PyPeT: A Python Perfusion Tool for Automated Quantitative Brain CT and MR Perfusion Analysis

Computed tomography perfusion (CTP) and magnetic resonance perfusion (MRP) are widely used in acute ischemic stroke assessment and other cerebrovascular conditions to generate quantitative maps of cerebral hemodynamics. While commercial perfusion analysis software exists, it is often costly, closed source, and lacks customizability. This work introduces PyPeT, an openly available Python Perfusion Tool for head CTP and MRP processing. PyPeT is capable of producing cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and time-to-maximum (Tmax) maps from raw four-dimensional perfusion data. PyPeT aims to make perfusion research as accessible and customizable as possible. This is achieved through a unified framework in which both CTP and MRP data can be processed, with a strong focus on modularity, low computational burden, and significant inline documentation. PyPeT's outputs can be validated through an extensive debug mode in which every step of the process is visualized. Additional validation was performed via visual and quantitative comparison with reference perfusion maps generated by three FDA-approved commercial perfusion tools and a research tool. These comparisons show a mean SSIM around 0.8 for all comparisons, indicating a good and stable correlation with FDA-approved tools. The code for PyPeT is openly available at our GitHub https://github.com/Marijn311/CT-and-MR-Perfusion-Tool

Automatically Generating Narrative-Style Radiology Reports from Volumetric CT Images; a Proof of Concept

The world faces a shortage of radiologists, leading to longer treatment times and increased stress, negatively impacting patient safety and workforce morale. Integrating artificial intelligence to interpret radiographic images and generate descriptive reports offers a promising solution. However, limited research exists on generating natural language descriptions for volumetric medical images. This study introduces a deep learning-based proof of concept model to accurately identify abnormalities in volumetric CT data and generate narrative-style reports. Various encoder-decoder models were assessed for their efficacy in clinically relevant and surrogate tasks. Clinically relevant tasks involved identifying and describing pulmonary nodules and pleural effusions, while surrogate tasks involved recognizing and describing artificial abnormalities such as mirroring, rotation, and lung lobe occlusion. The results show high accuracy in detecting combinations of artificial abnormalities, with the best model achieving a classification accuracy of 0.97 on an independent dataset with a homogeneously distributed 11-class problem. Furthermore, the best model consistently generated coherent radiology reports in natural language, with a next-word prediction accuracy of 0.84. Additionally, 65% of these reports were factually accurate regarding the identified artificial abnormalities. Unfortunately, these models did not replicate this success for clinically relevant tasks. Overall, this study provides a working proof of concept model for a challenge yet to be fully addressed by the scientific community. Given the success on surrogate tasks, the leap to clinically relevant tasks seems feasible. Acquiring a significantly larger high-quality dataset appears to be the most promising path forward, alongside more computational resources for end-to-end model training.