使用磁共振成像 (MRI) 测量非参数体素内平均扩散率分布 (MDD) 是检测生理变化期间细胞内扩散率变化的敏感方法。神经胶质瘤的组织学和分子分类对于预后和治疗至关重要，亚型之间具有不同的水扩散动力学。我们开发了一种使用全连接网络 (FCN) 的数据驱动方法，以提高计算不同 SNR 下的 MDD 的速度和稳定性，使肿瘤能够微观结构映射，并测试其在识别 MIB-1 标记指数 (LI) 水平和神经胶质瘤分子状态方面的可靠性。训练 FCN 来学习模拟扩散衰减曲线和真实 MDD 之间的映射。我们使用各种扩散系数和随机 SNR ε [ 30 , 300 ] $ \in [ {30,\ 300} ]$ 执行了 5 000 000 条模拟曲线。 80%的模拟曲线用于FCN训练，10%用于验证，其余为FCN性能评估的外部测试。收集体内数据以评估其临床可靠性。 101 名神经胶质瘤患者（44 岁 ± $ \pm $ 14，67 名男性）和 6 名健康对照者接受了 3.0 T MRI 检查，采用自旋回波-回波平面成像 (SE-EPI) 扩散加权成像 (DWI) 序列。训练后的 FCN 用于逐个体素地计算每个脑体素的 MDD。我们使用模糊 C 均值算法对肿瘤体素的 MDD 进行聚类，促进不同神经胶质瘤组织的表征。通过 MDD 的截面积分进行定量评估，由六个频带划分以得出信号分数 ( f n , n = 1 - 6 ${{f}_n},\ n = 1 -6$ ) 和最大峰值的扩散率 ( D p e a k ${{D}_{peak}}$ )。余弦相似度得分 (CSS) 用于 MDD 相似度。采用ANOVA和Mann-Whitney U检验进行差异分析。使用Logistic回归和接受者算子特征曲线下面积（AUC）进行分类评估。模拟结果表明，基于FCN的MDD方法（FCN-MDD）比基于非负最小二乘的MDD（NNLS-医学博士）。对于体内数据，FCN-MDD 获得的 ET 和 NET 光谱比 NNLS-MDD 更容易区分。分数图描绘了不同肿瘤组织的特征（增强和非增强肿瘤、水肿和坏死）。 f 3 , f 4 , D p e a k ${{f}_3},\ {{f}_4},{{D}_{peak}}$ 分别与 MIB-1 呈正相关和负相关 ( r = 0.568 , r = - 0.521，r = - 0.654 $r = 0.568，\ r = - 0.521，\ r = - 0.654$，所有 p < 0.001 $p

Measuring non-parametric intravoxel mean diffusivity distributions (MDDs) using magnetic resonance imaging (MRI) is a sensitive method for detecting intracellular diffusivity changes during physiological alterations. Histological and molecular glioma classifications are essential for prognosis and treatment, with distinct water diffusion dynamics among subtypes.We developed a data-driven approach using a fully connected network (FCN) to enhance the speed and stability of calculating MDDs across varying SNRs, enable tumor microstructural mapping, and test its reliability in identifying MIB-1 labeling index (LI) levels and molecular status of gliomas.An FCN was trained to learn the mapping between the simulated diffusion decay curves and the ground truth MDDs. We performed 5 000 000 simulation curves with various diffusivity components and random SNR ∈ [ 30 , 300 ] $ \in [ {30,\ 300} ]$ . Eighty percent of simulation curves were used for the FCN training, 10% for validation, and the others were external tests for the FCN performance evaluation. In vivo data were collected to evaluate its clinical reliability. One hundred one patients (44 years ± $ \pm $ 14, 67 men) with gliomas and six healthy controls underwent a 3.0 T MRI examination with a spin echo-echo planar imaging (SE-EPI) diffusion-weighted imaging (DWI) sequence. The trained FCN was employed to calculate MDDs of each brain voxel by voxel. We used the Fuzzy C-means algorithm to cluster the MDDs of tumor voxels, facilitating the characterization of distinct glioma tissues. Quantitative assessments were conducted through sectional integrals of the MDDs, demarcated by six bands to derive signal fractions ( f n , n = 1 - 6 ${{f}_n},\ n = 1 -6$ ) and diffusivities of the maximum peaks ( D p e a k ${{D}_{peak}}$ ). Cosine similarity scores (CSS) were used for MDD similarity. ANOVA and Mann-Whitney U test were used for difference analysis. Logistic regression and area under the receiver operator characteristic curve (AUC) were used for classification evaluation.The simulation results showed that the FCN-based MDD approach (FCN-MDD) achieved higher CSS than non-negative least squares-based MDD (NNLS-MDD). For in vivo data, the spectra of ET and NET obtained by FCN-MDD are more distinguishable than NNLS-MDD. Fraction maps delineate the characteristics of different tumor tissues (enhancing and non-enhancing tumor, edema, and necrosis). f 3 , f 4 , D p e a k ${{f}_3},\ {{f}_4},{{D}_{peak}}$ showed a positive and negative correlation with MIB-1 respectively ( r = 0.568 , r = - 0.521 , r = - 0.654 $r = 0.568,\ r = - 0.521,\ r = - 0.654$ , all p < 0.001 $p < 0.001$ ). The AUC of D p e a k ${{D}_{peak}}$ for predicting MIB-1 LI levels was 0.900 (95% CI, 0.826-0.974), versus 0.781 (0.677-0.886) of ADC. The highest AUC of isocitrate dehydrogenase (IDH) mutation status, assessed by a logistic regression model ( f 1 + f 3 ${{f}_1} + {{f}_3}$ ) was 0.873 (95% CI, 0.802-0.944).The proposed FCN-MDD method was more robust to variations in SNR and less reliant on empirically set regularization values than the NNLS-MDD method. FCN-MDD also enabled qualitative and quantitative evaluation of the composition of gliomas.© 2024 American Association of Physicists in Medicine.