Basic SPECT :Basic SPECT T.K.Marshel R.T. (R)(N)(CT)(MR)(NCT)(PET)CNMT


2-Dimensional vs. 3 Dimensional Images :2-Dimensional vs. 3 Dimensional Images Our world is a 3D world. Objects have a height, width and depth to them. However, when we “image” a real world object, we usually make a 2D representation of that object. NM produces similar 2D “projections” of the 3D radionuclide distribution inside the body or organ.


Examples of Tomographic Imaging Modalities :Examples of Tomographic Imaging Modalities CT MRI SPECT PET


Advantages of Tomographic Imaging over Planar Images :Advantages of Tomographic Imaging over Planar Images Improved contrast and easier interpretation Overlapping organ tissue below and above the slice of interest is eliminated. 3D representation of the organ is created. The counts in a given pixel represent the counts in an actual volume of tissue. Images can be reconstructed at different and arbitrary angles. Absolute quantitation of activity is possible. Functional images of organ activity can be created from mathematical analyis of the quantitative images Direct measurement of physiological function


SPECT Reconstruction :SPECT Reconstruction Image reconstruction is the process of transforming a set of 2D projections into 3D images.


Common Algorithms for image reconstruction :Common Algorithms for image reconstruction Simple Backprojection Filtered Backprojection Fourier Transformation Reconstruction Classical Iterative Reconstruction Maximum likelihood Expectation Maximazation OSEM: Ordered subset Expectation Maximization


Simple Backprojection Reconstruction :Simple Backprojection Reconstruction The simpliest image reconstruction method. It makes no assumptions about the form of the image before reconstruction The major problem with SBP recon is that it leaves “extra” counts on the image in the wrong places. Creates a “Star” or “Spoke” pattern


Filtered Backprojection :Filtered Backprojection Used to remove the “Star” or “Spoke” artifact from the SBP recon images. Can be done before backprojection (pre-filtering) or after backprojection (post-filtering). Is usually done in the frequency space domain (to be discussed later) However, it can also be done in the spatial domain by a process called convolution.


Fourier Transformation Reconstruction :Fourier Transformation Reconstruction The Fourier Transform is a mathematical operation that changes a projections data from being a function of counts per pixel to a completely equivalent function of amplitude vs. cycles/pixel. This transformation projection is in what is called the spatial frequency domain. The reconstruction produces the same image as the filtered backprojection.


Iterative Reconstruction Methods :Iterative Reconstruction Methods This method of reconstruction involves solving a set of algebraic equations to reconstruct the image. This method is very computer intensive and until recently the computer power needed to perform this reconstruction hadn’t been available in most NM systems.


Iterative Reconstruction MethodsGeneral Process :Iterative Reconstruction MethodsGeneral Process The computer makes and initial “guess” at the form of the reconstructed image and creates and initial “guess” image. The initial “guess” image matrix is reprojected along the original projections. The projection of the “guess” image is compared to the real projection data. The counts/pixel in the image matrix are adjusted until the projection agrees with the real projection data. Steps 2-4 are repeated for each angle ad projection The process stops when most of the projection of the images are close to the values of the original projection data. (called convergence)


Filtered Back Projection vs. Iterative Reconstruction Methods :Filtered Back Projection vs. Iterative Reconstruction Methods FBP a. Resultant images are reconstructed from direct calculations from collected “projection” of activity. b. Assumes no “attenuation” of activity c. Has no mechanism for attenuation corrections.


Iterative Reconstruction Methods :Iterative Reconstruction Methods Has the ability to model the physical processes of image acquisition. Can correct some of the factors that degrade images in FBP Can correct for attenuation of activity Can correct for depth dependent blurring Can correct for scatter effects


Modern Iterative Methods :Modern Iterative Methods Maximum Likelihood Expectation Maximization: a. Very popular method with good results but computationally very slow and has a slow convergence rate. OSEM: Ordered subset Expectation Maximization a. Newer and faster than ML-EM method


Main differences between these methods involve: :Main differences between these methods involve: The choice of the initial “guess” image. The way the image matrix is changed after comparison to the projection data. How the approximated image is tested for “convergence” (How the program determines whether it is close enough to the projection data to stop the interations)


: Simple example of the Algebraic Iterative Reconstruction method.