Optimization of Tracer Dose for Scintigraphic Imagery

Myocardial scintigraphy is a non-invasive isotope examination that has played a central role in the management of these coronary heart diseases for decades.it has proven its performance in nuclear cardiology, mainly for the diagnosis of ischemia by making it possible to analyze the myocardial perfusion, and precisely, to evaluate the quality of the irrigation by the arteries and the coronaries, as well as for the diagnosis of coronary heart disease. It is based on the injection of an intravenous radioactive tracer, which, once injected, is absorbed by the heart muscle. The radiation emitted by the radioactive tracer is converted into an image by computer tomography. However, these scintigraphic images suffer from poor spatial resolution in particular, in obese patients, it is difficult to obtain images of sufficient quality using the recommended standard doses due to the attenuation of gamma-rays by soft tissues (fat, fibrous tissues, etc.). This phenomenon prompts the nuclear physician to overdose the tracer and the dose of radiation received exceeds the admissible regulatory limits. In this paper we propose a machine learning model that predict the dose of tracer based on patient's morphological parameters to obtain images of sufficient quality to support the cardiovascular diagnosis while exposing him to the lowest possible doses of radiation. We show the body weight is not the best-predicting parameter for image quality.