Automated patient registration in low field MRI using deep learning-based height and weight estimation with 3D camera

Motivation: Automated patient positioning and Specific Absorption Rate (SAR) estimation in MRI is crucial for optimized image quality. Achieving these objectives necessitates precise patient parameter estimation. Typically, manual estimation of patient parameters, such as height and weight, is error-prone and time-intensive. Goal(s): To assess the 3D camera's potential for acquiring depth images suitable for deep learning (DL)-based estimation of patient height and weight. Approach: We employed 3D camera technology to capture depth images of patients on MRI tables, enabling DL-based height and weight estimation. Results: Our evaluation study demonstrated the 3D camera's effectiveness in acquiring depth images for accurate patient height and weight estimation. Impact: Current deep learning-driven 3D camera methods enhance MR imaging workflows with the goal of achieving standardized and higher-quality image acquisition by accurately predicting patient height and weight.