# US 2019/0244347 A1 · Methods and systems for utilizing quantitative imaging

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- Number: US 2019/0244347 A1
- Document ID: US20190244347A1
- Status: Published application
- Kind: A1
- Application number: 16/203,418
- Filed: 2018-11-28
- Published: 2019-08-08
- Earliest claimed: 2015-08-14
- Assignees: Elucid Bioimaging Inc.
- Applicants: Elucid Bioimaging Inc.
- Inventors: Andrew J. Buckler; Kjell Johnson; Xiaonan Ma; Keith A. Moulton; Mark A. Buckler; Vladimir Valtchinov; David S. Paik
- Web page: https://patentgrep.com/patent/US20190244347A1

## CPC

- G06T 7/0012: {Biomedical image inspection}
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- A61B 6/032: {Transmission computed tomography [CT]}
- G06F 18/211: Selection of the most significant subset of features
- G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade}
- G06F 18/24: Classification techniques
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/09: Supervised learning
- G06N 3/096: Transfer learning
- G06N 20/00: Machine learning
- G06T 3/00: Geometric image transformations in the plane of the image
- G06T 5/73: Deblurring; Sharpening
- G06T 7/11: Region-based segmentation
- G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI]
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/82: using neural networks
- G06V 20/69: Microscopic objects, e.g. biological cells or cellular parts
- G16H 30/40: for processing medical images, e.g. editing
- G06T 2207/10048: Infrared image
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/10101: Optical tomography; Optical coherence tomography [OCT]
- G06T 2207/10104: Positron emission tomography [PET]
- G06T 2207/10108: Single photon emission computed tomography [SPECT]
- G06T 2207/10132: Ultrasound image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30096: Tumor; Lesion
- G06T 2207/30104: Vascular flow; Blood flow; Perfusion
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

## Claims

### Claim 1

A method for computer aided phenotyping of a pathology using an enriched radiological dataset, the method comprising:
receiving a radiological dataset for a patient;
enriching the dataset by performing analyte measurement and/or classification of one or more of: (i) anatomic structure, (ii) shape or geometry or (iii) tissue characteristic, type, or character, with objective validation for a set of analytes relevant to a pathology;
using a machine learned classification approach based on known ground truths to process the enriched dataset and determine a phenotype for the pathology.

### Claim 2 (depends on 1)

The method of claim 1, wherein enriching the dataset further includes spatial transformations of the dataset to accentuate biologically-significant spatial context.

### Claim at position 3

-4. (canceled)

### Claim 5 (depends on 1)

The method of claim 1, wherein the analyte measurement and/or classification of anatomic structure, shape, or geometry and/or tissue characteristic, type, or character includes semantic segmentation to identify and classify regions of interest in the radiological dataset.

### Claim 6 (depends on 5)

The method of claim 5, wherein the regions of interest are identified with respect to cross-sections of a pathology-specific structure in the radiological dataset.

### Claim 7

(canceled)

### Claim 8 (depends on 1)

The method of claim 1, wherein enriching the dataset includes both (i) semantic segmentation to identify and classify regions of interest in cross-sections of a pathology-specific structure in the radiological dataset to produce an annotated dataset and (ii) spatially transforming the annotated dataset with respect to the cross-sections of a pathology-specific structure to produce a pathology-appropriate transformed dataset.

### Claim 9 (depends on 1)

The method of claim 1, wherein an image volume in the radiological dataset is preprocessed to form a region of interest containing a physiological target, lesion, and/or set of lesions that is to be analyzed.

### Claim 10 (depends on 9)

The method of claim 9, wherein the region of interest and/or the physiological target, lesion, and/or set of lesions are at least one of (i) automatically determined from the radiological dataset or (ii) identified by a user from the radiological dataset.

### Claim 11 (depends on 9)

The method of claim 9, wherein the region of interest includes one or more cross sections, each composed of projections through that volume.

### Claim at position 11

-24. (canceled)

### Claim 25 (depends on 9)

The method of claim 9, wherein the pre-processing the image volume includes deblurring or restoring using a patient-specific point spread determination algorithm to mitigate artifacts or image limitations that result from the image formation process.

### Claim 26

(canceled)

### Claim 27 (depends on 1)

The method of claim 1, wherein the machine learned classification approach is use of a trained convolutional neural network (CNN), wherein the CNN is based on a refactoring of AlexNET, Inception, CaffeNet, or other open source or commercially available framework.

### Claim 28 (depends on 1)

The method of claim 1, wherein the dataset is enriched by visually using different colors to represent different analyte sub-regions.

### Claim 29

(canceled)

### Claim 30 (depends on 1)

The method of claim 1, wherein dataset enrichment includes ground truth annotation of analyte sub-regions as well as providing a spatial context of how such analytes sub-regions present in cross-section.

### Claim 31 (depends on 30)

The method of claim 30, wherein the spatial context provides a common basis for analysis of enhanced dataset relative to histological cross-sections.

### Claim at position 19

-33. (canceled)

### Claim 34 (depends on 30)

The method of claim 30, wherein the spatial context includes providing a coordinate system based on polar coordinates relative to a centroid of each cross-section.

### Claim 35

(canceled)

### Claim 36 (depends on 1)

The method of claim 1, wherein dataset enrichment includes ground truth annotation of analyte sub-regions using ex vivo classification independent of or in conjunction with image-based classification based on a common spatial context between the radiological dataset and ex vivo data

### Claim 37 (depends on 36)

The method of claim 36, wherein enriched dataset is visualized using different colors to represent different analyte sub-regions, wherein colors for visualizing the enhanced dataset are selected to correspond to colors utilized in the ex vivo data.

### Claim 38 (depends on 36)

The method of claim 36, wherein the enriched dataset includes a visual overlay of ex-vivo data over the radiological data.

### Claim at position 25

-40. (canceled)

### Claim 41 (depends on 1)

The method of claim 1, wherein the radiological dataset includes computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MRI), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), and/or or single-photon emission tomography (SPECT) diagnostic images.

### Claim 42 (depends on 41)

The method of claim 41, wherein enriching the dataset includes using image deblurring or restoring is used to identify lesions of interest and extract pathology composition quantitatively.

### Claim 43 (depends on 41)

The method of claim 41, wherein enriching the dataset includes spatially transforming cross-sectional segmented images into an ‘pathology-appropriate transformed’ reference frame.

### Claim 44

(canceled)

### Claim 45 (depends on 1)

The method of claim 1, further comprising using a machine learned classification approach based on known ground truths to process the enriched dataset and determine a predictive outcome related to the pathology.