Roberson Peter L, McLaughlin P William, Narayana Vrinda, Troyer Sara, Hixson George V, Kessler Marc L
University of Michigan, Department of Radiation Oncology, Ann Arbor, Michigan 48109, USA.
Med Phys. 2005 Feb;32(2):473-82. doi: 10.1118/1.1851920.
Post-implant dosimetric analysis for permanent implant of the prostate benefits from the use of a computed tomography (CT) dataset for optimal identification of the radioactive source (seed) positions and a magnetic resonance (MR) dataset for optimal description of the target and normal tissue volumes. The CT/MR registration process should be fast and sufficiently accurate to yield a reliable dosimetric analysis. Since critical normal tissues typically reside in dose gradient regions, small shifts in the dose distribution could impact the prediction of complication or complication severity. Standard procedures include the use of the seed distribution as fiducial markers (seed match), a time consuming process that relies on the proper identification of signals due to the same seed on both datasets. Mutual information (MI) is more efficient because it uses image data requiring minimal preparation effort. A comparison of MI registration and seed-match registration was performed for twelve patients. MI was applied to a volume limited to the prostate and surrounding structures, excluding most of the pelvic bone structures (margins around the prostate gland were approximately 2 cm right-left, approximately 1 cm anterior-posterior, and approximately 2 cm superior-inferior). Seeds were identified on a 2 mm slice CT dataset using an automatic seed identification procedure on reconstructed three-dimensional data. Seed positions on the 3 mm slice thickness T2 MR data set were identified using a point-and-click method on each image. Seed images were identified on more than one MR slice, and the results used to determine average seed coordinates for MR images and matched seed pairs between CT and MR images. On average, 42% (19%-64%) of the seeds (19-54 seeds) were identified and matched to their CT counterparts. A least-squares method applied to the CT and MR seed coordinates was used to produce the optimum seed-match registration. MI registration and seed match registration angle differences averaged 0.5 degrees, which was not significantly different from zero. Translation differences averaged 0.6 (1.2 standard deviation) mm right-left, -0.5(1.5) mm posterior-anterior, and -1.2(2.0) mm inferior-superior. Registration error estimates were approximately 2 mm for both the MI and seed-match methods. The observed standard deviations in the offset values were consistent with propagation of error. Registration methods as applied here using mutual information and seed matching are consistent, except for a small systematic difference in the inferior-superior axis for a minority of cases (approximately 15%). Cases registered with mutual information and with bony anatomy misregistration of greater than approximately 5 mm should be evaluated for rescan or seed-match registration. The improvement in efficiency of use for the MI registration method is substantial, approximately 30 min compared to several hours using seed match registration.
前列腺永久性植入术后的剂量学分析受益于使用计算机断层扫描(CT)数据集来优化识别放射源(种子源)位置,以及使用磁共振(MR)数据集来优化描述靶区和正常组织体积。CT/MR配准过程应快速且足够准确,以产生可靠的剂量学分析。由于关键正常组织通常位于剂量梯度区域,剂量分布的微小变化可能会影响并发症或并发症严重程度的预测。标准程序包括使用种子源分布作为基准标记(种子源匹配),这是一个耗时的过程,依赖于在两个数据集中正确识别同一种子源产生的信号。互信息(MI)更高效,因为它使用的图像数据所需的准备工作最少。对12名患者进行了MI配准和种子源匹配配准的比较。MI应用于仅限于前列腺及周围结构的体积,不包括大部分骨盆骨结构(前列腺周围的边界在左右约2 cm、前后约1 cm、上下约2 cm)。在重建的三维数据上使用自动种子源识别程序在2 mm层厚的CT数据集中识别种子源。在3 mm层厚的T2 MR数据集上,通过在每个图像上点击的方法识别种子源位置。在多个MR切片上识别种子源图像,并将结果用于确定MR图像的平均种子源坐标以及CT和MR图像之间匹配的种子源对。平均而言,42%(19%-64%)的种子源(19-54个种子源)被识别并与它们在CT上的对应物匹配。应用于CT和MR种子源坐标的最小二乘法用于产生最佳种子源匹配配准。MI配准和种子源匹配配准的角度差异平均为0.5度,但与零无显著差异。平移差异平均为左右0.6(1.2标准差)mm、前后-0.5(1.5)mm、上下-1.2(2.0)mm。MI和种子源匹配方法的配准误差估计均约为2 mm。观察到的偏移值标准差与误差传播一致。除少数情况(约15%)在上下轴上存在小的系统差异外,这里使用互信息和种子源匹配应用的配准方法是一致的。对于互信息配准且骨解剖结构配准误差大于约5 mm的病例,应评估是否重新扫描或进行种子源匹配配准。MI配准方法的使用效率有显著提高,与使用种子源匹配配准的数小时相比,大约为30分钟。