Virus is known to resonate in the confined-acoustic dipolar mode with microwave of the same frequency. However this effect was not considered in previous virus-microwave interaction studies and microwave-based virus epidemic prevention. Here we show that this structure-resonant energy transfer effect from microwaves to virus can be efficient enough so that airborne virus was inactivated with reasonable microwave power density safe for the open public. We demonstrate this effect by measuring the residual viral infectivity of influenza A virus after illuminating microwaves with different frequencies and powers. We also established a theoretical model to estimate the microwaves power threshold for virus inactivation and good agreement with experiments was obtained. Such structure-resonant energy transfer induced inactivation is mainly through physically fracturing the virus structure, which was confirmed by real-time reverse transcription polymerase chain reaction. These results provide a pathway toward establishing a new epidemic prevention strategy in open public for airborne virus.

Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This work surpasses current benchmark levels and demonstrates a reliable AI-assisted system for SRS treatment planning for multiple brain metastases.

We performed in vivo THz transmission imaging study on a subcutaneous xenograft mouse model for early human breast cancer detection. With a THz-fiber-scanning transmission imaging system, we continuously monitored the growth of human breast cancer in mice. Our in vivo study not only indicates that THz transmission imaging can distinguish cancer from the surrounding fatty tissue, but also with a high sensitivity. Our in vivo study on the subcutaneous xenograft mouse model will encourage broad and further investigations for future early cancer screening by using THz imaging system.

We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in the United States, is asymptomatic, often detected incidentally, and often missed by radiologists. Our model architecture is a modified 3D U-Net combined with ellipse fitting that performs aorta segmentation and AAA detection. The study uses 321 abdominal-pelvic CT examinations performed by Massachusetts General Hospital Department of Radiology for training and validation. The model is then further tested for generalizability on a separate set of 57 examinations with differing patient demographics and acquisition characteristics than the original dataset. DeepAAA achieves high performance on both sets of data (sensitivity/specificity 0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans and works with image volumes with varying numbers of images. We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection. It is expected that the model can serve as an effective background detector in routine CT examinations to prevent incidental AAAs from being missed.

Background Stereotactic radiosurgery (SRS), a validated treatment for brain tumors, requires accurate tumor contouring. This manual segmentation process is time-consuming and prone to substantial inter-practitioner variability. Artificial intelligence (AI) with deep neural networks have increasingly been proposed for use in lesion detection and segmentation but have seldom been validated in a clinical setting. Methods We conducted a randomized, cross-modal, multi-reader, multi-specialty, multi-case study to evaluate the impact of AI assistance on brain tumor SRS. A state-of-the-art auto-contouring algorithm built on multi-modality imaging and ensemble neural networks was integrated into the clinical workflow. Nine medical professionals contoured the same case series in two reader modes (assisted or un-assisted) with a memory washout period of 6 weeks between each section. The case series consisted of ten algorithm-unseen cases, including five cases of brain metastases, three of meningiomas and two of acoustic neuromas. Among the nine readers, three experienced experts determined the ground truths of tumor contours. Results With the AI assistance, the inter-reader agreement significantly increased (Dice similarity coefficient [DSC] from 0.86 to 0.90, P<0.001). Algorithm-assisted physicians demonstrated a higher sensitivity for lesion detection than un-assisted physicians (91.3% versus 82.6%, P=0.030). AI assistance improved contouring accuracy, with an average increase in DSC of 0.028, especially for physicians with less SRS experience (average DSC from 0.847 to 0.865, P=0.002). In addition, AI assistance improved efficiency with a median of 30.8%-time savings. Less-experienced clinicians gained prominent improvement on contouring accuracy but less benefit in reduction of working hours. By contrast, SRS specialists had a relatively minor advantage in DSC, but greater timesaving with the aid of AI. Conclusions Deep learning neural networks can be optimally utilized to improve accuracy and efficiency for the clinical workflow in brain tumor SRS.