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Developing and testing a robotic MRI/CT fusion biopsy technique using a purpose-built interventional phantom.

Edward W Johnston1,2, Nicos Fotiadis3,4, Craig Cummings4

  • 1Royal Marsden Hospital, 203 Fulham Road, London, SW3 6JJ, UK. edwilliamjohnston@gmail.com.

European Radiology Experimental
|November 21, 2022
PubMed
Summary

A new robotic MRI/CT-fusion biopsy technique was developed and validated using a reproducible phantom. This accurate and reliable method is suitable for clinical application within acceptable timeframes.

Keywords:
Image-guided biopsyMagnetic resonance imagingMultimodal imagingPhantoms (imaging)Robotics

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Area of Science:

  • Medical Imaging
  • Biopsy Technology
  • Robotics in Medicine

Background:

  • Magnetic resonance imaging (MRI) aids tumor targeting in biopsies.
  • Correlating histology with MRI signals is vital for imaging biomarker validation.
  • Robotic MRI/computed tomography (CT) fusion biopsy requires development for precise correlation.

Purpose of the Study:

  • To develop and validate a reproducible biopsy phantom.
  • To test the accuracy and reliability of robotic MRI/CT-fusion biopsy.
  • To assess the clinical applicability of the developed biopsy technique.

Main Methods:

  • Prospectively assessed T1/T2 relaxation times and Hounsfield units (HU) in various phantoms.
  • Constructed four biopsy phantoms with 12 MRI-visible targets each.
  • Developed a technical pipeline and tested intra/interoperator reliability for robotic biopsies.

Main Results:

  • T1, T2, and HU repeatability showed 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%.
  • Phantom reproducibility was high (Dice Similarity Coefficient [DSC] 0.93/0.92).
  • Achieved 100% hit rate with robotic biopsies in clinically acceptable times (32 min for 12 targets).

Conclusions:

  • A reproducible biopsy phantom was successfully developed and validated.
  • The robotic MRI/CT-fusion biopsy technique proved highly accurate and reliable.
  • The technique is suitable for clinical application within acceptable timeframes.