Non-rigid registration between 3D ultrasound and CT ... - isl, ee, kaist

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134 DLeeet alTable 3. Accuracy test of lesion registration for five patient datasets.Rigid(vessels)Non-rigid(vessels)Non-rigid(vessels andsurfaces)Lesion type DC OM DC OM DC OMPatient (diameter (mm)) (mm) (%) (mm) (%) (mm) (%)A Hemangioma (10.3) 3.42 68 0.52 100 0.47 100B Hemangioma (23.2) 10.50 62 4.52 95 4.48 94C Cyst_1 (12.2) 4.25 71 1.21 99 1.36 99Cyst_2 (22.7) 5.08 81 4.54 88 2.75 90D Cyst (18.3) 12.40 20 2.56 87 3.31 86E Cyst_1 (6.2) 6.44 0 6.43 10 3.86 88Cyst_2 (18.9) 7.85 59 5.25 86 3.62 90Average 7.13 52 3.58 81 2.84 92range from 0.47 to 4.48 mm, which are considered to be acceptable for image-guided liverintervention (Lange et al 2004). Additionally, in cyst_2 in C and in both cysts in E, the use ofthe surface information improves the registration accuracy. Meanwhile, it is noted in cyst_1in C and in the cyst in D, the DC values of the proposed algorithm show a slight increasecompared to those of the non-rigid registration process based on the vessels. However, it isconsidered that the accuracy of lesion registration is not degraded in the proposed algorithm,as the OM values are mostly preserved. Among the datasets, we select the registrationresults for datasets C and E that include two identifiable lesions. These are shown infigure 11. In this figure, the white contours of lesions of a US image are superimposedonto the registered CT image. These contours help to illustrate that the proposed algorithmprovides very good registration accuracy for lesions and for the anatomical features of thevessels and the surface.Based on the quantitative evaluations above, the proposed algorithm is shown to provideaccurate non-rigid registration results. These accurate results stem from the efficientmanipulation of the anatomical information in the non-rigid registration framework. Asthe proposed registration algorithm can offset local deformation of the liver due to the USprobe pressure and/or due to the different respiration phases between US and CT images, weexpect that the algorithm can be applied to a range of clinical areas in which high registrationaccuracy is required, such as liver surgery. The proposed algorithm can provide new clinicalsolutions pertaining to image-guided diagnoses and intervention strategies.To obtain the final registered CT image in our experiments, user interaction was notrequired except for anatomical feature segmentation in the CT preprocessing step which canbe considered a preoperative procedure. Hence, the proposed algorithm is fully automatic in theintraoperative procedure. For real-time intraoperative applications, however, the computationtime of the algorithm is a bottleneck, since two main non-rigid registration steps in figure 4requires several hours of execution time on a standard PC. The acceleration of computationtime is required as future work for real-time applications.An application example of the proposed algorithm is a real-time concurrent displaysystem for 3D US and CT images. In such a system, multiple-respiratory-phase CT imagesare obtained through the non-rigid registration of a pre-scanned single-respiratory-phase CTimage to real-time multiple-respiratory-phase US images. This display would be very usefulfor image-guided intervention involving the liver. Given that computation time of the 3D
Non-rigid registration between 3D ultrasound and CT images of the liver 135USRigid(vessels)Non-rigid(vessels)Non-rigid(vessels + surface)(a)USRigid(vessels)Non-rigid(vessels)(b)Non-rigid(vessels + surface)Figure 11. Registration results for two patient datasets. From the top left, a B-mode US slice andits registered CT slices are shown. The superimpositions of the contours of the lesions extractedfrom the US slice are depicted for (a) patient C and (b) patient E in table 3.non-rigid registration process is too slow for real-time execution, however, it is necessaryto improve the computation time or to consider a system consisting of a preoperative stagerequiring a high computational load and an intraoperative stage requiring a low computationalload (Blackall et al 2005, Huang et al 2009). These approaches using the proposed algorithmare left for future work.4. ConclusionsAn accurate registration algorithm for the 3D B-mode US and CT images of the liver ispresented in this paper. For accurate registration, we utilize the anatomical features of the
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