Magnetic Resonance Imaging (MRI) provides excellent soft tissue contrast with one significant limitation of slow data acquisition. Dynamic Contrast Enhanced MRI (DCE-MRI) is one of the widely employed techniques to estimate tumor tissue physiological parameters using contrast agents. DCE-MRI data acquisition and reconstruction requires high spatiotemporal resolution, especially during the post-contrast phase. The region of Interest Compressed Sensing (ROICS) is based on Compressed Sensing (CS) framework and works on the hypothesis that limiting CS to an ROI can achieve superior CS performance. In this work, ROICS has been demonstrated on breast DCE-MRI data at chosen acceleration factors and the results are compared with conventional CS implementation. Normalized Root Mean Square Error (NRMSE) was calculated to compare ROICS with CS quantitatively. CS and ROICS reconstructed images were used to compare Ktrans and ve values derived using standard Tofts Model (TM). This also validated the superior performance of ROICS over conventional CS. ROICS generated Concentration Time Curves (CTC's) at chosen acceleration factors follow similar trend as the ground truth data as compared to CS. Both qualitative and quantitative analyses show that ROICS outperforms CS particularly at acceleration factors of 5× and above.

中文翻译：

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基于感兴趣区域压缩感测的加速动态对比度增强MRI。

磁共振成像（MRI）提供了出色的软组织对比度，但数据采集缓慢的一个重要限制。动态对比度增强MRI（DCE-MRI）是广泛使用的使用造影剂估算肿瘤组织生理参数的技术之一。DCE-MRI数据采集和重建需要较高的时空分辨率，尤其是在对比后阶段。感兴趣区域压缩感知（ROICS）基于压缩感知（CS）框架，并基于以下假设进行工作：将CS限制为ROI可以实现出色的CS性能。在这项工作中，已在选定的加速因子下对乳房DCE-MRI数据进行了ROICS的论证，并将结果与​​常规CS实施进行了比较。计算归一化均方根误差（NRMSE），以定量比较ROICS和CS。CS和ROICS重建图像用于比较使用标准Tofts Model（TM）得出的Ktrans和ve值。这也证明了ROICS优于传统CS的性能。与CS相比，ROICS在选定的加速因子下生成的浓度时间曲线（CTC）遵循与地面真实数据相似的趋势。定性和定量分析均显示ROICS优于CS，特别是在5倍及以上的加速因子时。