Quiescent frame, contrast-enhanced coronary magnetic resonance angiography reconstructed using limited number of physiologic frames from 5D free-running acquisitions

IF 2.1 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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引用次数: 0

Abstract

Background

5D, free-running imaging resolves sets of 3D whole-heart images in both cardiac and respiratory dimensions. In an application such as coronary imaging when a single, static image is of interest, computationally expensive offline iterative reconstruction is still needed to compute the multiple 3D datasets.

Purpose

Evaluate how the number of physiologic bins included in the reconstruction affects the computational cost and resulting image quality of a single, static volume reconstruction.

Study type

Retrospective.

Subjects

15 pediatric patients following Ferumoxytol infusion (4 mg/kg).

Field strength/Sequence

1.5 T/Ungated 5D free-running GRE sequence.

Assessment

The raw data of each subject were binned and reconstructed into a 5D (x-y-z-cardiac-respiratory) images. 1, 3, 5, 7, and 9 bins adjacent to both sides of the retrospectively determined cardiac resting phase and 1, 3 bins adjacent to the end-expiration phase are used for limited frame reconstructions. The static volume within each limited reconstruction was compared with the corresponding full 5D reconstruction using the structural similarity index measure (SSIM). A non-linear regression model was used to fit SSIM with the percentage of data used compared to full reconstruction (% data). A linear regression model was used to fit computation time with % raw data used. Coronary artery sharpness is measured on each limited reconstructed images to determine the minimal number of cardiac and respiratory bins needed to preserve image quality.

Statistical tests

The coefficient of determination (R2) is computed for each regression model.

Results

The % of data used in the reconstruction was linearly related to the computational time (R2 = 0.99). The SSIM of the static image from the limited reconstructions is non-linearly related with the % of data used (R2 = 0.80). Over the 15 patients, the model showed SSIM of 0.9 with 18% of data, and SSIM of 0.96 with 30% of data. The coronary artery sharpness of images reconstructed using no less than 5 cardiac and all respiratory phases is not significantly different from the full reconstructed images using all cardiac and respiratory bins.

Data conclusion

Reconstruction using only a limited number of acquired physiological states can linearly reduce the computational cost while preserving similarity to the full reconstruction image. It is suggested to use no less than 5 cardiac and all respiratory phases in the limited reconstruction to best preserve the original quality seen on the full reconstructed images.

利用 5D 自由运行采集的有限生理帧重建的静态帧、对比度增强冠状动脉磁共振血管造影。
背景:5D、自由运行成像可解析心脏和呼吸两个维度的三维全心图像集。在冠状动脉成像等应用中,当感兴趣的是单个静态图像时,仍然需要计算昂贵的离线迭代重建来计算多个三维数据集。目的:评估重建中包含的生理分区数量如何影响单个静态容积重建的计算成本和图像质量:研究对象15名输注阿魏酸后的儿科患者(4毫克/千克):场强/序列:1.5 T/Ungated 5D 自由运行 GRE 序列:对每个受试者的原始数据进行分档,并重建为 5D(x-y-z-心脏-呼吸)图像。回溯确定的心脏静息期两侧相邻的 1、3、5、7 和 9 个分区以及呼气末期相邻的 1、3 个分区用于有限帧重建。使用结构相似性指数测量法(SSIM)将每个有限重建中的静态体积与相应的完整 5D 重建进行比较。使用非线性回归模型将 SSIM 与完整重建数据的使用百分比(数据百分比)进行拟合。线性回归模型用于拟合计算时间与所用原始数据百分比。冠状动脉清晰度在每个有限的重建图像上进行测量,以确定保持图像质量所需的最小心脏和呼吸分区数:统计测试:计算每个回归模型的决定系数(R2):结果:重建中使用的数据百分比与计算时间呈线性关系(R2 = 0.99)。有限重建的静态图像的 SSIM 与所用数据百分比呈非线性关系(R2 = 0.85)。在 15 名患者中,该模型使用 22% 的数据显示 SSIM 为 0.9,使用 45% 的数据显示 SSIM 为 0.95。使用不少于 5 个心动相位和所有呼吸相位重建的图像的冠状动脉清晰度与使用所有心动相位和呼吸相位重建的完整图像没有显著差异:数据结论:仅使用有限数量的采集生理状态进行重建可线性降低计算成本,同时保持与完整重建图像的相似性。建议在有限重建中使用不少于 5 个心脏相位和所有呼吸相位,以最好地保留完整重建图像的原始质量。
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来源期刊
Magnetic resonance imaging
Magnetic resonance imaging 医学-核医学
CiteScore
4.70
自引率
4.00%
发文量
194
审稿时长
83 days
期刊介绍: Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.
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