Order statistic-neural network hybrid filters for gamma camera-bremsstrahlung image restoration

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Abstract
An order statistic and neural network hybrid filter (OSNNH) is proposed for the restoration of gamma camera images using the measured modulation transfer function. Planar images of beta-emitting radionuclides are used to evaluate the filter because they exhibit higher degradation than images of single photon emitters due to increased photon scattering and collimator septal penetration. The filter performance is quantitatively evaluated and compared to that of the Wiener filter by investigating the relationship between the externally measured counts from sources of phosphorous-32 ((32)P) at various depths in water. An effective linear attenuation coefficient for (32)P is determined to be equal to 0.13 cm(-1) and 0.14 cm(-1) for the OSNNH and the Wiener filters, respectively. Evaluation of phantom and patient filtered images demonstrates that the OSNNH filter avoids ring effects caused by the ill-conditioned blur matrix and noise overriding caused by matrix inversion, typical of other restoration filters such as the Wiener.