基于神经网络的自适应加权对数差减法在双能透视中用于无标记肿瘤跟踪。

Adaptive weighted log subtraction based on neural networks for markerless tumor tracking using dual-energy fluoroscopy.

机构信息

Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, 60153, USA.

Varian Medical Systems, 3120 Hansen Way, Palo Alto, CA, 94304, USA.

出版信息

Med Phys. 2020 Feb;47(2):672-680. doi: 10.1002/mp.13941. Epub 2020 Jan 10.

DOI:10.1002/mp.13941

PMID:31797397

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7015793/

Abstract

PURPOSE

To present a novel method, based on convolutional neural networks (CNN), to automate weighted log subtraction (WLS) for dual-energy (DE) fluoroscopy to be used in conjunction with markerless tumor tracking (MTT).

METHODS

A CNN was developed to automate WLS (aWLS) of DE fluoroscopy to enhance soft tissue visibility. Briefly, this algorithm consists of two phases: training a CNN architecture to predict pixel-wise weighting factors followed by application of WLS subtraction to reduce anatomical noise. To train the CNN, a custom phantom was built consisting of aluminum (Al) and acrylic (PMMA) step wedges. Per-pixel ground truth (GT) weighting factors were calculated by minimizing the contrast of Al in the step wedge phantom to train the CNN. The pretrained model was then utilized to predict pixel-wise weighting factors for use in WLS. For comparison, the weighting factor was manually determined in each projection (mWLS). A thorax phantom with five simulated spherical targets (5-25 mm) embedded in a lung cavity, was utilized to assess aWLS performance. The phantom was imaged with fast-kV dual-energy (120 and 60 kVp) fluoroscopy using the on-board imager of a commercial linear accelerator. DE images were processed offline to produce soft tissue images using both WLS methods. MTT was compared using soft tissue images produced with both mWLS and aWLS techniques.

RESULTS

Qualitative evaluation demonstrated that both methods achieved soft tissue images with similar quality. The use of aWLS increased the number of tracked frames by 1-5% compared to mWLS, with the largest increase observed for the smallest simulated tumors. The tracking errors for both methods produced agreement to within 0.1 mm.

CONCLUSIONS

A novel method to perform automated WLS for DE fluoroscopy was developed. Having similar soft tissue quality as well as bone suppression capability as mWLS, this method allows for real-time processing of DE images for MTT.

摘要

目的

提出一种新的方法，基于卷积神经网络（CNN），实现双能（DE）透视加权对数减法（WLS）的自动化，以便与无标记肿瘤跟踪（MTT）结合使用。

方法

开发了一个 CNN 来实现 DE 透视的自动 WLS（aWLS），以增强软组织的可视性。简要地说，该算法包括两个阶段：训练一个 CNN 架构来预测像素级加权因子，然后应用 WLS 减法来减少解剖噪声。为了训练 CNN，构建了一个由铝（Al）和亚克力（PMMA）阶跃楔块组成的定制体模。通过最小化阶跃楔体模中 Al 的对比度来计算每个像素的地面真实（GT）加权因子，以训练 CNN。然后利用预训练模型预测像素级加权因子，用于 WLS。为了比较，在每个投影中手动确定加权因子（mWLS）。利用一个嵌入在肺腔中的五个模拟球形靶（5-25 毫米）的胸腔体模来评估 aWLS 的性能。使用商业直线加速器的机载成像仪对体模进行快速千伏双能（120 和 60 kVp）透视成像。离线处理 DE 图像，使用两种 WLS 方法生成软组织图像。使用 mWLS 和 aWLS 技术生成的软组织图像比较 MTT。