The aim is to produce and demonstrate proper functioning of software for automatic determination of directions for fibular repositioning with the ultimate goal of application to a robotic reduction procedure that can reduce the time and complexity of the procedure as well as provide the benefits of reduced error in ideal final fibular position, improved syndesmosis restoration and reduced incidence of post-traumatic osteoarthritis. The focus of this product will be developing and testing the image guidance software, from the input of preoperative images through the steps of automated segmentation and registration until the output of a final transformation that can be used as instructions to a robot on how to reposition the fibula, but will not involve developing or implementing the hardware of the robot itself. Background Ankle fractures occur with a frequency of around 174 cases per 100,000 adults per year, with over 5 million yearly cases in the U.S. alone (Goost et al), affecting mainly young active people and the elderly. Ankle fractures most commonly involve a fracture in the lower fibula which can also result in disruption of the syndesmosis, or the alignment of other bones and ligaments within the ankle joint, if the fibula is displaced. This is due to the displacement of the lower fibula causing damage and forceful shifting of these ligaments and other connective tissue.

Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning technology for image reconstruction,various deep learning methods have been also suggested for metal artifact removal, among which supervised learning methods are most popular. However, matched non-metal and metal image pairs are difficult to obtain in real CT acquisition. Recently, a promising unsupervised learning for MAR was proposed using feature disentanglement, but the resulting network architecture is complication and difficult to handle large size clinical images. To address this, here we propose a much simpler and much effective unsupervised MAR method for CT. The proposed method is based on a novel beta-cycleGAN architecture derived from the optimal transport theory for appropriate feature space disentanglement. Another important contribution is to show that attention mechanism is the key element to effectively remove the metal artifacts. Specifically, by adding the convolutional block attention module (CBAM) layers with a proper disentanglement parameter, experimental results confirm that we can get more improved MAR that preserves the detailed texture of the original image.

Segmentation is highly important both for examination and planning of knee replacement, hip replacement, shoulder surgery, lesion detection, osteotomy and many other orthopedic procedures. RSIP Vision's CTO Ilya Kovler explains how to improve the segmentation in orthopedics with deep learning. Deep learning is repeatedly being proven to be the most powerful framework for various tasks, and segmentation in orthopedics is no exception. Generic out-of-the-box solutions exist and can produce fair results, but carefully crafted and tailored solutions are needed to make the most out of a deep learning approach. Choosing the correct input, selecting the most suitable neural network architecture and incorporating task-specific prior knowledge into the model can all significantly improve the results.

--The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT images. We construct three-dimensional adversarial nets with a regularized loss function designed for metal artifacts from multiple dental fillings. The results of experiments using 915 CT volumes from real patients demonstrate that the proposed framework has an outstanding capacity to reduce strong artifacts and to recover underlying missing voxels, while preserving the anatomical features of soft tissues and tooth structures from the original images. EDICAL procedures such as diagnosis, surgical planning, and radiotherapy can be seriously degraded by the presence of metal artifacts in computed tomography (CT) imaging. Metal objects such as dental fillings, fixation devices, and other electric instruments implanted in patients' bodies inhibit X-ray propagation [1], preventing accurate calculation of the CT values during image reconstruction and yielding dark bands or streak artifacts in the CT images [2][3]. To correct the images, missing CT values for the underlying anatomical features must be compensated at the same time as the artifacts are removed. Although doctors make clinical efforts to manually collect such artifacts, this is a labor-intensive and time-consuming task. M. Nakao and T. Matsuda are with the Graduate School of Informatics, Kyoto University, Y oshida-Honmachi, Sakyo, Kyoto 606-8501, JAP AN; email: megumi@i.kyoto-u.ac.jp.

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners. The presence of hyper-dense materials in a scene, such as metals, can strongly attenuate X-rays, producing severe streaking artifacts in the reconstruction. These metal artifacts can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic non-adaptive interpolation-based projection data completion methods or direct image post-processing methods. These standard approaches have had limited success. Motivated primarily by security applications, we present a new deep-learning-based metal artifact reduction (MAR) approach that tackles the problem in the projection data domain. We treat the projection data corresponding to metal objects as missing data and train an adversarial deep network to complete the missing data in the projection domain. The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts. This new approach results in an end-to-end MAR algorithm that is computationally efficient so practical and fits well into existing CT workflows allowing easy adoption in existing scanners. Training deep networks can be challenging, and another contribution of our work is to demonstrate that training data generated using an accurate X-ray simulation can be used to successfully train the deep network when combined with transfer learning using limited real data sets. We demonstrate the effectiveness and potential of our algorithm on simulated and real examples.