Single Photon Emission Computed Tomography (SPECT) imaging is pivotal for medical diagnosis, providing crucial insights into a patient's health. This medical imaging technique produces 3D images of a radioactive tracer's distribution in the body, detected by a gamma camera. SPECT is used to visualize physiological processes in organs, diagnosing various diseases, thus providing essential functional information. However, interpreting SPECT images just like other medical imaging results of organs near the lungs often encounters challenges due to artifacts induced by respiratory motion, leading to image noise and compromised quality. This study aims to enhance SPECT image clarity and fidelity for organs near the lungs using center of gravity estimation and event avoidance methods. By tracking respiratory movements with a motion camera, image clarity is refined. Additionally, methods to enhance image quality without the motion camera are explored. Calculating the barycenter allows for more accurate organ positioning within specific timeframes, while eliminating motion in others streamlines events. The amalgamation of these methods results in a notable reduction in motion artifacts and image blurring, with motion-corrected images exhibiting superior delineation of structures compared to uncorrected ones. This process is helpful for the surgeon targeting the injection.