■不同的图像模态捕获患者的不同方面。期望产生在单个图像中捕获所有这些特征的图像。这项研究探讨了多模态图像融合方法增强磁共振成像(MRI)肿瘤对比度及其在不同患者之间的一致性的潜力,可以在一张图像中清晰地捕获解剖结构和肿瘤对比度,使基于MRI的目标描绘更准确和有效。
■使用来自80例鼻咽癌(NPC)患者的T1加权(T1-w)和T2加权(T2-w)磁共振(MR)图像。一种新颖的图像融合方法,图像融合的像素梯度模型(PGMIF),它基于像素梯度来捕获形状和生成对抗网络(GAN)项来捕获图像对比度,被介绍了。将PGMIF与几种流行的融合方法进行了比较。融合方法的性能使用两个指标进行量化:肿瘤对比噪声比(CNR),旨在测量边缘的对比度,和广义Sobel算子分析,旨在测量边缘的清晰度。
■PGMIF方法产生最高的CNR[中位数(mdn)=1.208,四分位距(IQR)=1.175-1.381]。与T1-w(mdn=1.044,IQR=0.957-1.042,P<5.60×10-4)和T2-wMR图像(mdn=1.111,IQR=1.023-1.182,P<2.40×10-3)相比,并优于其他融合模型:图像间最大比较梯度模型(GMMCI)(mdn=0.967,IQR=0.795-0.982,P<5.60×10-4),具有加权损失的深度学习模型(DLMWL)(mdn=0.883,IQR=0.832-0.943,P<5.60×10-4),像素加权平均(PWA)(mdn=0.875,IQR=0.806-0.972,P<5.60×10-4)和图像最大值(MoI)(mdn=0.863,IQR=0.823-0.991,P<5.60×10-4)。就广义Sobel算子分析而言,一种基于Sobel算子测量对比度增强的方法,PGMIF再次表现出最高的广义索贝尔算子(mdn=0.594,IQR=0.579-0.607;mdn=0.692,IQR=0.651-0.718,与T1-w和T2-w图像进行比较),比较:GMMCI(mdn=0.491,IQR=0.458-0.507,P<5.60×10-4;mdn=0.495,IQR=0.487-0.533,P<5.60×10-4),DLMWL(mdn=0.292,IQR=0.248-0.317,P<5.60×10-4;mdn=0.191,IQR=0.179-0.243,P<5.60×10-4),PWA(mdn=0.423,IQR=0.383-0.455,P<5.60×10-4;mdn=0.448,IQR=0.414-0.463,P<5.60×10-4)和MoI(mdn=0.437,IQR=0.406-0.479,P<5.60×10-4;mdn=0.540,IQR=0.521-与其他方法相比,具有出色的对比度增强和清晰度。
■基于肿瘤CNR和广义Sobel算子分析,提出的PGMIF方法证明了其增强MRI肿瘤对比度的能力,同时保持输入图像的解剖结构。它为放射治疗中的NPC肿瘤描绘提供了希望。
UNASSIGNED: Different image modalities capture different aspects of a patient. It is desirable to produce images that capture all such features in a single image. This research investigates the potential of multi-modal image fusion method to enhance magnetic resonance imaging (MRI) tumor contrast and its consistency across different patients, which can capture both the anatomical structures and tumor contrast clearly in one image, making MRI-based target delineation more accurate and efficient.
UNASSIGNED: T1-weighted (T1-w) and T2-weighted (T2-w) magnetic resonance (MR) images from 80 nasopharyngeal carcinoma (NPC) patients were used. A novel image fusion method, Pixelwise Gradient Model for Image Fusion (PGMIF), which is based on the pixelwise gradient to capture the shape and a generative adversarial network (GAN) term to capture the image contrast, was introduced. PGMIF is compared with several popular fusion methods. The performance of fusion methods was quantified using two metrics: the tumor contrast-to-noise ratio (CNR), which aims to measure the contrast of the edges, and a Generalized Sobel Operator Analysis, which aims to measure the sharpness of edge.
UNASSIGNED: The PGMIF method yielded the highest CNR [median (mdn) =1.208, interquartile range (IQR) =1.175-1.381]. It was a statistically significant enhancement compared to both T1-w (mdn =1.044, IQR =0.957-1.042, P<5.60×10-4) and T2-w MR images (mdn =1.111, IQR =1.023-1.182, P<2.40×10-3), and outperformed other fusion models: Gradient Model with Maximum Comparison among Images (GMMCI) (mdn =0.967, IQR =0.795-0.982, P<5.60×10-4), Deep Learning Model with Weighted Loss (DLMWL) (mdn =0.883, IQR =0.832-0.943, P<5.60×10-4), Pixelwise Weighted Average (PWA) (mdn =0.875, IQR =0.806-0.972, P<5.60×10-4) and Maximum of Images (MoI) (mdn =0.863, IQR =0.823-0.991, P<5.60×10-4). In terms of the Generalized Sobel Operator Analysis, a measure based on Sobel operator to measure contrast enhancement, PGMIF again exhibited the highest Generalized Sobel Operator (mdn =0.594, IQR =0.579-0.607; mdn =0.692, IQR =0.651-0.718 for comparison with T1-w and T2-w images), compared to: GMMCI (mdn =0.491, IQR =0.458-0.507, P<5.60×10-4; mdn =0.495, IQR =0.487-0.533, P<5.60×10-4), DLMWL (mdn =0.292, IQR =0.248-0.317, P<5.60×10-4; mdn =0.191, IQR =0.179-0.243, P<5.60×10-4), PWA (mdn =0.423, IQR =0.383-0.455, P<5.60×10-4; mdn =0.448, IQR =0.414-0.463, P<5.60×10-4) and MoI (mdn =0.437, IQR =0.406-0.479, P<5.60×10-4; mdn =0.540, IQR =0.521-0.636, P<5.60×10-4), demonstrating superior contrast enhancement and sharpness compared to other methods.
UNASSIGNED: Based on the tumor CNR and Generalized Sobel Operator Analysis, the proposed PGMIF method demonstrated its capability of enhancing MRI tumor contrast while keeping the anatomical structures of the input images. It holds promises for NPC tumor delineation in radiotherapy.