Dynamic contrast-enhanced (DCE) breast MRI requires high spatial resolution in order to see lesion features such as shape, size, borders or heterogeneity, but high temporal resolution in order to resolve contrast dynamics. In MRI, there are normally fundamental tradeoffs between encoding and scan time. Compressed sensing (CS) accelerates imaging by acquiring a subset of the full k-space data [1]. Randomly subsampling data and constraining the reconstruction for certain properties can reconstruct artifact-free images. Typically, the “constraint” is to enforce that the image is compressible or “sparse.” CS has been successfully applied to MRI [2], with consistent benefits in a clinical setting [3]. It should be noted that CS uses a complementary mechanism to parallel imaging, so both methods can be used together, which is important for breast MRI where coil geometry enables routine use of parallel imaging.
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