SkullGAN Filtered Synthetic Skulls

To address the challenges of limited and heterogeneous skull CT data for training deep learning models in transcranial ultrasound (TUS) applications, we developed SkullGAN, a generative adversarial network designed to generate high-quality synthetic skull CT slices. SkullGAN leverages real CT data from 38 healthy subjects to produce synthetic images that closely mimic the radiological features of real skulls, as evaluated through skull density ratio, mean thickness, mean intensity, t-SNE analysis, a Fréchet inception distance (FID) score of 49, and a visual Turing test (VTT) with expert radiologists achieving 60% mean accuracy in distinguishing real from synthetic images. These synthetic skull slices were utilized to train TUSNet, a novel deep learning framework for end-to-end transcranial ultrasound simulation and phase aberration correction. TUSNet achieves unparalleled efficiency and accuracy, computing ultrasound pressure fields and phase corrections in 21 milliseconds (over 1200× faster than k-Wave) with a mean focal positioning error of 0.18 mm and 98.3% peak pressure accuracy. This dataset includes the 5,000 synthetic skull CTs that we sampled from SkullGAN for our training, along with indices of similar skulls that should be excluded to yield the TUSNet training set. More details about this filtering are available on the TUSNet Github repository. By publishing this data (and the SkullGAN model), we aim to advance TUS research by providing a scalable and accessible resource for training and validating deep learning models.

为解决经颅超声（transcranial ultrasound, TUS）应用中深度学习模型训练所需颅骨CT数据有限且异质性突出的难题，我们研发了SkullGAN——一款专为生成高质量合成颅骨CT切片而设计的生成对抗网络（generative adversarial network）。SkullGAN利用38名健康受试者的真实CT数据，生成可高度贴合真实颅骨放射学特征的合成图像。我们通过颅骨密度比、平均厚度、平均强度、t-SNE分析、弗雷歇初始距离（Fréchet inception distance, FID）得分为49，以及由放射学专家参与的视觉图灵测试（visual Turing test, VTT）对模型效果进行评估：专家区分真实与合成图像的平均准确率达60%。我们将这些合成颅骨切片用于训练TUSNet——一款面向端到端经颅超声仿真与相位畸变校正的新型深度学习框架。TUSNet实现了前所未有的效率与精度：可在21毫秒内完成超声压力场与相位校正计算（较k-Wave快1200余倍），平均焦点定位误差为0.18 mm，峰值压力准确率达98.3%。本数据集包含我们从SkullGAN中采样得到的5000张合成颅骨CT图像，以及构建TUSNet训练集时需剔除的相似颅骨索引信息。有关该筛选流程的更多细节，可查阅TUSNet的GitHub代码仓库。我们公开此数据集（及SkullGAN模型），旨在为经颅超声领域的深度学习模型训练与验证提供可扩展、易获取的资源，以推动该领域的研究进展。

搜集汇总

背景与挑战

背景概述

该数据集包含5000个合成的头骨CT切片，由SkullGAN生成，用于训练深度学习模型TUSNet，旨在促进经颅超声研究的发展。数据集还包括需要排除的相似头骨的索引信息。