Open-source image registration for MRI–TRUS fusion-guided prostate interventions
- 711 Downloads
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).
- 2.Hegde VJ, Mulkern RV, Panych LP, Fennessy FM, Fedorov A, Maier SE, Tempany CM (2013) Multiparametric MRI of prostate cancer: An update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging 37(5):1035–1054CrossRefPubMedCentralPubMedGoogle Scholar
- 7.Hu Y, Ahmed HA, Taylor Z, Allen C, Emberton M, Hawkes D, Barratt D (2012) MR to ultrasound registration for image-guided prostate interventions. Med Image Anal 16(3): 687–703. ISSN: 1361–8415Google Scholar
- 8.Moradi M et al (2012) Two solutions for registration of ultrasound to MRI for image-guided prostate interventions. In: Engineering in Medicine and Biology Society (EMBC), 2012 annual international conference of the IEEE, pp 1129–1132Google Scholar
- 9.Sun Y, Yuan J, Rajchl M, Qiu W, Romagnoli C, Fenster A (2013) Efficient convex optimization approach to 3D non-rigid MR–TRUS registration. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) MICCAI, vol. 8149. Springer, Berlin, pp 195–202. ISBN: 978-3-642-40810-6Google Scholar
- 11.Smith WL et al (2007) Prostate volume contouring: a 3D analysis of segmentation using 3DTRUS, CT, and MR. Int J Radiat Oncol Biol Phys 67(4):1238–1247. ISSN: 0360–3016Google Scholar
- 15.Johnson HJ, Harris G, Williams K (2007) BRAINSFit: mutual information registrations of whole-brain 3D images, using the insight toolkit. Insight JGoogle Scholar
- 17.Penzkofer T et al (2014) Transperineal In-Bore 3-T MR imaging-guided prostate biopsy: a prospective clinical observational study. Radiology 274:170–180 Google Scholar
- 18.Maurer CR, Raghavan V (2003) A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell 25(2):265–270Google Scholar
- 19.Gelas A, Gouaillard A, Megason S (2008) Surface meshes incremental decimation framework. Insight JGoogle Scholar
- 22.Shah A, Zettinig O, Maurer T, Precup C, Schulte zu Berge C, Frisch B, Navab N (2014) An open source multimodal image-guided prostate biopsy framework. In: 3rd workshop on clinical image-based procedures: translational research in medical imaging (CLIP), 17th MIC-CAIGoogle Scholar
- 24.Heijmink SWTPJ, Scheenen TWJ, van Lin ENJT, Visser AG, Kiemeney LALM, Witjes JA, Barentsz JO (2009) Changes in prostate shape and volume and their implications for radiotherapy after introduction of endorectal balloon as determined by MRI at 3T. Int J Radiat Oncol Biol Phys 73(5):1446–1453CrossRefPubMedGoogle Scholar