What do image reconstruction algorithms in CT refer to?

Image reconstruction algorithms in CT refer to the mathematical techniques and processes used to convert raw data collected by the CT scanner into a visual representation of the scanned object. Kernels play a crucial role in this process as they are specific mathematical functions used to filter and manipulate the acquired data.

Kernels are applied to the raw data to enhance certain features, reduce noise, and improve the overall quality of the images being reconstructed. The choice of kernel can significantly affect the final image quality, including its resolution and contrast. This manipulation happens during the reconstruction phase, making kernels a fundamental component of the algorithms used in CT imaging.

In contrast, slices refer to the individual images obtained after the reconstruction is complete, reconstructions describe the overall process and resultant images, while filters generally refer to the broader category of methods used to process signals or data but do not specifically pinpoint the mathematical functions as kernels do in image reconstruction algorithms.