Claire-Anne Reidel | Searcharxiv

arXiv2026

Measurement of 80-200 MeV/n $^{16}$O nuclear cross-section on Carbon and Polyethylene targets with the nuclear emulsion detector of the FOOT experiment

Accurate knowledge of nuclear fragmentation cross-sections is essential for optimizing charged particle therapy. In this study, conducted within the framework of the FOOT (FragmentatiOn Of Target) experiment, we present the first measurements with a large angular acceptance of total charge-changing cross-section and the cross-section for the production of fragments (production cross-section) for $^{16}$O ions interacting with Carbon (C) and Polyethylene (C$_2$H$_4$) targets in the kinetic energy range of 80 to 200 MeV/nucleon. Measurements were performed using the Emulsion Cloud Chamber (ECC) technique, which combines high spatial resolution and angular acceptance, up to 45$^\circ$. The results are compared with Monte Carlo model predictions. Moreover, the total charge-changing and fragment production cross-sections for $^{16}$O on Hydrogen in the same energy range are derived.

nucl-ex↗

arXiv2025

A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.

cs.CV↗