Extracellular volume fraction mapping in the myocardium, part 1: evaluation of an automated method.
Extracellular volume fraction mapping in the myocardium, part 1: evaluation of an automated method.
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DOI：
10.1186/1532-429x-14-63
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发表时间：
2012-09-10
期刊：
影响因子：
--
通讯作者：
Arai AE
中科院分区：
文献类型：
--
作者：
Kellman P;Wilson JR;Xue H;Ugander M;Arai AE
心肌细胞外体积分数（ECV）紊乱，如弥漫性或局灶性心肌纤维化或水肿，是心脏病的标志。弥漫性ECV变化很难通过心血管磁共振（CMR）评估或量化，仅使用常规晚期钆增强（LGE）或造影前或造影后的t1定位。ECV测量绕过了混淆t1加权图像或t1图的因素，并已被证明与弥漫性心肌纤维化密切相关。本研究的目的是开发和评估一种自动生成ECV像素图的方法，该方法对临床工作流程具有足够的可靠性。ECV图是根据对比前和对比后获得的t1图自动生成的。该算法结合了由于屏气不足和屏气之间的错误登记而发生的呼吸运动的纠正，以及自动识别血库。从非诊断性(1)到优秀(5)的5分制对图像进行视觉评分。ECV图谱质量评分为4.23±0.83 (m±SD)，对338例患者的600张ECV图谱进行评分，优良率为83%。对比前后图像的联合配准提高了81%的ECV地图的图像质量。采用运动校正和共配准值时，正常心肌的ECV为25.4±2.5% (m±SD)；未采用运动校正和共配准值时，ECV为31.5±8.7%。全自动运动校正和屏气联合配准显著提高了ECV图的质量，从而使ECV图的生成在临床工作流程中成为可能。
Disturbances in the myocardial extracellular volume fraction (ECV), such as diffuse or focal myocardial fibrosis or edema, are hallmarks of heart disease. Diffuse ECV changes are difficult to assess or quantify with cardiovascular magnetic resonance (CMR) using conventional late gadolinium enhancement (LGE), or pre- or post-contrast T1-mapping alone. ECV measurement circumvents factors that confound T1-weighted images or T1-maps, and has been shown to correlate well with diffuse myocardial fibrosis. The goal of this study was to develop and evaluate an automated method for producing a pixel-wise map of ECV that would be adequately robust for clinical work flow. ECV maps were automatically generated from T1-maps acquired pre- and post-contrast calibrated by blood hematocrit. The algorithm incorporates correction of respiratory motion that occurs due to insufficient breath-holding and due to misregistration between breath-holds, as well as automated identification of the blood pool. Images were visually scored on a 5-point scale from non-diagnostic (1) to excellent (5). The quality score of ECV maps was 4.23 ± 0.83 (m ± SD), scored for n = 600 maps from 338 patients with 83% either excellent or good. Co-registration of the pre-and post-contrast images improved the image quality for ECV maps in 81% of the cases. ECV of normal myocardium was 25.4 ± 2.5% (m ± SD) using motion correction and co-registration values and was 31.5 ± 8.7% without motion correction and co-registration. Fully automated motion correction and co-registration of breath-holds significantly improve the quality of ECV maps, thus making the generation of ECV-maps feasible for clinical work flow.