Statement Of Work
Introduction:
Disease, we have all heard this term before, and it is mostly associated with the feelings of depression, nervousness, and even immense sadness. The list can go on about the feelings that are associated with the word disease, but what if there was a way to better the chance of preventing certain diseases. For example using computational models can become a turning point in how medicine is practiced and how the process of models can address different diseases. These techniques can shape the future of modern medicine.
Objective:
There is only so much that can be done when someone is diagnosed with a disease. The modern practice of medicine is doing its best to treat and cure those patients associated with it. Research in computational modeling can have breakthroughs with how medicine is practiced. Mathematical models using Non-Uniform Rational B-Splines (NURBS) can change the way we treat these diseases and can potentially affect millions of lives. In order to create these models an artery segment is idealized in order to create a generalized mathematical basis. By gathering medical images we can generate stacks of cross sectional images and pull out areas of interest to create a computational model. Then by using a skeletonization process we can process a center line creating arterial path lines that are used to build these solid fluid models. As said previously, a solid NURBS mesh can be constructed employing isogeometric analysis (IGA) which constitutes the fluid structure of the computational model. This can lead to huge breakthroughs in the way modern medicine is practiced today.
What is being done:
We are building an infrastructure, specifically a modeling pipeline to make the above process streamlined. As detailed previously, there are computational models out there that can simulate the way an idealized structure behaves. We are creating patient-specific models and this begins by obtaining information from medical images. These imaging devices can generate stacks of cross sectional images in which biological substances are represented in pixels as grey scale intensities at each slice. From this we can cut out certain areas that we want to obtain and use a process that builds a three dimensional solid fluid computational model. Through the computational model we can address important design questions such as, the optimum delivery location, drug release rate, and nanoparticle surface characteristics, thereby changing the way modern practices are used.
How will it be done:
0. C++ Code - Input Parameters
1. MeshFix Software - Meshlab
2. YouTube References (https://www.youtube.com/watch?v=wHQY0o8RdS4)
3. Programming languages C/C++
4. Medical Imaging Processing - ITK-SNAP software
5. Image Stack - DICOM
6. Skeletalization Software - Starlab-Mcfskel
Assumptions associated with each task:
With each task in front, we will encounter failure, but this will lead us closer to success.
0. NURBS elements will help us create a continuous geometric elements for each of our models.
1. Visual Studio will help debug and build code for the Windows environment.
2. MeshLab and MeshViewer can enhance the way we see the model.
3. YouTube references can help us better understand what we need to do.
4. The programming languages will help us create simpler code to create for each model.
Deliverables that will be handed off to the client for review and approval:
0. Well handed document explaining what has been accomplished
1. Presentation
2. Poster
Schedule:
2014.06.09 – 2014.09.01
Additional or Key Assumptions:
0. This research is at the forefront of the field’s technology, so every successful task can lead to major benefits in the field of medicine. A potential breakthrough could lead to significant changes in how medicine is eventually practiced.
1. More discoveries can come out of this research as we find more from what we are specifically investigating.
Introduction:
Disease, we have all heard this term before, and it is mostly associated with the feelings of depression, nervousness, and even immense sadness. The list can go on about the feelings that are associated with the word disease, but what if there was a way to better the chance of preventing certain diseases. For example using computational models can become a turning point in how medicine is practiced and how the process of models can address different diseases. These techniques can shape the future of modern medicine.
Objective:
There is only so much that can be done when someone is diagnosed with a disease. The modern practice of medicine is doing its best to treat and cure those patients associated with it. Research in computational modeling can have breakthroughs with how medicine is practiced. Mathematical models using Non-Uniform Rational B-Splines (NURBS) can change the way we treat these diseases and can potentially affect millions of lives. In order to create these models an artery segment is idealized in order to create a generalized mathematical basis. By gathering medical images we can generate stacks of cross sectional images and pull out areas of interest to create a computational model. Then by using a skeletonization process we can process a center line creating arterial path lines that are used to build these solid fluid models. As said previously, a solid NURBS mesh can be constructed employing isogeometric analysis (IGA) which constitutes the fluid structure of the computational model. This can lead to huge breakthroughs in the way modern medicine is practiced today.
What is being done:
We are building an infrastructure, specifically a modeling pipeline to make the above process streamlined. As detailed previously, there are computational models out there that can simulate the way an idealized structure behaves. We are creating patient-specific models and this begins by obtaining information from medical images. These imaging devices can generate stacks of cross sectional images in which biological substances are represented in pixels as grey scale intensities at each slice. From this we can cut out certain areas that we want to obtain and use a process that builds a three dimensional solid fluid computational model. Through the computational model we can address important design questions such as, the optimum delivery location, drug release rate, and nanoparticle surface characteristics, thereby changing the way modern practices are used.
How will it be done:
0. C++ Code - Input Parameters
1. MeshFix Software - Meshlab
2. YouTube References (https://www.youtube.com/watch?v=wHQY0o8RdS4)
3. Programming languages C/C++
4. Medical Imaging Processing - ITK-SNAP software
5. Image Stack - DICOM
6. Skeletalization Software - Starlab-Mcfskel
Assumptions associated with each task:
With each task in front, we will encounter failure, but this will lead us closer to success.
0. NURBS elements will help us create a continuous geometric elements for each of our models.
1. Visual Studio will help debug and build code for the Windows environment.
2. MeshLab and MeshViewer can enhance the way we see the model.
3. YouTube references can help us better understand what we need to do.
4. The programming languages will help us create simpler code to create for each model.
Deliverables that will be handed off to the client for review and approval:
0. Well handed document explaining what has been accomplished
1. Presentation
2. Poster
Schedule:
2014.06.09 – 2014.09.01
Additional or Key Assumptions:
0. This research is at the forefront of the field’s technology, so every successful task can lead to major benefits in the field of medicine. A potential breakthrough could lead to significant changes in how medicine is eventually practiced.
1. More discoveries can come out of this research as we find more from what we are specifically investigating.