Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws

Myocardial perfusion quantification using contrast-enhanced ultrasound offers a bedside non-ionizing alternative to nuclear imaging modalities. However, its clinical adoption is hindered by time-consuming manual labelling. Automated segmentation has proved challenging due to a paucity of in-domain training data. Adapting strategies currently used to optimise large language models for large datasets, we apply neural scaling laws to predict network performance for myocardial segmentation. We extrapolate performance on subsets of the data to determine optimal network size on the CAMUS echocardiography dataset and a 25-patient contrast-enhanced ultrasound (CEUS) dataset. Finally, we validate the clinical utility of our models by comparing the final myocardial perfusion parameters with those obtained by a senior cardiologist. Extrapolation based on the scaling law is predictive of test loss at the full dataset size, allowing us to select two networks that obtained state-of-the-art performance on CAMUS with a 240-fold reduction in parameter count. We observe the gradient of the scaling law transfers from CAMUS to the CEUS dataset with a bias in the predicted losses. The automatically segmented masks perform equivalently to a senior cardiologist in myocardial perfusion quantification. These results establish neural scaling laws as a practical tool for data-driven compute-optimal model design for small imaging datasets.

Determining the utility of ultrafast nonlinear contrast enhanced and super resolution ultrasound for imaging microcirculation in the human small intestine

The regulation of intestinal blood flow is critical to gastrointestinal function. Imaging the intestinal mucosal micro-circulation in vivo has the potential to provide new insight into the gut physiology and pathophysiology. We aimed to determine whether ultrafast contrast enhanced ultrasound (CEUS) and super-resolution ultrasound localisation microscopy (SRUS/ULM) could be a useful tool for imaging the small intestine microcirculation in vivo non-invasively and for detecting changes in blood flow in the duodenum. Ultrafast CEUS and SRUS/ULM were used to image the small intestinal microcirculation in a cohort of 20 healthy volunteers (BMI<25). Participants were imaged while conscious and either having been fasted, or following ingestion of a liquid meal or water control, or under acute stress. For the first time we have performed ultrafast CEUS and ULM on the human small intestine, providing unprecedented resolution images of the intestinal microcirculation. We evaluated flow speed inside small vessels in healthy volunteers (2.78 +/- 0.05 mm/s, mean +/- SEM) and quantified changes in the perfusion of this microcirculation in response to nutrient ingestion. Perfusion of the microvasculature of the intestinal mucosa significantly increased post-prandially (36.2% +/- 12.2%, mean +/- SEM, p<0.05). The feasibility of 3D SRUS/ULM was also demonstrated. This study demonstrates the potential utility of ultrafast CEUS for assessing perfusion and detecting changes in blood flow in the duodenum. SRUS/ULM also proved a useful tool to image the microvascular blood flow in vivo non-invasively and to evaluate blood speed inside the microvasculature of the human small intestine.

Enhanced Acoustic Beamforming with Sub-Aperture Angular Multiply and Sum -- in vivo and in Human Demonstration

Power Doppler ultrasound is in widespread clinical use for non-invasive vascular imaging but the most common current method - Delay and Sum (DAS) beamforming - suffers from limited resolution and high side-lobes. Here we propose the Sub-Aperture Angular Multiply and Sum (SAMAS) algorithm; it combines the advantages of two recent non-linear beamformers, Frame Multiply and Sum (FMAS) which uses signal temporal coherence and the acoustic sub-aperture (ASAP) algorithm, which uses signal spatial coherence, respectively. Following in vitro experiments to optimise the algorithm, particularly the use of phase information and sub-aperture pairing, it was evaluated in vivo, first in a rabbit kidney and then in human lymph node, using ultrafast ultrasound images obtained with intravenous contrast agents. The SAMAS algorithm improved the CNR and SNR across all tests, on average raising the CNR by 11 dB and the SNR by 18 dB over DAS in vivo. This work demonstrates a promising vascular imaging method that could have widespread clinical utility.