Chapter-4 Single-Photon emission computed tomography (SPECT)

Chapter-4 Single-Photon emission computed tomography (SPECT)
Reconstruction (Simple and Filtered back projection) Filtering Filtering is a mathematical technique applied during reconstruction to improve the appearance of the image. Filters have different types I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering Domain of Images Domain of Images I have to add some introduction History of gamma camera and uses Spatial domain Frequency domain When image data is represented in counts per pixel, this data is said to be in spatial domain Filtering can be performed on this data as it is but proves to be computationally burdensome This is when the data is represented as a series of sine waves. The data is said to be transformed into the frequency domain (fourier transform) It is easier to perform filtering in this domain

Reconstruction (Simple and Filtered back projection) Filtering Spatial and Frequency domains of Images These two domains are not entirely independent. In fact, they only represent different views of the underlying data The information obtained by the camera is not changed by this transformation of the collected data from the spatial to the frequency domain; all that is changed is the method of describing the data. more generally, we can say that data can be transformed from one domain into another with neither gain nor loss of the contained information. I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) The only difference between simple and FBP is that in the latter method, the profiles are modified by a reconstruction filter applied before they are back-projected across the image. The filter has both positive and negative values. The negative portions of the filtered profiles near the central peak “subtract out” some of the projected intensity next to the peak. I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) Kernal has negative values for the peripheral pixels and a positive value in the centre. This filter tends to enhance the edges and reduce the intensity of the star artifact A simple version of this filter is with Kernal consists of central value +2, surrounded by value of -1. I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) This Kernal is sequentially applied to each pixel of the array. In the resulting array, the outer values are zero or negative I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) in a similar fashion, the kernal is applied to the second array of the example in the figure. When these filtered arrays are backprojected, their peripheral negative values cancel counts in a manner that removes the portion of the rays adjacent to the image of the disk. I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) The relative depression of counts surrounding the backprojected disk helps to separate it from the background. I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) This figure is a graphic representation of the process The top panels demonstrate the process of back-projectiong rectangles to create a disk I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) Each swipe of the paint roller represents a ray. In the upper right image, the combined rays create a disk with indistinct edges. I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) The bottom images demonstrate the effect of a simple edge enhancement filter in which negative values are used to border each rectangle prior to backprojection (represented by the small white squares on either side of each rectangle) I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering data represented in the spatial domain in order to reduce star artifacts (spatial filtering to reduce the star artifacts) These negative values cancel contributions from adjacent ray-sums and the circle’s edge is seen more clearly (bottom right). I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering I have to add some introduction History of gamma camera and uses

Reconstruction (Simple and Filtered back projection) Filtering Types of filter High-pass filter (Ex: ramp filter) Low-pass filter (Ex: Hann, Hamming, Butterworth filters) I have to add some introduction History of gamma camera and uses

Reconstruction Reconstruction is the process of creating trans-axial slices from projection views. There are two basic approaches to creating the trans-axial slices. Filtered backprojection Iterative reconstruction I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction A relatively elegant technique called iterative reconstruction is steady replacing FBP Images reconstructed with this technique exhibit significantly less star artifact than those created using FBP The algorithm approaches the true image, by means of successive approximations, or estimate. I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction In iterative reconstruction, the computer starts with an initial “guess”-estimate of the data to produce a set of transaxial slices Often the initial estimate is very simple, such as a blank or uniform image. These slices are then used to create a second set of projection views (using a process called forward projection, which is inverse of BP) which are compared to the original projections views as acquired from the patient. I have to add some introduction History of gamma camera and uses It is performed by summing up the intensities along the potential ray paths for all projections through the estimated image

Reconstruction Iterative Reconstruction Unlikely that the initial estimate closely resemble the true image the transaxial slices from the computer’s estimate are then modified using the difference between, or ratio of, the two sets of projections views. I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction A new set of transaxial slices reconstructed from this modified, or second, estimate are then used to create a set of projection views which are compared to the original projection views. I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction A gain these projection views are compared to the original projection views. If the process proceeds efficiently, each iteration generates a new set of projection views that more closely approximate the original projection views. I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction The update-and-compare process is repeated until the difference between the forward-projected profiles for the estimated image and the actual recorded profiles falls bellow some specified threshold I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction Simplified representation I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction In practice most iterative reconstruction are terminated at a pre-determined number of iterations, that is, when the radiologists is satisfied with the overall image quality, instead of allowing them to progress until the difference between the estimated and projection views reaches a set value. In general the image resolution improves with increasing number of iterations . However, beyond a certain reasonable number of iterations, further improvements in resolution can only be accomplished at the cost of increased image noise. I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction The two basic components of iterative reconstruction algorithms are; The method for comparing the estimated and actual profiles cost function which measures the difference between the profiles generated by forward projections through the estimated image and profiles actually recorded from the scanned object The method by which the image is updated on the basis of this comparison the search or update function, which uses the output of the cost function to update the estimated image I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction The general goal of algorithm development is to devise versions of these functions that produce convergence of the estimated image toward the true image as rapidly and accurately as possible. A number of methods have been developed to speed up these advanced algorithms. One of the most popular is called ordered subsets expectation maximization (OSEM) I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction (Adv) I have to add some introduction History of gamma camera and uses

Reconstruction Iterative Reconstruction (disadva) I have to add some introduction History of gamma camera and uses

Chapter-4 Single-Photon emission computed tomography (SPECT)

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Chapter-4 Single-Photon emission computed tomography (SPECT)

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