In the domain of brain imaging of small animals including rats, ultrasound (US) imaging is an appealing tool because it offers a high frame rate, easy access, and involves no radiation. However, the rat skull causes artifacts that influence brain image quality in terms of contrast and resolution. Therefore, minimizing the skull-induced artifacts in US imaging is a significant challenge. Unfortunately, the amount of literature on rat skull-induced artifacts is limited, and there is a particular lack of studies exploring reducing skull-induced artifacts. Due to the difficulty of experimentally imaging the same rat brain with and without a skull, numerical simulation becomes a reasonable approach to studying skull-induced artifacts. In this work, we investigated the effects of skull-induced artifacts by simulating a grid of point targets inside the skull cavity and quantifying the pattern of skull-induced artifacts. With the capacity to automatically capture the artifact pattern given a large amount of paired training data, deep learning (DL) models can effectively reduce image artifacts in multiple modalities. This work explored the feasibility of using DL-based methods to reduce skull-induced artifacts in US imaging. Simulated data were used to train a U-Net-derived, image-to-image regression network. US channel data with artifact signals served as inputs to the network, and channel data with reduced artifact signals were the regression outcomes. Results suggest the proposed method can reduce skull-induced artifacts and enhance target signals in B-mode images.
The advancement in bio-engineering technology has enabled tissues to be artificially cultivated from human cells, providing the opportunity to model disease and discover potential treatments 1 . Blood vessel is an important category of human tissues that can be artificially engineered to facilitate the development of treatment plans for vascular diseases. The growth of tissue engineered blood vessels (TEBVs) is a costly procedure, and effective quality control during the growing process could help reduce waste and optimize the cultivation process. Imaging technologies, such as optical coherence tomography5,6 (OCT), have been applied to obtain cross-sectional images of TEBVs, which could be used as a nondestructive method to assess blood vessel during cultivation. Ultrasound (US) imaging has been widely accepted in clinical practice due to its real-time imaging capacity and zero radiation emission; and compared to optics-based imaging modality it is more accessible financially. We implemented an US computer tomography (USCT) based monitoring system on assisting quality control in TEBV growth. In this prototype, a single element transducer is placed in a circular stand that rotates around the TEBV bioreactor to collect A-lines from different angles. Mechatronics systems are used to actuate the transducer for circular motion. A circular back-projection method is used in image reconstruction. Experiments were carried out with point phantom and the bioreactor to validate the imaging functionality of the prototype. Reconstructed images provide validation to the feasibility of using USCT to monitor the growth of TEBV growth.
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