What mathematical technique is most useful for enhancing spatial resolution in CT imaging?

Filtered back-projection is a key mathematical technique used in CT imaging that enhances spatial resolution. This process involves reconstructing images from the raw data acquired by the CT scanner. During filtering, high-frequency components, which are crucial for detailing small structures and ensuring a clear image, are preserved while minimizing lower-frequency noise that can obscure detail. By applying this technique, the algorithm effectively reverses the projection process, allowing for a more accurate representation of the scanned object, improving clarity and detail in the resulting images.

In contrast, interpolation refers to estimating values at unknown points based on known values, and while it can be useful in some contexts within imaging, it does not specifically enhance spatial resolution in the way filtered back-projection does. Fourier transformation is a mathematical technique used to analyze frequencies in images and can be important for understanding data, but it is not the direct method employed for image reconstruction in the context of CT spatial resolution. Geometric correction is used to correct distortions in images but does not intrinsically enhance spatial resolution like filtered back-projection does.