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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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PPEA: Personalized positioning and exposure assistant based on multi-task shared pose estimation transformer.

Jie Zhao1,2, Jianqiang Liu2, Chunfeng Yang1

  • 1Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|August 13, 2025
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Summary
This summary is machine-generated.

This study introduces an AI assistant for digital radiography (DR) of hands and feet. It automatically recognizes patient positioning and recommends optimal exposure parameters to improve image quality and reduce retakes.

Keywords:
Digital radiographyautomatic exposure controlfoot pose estimationhand pose estimationpatient positioningtransformer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiography

Background:

  • Accurate patient positioning and exposure settings are critical for diagnostic quality in hand and foot digital radiography (DR).
  • Variations in patient anatomy and positioning requirements necessitate personalized approaches to optimize imaging protocols.
  • Minimizing image retakes is essential for efficiency and reducing patient radiation exposure.

Purpose of the Study:

  • To develop and evaluate a personalized positioning and exposure assistant for hand and foot DR.
  • To enable automatic recognition of hand and foot positions and recommend tailored exposure parameters.
  • To enhance diagnostic accuracy and reduce errors in clinical radiography settings.

Main Methods:

  • Development of a three-module assistant: Progressive Iterative Hand-Foot Tracker (PIHFT) for pose estimation, Multi-Task Shared Pose Estimation Transformer (MTSPET) for robust hand and foot pose recognition, and Domain Expertise-embedded Positioning and Exposure Assistant (DEPEA) for parameter recommendation.
  • Training MTSPET on two newly collected datasets, demonstrating superior performance over existing methods like MediaPipe for hand pose estimation and successful transfer learning to foot pose estimation.
  • Integration of key-point coordinates with clinical requirements in DEPEA to infer exposure areas and Regions of Interest (ROIs) for Digital Automatic Exposure Control (DAEC).

Main Results:

  • MTSPET achieved superior hand pose estimation compared to MediaPipe and effectively applied this to foot pose estimation.
  • The DEPEA module successfully combined pose data with clinical expertise to determine positioning and exposure parameters.
  • A preliminary clinical trial indicated strong agreement between the assistant's outputs and manual annotations, validating its clinical utility.

Conclusions:

  • The proposed personalized positioning and exposure assistant effectively automates the recognition of hand and foot positions in DR.
  • The system provides accurate recommendations for exposure parameters, contributing to improved image quality and reduced retakes.
  • This AI-driven approach lays the groundwork for personalized, patient-specific imaging strategies in medical radiography, enhancing diagnostic outcomes.