|
| |
|
|
MRI Data Processing Acceleration on GPU
Kešner, Filip ; Nečas, Ondřej (referee) ; Polok, Lukáš (advisor)
This BSc Thesis was performed during a study stay at the Universita della Svizzera italiana, Swiss. The identification of trajectories of neuron fibres within the human brain is of great importance in many medical applications as the neural diagnostics, neuronavigation, treatment of epilepsy, surgical removal of tumors and etc. By using diffusion MRI-data as input, and by employing Monte-Carlo like methods, possible trajectories are generated and the most likely ones can be visualized. These can serve as input for advanced medical diagnosis and treatments. Due to the huge amount of data to be analyzed and many iterations, this is a time consuming process. For the purposes such as statistical analysis and comparsion over several datasets or several patients, computational time requirements are enourmous. Faster diagnosis can improve routine throughput and provide earlier treatment of illness. At this time, there exists only a very few implementations of neural tractography sof tware. For probabilistic neural tractography is the list of software even thiner. Today's implementations using standard serial CPU execution suffer from high time consumption. The goal is to provide an efficient implementation which makes use of GPGPUs and exploits parallelism in the method. For the GPU implementation, a comparsion of CUDA and OpenCL technologies will be provided, using the more suitable one.
|
|
|
Tracking of Axonal Bundles in Diffusion MRI Brain Images
Piskořová, Z.
The aim of this work is to design tracking algorithm which will be able to track brain axonal bundles in diffusion weighted MRI data. Estimation of anisotropic diffusion profile inside voxels was performed by diffusion tensor imaging model (DTI). Tracing is based on the 4th order Runge-Kutta method. Algorithm is implemented in the MATLAB computing environment and is tested on real data biological phantom.
|
|
|
Simulation of synthetic diffusion tensor data
Labudová, K.
First there is the Gaussian diffusion tensor model with the most important equations introduced. The other part of this work deals with the software and its current state of development. The software calculates diffusion tensor data from diffusion tensor and acquisition parameters successfully. User can add noise to simulated data and then estimate diffusion tensor in reverse. Original and estimated diffusion tensors are displayed, compared and their deviation in space determined.
|
guest ::
