Difference between revisions of "NA-MIC/Projects/Collaboration/MGH RadOnc"

From NAMIC Wiki
Jump to: navigation, search
Line 1: Line 1:
{|
 
|[[Image:ProjectWeek-2008.png|thumb|320px|Return to [[2008_Summer_Project_Week|Project Week Main Page]] ]]
 
|[[Image:abdomen.jpg|thumb|320px|Registration of abdomen.]]
 
|}
 
 
 
__NOTOC__
 
__NOTOC__
  
===Key Investigators===
+
As requested at the Salt Lake City meeting, I have gathered a few images related to both segmentation and registration problems in radiation therapy. 
* MGH: Greg Sharp, Marta Peroni
 
* NA-MIC: Steve Pieper, Wendy Plesniak
 
* BWH: Nicole Aucoin (fiducials), Katie Hayes (CTest)
 
  
 +
===Interactive Segmentation===
  
<div style="margin: 20px;">
+
Rad Onc departments use interactive segmentation every day for both research and patient care.
 +
Prior to treatment planning, the target and critical structures are delineated in CT. The current state of the art is manual segmentation in axial view. A outline tool, used delineate the boundary, is generally prefered over a paintbrush tool that fills pixels. Commercial products generally support some subset of the following tools to assist the operator.
  
<div style="width: 23%; float: left; padding-right: 3%;">
+
1 contour interpolation between slices
 +
2 boundary editing
 +
3 mixed axial/coronal/sagittal drawing
 +
4 livewire or intelligent scissors
 +
5 drawing constraints (e.g. constraints on volume overlap/distance)
 +
6 post-processing tools to nudge or smooth the boundary
  
<h1>Objective</h1>
+
===Adaptive RT===
The long term objective is to improve interactive tools for radiation therapy planning.  Specifically, we would like to improve the reliability and usability of image registration and segmentation of critical organs.
 
  
</div>
+
Below are some examples of anatomic change in head & neck and thorax. 
  
<div style="width: 33%; float: left; padding-right: 3%;">
+
{|
 
+
|[[Image:gcsGUI.png|thumb|320px|Simple GUI for plastimatch]]
<h1>Approach, Plan</h1>
+
|[[Image:gcsImage.jpg|thumb|320px|Registration output in Slicer]]
Workshop goals
+
|[[Image:gcsHeartLung.jpg|thumb|320px|Conversion of DICOM RT structures]]
* Complete the CLP interface between our in-house registration software (plastimatch) and slicer3
+
|}
* Get ideas how the user interface can be implemented
 
* Learn how to interface with slicer3 fiducial and label maps for interactive deformable registration
 
* Learn how CTest can be used for automated testing
 
 
 
</div>
 
  
<div style="width: 38%; float: left;">
 
  
<h1>Progress</h1>
+
===General Discussion of Registration===
Since last year
 
* Plastimatch software released under open source software license (BSD-style).
 
* The scaffolding code for slicer3 CLP is implemented, but not working because plastimatch doesn't read nrrd (yet).
 
  
</div>
+
Deformable registration is still not as reliable as it should be. Image acquisition has residual artifacts which cause unrealistic deformations. Registration algorithms are not always robust, and require experimentation and tuning. Validation of registration results is not easy, since there are inadequate tools. Temporal regularization is generally not done, because of slow algorithms and large memory footprints. And so on.
  
<br style="clear: both;" />
+
===4D-CT Registration in Thorax===
  
</div>
+
Thorax is a special case.  Patient images are acquired using 4D-CT, and radiation dose can computed for 3D volumes at each breathing phase. The volumes are aligned using deformable registration, and radiation dose is accumulated in a reference phase (e.g. exhale). Ideally this procedure is repeated to perform 4D treatment plan optimization.
  
===Progress===
+
The sliding of the lungs against the chest wall is difficult to model. We sometimes segment the images at the pleural boundary. This allows us to separate the moving set of organs from the non-moving set, which are registered separately. Ideally we would always do this, but segmentation is manual and therefore we usually skip this step.
  
