Journal of Digital Imaging

, Volume 30, Issue 3, pp 350–357 | Cite as

Automating Perforator Flap MRA and CTA Reporting

  • 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
Article

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.

Keywords

Breast reconstruction Magnetic resonance angiography Computed tomographic angiography Perforator Autologous flap Automated reporting 

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Copyright information

© Society for Imaging Informatics in Medicine 2017

Authors and Affiliations

  • Christopher J. Lange
    • 1
    • 2
  • Nanda Deepa Thimmappa
    • 1
  • Srikanth R. Boddu
    • 1
  • Silvina P. Dutruel
    • 1
  • Mengchao Pei
    • 1
  • Zerwa Farooq
    • 1
  • Ashkan Heshmatzadeh Behzadi
    • 1
  • Yi Wang
    • 1
  • Ramin Zabih
    • 1
    • 2
    • 3
  • Martin R. Prince
    • 1
    • 4
  1. 1.Department of RadiologyWeill Medical College of Cornell UniversityNew YorkUSA
  2. 2.Department of Computer ScienceCornell UniversityIthacaUSA
  3. 3.GoogleNew YorkUSA
  4. 4.Department of RadiologyColumbia UniversityNew YorkUSA

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