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.
Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of cardiovascular diseases but also for prediction of cardiac future events. Current imaging modalities has limitations that could degrade the accuracy of the analysis indices. In this paper, we present a projection-based motion estimation method for x-ray CT that estimates cardiac motion with high spatio-temporal resolution using projection data and a reference 3D volume image. The experiment using a synthesized digital phantom showed promising results for motion analysis.
We have developed a digitally synthesized patient which we call “Zach” (Zero millisecond Adjustable
Clinical Heart) phantom, which allows for an access to the ground truth and assessment of image-based
cardiac functional analysis (CFA) using CT images with clinically realistic settings. The study using Zach
phantom revealed a major problem with image-based CFA: "False dyssynchrony." Even though the true
motion of wall segments is in synchrony, it may appear to be dyssynchrony with the reconstructed cardiac
CT images. It is attributed to how cardiac images are reconstructed and how wall locations are updated
over cardiac phases. The presence and the degree of false dyssynchrony may vary from scan-to-scan,
which could degrade the accuracy and the repeatability (or precision) of image-based CT-CFA exams.
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