Real-time complex light field generation through a multi-core fiber with deep learning

Resolution-enhanced multi-core fiber imaging learned on a digital twin for cancer diagnosis

Significance

Deep learning enables label-free all-optical biopsies and automated tissue classification. Endoscopic systems provide intraoperative diagnostics to deep tissue and speed up treatment without harmful tissue removal. However, conventional multi-core fiber (MCF) endoscopes suffer from low resolution and artifacts, which hinder tumor diagnostics.

Aim

We introduce a method to enable unpixelated, high-resolution tumor imaging through a given MCF with a diameter of around 0.65 mm and arbitrary core arrangement and inhomogeneous transmissivity.

Approach

Image reconstruction is based on deep learning and the digital twin concept of the single-reference-based simulation with inhomogeneous optical properties of MCF and transfer learning on a small experimental dataset of biological tissue. The reference provided physical information about the MCF during the training processes.

Results

For the simulated data, hallucination caused by the MCF inhomogeneity was eliminated, and the averaged peak signal-to-noise ratio and structural similarity were increased from 11.2 dB and 0.20 to 23.4 dB and 0.74, respectively. By transfer learning, the metrics of independent test images experimentally acquired on glioblastoma tissue ex vivo can reach up to 31.6 dB and 0.97 with 14 fps computing speed.

Conclusions

With the proposed approach, a single reference image was required in the pre-training stage and laborious acquisition of training data was bypassed. Validation on glioblastoma cryosections with transfer learning on only 50 image pairs showed the capability for high-resolution deep tissue retrieval and high clinical feasibility.

Honeycomb effect elimination in differential phase fiber-bundle-based endoscopy

Fiber-bundle-based endoscopy, with its ultrathin probe and micrometer-level resolution, has become a widely adopted imaging modality for in vivo imaging. However, the fiber bundles introduce a significant honeycomb effect, primarily due to the multi-core structure and crosstalk of adjacent fiber cores, which superposes the honeycomb pattern image on the original image. To tackle this issue, we propose an iterative-free spatial pixel shifting (SPS) algorithm, designed to suppress the honeycomb effect and enhance real-time imaging performance. The process involves the creation of three additional sub-images by shifting the original image by one pixel at 0, 45, and 90 degree angles. These four sub-images are then used to compute differential maps in the x and y directions. By performing spiral integration on these differential maps, we reconstruct a honeycomb-free image with improved details. Our simulations and experimental results, conducted on a self-built fiber bundle-based endoscopy system, demonstrate the effectiveness of the SPS algorithm. SPS significantly improves the image quality of reflective objects and unlabeled transparent scattered objects, laying a solid foundation for biomedical endoscopic applications.

Multimode Optical Interconnects on Silicon Interposer Enable Confidential Hardware-to-Hardware Communication

Following Moore's law, the density of integrated circuits is increasing in all dimensions, for instance, in 3D stacked chip networks. Amongst other electro-optic solutions, multimode optical interconnects on a silicon interposer promise to enable high throughput for modern hardware platforms in a restricted space. Such integrated architectures require confidential communication between multiple chips as a key factor for high-performance infrastructures in the 5G era and beyond. Physical layer security is an approach providing information theoretic security among network participants, exploiting the uniqueness of the data channel. We experimentally project orthogonal and non-orthogonal symbols through 380 μm long multimode on-chip interconnects by wavefront shaping. These interconnects are investigated for their uniqueness by repeating these experiments across multiple channels and samples. We show that the detected speckle patterns resulting from modal crosstalk can be recognized by training a deep neural network, which is used to transform these patterns into a corresponding readable output. The results showcase the feasibility of applying physical layer security to multimode interconnects on silicon interposers for confidential optical 3D chip networks.

Quantitative phase imaging through an ultra-thin lensless fiber endoscope

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging with microscale lateral resolution and nanoscale axial sensitivity of the optical path length. The incident complex light field at the measurement side is precisely reconstructed from the far-field speckle pattern at the detection side, enabling digital refocusing in a multi-layer sample without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, three-dimensional imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications.