Jing Juntong, Mekhanik Anthony, Schellenberg Melanie, Murray Victor, Cohen Ouri, Otazo Ricardo
Weill Cornell Graduate School of Medical Sciences, New York, NY, United States.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Magn Reson Imaging. 2025 Apr;117:110310. doi: 10.1016/j.mri.2024.110310. Epub 2024 Dec 20.
Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI. DCE-Movienet offers rapid reconstruction of high spatiotemporal resolution 4D MRI data, reducing reconstruction time of the full acquisition to only 0.66 s, which is significantly shorter than compressed sensing's order of 10 min-long reconstructions, without affecting image quality. DCE-Qnet can then perform comprehensive quantification of perfusion parameter maps (K, v, v), and other parameters affecting quantification (T1, B1, and BAT) from a single contrast-enhanced acquisition. The end-to-end deep learning pipeline was implemented to process data acquired with a golden-angle stack-of-stars k-space trajectory and validated on healthy volunteers and a cervical cancer patient against a compressed sensing reconstruction. The end-to-end deep learning DCE-MRI technique addresses key limitations in DCE-MRI in terms of speed and quantification robustness, which is expected to improve the performance of DCE-MRI in a clinical setting.
动态对比增强（DCE）磁共振成像（MRI）是一种重要的成像工具，可用于评估肿瘤血管生成，从而更好地刻画肿瘤范围和异质性，并对治疗反应进行早期评估。然而，由于采集和量化性能方面的挑战以及缺乏自动化工具，定量DCE-MRI在临床上的应用仍然有限。本研究提出了一种端到端的深度学习流程，该流程利用一种名为DCE-Movienet的新型深度重建网络和一种先前开发的名为DCE-Qnet的深度量化网络，用于快速定量DCE-MRI。DCE-Movienet能够快速重建高时空分辨率的4D MRI数据，将完整采集的重建时间缩短至仅0.66秒，这明显短于压缩感知所需的长达10分钟的重建时间，且不影响图像质量。然后，DCE-Qnet可以从单次对比增强采集中对灌注参数图（K、v、v）以及其他影响量化的参数（T1、B1和BAT）进行全面量化。该端到端的深度学习流程被用于处理采用金角星状k空间轨迹采集的数据，并在健康志愿者和一名宫颈癌患者身上针对压缩感知重建进行了验证。端到端的深度学习DCE-MRI技术在速度和量化稳健性方面解决了DCE-MRI的关键局限性，有望提高DCE-MRI在临床环境中的性能。
