1. Evaluation of Preoperative Low-flow Areas in STA-MCA Bypass Surgery Using Vascular Fusion Map Image
- Author
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Tanaka, Riki, Jankovic, Dragan, Katayama, Tomoko, Okubo, Mai, Sasaki, Kento, Tamura, Takamitsu, Yamada, Yasuhiro, Komatsu, Fuminari, and Kato, Yoko
- Subjects
Preoperative care -- Evaluation ,Cardiopulmonary bypass -- Complications and side effects -- Patient outcomes ,CT imaging -- Evaluation - Abstract
Background: The superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery requires an anastomosis of the STA to an MCA with diminished blood flow. However, identifying the precise location of the MCA with reduced flow preoperatively is challenging as it often remains nonvisualized. To address this issue, we developed a novel technique, the area target bypass (ATB) method, to infer the location of the responsible vessel for low-flow areas. Objective: The cornerstone of the ATB method lies in the utilization of the vascular fusion map (VFM). The VFM integrates 3D perfusion and 3D vascular images, enabling simultaneous evaluation of cerebral surface vessels and regions with reduced blood flow. This study aimed to assess the efficacy of the STA-MCA bypass surgery adopting the ATB method. Methods: Between August 2022 and March 2023, we conducted eight STA-MCA bypass surgeries using the ATB method. For each case, the VFM was generated using the MTT and DLY parameters, and blood flow improvement was evaluated based on the VFM score, determined by an average score from seven experts. Results: In all cases, the target vessel was identified either preoperatively or during craniotomy, with postoperative patency of the STA-MCA bypass confirmed. Out of the eight cases, seven demonstrated improved blood flow with a VFM score exceeding 1. No complications were reported. Conclusion: The introduction of the ATB method has proven its potential in accurately pinpointing optimal anastomosis sites. Keywords: 3D-image, bypass, diagnosis, surgery, Author(s): Riki Tanaka (corresponding author) [1]; Dragan Jankovic [1,2]; Tomoko Katayama [3]; Mai Okubo [3]; Kento Sasaki [1]; Takamitsu Tamura [1]; Yasuhiro Yamada [1]; Fuminari Komatsu [1]; Yoko Kato [1] [...]
- Published
- 2024
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