Clinic of Nuclear Medicine, University Hospital Erlangen, 91054 Erlangen, Germany. CIML Group, Biophysics, University of Regensburg, 93040 Regensburg, Germany. Pattern Recognition Lab, University of Erlangen-Nürnberg, 91058 Erlangen, Germany.
Phys Med Biol. 2019 Dec 19;64(24):245011. doi: 10.1088/1361-6560/ab5b81.
In [Formula: see text] radionuclide therapies, dosimetry is used for determining patient-individual dose burden. Standard approaches provide whole organ doses only. For assessing dose heterogeneity inside organs, voxel-wise dosimetry based on 3D SPECT/CT imaging could be applied. Often, this is achieved by convolving voxel-wise time-activity-curves with appropriate dose-voxel-kernels (DVK). The DVKs are meant to model dose deposition, and can be more accurate if modelled for the specific tissue type under consideration. In literature, DVKs are often not adapted to these inhomogeneities, or simple approximation schemes are applied. For 26 patients, which had previously undergone a [Formula: see text] -PSMA or -DOTATOC therapy, decay maps, mass-density maps as well as tissue-type maps were derived from SPECT/CT acquisitions. These were used for a voxel-based dosimetry based on convolution with DVKs (each of size [Formula: see text]) obtained by four different DVK methods proposed in literature. The simplest only considers a spatially constant soft-tissue DVK (herein named 'constant'), while others either take into account only the local density of the center voxel of the DVK (herein named 'center-voxel') or scale each voxel linearly according to the proper mass density deduced from the CT image (herein named 'density') or considered both the local mass density as well as the direct path between the center voxel and any voxel in its surrounding (herein named 'percentage'). Deviations between resulting dose values and those from full Monte-Carlo simulations (MC simulations) were compared for selected organs and tissue-types. For each DVK method, inter-patient variability was considerable showing both under- and over-estimation of energy dose compared to the MC result for all tissue densities higher than soft tissue. In kidneys and spleen, 'constant' and 'density'-scaled DVKs achieved estimated doses with smallest deviations to the full MC gold standard (∼[Formula: see text] underestimation). For low and high density tissue types such as lung and adipose or bone tissue, alternative DVK methods like 'center-voxel'- and 'percentage'- scaled achieved superior results, respectively. Concerning computational load, dose estimation with the DVK method 'constant' needs about 1.1 s per patient, center-voxel scaling amounts to 1.2 s, density scaling needs 1.4 s while percentage scaling consumes 860.3 s per patient. In this study encompassing a large patient cohort and four different DVK estimation methods, no single DVK-adaption method was consistently better than any other in case of soft tissue kernels. Hence in such cases the simplest DVK method, labeled 'constant', suffices. In case of tumors, often located in tissues of low (lung) or high (bone) density, more sophisticated DVK methods excel. The high inter-patient variability indicates that for evaluating new algorithms, a sufficiently large patient cohort needs to be involved.
在 [公式:见文本] 放射性核素治疗中,剂量学用于确定患者个体的剂量负担。标准方法仅提供整体器官剂量。为了评估器官内的剂量不均匀性,可以应用基于 3D SPECT/CT 成像的体素剂量学。通常,这是通过将体素时间活动曲线与适当的剂量体素核(DVK)卷积来实现的。DVK 旨在模拟剂量沉积,如果针对特定考虑的组织类型进行建模,则可以更准确。在文献中,DVK 通常不适应这些不均匀性,或者应用简单的近似方案。对于之前接受过 [公式:见文本] -PSMA 或 -DOTATOC 治疗的 26 名患者,从 SPECT/CT 采集推导出衰减图、质量密度图以及组织类型图。这些用于基于卷积的体素剂量学,卷积使用来自文献中提出的四种不同 DVK 方法的 DVK(每个大小为 [公式:见文本])。最简单的方法仅考虑空间上恒定的软组织 DVK(本文称为“常数”),而其他方法要么仅考虑 DVK 中心体素的局部密度(本文称为“中心体素”),要么根据从 CT 图像推导出的适当质量密度对每个体素进行线性缩放(本文称为“密度”),要么同时考虑局部质量密度以及中心体素与周围任何体素之间的直接路径(本文称为“百分比”)。对于选定的器官和组织类型,比较了来自不同 DVK 方法的结果剂量值与来自完全蒙特卡罗模拟(MC 模拟)的结果剂量值之间的差异。对于所有组织密度高于软组织的组织,对于每种 DVK 方法,患者间的变异性都很大,与 MC 结果相比,能量剂量都存在低估或高估的情况。在肾脏和脾脏中,“常数”和“密度”缩放的 DVK 实现了与完整 MC 黄金标准(约 [公式:见文本] 低估)最小偏差的估计剂量。对于密度低和高的组织类型,如肺和脂肪或骨组织,替代的 DVK 方法,如“中心体素”和“百分比”缩放,分别取得了更好的结果。关于计算负荷,使用 DVK 方法“常数”对每位患者进行剂量估计大约需要 1.1 秒,中心体素缩放需要 1.2 秒,密度缩放需要 1.4 秒,而百分比缩放每个患者需要 860.3 秒。在这项研究中,纳入了大量患者队列和四种不同的 DVK 估计方法,在软组织核的情况下,没有一种 DVK 适应方法始终优于其他方法。因此,在这种情况下,最简单的 DVK 方法,标记为“常数”,就足够了。在肿瘤的情况下,肿瘤通常位于低密度(肺)或高密度(骨)组织中,更复杂的 DVK 方法表现出色。高患者间变异性表明,为了评估新算法,需要有足够大的患者队列参与。