CT RECONSTRUCTION FROM PARALLEL AND FAN-BEAM PROJECTIONS BY A 2-D DISCRETE RADON TRANSFORM

CT RECONSTRUCTION FROM PARALLEL AND FAN-BEAM PROJECTIONS BY A 2-D DISCRETE RADON TRANSFORM

The discrete Radon transform (DRT) was defined by Abervuch et al. as an analog of the continuous Radon transform for discrete data. Both the DRT and its inverse are computable in operations for images of size. In this project, we demonstrate the applicability of the inverse DRT for the reconstruction of a 2-D object from its continuous projections. The DRT and its inverse are shown to model accurately the continuum as the number of samples increases. Numerical results for the reconstruction from parallel projections are presented. We also show that the inverse DRT can be used for reconstruction from fan-beam projections with equispaced detectors.

Existing System:

Tomographic reconstruction underlies nearly all diagnostic imaging modalities, including X-ray computed tomography (CT), positron emission tomography, single-photon emission tomography, and certain acquisition methods for magnetic resonance imaging.

It is also widely used for nondestructive evaluation in manufacturing and, more recently, for airport baggage security. Reconstruction in tomography means a recovery (inversion) from samples of either the X-ray transform (set of line-integral projections) or the Radon transform (set of integrals on planes) of an unknown object density distribution.

Proposed System:

In fact, they can be implemented using only 1-D FFTs. The underlying transforms for these algorithms are proven in and to be algebraically accurate, to preserve the geometric properties of the continuous transforms, and to be invertible and rapidly computable.
In addition, it is shown in that the discrete Radon transform (DRT) converges to the continuous Radon transform as the discretization step goes to zero.

This property is of major theoretical and computational importance since it shows that the discrete transform indeed approximates the continuous transform; thus, it can be used to replace the continuous transform in digital implementations.
The equally sloped tomography (EST) method introduced is an iterative one, which allows reconstructions from a limited number of noisy projections through the use of total-variation regularization.
The use of regularization also allows for the reduction of aliasing artifacts, which originated from incorrect sampling [e.g., inaccessible information beyond the resolution circle, These works extend innovatively the pseudo polar representation on which we based our algorithm to overcome the problem of reconstruction from a limited number of noisy projections when, e.g., dose reduction is requested.

Software Requirements:
.Net
Front End – ASP.Net
Language – C#.Net
Back End – SQL Server
Windows XP

Hardware Requirements:
RAM : 512 Mb
Hard Disk : 80 Gb
Processor : Pentium IV

Comments are closed.