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Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.基于新型对抗性语义结构深度学习的脑 PET/MRI 磁共振成像衰减校正。
Eur J Nucl Med Mol Imaging. 2019 Dec;46(13):2746-2759. doi: 10.1007/s00259-019-04380-x. Epub 2019 Jul 1.
2
Accurate hybrid template-based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications.在同时进行的PET/MR脑成像应用中,使用UTE图像进行基于混合模板和基于MR的准确衰减校正。
BMC Med Imaging. 2018 Nov 6;18(1):41. doi: 10.1186/s12880-018-0283-3.
3
Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction.Dixon-VIBE 深度学习(DIVIDE)用于骨盆 PET/MR 衰减校正的伪 CT 合成。
J Nucl Med. 2019 Mar;60(3):429-435. doi: 10.2967/jnumed.118.209288. Epub 2018 Aug 30.
4
Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network.使用卷积神经网络为 PET/MRI 神经成像的 PET 衰减校正生成患者特异性透射数据。
J Nucl Med. 2019 Apr;60(4):555-560. doi: 10.2967/jnumed.118.214320. Epub 2018 Aug 30.
5
Improving PET/MR brain quantitation with template-enhanced ZTE.基于模板增强 ZTE 提高 PET/MR 脑定量分析准确性。
Neuroimage. 2018 Nov 1;181:403-413. doi: 10.1016/j.neuroimage.2018.07.029. Epub 2018 Jul 19.
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Medical Image Synthesis with Context-Aware Generative Adversarial Networks.基于上下文感知生成对抗网络的医学图像合成
Med Image Comput Comput Assist Interv. 2017 Sep;10435:417-425. doi: 10.1007/978-3-319-66179-7_48. Epub 2017 Sep 4.
7
Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.基于 Dixon 和 ZTE MR 图像的深度神经网络在脑 PET 成像中的衰减校正。
Phys Med Biol. 2018 Jun 13;63(12):125011. doi: 10.1088/1361-6560/aac763.
8
Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning.基于零切线的头部伪 CT 图像转换及其在 PET/MR 衰减校正和 MR 引导放射治疗计划中的应用。
Magn Reson Med. 2018 Oct;80(4):1440-1451. doi: 10.1002/mrm.27134. Epub 2018 Feb 18.
9
Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI.零回波时间和 Dixon 深度伪 CT(ZeDD CT):使用多参数 MRI 的深度卷积神经网络直接生成用于骨盆 PET/MRI 衰减校正的伪 CT 图像。
J Nucl Med. 2018 May;59(5):852-858. doi: 10.2967/jnumed.117.198051. Epub 2017 Oct 30.
10
Rapid dual-echo ramped hybrid encoding MR-based attenuation correction (dRHE-MRAC) for PET/MR.基于快速双回波 ramped 混合编码磁共振的衰减校正(dRHE-MRAC)用于 PET/MR。
Magn Reson Med. 2018 Jun;79(6):2912-2922. doi: 10.1002/mrm.26953. Epub 2017 Oct 2.

基于磁共振成像的正电子发射断层显像衰减校正,采用联合超短回波时间/多回波狄克逊采集技术。

MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.

作者信息

Han Paul Kyu, Horng Debra E, Gong Kuang, Petibon Yoann, Kim Kyungsang, Li Quanzheng, Johnson Keith A, El Fakhri Georges, Ouyang Jinsong, Ma Chao

机构信息

Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, MA, 02114, USA.

Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA.

出版信息

Med Phys. 2020 Jul;47(7):3064-3077. doi: 10.1002/mp.14180. Epub 2020 May 11.

DOI:10.1002/mp.14180
PMID:32279317
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7375929/
Abstract

PURPOSE

To develop a magnetic resonance (MR)-based method for estimation of continuous linear attenuation coefficients (LACs) in positron emission tomography (PET) using a physical compartmental model and ultrashort echo time (UTE)/multi-echo Dixon (mUTE) acquisitions.

METHODS

We propose a three-dimensional (3D) mUTE sequence to acquire signals from water, fat, and short T components (e.g., bones) simultaneously in a single acquisition. The proposed mUTE sequence integrates 3D UTE with multi-echo Dixon acquisitions and uses sparse radial trajectories to accelerate imaging speed. Errors in the radial k-space trajectories are measured using a special k-space trajectory mapping sequence and corrected for image reconstruction. A physical compartmental model is used to fit the measured multi-echo MR signals to obtain fractions of water, fat, and bone components for each voxel, which are then used to estimate the continuous LAC map for PET attenuation correction.

RESULTS

The performance of the proposed method was evaluated via phantom and in vivo human studies, using LACs from computed tomography (CT) as reference. Compared to Dixon- and atlas-based MRAC methods, the proposed method yielded PET images with higher correlation and similarity in relation to the reference. The relative absolute errors of PET activity values reconstructed by the proposed method were below 5% in all of the four lobes (frontal, temporal, parietal, and occipital), cerebellum, whole white matter, and gray matter regions across all subjects (n = 6).

CONCLUSIONS

The proposed mUTE method can generate subject-specific, continuous LAC map for PET attenuation correction in PET/MR.

摘要

目的

开发一种基于磁共振(MR)的方法,使用物理隔室模型和超短回波时间(UTE)/多回波狄克逊(mUTE)采集来估计正电子发射断层扫描(PET)中的连续线性衰减系数(LAC)。

方法

我们提出一种三维(3D)mUTE序列,在一次采集中同时从水、脂肪和短T成分(如骨骼)获取信号。所提出的mUTE序列将3D UTE与多回波狄克逊采集相结合,并使用稀疏径向轨迹来加快成像速度。使用特殊的k空间轨迹映射序列测量径向k空间轨迹中的误差,并在图像重建时进行校正。使用物理隔室模型拟合测量的多回波MR信号,以获得每个体素的水、脂肪和骨成分的分数,然后用于估计PET衰减校正的连续LAC图。

结果

通过体模和人体研究评估了所提出方法的性能,使用计算机断层扫描(CT)的LAC作为参考。与基于狄克逊和图谱的MRAC方法相比,所提出的方法产生的PET图像与参考图像具有更高的相关性和相似性。在所提出方法重建的PET活性值的相对绝对误差在所有受试者(n = 6)的四个脑叶(额叶、颞叶、顶叶和枕叶)、小脑、整个白质和灰质区域中均低于5%。

结论

所提出的mUTE方法可以生成用于PET/MR中PET衰减校正的个体特异性连续LAC图。