Flow Chart of FBP.. BME 525 HW 1: Programming assignment The Filtered Back-projection Image reconstruction using Shepp-Logan filter You can use any programming.

Slides:



Advertisements
Similar presentations
Direct Fourier Reconstruction
Advertisements

Mark Mirotznik, Ph.D. Associate Professor The University of Delaware
© by Yu Hen Hu 1 ECE533 Digital Image Processing Image Enhancement in Frequency Domain.
Image reconstruction and analysis for X-ray computed microtomography Lucia Mancini 1, Francesco Montanari 2, Diego Dreossi 3 1 Elettra - Trieste 2 A.R.P.A.
Image Reconstruction T , Biomedical Image Analysis Seminar Presentation Seppo Mattila & Mika Pollari.
Medical Imaging Mohammad Dawood Department of Computer Science University of Münster Germany.
IPIM, IST, José Bioucas, X-Ray Computed Tomography Radon Transform Fourier Slice Theorem Backprojection Operator Filtered Backprojection (FBP) Algorithm.
Medical Image Analysis Image Formation Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.
CT Physics V.G.Wimalasena Principal School of radiography.
IMAGE, RADON, AND FOURIER SPACE
BMME 560 & BME 590I Medical Imaging: X-ray, CT, and Nuclear Methods Tomography Part 2.
BMME 560 & BME 590I Medical Imaging: X-ray, CT, and Nuclear Methods Tomography Part 3.
Project Overview Reconstruction in Diffracted Ultrasound Tomography Tali Meiri & Tali Saul Supervised by: Dr. Michael Zibulevsky Dr. Haim Azhari Alexander.
ECE 501 Introduction to BME ECE 501 Dr. Hang. Part VI Medical Imaging Computed Tomography ECE 501 Dr. Hang.
Image Filtering CS485/685 Computer Vision Prof. George Bebis.
RADON TRANSFORM A small introduction to RT, its inversion and applications Jaromír Brum Kukal, 2009.
7. Neighbourhood operations A single pixel considered in isolation conveys information on the intensity and colour at a single location in an image, but.
MRI, FBP and phase encoding. Spins Precession RF pulse.
Image reproduction. Slice selection FBP Filtered Back Projection.
2D Fourier Theory for Image Analysis Mani Thomas CISC 489/689.
Back Projection Reconstruction for CT, MRI and Nuclear Medicine
Projection generation Object/ Phantom f(x,y) Sinogram, p(t,  ) t  Projection, p(t,  =0) 
DREAM PLAN IDEA IMPLEMENTATION Introduction to Image Processing Dr. Kourosh Kiani
Input image Output image Transform equation All pixels Transform equation.
For example, consider a simple 2-row by 2-column reconstruction Matrix. Views are collected at 4 angles, 0 (left to right), 90 (top to bottom), 45 (diagonal),
Application of Digital Signal Processing in Computed tomography (CT)
Maurizio Conti, Siemens Molecular Imaging, Knoxville, Tennessee, USA
Medical Image Analysis Dr. Mohammad Dawood Department of Computer Science University of Münster Germany.
CS448f: Image Processing For Photography and Vision Wavelets Continued.
G Practical MRI 1 The Fourier Transform
These slides based almost entirely on a set provided by Prof. Eric Miller Imaging from Projections Eric Miller With minor modifications by Dana Brooks.
Seeram Chapter 7: Image Reconstruction
Optimizing Katsevich Image Reconstruction Algorithm on Multicore Processors Eric FontaineGeorgiaTech Hsien-Hsin LeeGeorgiaTech.
Unit 5: Geometric Transformations.
Filtered Backprojection. Radon Transformation Radon transform in 2-D. Named after the Austrian mathematician Johann Radon RT is the integral transform.
Under Supervision of Dr. Kamel A. Arram Eng. Lamiaa Said Wed
Computed Tomography References The Essential Physics of Medical Imaging 2 nd ed n. Bushberg J.T. et.al Computed Tomography 2 nd ed n : Seeram Physics of.
Medical Image Analysis Image Reconstruction Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.
Filtering Robert Lin April 29, Outline Why filter? Filtering for Graphics Sampling and Reconstruction Convolution The Fourier Transform Overview.
Digital Image Processing Lecture 6: Image Geometry
Image Reconstruction from Projections Antti Tuomas Jalava Jaime Garrido Ceca.
LOGO ภาควิชาอิเล็กทรอนิกส์ คณะวิศวกรรมศาสตร์ สถาบันเทคโนโลยีพระจอมเกล้าเจ้าคุณทหารลาดกระบัง.
CT Image Reconstruction. CT Please read Ch 13. Homework is due 1 week from today at 4 pm.
Digital Image Processing Chapter 4 Image Enhancement in the Frequency Domain Part I.
Lecture 7: Sampling Review of 2D Fourier Theory We view f(x,y) as a linear combination of complex exponentials that represent plane waves. F(u,v) describes.
Single Photon Emission Computed Tomography
RADON TRANSFORM A small introduction to RT, its inversion and applications Jaromír Brum Kukal, 2009.
Image Subtraction Mask mode radiography h(x,y) is the mask.
COMP322/S2000/L171 Robot Vision System Major Phases in Robot Vision Systems: A. Data (image) acquisition –Illumination, i.e. lighting consideration –Lenses,
Computed Tomography Diego Dreossi Silvia Pani University of Trieste and INFN, Trieste section.
1 Reconstruction Technique. 2 Parallel Projection.
Single-Slice Rebinning Method for Helical Cone-Beam CT
HYPR Project Presentation By Nasser Abbasi HYPR Input-Output view.
Ultrasound Computed Tomography 何祚明 陳彥甫 2002/06/12.
Date of download: 5/28/2016 Copyright © 2016 SPIE. All rights reserved. (a) Cone beam CT breast imaging system. This imaging system’s x-ray tube is the.
Chapter-4 Single-Photon emission computed tomography (SPECT)
Fourier transform.
Spatial Image Enhancement
Single Photon Emission Tomography
Tianfang Li Quantitative Reconstruction for Brain SPECT with Fan-Beam Collimators Nov. 24th, 2003 SPECT system: * Non-uniform attenuation Detector.
A New Modality for Microwave Tomographic Imaging: Transit Time Tomography Matt Trumbo 3/30/06.
Reconstructing Shredded Documents
Image Enhancement in the
Fundamentals of Spatial Filtering
9th Lecture - Image Filters
Intensity Transformation and Spatial Filtering
Fast Hierarchical Back Projection
Translations, Reflections, & Rotations
Lecture 13: CT Reconstruction
Filtering Removing components from an image is know as “image filtering”. If we remove the high frequency components, we tend to remove the noise. This.
Presentation transcript:

