Imaging
description
Transcript of Imaging
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ImagingImaging
PETPET
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Course LayoutCourse LayoutClassDateClass + Content
I23.2.2005Physical Principles of PET
II2.3.2005Physical principles of MRI
III9.3.2005Imaging applications
IV16.3.2005Image Reconstruction PET and MRI
V23.3.2005Automatic Image Alignment
VI30.3.2005PCA
VII6.4.2005No Class
VIII13.4.2005GLM
IX4.5.2005GLM relation to classical tests (Anova, T-test..)
X18.5.2005Covariates
XI25.5.2005Gaussian fields Theory
XII1.6.2005Specific experiment design and analysis
XIII8.6.2005Specific experiment design and analysis
XIV15.6.2005Correction for multiple measurements
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Talk LayoutTalk Layout SPECT (Short introduction)SPECT (Short introduction) PET – Physical principles and PET – Physical principles and
StructureStructure PET correctionsPET corrections PET image reconstructionPET image reconstruction PET Typical applications in Brain PET Typical applications in Brain
ScienceScience
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Principle of radionuclide Principle of radionuclide imagingimaging
Introduce radioactive Introduce radioactive substance into bodysubstance into body
Allow for distribution and Allow for distribution and uptake/metabolism of uptake/metabolism of compoundcompound Functional ImagingFunctional Imaging!!
Detect regional variations Detect regional variations of radioactivity as of radioactivity as indication of presence or indication of presence or absence of specific absence of specific physiologic functionphysiologic function
Detection by “gamma Detection by “gamma camera” or detector arraycamera” or detector array
(Image reconstruction)(Image reconstruction)
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Single photon emission Single photon emission CT (SPECT)CT (SPECT)
Single photon counting:Single photon counting: Windowing (reduces scatter, Windowing (reduces scatter,
background)background) Counting statistics limited Counting statistics limited
by patient radiation doseby patient radiation dose ~ 30 min examination w/ ~ 30 min examination w/
cameracamera First SPECT 1963 (Mark First SPECT 1963 (Mark
IV) used array of detectorsIV) used array of detectors Rotation, TranslationRotation, Translation High count ratesHigh count rates Many componentsMany components Mostly single-sliceMostly single-slice
Rotating camera:Rotating camera: Multiple slicesMultiple slices Multi-camera systems Multi-camera systems
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SPECT applicationsSPECT applications Brain: Brain:
Perfusion (stroke, Perfusion (stroke, epilepsy, epilepsy, schizophrenia, schizophrenia, dementia [Alzheimer])dementia [Alzheimer])
TumorsTumors Heart:Heart:
Coronary artery Coronary artery diseasedisease
Myocardial infarctsMyocardial infarcts RespiratoryRespiratory LiverLiver KidneyKidney
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PETPET
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Positron emissionPositron emission
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Scintillation DetectionScintillation Detection
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1-to-1 CouplingExcellent livetime characteristics, but expensive, and limited in size to smallest available PMT (~1cm2).
Block DetectorIndividual crystals “pipe” light to detectors.More complex, but required with low light output
Anger CameraLight from scintillator is distributed among several PMT’s; measured distribution determines location.Poor livetime, but can have good resolution with enough light output--NaI(Tl).
Detector AssembliesDetector Assemblies
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Block DetectorBlock Detector
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PET evolutionPET evolution
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PETPET
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Coincidence DetectionCoincidence Detection
DET 1 DET 2
Pulse Processing
AND
Pulse Processing
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CoincidencesCoincidences
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Coincidence EventsCoincidence Events
1
1. Detected True Coincidence Event
2
2. True Event Lost to Sensitivity or Deadtime
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3. True Event Lost to Photon Attenuation
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4. Scattered Coincidence Event
5a 5b
5a,b. Random Coincidence Event
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Accidental (random) Accidental (random) coincidences:coincidences:
Two unrelated annihilation photons reach two Two unrelated annihilation photons reach two opposing detectors within the time window of opposing detectors within the time window of the coincidence resolving time the coincidence resolving time (~10-20 ns)(~10-20 ns)
detector i
detector j
1
2
D d
2Aij i jC f C C
: Pulse legth (2 = resolving time)f : Fraction of detectors involved f ~ 1 Ci,Cj: Individual (single) count rates
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Attenuation CorrectionAttenuation Correction
0 0
( ') ' ( ') ' ( ') '
1 2
x R R
x
x dx x dx x dx
p p p e e e
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Scatter EliminationScatter Elimination
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Filtered Back ProjectionFiltered Back Projection
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FilteredFiltered B Baack Projectionck Projection
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Filtered Back Projection
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Filtered backprojection• Filter the measured projection data at different projection angles with a special function.• Backproject the filtered projection data to form the reconstructed image.
Filtering can be implemented in 2 ways, in the spatial domain, the filter operation is equivalent to to convolving the measured projection data using a special convolving function h(t)
p t p t h t, ( , ) ( , ) ( )
More efficient multiplication will be in the spatial frequency domain.• FFT the measured projection data into the frequency domain:p(,)=FT {p(t, )• Multiply the the fourier transform projections with the special function.•Inverse Fourier transform the product p’(,).
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2D Vs. 3D2D Vs. 3D
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RandomsRandoms
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ScattersScatters