Browsing by Subject "Image Synthesis"
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Item Embargo A Conditional Generative Adversarial Network (cGAN) Based 2D MP-RAGE MR Image Synthesis Method(2024) Zeng, ZiyiPurpose: A deep learning framework based on a conditional Generative Adversarial Network (cGAN) was developed to synthesize high-contrast Magnetization Prepared Rapid Gradient Echo (MP-RAGE) images from common spin-echo MR imaging sequences. This framework utilizes combinations of inputs from T1-weighted (T1-w), T2-weighted (T2-w), and Proton Density-weighted (PD-w) images. The primary objective was to augment the diversity of clinical data by capitalizing on the inherent advantages of MP-RAGE imaging, such as superior contrast, while mitigating its susceptibility to metal artifacts.Methods and Materials: A cGAN image synthesis model, incorporating a U-Net-based generator and a Patch GAN discriminator, was developed. The training was conducted across four distinct configurations, employing combinations of T1-w, T2-w, and PD-w images as inputs to synthesize MP-RAGE images, with and without Proton Density (PD) information, designated as PD-0 and PD-1, respectively. For training, data from 51 patients, comprising 8,160 slices, were used, following a training-to-validation ratio of 90:10. For prediction, data from 14 patients, comprising 2,240 slices, were utilized. The efficacy of the synthesized MP-RAGE images was evaluated using a suite of quantitative metrics, including Mean Absolute Error (MAE), Normalized Cross-Correlation (NCC), Percentage Mutual Information (PMI), and Structural Similarity Index (SSIM). Additionally, a Freesurfer brain segmentation task was performed on both synthesized and ground truth brain images, with the fidelity of synthesized images being indirectly assessed by the calculated Dice coefficient. Results: It was observed that the cGAN-synthesized MP-RAGE images exhibited comparable contrast to the ground truth in the axial view. A decrease in input channel numbers resulted in diminished contrast between certain anatomical structures in the synthetic MP-RAGE images, albeit within an acceptable range. The MAE approached (0.02±0.01), the PMI for two Three-in-One-out synthesis approached(0.76±0.07), the NCC was about (0.91±0.05), and the SSIM was about (0.9±0.1). The Freesurfer segmentation results showed desirable Dice coefficients (mostly above 0.8) for different kinds of inputs, except the One-in-One-out T1-w synthesis. Conclusion: The cGAN framework developed in this study has proven to be a robust and versatile tool for synthesizing high-contrast MP-RAGE images, even in scenarios with single-channel input images. The Freesurfer segmentation results demonstrated that the synthesized MP-RAGE images are highly similar to the ground truth in segmentation tasks, underscoring the potential clinical and research value of the proposed image synthesis model.
Item Embargo Efficient Synthesizing High Quality Digital Cortex Phantoms through a Novel Combination of GAN and CNN Methods(2024) Pan, JiongliPurpose: Imaging data scarcity is an intrinsic challenge in medical research partly due to resource limitations and privacy concerns. Digital human phantom is one approach to mitigate such challenge. The aim of this study is to propose an innovative method combining Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) to virtually synthesize large scale and high-quality brain cortex digital phantoms.Method: High resolution brain MRIs (MP-RAGE) of 369 patients were used to generate brain segmentations using FreeSurfer software. Total of 14,200 segmented 2D cortex phantoms were extracted and served as ground truth. A GAN was developed to learn and synthesize new cortex phantoms. 28,955 phantoms were generated and manually classified into ‘real’ and ‘fake’ groups, where 14,364 were labeled as ‘real’ while rest were labeled as ‘fake’ due to mal/poor morphology and/or noise. To facilitate automatic phantom quality classification, a CNN was designed and trained on the total of 43,155 phantoms, where the training and testing split was 7:3. Receiver Operating Characteristic Curve (ROC) analysis and Area Under the Curve (AUC) evaluation were performed on the CNN. The GAN and CNN together constitute the automatic cortex digital phantom generation network. To visually evaluate the realism of the model-generated phantoms, two experiments were conducted: the first involved binary classification of real and generated phantoms, and the second added a ‘Cannot Decide’ option to the previous task. Eight volunteers were recruited for these experiments. In addition to visual assessments, the Fréchet Inception Distance (FID) was employed as a quantitative metric to evaluate the generated phantoms. Results: The CNN-based filtering network attained an accuracy of 97% and an AUC of 0.99. This integrated GAN generation and CNN classification network generated over 130,000 high-quality phantoms in less than one hour on a PC (NVIDIA GeForce GTX 1060), with only 6 phantoms contained minor noise on the phantom border. The morphological quality of the final phantoms received positive approval from visual evaluation experiments, demonstrating that the ability to distinguish synthetic from real phantoms in paired comparisons was about 50%. The FID score of 9.61 suggests a high level of diversity in the synthetic phantoms. Conclusion: This study demonstrated a novel and efficient method for generating a large-scale synthetic medical digital phantom by integrating two deep-learning models, GAN and CNN. The synthesized digital phantoms not only showed realistic morphology but also maintains commendable data diversity. Additionally, this approach offers a solution to the time-consuming and subjective nature of manually assessing the phantom quality generated by GAN networks. The digital phantoms synthesized have potential to be utilized in various phantom research areas.