Wave-Cam: A novel micro-radar imaging array for non-rigid motion estimation in hybrid medical imaging

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Abstract During medical imaging, patient motion is unavoidable and can result in significant image degradation. Heart disease and cancer account for almost half of all deaths in the U.S. and considering the prevalence of imaging for diagnosis, therapeutic monitoring, and surgical planning for these diseases, degraded images due to patient motion can have significant implications. Furthermore, in multi-modal imaging the potential for misalignment from patient motion between sequential acquisitions distorts intermodality alignment decreasing the utility of imaging. Existing strategies for motion correction are classified as either: 1) data-driven by utilizing imaging data or 2) use of a device external to the imaging system(s) that estimates motion of internal volumes of interest (VOI) by tracking the motion of a surrogate, such as the patient’s body surface. External motion tracking (EMT) is a popular alternative to data-driven approaches as it is unaffected by modality characteristics, such noise levels and radiotracer distribution and can be used for tracking in multi-modal systems. The majority of EMT cameras actively illuminate patients with visible or near infrared electromagnetic radiation that is reflected back by patient garments. The opaqueness of garments to this type of radiation prevents tracking of the patient’s actual body surface, thereby adding a level of uncertainty when estimating the motion. Thus, if the patient is clothed, then current EMTs only track garments or markers which are themselves acting as surrogates to the patient’s body surface, which is tantamount to tracking a surrogate to another surrogate to the VOI. Our premise is that patient garments pose an unsurmountable barrier to EMTs that limit their motion tracking and compensation performance in clinical imaging. As such, we propose to develop a novel EMT that utilizes a part of the electromagnetic spectrum for which clothing is transparent, thus overcoming this limitation, and in turn transforming the way motion tracking is performed in clinical settings.