Kento Morita

Computer-aided Surgical Planning of Anterior Cruciate Ligament Reconstruction in MR Images☆

Abstract Anterior cruciate ligament (ACL) injury causes knee joint instability, and effects on sports performance. Therefore, ACL reconstruction is essential to keep their high performance. It is well known that the outcome of ACL reconstruction is strongly related to the placement and orientation of the bone tunnel. Therefore, optimization of tunnel drilling technique is an important factor to obtain satisfactory surgical results. Current procedure relies on arthroscopic evaluation and there is a risk of damaging arteries and ligaments during surgery. The damages may reduce the accuracy and reproducibility of ACL reconstruction. As a postoperative evaluation method, a quadrant method has been used to evaluate the placement and orientation of the bone tunnel in X-ray radiography. This study proposes a computer-aided surgical planning system for evaluating ACL insertion site and orientation using magnetic resonance (MR) images. We first introduce MR image based the quadrant method to determine the ACL insertion site for preoperative patients. It also evaluates the 3-D spatial relationship between the planning femoral drilling hole and arteries around the femoral condyle. This system has been applied to ACL injured patients, it may increase the accuracy and reproducibility of ACL bone tunnel, and it can evaluate a risk of damaging the surrounding arteries and ligaments.

Neonatal brain MRI normalization with 3-D cerebral sulci registration

MR image registration (IR) has been used in brain function analysis, voxel-based-morphometry, etc. The conventional IR methods mainly use MR signal based likelihood. However, they cannot prevent miss registration of different gyri because they do not evaluate correspondence of sulci. Also, we cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal and sulcal width. This paper focuses on neonatal brain MR images, and introduces a new feature called sulcal-distribution index (SDI), which is calculated from MR signal around the cerebral surface. Next, this paper proposes a non-rigid 3-D IR method based on a flattening with SDI. The likelihood used is mutual information of SDI. The new method evaluates the correspondence of cerebral sulci. And, the method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the MR signal around the cerebral surface. Results in 3 neonates (modified age; 3-5 weeks) showed that the method registered one brain with the other brain successfully.

Particle filter based implanted knee kinematics analysis for the postoperative evaluation

Total knee arthroplasty (TKA) improves patients Quality of Life (QoL) whose knee has pain caused by aging and diseases. During the TKA surgery, the physician subjectively selects the size and type of the TKA prosthesis. The implanted knee kinematics in-vivo is essential for the evaluation of its function after the surgery. The 2-D/3-D still image registration based conventional methods do not consider the temporal continuity of the knee kinematics. This study proposes a kinematics analysis method for implanted knee using particle filter. Particle filter algorithm requires high computational cost for the accurate outcome. This paper proposes the new prediction model which evaluates the relative pose/position of the femoral and the tibial implants. The experimental results showed that the smooth estimation results were obtained with low computational time.

Clinical big image data based pre-operative planning in ACL reconstruction

In recent years, medical institutions have very big data including medical images. The big image data analysis using the collected medical images is effective to increase the accuracy and the reproducibility of the surgery. Anterior cruciate ligament (ACL) injury causes knee joint instability, and affects on sports performance. Therefore, ACL reconstruction surgery is essential to keep their performance high and to prevent osteoarthrosis. We have proposed a MR image based pre-operative planning system of ACL reconstruction. The system manually applies the Quadrant method to the synthesized pseudo radiograph. This paper proposes a fully automated pre-operative planning system based on the clinical big image data analysis. The experimental results showed that the proposed method successfully estimated the bone tunnel opening site to insert the ACL.

Blumensaat's line detection for Quadrant method on MR images

Anterior cruciate ligament (ACL) injury causes knee joint instability, and affects on sports performance. Therefore, ACL reconstruction is essential to keep their performance high and to prevent osteoarthrosis. It is well known that the outcome of ACL reconstruction is strongly related to the placement and orientation of the bone tunnel. 2-D X-ray radiograph and CT images have been used to evaluate the placement and orientation of the bone tunnel. Quadrant method evaluates the bone tunnel placement based on the Blumensaats line which has high intensity on 2-D X-ray lateral radiograph. There is problem of invasiveness using X-ray radiograph or CT image. Therefore, we have proposed an MR image based computer-aided surgical planning of ACL reconstruction. The system evaluates the bone tunnel placement and orientation based on Quadrant method. The remained problem of our system is Blumensaats line is manually determined. This paper proposes that a method to synthesize the pseudo lateral radiograph from MR images, and extract the Blumensaats line on the synthesized pseudo lateral radiograph. The experimental results showed that the proposed method successfully determined the Blumensaats line on the pseudo lateral radiograph.

ICP based neonatal brain MRI normalization method

Magnetic resonance (MR) images are widely used to diagnose cerebral diseases. The diseases may deform the brain shape, and the deformed region differs among types of diseases. To evaluate the brain shape deformation, MR image registration (IR) has been used. There are some IR methods for brain MR images but they mainly use MR signal based likelihood. We cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal distribution and brain shape. This paper focuses on neonatal brain MR images, and introduces a sulcus extraction method using Hessian matrix based on a feature called sulcal-distribution index (SDI). SDI is calculated from MR signal on the cerebral surface. Next, this paper proposes an iterative closest point (ICP) based brain shape registration method using the extracted sulci. The proposed method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the correspondence of cerebral sulci distribution. Results in seven neonates (modified age was between 3 weeks and 2 years) showed that the method registered one brain with the other brain successfully.