Difference between revisions of "2009 Winter Project Week MRSI"

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|[[Image:Mrsi slicer.jpg|thumb|190px|Color-coded MRSI in the localization and grading of brain tumors.]]
 
|[[Image:Mrsi slicer.jpg|thumb|190px|Color-coded MRSI in the localization and grading of brain tumors.]]
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|[[Image:Spectral fitting mrs.png|thumb|220px|Fitting metabolite models to the MRS signal of tumorous brain tissue. Top: Original FID signal and estimated baseline. Bottom: Estimated resonance lines for three relevant metabolites.]]
 
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===Key Investigators===
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* BWH SPL / MIT CSAIL: Bjoern Menze
  
===Key Investigators===
 
* SPL, MIT: Bjoern Menze
 
  
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<h1>Objective</h1>
 
<h1>Objective</h1>
Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases - such as tumors in brain, breast and prostate - can be can be associated with characteristic changes in the metabolic level. Thus, proton MRSI is in principle very well suited for the detection, localization and grading of these diseases. A major challenge in MRSI, however, lies in the postprocessing and evaluation of the acquired spectral volumes, and the availability of MRSI data processing routines.
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Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases - such as tumors in brain, breast and prostate - can be can be associated with characteristic changes in the metabolic level. Thus, proton MRSI is in principle very well suited for the detection, localization and grading of these diseases. A major challenge in MRSI, however, lies in the post-processing and evaluation of the acquired spectral volumes.
  
The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI, and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities in Slicer.
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The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.
 
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<h1>Approach, Plan</h1>
 
<h1>Approach, Plan</h1>
The standard approach in the analysis of magnetic resonance spectra is the fitting of resonance-line shaped model functions to the spectral pattern, often referred to as "quantification". The most likely parameter estimate for a given signal model is determined with a nonlinear least squares approach, additional steps in the signal processing remove broad baselines and residuals of the water peak.
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Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models.
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The plan for the project week is to integrate these routines - for data in/out, global registration, and visualization of the metabolite maps - into Slicer using the Slicer-Python interface.  
 
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<h1>Progress</h1>
 
<h1>Progress</h1>
Defined a signal processing pipeline and identified resources for the implementation.
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Designed a basic visualization/interaction of spectra and fitting result using the Python interface.
 
 
 
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Latest revision as of 20:58, 2 February 2009

Home < 2009 Winter Project Week MRSI
Color-coded MRSI in the localization and grading of brain tumors.
Fitting metabolite models to the MRS signal of tumorous brain tissue. Top: Original FID signal and estimated baseline. Bottom: Estimated resonance lines for three relevant metabolites.


Key Investigators

  • BWH SPL / MIT CSAIL: Bjoern Menze


Objective

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic method used to determine the relative abundance of specific metabolites at arbitrary locations in vivo. Certain diseases - such as tumors in brain, breast and prostate - can be can be associated with characteristic changes in the metabolic level. Thus, proton MRSI is in principle very well suited for the detection, localization and grading of these diseases. A major challenge in MRSI, however, lies in the post-processing and evaluation of the acquired spectral volumes.

The objective of the current project is to develop a module proving the means for the processing and visualization of MRSI -- and thus for a joint analysis of magnetic resonance spectroscopic images together with other imaging modalities -- in Slicer.

Approach, Plan

Spectral fitting routines have been implemented, using a HSVD filter for water peak removal and baseline estimation, and a constrained non-linear least squares optimization for the fit of the resonance line models.

The plan for the project week is to integrate these routines - for data in/out, global registration, and visualization of the metabolite maps - into Slicer using the Slicer-Python interface.

Progress

Designed a basic visualization/interaction of spectra and fitting result using the Python interface.