Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

US \& MRI Image Fusion Based on Markerless Skin Registration (2307.14288v4)

Published 26 Jul 2023 in cs.CV

Abstract: This paper presents an innovative automatic fusion imaging system that combines 3D CT/MR images with real-time ultrasound (US) acquisition. The system eliminates the need for external physical markers and complex training, making image fusion feasible for physicians with different experience levels. The integrated system involves a portable 3D camera for patient-specific surface acquisition, an electromagnetic tracking system, and US components. The fusion algorithm comprises two main parts: skin segmentation and rigid co-registration, both integrated into the US machine. The co-registration software aligns the surface extracted from CT/MR images with patient-specific coordinates, facilitating rapid and effective fusion. Experimental testing in different settings validates the system's accuracy, computational efficiency, noise robustness, and operator independence. The co-registration error remains under the acceptable range of~$1$ cm.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (27)
  1. Pointnetlk: Robust & efficient point cloud registration using pointnet. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7163–7172, 2019.
  2. Multimodality image fusion–guided procedures: technique, accuracy, and applications. Cardiovascular and interventional radiology, 35:986–998, 2012.
  3. An automatic algorithm for skin surface extraction from mr scans. https://cds.ismrm.org/ismrm-2000/PDF3/0672.pdf. (undefined 23/12/2023 11:11).
  4. Multiparametric mri and targeted prostate biopsy: Improvements in cancer detection, localization, and risk assessment. Central European Journal of Urology, 69(1):9, 2016.
  5. Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine bone mineral density, and blood biomarkers in women with type 2 diabetes mellitus? Journal of Magnetic Resonance Imaging, 35(1):117–124, 2012.
  6. Automated alignment of perioperative mri scans: A technical note and application in pediatric epilepsy surgery. Technical report, Wiley Online Library, 2016.
  7. Depth camera d415 – intel® realsense™ depth and tracking cameras. https://www.intelrealsense.com/depth-camera-d415/. (Accessed on 03/09/2023).
  8. The value of magnetic resonance imaging and ultrasonography (mri/us)-fusion biopsy platforms in prostate cancer detection: A systematic review. BJU international, 117(3):392–400, 2016.
  9. Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography. Journal of biomedical optics, 18(8):086007–086007, 2013.
  10. Marching cubes: A high resolution 3d surface construction algorithm. ACM Siggraph Computer Graphics, 21(4):163–169, 1987.
  11. An automated skin segmentation of breasts in dynamic contrast-enhanced magnetic resonance imaging. Scientific Reports, 8(1):6159, 2018.
  12. Min Woo Lee. Fusion imaging of real-time ultrasonography with ct or mri for hepatic intervention. Ultrasonography, 33(4):227, 2014.
  13. Respiratory motion prediction using fusion-based multi-rate kalman filtering and real-time golden-angle radial mri. IEEE Transactions on Biomedical Engineering, 67(6):1727–1738, 2019.
  14. External hardware and sensors, for improved mri. Journal of Magnetic Resonance Imaging, 57(3):690–705, 2023.
  15. Point set registration: Coherent point drift. Transactions on Pattern Analysis and Machine Intelligence, 32(12):2262–2275, 2010.
  16. Edge detector-based automatic segmentation of the skin layers and application to moisturization in high-resolution 3 tesla magnetic resonance imaging. Skin Research and Technology, 25(3):339–346, 2019.
  17. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 652–660, 2017.
  18. A robust point-matching algorithm for autoradiograph alignment. Medical Image Analysis, 1(4):379–398, 1997.
  19. Georgios Sakas. Trends in medical imaging: From 2d to 3d. Computers & Graphics, 26(4):577–587, 2002.
  20. Ultrasound-driven cardiac mri. Physica Medica, 70:161–168, 2020.
  21. Augmented reality for interventional oncology: Proof-of-concept study of a novel high-end guidance system platform. European Radiology Experimental, 2:1–9, 2018.
  22. Medical robotics for ultrasound imaging: Current systems and future trends. Current Robotics Reports, 2:55–71, 2021.
  23. Automated abdominal segmentation of ct scans for body composition analysis using deep learning. Radiology, 290(3):669–679, 2019.
  24. Fully automatic breast segmentation in 3d breast mri. In 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pages 1024–1027. IEEE, 2012.
  25. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on ct images. Computer Methods and Programs in Biomedicine, 144:97–104, 2017.
  26. A survey of iterative closest point algorithm. In 2017 Chinese Automation Congress (CAC), pages 4395–4399. IEEE, 2017.
  27. Subject-specific real-time respiratory liver motion compensation method for ultrasound-mri/ct fusion imaging. International Journal of Computer Assisted Radiology and Surgery, 10:517–529, 2015.

Summary

We haven't generated a summary for this paper yet.