PIAA: Pre-imaging all-round assistant for digital radiography

Article type: Research Article

Authors: Zhao, Jie^{a; b} | Liu, Jianqiang^b | Wang, Shijie^a | Zhang, Pinzheng^a | Yu, Wenxue^a | Yang, Chunfeng^{a; *} | Zhang, Yudong^{a; *} | Chen, Yang^{a; *}

Affiliations: [a] Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China | [b] Careray Digital Medical Technology Co., Ltd., Suzhou, China

Correspondence: [*] Corresponding authors: Chunfeng Yang, Yudong Zhang and Yang Chen, Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China. E-mail: chunfeng.yang@seu.edu.cn, yudongzhang@seu.edu.cn and chenyang.list@seu.edu.cn.

Abstract: BACKGROUND: In radiography procedures, radiographers’ suboptimal positioning and exposure parameter settings may necessitate image retakes, subjecting patients to unnecessary ionizing radiation exposure. Reducing retakes is crucial to minimize patient X-ray exposure and conserve medical resources. OBJECTIVE: We propose a Digital Radiography (DR) Pre-imaging All-round Assistant (PIAA) that leverages Artificial Intelligence (AI) technology to enhance traditional DR. METHODS: PIAA consists of an RGB-Depth (RGB-D) multi-camera array, an embedded computing platform, and multiple software components. It features an Adaptive RGB-D Image Acquisition (ARDIA) module that automatically selects the appropriate RGB camera based on the distance between the cameras and patients. It includes a 2.5D Selective Skeletal Keypoints Estimation (2.5D-SSKE) module that fuses depth information with 2D keypoints to estimate the pose of target body parts. Thirdly, it also uses a Domain expertise (DE) embedded Full-body Exposure Parameter Estimation (DFEPE) module that combines 2.5D-SSKE and DE to accurately estimate parameters for full-body DR views. RESULTS: Optimizes DR workflow, significantly enhancing operational efficiency. The average time required for positioning patients and preparing exposure parameters was reduced from 73 seconds to 8 seconds. CONCLUSIONS: PIAA shows significant promise for extension to full-body examinations.

Keywords: Digital radiography, patient positioning, deep learning, domain expertise, human pose estimation, automatic exposure control, RGB-D camera

DOI: 10.3233/THC-240639

Journal: Technology and Health Care, vol. Pre-press, no. Pre-press, pp. 1-16, 2024