Ultrasound image quality is often inherently limited by the physical dimensions of the imaging transducer. We hypothesize that, by collecting synthetic aperture data sets over a range of aperture positions while precisely tracking the position and orientation of the transducer, we can synthesize large effective apertures to produce images with improved resolution and target detectability. We analyze the two largest limiting factors for coherent signal summation: aberration and mechanical uncertainty. Using an excised canine abdominal wall as a model phase screen, we experimentally observed an effective arrival time error ranging from 18.3 ns to 58 ns (root-mean-square error) across the swept positions. Through this clutter-generating tissue, we observed a 72.9% improvement in resolution with only a 3.75 dB increase in side lobe amplitude compared to the control case. We present a simulation model to study the effect of calibration and mechanical jitter errors on the synthesized point spread function. The relative effects of these errors in each imaging dimension are explored, showing the importance of orientation relative to the point spread function. We present a prototype device for performing swept synthetic aperture imaging using a conventional 1-D array transducer and ultrasound research scanner. Point target reconstruction error for a 44.2 degree sweep shows a reconstruction precision of 82.8 $\mu {\rm m}$ and 17.8 $\mu {\rm m}$ in the lateral and axial dimensions respectively, within the acceptable performance bounds of the simulation model. Improvements in resolution, contrast and contrast-to-noise ratio are demonstrated in vivo and in a fetal phantom.

Novel severe acute respiratory syndrome coronavirus 2 (COVID-19) has become a pandemic of epic proportions, and global response to prepare health systems worldwide is of utmost importance. 2-dimensional (2D) lung ultrasound (LUS) has emerged as a rapid, noninvasive imaging tool for diagnosing COVID-19 infected patients. Concerns surrounding LUS include the disparity of infected patients and healthcare providers, and importantly, the requirement for substantial physical contact between the patient and operator, increasing the risk of transmission. New variants of COVID-19 will continue to emerge; therefore, mitigation of the virus's spread is of paramount importance. A tele-operative robotic ultrasound platform capable of performing LUS in COVID-19 infected patients may be of significant benefit, especially in low- and middle-income countries. The authors address the issues mentioned above surrounding the use of LUS in COVID-19 infected patients and the potential for extension of this technology in a resource-limited environment. Additionally, first-time application, feasibility, and safety were validated in healthy subjects. Preliminary results demonstrate that our platform allows for the successful acquisition and application of robotic LUS in humans.

Ultrasonography is a widely used imaging modality to visualize anatomical structures due to its low cost and ease of use; however, it is challenging to acquire acceptable image quality in deep tissue. Synthetic aperture (SA) is a technique used to increase image resolution by synthesizing information from multiple subapertures, but the resolution improvement is limited by the physical size of the array transducer. With a large F-number, it is difficult to achieve high resolution in deep regions without extending the effective aperture size. We propose a method to extend the available aperture size for SA—called synthetic tracked aperture ultrasound (STRATUS) imaging—by sweeping an ultrasound transducer while tracking its orientation and location. Tracking information of the ultrasound probe is used to synthesize the signals received at different positions. Considering the practical implementation, we estimated the effect of tracking and ultrasound calibration error to the quality of the final beamformed image through simulation. In addition, to experimentally validate this approach, a 6 degree-of-freedom robot arm was used as a mechanical tracker to hold an ultrasound transducer and to apply in-plane lateral translational motion. Results indicate that STRATUS imaging with robotic tracking has the potential to improve ultrasound image quality.

Under the ceaseless global COVID-19 pandemic, lung ultrasound (LUS) is the emerging way for effective diagnosis and severeness evaluation of respiratory diseases. However, close physical contact is unavoidable in conventional clinical ultrasound, increasing the infection risk for health-care workers. Hence, a scanning approach involving minimal physical contact between an operator and a patient is vital to maximize the safety of clinical ultrasound procedures. A robotic ultrasound platform can satisfy this need by remotely manipulating the ultrasound probe with a robotic arm. This paper proposes a robotic LUS system that incorporates the automatic identification and execution of the ultrasound probe placement pose without manual input. An RGB-D camera is utilized to recognize the scanning targets on the patient through a learning-based human pose estimation algorithm and solve for the landing pose to attach the probe vertically to the tissue surface; A position/force controller is designed to handle intraoperative probe pose adjustment for maintaining the contact force. We evaluated the scanning area localization accuracy, motion execution accuracy, and ultrasound image acquisition capability using an upper torso mannequin and a realistic lung ultrasound phantom with healthy and COVID-19-infected lung anatomy. Results demonstrated the overall scanning target localization accuracy of 19.67 ± 4.92 mm and the probe landing pose estimation accuracy of 6.92 ± 2.75 mm in translation, 10.35 ± 2.97 deg in rotation. The contact force-controlled robotic scanning allowed the successful ultrasound image collection, capturing pathological landmarks.