{"id":2344,"date":"2024-02-25T16:15:39","date_gmt":"2024-02-25T16:15:39","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/shachar-avni\/"},"modified":"2024-02-25T16:15:39","modified_gmt":"2024-02-25T16:15:39","slug":"shachar-avni","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/shachar-avni\/","title":{"rendered":"Shachar Avni"},"content":{"rendered":"
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Shachar Avni<\/h1>\n<\/div>\n
Masters Student<\/div>\n
School of Computing<\/div>\n
Queen’s University<\/div>\n
Member from 2008<\/em> to 2010<\/em><\/div>\n
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Avni, Shachar; Vikal, Siddharth; Fichtinger, Gabor<\/p>

Design of a predictive targeting error simulator for MRI-guided prostate biopsy<\/a> Conference<\/span> <\/p>

Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, <\/span>SPIE <\/span>SPIE, <\/span>San Diego, California, USA, <\/span>2010<\/span>, ISBN: 0277-786X<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@conference{Avni2010,
\r\ntitle = {Design of a predictive targeting error simulator for MRI-guided prostate biopsy},
\r\nauthor = {Shachar Avni and Siddharth Vikal and Gabor Fichtinger},
\r\nurl = {https:\/\/labs.cs.queensu.ca\/perklab\/wp-content\/uploads\/sites\/3\/2024\/02\/Avni2010.pdf},
\r\ndoi = {http:\/\/dx.doi.org\/10.1117\/12.844476},
\r\nisbn = {0277-786X},
\r\nyear  = {2010},
\r\ndate = {2010-01-01},
\r\nurldate = {2010-01-01},
\r\nbooktitle = {Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling},
\r\npages = {76251A-1 - 76251A-8},
\r\npublisher = {SPIE},
\r\naddress = {San Diego, California, USA},
\r\norganization = {SPIE},
\r\nabstract = {<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>},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
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<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><\/div>
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