Deep learning based estimation of patient anthropometric data for intelligent scan planning in MR

Estimating patient height and weight is used to determine the optimum SAR (Signal Modelling) for the patient. We used a deep learning approach to first generate the point cloud of the person (Data Processing) followed by prediction of the height and weight (Data Analysis). This tackles the problems of heavy occlusion which occurs in the MR imaging scenario in the form of coils/blankets and different positions in which the patient will be placed. We achieved a MAE score of 4.9 cm on the height and 6.2 kg on the weight. This is a promising solution to an important problem.