[en] Aim: Detect patient motion during SPECT imaging. Material and Method: SPECT study is carried out on a patient's body organ, such as the heart, and frame of image data are thereby acquired. The image data in these frames are subjected to a series of mappings and computations, from which frame containing a significant quantity of organ motion can be identified. Quantification of motion occurs by shifting some of the mapped data within a predetermined range, and selecting that data shift which minimizes the magnitude of a motion sensitive mathematical function. The sensitive mathematical function is constructed from all set of image frames using the pixel data within a region covering the body organ. Using cine display of planar image data, the operator defines the working region by marking two points, which define two horizontal lines covering the area of the body organ. This is the only operator intervention. The mathematical function integrates pixel data from all set of image frames and therefore does not use derivatives which may cause distortion in noisy data. Moreover, as a global function, this method is superior than that using frame-to-frame cross-correlation function to identify motion between adjacent frames. Using standard image processing software, the method was implemented computationally. Ten SPECT studies with movement (Sestamibi cardiac studies and 99m-ECD brain SPECT studies) were selected plus two others with no movement. The acquisition SPECT protocol for the cardiac study was as follow: Step and shoot mode, non-circular orbit, 64 stops 20s each, 64x64x16 matrix and LEHR colimator. For the brain SPECT, 128 stops over 360⁰ were used. Artificial vertical displacements (±1-2 pixels) over several frames were introduced in those studies with no movement to simulate patient motion. Results: The method was successfully tested in all cases and was capable to recognize SPECT studies with no body motion as well as those with body motion (both from the patient and artificially imposed). The method is fast (<20s) and robust as compared with manual or other semi-automatic detection of body organ motions in nuclear medicine studies. Conclusion: A fast and robust semi-automatic patient motion detection and correction for SPECT studies has been developed