scholarly journals Development of an Evaluation System for Magnetic Resonance Imaging Based Three-Dimensional Modeling of a Transfemoral Prosthetic Socket Using Finite Elements

2019 ◽  
Vol 9 (18) ◽  
pp. 3662
Author(s):  
Mohd Syahmi Jamaludin ◽  
Akihiko Hanafusa ◽  
Yamamoto Shinichirou ◽  
Yukio Agarie ◽  
Hiroshi Otsuka ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Soft Tissue ◽  
Computer Aided Design ◽  
Evaluation System ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Tissue Deformation ◽  
Soft Tissue Deformation ◽  
Prosthetic Socket

Recent technologies have suggested the utilization of three-dimensional (3D) printing technology to enhance the fabrication accuracy of prosthetics. Accordingly, simulations are used to obtain precise parameters for subject-specified prosthetic socket. This study proposes an evaluation system to measure the accuracy of a subject-specific 3D transfemoral residuum model during the interaction with the socket in conjunction with the application of finite element methods. The proposed system can be used in future validations of socket fabrication. The evaluation is based on the measurement of the residuum’s soft tissue deformation inside two types of prosthetic sockets. In comparison with other studies, the 3D models were constructed with magnetic resonance images (MRI) with the aid of computer-aided design (CAD) software. The measurement of soft tissue deformation was conducted based on the measurement of the volumetric value of fat, muscle and skin in the pre- and post-donning phases. The result yielded a promising correlation coefficient value between the simulation and the experiment in the soft tissue deformation evaluation. The relation of the muscle–fat ratio in the residuum is extremely important in the determination of the ability of the prosthetic to deform. The environment during the socket fitting session was similar to that defined by the set boundary conditions in simulations. In view of the promising results of this study, the evaluation system proposed herein is considered reliable and is envisaged to be used in future research.

Simulation for Needle Deflection and Soft Tissue Deformation in Needle Insertion

2010 ◽  
Vol 139-141 ◽  
pp. 889-892
Author(s):  
De Dong Gao ◽  
Hao Jun Zheng
Keyword(s):  
Finite Element ◽  
Soft Tissue ◽  
Three Dimensional ◽  
Needle Insertion ◽  
Tissue Deformation ◽  
Spring Model ◽  
Ansys Software ◽  
Soft Tissue Deformation ◽  
Needle Deflection ◽  
Element Method

Needle deflection and soft tissue deformation are the most important factors that affect accuracy in needle insertion. Based on the quasi-static thinking and needle forces, an improved virtual spring model and a finite element method are presented to analyze needle deflection and soft tissue deformation when a needle is inserted into soft tissue. According to the spring model, the trajectory of the needle tip is calculated with MATLAB using different parameters. With the superposed element method, the two and three dimensional quasi-static finite element models are created to simulate the dynamic process of soft tissue deformation using ANSYS software. The two methods will be available for steering the flexible needle to hit the target and avoid the obstacles precisely in the robot-assisted needle insertion.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Tensegric-Modeling-Based Soft Tissue Deformation for Shaping-Undergarments

2006 ◽  
Vol 3 (1-4) ◽  
pp. 359-366 ◽  
Author(s):  
Kazuya G. Kobayashi ◽  
Taro Ichizawa ◽  
Katsutoshi Ootsubo
Keyword(s):  
Soft Tissue ◽  
Tissue Deformation ◽  
Soft Tissue Deformation

The Safety of Operating on Breasts With a History of Prior Reduction Mammoplasty: Dynamic Magnetic Resonance Imaging Analysis of Angiogenesis

2021 ◽  
Author(s):  
Joseph Kyu-hyung Park ◽  
Seokwon Park ◽  
Chan Yeong Heo ◽  
Jae Hoon Jeong ◽  
Bola Yun ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Three Dimensional ◽  
Venous Drainage ◽  
Reduction Mammoplasty ◽  
Magnetic Resonance Images ◽  
Areolar Complex ◽  
Internal Mammary ◽  
Initial Procedure ◽  
Nipple Sparing Mastectomy ◽  
Nipple Sparing

Abstract Background Vascularity of the nipple-areolar complex (NAC) is altered after reduction mammoplasty, which increases complications risks after repeat reduction or nipple-sparing mastectomy. Objectives To evaluate angiogenesis of the NAC via serial analysis of breast magnetic resonance images (MRIs). Methods Breast MRIs after reduction mammoplasty were analyzed for 35 patients (39 breasts) using three-dimensional reconstructions of maximal intensity projection images. All veins terminating at the NAC were classified as internal mammary, anterior intercostal, or lateral thoracic in origin. The vein with the largest diameter was considered the dominant vein. Images were classified based on the time since reduction: <6 months, 6-12 months, 12-24 months, >2 years. Results The average number of veins increased over time: 1.17 (<6 months), 1.56 (6–12 months), 1.64 (12–24 months), 1.73 (>2 years). Within 6 months, the pedicle was the only vein. Veins from other sources began to appear at 6–12 months. In most patients, at least two veins were available after 1 year. After 1 year, the internal mammary vein was the most common dominant vein regardless of the pedicle used. Conclusions In the initial 6 months after reduction mammoplasty, the pedicle is the only source of venous drainage; however, additional sources are available after 1 year. The internal thoracic vein was the dominant in most patients. Thus, repeat reduction mammoplasty or nipple-sparing mastectomy should be performed ≥1 year following the initial procedure. After 1 year, the superior or superomedial pedicle may represent the safest option when the previous pedicle is unknown.

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