直接从稀疏k-space数据分割心脏，跳过图像重建步骤。
Reconstruction-free segmentation from undersampled k-space using transformers
- 用Transformer编码全局k-space信息生成潜在特征
- 在高加速因子下分割精度优于传统图像基方法
- 适合需要快速心脏结构功能评估的临床场景
高加速因子限制了MRI图像重建质量。当分割模型作为独立后续处理时，这一限制同样影响分割性能。本文目标是直接从稀疏k-space数据生成分割结果，无需中间图像重建。方法上，采用Transformer架构将全局k-space信息编码为潜在特征，解码时以这些特征条件化查询坐标，输出分割类别概率。结果显示，在高加速因子下，该模型的分割性能优于基于图像的分割基线。本方法实现了心脏分割直接从欠采样k-space数据完成，避免了中间图像重建步骤，有望在更高加速因子下评估心肌结构与功能。
原文摘要 · Abstract (English)
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.
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