Li Ran, Eldeniz Cihat, Wang Keyan, Nguyen Natalie, Schindler Thomas H, Huang Qi, Peterson Linda R, Yang Yang, Yan Yan, Cheng Jingliang, Woodard Pamela K, Zheng Jie
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, 63110, United States.
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Radiol Adv. 2024 Nov;1(4). doi: 10.1093/radadv/umae026. Epub 2024 Oct 26.
To develop a new deep learning enabled cardiovascular magnetic resonance (CMR) approach for noncontrast quantification of myocardial oxygen extraction fraction (mOEF) and myocardial blood volume (MBV) in vivo.
An asymmetric spin-echo prepared CMR sequence was created in a 3 T MRI clinical system. A UNet-based fully connected neural network was developed based on a theoretical model of CMR signals to calculate mOEF and MBV. Twenty healthy volunteers (20-30 years old, 11 females) underwent CMR scans at 3 short-axial slices (16 myocardial segments) on 2 different days. The reproducibility was assessed by the coefficient of variation. Ten patients with chronic myocardial infarction were examined to evaluate the feasibility of this CMR method to detect abnormality of mOEF and MBV.
Among the volunteers, the average global mOEF and MBV on both days was 0.58 ± 0.07 and 9.5% ± 1.5%, respectively, which agreed well with data measured by other imaging modalities. The coefficient of variation of mOEF was 8.4%, 4.5%, and 2.6%, on a basis of segment, slice, and participant, respectively. No significant difference in mOEF was shown among 3 slices or among different myocardial segments. Female participants showed significantly higher segmental mOEF than male participants ( < .001). Regional mOEF decrease 40% in CMR-confirmed myocardial infarction core, compared to normal myocardial regions.
The new deep learning-enabled CMR approach allows noncontrast quantification of mOEF and MBV with good to excellent reproducibility. This technique could provide an objective contrast-free means to assess and serially measure hypoxia-relief effects of therapeutic interventional strategies to save viable myocardial tissues.
开发一种新的基于深度学习的心血管磁共振（CMR）方法，用于在体无创定量心肌氧摄取分数（mOEF）和心肌血容量（MBV）。
在3T MRI临床系统中创建了一种非对称自旋回波准备的CMR序列。基于CMR信号的理论模型开发了一个基于U-Net的全连接神经网络，用于计算mOEF和MBV。20名健康志愿者（年龄20 - 30岁，女性11名）在2个不同日期对3个短轴切片（16个心肌节段）进行CMR扫描。通过变异系数评估可重复性。对10例慢性心肌梗死患者进行检查，以评估这种CMR方法检测mOEF和MBV异常的可行性。
在志愿者中，两天的平均整体mOEF和MBV分别为0.58±0.07和9.5%±1.5%，与其他成像方式测量的数据吻合良好。基于节段、切片和参与者，mOEF的变异系数分别为8.4%、4.5%和2.6%。3个切片之间或不同心肌节段之间的mOEF无显著差异。女性参与者的节段性mOEF显著高于男性参与者（P <.001）。与正常心肌区域相比，CMR证实的心肌梗死核心区域的局部mOEF降低了40%。
新的基于深度学习的CMR方法能够对mOEF和MBV进行无创定量，具有良好至优异的可重复性。该技术可为评估和连续测量治疗性干预策略对挽救存活心肌组织的缺氧缓解效果提供一种客观的无对比剂手段。
