Deep-learning assisted photoacoustic histology for real-time intraoperative pathological diagnosis

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Project Summary Despite the advances in cancer treatment, surgery remains the cornerstone, and more than 80% of cancer patients have a surgical procedure at some point in their cancer evolution. In oncology surgery, intraoperative pathology provides surgical guidance and identification of tumor margins, allowing confirmation of complete tumor resection before oncology surgeons close the surgical wound and helping patients avoid a second tumor resection surgery. Most localized tumors with negative margin resection show improved patient outcomes and a lower chance of tumor recurrence. However, the intraoperative frozen section technique suffers from a series of limitations: tissue loss, compromised quality due to freezing artifacts, suboptimal cutting of fatty specimens, and inability to diagnose bony lesions. In our preliminary results, we have developed the 3D contour scanning ultraviolet photoacoustic microscopy (UV- PAM) to acquire histology-like images of thick bone specimens, which addresses the long-standing challenge of intraoperative bone histology. The rapid photoacoustic histology images of bone specimens well match the conventional histology images stained by hematoxylin and eosin (H&E), allowing pathologists to identify the cancerous features following existing pattern recognition parameters readily. Although these results showed the feasibility of intraoperative photoacoustic histology of bone specimens, the system has a relatively slow imaging speed fundamentally limited by the low laser repetition rate of UV lasers and applies only to only bone specimens. This research proposal aims to develop a high-throughput photoacoustic histology platform for pathologists and surgeons to diagnose intraoperatively and remotely with an imaging speed at least 100 times faster than any published reflection-mode UV-PAM systems. Specific Aim 1: Develop a structured illumination UV-PAM for ultrafast histology imaging of slide-free specimens. Aim 1.1. We will develop an ultrafast reflection mode UV-PAM using multifocal illumination with a single element transducer. Aim 1.2. We will design and fabricate DOEs for structured illumination UV-PAM with an extended depth of focus for slide-free specimens with irregular surfaces to allow high-throughput imaging of slide-free specimens in clinical settings. Specific Aim 2: Implement neural networks for virtual staining of photoacoustic histology and real-time intraoperative diagnosis. Aim 2.1. We will implement neural networks and unsupervised deep learning techniques to virtually stain photoacoustic images in various tissue types. The utilization of virtual stained PAM images for intraoperative diagnostic will be evaluated by pathologists in clinical practices. Aim 2.2. We will develop and train a deep learning neural network to classify tumor types and stages in different tissues using photoacoustic histology to build a computer-aided platform for real-time intraoperative diagnosis.