In many clinical applications, it is desirable to perform computed tomography (CT) scans during the peak of the contrast uptake of an organ. Typically, a patient is first scanned continuously (with a low technique) at a fixed location and a series of CT images is generated. The average CT number within a predefined region of interest (ROI) is calculated and plotted for all the acquired images. When the contrast uptake within the ROI is judged to be adequate by an operator, a scan command is initiated and normal CT scans are performed. The methodology has been shown to be quite effective in improving the image quality of the CT examinations. When the scheme is applied to body scans (chest or abdomen), however, two factors might affect the effectiveness of this approach. The first relates to the fact that patient motion is not always avoidable in body scans. As a result, the predefined ROI might not always register at the same anatomical location. Another factor is related to the fact that a significant delay is encountered between the initiation of a scan command and the actual helical scan, due to mechanical delay of the CT system, time to move the patient table to the desired location, and the patient prep-time. Because of the contrast agent can pass through a patient organ in a relatively short period of time (10 - 30 seconds), the inherit delay often leads to a suboptimal scan since the peak of the contrast uptake is missed. In order to overcome these difficulties, we propose two schemes. The location of ROI is adapted from one image to the next to ensure the best feature match. This is accomplished by performing correlation calculation between the selected ROI in the pre-injection image and various ROIs in the neighboring locations in the new image. The final ROI location is determined based on the peak of the correlation coefficient. We also use a predictive algorithm to estimate the future uptake of the contrast agent. At the start of the scan, the average CT numbers are measured and sent to the predictor. The predictor uses all the measured samples taken up to that time to predict the average CT number a few seconds, t, later. When the next sample becomes available, the predictor will compare its prediction with the actual measurement and modify its prediction parameters to best predict the future samples. Computer simulations and clinical experiments have shown that significant improvement in the optimization of the contrast uptake can be achieved with this method. As a result, the entire CT examination is better centered on the peak of the contrast curve and an optimal contrast enhancement in CT image scan be achieved.