Wang Yinghui, Wang Lu, Feng Yidan, Chen Zhi, Qin Jing, Li Tian, Cai Jing
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Centre for Smart Health, Hong Kong Polytechnic University, Hong Kong SAR, China.
Med Phys. 2025 Sep;52(9):e18101. doi: 10.1002/mp.18101.
Four-dimensional magnetic resonance imaging (4D-MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D-MRI methods are limited by an inherent trade-off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.
This study aims to develop a technique that leverages information across multiple frames to mitigate spatial undersampling, thereby enabling superior spatiotemporal resolution in abdominal 4D-MRI.
We introduce a novel reconstruction approach for 4D-MRI that leverages respiratory-synchronized frames to reconstruct target frames with enhanced image quality. Specifically, we introduce a multi-frame collaborative reconstruction network (MCR-Net) that capitalizes on inter-frame correlations and complementary information for faithful reconstruction. MCR-Net integrates two key mechanisms: the Inter-frame mutual-attention mechanism (IMM) and the structure-aware consolidation module (SaCM). IMM enhances feature extraction by exploiting correlations among neighboring respiratory-synchronized frames, thereby reinforcing shared anatomical features while suppressing random artifacts and noise. SaCM consolidates structural information across frames by leveraging context-aware residual learning, enhancing high-frequency details, and filtering irrelevant data during multi-frame fusion, thus significantly improving the clarity and anatomical integrity.
Experimental evaluations on clinical patient datasets (training: n = 20; validation: n = 6) demonstrate that our method significantly outperforms nine state-of-the-art reconstruction approaches in both visual quality and quantitative accuracy. MCR-Net achieves superior performance in MAE, SSIM, and PSNR, outperforming the next-best methods by 3.77%, 1.03%, and 6.74%, respectively. Furthermore, our experiments validate that MCR-Net enhances registration accuracy compared to original low-quality 4D-MRI by 10.66%, 3.60%, and 1.94% in MAE, SSIM, and NCC metrics. Additionally, simulations demonstrate that MCR-Net effectively maintains high image quality even under significantly increased undersampling ratios.
Our findings demonstrate that MCR-Net effectively suppresses artifacts and recovers missing anatomical structures from undersampled acquisitions, underscoring its potential to enhance 4D-MRI's spatiotemporal resolution and advance clinical applications in abdominal radiotherapy.
四维磁共振成像（4D-MRI）在精确腹部放射治疗引导方面具有巨大潜力。然而，当前的4D-MRI方法受到空间分辨率和时间分辨率之间固有权衡的限制，导致图像质量受损，表现为空间分辨率低和显著的运动伪影，阻碍了临床应用。尽管最近有进展，但现有方法未能充分利用冗余帧信息，并且难以从高度欠采样的采集中恢复结构细节。
本研究旨在开发一种利用多帧信息来减轻空间欠采样的技术，从而在腹部4D-MRI中实现卓越的时空分辨率。
我们为4D-MRI引入了一种新颖的重建方法，该方法利用呼吸同步帧来重建具有更高图像质量的目标帧。具体而言，我们引入了一种多帧协作重建网络（MCR-Net），该网络利用帧间相关性和互补信息进行准确重建。MCR-Net集成了两个关键机制：帧间互注意力机制（IMM）和结构感知整合模块（SaCM）。IMM通过利用相邻呼吸同步帧之间的相关性来增强特征提取，从而强化共享的解剖特征，同时抑制随机伪影和噪声。SaCM通过利用上下文感知残差学习来整合跨帧的结构信息，增强高频细节，并在多帧融合过程中过滤无关数据，从而显著提高清晰度和解剖完整性。
对临床患者数据集（训练：n = 20；验证：n = 6）的实验评估表明，我们的方法在视觉质量和定量准确性方面均显著优于九种最先进的重建方法。MCR-Net在平均绝对误差（MAE）、结构相似性指数（SSIM）和峰值信噪比（PSNR）方面实现了卓越性能，分别比次优方法高出3.77%、1.03%和6.74%。此外，我们的实验验证了与原始低质量4D-MRI相比，MCR-Net在MAE、SSIM和归一化互相关（NCC）指标上的配准准确性分别提高了10.66%、3.60%和1.94%。此外，模拟表明，即使在欠采样率显著增加的情况下，MCR-Net也能有效保持高图像质量。
我们的研究结果表明，MCR-Net有效地抑制了伪影，并从欠采样采集中恢复了缺失的解剖结构，突出了其增强4D-MRI时空分辨率以及推进腹部放射治疗临床应用的潜力。
