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We frequently find in medical imaging that patient data are “sparse.” That is, when the recorded data are decomposed into an appropriate basis, the information related to a specific clinical task is found in a small compact subspace. Sampling sparse data appropriately enables high frame-rate imaging with minimal loss of image quality. It also enables efficient implementation of machine-learning and other analysis techniques designed to enhance the diagnostic performance of that modality. These ideas are fundamentally changing how we approach data sampling in image science.
We are leveraging the advantages of sparsity in developing a new power-Doppler imaging method using data from commercial ultrasound instruments. Our methods significantly increase blood-signal sensitivity and specificity for slow, spatially disorganized patterns of blood flow as is characteristic of peripheral perfusion. We arrange the recorded echo data into high-dimensional arrays that are decomposed into basis sets to effectively separate strong tissue echo signals from the much weaker blood signals of perfusion. That is, we first expand echo-data dimensionality to capture and then isolate the perfusion subspace before reducing dimensionality to render an image. This combination of pulse sampling and clutter filtering enhances peripheral perfusion images such that injectable contrast media are no longer required. In preclinical mouse studies, we find our methods can significantly enhance the effectiveness of sonography at assessing the time course of revascularization in an ischemic hindlimb. The clinical application we are pursuing is serial assessment of peripheral arterial disease in diabetic patients.
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