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{"name":"Ct-tutorial","tagline":"Tutorial code for CT reconstruction with high dynamic range images","body":"# CT-Tutorial\r\nTutorial code for CT reconstruction with high dynamic range images\r\n\r\n### Conversion of a CT image to a noisy sinogram and reconstruction with noise and without noise.  \r\n\r\nThis notebook is a tutorial on reconstruction of high-dynamic range CT images with proper physical scaling and a realistic noise model. \r\n\r\n### Files:\r\n\r\n- CT_Tutorial.ipynb has code to do forward radon projection and reconstruction using filtered backprojection along with a tutorial describing each step.  A key contribution is a description of the units and intensity scaling that appears in real CT images.  \r\n- make_data.ipynb has code to create high dynamic range images that have some properties similar to those from baggage scans.  \r\n\r\n**Outline of processing in CT_Tutorial.ipynb:**\r\n\r\n1. Read image: in Hounsfield units (air=0, water=1000)\r\n2. Scale so that air=0, water=1\r\n3. Mask for circular ROI\r\n4. Take the raw sinogram using a specified number of views\r\n5. Scale to account for pixel pitch and x-ray density\r\n6. Add noise to the sinogram using the correct variance\r\n7. Reconstruct from the original and noisy sinogram\r\n8. Display the reconstructions and sinograms\r\n\r\n### Examples images and reconstructions, shown at several intensity windows.\r\n\r\n![Example images](http://www.math.purdue.edu/~buzzard/images/test.png)","note":"Don't delete this file! It's used internally to help with page regeneration."}