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We present a clustering method to extract the depth of interaction (DOI) information from an 8 mm thick crystal version of our continuous miniature crystal element (cMiCE) small animal PET detector. This clustering method, based on the maximum-likelihood (ML) method, can effectively build look-up tables (LUT) for different DOI regions. Combined with our statistics-based positioning (SBP) method, which uses a LUT searching algorithm based on the ML method and two-dimensional mean-variance LUTs of light responses from each photomultiplier channel with respect to different gamma ray interaction positions, the position of interaction and DOI can be estimated simultaneously. Data simulated using DETECT2000 were used to help validate our approach. An experiment using our cMiCE detector was designed to evaluate the performance. Two and four DOI region clustering were applied to the simulated data. Two DOI regions were used for the experimental data. The misclassification rate for simulated data is about 3.5% for two DOI regions and 10.2% for four DOI regions. For the experimental data, the rate is estimated to be approximately 25%. By using multi-DOI LUTs, we also observed improvement of the detector spatial resolution, especially for the corner region of the crystal. These results show that our ML clustering method is a consistent and reliable way to characterize DOI in a continuous crystal detector without requiring any modifications to the crystal or detector front end electronics. The ability to characterize the depth-dependent light response function from measured data is a major step forward in developing practical detectors with DOI positioning capability.

Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD). Methods 3170 gastric cancer and 5981 benign images were collected to train the DCNN to detect EGC. A total of 24549 images from different parts of stomach were collected to train the DCNN to monitor blind spots. Class activation maps were developed to automatically cover suspicious cancerous regions. A grid model for the stomach was used to indicate the existence of blind spots in unprocessed EGD videos. Results The DCNN identified EGC from non-malignancy with an accuracy of 92.5 %, a sensitivity of 94.0 %, a specificity of 91.0 %, a positive predictive value of 91.3 %, and a negative predictive value of 93.8 %, outperforming all levels of endoscopists. In the task of classifying gastric locations into 10 or 26 parts, the DCNN achieved an accuracy of 90 % or 65.9 %, on a par with the performance of experts. In real-time unprocessed EGD videos, the DCNN achieved automated performance for detecting EGC and monitoring blind spots. Conclusions We developed a system based on a DCNN to accurately detect EGC and recognize gastric locations better than endoscopists, and proactively track suspicious cancerous lesions and monitor blind spots during EGD.

The concept of pure optical photoacoustic microscopy(POPAM) was proposed based on optical rastering of a focused excitation beam and optically sensing the photoacoustic signal using a microring resonator fabricated by a nanoimprinting technique. After the refinements of the microring’s working wavelength and in the resonator structure and mold fabrication, an ultrahigh Q factor of 3.0×105 was achieved which provided high sensitivity with a noise equivalent detectable pressure(NEDP) value of 29Pa. This NEDP is much lower than the hundreds of Pascals achieved with existing optical resonant structures such as etalons, fiber gratings and dielectric multilayer interference filters available for acoustic measurement. The featured high sensitivity allowed the microring resonator to detect the weak photoacoustic signals from micro- or submicroscale objects. The inherent superbroad bandwidth of the optical microring resonator combined with an optically focused scanning beam provided POPAM with high resolution in the axial as well as both lateral directions while the axial resolution of conventional photoacoustic microscopy (PAM) suffers from the limited bandwidth of PZT detectors. Furthermore, the broadband microring resonator showed similar sensitivity to that of our most sensitive PZT detector. The current POPAM system provides a lateral resolution of 5 μm and an axial resolution of 8 μm, comparable to that achieved by optical microscopy while presenting the unique contrast of optical absorption and functional information complementing other optical modalities. The 3D structure of microvasculature, including capillary networks, and even individual red blood cells have been discerned successfully in the proof-of-concept experiments on mouse bladders ex vivo and mouse ears in vivo. The potential of approximately GHz bandwidth of the microring resonator also might allow much higher resolution than shown here in microscopy of optical absorption and acoustic propagation properties at depths in unfrozen tissue specimens or thicker tissue sections, which is not now imageable with current optical or acoustic microscopes of comparable resolution.

We demonstrate an ultrasonic detector with unprecedented broad bandwidth and high sensitivity, based on an imprinted polymer optical microring. It has an acoustic response of up to 350 MHz at −3 dB and noise-limited detectable pressure as low as 105 Pa in this frequency range. Application of such a detector in photoacoustic imaging leads to improved axial resolving ability compared with using the conventional ultrasound detector, and sub-3 μm axial resolution is achieved, which is more than a 2-fold improvement with respect to the reported record. The device's miniaturized cavity height guarantees its broadband response, and at the same time, its high optical quality factor ensures the detection sensitivity. Our work suggests that the polymer-based miniature microring resonator works as a high-performance ultrasound detector and has potential for acquiring volumetric photoacoustic images with cellular/subcellular resolution in three dimensions.