OpenPros: 一个大规模数据集用于有限视角前列腺超声计算机断层扫描
OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography
AI总结：
OpenPros是一个大规模数据集，用于评估基于学习的方法在有限视角前列腺USCT中的性能，旨在解决重建精度和鲁棒性等挑战。
摘要：
前列腺癌是男性中最常见且致命的癌症之一，推动了开发准确且易于获取的成像技术以实现早期检测。超声计算机断层扫描（USCT）重建定量组织参数，如声速（SOS），是一种有前途的低成本替代现有方法。然而，由于有限角度采集、强组织异质性、骨诱导波扭曲以及缺乏大规模、解剖学真实的数据集，前列腺USCT仍具挑战性。我们引入OPENPROS，第一个大规模的前列腺USCT有限角度基准数据集，旨在系统评估机器学习方法用于定量反问题。OPENPROS包含超过280,000对真实2D SOS地图和对应的超声全波形数据，这些数据来自4个临床MRI/CT扫描和62个体外前列腺标本的实验超声测量。在临床真实配置下，使用开源的有限差分时域和龙格-库塔求解器模拟波传播。我们提供了标准化的训练、分布内和分布外基准，并评估了代表性的深度学习基线。虽然基于学习的方法在推理速度和重建准确性上显著优于基于物理的方法，但结果突显了在鲁棒性、泛化能力和高分辨率重建质量方面的持续挑战。通过公开发布OPENPROS，我们建立了严格的标准基准，以支持反问题、物理引导学习和操作学习的研究，并弥合机器学习研究与实际USCT部署之间的差距。该数据集可在https://open-pros.github.io/上获取。
英文摘要：
Prostate cancer is one of the most prevalent and deadly cancers among men, motivating the development of accurate and accessible imaging technologies for early detection. Ultrasound computed tomography (USCT) reconstructs quantitative tissue parameters such as speed-of-sound (SOS) and is a promising low-cost alternative to existing modalities. However, prostate USCT remains challenging due to limited-angle acquisition, strong tissue heterogeneity, bone-induced wave distortion, and the lack of large-scale, anatomically realistic datasets for method development and evaluation. We introduce OPENPROS, the first large-scale benchmark dataset for limited-angle prostate USCT, designed to systematically evaluate machine learning methods for quantitative inverse problems. OPENPROS contains over 280,000 paired samples of realistic 2D SOS maps and corresponding ultrasound full-waveform data, generated from anatomically accurate 3D digital prostate models derived from 4 clinical MRI/CT scans and 62 ex vivo prostate specimens with experimental ultrasound measurements. Wave propagation is simulated under clinically realistic configurations using open-source finite-difference time-domain and Runge-Kutta solvers. We provide standardized training, in-distribution, and out-of-distribution benchmarks and evaluate representative deep learning baselines. While learning-based methods substantially improve inference speed and reconstruction accuracy over physics-based approaches, results highlight persistent challenges in robustness, generalization, and high-resolution reconstruction quality. By publicly releasing OPENPROS, we establish a rigorous benchmark to support research in inverse problems, physics-guided learning, and operator learning, and to bridge the gap between machine learning research and practical USCT deployment. The dataset is available at https://open-pros.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
