首页|面向急性缺血性脑卒中的CT生成MRI算法

面向急性缺血性脑卒中的CT生成MRI算法

CT-Generated MRI Algorithm for Acute Ischemic Stroke

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 维普
  • 万方数据
急性缺血性脑卒中病灶在计算机断层扫描(CT)上表现不明显,但在核磁共振成像(MRI)上可以清晰显示.然而,当患者体内有金属植入物等特殊情况则无法进行MRI检测,使得患者的治疗受到延误.通过CT生成MRI可在急性缺血性脑卒中的诊断和治疗中起到至关重要的作用,但现有的医学影像跨模态生成方法从CT得到的MRI缺乏病灶信息且边界模糊.为了解决上述问题,提出一种基于影像组学和扩散生成对抗网络的急性缺血性脑卒中CT生成MRI算法,通过影像组学在CT上进行病灶特征增强,突出生成MRI的病灶信息,引入梯度损失为生成图像与真实图像增加边缘感知约束,提升生成MRI的质量.在ISLES2018挑战赛数据集上的实验结果表明,该算法生成的MRI在整体上的峰值信噪比为23.051 dB,结构相似度为0.798,皮尔逊相关系数为0.969,并且病灶区域的互信息为2.075,与现有的生成模型相比,该算法的各项指标均达到最优.此外,经3名经验丰富的放射科医生在生成的MRI上确定病灶并进行阳性/阴性分类,其中生成的MRI中无错误病灶,且分类准确率可达到86.61%,可作为一种辅助工具协助医生进行诊断.
The lesions of acute ischemic stroke are not clearly discernible on Computed Tomography(CT),but they are distinctly visible in Magnetic Resonance Imaging(MRI).However,in cases where patients have a metal implant in their body,MRI detection becomes unfeasible,delaying the patient's treatment.MRI generated from CT plays a crucial role in the diagnosis and treatment of acute ischemic stroke.However,MRI obtained from CT using the current medical image cross-modal generation method lacks lesion information and exhibits blurred boundaries.To address these issues,this study proposes a CT-generated MRI algorithm for acute ischemic stroke based on radiomics and diffusion Generative Adversarial Network(GAN).Through imaging radiomics,lesion features are enhanced on CT to highlight lesion information generated by MRI.Additionally,gradient loss is introduced to increase the edge perception constraints between generated and real images,thereby enhancing the quality of the generated MRI.The results of experiments conducted on the ISLES2018 challenge dataset show that the overall Peak Signal-to-Noise Ratio(PSNR)of the generated MRI is 23.051 dB,the Structural Similarity(SSIM)is 0.798,the Pearson Correlation Coefficient(PCC)is 0.969,and the Mutual Information(MI)of the lesion region is 2.075.These results indicate that the proposed algorithm is optimal compared with the existing generation models.In addition,three experienced radiologists identified the lesions on the generated MRI and classified them as positive or negative.No incorrect lesions are identified in the generated MRI,and the classification accuracy reached 86.61%,demonstrating its potential as an auxiliary tool to assist doctors in diagnosis.

medical image generationradiomicsdiffusion Generated Adversarial Network(GAN)Computed Tomography(CT)Magnetic Resonance Imaging(MRI)

张美美、秦品乐、柴锐、曾建潮、翟双姣、闫俊义、冯二燕

展开 >

中北大学计算机科学与技术学院,山西 太原 030051

医学图像生成 影像组学 扩散生成对抗网络 计算机断层扫描 核磁共振成像

山西省科技重大专项计划"揭榜挂帅"项目山西省科学技术厅自由探索类项目山西省基础研究计划自由探索类青年科学研究项目

202110101010101820210302123033202203021222049

2024

10.19678/j.issn.1000-3428.0068435

计算机工程
华东计算技术研究所　上海市计算机学会

计算机工程

CSTPCD北大核心
影响因子：0.581
ISSN：1000-3428
年,卷(期)：2024.50(2)
  • 2