Fig 9 - uploaded by Derek Gould
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Programmed diaphragmatic behavior: the central tendon of the diaphragm, presented here in white, is considered to have a stiff translation in the longitudinal plane. In comparison, the muscular portion (red), with the exception of regions directly attached to the spine, passively deform in response to tendon displacement. The initial and final positions of the diaphragm are depicted above by opaque and transparent boundaries respectively.
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The use of simulation to train skills has grown exponentially in the last few decades, especially in medicine, where simulator models can provide a platform where trainees can practice procedures without risk or harm to patients. Potential advantages of computer based simulator models over other forms of medical simulators include: (i) anatomical
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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and n...
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... We used a 3D CT scan image (resolution: 0.927 × 0.927 × 0.3 mm 3 ) that 96 was acquired for medical reasons, see our previous work [5]. The diaphragm was manually 97 segmented in 3D following visual cues as explained in [17]. The labeled voxels were exported 98 to a triangle mesh with the Marching Cube algorithm and the mesh was simplified with a 99 decimation algorithm [18]. ...
The thoracic diaphragm is the muscle that drives the respiratory cycle of a human being. Using a system of partial differential equations (PDEs) that models linear elasticity we compute displacements and stresses in a two-dimensional cross section of the diaphragm in its contracted state. The boundary data consists of a mix of displacement and traction conditions. If these are imposed as they are, and the conditions are not compatible, this leads to reduced smoothness of the solution. Therefore, the boundary data is first smoothed using the least-squares radial basis function generated finite difference (RBF-FD) framework. Then the boundary conditions are reformulated as a Robin boundary condition with smooth coefficients. The same framework is also used to approximate the boundary curve of the diaphragm cross section based on data obtained from a slice of a computed tomography (CT) scan. To solve the PDE we employ the unfitted least-squares RBF-FD method. This makes it easier to handle the geometry of the diaphragm, which is thin and non-convex. We show numerically that our solution converges with high-order towards a finite element solution evaluated on a fine grid. Through this simplified numerical model we also gain an insight into the challenges associated with the diaphragm geometry and the boundary conditions before approaching a more complex three-dimensional model.
... We used a 3D CT scan image (resolution: 0.927 × 0.927 × 0.3 mm 3 ) that was acquired for medical reasons, see our previous work [5]. The diaphragm was manually segmented in 3D following visual cues as explained in [17]. The labeled voxels were exported to a triangle mesh with the Marching Cube algorithm and the mesh was simplified with a decimation algorithm [18]. ...
The thoracic diaphragm is the muscle that drives the respiratory cycle of a human being. Using a system of partial differential equations (PDEs) that models linear elasticity we compute displacements and stresses in a two-dimensional cross section of the diaphragm in its contracted state. The boundary data consists of a mix of displacement and traction conditions. If these are imposed as they are, and the conditions are not compatible, this leads to reduced smoothness of the solution. Therefore, the boundary data is first smoothed using the least-squares radial basis function generated finite difference (RBF-FD) framework. Then the boundary conditions are reformulated as a Robin boundary condition with smooth coefficients. The same framework is also used to approximate the boundary curve of the diaphragm cross section based on data obtained from a slice of a computed tomography (CT) scan. To solve the PDE we employ the unfitted least-squares RBF-FD method. This makes it easier to handle the geometry of the diaphragm, which is thin and non-convex. We show numerically that our solution converges with high-order towards a finite element solution evaluated on a fine grid. Through this simplified numerical model we also gain an insight into the challenges associated with the diaphragm geometry and the boundary conditions before approaching a more complex three-dimensional model.
... Automated segmentation methods are currently not able to identify the diaphragm that is barely visible in the images. Therefore, the diaphragm was manually segmented on a Wacom tablet using a method similar to the description in [14]. The segmentation time is roughly six hours for one 3-D image. ...
... Automated segmentation methods are currently not able to identify the diaphragm that is barely visible in the images. Therefore, the diaphragm was manually segmented on a Wacom tablet using a method similar to the description in [14]. The segmentation time is roughly 6 h for one 3-D image. ...
The diaphragm is the main muscle that regulates the human respiration. When a patient is put under controlled mechanical ventilation, the diaphragm is exposed to forces that damage the muscle function. The long-term aim of this work is to study this process through numerical simulation. Here, we take the first steps in developing a meshless numerical simulation method for the diaphragm. We describe how the diaphragm geometry can be extracted from medical images, and then be used in the meshless method. We show that for a thin volume like the diaphragm, the resolution of the thin dimension is highly relevant for the accuracy of the approximation, and we also show that the method converges for a test case, where realistic displacements are used as boundary conditions.
... The group modelled respiratory diaphragm motion for integration into surgical training and planning simulators. Villard (Villard et al., 2009) went a step further, including rib cage respiratory movement, soft tissue behaviour, and a collection of virtual patients and their organs, segmented from CT scans of actual patients in their liver biopsy simulator. These forward strides have necessitated parallel advances in the area of organ modelling. ...
