标题： 无需重建的从欠采样k空间中进行分割的变压器方法 显示英文标题

标题： Reconstruction-free segmentation from undersampled k-space using transformers

摘要： 动机：高加速因子对MRI图像重建设定了限制。 当将这些过程视为后续的独立过程时，该限制扩展到了分割模型。 目标：我们的目标是直接从稀疏k空间测量中生成分割结果，而无需中间图像重建。 方法：我们采用Transformer架构，将全局k空间信息编码为潜在特征。 生成的潜在向量在解码过程中对查询坐标进行条件约束，以生成分割类别概率。 结果：与基于图像的分割基线相比，该模型能够在高加速因子下生成更好的分割结果。 影响：直接从欠采样k空间样本进行心脏分割避免了中间图像重建步骤的需求。 这使得在比依赖图像作为输入的方法更高的加速因子下评估心肌结构和功能成为可能。 显示英文摘要

摘要： Motivation: High acceleration factors place a limit on MRI image reconstruction. This limit is extended to segmentation models when treating these as subsequent independent processes. Goal: Our goal is to produce segmentations directly from sparse k-space measurements without the need for intermediate image reconstruction. Approach: We employ a transformer architecture to encode global k-space information into latent features. The produced latent vectors condition queried coordinates during decoding to generate segmentation class probabilities. Results: The model is able to produce better segmentations across high acceleration factors than image-based segmentation baselines. Impact: Cardiac segmentation directly from undersampled k-space samples circumvents the need for an intermediate image reconstruction step. This allows the potential to assess myocardial structure and function on higher acceleration factors than methods that rely on images as input.