Photoacoustic (PA) imaging is a hybrid imaging technology which combines the best of optical (contrast) and ultrasound (resolution) imaging. PA imaging uses intrinsic contrast agents in the body like blood (hemoglobin), melanin, etc. But the contrast from the intrinsic contrast agents might not be sufficient for different applications. So, external contrast agents are needed for improving the contrast of PA images. Organic dyes, inorganic dyes, and nanomaterials can be used as photoacoustic contrast agents. The major issue with using external contrast agent is that they often need FDA approval to be used for in-vivo studies. Availability of FDA approved contrast agents for PA imaging is very limited. In this work, we present the feasibility of using food and food-based dyes as photoacoustic contrast agents. We use commonly used foods like coffee, tea, chocolate and food colorants as contrast agents. We use alpinion’s E-CUBE dual mode ultrasound and photoacoustic imaging system with mobile Nd:YAG laser pumped by OPO laser to demonstrate the efficiency of the contrast agents. The contrast agents were compared with methylene blue. Out of the many different agents we tested, coffee, chocolate and few other dyes proved as efficient photoacoustic contrast agents. We performed photoacoustic spectroscopy to identify at which wavelength the dye performed best. We also tested the contrast agents for imaging sentinel lymph nodes in rats and the results are very similar to methylene blue. This will enable the transition of photoacoustic imaging to the clinics more easily.
In a standard computed tomography (CT) image, pixels having the same Hounsfield Units (HU) can correspond to different materials and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but DECT scanners are not widely available as singleenergy CT (SECT) scanners. Here we develop a deep learning approach to perform DECT imaging by using standard SECT data. The end point of the deep learning approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. We retrospectively studied 22 patients who received contrast-enhanced abdomen DECT scan. The difference between the predicted and original high-energy CT images are 3.47 HU, 2.95 HU, 2.38 HU, and 2.40 HU for spine, aorta, liver and stomach, respectively. The difference between virtual non-contrast (VNC) images obtained from original DECT and deep learning DECT are 4.10 HU, 3.75 HU, 2.33 HU and 2.92 HU for spine, aorta, liver and stomach, respectively. The aorta iodine quantification difference between iodine maps obtained from original DECT and deep learning DECT images is 0.9%. This study demonstrates that highly accurate DECT imaging with single low-energy data is achievable by using a deep learning approach. The proposed method can significantly simplify the DECT system design, reducing the scanning dose and imaging cost.