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Sub-segmental quantification of single (stress)-pass perfusion CMR improves the diagnostic accuracy for detection of obstructive coronary artery disease.
Journal of Cardiovascular Magnetic Resonance ( IF 6.4 ) Pub Date : 2020-02-06 , DOI: 10.1186/s12968-020-0600-1
Melanie T P Le 1 , Niloufar Zarinabad 1 , Tommaso D'Angelo 1, 2 , Ibnul Mia 1 , Robert Heinke 1 , Thomas J Vogl 3 , Andreas Zeiher 4 , Eike Nagel 1 , Valentina O Puntmann 1, 4
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BACKGROUND Myocardial perfusion with cardiovascular magnetic resonance (CMR) imaging is an established diagnostic test for evaluation of myocardial ischaemia. For quantification purposes, the 16 segment American Heart Association (AHA) model poses limitations in terms of extracting relevant information on the extent/severity of ischaemia as perfusion deficits will not always fall within an individual segment, which reduces its diagnostic value, and makes an accurate assessment of outcome data or a result comparison across various studies difficult. We hypothesised that division of the myocardial segments into epi- and endocardial layers and a further circumferential subdivision, resulting in a total of 96 segments, would improve the accuracy of detecting myocardial hypoperfusion. Higher (sub-)subsegmental recording of perfusion abnormalities, which are defined relatively to the normal reference using the subsegment with the highest value, may improve the spatial encoding of myocardial blood flow, based on a single stress perfusion acquisition. OBJECTIVE A proof of concept comparison study of subsegmentation approaches based on transmural segments (16 AHA and 48 segments) vs. subdivision into epi- and endocardial (32) subsegments vs. further circumferential subdivision into 96 (sub-)subsegments for diagnostic accuracy against invasively defined obstructive coronary artery disease (CAD). METHODS Thirty patients with obstructive CAD and 20 healthy controls underwent perfusion stress CMR imaging at 3 T during maximal adenosine vasodilation and a dual bolus injection of 0.1 mmol/kg gadobutrol. Using Fermi deconvolution for blood flow estimation, (sub-)subsegmental values were expressed relative to the (sub-)subsegment with the highest flow. In addition, endo-/epicardial flow ratios were calculated based on 32 and 96 (sub-)subsegments. A receiver operating characteristics (ROC) curve analysis was performed to compare the diagnostic performance of discrimination between patients with CAD and healthy controls. Observer reproducibility was assessed using Bland-Altman approaches. RESULTS Subdivision into more and smaller segments revealed greater accuracy for #32, #48 and # 96 compared to the standard #16 approach (area under the curve (AUC): 0.937, 0.973 and 0.993 vs 0.820, p < 0.05). The #96-based endo-/epicardial ratio was superior to the #32 endo-/epicardial ratio (AUC 0.979, vs. 0.932, p < 0.05). Measurements for the #16 model showed marginally better reproducibility compared to #32, #48 and #96 (mean difference ± standard deviation: 2.0 ± 3.6 vs. 2.3 ± 4.0 vs 2.5 ± 4.4 vs. 4.1 ± 5.6). CONCLUSIONS Subsegmentation of the myocardium improves diagnostic accuracy and facilitates an objective cut-off-based description of hypoperfusion, and facilitates an objective description of hypoperfusion, including the extent and severity of myocardial ischaemia. Quantification based on a single (stress-only) pass reduces the overall amount of gadolinium contrast agent required and the length of the overall diagnostic study.

中文翻译:

单次(压力)灌注CMR的亚细分化量化可提高检测梗阻性冠状动脉疾病的诊断准确性。

背景技术具有心血管磁共振(CMR)成像的心肌灌注是用于评估心肌缺血的公认诊断测试。出于量化目的,美国心脏协会(AHA)的16段模型在提取有关缺血性程度/严重程度的相关信息方面存在局限性,因为灌注不足并不总是属于单个段,这降低了其诊断价值,并且难以准确评估结果数据或进行各种研究的结果比较。我们假设将心肌节段分为心外膜层和心内膜层以及进一步的圆周细分,从而形成总共96个节段,将提高检测心肌灌注不足的准确性。灌注异常的较高(亚)细分记录,相对于使用最高值的细分细分相对于正常参考定义的“像素”,可以基于单个应力灌注采集来改善心肌血流的空间编码。目的对基于透壁段(16个AHA和48个段)与细分为心外膜和心内膜(32个)段以及进一步将圆周细分为96个(子)段的细分方法进行概念比较研究,以针对侵入性诊断准确性定义为阻塞性冠状动脉疾病(CAD)。方法30例阻塞性CAD患者和20例健康对照者在最大腺苷血管舒张和0.1毫克/千克加多布特罗的两次大剂量注射期间于3 T接受灌注应力CMR成像。使用费米反卷积进行血流估计,相对于流量最高的(子)细分表示(子)细分值。此外,根据32个和96个(亚)子段计算心内膜/心外膜血流比率。进行了接收器操作特征(ROC)曲线分析,以比较具有CAD的患者与健康对照组之间的鉴别诊断性能。使用Bland-Altman方法评估观察者的可重复性。结果与标准的16号方法相比,细分为更多和较小的段可显示#32,#48和#96的准确性更高(曲线下面积(AUC):0.937、0.973和0.993与0.820,p <0.05)。基于#96的心内膜/心外膜比率优于#32内心膜/心外膜比率(AUC 0.979,而0.932,p <0.05)。与#32,#48和#96相比,#16模型的测量结果显示了略微更好的重现性(平均差±标准差:2.0±3.6 vs. 2.3±4.0 vs 2.5±4.4 vs. 4.1±5.6)。结论心肌的细分可以提高诊断的准确性,并有助于基于临界值的低灌注描述,并有助于对低灌注的客观描述,包括心肌缺血的程度和严重性。基于单次(仅应力)通过的量化减少了所需的contrast造影剂总量以及整个诊断研究的时间。结论心肌的细分可以提高诊断的准确性,并有助于基于临界值的低灌注描述,并有助于对低灌注的客观描述,包括心肌缺血的程度和严重性。基于单次(仅应力)通过的量化减少了所需的contrast造影剂总量以及整个诊断研究的时间。结论心肌的细分可以提高诊断的准确性,并有助于基于临界值的低灌注描述,并有助于对低灌注的客观描述,包括心肌缺血的程度和严重性。基于单次(仅应力)通过的量化减少了所需的contrast造影剂总量以及整个诊断研究的时间。
更新日期:2020-04-22
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