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Automating Perforator Flap MRA and CTA Reporting

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Abstract

Surgical breast reconstruction after mastectomy requires precise perforator coordinates/dimensions, perforator course, and fat volume in a radiology report. Automatic perforator reporting software was implemented as an OsiriX Digital Imaging and Communications in Medicine (DICOM) viewer plugin. For perforator analysis, the user identifies a reference point (e.g., umbilicus) and marks each perforating artery/vein bundle with multiple region of interest (ROI) points along its course beginning at the muscle–fat interface. Computations using these points and analysis of image data produce content for the report. Post-processing times were compared against conventional/manual methods using de-identified images of 26 patients with surgically confirmed accuracy of perforator locations and caliber. The time from loading source images to completion of report was measured. Significance of differences in mean processing times for this automated approach versus the conventional/manual approach was assessed using a paired t test. The mean conventional reporting time for our radiologists was 76 ± 27 min (median 65 min) compared with 25 ± 6 min (median 25 min) using our OsiriX plugin (p < 0.01). The conventional approach had three reports with transcription errors compared to none with the OsiriX plugin. Otherwise, the reports were similar. In conclusion, automated reporting of perforator magnetic resonance angiography (MRA) studies is faster compared with the standard, manual approach, and transcription errors which are eliminated.

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References

  1. Minqiang X, Lanhua M, Jie L, Dali M, Jinguo L. The value of multidetector-row CT angiography for pre-operative planning of breast reconstruction with deep inferior epigastric arterial perforator flaps. Br J Radiol. 2010;83(985):40–3.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Rozen WM, Ashton MW. Modifying techniques in deep inferior epigastric artery perforator flap harvest with the use of pre-operative imaging. ANZ J Surg. 2009;79 (9):598–603

    Article  PubMed  Google Scholar 

  3. Zou Z, Kate Lee H, Levine JL, Greenspun DT, Allen RJ et al. Gadofosveset trisodium-enhanced abdominal perforator MRA. J Magn Reson Imaging. 2012;35 (3):711–6.

    Article  PubMed  Google Scholar 

  4. Masia J, Clavero JA, Larrañaga JR, Alomar X, Pons G, Serret P. Multidetector-row computed tomography in the planning of abdominal perforator flaps. J Plast Reconstr Aesthet Surg 2006; 59:594–9.

    Article  CAS  PubMed  Google Scholar 

  5. Agrawal MD, Thimmappa ND, Vasile JV, Levine JL, Allen RJ et al. Autologous breast reconstruction: pre-operative magnetic resonance angiography for perforator flap vessel mapping. J Reconstr Microsurg. 2015; 31(1):1–11

    PubMed  Google Scholar 

  6. Eylert G, Deutinger M, Stemberger A, Huber W, Gösseringer N. Evaluation of the perforator CT-angiography with a cancer risk assessment in DIEP flap breast reconstruction. J Plast Reconstr Aesthet Surg. 2015;68(4):e80–2.

    Article  PubMed  Google Scholar 

  7. Thimmappa, N, Pei, M, Wang, Y, Chen, C, Ahn, C et al., Semi-automated perforator flap reporting. Radiological Society of North America 2012 Scientific Assembly and Annual Meeting, Chicago IL. http://archive.rsna.org/2012/12023099.html Accessed November 10, 2016.

  8. Chae MP, Hunter-Smith DJ, Rozen WM. Comparative study of software techniques for 3D mapping of perforators in deep inferior epigastric artery perforator flap planning. Gland Surg. 2016;5(2):99–106

    PubMed  PubMed Central  Google Scholar 

  9. Eder M, Raith S, Jalali J, Müller D, Harder Y, et al. Three-dimensional prediction of free-flap volume in autologous breast reconstruction by CT angiography imaging. Int J Comput Assist Radiol Surg. 2014;9(4):541–9

    Article  PubMed  Google Scholar 

  10. Hummelink S, Hameeteman M, Hoogeveen Y, Slump CH, Ulrich DJ, Schultze Kool LJ. Preliminary results using a newly developed projection method to visualize vascular anatomy prior to DIEP flap breast reconstruction. J Plast Reconstr Aesthet Surg. 2015;68(3):390–4.

    Article  CAS  PubMed  Google Scholar 

  11. Zöllner FG, Daab M, Sourbron SP, Schad LR, Schoenberg SO, Weisser G. An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited. BMC Med Imaging. 2016;16:7.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Blackledge MD, Collins DJ, Koh DM, Leach MO. Rapid development of image analysis research tools: bridging the gap between researcher and clinician with pyOsiriX. Comput Biol Med. 2016; 69: 203–12.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lo Presti G, Carbone M, Ciriaci D, Aramini D, Ferrari M, Ferrari V. Assessment of DICOM viewers capable of loading patient-specific 3D models obtained by different segmentation platforms in the operating room. J Digit Imaging. 2015; 28(5):518–27.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA

    Christopher J. Lange, Nanda Deepa Thimmappa, Srikanth R. Boddu, Silvina P. Dutruel, Mengchao Pei, Zerwa Farooq, Ashkan Heshmatzadeh Behzadi, Yi Wang, Ramin Zabih & Martin R. Prince

  2. Department of Computer Science, Cornell University, Ithaca, NY, USA

    Christopher J. Lange & Ramin Zabih

  3. Google, New York, NY, USA

    Ramin Zabih

  4. Department of Radiology, Columbia University, New York, NY, USA

    Martin R. Prince

Authors
  1. Christopher J. Lange
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  2. Nanda Deepa Thimmappa
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  3. Srikanth R. Boddu
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  4. Silvina P. Dutruel
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  5. Mengchao Pei
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  6. Zerwa Farooq
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  7. Ashkan Heshmatzadeh Behzadi
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  8. Yi Wang
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  9. Ramin Zabih
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  10. Martin R. Prince
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Corresponding author

Correspondence to Martin R. Prince.

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Lange, C.J., Thimmappa, N.D., Boddu, S.R. et al. Automating Perforator Flap MRA and CTA Reporting. J Digit Imaging 30, 350–357 (2017). https://doi.org/10.1007/s10278-017-9943-z

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  • DOI: https://doi.org/10.1007/s10278-017-9943-z

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