Dan Guangyu, Feng Cui, Zhong Zheng, Sun Kaibao, Zhong Ping-Shou, Hu Daoyu, Li Zhen, Zhou Xiaohong Joe
Center for Magnetic Resonance Research, University of Illinois Chicago, Illinois, USA.
Department of Biomedical Engineering, University of Illinois Chicago, Illinois, USA.
Med Phys. 2025 Jul;52(7):e17810. doi: 10.1002/mp.17810. Epub 2025 Apr 8.
In diffusion-weighted imaging (DWI), a large collection of diffusion models is available to provide insights into tissue characteristics. However, these models are limited by predefined assumptions and computational challenges, potentially hindering the full extraction of information from the diffusion MR signal.
This study aimed at developing a MOdel-free Diffusion-wEighted MRI (MODEM) method for tissue differentiation by using a machine learning (ML) algorithm based on raw diffusion images without relying on any specific diffusion model. MODEM has been applied to both simulation data and cervical cancer diffusion images and compared with several diffusion models.
With Institutional Review Board approval, 54 cervical cancer patients (median age, 52 years; age range, 29-73 years) participated in the study, including 26 in the early FIGO (International Federation of Gynecology and Obstetrics) stage (IB, 16; IIA, 10) and 28 the late stage (IIB, 8; IIIB, 14; IIIC, 1; IVA, 3; IVB, 2). The participants underwent DWI with 17 b-values (0 to 4500 s/mm) at 3 Tesla. Synthetic diffusion MRI signals were also generated using Monte-Carlo simulation with Gaussian noise doping under varying substrates. MODEM with multilayer perceptron and five diffusion models (mono-exponential, intra-voxel incoherent-motion, diffusion kurtosis imaging, fractional order calculus, and continuous-time-random-walk models) were employed to distinguish different substrates in the simulation data and differentiate different pathological states (i.e., normal vs. cancerous tissue; and early-stage vs. late-stage cancers) in the cervical cancer dataset. Accuracy and area under the receiver operating characteristic (ROC) curve were evaluated. Mann-Whitney U-test was used to compare the area under the curve (AUC) and accuracy values between MODEM and the five diffusion models.
For the simulation dataset, MODEM produced a higher AUC and better accuracy, particularly in scenarios where the noise level exceeded 5%. For the cervical cancer dataset, MODEM yielded the highest AUC and accuracy in cervical cancer detection (AUC, 0.976; accuracy, 91.9%) and cervical cancer staging (AUC, 0.773; accuracy, 69.2%), significantly outperforming any of the diffusion models (p < 0.05).
MODEM is useful for cervical cancer detection and staging and offers considerable advantages over analytical diffusion models for tissue characterization.
在扩散加权成像（DWI）中，有大量的扩散模型可用于深入了解组织特征。然而，这些模型受到预定义假设和计算挑战的限制，可能会阻碍从扩散磁共振信号中充分提取信息。
本研究旨在开发一种无模型扩散加权磁共振成像（MODEM）方法，通过使用基于原始扩散图像的机器学习（ML）算法来进行组织区分，而不依赖于任何特定的扩散模型。MODEM已应用于模拟数据和宫颈癌扩散图像，并与几种扩散模型进行了比较。
经机构审查委员会批准，54例宫颈癌患者（中位年龄52岁；年龄范围29 - 73岁）参与了本研究，其中26例处于国际妇产科联盟（FIGO）早期阶段（IB期16例；IIA期10例），28例处于晚期阶段（IIB期8例；IIIB期14例；IIIC期1例；IVA期3例；IVB期2例）。参与者在3特斯拉磁场下接受了具有17个b值（0至4500 s/mm²）的DWI检查。还使用蒙特卡罗模拟在不同底物下掺杂高斯噪声生成了合成扩散磁共振信号。采用具有多层感知器的MODEM和五种扩散模型（单指数模型、体素内不相干运动模型、扩散峰度成像模型、分数阶微积分模型和连续时间随机游走模型）来区分模拟数据中的不同底物，并区分宫颈癌数据集中的不同病理状态（即正常组织与癌组织；早期癌症与晚期癌症）。评估了准确性和受试者操作特征（ROC）曲线下面积。使用曼 - 惠特尼U检验比较MODEM与五种扩散模型之间的曲线下面积（AUC）和准确性值。
对于模拟数据集，MODEM产生了更高的AUC和更好的准确性，特别是在噪声水平超过5%的情况下。对于宫颈癌数据集，MODEM在宫颈癌检测（AUC为0.976；准确性为91.9%）和宫颈癌分期（AUC为0.773；准确性为69.2%）方面产生了最高的AUC和准确性，显著优于任何一种扩散模型（p < 0.05）。
MODEM对宫颈癌检测和分期有用，并且在组织特征表征方面比分析性扩散模型具有相当大的优势。
