Automated Patient Registration in Magnetic Resonance Imaging Using Deep Learning-Based Height and Weight Estimation with 3D Camera: A Feasibility Study

Iram Shahzadi; Birgi Tamersoy; Lynn Johann Frohwein; Sesha Subramanian; Christoph Moenninghoff; Julius Henning Niehoff; Jan Robert Kroeger; Alexey Surov; Jan Borggrefe

doi:10.1016/j.acra.2024.01.029

Automated Patient Registration in Magnetic Resonance Imaging Using Deep Learning-Based Height and Weight Estimation with 3D Camera: A Feasibility Study

Acad Radiol. 2024 Feb 16:S1076-6332(24)00050-3. doi: 10.1016/j.acra.2024.01.029. Online ahead of print.

Authors

Iram Shahzadi¹, Birgi Tamersoy², Lynn Johann Frohwein², Sesha Subramanian², Christoph Moenninghoff³, Julius Henning Niehoff³, Jan Robert Kroeger³, Alexey Surov³, Jan Borggrefe⁴

Affiliations

¹ Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany; Siemens Healthineers GmbH, Erlangen, Germany.
² Siemens Healthineers GmbH, Erlangen, Germany.
³ Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
⁴ Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany. Electronic address: jan.borggrefe@muehlenkreiskliniken.de.

PMID: 38368163
DOI: 10.1016/j.acra.2024.01.029

Abstract

Rationale and objectives: Accurate and efficient estimation of patient height and weight is crucial to ensure patient safety and optimize the quality of magnetic resonance imaging (MRI) procedures. Several height and weight estimation methods have been proposed for use in adult patient management, but none is widely established. Estimation by the medical technologists for radiology (MTR) based on personal experience remains to be the most common method. This study aimed to compare a novel deep learning (DL)-based 3-dimensional (3D) camera estimation method to MTR staff in terms of estimation accuracy.

Methods: A retrospective study was conducted to compare the accuracy of height and weight estimation with a DL-based 3D camera algorithm to the accuracy of height and weight estimation by the MTR. Depth images of the patients were captured during the regular imaging workflow on a low field 0.55 T MRI scanner (MAGNETOM Free.Max, Siemens Healthineers, Erlangen, Germany) and then processed retrospectively. Depth images of a total of 161 patients were used to validate the accuracy of the height and weight estimation algorithm. The accuracy of each estimation method was evaluated by computing the proportions of the estimates within 5% and 15% of actual height (PH05, PH15) and within 10% and 20% of actual weight (PW10, PW20). An acceptable accuracy for height estimation was predetermined to be PH05 = 95% and PH15 = 99% and an acceptable accuracy for weight estimation was predetermined to be PW10 = 70% and PW20 = 95%. The bias in height and weight estimation was measured by the mean absolute percentage error (MAPE).

Results: The retrospective study included 161 adult patients. For 148/161 patients complying with inclusion criteria, DL-based 3D camera algorithm outperformed the MTR in estimating the patient's height and weight in term of accuracy (3D camera: PH05 =98.6%, PH15 =100%, PW10 =85.1%, PW20 =95.9%; MTR: PH05 =92.5%, PH15 =100%, PW10 =75.0%, PW20 =93.2%). MTR had a slightly higher bias in their estimates compared to the DL-based 3D camera algorithm (3D camera: MAPE height=1.8%, MAPE weight=5.6%, MTR: MAPE height=2.2%, MAPE weight=7.5%) CONCLUSION: This study has demonstrated that the estimation of the patient's height and weight by a DL-based 3D camera algorithm is accurate and robust. It has the potential to complement the regular MRI workflows, by providing further automation during patient registration.

Keywords: 3D camera; Autopositioning; Deep learning; Height; Magnetic resonance imaging; Weight.