@conference {Poulin2017,
	title = {Validation Of MRI To US Registration For Focal Hdr Prostate Brachytherapy Treatment},
	booktitle = {Annual Scientific Meeting of the American Brachytherapy Society},
	volume = {16},
	number = {3},
	year = {2017},
	pages = {S56-S57},
	publisher = {Elsevier},
	organization = {Elsevier},
	address = {Boston, MA, USA},
	keywords = {Brachytherapy, contour propagation, MRI, Ultrasound},
	issn = {1538-4721},
	doi = {http://dx.doi.org/10.1016/j.brachy.2017.04.093},
	url = {http://www.brachyjournal.com/article/S1538-4721(17)30154-X/fulltext},
	author = {Eric Poulin},
	editor = {Karim Boudam and Csaba Pinter and Samuel Kadoury and Andras Lasso and Gabor Fichtinger and Cynthia Menard}
}
@article {Seifabadi2013a,
	title = {Real-Time 3D Needle Shape Tracking Using Fiber Bragg Grating Sensors for Prostate Percutaneous Interventions},
	year = {2013},
	address = {Crystal City, VA, USA},
	abstract = {<p>During prostate needle insertion, the gland and therefore targets move resulting in targeting inaccuracy. To compensate for this error, in a previous study we proposed robotic bevel-tip needle steering under live MRI. One important drawback is that tracking a flexible (20G) needle under real-time MRI is inaccurate due to the needle artifact and the image update frequency is relatively slow (\&gt;200 ms). As an alternative solution, we embedded some FBG strain sensors along the needle shaft at certain locations and reconstruct the needle shape from this information. Compared to the previous study on this topic by others, the following development have been done: 1) this study is extended to needle of higher gauge (thinner), 2) we guarantee below 0.5 mm accuracy at the needle tip for all insertion depth, 3) we extended it to bevel-tip needles (through modelling and mathematical formulation), 4) we visualized the 3D shape of the needle in 3D Slicer which is a commonly used planning and navigation software for prostate interventions, 5) our needle tracking is real-time.</p>
},
	keywords = {3D Slicer, MRI, needle tracking, real-time},
	author = {Reza Seifabadi and Esteban Escobar Gomez and Fereshteh Aalamifar and Gabor Fichtinger and Iulian Iordachita}
}
@conference {Avni2010,
	title = {Design of a predictive targeting error simulator for MRI-guided prostate biopsy},
	booktitle = {Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling},
	year = {2010},
	month = {2010},
	pages = {76251A-1 - 76251A-8},
	publisher = {SPIE},
	organization = {SPIE},
	address = {San Diego, California, USA},
	abstract = {<p>Multi-parametric MRI is a new imaging modality superior in quality to Ultrasound (US) which is currently used in standard prostate biopsy procedures. Surface-based registration of the pre-operative and intra-operative prostate volumes is a simple alternative to side-step the challenges involved with deformable registration. However, segmentation errors inevitably introduced during prostate contouring spoil the registration and biopsy targeting accuracies. For the crucial purpose of validating this procedure, we introduce a fully interactive and customizable simulator which determines the resulting targeting errors of simulated registrations between prostate volumes given user-provided parameters for organ deformation, segmentation, and targeting. We present the workflow executed by the simulator in detail and discuss the parameters involved. We also present a segmentation error introduction algorithm, based on polar curves and natural cubic spline interpolation, which introduces statistically realistic contouring errors. One simulation, including all I/O and preparation for rendering, takes approximately 1 minute and 40 seconds to complete on a system with 3 GB of RAM and four Intel Core 2 Quad CPUs each with a speed of 2.40 GHz. Preliminary results of our simulation suggest the maximum tolerable segmentation error given the presence of a 5.0 mm wide small tumor is between 4-5 mm. We intend to validate these results via clinical trials as part of our ongoing work.</p>
},
	keywords = {Biopsy, cancer, error, modeling, MRI, Prostate, segmentation, validation},
	isbn = {0277-786X},
	doi = {http://dx.doi.org/10.1117/12.844476},
	author = {Shachar Avni and Siddharth Vikal and Gabor Fichtinger}
}
@conference {Tadayyon2010a,
	title = {MRI-Guided prostate motion tracking by means of multislice-to-volume registration},
	booktitle = {SPIE Medical Imaging},
	year = {2010},
	month = {02/2010},
	keywords = {Biopsy, motion tracking, MRI, slice-to-volume registation},
	doi = {10.1117/12.844454},
	author = {Hadi Tadayyon and Gabor Fichtinger and Andras Lasso and Siddharth Vikal and Sean Gill}
}
