Row–column (RC) arrays have the potential to yield full 3-D ultrasound imaging with a greatly reduced number of elements compared to fully populated arrays. They, however, have several challenges due to their special geometry. This review article summarizes the current literature for RC imaging and demonstrates that full anatomic and functional imaging can attain a high quality using synthetic aperture (SA) sequences and modified delay-and-sum beamforming. Resolution can approach the diffraction limit with an isotropic resolution of half a wavelength with low sidelobe levels, and the field of view can be expanded by using convex or lensed RC probes. GPU beamforming allows for three orthogonal planes to be beamformed at 30 Hz, providing near real-time imaging ideal for positioning the probe and improving the operator’s workflow. Functional imaging is also attainable using transverse oscillation and dedicated SA sequence for tensor velocity imaging for revealing the full 3-D velocity vector as a function of spatial position and time for both blood velocity and tissue motion estimation. Using RC arrays with commercial contrast agents can reveal super-resolution imaging (SRI) with isotropic resolution below $20~ \mu \text{m}$ . RC arrays can, thus, yield full 3-D imaging at high resolution, contrast, and volumetric rates for both anatomic and functional imaging with the same number of receive channels as current commercial 1-D arrays.

A new approach for vascular super resolution imaging using the erythrocytes as targets (SURE imaging) is described and investigated. SURE imaging does not require fragile contrast agent bubbles, making it possible to use the maximum allowable mechanical index for ultrasound scanning for an increased penetration depth. A synthetic aperture ultrasound sequence was employed with 12 virtual sources using a 10 MHz GE L8-18i-D linear array hockey stick probe. The axial resolution was 1.20λ,(185.0μm) and the lateral resolution was 1.50λ,(231.3μm). Field IIpro simulations were conducted on 12.5 μm radius vessel pairs with varying separations. A vessel pair with a separation of 70 μm could be resolved, indicating a SURE image resolution below half a wavelength. A Verasonics research scanner was used for the in vivo experiments to scan the kidneys of Sprague-Dawley rats for up to 46 s to visualize their microvasculature by processing from 0.1 up to 45 s of data for SURE imaging, and for 46.8 s for super resolution (SR) imaging with a SonoVue contrast agent. Afterward, the renal vasculature was filled with the ex vivo micro-CT contrast agent Microfil, excised, and scanned in a micro-CT scanner at both a 22.6 μm voxel size for 11 hours, and for 20 hours in a 5 μm voxel size for validating the SURE images. Comparing the SURE and micro-CT images revealed that vessels with a diameter of 28 μm, five times smaller than the ultrasound wavelength, could be detected, and the dense grid of microvessels in the full kidney was shown for scan times between 1 to 10 s. The vessel structure in the cortex was also similar for the SURE and SR images. Fourier ring correlation indicated a resolution capability of 29 μm. SURE images are acquired in seconds rather than minutes without any patient preparation or contrast injection, making the method translatable to clinical use.

Super resolution ultrasound imaging using the erythrocytes (SURE) has recently been introduced. The method uses erythrocytes as targets instead of fragile microbubbles (MBs). The abundance of erythrocyte scatterers makes it possible to acquire SURE data in just a few seconds compared to several minutes in ultrasound localization microscopy (ULM) using MBs. A high number of scatterers can reduce the acquisition time, however, the tracking of uncorrelated and high-density scatterers is quite challenging. This paper hypothesizes that it is possible to detect and track erythrocytes as targets to obtain vascular flow images. A SURE tracking pipeline is used with modules for beamforming, recursive synthetic aperture imaging, motion estimation, echo canceling, peak detection, and recursive nearest neighbor tracker. The SURE tracking pipeline is capable of distinguishing the flow direction and separating tubes of a simulated Field II phantom with 125 to 25 μm wall-to-wall tube distances, as well as a 3D-printed hydrogel micro-flow phantom with 100 to 60 μm wall-to-wall channel distances. The comparison of an in-vivo SURE scan of a Sprague-Dawley rat kidney with ULM and micro-CT scans with voxel sizes of 26.5μm and 5μm demonstrated consistent findings. A microvascular structure composed of 16 vessels exhibited similarities across all imaging modalities. The flow direction and velocity profiles in the SURE scan were found to be concordant with those from ULM.

