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Retrospective Use of Breathing Motion Compensation Technology (MCT) Enhances Vessel Detection Software Performance

  • Clinical Investigation
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Abstract

Purpose

Cone beam CT (CBCT) with planning software is used in intra-arterial liver-directed therapies. Software accuracy relies on high CBCT image quality, which can be impaired by breathing motion. We assessed the impact of a specific MCT on software performance for procedure planning and navigation.

Materials and Methods

Institutional Review Board (IRB)-approved retrospective evaluation of liver-directed therapies from July 2015 to April 2018 was performed. CBCTs with at least one well-defined tumor and noticeable breathing motion were included. Each CBCT was reconstructed with and without breathing MCT (Motion Freeze, GE Healthcare). Automatic tumor-supplying vessel detection was performed on up to 4 tumors in each CBCT reconstruction (Liver ASSIST V.I., GE Healthcare). Vessel detection sensitivity and positive predictive value (PPV) were measured with and without MCT using Digital Subtracted Angiography (DSA) as reference. Preprocedural contrast-enhanced CT was also utilized in some cases to rule out the possibility of extrahepatic supplying vessels.

Results

MCT was applied retrospectively to 18 CBCTs with a total of 30 tumors. At least one supplying vessel was detected for 28/30 (93%) tumors with MCT versus 20/30 (66%) without. On the subgroup of 10 CBCTs (22 tumors, 76 feeders) in which the automatic vessel detection initially worked in both reconstructions, the average sensitivity and PPV increased from 63% (48/76) and 57% (48/84) before MCT to 83% (63/76) and 79% (63/80) after (p = 0.002 and p < 0.001).

Conclusion

Breathing MCT improves planning software performance in CBCT impaired by breathing motion.

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Funding

This study was not supported by any funding.

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Authors and Affiliations

  1. Radiology Department, Interventional Radiology Service, Memorial Sloan Kettering Cancer Center, 1275 York Avenue H-118, New York , NY, USA

    Fourat Ridouani, Hooman Yarmohammadi, Stephen B. Solomon & Adrian J. Gonzalez-Aguirre

  2. GE Healthcare - 283 rue de la minière, Buc, France

    Raphael Doustaly

Authors
  1. Fourat Ridouani
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  2. Raphael Doustaly
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  3. Hooman Yarmohammadi
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  4. Stephen B. Solomon
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  5. Adrian J. Gonzalez-Aguirre
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Corresponding author

Correspondence to Adrian J. Gonzalez-Aguirre.

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Conflict of interest

Homan Yarmohammadi has research funding from RSNA Scholar Grant, SIR Ernest Ring Academic Development, Guerbet Group and Thompson Foundation. He is an advisory board member of Management of Ascites Advisory Board of BD Medical and Management of HCC Advisory Board of Genentech. Stephen Solomon has consulting fees from BTG, Johnson & Johnson, Varian, XACT Robotics, Endoways and Aperture. He holds grants from GE Healthcare, AngioDynamics, Elestra, Johnson & Johnson. He is a shareholder of Aperture and Johnson & Johnson. All the other authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. For this retrospective study, formal consent is not required, and was waived by institutional IRB.

Informed Consent

This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748

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For this type of study, consent for publication is not required.

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Ridouani, F., Doustaly, R., Yarmohammadi, H. et al. Retrospective Use of Breathing Motion Compensation Technology (MCT) Enhances Vessel Detection Software Performance. Cardiovasc Intervent Radiol 44, 619–624 (2021). https://doi.org/10.1007/s00270-021-02767-8

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  • DOI: https://doi.org/10.1007/s00270-021-02767-8

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