What are CT parameters?

The authors seek to guide radiologists through the manipulation of 8 fundamental CT scan parameters that can be altered or optimized to reduce patient radiation dose, including detector configuration, tube current, tube potential, reconstruction algorithm, patient positioning, scan range, reconstructed slice thickness.

What is CT image reconstruction?

Image reconstruction in CT is a mathematical process that generates tomographic images from X-ray projection data acquired at many different angles around the patient. Image reconstruction has fundamental impacts on image quality and therefore on radiation dose.

What is the typical reconstruction matrix of a CT image?

The reconstruction matrix is the array of rows and columns of pixels in the reconstructed image. The display matrix is the matrix in the displayed image and can be equal to or larger than the reconstruction matrix size due to interpolation procedures.

What is reconstruction interval in CT?

Reconstruction interval – the spacing between adjacent slices – is independent of slice thickness in helical CT. The z-position of any given slice is determined by which projection is used to start the slice. Remember that to reconstruct an entire slice, you need 180 (plus fan angle) degrees of projection data.

What is CT image quality?

CT image quality, as in most imaging, is described in terms of contrast, spatial resolution, image noise, and artifacts.

What are the factors that affect image quality in CT?

The image quality is mainly determined by 3 factors: Resolution. Noise….Factors affecting z-sensitivity

  • Detector slice thickness. The wider (in the z-axis) the detector row, the lower the resolution.
  • Overlapping samples.
  • Focal spot.

What is FOV in CT?

The field of view (FOV) is defined as the dimensions of the exact anatomic region included in a scan.

What factors determine the quality of CT images?

The image quality is mainly determined by 3 factors: Resolution. Noise….Scanner factors

  • Focal spot. Size.
  • Detector size. Smaller detectors give higher resolution but more detectors within an area also means more partitions (dead space) and a reduced overall detection efficiency.
  • Detector design properties.