A Method to Determine Spectral Performance of Image Post-Processing Algorithms

The MTF and DQE of an x-ray detector are important metrics of performance. They are generally measured following IEC guidelines which requires the use of raw (unprocessed) images. However, many detectors now incorporate processing algorithms that are deeply embedded in the imaging chain and may be difficult or impossible to disable, blurring the line between processed and unprocessed image data, and questioning the relevance of MTF and DQE testing. This study develops a framework to represent the spectral performance of linear and shift-invariant digital-processing algorithms and examines their effects on MTF and DQE measurements with examples. Processing algorithms are represented as a cascade of operations where images are represented using an integrable impulse-sampled notation. This allows the use of Fourier transform theorems and relationships, which differ to a discrete Fourier transform notation, including a specific representation of signal and noise aliasing. It is concluded that: i) digital convolution of image data gives the same result, with the same aliasing artifacts, as a true convolution integral of presampling image data followed by sampling; ii) the slanted-edge method to measure MTF provides the presampling MTF even when processing algorithms operate on aliased image data; iii) the DQE is largely unaffected by LSI post processing, however spectral zero-crossings can suppress specific frequency content in both the MTF and DQE, and unsharp masking algorithms can decrease the DQE at low frequencies.