What does the Nyquist Theorem state regarding sampling rate?

The Nyquist Theorem is a fundamental principle in signal processing that states that to accurately reconstruct a signal without aliasing, it must be sampled at least twice the highest frequency contained in the signal. This means that the minimum sampling rate should be at least two times the frequency of the signal. In the context of MRI and other imaging techniques, this ensures that all the necessary information is captured to accurately represent the object or phenomenon being studied.

By sampling at least twice per cycle, the Nyquist Theorem helps to prevent loss of information and enables clearer and more accurate imaging. This is crucial in MRI to achieve high-resolution images and avoid artifacts that can arise from insufficient sampling.

The other options do not appropriately capture the essence of the Nyquist Theorem. Sampling once per cycle would lead to significant aliasing and loss of detail, while sampling at a rate equal to spatial resolution or slice thickness is insufficient for accurate signal representation and does not relate directly to the requirements outlined in the theorem. Consequently, the correct choice aligns with the principle that a minimum of twice the frequency must be sampled to maintain integrity in the imaging process.