Ozyoruk Kutsev Bengisu, Harmon Stephanie A, Lay Nathan S, Yilmaz Enis C, Bagci Ulas, Citrin Deborah E, Wood Bradford J, Pinto Peter A, Choyke Peter L, Turkbey Baris
Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Radiology and Biomedical Engineering Department, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
J Pers Med. 2024 Oct 9;14(10):1047. doi: 10.3390/jpm14101047.
BACKGROUND/OBJECTIVES: Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI).
By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2.
Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models.
These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows.
背景/目的：前列腺MRI中的表观扩散系数（ADC）图可揭示肿瘤特征，但其准确性可能会受到与患者运动或直肠气体相关的畸变伪影的影响。为应对这些挑战，我们提出了一种新颖的方法，该方法利用生成对抗网络从T2加权磁共振图像（T2W MRI）合成ADC图。
通过利用对比学习，我们的模型将轴向T2W MRI准确地映射到前列腺器官边界裁剪区域内的ADC图，通过从两种成像模态中学习相似和不相似的对来捕捉细微变化和复杂的结构细节。我们在来自506名患者的未配对T2加权图像和ADC图的综合数据集上训练我们的模型。在评估我们名为AI-ADC的模型时，我们将其与三种先进方法进行了比较：CycleGAN、CUT和StyTr2。
在来自195名患者的3240个2D MRI切片的测试数据集上，我们的模型表现出更高的平均结构相似性指数（SSIM），为0.863，而CycleGAN、CUT和StyTr2的值分别为0.855、0.797和0.824。同样，我们的模型实现了显著更低的弗雷歇因距离（FID）值，为31.992，而其他三个模型的值分别为43.458、179.983和58.784，表明其在生成ADC图方面的优越性能。此外，我们在公开可用的ProstateX数据集的147名患者上评估了我们的模型，与其他三个模型相比，它表现出更高的SSIM，为0.647，更低的FID，为113.876。
这些结果突出了我们提出的模型从T2W MRI生成ADC图的有效性，展示了其在增强临床诊断和放射学工作流程方面的潜力。
