Controlling the motion of microrobots based on feedback provided using an imaging modality is essential to make them clinically viable. In this study, we demonstrate the wireless magnetic-based motion control of paramagnetic microparticles using ultrasound feedback. This control is accomplished by pulling the microparticles using the magnetic field gradients towards the reference position through feedback provided by an ultrasound system. First, position of the microparticles is determined using the ultrasound images. Second, calibration of the ultrasound-based tracking of microparticles is achieved and verified using a calibrated microscopic system. Third, the feedback provided by the ultrasound system is used in the implementation of a proportional-derivative magneticbased control system. This control system allows us to achieve point-to-point control of microparticles with an average position tracking error of 48±59 µm, whereas a control system based on a microscopic system achieves an average position tracking error of 21±26 µm. The positioning accuracy accomplished using our ultrasound magnetic-based control system demonstrates the ability to control microrobotic systems in situations where visual feedback cannot be provided via microscopic systems.

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Breast cancer is the most common and the fifth deadliest cancer worldwide. In more advanced stages of cancer, cancer cells metastasize through lymphatic and blood vessels. Currently there is no satisfactory neoadjuvant (i.e., preoperative) diagnosis to assess whether cancer has spread to neighboring Axillary Lymph Nodes (ALN). This paper addresses the use of radar Microwave Imaging (MWI) to detect and determine whether ALNs have been metastasized, presenting an analysis of the performance of different artifact removal and beamformer algorithms in distinct anatomical scenarios. We assess distinct axillary region models and the effect of varying the shape of the skin, muscle and subcutaneous adipose tissue layers on single ALN detection. We also study multiple ALN detection and contrast between healthy and metastasized ALNs. We propose a new beamformer algorithm denominated Channel-Ranked Delay-Multiply-And-Sum (CR-DMAS), which allows the successful detection of ALNs in order to achieve better Signal-to-Clutter Ratio, e.g., with the muscle layer up to 3.07 dB, a Signal-to-Mean Ratio of up to 20.78 dB and a Location Error of 1.58 mm. In multiple target detection, CR-DMAS outperformed other well established beamformers used in the context of breast MWI. Overall, this work provides new insights into the performance of algorithms in axillary MWI.