Washington University

Duke University

Shiqi Xu

Quing Zhu

Pavan Chandra Konda

Xiang Dai

A microscopy system includes a lens, an image sensor, a planar array of light sources, and a first and second polarizing filter. The lens is disposed between the array and the image sensor. The image sensor has a field of view and is positioned to capture an image of a sample in a target area. Each light source provides light from a different direction to the target area. The first polarizing filter and the second polarizing filter are each positioned at a rotation angle. A method of microscopy imaging includes illuminating a sample positioned in a target area of a lens with a light source from an array of light sources, acquiring a set of images by an image sensor of the illuminated sample, and performing a reconstruction algorithm on the set of images to generate a composite high-resolution image over the field of view of the image sensor.

Method and system of polarization microscopy imaging

A diffuse optical tomography (DOT) system for generating a functional image of a lesion region of a subject includes a source subsystem configured to generate optical waves, a probe coupled to the source subsystem and configured to emit the optical waves generated by the source subsystem toward the lesion region and to detect optical waves reflected by the lesion region, a detection subsystem configured to convert the optical waves detected by the probe to digital signals, and a computing device including a processor and a memory. The memory includes instructions that program the processor to receive the digital signals sent from the detection subsystem, calculate an initial estimate of the functional image by solving an inverse optimization problem with a shape-regularized Fast Iterative Shrinkage-Thresholding algorithm (FISTA), reconstruct the functional image iteratively using a Finite Difference Method (FDM) or a Finite Element Method (FEM), and display the functional image.

Ultrasound-target-shape-guided sparse regularization to improve accuracy of diffused optical tomography

A diffuse optical tomography (DOT) system for generating a functional image of a lesion region of a subject is described. The DOT system includes a source subsystem configured to generate optical waves, a probe coupled to the source subsystem and configured to emit the optical waves generated by the source subsystem toward the lesion region and to detect optical waves reflected by the lesion region, a detection subsystem configured to convert the optical waves detected by the probe to digital signals, and a computing device including a processor and a memory. The memory includes instructions that program the processor to receive the digital signals sent from the detection subsystem and perform reconstruction using a depth-regularized reconstruction algorithm combined with a semi-automated interactive convolutional neural network (CNN) for depth-dependent reconstruction of absorption distribution.

Ultrasound-target-shape-guided sparse regularization to improve accuracy of diffused optical tomography and target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information

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Shiqi Xu