tissue contrast
TRANSCRIPT
Tissue Contrast
• intrinsic factors– relative quantity of protons
• tissue proton density
– relaxation properties of tissues• T1 & T2 relaxation
• secondary factors– flow
– contrast agents
Contrast
• the ability to discriminate different tissues based on their relative brightness
Basic Principle
• relatively intermediate intensity structures look bright on a dark background– important to remember with fatsat
• relatively intermediate intensity structures look dark on a light background
Caveat
• windowing affects the relative contrast of tissues– intensity values of pixels are relative
to one another, unlike CT
• windowing can make a solid tumor look like a “cyst”
T1 SET2 FSE
“CYST”“CYST”
T1 SET2 FSE
CYST?CYST?
T2 FSE T2 FSE
CYST?CYST?
Summary
• need visible differences in intensity to discriminate tissues
• surrounding tissues can make an intermediate signal tissue appear dark or bright
• windowing affects image and tissue contrast
Noise
• constant at a given machine setup
• reduces the ability to visualize low contrast structures
• adds to or subtracts from the average signal intensity of a given pixel
Noise
• increasing the available signal will reduce the relative effects of noise
• machine parameters must be chosen to maximize signal without significantly extending exam times
• S/N is a relative measure allowing for comparison in a variety of circumstances
frequency
SI
frequency
SI
Signal versus Noise
• high signal
• high SNR
• low signal
• low SNR
Noiseless Conditions
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Tissue A Tissue B
Tissue Type
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High Signal/Low Noise
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Tissue A Tissue B
Tissue Type
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Low Signal/High Noise
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Tissue A Tissue B
Tissue Type
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Noiseless Conditions
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Relative Pixel Location
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High Signal/Low Noise
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Relative Pixel Location
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Low Signal/High Noise
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Relative Pixel Location
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Image Contrast
100% noise
Image Contrast
80% noise
Image Contrast
60% noise
Image Contrast
40% noise
Image Contrast
20% noise
Image Contrast
0% noise
Factors Affecting SNR
• strength of main magnet
• coil selection• voxel size
• phase encoding
• number of averages
• receiver bandwidth
• pulse sequence parameters
SNR
SNR K voxel sizemeasurements
bandwid th= • •( )
• stronger main magnet
• proper imaging coil• larger voxel size
• decreased phase encoding
• increased number of averages
• decreased receiver bandwidth
• (pulse sequence parameters)
Factors INCREASING SNR
Stronger Main MagnetS/N effect Downside
• linear increase • less T1 weighting at high fields
• increased chemical shift effects in RO direction
Coil SelectionS/N effect Downside
• increase in signal with surface coils
• quadrature provides 40% increase S/N over linear
• phased array increased over quadrature
• limited coverage with surface coils
• more complex coils are more expensive
Larger Voxel SizeS/N effect Downside
• linear increase in either RO or PE direction
• linear increase with increased slice thickness
• decreased resolution
Decreased Phase EncodingsS/N effect Downside
• square root increase in signal to noise
• linear decrease in scan time
• decreased resolution in PE direction
• Gibb’s phenomenon in PE direction
Increased Signal AveragesS/N effect Downside
• square root increase in signal to noise
• linear increase in scan time
Decreased Receiver BWS/N effect Downside
• square root increase in signal to noise
• increase in chemical shift artifact in RO direction
Pulse Sequence Parameters
• SE imaging– increased TR provides nonlinear
increase in SNR with linear increase in scan time
– decreased TE provides nonlinear increase in SNR with no effect on scan time and less T2 weighting
Pulse Sequence Parameters
• GE imaging– complex effects
– maximum SNR typically between 30 and 60 degrees
– long TR sequences (2D)• increase SNR with increased flip angle
– short TR sequences (TOF & 3D)• decreased SNR with increased flip angle
SNR Application
• pituitary imaging– baseline:
• 16 cm FOV, 3 mm slice thickness, 192 phase encodes, 4 NEX
– new goal:• reduced scan time, same SNR
FOV RO PE
Slice Thickness
(mm) NEX
Imaging Time (TR=500 msec)
Relative SNR
160 256 192 3 4 6.40 43.30160 256 170 4 2 2.83 43.39190 256 192 3 2 3.20 43.18160 256 144 3 3 3.60 43.30
SNR Example
Fat Suppression and SNR
• non fat-suppressed image– each image pixel comprised of signal
from water and fat in the imaging voxel
• fat-suppression– reduces total signal by suppression of
fat from the voxel
– reduces SNR
frequency
SI
frequency
SI
Fat Suppression
• without fat suppresion
• high SNR
• with fat suppression
• lower SNR
waterplusfat water only