title: Accurate CT∕MR vessel-guided nonrigid registration of largely deformed livers.
abstract: OBJECTIVE
  Computer tomography (CT) scans are used for designing radiotherapy treatment plans. However, the tumor is often better visible in magnetic resonance (MR) images. For liver stereotactic body radiation therapy (SBRT), the planning CT scan is acquired while abdominal compression is applied to reduce tumor motion induced by breathing. However, diagnostic MR scans are acquired under voluntary breath-hold without the compression device. The resulting large differences in liver shape hinder the alignment of CT and MR image sets, which severely limits the integration of the information provided by these images. The purpose of the current study is to develop and validate a nonrigid registration method to align breath-hold MR images with abdominal-compressed CT images, using vessels that are automatically segmented within the liver.
  
  
  METHODS
  Contrast-enhanced MR and CT images of seven patients with liver cancer were used for this study. The registration method combines automatic vessel segmentation with an adapted version of thin-plate spline robust point matching. The vessel segmentation uses a multiscale vesselness measure, which allows vessels of various thicknesses to be segmented. The nonrigid registration is point-based, and progressively improves the correspondence and transformation between two point sets. Moreover, the nonrigid registration is capable of identifying and handling outliers (points with no counterpart in the other set). We took advantage of the strengths of both methods and created a multiscale registration algorithm. First, thick vessels are registered, then with each new iteration thinner vessels are included in the registration (strategy A). We compared strategy A to a straightforward approach where vessels of various diameters are segmented and subsequently registered (strategy B). To assess the transformation accuracy, residual distances were calculated for vessel bifurcations. For anatomical validation, residual distances were calculated for additional anatomical landmarks within the liver. To estimate the extent of deformation, the residual distances for the aforementioned anatomical points were calculated after rigid registration.
  
  
  RESULTS
  Liver deformations in the range of 2.8-10.7 mm were found after rigid registration of the CT and MR scans. Low residual distances for vessel bifurcations (average 1.6, range 1.3-1.9 mm) and additional anatomical landmarks (1.5, 1.1-2.4 mm) were found after nonrigid registration. A large amount of outliers were identified (25%-55%) caused by vessels present in only one of the image sets and false positives in the vesselness measure. The nonrigid registration was capable of handling these outliers as was demonstrated by the low residual distances. Both strategies yielded very similar results in registration accuracy, but strategy A was faster than strategy B (≥2.0 times).
  
  
  CONCLUSIONS
  An accurate CT∕MR vessel-guided nonrigid registration for largely deformed livers was developed, tested, and validated. The method, combining vessel segmentation and point matching, was robust against differences in the segmented vessels. The authors conclude that nonrigid registration is required for accurate alignment of abdominal-compressed and uncompressed liver anatomy. Alignment of breath-hold MR and abdominal-compressed CT images can be used to improve tumor localization for liver SBRT.
authors.affiliation: Department of Radiation Oncology, Erasmus Medical Center-Daniel den Hoed Cancer Center, Groene Hilledijk 301, 3075 EA Rotterdam, The Netherlands. e.vasquezosorio@erasmusmc.nl
authors.forename: Eliana M
authors.initials: EM
authors.lastname: Vasquez Osorio
authors.forename: Mischa S
authors.initials: MS
authors.lastname: Hoogeman
authors.forename: Alejandra
authors.initials: A
authors.lastname: Mendez Romero
authors.forename: Piotr
authors.initials: P
authors.lastname: Wielopolski
authors.forename: Andras
authors.initials: A
authors.lastname: Zolnay
authors.forename: Ben J M
authors.initials: BJ
authors.lastname: Heijmen
mesh_terms: D000792:Angiography / Q000379:methods*; D016477:Artifacts; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000098:blood supply* / Q000000981:diagnostic imaging* / Q000473:pathology; D008113:Liver Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*