Washington University

California Institute of Technology

Tomowave Laboratories, Inc.

Mark Anastasio

Lihong V Wang

Konstantin Maslov

Brendan Kelly

Liming Nie

Xin Cai

Chao Huang

Thomas Matthews

Robert W Schoonover

Alexander A Oraevsky

Sasa Mutic

Sergey A Ermilov

Quing Zhu

Hua Li

Andre Conjusteau

Yujia Chen

Huifeng Guan

Shihab Uddin

Bin Huang

Jun Xia

Systems and methods of reconstructing photoacoustic imaging data corresponding to a brain of a subject through a skull of a subject utilizing a reconstruction method that incorporates a spatial model of one or more acoustic properties of the brain and skull of the subject derived from an adjunct imaging dataset.

Transcranial photoacoustic/thermoacoustic tomography brain imaging informed by adjunct image data

Systems and methods reconstruct an image of a subject or object by performing an iterative reconstruction method to produce a plurality of intermediate images and the image of the subject, and transforming at least one selected intermediate image from the plurality of intermediate images using a quasi-projection operator. The quasi-projection operator includes a deep-learning model configured to map the at least one selected intermediate image to at least one regularized intermediate image. In addition, systems and methods for training the deep-learning model using a training data set that includes a plurality of training images and a plurality of corresponding training object images is disclosed.

Deep learning-assisted image reconstruction for tomographic imaging

A system and method for generating at least one optimized functional images of a lesion region of a subject is provided. The system includes a diffuse optical tomography (DOT) device, and a computing device. The DOT device is configured to acquire lesion functional data of an imaging volume including the lesion region of the breast and reference functional data from a corresponding imaging volume including healthy tissue within a contralateral breast. The computing device is programmed to generate at least one functional image of the breast by reconstructing the functional data at the plurality of regions including the lesion region and the surrounding adjacent background region. The functional images may be reconstructed by an optimization method regularized a preliminary estimate generated by applying a truncated pseudoinverse matrix of a weight matrix to the functional data. The optimized functional images relate to levels of hemoglobin at the voxels.

Systems and methods of optimizing functional images of a lesion region using guided diffuse optical tomography

Systems and methods for reconstructing an image of a subject or object based on a plurality of measured signals representing the subject or object obtained by an imaging device are disclosed. The systems and methods reconstruct the image of the subject or object by performing an iterative reconstruction method to produce a plurality of intermediate images and the image of the subject, and transforming at least one selected intermediate image from the plurality of intermediate images using a quasi-projection operator. The quasi-projection operator includes a deep-learning model configured to map the at least one selected intermediate image to at least one regularized intermediate image. In addition, systems and methods for training the deep-learning model using a training data set that includes a plurality of training images and a plurality of corresponding training object images is disclosed.

Provided herein are the systems, methods, components for a three-dimensional tomography system. The system is a dual-modality imaging system that incorporates a laser ultrasonic system and a laser optoacoustic system. The dual-modality imaging system generates tomographic images of a volume of interest in a subject body based on speed of sound, ultrasound attenuation and/or ultrasound backscattering and for generating optoacoustic tomographic images of distribution of the optical absorption coefficient in the subject body based on absorbed optical energy density or various quantitative parameters derivable therefrom. Also provided is a method for increasing contrast, resolution and accuracy of quantitative information obtained within a subject utilizing the dual-modality imaging system. The method comprises producing an image of an outline boundary of a volume of interest and generating spatially or temporally coregistered images based on speed of sound and/or ultrasonic attenuation and on absorbed optical energy within the outlined volume.

Laser optoacoustic ultrasonic imaging system (LOUIS) and methods of use

Systems and methods for improving limited-view photoacoustic tomography using an acoustic reflector are described. In particular, an acoustic reflector and ultrasonic transducer array are integrated to provide a virtual array that enhances the field of view of the ultrasonic transducer array, thereby improving the quality of photoacoustic tomography images obtained using the systems and methods described herein.

Photoacoustic computed tomography with a biplanar acoustic reflector

A method and systems of reconstructing a complex-valued X-ray refractive index distribution of an object having undergone X-ray phase-contrast tomography. The method includes acquiring at least one X-ray image of an object using an edge illumination X-ray phase-contrast tomography (EIXPCT) model, discretizing the model, jointly reconstructing the complex-valued refractive index distribution of the object using penalized least squares estimation of real and imaginary parts of the distribution, and solving the penalized least squares estimation using a batch gradient algorithm.

Single-shot method for edge illumination X-ray phase-contrast tomography

A method for optimizing a CT simulation protocol for different size patients for contouring and segmentation of organs and tumors. The method scanning different size phantoms, cadavers, or patients with a CT scanning x-ray tube to create a set of images and to create data sets for each different size phantoms, cadavers, or patients and then calculating an image quality index (IQI) as a benchmark for contouring accuracy. Finding an optimal IQI that characterizes patients of different sizes and using the optimal IQI to determine the accuracy of the contouring and segmentation of A CT simulation protocol for a patient.

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St. Louis, MO, United States of America

Mark Anastasio