Evaluation of Deformable Image Registration for Three-Dimensional Temporal Subtraction of Chest Computed Tomography Images

Purpose. To perform lung image registration for reducing misregistration artifacts on three-dimensional (3D) temporal subtraction of chest computed tomography (CT) images, in order to enhance temporal changes in lung lesions and evaluate these changes after deformable image registration (DIR). Methods. In 10 cases, mutual information (MI) lung mask affine mapping combined with cross-correlation (CC) lung diffeomorphic mapping was used to implement lung volume registration. With advanced normalization tools (ANTs), we used greedy symmetric normalization (greedy SyN) as a transformation model, which involved MI-CC-SyN implementation. The resulting displacement fields were applied to warp the previous (moving) image, which was subsequently subtracted from the current (fixed) image to obtain the lung subtraction image. Results. The average minimum and maximum log-Jacobians were 0.31 and 3.74, respectively. When considering 3D landmark distance, the root-mean-square error changed from an average of 20.82 mm for Pfixed to Pmoving to 0.5 mm for Pwarped to Pfixed. Clear shadows were observed as enhanced lung nodules and lesions in subtraction images. The lesion shadows showed lesion shrinkage changes over time. Lesion tissue morphology was maintained after DIR. Conclusions. DIR (greedy SyN) effectively and accurately enhanced temporal changes in chest CT images and decreased misregistration artifacts in temporal subtraction images

Study on Image Registration and Automatic Detection of Lung Nodule for Temporal Subtraction from Thoracic CT Images

九州工業大学博士学位論文 学位記番号：工博甲第502号 学位授与年月日：令和2年9月25日第1章 序論|第2章 GGVF集中度とシフトベクトルの平滑化によるレジストレーション手法|第3章 Feature-driven FFDを用いたレジストレーション法|第4章 特徴量と機械学習による結節状陰影の自動検出法|第5章 3D-CNNによる経時的差分像上の結節状陰影自動検出|第6章 残差機能を付加した3D-CNNによる経時的差分像上の結節状陰影検出|第7章 考察|第8章 結論九州工業大学令和2年