 
{|
 
{|
Line 52: Line 41:
 
|[[Image:gcsHeartLung.jpg|thumb|320px|Conversion of DICOM RT structures]]
 
|[[Image:gcsHeartLung.jpg|thumb|320px|Conversion of DICOM RT structures]]
 
|}
 
|}
 +
 +
If you ignore the pleural boundary, registration of 4D-CT is considered "easy". 
 +
 +
1. Single-session imaging, so patient is already co-registered
 +
2. Single-session imaging, so no anatomic change
 +
3. High contrast of vessels against lung parenchema
 +
 +
State of the art is probably around 2-3 mm RMS error for point landmarks.
  
 
<div style="margin: 20px;">
 
<div style="margin: 20px;">

Revision as of 18:54, 29 January 2009

Home < NA-MIC < Projects < Collaboration < MGH RadOnc


As requested at the Salt Lake City meeting, I have gathered a few images related to both segmentation and registration problems in radiation therapy.

Interactive Segmentation

Rad Onc departments use interactive segmentation every day for both research and patient care. Prior to treatment planning, the target and critical structures are delineated in CT. The current state of the art is manual segmentation in axial view. A outline tool, used delineate the boundary, is generally prefered over a paintbrush tool that fills pixels. Commercial products generally support some subset of the following tools to assist the operator.

1 contour interpolation between slices 2 boundary editing 3 mixed axial/coronal/sagittal drawing 4 livewire or intelligent scissors 5 drawing constraints (e.g. constraints on volume overlap/distance) 6 post-processing tools to nudge or smooth the boundary

Adaptive RT

Below are some examples of anatomic change in head & neck and thorax.

Simple GUI for plastimatch
Registration output in Slicer
Conversion of DICOM RT structures


General Discussion of Registration

Deformable registration is still not as reliable as it should be. Image acquisition has residual artifacts which cause unrealistic deformations. Registration algorithms are not always robust, and require experimentation and tuning. Validation of registration results is not easy, since there are inadequate tools. Temporal regularization is generally not done, because of slow algorithms and large memory footprints. And so on.

4D-CT Registration in Thorax

Thorax is a special case. Patient images are acquired using 4D-CT, and radiation dose can computed for 3D volumes at each breathing phase. The volumes are aligned using deformable registration, and radiation dose is accumulated in a reference phase (e.g. exhale). Ideally this procedure is repeated to perform 4D treatment plan optimization.

The sliding of the lungs against the chest wall is difficult to model. We sometimes segment the images at the pleural boundary. This allows us to separate the moving set of organs from the non-moving set, which are registered separately. Ideally we would always do this, but segmentation is manual and therefore we usually skip this step.

Simple GUI for plastimatch
Registration output in Slicer
Conversion of DICOM RT structures

If you ignore the pleural boundary, registration of 4D-CT is considered "easy".

1. Single-session imaging, so patient is already co-registered 2. Single-session imaging, so no anatomic change 3. High contrast of vessels against lung parenchema

State of the art is probably around 2-3 mm RMS error for point landmarks.

Progress

  • Working CLP program
  • Got fiducials from Slicer -- Wow!
  • Preliminary CTest interface
  • DicomRT contour conversion

Todo

  • Convert from CLP to scriptable or loadable module
  • Improved visualization of registration output
  • Improved interactivity of fiducials
  • Export of deformed contours to DicomRT



References

  • GC Sharp, Z Wu, N Kandasamy, "A Data Structure for B-Spline Registration," AAPM 50, Houston TX, July 2008.
  • V Boldea, GC Sharp, SB Jiang, D Sarrut, “4D-CT lung motion estimation with deformable registration: quantification of motion nonlinearity and hysteresis,” Medical Physics, Vol 35, No 3, pp 1008-1018, March 2008.
  • Z Wu, E Rietzel, V Boldea, D Sarrut, GC Sharp, "Evaluation of deformable registration of patient lung 4DCT with sub-anatomical region segmentations," Medical Physics, Vol 35, No 2, pp 775-81, February 2008.
  • GC Sharp, N Kandasamy, H Singh, M Folkert, "GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration,” Physics in Medicine and Biology, Vol 52, No 19, pp 5771--83, October 7, 2007.