Abstract
The accurate distribution of transit times and mean transit times of organ’s metabolism is an important quantitative tool in nuclear medicine, having applications within renal, cardiac, liver and cerebral examinations. A deconvolution algorithm is such a technique that can be used to isolate the response of an organ from variations, which occur due the shape of the input function. Practical application of deconvolution requires the incorporation of additional information describing what constitutes an acceptable impulse response into the deconvolution process. Previous studies with several of the algorithms have suggested that sensitivity to noise is a significant problem and may account for some of the oscillatory impulse response computed from studies done with fragmented injections. It is thus desirable to evaluate a deconvolution algorithm capable of accurately handling the range of functions found in shunt quantification. Development of such an algorithm may extend the range of clinical studies that are correctable by deconvolution. Probabilistic and more accurate information concerning the impulse response may be weighted with the data into a statistical estimate of the impulse response. Our study applies curve fitting and wavelet transform based on appended curve technique to improve the accuracy of the impulse response curve and reduce the deconvolution error effectively. Compare with the traditional methods, our study can effectively reduce the root mean square error more than 30%.