Congratulations to Nethra and Junhao! Their first-author papers were recently published in Biomedical Optics Express and Ultrasound in Medicine and Biology, respectively.
Multispectral photoacoustic imaging of breast cancer tissue with histopathology validation describes the first known work to use multispectral photoacoustic imaging with optical wavelengths up to 2000 nm to provide pathological information about breast tissue without traditional staining staining. We leveraged machine learning to differentiate between healthy and cancerous tissues based on photoacoustic spectra. Using our custom imaging platform, this method generates a virtual stain of a microscope tissue slide in under 30 minutes, compared to the more than 24 hours required for conventional H&E staining. The co-registered ultrasound images provide complementary anatomical context to the pathological information derived from multispectral photoacoustic images. Our approach has the potential to enable real-time tumor margin determination during biopsy or surgery.
Comparative Assessment of Real-Time and Offline Short-Lag Spatial Coherence (SLSC) Imaging of Ultrasound Breast Masses is the first to evaluate the effectiveness of real-time SLSC imaging, relative to historical offline SLSC implementations, when differentiating fluid from solid masses. Real-time SLSC imaging reduced uncertainty about fluid masses that existed when reading B-mode images alone. The application of the generalized contrast-to-noise ratio (gCNR) to real-time SLSC images further enhanced the sensitivity and specificity of cystic versus solid differentiation to overcome residual reader uncertainty. These benefits are promising to reduce 2-year follow-up recommendations (i.e., BI-RADS 3 diagnoses) or recommendations for more invasive procedures (e.g., biopsy, aspiration).
