首页|期刊导航|分子影像学杂志|基于注意力门增强机制的脑钙化成分跨模态图像生成:一种MRI到CT的精准映射方法

基于注意力门增强机制的脑钙化成分跨模态图像生成:一种MRI到CT的精准映射方法OA

Attention gate-enhanced cross-modal image generation for brain calcification components:a precise MRI-to-CT mapping method

中文摘要英文摘要

目的 探索基于注意力门增强的APS模型(AG-APS)从MR合成高质量CT(sCT),用于颅内钙化成分的精准生成.方法 回顾性收集2022年1月~2024年12月南方医科大学南方医院和南方医院增城院区共134例脑部存在钙化成分的受试者,涵盖生理性及病理性钙化,共获取1478张MRI-CT配对轴向切片.本研究提出AG-APS模型,在生成器中引入注意力门模块,通过量化sCT与真实CT(rCT)的平均绝对误差(MAE)、峰值信噪比(PSNR)及结构相似性指标(SSIM),评估sCT图像质量,并与CycleGAN、U-Net、Pix2pix及LSeSim对比.采用消融实验分析并对结果进行统计学检验.结果 在全图生成任务中,AG-APS模型性能(MAE=0.032,PSNR=21.352 dB,SSIM=0.821)优于U-Net、Pix2Pix、LSeSim和CycleGAN等现有方法(P<0.05),表现最佳.在钙化的局部评估中,图像质量、结构保真性和纹理一致性上同样取得最优(MAE=0.102,PSNR=32.360 dB,SSIM=0.986,P<0.05).在钙化区域的假阳性检测中,当容差阈值为5%和10%时,假阳性率(FPR)分别为2.11%和0%.此外,消融实验结果验证了在生成器中引入AG模块对于提升模型生成质量的有效性和必要性.结论 AG-APS可实现脑MRI到高质量sCT的生成,具备颅内钙化成分的精确重建,有助于钙化成分识别并减少对CT的依赖,有效降低辐射风险,具备良好的临床应用前景.

Objective To investigate an attention gate-enhanced adversarial-pixel-structural consistency(AG-APS)model for synthesizing high-quality synthetic CT(sCT)images from magnetic resonance images,enabling precise generation of intracranial calcification components.Methods A total of 134 subjects with intracranial calcifications,including both physiological and pathological cases,were retrospectively collected from Nanfang Hospital and Nanfang Hospital Zengcheng Branch of Southern Medical University from January 2022 to December 2024.In total,1478 paired axial MR-CT slices were obtained.An AG-APS model was proposed by incorporating attention gate(AG)modules into the generator.The quality of the generated sCT images was quantitatively evaluated against real CT(rCT)using mean absolute error(MAE),peak signal-to-noise ratio(PSNR),and structural similarity index(SSIM),and compared with CycleGAN,U-Net,Pix2Pix,and LSeSim.Ablation experiments were conducted,and statistical analyses were performed.Results In the whole-image synthesis task,the AG-APS achieved superior performance(MAE=0.032,PSNR=21.352 dB,SSIM=0.821)compared with U-Net,Pix2Pix,LSeSim,and CycleGAN(P<0.05),demonstrating the best overall performance.For local evaluation of calcified regions,AG-APS also outperformed competing methods in image quality,structural fidelity,and textural consistency(MAE=0.102,PSNR=32.360 dB,SSIM=0.986),with significant improvements(P<0.05).In false-positive detection of calcification regions,the false-positive rate(FPR)was 2.11%and 0%when tolerance thresholds were set at 5%and 10%,respectively.Furthermore,ablation studies confirmed the effectiveness and necessity of introducing the AG module into the generator for enhancing synthesis quality.Conclusion The AG-APS model enables high-quality sCT generation from brain MR images,achieving precise reconstruction of intracranial calcifications.This approach facilitates calcification identification,reduces reliance on CT imaging,and lowers radiation exposure,underscoring its strong clinical potential.

吕祎君;贾铭;曾伟雄;林嘉泽;陈丽华;钟俊远;钟海舰;秦耿耿

赣南医科大学医学信息工程学院,江西 赣州 341000||南方医科大学南方医院影像诊断科,广东 广州 510515赣州市人民医院医学影像科,江西 赣州 341000南方医科大学南方医院影像诊断科,广东 广州 510515南方医科大学南方医院影像诊断科,广东 广州 510515赣南医科大学医学信息工程学院,江西 赣州 341000赣州市人民医院医学影像科,江西 赣州 341000赣南医科大学医学信息工程学院,江西 赣州 341000赣南医科大学医学信息工程学院,江西 赣州 341000||南方医科大学南方医院影像诊断科,广东 广州 510515

钙化生成对抗网络跨模态重建注意力机制计算机断层扫描磁共振成像

calcificationgenerative adversarial networkscross-modality reconstructionattention mechanismcomputed tomographymagnetic resonance imaging

《分子影像学杂志》 2026 (2)

154-160,7

国家自然科学基金(32571282)广东省自然科学基金(2024A1515011520)广东省省级科技计划项目(2024A1111120015)赣州市科技计划项目(2022-RC1349、2022-ZD1373)Supported by National Natural Science Foundation of China(32571282)

10.12122/j.issn.1674-4500.2026.02.03

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