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US-12620106-B2 - MRI-based textural analysis of trabecular bone

US12620106B2US 12620106 B2US12620106 B2US 12620106B2US-12620106-B2

Abstract

In an example method, a computer system receives one or more images of one or more bones of a patient. The one or more images are generated by a magnetic resonance imaging (MRI). The computer system determines one or more metrics indicative of an image texture of the one or more images; and determines at least one of a bone risk or a bone health of the patient based on the one or more metrics.

Inventors

  • Emily M. Stein
  • Ryan Breighner
  • Matthew F. Koff

Assignees

  • NEW YORK SOCIETY FOR THE RELIEF OF THE RUPTURED AND CRIPPLED, MAINTAINING THE HOSPITAL FOR SPECIAL SURGERY

Dates

Publication Date
20260505
Application Date
20210331

Claims (13)

  1. 1 . A method comprising: receiving, by a computer system, one or more images of one or more bones of a patient, wherein the one or more images are generated by a magnetic resonance imaging (MRI); determining, by the computer system, a plurality of first metrics indicative of an image texture of the one or more images, wherein the plurality of first metrics comprises: an inverse difference moment associated with the one or more images, wherein the inverse difference moment is associated with a plurality of spatial directions, an angular second moment of the one or more images, a contrast of the one or more images, and an entropy associated with the one or more images; determining, by the computer system, at least one of a bone risk or a bone health of the patient based on the plurality of first metrics, wherein determining the bone health of the patient comprises determining a second metric that indicates at least one of (i) whether the patient suffers from osteoporosis or (ii) a bone fracture risk for the patient, wherein the second metric is a weighted sum of the plurality of first metrics, wherein the weighted sum of the plurality of first metrics comprises: a first term representing a first weight multiplied by the inverse difference moment associated with the one or more images, a second term representing a second weight multiplied by the angular second moment of the one or more images, a third term representing a third weight multiplied by the contrast of the one or more images, a fourth term representing a fourth weight multiplied by the entropy associated with the one or more images, and wherein determining the second metric comprises summing at least the first term, second term, third term, and fourth term.
  2. 2 . The method of claim 1 , wherein the one or more bones comprise trabecular bone.
  3. 3 . The method of claim 1 , wherein the one or more bones comprise at least one of a vertebra, a radius, a pelvis, a femur, a tibia, a rib, or a clavicle.
  4. 4 . The method of claim 1 , wherein determining at least one of the bone risk or the bone health of the patient comprises: determining the bone fracture risk for the patient.
  5. 5 . The method of claim 1 , wherein determining at least one of the bone risk or the bone health of the patient comprises: determining a disorder associated with the one or more bones.
  6. 6 . The method of claim 1 , wherein determining at least one of the bone risk or the bone health of the patient comprises: determining that patient suffers from osteoporosis.
  7. 7 . The method of claim 1 , wherein determining at least one of the bone risk or the bone health of the patient comprises: determining a risk of complications associated an orthopedic procedure.
  8. 8 . The method of claim 1 , wherein determining at least one of the bone risk or the bone health of the patient comprises: determining a likelihood of success associated with an orthopedic procedure.
  9. 9 . The method of claim 1 , wherein determining the one or more first metrics indicative of the image texture of the one or more images comprises: determining a heterogeneity of the one or more images.
  10. 10 . The method of claim 1 , wherein the plurality of first metrics further comprises at least one of: an energy of the one or more images, a homogeneity of the one or more images, an autocorrelation of the one or more images, a correlation of the one or more images, a cluster shade of the one or more images, a histogram of the one or more images, a mean of the one or more images, a variance of the one or more images, a skewness of the one or more images, an absolute gradient of the one or more images, a gradient mean of the one or more images, a gradient variance of the one or more images, a gradient skewness of the one or more images, a gradient kurtosis of the one or more images, a proportion of pixels with non-zero gradient of the one or more images, a run length matrix of the one or more images, a short run length matrix of the one or more images, a long run length matrix of the one or more images, a run length non-uniformity of the one or more images, a gray level non-uniformity of the one or more images, or fraction run of the one or more images.
  11. 11 . The method of claim 1 , wherein each of the plurality of first metrics is associated with a plurality of spatial directions.
