Breast cancer is the leading cause for cancer-related deaths in women all over the world.¹ It occurs when cells in the breast start growing out of control due to accumulation of mutations in essential genes.² Various genetic and environmental factors lead to such mutations and the growth of tumor mass. The mass can be asymptomatic or can sometimes be felt during a physical breast exam. The tumor is considered malignant when it starts to invade the surrounding tissue. Malignant breast cancer can present itself in many types, in which most are derived from the mammary ductal epithelium. Up to 75% of these are defined as invasive breast carcinoma, no special type (IDC-NST). Another type of epithelial carcinoma is called the invasive lobular carcinoma, which comprises about 5% to 15%. Other rare types, such as tubular carcinoma, invasive cribriform carcinoma, and pure mucinous carcinoma³ constitute the rest. The WHO classification of tumors of the breast⁴ categorizes breast tumors and its subtypes. Sinn and Kreipe⁵ discussed and reviewed these in detail.

Objective: We present a new photoacoustic tomography system that provides visualization of angiographic features in a human breast with mammogram-like images. Methods: The system images a mildly compressed breast, from both top and bottom, using two 128-element, 2.25 MHz linear transducer arrays and line optical fiber bundles. The mild compression is achieved using plastic films, which is a more comfortable experience for the patient compared to rigid metal plates used in a traditional mammogram. Results: We could image a D cup-sized breast of 7 cm thickness within 1 minute and achieved a spatial resolution of around 1 mm in all directions. Conclusion: Our system possesses the benefits of portability, speedy scanning, and patient comfort. The craniocaudal-view images can be easily correlated with existing imaging modalities for data interpretation. Significance: Early cancer detection plays a critical role in overall cancer survival rate. Our system may address the limitations of mammogram and offer a radiation-free screening technique for patients with dense breasts.

In this paper, we review recent advances in on-chip sensors integrated with microfluidics for biological applications. Since the 1990s, much research has concentrated on developing a sensing system using optical phenomena such as surface plasmon resonance (SPR) and surface-enhanced Raman scattering (SERS) to improve the sensitivity of the device. The sensing performance can be significantly enhanced with the use of microfluidic chips to provide effective liquid manipulation and greater flexibility. We describe an optical image sensor with a simpler platform for better performance over a larger field of view (FOV) and greater depth of field (DOF). As a new trend, we review consumer electronics such as smart phones, tablets, Google glasses, etc. which are being incorporated in point-of-care (POC) testing systems. In addition, we discuss in detail the current optical sensing system integrated with a microfluidic chip.

Linear-array-based photoacoustic tomography has shown broad applications in biomedical research and preclinical imaging. However, the elevational resolution of a linear array is fundamentally limited due to the weak cylindrical focus of the transducer element. While several methods have been proposed to address this issue, they have all handled the problem in a less time-efficient way. In this work, we propose to improve the elevational resolution of a linear array through Deep-E, a fully dense neural network based on U-net. Deep-E exhibits high computational efficiency by converting the three-dimensional problem into a two-dimension problem: it focused on training a model to enhance the resolution along elevational direction by only using the 2D slices in the axial and elevational plane and thereby reducing the computational burden in simulation and training. We demonstrated the efficacy of Deep-E using various datasets, including simulation, phantom, and human subject results. We found that Deep-E could improve elevational resolution by at least four times and recover the object’s true size. 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.