A comparative study of three CT and MRI registration algorithms in nasopharyngeal carcinoma

Objective: To evaluate the image registration accuracy and efficiency of CT and MRI fusion using three algorithms in nasopharyngeal carcinoma (NPC). Methods and materials: Twelve sets of CT and MRI scans of 12 NPC patients were fused using three image registration algorithms, respectively: Mark‐and‐link, Interactive, and Normalized Mutual Information (NMI). Registration accuracy was evaluated by performing statistical analysis of the coordinate differences between CT and MR anatomical landmarks along the x‐, y‐ and z‐axes. The time required to complete the registration process using three algorithms was also recorded. One‐way ANOVA was used to analyze the difference of the three registration methods. Results: The mean time required for CT/MRI registration using the three different registration algorithms, mark‐and‐link, interactive, and NMI, was 6.25 min, 5.25 min, and 5.15 min, respectively. The mark‐and‐link method was more time consuming (F=8.74,p=0.001); however no statistical difference was found between the time required using interactive and NMI methods (p=0.77). Mean registration errors of the three methods along the x‐axis were 0.66 mm, 0.70 mm, and 0.68 mm, respectively (F=0.09,p=0.91). Along the y‐axis, the mean registration errors were 1.03 mm, 1.04 mm, and 1.03 mm, respectively (F=0.02,p=0.98). Along the z‐axis, they were 0.58 mm, 0.64 mm, and 0.56 mm, respectively (F=0.21,p=0.81). Conclusions: All three registration algorithms, mark‐and‐link, interactive, and NMI, can provide accurate CT/MRI registration. However the mark‐and‐link method was most time consuming. PACS number: 87.57.nj

[1]  Johannes Bernarding,et al.  Prospective registration of human head magnetic resonance images for reproducible slice positioning using localizer images , 2004, Journal of magnetic resonance imaging : JMRI.

[2]  Richard A. Robb,et al.  New approach to 3-D registration of multimodality medical images by surface matching , 1992, Other Conferences.

[3]  A. Aisen,et al.  Integration of magnetic resonance imaging into radiation therapy treatment planning: I. Technical considerations. , 1987, International journal of radiation oncology, biology, physics.

[4]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[5]  D. Hill,et al.  Registration of MR and CT images for skull base surgery using point-like anatomical features. , 1991, The British journal of radiology.

[6]  Paul F. Hemler,et al.  Grey value correlation techniques used for automatic matching of CT and MR brain and spine images , 1994, Other Conferences.

[7]  Alireza Kassaee,et al.  Comparison of manual vs. automated multimodality (CT-MRI) image registration for brain tumors. , 2005, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[8]  Tohru Shiga,et al.  Image fusion between 18FDG-PET and MRI/CT for radiotherapy planning of oropharyngeal and nasopharyngeal carcinomas. , 2002, International journal of radiation oncology, biology, physics.

[9]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

[10]  H. Huisman,et al.  Clinical validation of the normalized mutual information method for registration of CT and MR images in radiotherapy of brain tumors , 2004, Journal of applied clinical medical physics.

[11]  Colin Studholme,et al.  Automated 3-D registration of MR and CT images of the head , 1996, Medical Image Anal..

[12]  Andrew W. Beavis,et al.  Quality assurance of registration of CT and MRI data sets for treatment planning of radiotherapy for head and neck cancers , 2004, Journal of applied clinical medical physics.

[13]  M Soltys,et al.  Improving treatment planning accuracy through multimodality imaging. , 1996, International journal of radiation oncology, biology, physics.

[14]  H Shirato,et al.  Magnetic resonance imaging system for three-dimensional conformal radiotherapy and its impact on gross tumor volume delineation of central nervous system tumors. , 2001, International journal of radiation oncology, biology, physics.

[15]  Robert H. Anderson,et al.  On Technical Considerations , 1995, Writing Ethnography (Second Edition).

[16]  B. Dawant,et al.  Effect of geometrical distortion correction in MR on image registration accuracy. , 1996, Journal of computer assisted tomography.

[17]  V S Khoo,et al.  A Comparison of clinical target volumes determined by CT and MRI for the radiotherapy planning of base of skull meningiomas. , 2000, International journal of radiation oncology, biology, physics.

[18]  D. Dearnaley,et al.  Magnetic resonance imaging (MRI): considerations and applications in radiotherapy treatment planning. , 1997, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[19]  A G Visser,et al.  Accuracy in radiation field alignment in head and neck cancer: a prospective study. , 1988, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[20]  Anil Sethi,et al.  Influence of MRI on target volume delineation and IMRT planning in nasopharyngeal carcinoma. , 2003, International journal of radiation oncology, biology, physics.

[21]  R K Ten Haken,et al.  Design of MRI scan protocols for use in 3-D, CT-based treatment planning. , 1991, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.