# US 12,109,060 B2 · Noninvasive assessment of microvascular dysfunction

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- Number: US 12,109,060 B2
- Document ID: US12109060B2
- Status: Granted
- Kind: B2
- Application number: 17/068,368
- Filed: 2020-10-12
- Published: 2021-04-15
- Granted: 2024-10-08
- Earliest claimed: 2019-10-10
- Assignees: Medstar Health, Inc.; Oxford University Hospitals Nhs Foundation Trust
- Applicants: Medstar Health, Inc.; Oxford University Hospitals Nhs Foundation Trust
- Inventors: Hector Manuel Garcia Garcia; Giovanni Luigi De Maria; Adrian P. Banning
- Web page: https://patentgrep.com/patent/US12109060B2

## CPC

- A61B 6/5217: {extracting a diagnostic or physiological parameter from medical diagnostic data}
- A61B 5/02007: {Evaluating blood vessel condition, e.g. elasticity, compliance}
- A61B 5/02028: {Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction}
- A61B 5/02125: {of pulse wave propagation time}
- A61B 5/4839: {combined with drug delivery}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 6/481: {involving the use of contrast agents}
- A61B 6/486: {involving generating temporal series of image data}
- A61B 6/504: {for diagnosis of blood vessels, e.g. by angiography}
- A61B 6/507: {for determination of haemodynamic parameters, e.g. perfusion CT}
- A61B 6/5294: {involving using additional data, e.g. patient information, image labeling, acquisition parameters}
- A61B 8/0891: {for diagnosis of blood vessels}
- A61B 8/5207: {involving processing of raw data to produce diagnostic data, e.g. for generating an image}
- A61B 5/0066: {Optical coherence imaging}
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- A61B 6/503: {for diagnosis of the heart}
- A61B 2576/00: Medical imaging apparatus involving image processing or analysis (A61B1/00009, A61B6/52 and A61B8/52 take precedence)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)

## Abstract

Systems and methods are provided for assessing microvascular dysfunction of a patient. A series of images is acquired of a region of interest within the body of the patient. An imaging-derived index of microcirculatory resistance is determined from the series of images. A clinical parameter representing microvascular dysfunction is assigned to the patient via a predictive model according to at least the imaging-derived index of microcirculatory resistance.

## Claims

### Claim 1

A method for assessing coronary microvascular dysfunction of a patient, the method comprising:
acquiring a first series of images of a region of interest within the patient;
determining a first angiography-derived index of microcirculatory resistance from the series of images and a measured first aortic pressure of the patient, wherein determining the imaging-derived index of microcirculatory resistance from the series of images comprises:
measuring the first aortic pressure of the patient;
determining an angiography-derived fractional flow reserve from the series of images;
determining, from the series of images, a transit time of a marker to travel between first and second reference points; and
calculating a product of a ratio of the number of frames necessary for the contrast dye to travel from the guiding catheter to the distal reference to a frame rate of the imager, the first aortic pressure, and the angiography-derived fractional flow reserve determined from the series of images;
assigning a first clinical parameter to the patient via a first predictive model trained on a set of training data representing the first clinical parameter and receiving at least the first angiography-derived index of microcirculatory resistance as an input and providing the first clinical parameter as an output, wherein the first clinical parameter is a categorical parameter that can assume a first value representing a presence of microvascular dysfunction, a second value representing an absence of microvascular dysfunction, and a third value representing an indeterminate result; and
performing the following steps if the first clinical parameter assumes the third value:
providing a vasodilator to the patient;
acquiring a second series of images of the region of interest;
determining a second angiography-derived index of microcirculatory resistance from the second series of images and a second measured aortic pressure of the patient; and
assigning a second clinical parameter to the patient via a second predictive model trained on a set of training data representing the second clinical parameter and receiving at least the second angiography-derived index of microcirculatory resistance as an input and providing the second clinical parameter as an output, wherein the second clinical parameter is a categorical parameter that can assume a first value representing the presence of microvascular dysfunction and a second value representing the absence of microvascular dysfunction.