What is the primary cause of aliasing in computed tomography?

Aliasing in computed tomography primarily results from undersampling, which refers to acquiring fewer data points than what is necessary to accurately represent the object being imaged. This insufficiency in data collection can lead to misinterpretation of the spatial frequency content of the image, causing structures to appear distorted or falsely represented. When the sampling rate is below the Nyquist frequency, which is the minimum rate required to accurately capture a signal, high-frequency information gets misrepresented and appears as lower frequency artifacts in the image, leading to the phenomenon known as aliasing.

In contrast, over-sampling may help in providing a more accurate representation by capturing more data points; thus, it does not contribute to aliasing. Reconstruction errors can occur due to improper algorithms or methods, but they are not fundamentally the same cause as undersampling. Incorrect calibration, while it can lead to measurement inaccuracies, does not directly influence the sampling strategy which is critical to preventing aliasing. Therefore, undersampling is recognized as the primary cause of aliasing in computed tomography.