2009 Winter Project Week MRSI - Revision history

Tkapur at 20:58, 2 February 2009

2009-02-02T20:58:55Z

Bmenze at 16:08, 9 January 2009

2009-01-09T16:08:01Z

Bmenze at 18:17, 15 December 2008

2008-12-15T18:17:09Z

Bmenze at 02:15, 5 December 2008

2008-12-05T02:15:30Z

Bmenze: New page: {| |Project Week Main Page ]] |[[Image:Mrsi slicer.jpg|thumb|190px|Color-coded MRSI in the localization and grading...

2008-12-05T02:02:29Z

New page: {| |thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page ]] |[[Image:Mrsi slicer.jpg|thumb|190px|Color-coded MRSI in the localization and grading...

New page

{|
|[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
|[[Image:Mrsi slicer.jpg|thumb|190px|Color-coded MRSI in the localization and grading of brain tumors.]]
|}

__NOTOC__

===Key Investigators===
* SPL, MIT: Bjoern Menze

__NOTOC__
<div style="margin: 20px;">
<div style="width: 40%; float: left; padding-right: 3%;">
<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.

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.
</div>

<div style="width: 27%; float: left; padding-right: 3%;">
<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.
</div>

<div style="width: 27%; float: left;">
<h1>Progress</h1>
Defined a signal processing pipeline and identified resources for the implementation.

</div>

<br style="clear: both;" />
</div>

@@ Line 18: / Line 18: @@
 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.
 </div>
 <h1>Progress</h1>
 </div>

@@ Line 4: / Line 4: @@
 |[[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.]]
 |}
 __NOTOC__
 <div style="margin: 20px;">
 <div style="width: 40%; float: left; padding-right: 3%;">
@@ Line 26: / Line 32: @@
 </div>

@@ Line 2: / Line 2: @@
 |[[Image:NAMIC-SLC.jpg|thumb|320px|Return to [[2009_Winter_Project_Week|Project Week Main Page]] ]]
 |[[Image:Mrsi slicer.jpg|thumb|190px|Color-coded MRSI in the localization and grading of brain tumors.]]
-|[[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: Nonlinear-least-squares fit of models for three relevant metabolites.]]
+|[[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.]]
 |}
 __NOTOC__
 <div style="margin: 20px;">
 <div style="width: 40%; float: left; padding-right: 3%;">
 <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.
+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.
+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.
 </div>
 <div style="width: 27%; float: left; padding-right: 3%;">
 <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.
+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.
 </div>
 <h1>Progress</h1>
 </div>
-<br style="clear: both;" />
+__NOTOC__
-</div>