Open-source image registration for MRI–TRUS fusion-guided prostate interventions
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We propose two software tools for non-rigid registration of MRI and transrectal ultrasound (TRUS) images of the prostate. Our ultimate goal is to develop an open-source solution to support MRI–TRUS fusion image guidance of prostate interventions, such as targeted biopsy for prostate cancer detection and focal therapy. It is widely hypothesized that image registration is an essential component in such systems.
The two non-rigid registration methods are: (1) a deformable registration of the prostate segmentation distance maps with B-spline regularization and (2) a finite element-based deformable registration of the segmentation surfaces in the presence of partial data. We evaluate the methods retrospectively using clinical patient image data collected during standard clinical procedures. Computation time and Target Registration Error (TRE) calculated at the expert-identified anatomical landmarks were used as quantitative measures for the evaluation.
The presented image registration tools were capable of completing deformable registration computation within 5 min. Average TRE was approximately 3 mm for both methods, which is comparable with the slice thickness in our MRI data. Both tools are available under nonrestrictive open-source license.
We release open-source tools that may be used for registration during MRI–TRUS-guided prostate interventions. Our tools implement novel registration approaches and produce acceptable registration results. We believe these tools will lower the barriers in development and deployment of interventional research solutions and facilitate comparison with similar tools.
KeywordsProstate cancer Targeted biopsy Image-guided interventions Image registration Magnetic resonance imaging Ultrasound
A.F., K.T., T.K., P.N. and C.T. were supported in part by the US National Institutes of Health, through the Grants R01 CA111288, P41 RR019703 and U24 CA180918. S.K., C.A.S., S.F. and P.A. were supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health (CIHR), the Networked Centres of Excellence on Graphics, Animation and New Media (GRAND) and Autodesk Research Inc. C.A.S. was supported by NSERC (PGSD) and UBC (FYF6456). C.Z. was supported by China Scholarship Council (201206105023).
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