lecture 6b

Exploration of Dynamic Medical

Volume Data

1

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

2

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

3

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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Subtraction images to analyze cerebralperfusion

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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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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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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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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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Principle of isoline generation with theMarching Squares approach

Isolines for isovalue 5 are computed by linearinterpolation along the grid cells

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Ten isolines depict a dynamic parameterderived from MRI mammography

The data and the resulting isolines are smoothed

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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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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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Exploration of MRI-mammography data witha lens

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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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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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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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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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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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Left: a malignant breast tumor visualized usinga MIP. Right: the same data visualized using a

CVP

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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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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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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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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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Enhancement curves are simultaneouslyderived for the symmetric regions in both

hemispheres

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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

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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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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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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Cutting Plane

Euclidean (left) versus geodesic distance(right) between surface points

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Cutting Plane

Cut boundary specification specified with apen on a digitizer tablet, which is shown

enlarged in the right image

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Cutting Plane

Cut boundary specification with a tactile inputdevice

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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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Cutting Plane

Definition of the plane E based on the (dashed)lines P drawn by the user

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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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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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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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Cutting Plane

The result of a virtual resection by means of adeformable mesh

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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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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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Cutting Plane

Application areas of virtual resectiontechniques

Liver surgery

Osteotomy planning

Craniofacial surgery

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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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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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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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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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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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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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Volume cutting

Left: two-dimensional representation of an objectsurface. Middle: voxelization of the cutting tool.

Right: the resulting cut surface

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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 with a virtual scalpel (left). The resultis shown in high quality (right)

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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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lecture 6b

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