## Abstract Image thresholding is critical to computer vision systems designed to detect very small numbers of contaminant particles from analysis of images acquired by in‐line process monitoring. The objective of this work was to obtain a thresholding method that would permit in‐line, “real‐time,”
Real-time catheter tracking and adaptive imaging
✍ Scribed by Daniel R. Elgort; Eddy Y. Wong; Claudia M. Hillenbrand; Frank K. Wacker; Jonathan S. Lewin; Jeffrey L. Duerk
- Publisher
- John Wiley and Sons
- Year
- 2003
- Tongue
- English
- Weight
- 242 KB
- Volume
- 18
- Category
- Article
- ISSN
- 1053-1807
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
Purpose
To evaluate the performance of a real‐time MR system for interventional procedures that adjusts specific image parameters in real time based on a catheter's speed of insertion.
Materials and Methods
The system was implemented using only the hardware provided with a standard short‐bore 1.5 T scanner (Siemens Magnetom Sonata) (with the exception of small tracking markers affixed to the catheter). The system tracks the position of an MR microcoil‐instrumented catheter and automatically updates the scan plane's position and orientation, as well as other features, including, but not limited to, field of view, resolution, tip angle, and TE. A real‐time feedback loop continuously localizes the tracking markers, updates the scan plane position and orientation, calculates the catheter's speed, adjusts the value of specific image parameters, then collects new image data, reconstructs an image, and provides it for immediate display. The system was evaluated in phantom and in vivo porcine experiments.
Results
The system is able to accurately localize a moving catheter in the abdominal aorta, calculate the device speed, and respond by adjusting specified image parameters 98% of the time, with precision of approximately 2 mm and 1.5°.
Conclusion
Simply slowing the speed of the catheter allows the clinician to adjust predetermined image parameters. This work also has the potential to build a degree of intelligence into the scanner, enabling it to react to changes in the clinical environment and automatically optimize specific image parameters. J. Magn. Reson. Imaging 2003;18:621–626. © 2003 Wiley‐Liss, Inc.
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