bioengineering 508: physical aspects of medical...
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Bioengineering 508:Physical Aspects of Medical Imaginghttp://courses.washington.edu/bioen508/
Organizer: Paul Kinahan, PhDAdam Alessio, PhDRuth Schmitz, PhD
Lawrence MacDonald, PhD
Imaging Research Laboratoryhttp://depts.washington.edu/nucmed/IRL/
Department of RadiologyUniversity of Washington Medical Center
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Bioengineering 508:Physical Aspects of Medical Imaging
Introduction to Medical Imaging1. Medical Imaging Modalities2. Modern Image Generation3. Intro to Image Quality
Adam Alessio, PhDDepartment of Radiology
University of Washington Medical [email protected]
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Nature of Medical Imaging
For this class:Medical Imaging: Non-invasive imaging of internal
organs, tissues, bones, etc.
Focus on:1. Macroscopic not microscopic2. in vivo (in the body) not in vitro (“in glass”, in the lab)3. Primarily human studies4. Primarily clinical diagnostic applications
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Nature of Medical Imaging
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!
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Types of Medical Imaging (Modalities)
Grouped by underlying physics:• X-Ray/CT• Ultrasound• Magnetic Resonance Imaging (MRI)• Nuclear Medicine• Optical• Magnetic Field• Electric Field• Thermal• Optoacoustic• Elastography
Major 4 that dominateclinical imaging, focusof this course
Primarily microscopic
Mainly research based
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Types of Medical Imaging (Modalities)
Nuclear medicineElectromagnetic Spectrum
For comparison, this iswavelength/frequency range of US,but US is NOT electromagnetic!
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Types of Medical Imaging (Modalities)
Classifications of Medical Images1. Anatomical vs. Functional
• Anatomy/Structure/Features vs. Physiology2. Emission vs. Transmission
• Where does energy imaged originate?3. Projection vs. Tomographic
• Projection--> 2D imaging, single plane, no depthinformation
• Tomographic (“tomo” = slice, graphy=image) --> volumetric
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Modern Image Generation
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
element2. Quantizing Samples
– Each discrete chunk must be represented by certain numberof bits
3. Visualization Techniques of quantized, sampled imagevolumes
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1. Sampling Continuous Information
Given a signal such as a sine wave withfrequency 1 Hz:
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Intro to Sampling Theory
We can sample the points at a uniform rate of 3Hz and reconstruct the signal:
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Intro to Sampling Theory
We can also sample the signal at a slower rate of2 Hz and still accurately reconstruct the signal:
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Intro to Sampling Theory
However, if we sample below 2 Hz, we don’t haveenough information to reconstruct the signal, and infact we may construct a different signal (an alias):
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Intro to Sampling Theory• Aliasing
– occurs when your sampling rate is not high enough to capture theamount 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.
• During image processing:– resampling discrete signal at a different rate– e.g. Image warping, zooming in, zooming out, etc.
• Nyquist criterion: Must sample at two times the highest frequency in thesignal for the samples to uniquely define the given signal
– Sampling below the Nyquist frequency can cause aliasing (CD sampling example)
�
FNyquist =SamplingRate
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Intro to Sampling Theory
• To perform sampling correctly in image space, needto understand structure of data/image
• Fourier: “Any periodic function can be rewritten as a weightedsum of sines and cosines of different frequencies.” - FourierSeries
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A sum of sines
• Our building block:•
• Add enough of them to getany signal f(x) you want
• Which one encodes thecoarse vs. fine structure ofthe signal?
• What would an image looklike with a lot of highfrequency content?
• What could you do to reducespeckled noise from animage?
)+!"xAsin(
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Fourier Transform
1D Example:• A signal composed of two sine
waves with frequency 2 Hz and 50Hz
• The Fourier Transform of thesignal shows these twofrequencies
frequency
Fourier Transform of f(x)
Signal f(x)
Low Freq
High Freq
High FreqHigh Freq
High Freq
In 2D:• Usually represent low
frequencies near origin, highfrequencies away from origin
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2D Fourier TransformsImage 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
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2D Fourier TransformsImage in space domain Image in frequency domain
(magnitude of frequency component)Image in frequency domain
(log magnitude of frequency component)
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Frequency Content
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Frequency Content
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Modern Image Generation
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
element2. Quantizing Samples
– Each discrete chunk must be represented by certain numberof bits
3. Visualization Techniques of quantized, sampled imagevolumes
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2. Quantization
• Only have finite storage available for each pictureelement
• 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=256x256x256possible colors)
– Many medical imaging modalities use intensity values of 12bits per pixel. (2^12=4096 possible gray levels)
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Color depth8 bits per pixel 5 bits per pixel 4 bits per pixel
3 bits per pixel 2 bits per pixel 1 bit per pixel