### Why did we use an iterative procedure while analyzing the CT performance?

## Why did we use an iterative procedure while analyzing the CT performance?

Successive iterations again reduce image noise until the real and predicted data converge. Finally, IR techniques utilizing the projection data only are akin to the previously described MBIR methods. Complex statistical, geometric, and physical models attempt to predict the projection data.

### What is iterative reconstruction technique?

Iterative reconstruction refers to an image reconstruction algorithm used in CT that begins with an image assumption, and compares it to real time measured values while making constant adjustments until the two are in agreement.

#### What is Fourier reconstruction?

The idea of Fourier reconstruction is very simple: Do a 1D Fourier transform on g with respect to the second variable for each . According to (6.1) this yields in all of. . Do a 2D inverse Fourier transform to obtain f.

**What is a reconstruction kernel?**

The kernel, also known as a convolution algorithm, refers to the process used to modify the frequency contents of projection data prior to back projection during image reconstruction in a CT scanner 1. This process corrects the image by reducing blurring 1.

**What is the main advantage of iterative reconstruction techniques versus filtered back projection?**

The major advantage of iterative reconstruction techniques is that they permit the emission and detection process to be accurately modelled. In contrast, the filtered back projection algorithm makes no allowance for the physics of emission including attenuation and scatter of the emitted photons.

## How does CT image reconstruction work?

CT makes use of filtered back projection reconstruction techniques, whereby each projection is convolved with a “filter”, and then back projected. When this procedure is performed for all 1000 or so projections, it is possible to achieve a perfect reconstruction of the scanned object.

### Are there any iterative methods for image reconstruction?

Spatial resolution, variance, ROI covariance (Huesman [30]), and autocorrelation have all been thoroughly analyzed (and empirical results agree with the analytical predictions). Only recently have such analyses been provided for some nonlinear reconstruction methods e.g., [31–42].

#### Why are annotated slides used in image reconstruction?

These annotated slides were prepared by Jeff Fessler for attendees of the ISBI tutorial on statisti- cal image reconstruction methods. The purpose of the annotation is to provide supplemental details, and particularly to provide ex- tensive literature references for further study.

**Are there any other examples of image reconstruction?**

Although the focus of examples in this course are PET / SPECT / CT, most of the principles apply equally well to other tomography problems like MR image reconstruction, optical / diffraction tomography, etc. 0.3 History

**Which is iterative method for direct Fourier Reconstruction?**

Bracewell’s classic paper on direct Fourier reconstruction also mentions a successive substitution approach [10] X-ray CT patent: [11] Early iterative methods for SPECT by Muehllehner [12] and Kuhl [13].

Why did we use an iterative procedure while analyzing the CT performance? Successive iterations again reduce image noise until the real and predicted data converge. Finally, IR techniques utilizing the projection data only are akin to the previously described MBIR methods. Complex statistical, geometric, and physical models attempt to predict the projection data. What is…