A unique deep learning network, Deep-E, is proposed, which utilizes 2D training data to solve a 3D problem. The novelty of this simulation method is to generate a 2D matrix in the axial-elevational plane using an arc-shaped transducer element, instead of generating a 3D matrix using the linear transducer arrays. Deep-E exhibited significant resolution improvement on the in vivo human breast data. In addition, we were able to restore deeper vascular structures and remove the noise artifact. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.
We developed the second-generation photoacoustic dual scan mammoscope (DSM) as a safe and effective modality for the breast imaging of patients with high breast density. Besides being a portable system with high resolution, DSM-2 has several improvements compared to the previous version: a larger field of view, better system stability, higher ultrasound imaging quality, and additional quasi-static elastography capability. The performance of the new system was demonstrated through clinical studies. The experiment result confirmed the capability of the second-generation DSM system as a powerful tool for breast imaging.
In this presentation, we introduce the initial patient imaging results of our dual-scan mammoscope system. The goal is to verify whether the system can differentiate malignant and healthy cases. We have imaged 38 patients with various cancer types and compared results of tumor breast with healthy breast for each patient. At 95% confidence level, we found that tumor breasts exhibit higher average photoacoustic signal amplitude, higher vessel signal amplitude, and stronger variation in background signals. We could also visualize different vascular features in and around the tumor region for different subtypes of tumors. Our preliminary results indicate that photoacoustic technology has a high potential for breast imaging.
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