Nöth Ulrike, Volz Steffen, Hattingen Elke, Deichmann Ralf
Brain Imaging Center (BIC), Goethe University Frankfurt, Schleusenweg 2-16, D-60528 Frankfurt am Main, Germany.
Brain Imaging Center (BIC), Goethe University Frankfurt, Schleusenweg 2-16, D-60528 Frankfurt am Main, Germany.
Neuroimage. 2014 May 15;92:106-19. doi: 10.1016/j.neuroimage.2014.01.050. Epub 2014 Feb 4.
A new method for motion correction of T2*-weighted data and resulting quantitative T2* maps is presented. For this method, additional data sets with a reduced number of phase encoding steps covering the k-space centre are acquired. Motion correction is based on a 3-step procedure: (1) calculation of improved input data sets with reduced artefact levels from the original data, (2) creation of a target data set free of movement artefacts on the basis of the improved input data sets, and (3) fitting of original data to the target data set, yielding an optimum combination of acquired k-space data which suppresses lines affected by movement. The method was tested on healthy subjects performing pre-trained movement. Motion correction was successful unless the same k-space line was affected by movement in all data sets acquired on a specific subject. The method was applied to patients suffering from subarachnoid haemorrhage (group 1) or tumours (group 2) with accompanying edema in the brain. Motion correction improved the interpretability of T2*-weighted patient data and resulting quantitative T2* maps considerably by allowing a clear delineation between ventricle and edema and a clear localisation of haemorrhage (group 1) or a clear delineation of tumour accompanying edema (group 2) which was not possible in data affected by movement.
本文提出了一种用于T2加权数据运动校正及由此生成定量T2图的新方法。对于该方法，需采集覆盖k空间中心且相位编码步数减少的额外数据集。运动校正基于三步程序：(1) 从原始数据计算出伪影水平降低的改进输入数据集；(2) 根据改进的输入数据集创建无运动伪影的目标数据集；(3) 将原始数据拟合到目标数据集，得到采集的k空间数据的最佳组合，从而抑制受运动影响的线条。该方法在进行过预训练运动的健康受试者身上进行了测试。除非在特定受试者采集的所有数据集中同一k空间线都受运动影响，否则运动校正即为成功。该方法应用于患有蛛网膜下腔出血（第1组）或肿瘤（第2组）且伴有脑内水肿的患者。运动校正通过清晰区分脑室和水肿以及明确出血定位（第1组）或清晰勾勒伴有水肿的肿瘤（第2组），极大地提高了T2加权患者数据及由此生成的定量T2图的可解释性，而这在受运动影响的数据中是无法实现的。
