Imaging PET

Course Layout Class + ContentDateClass Physical Principles of PET I Physical principles of MRI II Imaging applications III Image Reconstruction PET and MRI IV Automatic Image Alignment V PCA VI No Class VII GLM VIII GLM relation to classical tests (Anova, T-test..) IX Covariates X Gaussian fields Theory XI Specific experiment design and analysis XII Specific experiment design and analysis XIII Correction for multiple measurements XIV

Talk Layout SPECT (Short introduction) SPECT (Short introduction) PET – Physical principles and Structure PET – Physical principles and Structure PET corrections PET corrections PET image reconstruction PET image reconstruction PET Typical applications in Brain Science PET Typical applications in Brain Science

Principle of radionuclide imaging Introduce radioactive substance into body Introduce radioactive substance into body Allow for distribution and uptake/metabolism of compound  Functional Imaging! Allow for distribution and uptake/metabolism of compound  Functional Imaging! Detect regional variations of radioactivity as indication of presence or absence of specific physiologic function Detect regional variations of radioactivity as indication of presence or absence of specific physiologic function Detection by “ gamma camera ” or detector array Detection by “ gamma camera ” or detector array (Image reconstruction) (Image reconstruction)

Single photon emission CT (SPECT) Single photon counting: Single photon counting: Windowing (reduces scatter, background) Windowing (reduces scatter, background) Counting statistics limited by patient radiation dose Counting statistics limited by patient radiation dose ~ 30 min examination w/ camera ~ 30 min examination w/ camera First SPECT 1963 (Mark IV) used array of detectors First SPECT 1963 (Mark IV) used array of detectors Rotation, Translation Rotation, Translation High count rates High count rates Many components Many components Mostly single-slice Mostly single-slice Rotating camera: Rotating camera: Multiple slices Multiple slices Multi-camera systems Multi-camera systems

SPECT applications Brain: Brain: Perfusion (stroke, epilepsy, schizophrenia, dementia [Alzheimer]) Perfusion (stroke, epilepsy, schizophrenia, dementia [Alzheimer]) Tumors Tumors Heart: Heart: Coronary artery disease Coronary artery disease Myocardial infarcts Myocardial infarcts Respiratory Respiratory Liver Liver Kidney Kidney

PET

Positron emission

Scintillation Detection

1-to-1 Coupling Excellent livetime characteristics, but expensive, and limited in size to smallest available PMT (~1cm 2 ). Block Detector Individual crystals “pipe” light to detectors. More complex, but required with low light output Anger Camera Light 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 Assemblies

Block Detector

PET evolution

PET

Coincidence Detection DET 1 DET 2 Pulse Processing AND Pulse Processing

Coincidences

Coincidence Events 1 1. Detected True Coincidence Event 2 2. True Event Lost to Sensitivity or Deadtime 3 3. True Event Lost to Photon Attenuation 4 4. Scattered Coincidence Event 5a 5b 5a,b. Random Coincidence Event

Accidental (random) coincidences: Two unrelated annihilation photons reach two opposing detectors within the time window of the coincidence resolving time  (~10-20 ns) Two unrelated annihilation photons reach two opposing detectors within the time window of the coincidence resolving time  (~10-20 ns) detector i detector j 11 22 D d  : Pulse legth (2  = resolving time) f : Fraction of detectors involved f ~ 1 C i,C j : Individual (single) count rates

Attenuation Correction

Scatter Elimination

Filtered Back Projection

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) 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 ’ (,  ).

2D Vs. 3D

Randoms

Scatters