Granted Patent B2

US 12,408,839 · App. 17/302,041 · Granted Sep 9, 2025

Inventors: Lihong Wang (Arcadia, CA); Liming Nie (St. Louis, MO); Xin Cai (St. Louis, MO); Konstantin Maslov (Pasadena, CA); Mark A. Anastasio (St. Louis, MO); Chao Huang (St. Louis, MO); Robert W. Schoonover (St. Louis, MO)

Assignee: California Institute of Technology

A61B5/0095A61B5/0035A61B5/0042A61B5/055A61B5/4839A61B5/4848A61B5/725A61B5/7282A61B6/032A61M31/005G01N29/2418A61B6/501A61B2576/026

Abstract

Claims (25)

1. A method of photoacoustic imaging a brain within a skull of a subject, the method comprising:

obtaining an adjunct image dataset corresponding to the skull of the subject;

analyzing the adjunct image dataset to specify one or more parameters of a spatial model of one or more acoustic properties of the skull, wherein the one or more acoustic properties of the skull comprises a spatially-varying speed of sound associated with the skull;

obtaining one or more photoacoustic imaging signals corresponding to a region of the brain of the subject through the skull of the subject at a photoacoustic tomography (PAT) coordinate system;

registering the spatial model of the one or more acoustic properties of the skull of the subject to the PAT coordinate system to establish an imaging model; and

reconstructing a first photoacoustic image of the region of the brain from the one or more photoacoustic imaging signals by implementing an image reconstruction method, wherein the image reconstruction method is configured to utilize the one or more acoustic properties of the spatial model of the skull to mitigate aberrations of the one or more photoacoustic imaging signals induced by the skull of the subject by using the imaging model, wherein the image reconstruction method is configured to mitigate aberrations in part using the registration of the spatial model of the one or more acoustic properties of the skull to the PAT coordinate system.

2. The method of claim 1 , wherein the adjunct image dataset is obtained using at least one of an X-ray computed tomography (CT) scanning device, an ultrasound transducer device, and a magnetic resonance imaging (MRI) device.

3. The method of claim 1 , wherein the adjunct image dataset comprises a spatial map of porosity and the one or more parameters of the spatial model of the one or more acoustic properties comprise the spatially-varying speed of sound and an ambient density.

4. The method of claim 3 , wherein the image reconstruction method comprises a time-reversal reconstruction method or an iterative reconstruction method.

5. The method of claim 3 , wherein the image reconstruction method is a time-reversal reconstruction method, and wherein the time-reversal reconstruction method comprises solving discretized forms of acoustic equations describing a propagation of an acoustic wavefield though a medium subject to one or more initial conditions corresponding to an initial photoacoustically generated wavefield within the medium and an initial acoustic particle velocity of zero throughout the medium to obtain the first photoacoustic image corresponding to a spatial distribution of an initial acoustic wavefield.

6. The method of claim 5 , wherein the discretized forms of the acoustic equations are solved using a numerical algorithm comprising at least one of a real-space finite-element method, a real-space finite-difference method, and a k-space pseudospectral method.

7. The method of claim 5 , wherein the initial acoustic wavefield is proportional to an absorbed optical energy density.

8. The method of claim 5 , wherein the medium is a heterogenous medium.

9. The method of claim 3 , wherein the image reconstruction method is an iterative reconstruction method, the iterative reconstruction method comprising iteratively solving a discretized imaging model comprising a system matrix to obtain the first photoacoustic image corresponding to a spatial distribution of an initial acoustic wavefield, wherein the initial acoustic wavefield is proportional to an absorbed optical energy density.

10. The method of claim 9 , wherein the iterative reconstruction method comprises solving solves the discretized imaging model using an optimization relation.

11. The method of claim 10 , wherein the optimization relation is a total variation-regularized penalized least-squares cost function.

12. The method of claim 1 , further comprising:

generating a second photoacoustic image; and

applying a differential imaging method comprising subtracting the second photoacoustic image from the first photoacoustic image to obtain a differential image.

13. The method of claim 12 , further comprising applying a high-pass filter to the differential image.

14. The method of claim 12 , wherein the first photoacoustic image corresponds to the brain and the skull of the subject at a first condition and the second photoacoustic image corresponds to the brain and the skull of the subject at a second condition.

15. The method of claim 14 , wherein the first condition comprises an untreated baseline condition and the second condition comprises a condition after a treatment, wherein the treatment comprises one or more of a change in an endogenous contract agent, an introduction of an exogenous contract agent, a therapeutic treatment, or a performance of a cognitive task.

16. The method of claim 1 , wherein:

obtaining the one or more photoacoustic imaging signals comprises directing illumination from a laser source or a microwave source through the skull to illuminate the brain; and

the one or more photoacoustic imaging signals include acoustic signals generated by the brain in response to the illumination from the laser source or the microwave source.

Assignments (1)

Continuity (4)

References Cited (400)

Cited By (1)

US 12,593,986