Takao Masaki, Sugano Nobuhiko, Nishii Takashi, Tanaka Hisahi, Masumoto Jun, Miki Hidenobu, Sato Yoshinobu, Tamura Shinichi, Yoshikawa Hideki
Department of Orthopaedic Surgery, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan.
Magn Reson Imaging. 2005 Jun;23(5):665-70. doi: 10.1016/j.mri.2005.02.002.
The purpose of this study was to estimate the accuracy of a method in which three-dimensional (3D) magnetic resonance (MR) volume registration is used for monitoring hip joint disease. Data were analyzed using a normalized cross-correlation (NCC) algorithm involving a user-selected 3D box including the proximal femur. Most of the femoral head was not included in the 3D box because it can become deformed during the course of disease. The accuracy of registration around the femoral head was evaluated using five phantoms and clinical MR data of 17 patients with hip joint disease. In the phantom experiment, registration accuracy was evaluated using four fiducial markers attached to the femoral head. In the experiment using clinical data, registration accuracy was evaluated using a landmark in the femoral head. The registration accuracy in the phantom and clinical experiment was 0.43+/-0.18 mm (S.D.) and 1.12+/-0.46 mm (S.D.), respectively. The former is a value less than half the minimum dimension of a voxel (1.25 x 1.25 x 1.0 mm). Although the latter is slightly larger than the minimum dimension of a voxel, actual errors would be smaller because of the uncertainty in landmark localization. In conclusion, the present method based on an NCC algorithm can be used to accurately register serial MR images of the femoral heads with an error on the order of a voxel. We believe that this method is sufficiently accurate for monitoring hip joint diseases.
本研究的目的是评估一种使用三维（3D）磁共振（MR）体积配准来监测髋关节疾病的方法的准确性。使用归一化互相关（NCC）算法对数据进行分析，该算法涉及一个用户选择的包含股骨近端的3D框。股骨头的大部分未包含在3D框内，因为在疾病过程中它可能会变形。使用五个模型以及17例髋关节疾病患者的临床MR数据评估股骨头周围的配准准确性。在模型实验中，使用附着在股骨头上的四个基准标记评估配准准确性。在使用临床数据的实验中，使用股骨头内的一个标志点评估配准准确性。模型实验和临床实验中的配准准确性分别为0.43±0.18 mm（标准差）和1.12±0.46 mm（标准差）。前者的值小于体素最小尺寸（1.25×1.25×1.0 mm）的一半。尽管后者略大于体素的最小尺寸，但由于标志点定位的不确定性，实际误差会更小。总之，基于NCC算法的本方法可用于准确配准股骨头的系列MR图像，误差在体素量级。我们认为该方法对于监测髋关节疾病具有足够的准确性。
