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Multimodal image-guided prostate fusion biopsy based on automatic deformable registration

  • Original Article
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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Transrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, \(^{68}\hbox {Ga}\)-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance.

Methods

The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland.

Results

The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar–gelatin phantoms and clinical data of 13 patients confirm the validity of this approach.

Conclusion

The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

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Notes

  1. Available online: http://www.slicerigt.org.

  2. Computational Geometry Algorithms Library, available online: http://www.cgal.org.

  3. Available online: https://code.google.com/p/gmmreg/.

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Acknowledgments

We thank Wolfgang Wein, ImFusion GmbH, for providing his image processing framework, and the teams of radiology and nuclear medicine departments at our clinic for various phantom MRI scans. This work is partially supported by the EU 7th Framework Program projects Marie Curie Early Initial Training Network Fellowship (PITN-GA-2011-289355-PicoSEC-MCNet), EndoTOFPET-US (GA-FP7/2007-2013-256984), and SoftwareCampus program of the German Federal Ministry of Education and Research (BMBF, Förderkennzeichen 01IS12057). The authors declare that they have no conflict of interest. Informed consent was obtained from all individual participants included in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committees.

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Authors and Affiliations

  1. Computer-Aided Medical Procedures, Technische Universität München, Boltzmannstr. 3, 85748, Garching, Germany

    Oliver Zettinig, Amit Shah, Christoph Hennersperger, Christine Kroll, Fausto Milletarì, Julia Rackerseder, Christian Schulte zu Berge, Benjamin Frisch & Nassir Navab

  2. Urologische Klinik und Poliklinik, Technische Universität München, München, Germany

    Hubert Kübler, Tobias Maurer & Enno Storz

  3. Nuklearmedizinische Klinik und Poliklinik, Technische Universität München, München, Germany

    Matthias Eiber

  4. Johns Hopkins University, Baltimore, MD, USA

    Nassir Navab

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  1. Oliver Zettinig
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  2. Amit Shah
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Correspondence to Oliver Zettinig.

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Zettinig, O., Shah, A., Hennersperger, C. et al. Multimodal image-guided prostate fusion biopsy based on automatic deformable registration. Int J CARS 10, 1997–2007 (2015). https://doi.org/10.1007/s11548-015-1233-y

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  • DOI: https://doi.org/10.1007/s11548-015-1233-y

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