In recent years,radiotherapy based only on Magnetic Resonance(MR)images has become a hot spot for radiotherapy planning research in the current medical field.However,functional computed tomography(CT)is still needed f...In recent years,radiotherapy based only on Magnetic Resonance(MR)images has become a hot spot for radiotherapy planning research in the current medical field.However,functional computed tomography(CT)is still needed for dose calculation in the clinic.Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest,making radiotherapy based only on MR images possible.In this paper,we proposed a novel unsupervised image synthesis framework with registration networks.This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image and registering the cycle-consistent image with the input image.Furthermore,this paper added ConvNeXt blocks to the network and used large kernel convolutional layers to improve the network’s ability to extract features.This research used the collected head and neck data of 180 patients with nasopharyngeal carcinoma to experiment and evaluate the training model with four evaluation metrics.At the same time,this research made a quantitative comparison of several commonly used model frameworks.We evaluate the model performance in four evaluation metrics which achieve Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity(SSIM)are 18.55±1.44,86.91±4.31,33.45±0.74 and 0.960±0.005,respectively.Compared with other methods,MAE decreased by 2.17,RMSE decreased by 7.82,PSNR increased by 0.76,and SSIM increased by 0.011.The results show that the model proposed in this paper outperforms other methods in the quality of image synthesis.The work in this paper is of guiding significance to the study of MR-only radiotherapy planning.展开更多
文摘背景与目的:核磁共振成像(magnetic resonance imaging,MRI)图像对软组织结构具有较高的分辨率,但由于失真和缺乏剂量计算所需要的电子密度而限制了其在脑部肿瘤放射治疗中的应用,而MRI和CT图像融合可解决这一问题。本研究探讨MRI与CT的图像融合精度,及其对脑胶质瘤术后患者放疗临床靶区(clinical target volume,CTV)及危及器官(organs at risk,OARs)体积和中心位置的影响。方法:9例颅内胶质瘤术后患者MRI和CT图像采用标点法进行融合,评价其融合精度,分别采用体积法及几何中心法(center of geometry,COG)研究融合前后临床靶区和危及器官体积和中心的变化,测定病灶MRI-CT融合图像的COG与CT定位图像COG的距离,体积法测定病灶MRI与CT图像融合部分体积(VMRI-CT)占总体积(VMRI+CT)的百分比(PMRI-CT)。结果:采用人工标记法进行融合的精度小于1.5mm,完全达到脑部肿瘤的误差要求。融合后各危及器官体积无明显改变(P>0.05);9例患者中8例融合界面勾画的CTV体积比CT定位图像CTV体积减小13.85%～73.59%,1例体积增大10.35%;平均体积比较差异有统计学意义(P<0.05);融合后CTV的中心位置变化最大[(8.74±6.60)mm],其次为双眼[左右眼分别为(5.25±2.38)mm和(5.65±2.56)mm],脑干位置变化最小[(1.83±1.06)mm]。结论:采用人工标记的方法进行图像融合具有较高的融合精度,MRI与CT融合的方法可明显减少脑胶质瘤术后放疗CTV勾画的不确定性。
基金supported by the National Science Foundation for Young Scientists of China(Grant No.61806060)2019-2021,the Basic and Applied Basic Research Foundation of Guangdong Province(2021A1515220140)the Youth Innovation Project of Sun Yat-sen University Cancer Center(QNYCPY32).
文摘In recent years,radiotherapy based only on Magnetic Resonance(MR)images has become a hot spot for radiotherapy planning research in the current medical field.However,functional computed tomography(CT)is still needed for dose calculation in the clinic.Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest,making radiotherapy based only on MR images possible.In this paper,we proposed a novel unsupervised image synthesis framework with registration networks.This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image and registering the cycle-consistent image with the input image.Furthermore,this paper added ConvNeXt blocks to the network and used large kernel convolutional layers to improve the network’s ability to extract features.This research used the collected head and neck data of 180 patients with nasopharyngeal carcinoma to experiment and evaluate the training model with four evaluation metrics.At the same time,this research made a quantitative comparison of several commonly used model frameworks.We evaluate the model performance in four evaluation metrics which achieve Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Peak Signal-to-Noise Ratio(PSNR),and Structural Similarity(SSIM)are 18.55±1.44,86.91±4.31,33.45±0.74 and 0.960±0.005,respectively.Compared with other methods,MAE decreased by 2.17,RMSE decreased by 7.82,PSNR increased by 0.76,and SSIM increased by 0.011.The results show that the model proposed in this paper outperforms other methods in the quality of image synthesis.The work in this paper is of guiding significance to the study of MR-only radiotherapy planning.
