Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
Neuroimage. 2012 Feb 1;59(3):2266-73. doi: 10.1016/j.neuroimage.2011.09.061. Epub 2011 Oct 2.
Cerebral microbleeds (CMBs) are commonly detected on MRI and have recently received an increased interest, because they are associated with vascular disease and dementia. Identification and rating of CMBs on MRI images may be facilitated by semi-automatic detection, particularly on high-resolution images acquired at high field strength. For these images, visual rating is time-consuming and has limited reproducibility. We present the radial symmetry transform (RST) as an efficient method for semi-automated CMB detection on 7.0 T MR images, with a high sensitivity and a low number of false positives that have to be censored manually. The RST was computed on both echoes of a dual-echo T2*-weighted gradient echo 7.0 T MR sequence in 18 participants from the Second Manifestations of ARTerial disease (SMART) study. Potential CMBs were identified by combining the output of the transform on both echoes. Each potential CMB identified through the RST was visually checked by two raters to identify probable CMBs. The scoring time needed to manually reject false positives was recorded. The sensitivity of 71.2% is higher than that of individual human raters on 7.0 T scans and the required human rater time is reduced from 30 to 2 minutes per scan on average. The RST outperforms published semi-automated methods in terms of either a higher sensitivity or less false positives, and requires much less human rater time.
脑微出血 (CMB) 在磁共振成像 (MRI) 上很常见,最近受到了越来越多的关注,因为它们与血管疾病和痴呆有关。MRI 图像上 CMB 的识别和评分可以通过半自动检测来实现,特别是在高场强下采集的高分辨率图像上。对于这些图像,视觉评分既耗时又具有有限的可重复性。我们提出了径向对称变换 (RST) 作为一种在 7.0 T MRI 图像上进行半自动 CMB 检测的有效方法,该方法具有高灵敏度和低数量的需要手动屏蔽的假阳性。RST 在来自动脉疾病第二表现 (SMART) 研究的 18 名参与者的双回波 T2*-加权梯度回波 7.0 T MRI 序列的两个回波上进行计算。通过组合两个回波上的变换输出来识别潜在的 CMB。通过 RST 识别出的每个潜在 CMB 都由两名评分者进行视觉检查以识别可能的 CMB。记录手动拒绝假阳性所需的评分时间。71.2%的灵敏度高于 7.0 T 扫描时单个人类评分者的灵敏度,并且平均每个扫描的所需人类评分者时间从 30 分钟减少到 2 分钟。RST 在灵敏度或假阳性数量方面优于已发表的半自动方法,并且需要的人类评分者时间更少。