Computed tomography window blending in maxillofacial imaging

  • Nityanand MiskinEmail author
  • M. Travis CatonJr
  • Jeffrey P. Guenette
  • Jacob C. Mandell
Original Article



The purpose of this study was to demonstrate the ability of a custom window blending algorithm to depict multicompartmental disease processes of the maxillofacial region in a single image, using routine computed tomography (CT) DICOM data.


Five cases were selected from case files demonstrating trauma, infection, and malignancy of the maxillofacial region on routine CT examinations. Images were processed with a modified Relative Attenuation-Dependent Image Overlay (RADIO) window-blending algorithm in Adobe Photoshop controlled by ExtendScript.


The modified RADIO algorithm was able to demonstrate pertinent multicompartmental imaging findings in each of the examinations, allowing simultaneous visualization of clinically relevant bone and soft tissue findings in a single image, without needing to change window and level settings.


A custom window blending algorithm can demonstrate a range of multicompartmental pathology in the maxillofacial region in a single image.


Computed tomography Window blending Maxillofacial trauma Maxillofacial infection Maxillofacial malignancy 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Kimpe T, Tuytschaever T (2007) Increasing the number of gray shades in medical display systems--how much is enough? J Digit Imaging 20(4):422–432CrossRefGoogle Scholar
  2. 2.
    Mandell JC, Khurana B, Folio LR, Hyun H, Smith SE, Dunne RM, Andriole KP (2017) Clinical Applications of a CT Window Blending Algorithm: RADIO (Relative Attenuation-Dependent Image Overlay). J Digit Imaging 30(3):358–368CrossRefGoogle Scholar
  3. 3.
    Mandell JC, Wortman JR, Rocha TC, Folio LR, Andriole KP, Khurana B (2018) Computed Tomography Window Blending: Feasibility in Thoracic Trauma. Acad Radiol. 25(9):1190–1200CrossRefGoogle Scholar
  4. 4.
    Hammer MM, Mandell JC (2018) CT Window Blending for Evaluation of Multicompartmental Thoracic Pathology. J Comput Assist Tomogr 42(6):881–884CrossRefGoogle Scholar
  5. 5.
    Roth FS, Koshy JC, Goldberg JS, Soparkar CNS (2010) Pearls of orbital trauma management. Semin Plast Surg. 24(4):398–410CrossRefGoogle Scholar
  6. 6.
    Kataria G, Saxena A, Bhagat S, Singh B, Kaur M, Kaur G (2015) Deep Neck Space Infections: A Study of 76 Cases. Iran J Otorhinolaryngol 27(81):293–299PubMedPubMedCentralGoogle Scholar
  7. 7.
    Folio LR (2010) Combat radiology: diagnostic imaging of blast and ballistic injuries. Springer, New YorkCrossRefGoogle Scholar
  8. 8.
    Fayad LM, Jin Y, Laine AF, Berkmen YM, Pearson GD, Freedman B, van Heertum R (2002) Chest CT window settings with multiscale adaptive histogram equalization: pilot study. Radiology 223(3):845–852CrossRefGoogle Scholar
  9. 9.
    Zimmerman JB, Cousins SB, Hartzell KM, Frisse ME, Kahn MG (1989) A psychophysical comparison of two methods for adaptive histogram equalization. J Digit Imaging 2(2):82–91CrossRefGoogle Scholar
  10. 10.
    Pizer SM, Zimmerman JB, Staab EV (1984) Adaptive grey level assignment in CT scan display. J Comput Assist Tomogr. 8(2):300–305PubMedGoogle Scholar
  11. 11.
    Pizer SM et al (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process. 39(3):355–368CrossRefGoogle Scholar

Copyright information

© American Society of Emergency Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiology, Brigham and Women’s HospitalHarvard Medical SchoolBostonUSA

Personalised recommendations