Whole-body voxel-based internal dosimetry using deep learning.
Whole-body voxel-based internal dosimetry using deep learning.
复制标题
使用深度学习的全身基于体素的内部剂量测定。
DOI：
10.1007/s00259-020-05013-4
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发表时间：
2021-03
影响因子：
9.1
通讯作者：
Zaidi H
中科院分区：
文献类型：
--
作者：
Akhavanallaf A;Shiri I;Arabi H;Zaidi H
在精确医学时代，使用蒙特卡罗（MC）模拟计算患者特定剂量被认为是辐射危害风险-受益分析以及与患者结局相关性的金标准技术。因此，我们提出了一种新的方法来执行全身个性化器官水平剂量测定，考虑到活动分布的异质性，周围介质的不均匀性，以及使用深度学习算法的患者特定解剖结构。我们将体素尺度MIRD方法从单个S值内核扩展到对应于患者特定解剖结构的特定S值内核，以使用混合发射/透射图像集构建3D剂量图。在这种情况下，我们采用了深度神经网络（DNN）来预测沉积能量的分布，代表特定的S值，来自由人体几何结构组成的3D内核中心的单个源。训练数据集由从CT图像获得的密度图和使用Monte Carlo模拟生成的参考体素S值组成。因此，特定的S值核是从训练的模型和以类似于基于体素的MIRD形式的方式构建的全身剂量图推断的，即，将特定体素S值与活动图卷积。使用DNN预测的剂量图与使用MC模拟和两种基于MIRD的方法生成的参考进行了比较，包括单S值和多S值（SSV和MSV）和Olinda/EXM软件包。预测的特定体素S值核与作为参考的基于MC的核表现出良好的一致性，平均相对绝对误差（MRAE）为4.5 ± 1.8（%）。Bland和Altman分析显示DNN的最低剂量偏倚（2.6%）和最小方差（CI：-6.6，+ 1.3）。DNN、MSV和SSV之间估计吸收剂量相对于MC模拟参考的MRAE分别为2.6%、3%和49%。在器官水平剂量测量中，所提出的方法与MSV、SSV和Olinda/EXM之间的MRAE分别为5.1%、21.8%和23.5%。提出的基于DNN的WB内部剂量测定表现出与直接蒙特卡罗方法相当的性能，同时克服了核医学中传统剂量测定技术的局限性。
In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account the heterogeneity of activity distribution, non-uniformity of surrounding medium, and patient-specific anatomy using deep learning algorithms. We extended the voxel-scale MIRD approach from single S-value kernel to specific S-value kernels corresponding to patient-specific anatomy to construct 3D dose maps using hybrid emission/transmission image sets. In this context, we employed a Deep Neural Network (DNN) to predict the distribution of deposited energy, representing specific S-values, from a single source in the center of a 3D kernel composed of human body geometry. The training dataset consists of density maps obtained from CT images and the reference voxelwise S-values generated using Monte Carlo simulations. Accordingly, specific S-value kernels are inferred from the trained model and whole-body dose maps constructed in a manner analogous to the voxel-based MIRD formalism, i.e., convolving specific voxel S-values with the activity map. The dose map predicted using the DNN was compared with the reference generated using MC simulations and two MIRD-based methods, including Single and Multiple S-Values (SSV and MSV) and Olinda/EXM software package. The predicted specific voxel S-value kernels exhibited good agreement with the MC-based kernels serving as reference with a mean relative absolute error (MRAE) of 4.5 ± 1.8 (%). Bland and Altman analysis showed the lowest dose bias (2.6%) and smallest variance (CI: − 6.6, + 1.3) for DNN. The MRAE of estimated absorbed dose between DNN, MSV, and SSV with respect to the MC simulation reference were 2.6%, 3%, and 49%, respectively. In organ-level dosimetry, the MRAE between the proposed method and MSV, SSV, and Olinda/EXM were 5.1%, 21.8%, and 23.5%, respectively. The proposed DNN-based WB internal dosimetry exhibited comparable performance to the direct Monte Carlo approach while overcoming the limitations of conventional dosimetry techniques in nuclear medicine.
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影响因子：
9.3
作者：
Lee, Min Sun;Kim, Joong Hyun;Lee, Jae Sung
通讯作者：
Lee, Jae Sung
影响因子：
3.5
作者：
Lee, Choonik;Lee, Choonsik;Bolch, Wesley E.
通讯作者：
Bolch, Wesley E.
DOI：
10.1186/s13014-018-1065-3
发表时间：
2018-06-27
期刊：
Radiation oncology (London, England)
影响因子：
--
作者：
Andreo P
通讯作者：
Andreo P
影响因子：
4.6
作者：
Dan Nguyen;Long, Troy;Jiang, Steve
通讯作者：
Jiang, Steve
影响因子：
3.5
作者：
GIAP, HB;MACEY, DJ;BOYER, AL
通讯作者：
BOYER, AL