UCL Center for Translational Cardiovascular Imaging, University College London, London, UK.
Department of Cardiology, Royal Free London NHS Foundation Trust, London, UK.
Magn Reson Med. 2024 Jan;91(1):266-279. doi: 10.1002/mrm.29855. Epub 2023 Oct 6.
Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression).
A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05).
In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging.
HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.
交互式心脏 MRI 用于快速扫描规划和磁共振引导的介入。然而,实时采集和近乎实时可视化的要求限制了可实现的时空分辨率。本研究旨在通过优化欠采样螺旋采样并利用深度学习进行低延迟重建(深度伪影抑制)来提高交互式成像分辨率。
通过 HyperBand 对变量密度螺旋轨迹进行参数化和优化,为快速深度伪影抑制提供最佳候选轨迹。训练数据包括 692 个屏气 CINE。所开发的交互式序列在模拟中进行了测试,并前瞻性地在 13 名受试者中进行了测试(10 名用于图像评估,2 名用于导管插入术,1 名用于运动)。在前瞻性研究中,优化框架-HyperSLICE-在定量和定性图像指标方面与传统笛卡尔实时和屏气 CINE 成像进行了比较。使用 Friedman chi-squared 检验和事后 Nemenyi 检验(p<0.05)测试了统计差异。
在模拟中,与径向和均匀螺旋采样相比,使用优化螺旋采样的归一化均方根误差、峰值 SNR、结构相似性和拉普拉斯能量均具有统计学意义上的显著提高,尤其是在扫描计划更改后(结构相似性:0.71 与 0.45 和 0.43)。前瞻性地,HyperSLICE 能够实现比传统笛卡尔实时成像更高的空间和时间分辨率。该流水线在患者导管回撤期间得到了证明,能够快速重建用于交互式成像。
HyperSLICE 能够实现高空间和时间分辨率的交互式成像。优化螺旋采样使整体图像质量更好,并且能够更好地处理图像过渡,与径向和均匀螺旋轨迹相比具有优势。