Synthetic Aperture (SA) can be used for both anatomic and functional imaging, where tissue motion and blood velocity are revealed. Often, sequences optimized for anatomic B-mode imaging are different from functional sequences, as the best distribution and number of emissions are different. B-mode sequences demand many emissions for a high contrast, whereas flow sequences demand short sequences for high correlations yielding accurate velocity estimates. This paper hypothesizes that a single, universal sequence can be developed for linear array SA imaging. This sequence yields high quality linear and non-linear B-mode images as well as accurate motion and flow estimates for high and low blood velocities and super resolution images. Interleaved sequences with positive and negative pulse emissions for the same spherical virtual source were used to enable flow estimation for high velocities and make continuous long acquisitions for low velocity estimation. An optimized pulse inversion (PI) sequence with 2×12 virtual sources was implemented for four different linear array probes connected to either a Verasonics Vantage 256 scanner or the SARUS experimental scanner. The virtual sources were evenly distributed over the whole aperture and permuted in emission order for making flow estimation possible using 4, 8, or 12 virtual sources. The frame rate was 208 Hz for fully independent images for a pulse repetition frequency of 5 kHz, and recursive imaging yielded 5,000 images per second. Data were acquired from a phantom mimicking the carotid artery with pulsating flow and the kidney of a Sprague-Dawley rat. Examples include anatomic high contrast B-mode, non-linear B-mode, tissue motion, power Doppler, color flow mapping, vector velocity imaging and super resolution imaging derived from the same data set and demonstrates that all imaging modes can be shown retrospectively and quantitative data derived from it.

Background: Vascular imaging is essential for clinical practice, research, and the diagnosis and management of vascular diseases. Super-resolution ultrasound (SRUS) imaging is an emerging high-resolution imaging technique with broad applications in soft tissue vascular imaging. However, the impact of biological and clinical variables on its imaging accuracy is currently unknown. This study investigates these factors in an animal model and compares SRUS with contrast-enhanced µCT. Methods: Kidney scans from 29 Zucker rats (Zucker Diabetic Fatty and Zucker Lean) were retrospectively analysed. The left kidney was imaged in vivo using SRUS during microbubble infusion, then filled with Microfil and excised for ex vivo µCT. SRUS parameters and clinical variables were analysed, and SRUS scans were co-registered with µCT to compare vascular density measurements. Results: Mean arterial blood pressure and anaesthesia time showed significant linear relationships with SRUS microbubble detection and vascular track reconstruction. The anaesthesia time was also strongly correlated with vascular density measurement. Visualisation and velocity estimations of renal arteries were limited with SRUS. Ultrasound signal attenuation had significant impacts, particularly in cortical far-field imaging. Despite differences between kidney regions, the vascular density distribution did not differ considerably between SRUS and µCT datasets for whole-kidney imaging. Conclusions: This study outlines key factors SRUS users must consider for optimal technique use. Careful region selection and control of clinical variables ensure more reliable and comparable images. Further research is necessary to translate these findings from a rat model into clinical application.

Background: Approximately 50% of all oncological patients undergo radiation therapy, where personalized planning of treatment relies on gross tumor volume (GTV) delineation. Manual delineation of GTV is time-consuming, operator-dependent, and prone to variability. An increasing number of studies apply artificial intelligence (AI) techniques to automate such delineation processes. Methods: To perform a systematic review comparing the performance of AI models in tumor delineations within the body (thoracic cavity, esophagus, abdomen, and pelvis, or soft tissue and bone). A retrospective search of five electronic databases was performed between January 2017 and February 2025. Original research studies developing and/or validating algorithms delineating GTV in CT, MRI, and/or PET were included. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement and checklist (TRIPOD) were used to assess the risk, bias, and reporting adherence. Results: After screening 2430 articles, 48 were included. The pooled diagnostic performance from the use of AI algorithms across different tumors and topological areas ranged 0.62–0.92 in dice similarity coefficient (DSC) and 1.33–47.10 mm in Hausdorff distance (HD). The algorithms with the highest DSC deployed an encoder–decoder architecture. Conclusions: AI algorithms demonstrate a high level of concordance with clinicians in GTV delineation. Translation to clinical settings requires the building of trust, improvement in performance and robustness of results, and testing in prospective studies and randomized controlled trials.