Difference between revisions of "2017 Winter Project Week/3DSurgicalPlanningBreastReconstruction"

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Key Investigators  
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Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
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==Key Investigators==
 
* Michael Chae (Monash University, Australia)  
 
* Michael Chae (Monash University, Australia)  
 
* Andras Lasso (Queen’s University, Canada)  
 
* Andras Lasso (Queen’s University, Canada)  
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* David Hunter-Smith (Monash University, Australia)  
 
* David Hunter-Smith (Monash University, Australia)  
  
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Project description
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==Project Description==
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Objective  
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! style="text-align: left; width:27%" |  Objective
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At our institute, we have identified 2 useful tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer:  
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! style="text-align: left; width:27%" |  Approach and Plan
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# Volumetric analysis
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! style="text-align: left; width:27%" |  Progress and Next Steps
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# 3D-printed surgical planning template
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However, our method requires extensive manual handling and is subsequently slow. In order for us to deliver therapy in a timely manner and upscale our techniques to more patients, we are aiming to develop automated or semi-automated techniques.
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<!-- Objective bullet points -->
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Approach and plan
 
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1. Volumetric analysis:
 
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Our volumetric analysis technique has been described previously in this paper(1). But briefly, we can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation.
 
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We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques
 
  
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(1) Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.
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* Develop tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer:
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** Volumetric analysis
 +
** 3D-printed surgical planning template
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* Current method requires extensive manual handling and is subsequently slow. We are aiming to develop automated or semi-automated techniques.  
  
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2. 3D printed surgical planning template:
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<!-- Approach and Plan bullet points -->
  
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In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.
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* Volumetric analysis:
 +
** Current method ([Chae2014]): We can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation.
 +
** We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques
  
 +
* 3D printed surgical planning template: In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.
  
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Progress and next steps  
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<!-- Progress and Next steps bullet points (fill out at the end of project week -->
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*
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|}
  
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Background and references
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==Background and References==
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<!-- Use this space for information that may help people better understand your project, like links to papers, source code, or data -->
  
 
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)
 
1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)
  
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# Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.
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* [Chae2014] Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.
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# Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.
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* Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.
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# Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.
+
* Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.
−
# Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.
+
* Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.
 +
* Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.

Revision as of 18:37, 9 January 2017

Home < 2017 Winter Project Week < 3DSurgicalPlanningBreastReconstruction

Key Investigators

  • Michael Chae (Monash University, Australia)
  • Andras Lasso (Queen’s University, Canada)
  • Julian Smith (Monash University, Australia)
  • Warren Rozen (Monash University, Australia)
  • David Hunter-Smith (Monash University, Australia)

Project Description

Objective Approach and Plan Progress and Next Steps
  • Develop tools to help surgeons plan breast reconstructive surgeries in patients with breast cancer:
    • Volumetric analysis
    • 3D-printed surgical planning template
  • Current method requires extensive manual handling and is subsequently slow. We are aiming to develop automated or semi-automated techniques.
  • Volumetric analysis:
    • Current method ([Chae2014]): We can perform volumetric analysis on any imaging platforms (e.g. CT, MRI, 3D scanners). For CT/MRI scanners, we’d manually segment “areas of interest” (i.e. total breast tissue, mammary tissue, breast implants) on their axial slices in Osirix software. We’d refer to 3D-reconstructed image of the breasts (on 3D Slicer) to help guide areas that we’d need to segment. We’re increasing finding it easier to segment certain areas (i.e. mammary tissue, breast implants) from axial slices loaded on 3D slicer, instead of Osirix. For 3D Scanner-derived images, we’d upload the 3D file on to MeshMixer software, from which the file will be meshed (e.g. cutting, making planes). These files will be sent to Blender software for volume calculation.
    • We have been collecting the thresholding values used for segmenting breast tissues. We’d like to use these metrics, or other means, to automate/semi-automate breast volumetric analysis techniques
  • 3D printed surgical planning template: In autologous breast reconstruction, we raise a flap of tissue (i.e. abdominal wall fat) based on a perforator vessel called, DIEP (deep inferior epigastric artery perforators) perforators. These course through muscle called rectus abdominis (aka “six packs”). We’d like to 3D print the patient-specific DIEP perforators and their surrounding rectus abdominis muscle for surgical planning. Currently, we can achieve this on 3D Slicer (manual segmentation via watershed method, island effect tool, and thresholding) but this is very difficult and time-consuming. It’ll be a great opportunity to make this process easier and also automate/semi-automate it.

Background and References

1 in 8 women in the US will be diagnosed with breast cancer in their lifetime. As genetic testing for breast cancer, such as BRCA1/2, becomes more available, an increasing number of women will be diagnosed early and evidences show that more and more women are opting for aggressive surgery (i.e. mastectomy) early on to achieve cure. As a result, post-mastectomy breast reconstruction has become an important component of the holistic treatment of patients with breast cancer. Breast reconstruction with autologous tissue (i.e. one’s own tissue) bypasses risks associated with traditional implants and provides a stable, natural-appearing, long-term volume replacement. The most ideal source of tissue for breast reconstruction is the abdominal wall. These tissues are raised as a free flap tissue based on small vessels, called perforators. Unfortunately, there is a significant variance in perforator size and locations between individuals. Advancements in modern imaging technologies, such as computed tomographic angiography (CTA), has enabled surgeons to select the appropriate perforator and facilitate flap design, leading to improvements in clinical outcomes. However, their efficacy is limited by being displayed on a two-dimensional (2D) surface. In contrast, imaging-guided 3D-printed surgical planning solution can provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. (1-4)

  • [Chae2014] Chae, M. P., Hunter-Smith, D. J., Spychal, R. T., Rozen, W. M. 3D volumetric analysis for planning breast reconstructive surgery. Breast Cancer Res Treat 2014;146:457-460.
  • Rozen, W. M., Phillips, T. J., Ashton, M. W., Stella, D. L., Gibson, R. N., Taylor, G. I. Preoperative imaging for DIEA perforator flaps: a comparative study of computed tomographic angiography and Doppler ultrasound. Plast Reconstr Surg 2008;121:9-16.
  • Masia, J., Clavero, J. A., Larranaga, J. R., Alomar, X., Pons, G., Serret, P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006;59:594-599.
  • Chae, M. P., Rozen, W. M., McMenamin, P. G., Findlay, M. W., Spychal, R. T., Hunter-Smith, D. J. Emerging Applications of Bedside 3D Printing in Plastic Surgery. Front Surg 2015;2:25.
  • Gerstle, T. L., Ibrahim, A. M., Kim, P. S., Lee, B. T., Lin, S. J. A plastic surgery application in evolution: three-dimensional printing. Plast Reconstr Surg 2014;133:446-451.
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