Prepared by Nuhu Abdulhaqq Isa 20122792 Image Reconstruction Prepared by Nuhu Abdulhaqq Isa 20122792

Outline Nuclear Medicine Image Reconstruction Reconstruction Techniques Image Reconstruction Algorithms Digital Image Processing conclusion

Fundamental concept of Nuclear Medicine

Fundamental concept Scientifically and clinically defined as the use of a number of trace compounds labeled with radioactivity providing sufficient amount of data on the state of a disease. Radionuclide decay and emit gamma rays or high energy photons. External camera detects the gamma rays or photons and create an image

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Image Reconstruction What do you understand?

Definitions Algorithms are a collection of protocols developed to be followed in problem solving operation especially by a computer. image reconstruction can be thought as the collection of protocols to be followed in constructing 3D images from projection slices and utilizing the image quality parameters. Has fundamental impacts on image quality and therefore on radiation dose

Image Reconstruction Methods Analytical Reconstruction; 1D filter is employed across projection slices before back projecting (2D/3D) the new result on the image space. Iterative Reconstruction; reconstructs images by iteratively optimizing an objective function, which typically consists of a data fidelity term and an edge-preserving regularization term. The optimization process in IR involves iterations of forward projection and backprojection between image space and projection space.

Filtered Backprojection (FBP) Uses filtering and back projections. working principle is based on the Fourier theory the detector function is smeared out over the object domain. Done over all projection angles and summing them up from each directions. Resultant Image is severely blurred due to Fourier slice theorem. freer domain of the object is sampled such that low (smoothnes) frequencies are much denser than the high frequencies (sharpness).

Filtering A high pass filter is applied on the detector function to surpress the low frequencies to reduce blurriness. Firstly, the function is fourierly transformed using the fast Fourier transform FFT, the resulting spectrum then filtered The importance of high frequencies are increased frequencies higher than the cutoff are set to zero, to reduce noise in the reconstructed image. the filtered spectrum is transformed back to the Fourier transform, resulting in a new detector function using inverse fast filter transform

Pros and Cons of FBP Requires less processing time Good for normal imaging But not good for detailing

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Maximum Likelihood Estimation Method (ML-EM) the portable document format of the image may not contain probably the PDF of the missing measurement that decisively influence the estimation. So these missing information or measurement in this method will be roughly guessed or approximated iteratively, and carry out an ML estimation for each of the iteration.

Steps of MLEM initial image guess of the entire image set to a constant value. Image is forward projected into projection domain comparing your measured projections to the forward projections. A multiplicative correction factor is created as a result for each projection the image is back projected in to the image domain to get the correction factor for the initial image estimate.

The next step is to multiply the correction factor of image domain by the recent estimates of the image and a division using the weighing term that is based on the system model to apply the desired strength of the individual correction factor. This new image estimate is re entered in to the algorithm until it reaches it maximum likelihood solution.

Pros and Cons of MLEM lower image noise Higher image quality higher spatial resolution reduce image artifacts such as beam hardening, windmill, and metal artifacts. Takes more processing time Complicated Might never converge Edge artefacts

Parameters of Image Reconstruction Radiologist have very limited control over the inner workings of the reconstruction method Can adjust various parameters that potentially affect image quality. Trade off of one parameter with another for specific applications

Parameter Trade Offs Kernel (filter ) Slice thickness (1mm) Tradeoff between spatial resolution and noise for each kernel. Smoother kernel (low noise and low spatial resolution). A sharper kernel (high spatial resolution and high noise). Controls the spatial resolution in the longitudinal direction influencing the tradeoffs among resolution, noise, and radiation dose. Kernel (filter ) Slice thickness (1mm)

Digital signal processing What is Digital Signal?

What is a Digital Image? A digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels Real world is continuous – an image is simply a digital approximation of this. Images taken from Gonzalez & Woods, Digital Image Processing (2002)

Image Formats Common image formats include: 3 samples per point (Red, Green, and Blue) 1 sample per point (B&W or Grayscale) 4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity)

Definition Digital image processing is the use of computer algorithms to perform image processing on digital images. Can avoid problems such as the build-up of noise and signal distortion during processing. Since images are defined over two dimensions digital image processing may be modeled in the form of multidimensional systems.

What is Digital Image Processing? Digital image processing focuses on two major tasks Improvement of pictorial information for human interpretation Processing of image data for storage, transmission and representation for autonomous machine perception

Key Stages in Digital Image Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Image Aquisition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Image Enhancement Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Morphological Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Segmentation Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Object Recognition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Representation & Description Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Image Compression Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Key Stages in Digital Image Processing: Colour Image Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression

Why Digital Image Processing To calibrate patterns of various parts of the body and compare them with acquired medical imaging data for diagnosis.

Examples: Image Enhancement Image enhancement is to improve quality, remove noise etc

Original MRI Image of a Dog Heart Examples: Medicine Take slice from MRI scan of canine heart, and find boundaries between types of tissue Image with gray levels representing tissue density Use a suitable filter to highlight edges Original MRI Image of a Dog Heart Edge Detection Image

Example Of Patter Recognition Fingerprint recognition

Conclusion Image reconstruction is an intrinsic part of nuclear medicine. There are more works on image reconstruction algorithms and their developments for better image quality. Digital imaging is a good incorporation to medical imaging There is more that digital image processing can offer, you can explore it today.

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