  12. 12 . The method of claim 11 , wherein each of the spatial directions is a respective diagonal direction expressed according to a cubic kernel.
  13. 13 . The method of claim 11 , wherein the plurality of spatial directions comprise four diagonal directions expressed according to a cubic kernel.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a National Stage application under 35 U.S.C. § 371 of international Application No. PCT/US2021/025179, having an International Filing Date of Mar. 31, 2021, which claims priority to U.S. Provisional Patent Application No. 63/004,908, filed Apr. 3, 2021, the entire content of which is incorporated herein by reference. FIELD OF THE TECHNOLOGY The present technology relates to magnetic resonance imaging (MRI) and measurement of bone properties. BACKGROUND Osteoporosis and fragility fracture are extremely prevalent, and associated with significant morbidity, mortality and health care costs. However, osteoporosis is often overlooked, even among high risk individuals, leading to missed treatment opportunities. In North America, it has been estimated that up to 80% of individuals who sustain a fragility fracture are never evaluated or treated for osteoporosis. Bone mineral density (BMD) measured by dual energy x-ray absorptiometry (DXA) is the gold standard for diagnosis of osteoporosis. However, several structural and material properties beyond BMD independently contribute to overall fracture risk. Several techniques are currently available to measure different attributes of bone quality including high resolution peripheral quantitative computed tomography (QCT), central QCT, bone biopsy, high-resolution magnetic resonance imaging (MRI), and reference point indentation. However, these methods must be performed prospectively, limiting their application for screening. Trabecular bone score (TBS) is a DXA-derived technique currently used in clinical practice. This textural measurement derived from lumbar spine DXA images is able to discern attributes of trabecular microarchitecture based on trabecular bone distribution, and can discriminate and predict fracture independently of BMD. DXA is currently under-utilized in the assessment of osteoporosis as many high-risk patients are never screened. Opportunistic methods, using imaging obtained for other clinical purposes, are needed for the identification of individuals at risk for osteoporosis. SUMMARY In an aspect, a method includes receiving, by a computer system, one or more images of one or more bones of a patient, where the one or more images are generated by a magnetic resonance imaging (MRI); determining, by a computer system, one or more metrics indicative of an image texture of the one or more images, where the one or more metrics includes an inverse difference moment associated with the one or more images; and determining, by the computer system, at least one of a bone risk or a bone health of the patient based on the one or more metrics. Implementations of this aspect can include one or more of the following features. In some implementations, the one or more bones can include trabecular bone. In some implementations, the one or more bones can include at least one of a vertebra, a radius, a pelvis, a femur, a tibia, a rib, or a clavicle. In some implementations, determining at least one of the bone risk or the bone health of the patient can include determining a bone fracture risk for the patient. In some implementations, determining at least one of the bone risk or the bone health of the patient can include determining a disorder associated with the one or more bones. In some implementations, determining at least one of the bone risk or the bone health of the patient can include determining that patient suffers from osteoporosis. In some implementations, determining at least one of the bone risk or the bone health of the patient can include determining a risk of complications associated an orthopedic procedure. In some implementations, determining at least one of the bone risk or the bone health of the patient can include determining a likelihood of success associated with an orthopedic procedure. In some implementations, determining the one or more metrics indicative of the image texture of the one or more images can include determining a heterogeneity of the one or more images. In some implementations, the one or more metrics can further include an angular second moment associated with the one or more images. In some implementations, the one or more metrics can further include an entropy associated with the one or more images. In some implementations, the one or more metrics can further include at least one of: an energy of the one or more images, a contrast of the one or more images, a homogeneity of the one or more images, an autocorrelation of the one or more images, a correlation of the one or more images, a cluster shade of the one or more images, a histogram of the one or more images, a mean of the one or more images, a variance of the one or more images, a skewness of the one or more images, an absolute gradient of the one or more images, a gradient mean of the one or more images, a gradient variance of the one or more images, a gradient skewness of the one or more images, a gradient kurtosi