Introduction to Medical Visualization

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Outline

1. Visualization in Medicine

2. Computerized Medical Imaging

3. 2D and 3D Visualizations

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Visualization in Medicine

Scientific visualization

Deal with the analysis, visualization andexploration of datasets arising from measurements

or simulation of real world phenomena

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Visualization in Medicine

Goals and scenarios of scientific visualization

To explore data

To test a hypothesis based on measurements orsimulations and their visualization

The presentation of results

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Visualization in Medicine

Medical visualization

Deals with the analysis, visualization, andexploration of medical image data

A specialty of scientific visualization

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Visualization in Medicine

Neck dissection planning visualization

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Left: Relevant anatomical structures and potentially pathologic lymph nodes are displayed to support

neck dissection planning

Right: The distance between an enlarged and potentially malignant lymph node is color-coded to a

muscle to support the decision as to whether the muscle should be removed

Visualization in Medicine

Design of medical visualization systems

A process directed to understand the data

Interaction methods

Support users in navigating within the data

Support the interpretation and classification of the data

Support users in the storage of results

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Computerized Medical Imaging

Applications of medical visualization

Educational purposes

Visualization techniques are the core of anatomy andsurgery education systems

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Computerized Medical Imaging

Applications of medical visualization

Diagnosis

The diagnosis of radiological data benefits frominteractive 2D and 3D visualizations

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Computerized Medical Imaging

Applications of medical visualization

Treatment planning

Interactive 3D visualizations of the relevant anatomicaland pathologic structures enhance the planning of

surgical interventions, radiation treatment, and

minimally invasive interventions

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Computerized Medical Imaging

Applications of medical visualization

Intraoperative support

Medical visualization based on 3D data is entering theoperating room (OR)

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Computerized Medical Imaging

Computer support

Not to replace medical doctors

But to support and assist physicians

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2D and 3D Visualizations

2D techniques

Allow an accurate examination and processing

Each pixel can be seen and selected

Support precise exploration and analysis of thedata

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2D and 3D Visualizations

3D techniques

Often a comprehensible overall picture

Physicians who carry out interventions stronglybenefit from interactive 3D visualizations

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2D and 3D Visualizations

A simultaneous employment of 2D and 3Dvisualizations

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Left: A 3D surface visualization of the relevant anatomical structures for surgery planning

Right: The CT slices from which the data have been extracted

Medical Image Data &

Visual Perception

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Outline

1. Medical Image Data

2. Data Artifacts

3. Sensitivity and Specificity

4. Visual Perception

5. Summary

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Medical Image Data

Volumetric data

Usually represented as a stack of individual images

Each image represents a thin slice of the scanned bodypart and is composed of individual pixels

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Medical Image Data

2D grid

All pixels of an image are arranged on the gridpoints of the grid

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Medical Image Data

3D grid

All voxels are arranged on a 3D grid

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Medical Image Data

Volume cell

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Medical Image Data

Features of cartesian grid

Constant or regular spacing in each dimension

Regular geometry that can be computed by the gridindex and the spacing

Regular topology

It is only composed of cuboid cells

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Medical Image Data

Interpolation

Nearest-neighbor interpolation

Modest computational costs

Low visual quality

Trilinear interpolation

More sophisticated

High visual quality

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Medical Image Data

Trilinear interpolation

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Medical Image Data

6-neighborhood (left) and 26-neighborhood(right)

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Data Artifacts

Sampling theorem

A signal has to be sampled at least with twice thefrequency

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Data Artifacts

Aliasing

A phenomenon that is directly related to sampling

It is caused by an incorrectly reconstructed signal,due to insufficient sampling

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Data Artifacts

Moir artifact

The sampling rate is increased from the left toright and the Moir artifacts are reduced

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Data Artifacts

Undersampling artifact

Different slice and pixel distance in anisotropicdatasets can lead to insufficient sampling

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Data Artifacts

Solutions to overcome undersampling

Increase the sampling rate until we satisfy theNyquist rate

Band-limit the original signal by performing a lowpass filtering step

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Data Artifacts

Low pass filters

A box filter

A triangle filter

A Gaussian filter

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Data Artifacts

Side effects of smoothing

If too many frequencies are removed by a low passfilter, details will disappear

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Data Artifacts

Partial volume effect

Due to limited resolution at volume reconstruction,large intensity differences cannot properly be

reconstructed

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Data Artifacts

Partial volume effect

The thin membrane Lamina Terminalis at the floorof the third cerebral ventricle could not be fully

reconstructed

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Data Artifacts

Partial volume effect

False connections are due to the incompletelyreconstructed septum between the upper lateral

cerebral ventricles

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Data Artifacts

Interpolation artifacts

Central differences are typically used to estimatethe gradients of volume datasets

Will generate artifacts if the intensity differencesare large or the grid spacing is anisotropic

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Data Artifacts

Interpolation artifacts

Block artifacts of an isosurface reconstruction ofthe label volume of a bronchi dataset

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Data Artifacts

Interpolation issues on anisotropic grids

Different voxel distances in different spatialorientations are not properly addressed

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Data Artifacts

Interpolation issues on anisotropic grids

Staircasing artifacts (left) and a magnification ofthe marked area (right). The flipping normal

direction demonstrates the origin of the artifact

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Lighting calculation

Data Artifacts

Remedies for anisotropic grid spacing

Correct the sample point normals according to thespacing

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Data Artifacts

Remedies for anisotropic grid spacing

Resample the data volume into an isotropic griddataset using an appropriate reconstruction filter

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Data Artifacts

Signal artifacts

Caused by the data acquisition techniquesthemselves

The most notorious signal artifacts are metalartifacts

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Data Artifacts

Signal artifacts

Metallic implants degrade CT image quality in themaxillary region (left) and beam-hardening artifact

near the base of the skull (right)

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Sensitivity and Specificity

Categories for the evaluation of diagnosticprocedures

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Sensitivity and Specificity

Sensitivity is the probability of correctlyreporting an abnormality

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Sensitivity and Specificity

Specificity is the probability of correctlyreporting that no abnormality exists

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Sensitivity and Specificity

Examples of ROC (Receiver OperatingCharacteristic) curves A good curve indicates that a diagnostic procedure

has a high sensitivity and specificity

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Visual Perception

Gray value perception

The human eye is relatively less sensitive in thebrightest areas of an image

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Visual Perception

Aspects of visual perception

Just-Noticeable Differences

The smallest luminance difference of a given referenceintensity that the average human observer can still

perceive

Spatial Resolution

Contrast Perception

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Visual Perception

Color spaces

Device-oriented color spaces

The color is defined in away which corresponds to thephysical realization of color output of that device

Intuitive color spaces

The color is defined in a way that adheres to naturalproperties of color, such as brightness

Perceptually uniform color spaces

The color is defined such that the Euclidean distancebetween a pair of colors corresponds to the perceived

difference between these colors50

Visual Perception

Color scales for encoding scalar values

Rainbow scale

The full hue range of the HSV color model is mapped toa selected color range

Isomorphic colormap

Either saturation or luminance is increased in amonotone manner

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Visual Perception

Discrete color scales

Used to convey whether a value is in a certaininterval

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Visual Perception

Bi- and trivariate color scales

Map two or three scalar values to a single color byusing separate components of a color space

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Summary

The datasets are subject to the samplingtheorem

Gray intensities are not perceived linearly

The same problem arises in colorrepresentations

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