In total hip arthroplasty, analysis of postoperative images is important to evaluate surgical outcome. Since CT is most prevalent modality in orthopedic surgery, we aimed at the analysis of CT image. The challenge in this work is the metal artifact in postoperative CT caused by the metallic implant, which reduces the accuracy of segmentation especially in the vicinity of the implant. Our goal was to develop an automated segmentation method of the muscles in the postoperative CT images. In this paper, we propose a method that combines Normalized Metal Artifact Reduction (NMAR), which is one of the state-of-the-art metal artifact reduction methods, and a CNN- based segmentation using the U-Net architecture. We conducted experiments using simulated images and real images of the lower extremity to evaluate the segmentation accuracy of 19 muscles that are contaminated with metallic artifact. The training dataset we used is 20 CTs that were manually traced by an expert surgeon. In simulation study, the proposed method improved the average symmetric surface distance (ASD) from 1.85 ± 1.63 mm to 1.24 ± 0.67 mm (mean ± std). The real image study using two CTs with the ground truth of gluteus maximus, medius and minimus muscles showed the reduction of ASD from 1.67 ± 0.40 mm to 1.52 ± 0.47 mm. Our future work includes the end-to-end convolutional neural network for metal artifact reduction and musculoskeltal segmentation and to establish a ground truth dataset by performing non-rigid registration between the postoperative and preoperative CT of the same patient.
High-resolution medical images are crucial for medical diagnosis, and for planning and assisting surgery. Micro computed tomography (micro CT) can generate high-resolution 3D images and analyze internal micro-structures. However, micro CT scanners can only scan small objects and cannot be used for in-vivo clinical imaging and diagnosis. In this paper, we propose a super-resolution method to reconstruct micro CT-like images from clinical CT images based on learning a mapping function or relationship between the micro CT and clinical CT. The proposed method consists of following three steps: (1) Pre-processing: This involves the collection of pairs of clinical CT images and micro CT images for training and the registration and normalization of each pair. (2) Training: This involves learning a non-linear mapping function between the micro CT and clinical CT by using training pairs. (3) Processing (testing) step: This involves enhancing a new CT image, which is not included in the training data set, by using the learned mapping function.