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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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.
Here, in order to determine the magnitude of velocity and acceleration for point...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...

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Updated: May 28, 2026

A Protocol for Real-time 3D Single Particle Tracking
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Published on: January 3, 2018

MRIgRT real-time target tracking: TrackRAD2025 challenge report.

Tom Julius Blöcker1, Pia A W Görts2, Yiling Wang3

  • 1Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.

Medical Image Analysis
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

TrackRAD2025 benchmarked algorithms for MRI-guided radiotherapy (MRIgRT) target tracking. Foundation models achieved high accuracy, comparable to experts, advancing precise cancer treatment delivery.

Keywords:
Deep learningMRI-guidanceMRI-linacMotion managementRespiratory motion

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology
  • Artificial Intelligence in Medicine

Background:

  • Magnetic resonance imaging (MRI)-guided radiotherapy (MRIgRT) enhances real-time motion management during treatment using cine-MRI.
  • Current target localization methods in MRIgRT, such as template matching and deformable image registration, have limitations.
  • Advanced localization algorithms are crucial for improving the precision and efficiency of radiotherapy delivery.

Purpose of the Study:

  • To establish a benchmark dataset and challenge (TrackRAD2025) for evaluating algorithms in MRIgRT target tracking using 2D+t cine-MRI.
  • To compare the performance of various algorithms in propagating target segmentation masks across dynamic MRI sequences.
  • To assess the impact of different factors, like field strength and anatomical site, on tracking accuracy and speed.

Main Methods:

  • The TrackRAD2025 challenge provided a multi-institutional dataset of cine-MRI scans from 585 cancer patients.
  • Participants developed algorithms to track target segmentation masks across frames in sagittal cine-MRI data.
  • Submitted algorithms were evaluated on the challenge platform using geometric accuracy, surrogate dose accuracy, and execution speed metrics.

Main Results:

  • The top five algorithms achieved high performance, with mean Dice similarity coefficients >0.87 and Euclidean center distances <2.1 mm, comparable to interobserver variability.
  • Foundation models, with or without finetuning, were prominent among the top-performing solutions.
  • MRI field strength had minimal impact; tracking performance was better in the pelvis (reduced motion) than in the thorax and abdomen.

Conclusions:

  • TrackRAD2025 successfully benchmarked MRIgRT target tracking algorithms on diverse, multi-institutional cine-MRI data.
  • Foundation models show significant promise for clinical translation in MRIgRT target tracking.
  • The results provide a foundation for developing more precise and efficient radiotherapy delivery systems.