Nobuhiko Sugano

Development of a computer-integrated femoral head fracture reduction system

The exertion and radiation exposure of a doctor both increase with the number of femur fracture patients treated. The authors have developed a robotic system to assist in the operation, lessen these problems, and improve the efficacy of repair. The robot has 6 degrees of freedom, with a 6-axis force sensor installed to enable a power assist capability for the surgeon and to measure the force applied to the patients femur. The following three operation modes are provided: JOG mode using a teaching pendant, a power assisted mode and an automatic operation mode. Experiments were successfully performed to evaluate the capabilities of the developed system. The basic data needed to apply the system in clinical use were obtained.

Patient-Specific Skeletal Muscle Fiber Modeling from Structure Tensor Field of Clinical CT Images

We propose an optimization method for estimating patient-specific muscle fiber arrangement from clinical CT. Our approach first computes the structure tensor field to estimate local orientation, then a geometric template representing fiber arrangement is fitted using a B-spline deformation by maximizing fitness of the local orientation using a smoothness penalty. The initialization is computed with a previously proposed algorithm that takes account of only the muscle’s surface shape. Evaluation was performed using a CT volume (1.0 mm(^text {3})/voxel) and high resolution optical images of a serial cryo-section (0.1 mm(^text {3})/voxel). The mean fiber distance error at the initialization of 6.00 mm was decreased to 2.78 mm after the proposed optimization for the gluteus maximus muscle, and from 5.28 mm to 3.09 mm for the gluteus medius muscle. The result from 20 patient CT images suggested that the proposed algorithm reconstructed an anatomically more plausible fiber arrangement than the previous method.

Registration-Based Patient-Specific Musculoskeletal Modeling Using High Fidelity Cadaveric Template Model

We propose a method to construct patient-specific musculoskeletal model using a template obtained from a high fidelity cadaver images. Musculoskeletal simulation has been traditionally performed using a string-type muscle model that represent the line-of-forces of a muscle with strings, while recent studies found that a more detailed model that represents muscle’s 3D shape and internal fiber arrangement would provide better simulation accuracy when sufficient computational resources are available. Thus, we aim at reconstructing patient-specific muscle fiber arrangement from clinically available modalities such as CT or (non-diffusion) MRI. Our approach follows a conventional biomedical modeling approach which first constructs a highly accurate generic template model which is then registered using the patient-specific measurement. Our template is created from a high-resolution cryosectioned volume and newly proposed registration method aligns the surface of bones and muscles as well as the local orientation inside the muscle (i.e., muscle fiber direction). The evaluation was performed using cryosectioned volumes of two cadavers, one of which accompanies images obtained from clinical CT and MRI. Quantitative evaluation demonstrated that the mean fiber distance error between the one estimated from CT and the ground truth was 4.16, 3.76, and 2.45 mm for the gluteus maximus, medius, and minimus muscles, respectively. The qualitative visual assessment on 20 clinical CT images suggested plausible fiber arrangements that would be able to be translated to biomechanical simulation.

Clinical Application of Navigation in the Surgical Treatment of a Pelvic Ring Injury and Acetabular Fracture

The purpose of this chapter is to review current evidence on indications, techniques, and outcomes of computer-navigated surgical treatment of pelvic ring injuries and acetabular fractures, particularly computer-navigated screw fixation.Iliosacral screw fixation of pelvic ring injury using navigation is attracting attention because the biomechanical stabilization of posterior pelvic ring disruption is of primary importance and is widely indicated because it does not require complete reduction of the fracture site. A cadaver study with a simulated zone II sacral fracture demonstrated a substantial compromise in the space available for iliosacral screws with displacements greater than 10 mm. It is possible to reduce the fracture fragment prior to intraoperative imaging in 2D or 3D fluoroscopic navigation. The use of 3D fluoroscopic navigation reportedly results in lower rates of iliosacral screw malpositioning than the use of the conventional technique or 2D fluoroscopic navigation. Moreover, compared with the conventional technique, it reduces radiation exposure and lowers revision rates. However, the malposition rate associated with 3D fluoroscopic navigation ranges from 0% to 31%, demonstrating that there is still room to improve the navigation performance.Conversely, complete articular surface reduction is required when treating a displaced acetabular fracture to prevent residual hip pain and subsequent osteoarthritic changes. Treating a severely displaced acetabular fracture by screw fixation is very challenging, even with the use of 3D fluoroscopic navigation, because of the difficulty in performing closed anatomical reduction. The indication for percutaneous screw fixation is limited to cases with a small articular displacement. Using 3D fluoroscopic navigation for open surgeries reportedly improves the quality of radiographic fracture reduction, limits the need for an extended approach, and lowers the complication rate.In conclusion, percutaneous screw fixation for pelvic ring injuries is widely indicated, and navigation makes these procedures safe and reliable. The indication for percutaneous screw fixation of acetabular fractures is limited to cases with a small articular displacement. Using 3D fluoroscopic navigation when performing open surgeries is reported to be useful in evaluating fracture reduction and screw position.

Reconstruction of micro CT-like images from clinical CT images using machine learning: a preliminary study

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.