Tactile perception plays an important role in medical simulation and training, specifically in surgery. The surgeon must feel organic tissue hardness, evaluate anatomical structures, measure tissue properties, and apply appropriate force control actions for safe tissue manipulation. Development of novel cost effective haptic-based simulators and their introduction in the minimally invasive surgery learning cycle can absorb the learning curve for residents. Receiving pre-training in a core set of surgical skills can reduce skill acquisition time and risks. We present the development of a cost-effective visuo-haptic simulator for the liver tissue, designed to improve practice-based education in minimally invasive surgery. Such systems can positively affect the next generations of learners by enhancing their knowledge in connection with real-life situations while they train in mandatory safe conditions.
... Ribs and spines are also manually segmented in order to properly separate the different ribs. The rotation centers are computed using the inertia matrix as described in [42]. ...
We present a framework that combines evolutionary optimisation, soft tissue modelling and ray tracing on GPU to simultaneously compute the respiratory motion and X-ray imaging in real-time. Our aim is to provide validated building blocks with high fidelity to closely match both the human physiology and the physics of X-rays. A CPU-based set of algorithms is presented to model organ behaviours during respiration. Soft tissue deformation is computed with an extension of the Chain Mail method. Rigid elements move according to kinematic laws. A GPU-based surface rendering method is proposed to compute the X-ray image using the Beer-Lambert law. It is provided as an open-source library. A quantitative validation study is provided to objectively assess the accuracy of both components: i) the respiration against anatomical data, and ii) the X-ray against the Beer-Lambert law and the results of Monte Carlo simulations. Our implementation can be used in various applications, such as interactive medical virtual environment to train percutaneous transhepatic cholangiography in interventional radiology, 2D/3D registration, computation of digitally reconstructed radiograph, simulation of 4D sinograms to test tomography reconstruction tools.
... In [1], [11], visuo-haptic simulation of respiratory motion of a virtual patient model was presented for liver biopsy and methods for parameter estimation for this simulation are given in [12], [13]. Another visuo-haptic simulation approach is proposed in [2], where the motion of the liver was integrated in an ultrasound simulation. ...
... m =b + Bẑ(t) (11) for which B can be estimated by multivariate regression. With given B, Eq. 11 can be rewritten as the linear combination of three column vectors a 1. ...
This article presents methods for direct visuo-haptic 4D volume rendering of virtual patient models under respiratory motion. Breathing models are computed based on patient-specific 4D CT image data sequences. Virtual patient models are visualized in real-time by ray casting based rendering of a reference CT image warped by a time-variant displacement field, which is computed using the motion models at run-time. Furthermore, haptic interaction with the animated virtual patient models is provided by using the displacements computed at high rendering rates to translate the position of the haptic device into the space of the reference CT image. This concept is applied to virtual palpation and the haptic simulation of insertion of a virtual bendable needle. To this aim, different motion models that are applicable in real-time are presented and the methods are integrated into a needle puncture training simulation framework, which can be used for simulated biopsy or vessel puncture in the liver. To confirm real-time applicability, a performance analysis of the resulting framework is given. It is shown that the presented methods achieve mean update rates around 2000 Hz for haptic simulation and interactive frame rates for volume rendering and thus are well suited for visuo-haptic rendering of virtual patients under respiratory motion.
... Simulation is useful in planning and education of complicated 3D needle biopsy process (Ra et al., 2002). In ultrasound-‐controlled biopsy for liver, simulator based on virtual reality provides a feedback related with a realistic vision and contact (Ni et al., 2011;
Villard
et
al.,
2011). These
simulations
plays
an important role in obtaining 3D images necessary in making practice in the order that should also be followed in biopsy of prostate (Deguchi et al., 2006). ...
Together with developing computer technology, a trail is also blazed in medical education. Virtual model formation by three-‐dimensional imaging and reconstruction is a technology used especially in anatomy education as well as surgery, pathology, biopsy, forensic medicine, sports medicine and plastic reconstruction. These models, as used in anatomy education of human and veterinary medicine, became a more attractive material for students by decreasing the number of cadavers. The models obtained are used in planning of surgery and biopsy, comprehension of pathology, in biopsy education and in measuring all organs and structures with high accuracy as a result of autopsy in forensic medicine. Moreover, several experiments and observations performed on virtual models in sports medicine are used in prevention of disablements, in the determination of deformations in structures and in revealing possible results of postoperative period in plastic reconstruction. Thus, virtual anatomic models benefited in education, diagnosis and treatment period in medical field will be more developed and commonly used in the future.
... The Vs1 represented the TSH level of Human Body. If the output of U3 and U4 were both high (9.984nV) then the TSH level (> 3.0 uIU/mL) was high [16]. If the outputs of both OPAmps were low (4.898V) then the TSH level (<0.3uIU/mL) was low. ...
... The Vs1 represents the TSH level of Human Body. If the output of U3 and U4 are both high (9.984nV) then the TSH level (> 3.0 uIU/mL) is high [21]. If the outputs of both OPAmps are low (4.898V) then the TSH level (<0.3uIU/mL) is low. ...
