Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in suboptimal end-to-end performance. In this work, we propose TACKLE as a unified framework for designing CS-MRI systems tailored to specific tasks. Leveraging recent co-design techniques, TACKLE jointly optimizes subsampling, reconstruction, and prediction strategies to enhance the performance on the downstream task. Our results on multiple public MRI datasets show that the proposed framework achieves improved performance on various tasks over traditional CS-MRI methods. We also evaluate the generalization ability of TACKLE by experimentally collecting a new dataset using different acquisition setups from the training data. Without additional fine-tuning, TACKLE functions robustly and leads to both numerical and visual improvements.

翻译：压缩感知磁共振成像（CS-MRI）旨在从欠采样测量中恢复视觉信息以完成诊断任务。传统CS-MRI方法通常分别处理测量欠采样、图像重建和任务预测，导致端到端性能次优。本文提出TACKLE作为统一框架，用于设计针对特定任务的CS-MRI系统。利用近期协同设计技术，TACKLE联合优化欠采样、重建和预测策略以提升下游任务性能。在多个公开MRI数据集上的结果表明，该框架在各类任务上均优于传统CS-MRI方法。我们还通过实验采集与训练数据具有不同采集设置的新数据集，评估了TACKLE的泛化能力。无需额外微调，TACKLE即可稳健运行，并在数值指标和视觉效果上均取得提升。