Department of Radiology, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
MAGMA. 2013 Apr;26(2):229-38. doi: 10.1007/s10334-012-0332-9. Epub 2012 Aug 15.
This study proposes a scale space based algorithm for automated segmentation of single-shot tagged images of modest SNR. Furthermore the algorithm was designed for analysis of discontinuous or shearing types of motion, i.e. segmentation of broken tag patterns.
The proposed algorithm utilises non-linear scale space for automatic segmentation of single-shot tagged images. The algorithm's ability to automatically segment tagged shearing motion was evaluated in a numerical simulation and in vivo. A typical shearing deformation was simulated in a Shepp-Logan phantom allowing for quantitative evaluation of the algorithm's success rate as a function of both SNR and the amount of deformation. For a qualitative in vivo evaluation tagged images showing deformations in the calf muscles and eye movement in a healthy volunteer were acquired.
Both the numerical simulation and the in vivo tagged data demonstrated the algorithm's ability for automated segmentation of single-shot tagged MR provided that SNR of the images is above 10 and the amount of deformation does not exceed the tag spacing. The latter constraint can be met by adjusting the tag delay or the tag spacing.
The scale space based algorithm for automatic segmentation of single-shot tagged MR enables the application of tagged MR to complex (shearing) deformation and the processing of datasets with relatively low SNR.
本研究提出了一种基于尺度空间的算法，用于自动分割具有适度信噪比的单次标记图像。此外，该算法旨在分析不连续或剪切类型的运动，即分割断裂的标记模式。
所提出的算法利用非线性尺度空间自动分割单次标记的图像。在数值模拟和体内实验中评估了该算法自动分割标记剪切运动的能力。在 Shepp-Logan 体模中模拟了典型的剪切变形，允许定量评估算法的成功率作为 SNR 和变形量的函数。为了进行定性的体内评估，获取了显示小腿肌肉变形和健康志愿者眼球运动的标记图像。
数值模拟和体内标记数据均表明，该算法能够自动分割 SNR 高于 10 的单次标记的磁共振图像，并且变形量不超过标记间隔。通过调整标记延迟或标记间隔可以满足后者的约束条件。
基于尺度空间的单次标记磁共振自动分割算法能够将标记磁共振应用于复杂（剪切）变形，并处理 SNR 相对较低的数据集。
