Efficient Synthesizing High Quality Digital Cortex Phantoms through a Novel Combination of GAN and CNN Methods

Purpose: 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.