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Low spatial resolution and the related partial volume effects limit the diagnostic potential of brain SPECT imaging. Here a technique for the compensation of such degrading factors is proposed, which also requires the T1-weighted MR image of the same patient. Basically, after a coregistration step, the high frequency part of the MR, which would be unrecoverable by the set SPECT acquisition system+reconstruction algorithm, is extracted and added to the SPECT image. The tuning of the influence of the MR on the SPECT can be performed very quickly, any iterative reconstruction algorithm can be used and, in the case that the SPECT projections are not available, the proposed technique can also be applied directly to the SPECT image, provided that the performance of the scanner is known. The procedure does not significantly affect quantitation over Regions Of Interest (ROIs) whose dimensions are larger than the SPECT resolution distance.

The pattern recognition problem in RICH counters concerns the identification of an unknown number of imperfect roughly-circular rings made of a low number of discrete points in presence of background. We present some preliminary results obtained using the Possibilistic C-Spherical Shell algorithm. In particular, we show that the algorithm is very tolerant and robust to noise (outliers rate) level. Moreover, for complex images full of rings, we introduce an iterative scheme that greatly improves performance. Also, the rings are not required to be complete, arcs are sufficient to recognize the underlying rings.