) of the same boundary
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Virtual Resection with a Deformable
Cutting Plane
Application areas of virtual resectiontechniques
Liver surgery
Osteotomy planning
Craniofacial surgery
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Virtual Resection with a Deformable
Cutting Plane
Conventional osteotomy planning based on astereolithographic model (left). Virtual
resection based on a 3D model of the patients
bones (middle, right)
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Virtual Resection with a Deformable
Cutting Plane
Efficient visualization of virtual resections
Efficiency is an important aspect for virtualresection and clipping with arbitrary geometries
It is desirable that a high update-rate be achievedwithout compromising accuracy
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Virtual Resection with a Deformable
Cutting Plane
Visualization parameters
The realistic approach (to remove the resectionvolume entirely) is only one of several possibilities
The resection volume can be regarded as a newvisualization object that can be flexibly
parameterized
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Virtual Resection with a Deformable
Cutting Plane
Combination of resection proposals and virtualresection
A completely different approach to resectionspecification is to propose to the surgeon which
part of an organ has to be resected
The resection proposal might be presented asadditional information when the deformable
cutting plane is specified
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Virtual Resection with a Deformable
Cutting Plane
Combining resection proposals and interactiveresection for liver surgery planning
The resection proposal (dark red) is presentedwhile the user interactively specifies the resection
region (with the yellow line)
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Cutting Medical Volume Data
Cutting facilities are important for surgerysimulation
Users move a cutting device through medicalvolume data and simulate cutting procedures
Collision detection and tactile feedback areessential for educational purposes when
prospective surgeons are trained
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Cutting Medical Volume Data
High-quality representation of cut surfaces
Surgical cutting is a challenging applicationbecause the requirements for accuracy and speed
are high, and arbitrary shapes are involved
If virtual resection is accomplished by a volumerepresentation, the resolution of the cut surface is
limited to the resolution of the underlying data
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Cutting Medical Volume Data
Volume cutting
Left: two-dimensional representation of an objectsurface. Middle: voxelization of the cutting tool.
Right: the resulting cut surface
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Cutting Medical Volume Data
Progressive cutting
Left: only the left area has to be considered.Middle: representation of the new cut surface.
Right: the resulting cut surface
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Cutting Medical Volume Data
Cutting with a virtual scalpel (left). The resultis shown in high quality (right)
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Cutting Medical Volume Data
Virtual resection vs. surgery simulation
Virtual resection techniques are intended forexperienced surgeons who are actually planning a
surgical procedure
Virtual resection is not focused on the realisticsimulation of a procedure but on decision support
based on the interaction with the data of a
particular patient
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Summary
Virtual resection is an essential feature forsurgery planning, particularly for internalorgans, such as the kidney, liver, and pancreas
There are some similarities between virtualresection and surgery simulation concerningthe representation and visualization of the data
Hardware support for 3D texture-mappingcombined with multi-texturing is essential fora good performance
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