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Diffusion Tensor Imaging in the Cervical Spinal Cord: Implementing methods for acquisition, processing and evaluation

Szczepankiewicz, Filip (2011)
Medical Physics Programme
Abstract (Swedish)
Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging technique that is sensitive to the diffusion movement of water contained in tissue. It is used to quantify parameters such as the diffusivity and its directionality. Because diffusion is intimately connected to the tissue microarchitecture DTI is one of the few techniques that can quantify tissue microstructure non-invasively in vivo. Further, it has been established that DTI parameters such as mean diffusivity and fractional anisotropy are sensitive to the influence of a variety of diseases, among them are neurodegenerative diseases such as multiple sclerosis. Although still in its cradle several papers have reported successful implementation of DTI in the spinal... (More)
Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging technique that is sensitive to the diffusion movement of water contained in tissue. It is used to quantify parameters such as the diffusivity and its directionality. Because diffusion is intimately connected to the tissue microarchitecture DTI is one of the few techniques that can quantify tissue microstructure non-invasively in vivo. Further, it has been established that DTI parameters such as mean diffusivity and fractional anisotropy are sensitive to the influence of a variety of diseases, among them are neurodegenerative diseases such as multiple sclerosis. Although still in its cradle several papers have reported successful implementation of DTI in the spinal cord, noting that there are still challenges to be solved before it can be clinically implemented.

The aim of this work was to create, optimize and implement a DTI protocol of the cervical spine in a clinical setting and to evaluate its potential as a complement to current conventional diagnostics.

The main work was performed using the Siemens Skyra (3T) located at the Lund University Hospital. The imaging protocol was based on similar protocols used in DTI of the brain although several modifications were implemented to adapt the acquisition to the anatomically complicated region of the spine. The protocol was designed to include three imaging series; the main DTI series; a turbo spin echo (TSE) reference series for distortion correction purposes; and a high resolution T2-weighted series for conventional diagnostics. An automated tractography based segmentation method was devised to facilitate fast and reproducible evaluation of the DTI parameters.

The method was tested by means of validation and an in vivo pilot study. Validation was performed to gauge system performance. The pilot study was performed as a proof of concept for the acquisition, processing and evaluation methods.

Results show that using DTI parameters for individual diagnostics is currently limited by the large inter and intra-subject variation, however the in vivo pilot study proved the suggested methods to be viable in a clinical setting; adding only 5 minutes to the standard scan time and allowing for quick and highly automated evaluation of data which is suitable for large group comparisons. (Less)
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author
Szczepankiewicz, Filip
supervisor
organization
year
type
H2 - Master's Degree (Two Years)
subject
keywords
MRI
language
English
id
2273095
date added to LUP
2012-01-02 15:15:24
date last changed
2013-10-25 11:37:15
@misc{2273095,
  abstract     = {Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging technique that is sensitive to the diffusion movement of water contained in tissue. It is used to quantify parameters such as the diffusivity and its directionality. Because diffusion is intimately connected to the tissue microarchitecture DTI is one of the few techniques that can quantify tissue microstructure non-invasively in vivo. Further, it has been established that DTI parameters such as mean diffusivity and fractional anisotropy are sensitive to the influence of a variety of diseases, among them are neurodegenerative diseases such as multiple sclerosis. Although still in its cradle several papers have reported successful implementation of DTI in the spinal cord, noting that there are still challenges to be solved before it can be clinically implemented. 

The aim of this work was to create, optimize and implement a DTI protocol of the cervical spine in a clinical setting and to evaluate its potential as a complement to current conventional diagnostics.

The main work was performed using the Siemens Skyra (3T) located at the Lund University Hospital. The imaging protocol was based on similar protocols used in DTI of the brain although several modifications were implemented to adapt the acquisition to the anatomically complicated region of the spine. The protocol was designed to include three imaging series; the main DTI series; a turbo spin echo (TSE) reference series for distortion correction purposes; and a high resolution T2-weighted series for conventional diagnostics. An automated tractography based segmentation method was devised to facilitate fast and reproducible evaluation of the DTI parameters.

The method was tested by means of validation and an in vivo pilot study. Validation was performed to gauge system performance. The pilot study was performed as a proof of concept for the acquisition, processing and evaluation methods.

Results show that using DTI parameters for individual diagnostics is currently limited by the large inter and intra-subject variation, however the in vivo pilot study proved the suggested methods to be viable in a clinical setting; adding only 5 minutes to the standard scan time and allowing for quick and highly automated evaluation of data which is suitable for large group comparisons.},
  author       = {Szczepankiewicz, Filip},
  keyword      = {MRI},
  language     = {eng},
  note         = {Student Paper},
  title        = {Diffusion Tensor Imaging in the Cervical Spinal Cord: Implementing methods for acquisition, processing and evaluation},
  year         = {2011},
}