提出可处理时间偏移与拉伸的非负矩阵分解方法，提升脑组织分层精度。
Shift- and stretch-invariant non-negative matrix factorization with an application to brain tissue delineation in emission tomography data
- 在频域中建模时间偏移与拉伸，通过相位调整和零填充实现
- 在合成数据与真实脑部发射数据上显著改善组织结构解析
- 适合动态神经影像分析，尤其对血流或脑脊液示踪剂研究者
动态神经影像数据（如放射性示踪剂在血液或脑脊液中的传输测量）常呈现类扩散特性，导致距离相关的时序延迟、尺度差异和时间拉伸，限制了传统线性建模与分解方法的效果。为此，我们提出一种抗平移与拉伸的非负矩阵分解框架。该方法在频域中同时估计整数与非整数时间偏移及时间拉伸，其中偏移对应相位变化，拉伸通过零填充或截断处理。模型基于PyTorch实现（https://github.com/anders-s-olsen/shiftstretchNMF）。在合成数据与脑部发射断层扫描数据上的实验表明，该方法能有效建模时间拉伸，提供更精细的脑组织结构表征。
原文摘要 · Abstract (English)
Dynamic neuroimaging data, such as emission tomography measurements of radiotracer transport in blood or cerebrospinal fluid, often exhibit diffusion-like properties. These introduce distance-dependent temporal delays, scale-differences, and stretching effects that limit the effectiveness of conventional linear modeling and decomposition methods. To address this, we present the shift- and stretch-invariant non-negative matrix factorization framework. Our approach estimates both integer and non-integer temporal shifts as well as temporal stretching, all implemented in the frequency domain, where shifts correspond to phase modifications, and where stretching is handled via zero-padding or truncation. The model is implemented in PyTorch (https://github.com/anders-s-olsen/shiftstretchNMF). We demonstrate on synthetic data and brain emission tomography data that the model is able to account for stretching to provide more detailed characterization of brain tissue structure.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。