Flow Chart of FBP.

BME 525 HW 1: Programming assignment The Filtered Back-projection Image reconstruction using Shepp-Logan filter You can use any programming languages, Fortran, C, C++ or Matlab ( without employing built-in functions for image reconstruction, radon( ) and iradon( ) ). Example of steps:  Step1 : Creating Phantom image Hint: - Using an odd sized image might ease computations by having a center ray. - Make 2 phantom images : small one for testing program ( 63x63 ) large one for presentation ( 255x255 )

 Step2 : Calculate Projection data and Construct Sinogram: 1. Define the step-size of angle, theta. 2. Define the number of rays per view, m. 3. Calculate the projection data - The projection of the image intensity along a radial line oriented at angle theta k and distance t k from origin. : Line integral along a line original phantom f(x,y) - Rotate f(x,y) by a given theta rotated and interpolated f(x,y)

- Sum all pixel values on each row of the rotated and interpolated f(x,y): This is to calculate projection data or ray sums or line integrals. ray sum

4. Construct Sinogram - Sinogram is a collection of Projection data for all theta ( n x m matrix ) - Repeat step 3 for all theta you define sinogram

 Step 3: Shepp-Logan filtering and Reconstruct phantom image 1. Design Shepp-Logan fileter H SL (w) 2. Filtering Signogram in frequency or spatial domain. sinogram before filtering sinogram after S-L filtering

3. Reconstruct image from filtered sinogram. 64*64256*256 Original