金星p瓣植入术患者特异性混合现实:一种新的手术计划方法

IF 1.2 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS
Angelo Fabio d’Aiello , Francesca Bevilacqua , Angelo Micheletti , Diana Gabriela Negura , Giulia Pasqualin , Luca Giugno , Luciane Piazza , Laura Schianchi , Sara Boveri , Alessandro Giamberti , Massimo Chessa
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引用次数: 0

摘要

先天性心脏病(CHD)患者的生存率有所提高,但肺反流(PR)等并发症往往需要再次干预。经导管肺动脉瓣植入术(TPVI)带有自膨胀瓣膜,如金星p型瓣膜,拓宽了治疗选择。准确的程序规划,特别是阀门定径,仍然是一个重大挑战。混合现实(MxR)技术实现了针对患者的方法,提高了程序规划的准确性。目的探讨磁共振成像在静脉- p瓣膜置入术中的应用价值。材料和方法本研究纳入2023年1月至2024年6月期间接受金星p瓣植入术的患者,使用ARTICOR®软件根据CT数据生成全息模型。两个独立的操作者使用这些模型进行程序规划。评估操作者之间的一致性。将预测结果与植入瓣膜尺寸进行比较,以评估一致性。结果29例患者中,26例成功行静脉- p瓣膜植入术。操作人员对阀门直径的一致性达到60% (n = 15/26),对阀门长度的一致性超过88% (n = 23/26)。全息模型在预测瓣膜长度、入路类型(92%)和直径(50%)方面达到了96% (n = 25/26)的一致性(n = 13/26)。全息模型增强了程序规划,实现了更好的可视化和协作决策。虽然该方法对阀门长度预测非常有效,但在预测阀门直径方面的局限性突出了改进方法的必要性,例如计算建模或机器学习。结论患者特异性全息模型是TPVI规划的有效工具。技术的进步和跨学科合作对于克服目前的局限性和推进介入心脏病学的程序规划和相关结果至关重要。摘要本研究评估了混合现实(MxR)技术在经导管肺动脉瓣植入术(TPVI)中的应用。根据CT数据创建全息模型,以帮助确定阀门尺寸和植入策略,两名操作员比较阀门直径和长度的测量结果。在26例成功植入的患者中,操作人员对瓣膜直径的一致性为60%,瓣膜长度的一致性为88%。使用全息模型的预测显示植入瓣膜长度的一致性为96%,入路类型的一致性为92%,瓣膜直径的一致性为50%。这些结果强调了MxR在改善TPVI计划方面的潜力,尽管瓣膜直径的有限准确性表明需要进一步的改进,例如计算建模或机器学习,以优化介入心脏病学的程序结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patient-specific mixed reality for venus P-valve implantation: A novel approach to procedural planning

Background

Survival rates for patients with congenital heart disease (CHD) have improved, but complications like pulmonary regurgitation (PR) often require re-interventions. Transcatheter pulmonary valve implantation (TPVI) with self-expandable valves, such as the Venus P-Valve, has broadened treatment options. Accurate procedural planning, particularly valve sizing, remains a significant challenge. Mixed Reality (MxR) technology enables a patient-specific approach that enhances procedural planning accuracy.

Aim

To evaluate the use of MxR in planning Venus-P valve implantation.

Materials and methods

This study included patients undergoing Venus P-Valve implantation with holographic models generated from CT data using ARTICOR® software from January 2023 to June 2024. Two independent operators used these models for procedural planning. Concordance between operators was assessed. Predictions were compared with implanted valve dimensions to evaluate concordance.

Results

Of 29 eligible patients, 26 underwent successful Venus-P valve implantation. Concordance between the operators reached 60 % (n = 15/26) for valve diameter and over 88 % (n = 23/26) for valve length. Holographic models achieved 96 % (n = 25/26) concordance in predicting valve length, type of the approach (92 %) and 50 % (n = 13/26) concordance for diameter.

Discussion

Holographic models enhanced procedural planning, enabling better visualization and collaborative decision-making. While highly effective for valve length predictions, and type of the approach limitations in predicting valve diameter highlight the need for improved methods, such as computational modelling or machine learning.

Conclusion

Patient-specific holographic models are promising tools for TPVI planning. Advancements in technology and interdisciplinary collaboration are critical to overcoming current limitations and advancing procedural planning and related outcomes in interventional cardiology.

Condensed abstract

This study assesses the use of mixed reality (MxR) technology for procedural planning in transcatheter pulmonary valve implantation (TPVI) with the Venus P-Valve. Holographic models were created from CT data to aid in valve sizing and implantation strategies, with two operators comparing measurements for valve diameter and length. Among 26 patients who underwent successful implantation, concordance between operators was 60 % for valve diameter and 88 % for valve length. Predictions using holographic models showed 96 % concordance for implanted valve length, type of the approach (92 %) and 50 % concordance for valve diameter. These results highlight the potential of MxR for improving TPVI planning, though the limited accuracy for valve diameter suggests a need for further advancements, such as computational modeling or machine learning, to optimize procedural outcomes in interventional cardiology.
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来源期刊
International journal of cardiology. Congenital heart disease
International journal of cardiology. Congenital heart disease Cardiology and Cardiovascular Medicine
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