This conference presentation was prepared for the Optical Diagnostics and Sensing XXIII: Toward Point-of-Care Diagnostics conference at SPIE BiOS, 2023.
High-resolution and label-free histological imaging modalities provide cell nuclear contrast that is analogous to standard hematoxylin and eosin (H&E) histological staining. Recently, a rapid and slide-free imaging technique, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP), has been developed to image thick and unprocessed tissues with subcellular resolution. Here, we propose a fast and low-cost light-emitting diode (LED) based CHAMP imaging system assisted by deep learning. The low-resolution widefield LED images can be translated into high-resolution LED-CHAMP images that highly resemble Laser-CHAMP images by enhanced super-resolution generative adversarial networks (ESRGAN). Moreover, LED-CHAMP images can be further translated into virtual H&E-stained images comparable to standard H&E histology by virtual staining models. The versatility of LEDCHAMP is experimentally demonstrated using mouse brain thin slices and thick sections, which takes only five minutes for imaging tissue surface area with 10 × 10 mm2. The promising LED-CHAMP workflow enables fast, low-cost, and comparable image quality for intraoperative assessment.
This paper proposes a fast whole-organ histological imaging method with real-time staining and mechanical sectioning. Time-consuming and laborious sample processing procedures are not needed. The imaged tissue block will be labeled along with the serial sectioning and optical scanning to improve the overall speed and the uniformity of staining. A super-resolution network (ESRGAN) and an optical-sectioning imaging technique (HiLo microscopy) have been applied to optimize the imaging speed and resolution. The proposed system can realize whole-organ histological imaging within hours to days, depending on the volume of the imaged sample.
Lung cancer is one of the leading causes of cancer mortality worldwide, with an estimated 2.2 million new cancer cases and 1.8 million deaths in 2020. Adenocarcinoma is the most common type of non-small cell lung cancer (NSCLC), which is usually developed with a mixture of histologic subtypes. Surgery to remove the affected tissue or tumor is the most curative treatment option for the early-stage NSCLC currently. The clinical diagnosis of NSCLC based on pathological analysis of formalin-fixed and paraffin-embedded (FFPE) tissues is laborious and time-consuming, failing to guide surgeons intraoperatively. Although frozen section can serve as a rapid alternative to FFPE histology, it still requires a turnaround time of 20–30 minutes during surgery. Besides, the diagnostic accuracy of the frozen section could be affected due to the tissue freezing artifacts and inadequate sampling of resection margins. Here, we propose a rapid histological imaging method, termed microscopy with ultraviolet single-plane illumination (MUSI), which enables label-free and non-destructive imaging of freshly excised and unprocessed tissues. The MUSI system allows the surgical specimens with large irregular surfaces to be scanned in a label-free manner at a speed of 0.65 mm2/s with a subcellular resolution, showing great potential as an assistive imaging platform that can provide immediate feedback to surgeons and pathologists for intraoperative decision-making. We demonstrate that MUSI can differentiate between different subtypes of human lung adenocarcinomas, revealing diagnostically important features that are comparable to the gold standard FFPE histology, holding great promise to revolutionize the current practice of surgical pathology.
Ultraviolet-based photoacoustic microscopy (UV-PAM) has recently been demonstrated as a promising tool to overcome the time-consuming sample preparation procedure in traditional pathological analysis. In order to achieve high-speed UVPAM for clinical usage, we implemented UV-PAM with a single-axis galvo mirror scanner. With our UV laser operating at a repetition rate of 55 kHz, our system produced images ~5.5 times faster than the previously reported point-by-point raster scanning based UV-PAM, with a lateral resolution of ~1.0 μm. Histology-like images of a mouse brain slice were acquired by our system, showing its potential as an intraoperative imaging tool for surgical margin assessment.
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