An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters
An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters
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一种用于估计体素内不相干运动参数的无监督卷积神经网络方法
DOI：
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发表时间：
2022
影响因子：
3.5
通讯作者：
Hsuan
中科院分区：
文献类型：
--
作者：
Hsuan
客观的。通过将双指数模型拟合到多个 b 值扩散加权磁共振成像 (DW-MRI) 获得的体素内不相干运动 (IVIM) 成像已被证明是适合不同临床应用的有前途的工具。最近，提出了几种深度神经网络（DNN）方法来生成 IVIM 成像。方法。在这项研究中，我们提出了一种无监督卷积神经网络（CNN）方法来估计 IVIM 参数。我们使用模拟和真实腹部 DW-MRI 数据来评估所提出的基于 CNN 的方法的性能，并将结果与非线性最小二乘拟合（TRR，信任区域反射算法）和基于前馈反向传播 DNN 的方法获得的结果进行比较。主要结果。仿真结果表明，基于 DNN 和 CNN 的方法均比 TRR 方法具有更低的变异系数，但基于 CNN 的方法提供了更准确的参数估计。从真实 DW-MRI 数据获得的结果表明，TRR 方法产生了许多有偏差的 IVIM 参数估计值，达到了参数上限和下限。相比之下，基于 DNN 和 CNN 的方法产生的 IVIM 参数估计偏差较小。总体而言，基于 DNN 和 CNN 的方法获得的灌注分数和扩散系数接近文献值。然而，与基于 CNN 的方法相比，TRR 和基于 DNN 的方法都倾向于产生增加的伪扩散系数（55%–180%）。意义。我们的初步结果表明，使用 CNN 估计 IVIM 参数是可行的。
Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.
空间约束不相干运动方法改善了肝脏和脾脏中的扩散加权 MRI 信号衰减分析。
影响因子：
3.8
作者：
Taimouri,Vahid;Afacan,Onur;Perez-Rossello,JeannetteM;Callahan,MichaelJ;Mulkern,RobertV;Warfield,SimonK;Freiman,Moti
通讯作者：
Freiman,Moti