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    3. Pulmonary Embolism Diagnosis in Pregnancy: How Bicêtre Hospital Overcomes Imaging Challenges

    Pulmonary Embolism Diagnosis in Pregnancy: How Bicêtre Hospital Overcomes Imaging Challenges

    5min
    Alfredo Cantarinha
    Published on May 17, 2018
    CT image shows bilateral pulmonary embolism in pregnant woman.

    CT imaging challenges for PE diagnosis in pregnancy

    Fig. 1: CTPA of bilateral pulmonary embolism in a 25-year-old patient (28 weeks of amenorrhea).

    Overcoming CT imaging limitations

    Non-diagnostic images can be reduced by the GPS-protocol from Bicêtre Hospital, France.
    Fig. 2: Non-optimized CTPA in an emergency setting of a patient 28 weeks of amenorrhea. With an achieved density of 140 HU measured in the left/ right main pulmonary artery, this CT must be rated as non-diagnostic for evaluation of PE.
    Challenges for PE diagnosis in pregnant women and GPS protocol from Bicêtre Hospital in France.

    Overview of potential problems and GPS protocol

    Comparison of HU in the pulmonary arteries shows better outcome with use of the GPS protocol from Bicêtre Hospital in France.

    Fig. 4: Comparison of HU in the pulmonary arteries with (orange) and without (purple) use of the GPS protocol in patient cohort (n=30 in June 2016) and control group (n=37).[18]

    Conclusion

    By Alfredo Cantarinha
    Alfredo Cantarinha, Department of Radiology, CHU Bicêtre, France

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    15. Heyer CM, Mohr PS, Lemburg SP, Petersgut SA, Nicolas V. Image quality and radiation exposure at pulmonary CT angiography with 100- or 120-kVp protocol: prospective randomized study. Radiology. 2007; 245(2):577-83.

    16. Cordoliani YS, Hazebroucq V, Sarazin JL, Lévêque C, Vincent B, Jouan, E. Irradiation et bonnes pratiques en tomodensitométrie hélicoïdale. J Radiol. 1999; 80(9):903-11.

    17. Menu Y, Lacout A, Kone T, Rangheard AS. Conduite à tenir en cas de suspicion d’embolie pulmonaire. Feuillets de Radiologie 2007 ; 47(3) :179-189.

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    1. The statements by Siemens Healthineers customers described herein are based on results that were achieved in the customer’s unique setting. Since there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.