Download Bioengineering 508: Physical Aspects of Medical Imaging Nature of

Bioengineering 508:
Physical Aspects of Medical Imaging
Bioengineering 508:
Physical Aspects of Medical Imaging
http://courses.washington.edu/bioen508/
Introduction to Medical Imaging
1. Medical Imaging Modalities
2. Modern Image Generation
3. Intro to Image Quality
Organizer: Paul Kinahan, PhD
Adam Alessio, PhD
Ruth Schmitz, PhD
Lawrence MacDonald, PhD
Adam Alessio, PhD
Department of Radiology
University of Washington Medical Center
[email protected]
Imaging Research Laboratory
http://depts.washington.edu/nucmed/IRL/
Department of Radiology
University of Washington Medical Center
Alessio - BIO508
Nature of Medical Imaging
Alessio - BIO508
Nature of Medical Imaging
For this class:
Medical Imaging: Non-invasive imaging of internal
organs, tissues, bones, etc.
Focus on:
1. Macroscopic not microscopic
2. in vivo (in the body) not in vitro (“in glass”, in the lab)
3. Primarily human studies
4. Primarily clinical diagnostic applications
Alessio - BIO508
QUICK CAVEAT
•
•
•
Powerpoint Slides are just a vehicle for major topics
These do not have all the information discussed in
class!
Taking notes to supplement slides is probably a
good idea!
Alessio - BIO508
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Types of Medical Imaging (Modalities)
Types of Medical Imaging (Modalities)
Electromagnetic Spectrum
Grouped by underlying physics:
• X-Ray/CT
Major 4 that dominate
• Ultrasound
clinical imaging, focus
• Magnetic Resonance Imaging (MRI)
of this course
• Nuclear Medicine
• Optical
Primarily microscopic
• Magnetic Field
• Electric Field
Mainly research based
• Thermal
• Optoacoustic
• Elastography
Alessio - BIO508
Alessio - BIO508
Types of Medical Imaging (Modalities)
Classifications of Medical Images
1. Anatomical vs. Functional
•
Nuclear medicine
Modern Image Generation
From continuous real world to a meaningful image
(on computer):
1. Sampling Continuous Information
Anatomy/Structure/Features vs. Physiology
– Information and sampling technique varies widely for each
modality- Topic for later lectures
– Computer can only hold discrete chunks of data
– Pixel = a single picture element; Voxel = a single volume
element
2. Emission vs. Transmission
•
Where does energy imaged originate?
3. Projection vs. Tomographic
•
•
Alessio - BIO508
Projection--> 2D imaging, single plane, no depth
information
Tomographic (“tomo” = slice, graphy=image) --> volumetric
For comparison, this is
wavelength/frequency range of US,
but US is NOT electromagnetic!
2. Quantizing Samples
– Each discrete chunk must be represented by certain number
of bits
3. Visualization Techniques of quantized, sampled image
volumes
Alessio - BIO508
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1. Sampling Continuous Information
Given a signal such as a sine wave with
frequency 1 Hz:
Alessio - BIO508
Intro to Sampling Theory
We can also sample the signal at a slower rate of
2 Hz and still accurately reconstruct the signal:
Alessio - BIO508
Intro to Sampling Theory
We can sample the points at a uniform rate of 3
Hz and reconstruct the signal:
Alessio - BIO508
Intro to Sampling Theory
However, if we sample below 2 Hz, we don’t have
enough information to reconstruct the signal, and in
fact we may construct a different signal (an alias):
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Intro to Sampling Theory
•
Intro to Sampling Theory
Aliasing
– occurs when your sampling rate is not high enough to capture the
amount of detail in your image
– Can give you the wrong signal/image—an alias
– Where can it happen in graphics?
• During image synthesis:
– sampling continuous signal into discrete signal
– e.g. ray tracing, line drawing, function plotting, etc.
• To perform sampling correctly in image space, need
to understand structure of data/image
•
• During image processing:
– resampling discrete signal at a different rate
– e.g. Image warping, zooming in, zooming out, etc.
•
Fourier: “Any periodic function can be rewritten as a weighted
sum of sines and cosines of different frequencies.” - Fourier
Series
Nyquist criterion: Must sample at two times the highest frequency in the
signal for the samples to uniquely define the given signal
FNyquist =
SamplingRate
2
– Sampling below the Nyquist frequency can cause aliasing (CD sampling example)
Alessio - BIO508
Alessio - BIO508
A sum of sines
•
•
Our building block:
•
Add enough of them to get
any signal f(x) you want
Which one encodes the
coarse vs. fine structure of
the signal?
What would an image look
like with a lot of high
frequency content?
What could you do to reduce
speckled noise from an
image?
•
•
•
Asin("x + ! )
Fourier Transform
Signal f(x)
1D Example:
•
A signal composed of two sine
waves with frequency 2 Hz and 50
Hz
•
The Fourier Transform of the
signal shows these two
frequencies
In 2D:
•
Usually represent low
frequencies near origin, high
frequencies away from origin
High Freq
High Freq
frequency
Low Freq
High Freq
Alessio - BIO508
Fourier Transform of f(x)
High Freq
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2D Fourier Transforms
Image in frequency domain
Image in space domain (magnitude of frequency component)
2D Fourier Transforms
Image in frequency domain
(log magnitude of frequency component)
Image in space domain
Image in frequency domain
(magnitude of frequency component)
Image in frequency domain
(log magnitude of frequency component)
Original
After low-pass
After high-pass
Alessio - BIO508
Frequency Content
Alessio - BIO508
Alessio - BIO508
Frequency Content
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Modern Image Generation
2. Quantization
From continuous real world to a meaningful image
(on computer):
1. Sampling Continuous Information
– Information and sampling technique varies widely for each
modality- Topic for later lectures
– Computer can only hold discrete chunks of data
– Pixel = a single picture element; Voxel = a single volume
element
2. Quantizing Samples
– Each discrete chunk must be represented by certain number
of bits
• Only have finite storage available for each picture
element
• Digital images have “digitized” intensity values.
Continuous values are quantized into discrete values.
– Example: “Truecolor” on computer displays use 24 bits for
each pixel (8bits blue, 8 bits red, 8bits green=256x256x256
possible colors)
– Many medical imaging modalities use intensity values of 12
bits per pixel. (2^12=4096 possible gray levels)
3. Visualization Techniques of quantized, sampled image
volumes
Alessio - BIO508
Alessio - BIO508
Color depth
8 bits per pixel
5 bits per pixel
4 bits per pixel
3 bits per pixel
2 bits per pixel
1 bit per pixel
Alessio - BIO508
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