IP Library › Granted Patent US 12,471,853
Granted Patent B2
US 12,471,853 · App. 17/905,069 · Granted Nov 18, 2025

3D image analysis platform for neurological conditions

Inventor: Darin T. Okuda (Dallas, TX)
Assignee: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM
A61B5/7275A61B5/0042A61B5/055A61B5/4064A61B5/407A61B5/4076A61B5/4842G06T7/0012G06T7/11G06T7/38G06T7/62G06T7/64G06V10/25G06T2207/10088G06T2207/30016G06T2207/30096
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,471,853
App. No.
17/905,069
Granted
Nov 18, 2025
Kind
B2
Abstract

Disclosed herein are systems and methods of analyzing 3D structure of a portion of the CNS. An analytics module may be used to calculate one or more metrics the describe changes in the 3D structure of a CNS structure over time. The one or more metrics may be used to identify patterns of structural change prior to progressive symptom development. Healthcare providers may use the one or more metrics and or patterns of structural change to diagnose neurological conditions, track the progress of neurological conditions in the patient, and determine the patient's risk of progressive disease development. The 3D structure analytics techniques described herein may also be used to develop treatments and create a care delivery that is individualized for each patient.

Claims (48)

1 . A method of analyzing a three-dimensional (3D) structure of a central nervous system (CNS) structure comprising:

capturing, by a 3D imaging device, image data of a portion of the CNS structure of a patient at a first time point;

accessing image data of the portion of the CNS structure of a patient at a second time point, wherein the second time point is after the first time point;

generating, from the image data, a first 3D representation of the portion of the CNS structure at the first time point and a second 3D representation of the portion of the CNS structure at the second time point;

calculating one or more metrics that describe at least one of a 3D structural property of the CNS structure at the first time point, a 3D structural property of the CNS structure at the second time point, and a change in the CNS structure between the first time point and the second time point;

identifying one or more patterns of structural change in the CNS structure from the one or more metrics; and

determining a probability of a particular course of development for a neurological condition of the patient based on the one or more patterns of structural change.

2 . The method of claim 1 , further comprising:

segmenting the image data to extract a region of interest (ROI) of the CNS structure that is included in the image data; and

aligning the first 3D representation and the second 3D representation using an intensity matching technique.

3 . The method of claim 2 , wherein the ROI is clinically relevant and uniformly repeatable across a wide variety of different patients with degenerative neurological conditions.

4 . The method of claim 1 , wherein the CNS structure is a portion of at least one of a brain and a spinal cord.

5 . The method of claim 1 , wherein the one or more metrics include at least one of a volume measurement, a surface area measurement, a displacement measurement, and a surface complexity measurement.

6 . The method of claim 1 , wherein the one or more metrics include a compliance metric that describes the change in a property of the CNS structure over time.

7 . The method of claim 1 , further comprising diagnosing a neurological condition or tracking the progress of the neurological condition based on at least one of the one or more metrics and the patterns of structural change.

8 . The method of claim 1 , wherein the determining a probability of a particular course of development for the neurological condition further comprises:

comparing the one or more metrics to one or more thresholds; and

determining the neurological condition is on a progressive course of development response to determining at least one metric exceeds the threshold for that metric.

9 . The method of claim 8 , wherein the one or more thresholds include:

a volume-based threshold that is exceeded when a change in volume of the ROI of the CNS structure exceeds a threshold volume change;

an area-based threshold that is exceeded when a change in a surface area of the ROI of the CNS structure exceeds a threshold surface area change; and

a surface complexity threshold that is exceeded when a change in the surface complexity of the ROI of the CNS structure exceeds a threshold surface complexity change.

10 . The method of claim 9 , wherein the value of at least one of the volume-based threshold, the area-based threshold, and the surface complexity threshold is specific to a race of the patient.

11 . A system for analyzing the three-dimensional (3D) structure of a central nervous system (CNS) structure comprising:

a 3D imaging device configured to capture image data of a portion of the CNS structure of a patient at a first time point; and

an analytics module configured to cause one or more processors to perform the operations of:

accessing image data of the portion of the CNS structure of a patient at a second time point, wherein the second time point is after the first time point;

generating, from the image data, a first 3D representation of the portion of the CNS structure at the first time point and a second 3D representation of the portion of the CNS structure at the second time point;

calculating one or more metrics that describe at least one of a 3D structural property of the CNS structure at the first time point, a 3D structural property of the CNS structure at the second time point, and a change in the CNS structure between the first time point and the second time point;

identifying one or more patterns of structural change in the CNS structure form the one or more metrics; and

determining a probability of a particular course of development for a neurological condition of the patient based on the one or more patterns of structural change.

12 . The system of claim 11 , wherein the analytics module is further configured to cause the processor to perform the operations of:

segmenting the image data to extract a region of interest (ROI) of the CNS structure that is included in the image data; and

aligning the first 3D representation and the second 3D representation using an intensity matching technique.

13 . The system of claim 12 , wherein the ROI is clinically relevant and uniformly repeatable across a wide variety of different patients with degenerative neurological conditions.

14 . The system of claim 11 , wherein the CNS structure is a portion of at least one of a brain and a spinal cord.

15 . The system of claim 11 , wherein the one or more metrics include at least one of a volume measurement, a surface area measurement, a displacement measurement, and a surface complexity measurement.

16 . The system of claim 11 , wherein the one or more metrics include a compliance metric that describes the change in a property of the CNS structure over time.

17 . The system of claim 11 , wherein the analytics module is further configured to cause the processor to perform the operations of:

diagnosing a neurological condition or tracking the progress of the neurological condition based on at least one of the one or more metrics and the patterns of structural change.

18 . The system of claim 11 , wherein the analytics module is further configured to cause the processor to perform the determining a probability of a particular course of development for the neurological condition by:

comparing the one or more metrics to one or more thresholds; and

determining the neurological condition is on a progressive course of development in response to determining at least one metric exceeds the threshold for that metric.

19 . The system of claim 11 , wherein the one or more thresholds include:

a volume-based threshold that is exceeded when a change in volume of a region of the CNS structure exceeds a threshold volume change;

an area-based threshold that is exceeded when a change in a surface area of a region of the CNS structure exceeds a threshold surface area change; and

a surface complexity threshold that is exceeded when a change in the surface complexity of a region of the CNS structure exceeds a threshold surface complexity change.

20 . The system of claim 19 , wherein the value of at least one of the volume-based threshold, the area-based threshold, and the surface complexity threshold is specific to a race of the patient.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 7, 2022
From: OKUDA, DARIN T.
To: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM
Reel/Frame 061015/0374 →
Continuity (2)
Provisional Application 63018103 · Apr 30, 2020
Related Publication 20230089375A1 · Mar 23, 2023
References Cited (73)
US 6819952B2 · Pfefferbaum · 2004 [cited by examiner]
US 11589800B2 · Tseng · 2023 [cited by examiner]
US 11666219B2 · Fox · 2023 [cited by examiner]
US 11842491B2 · Conklin · 2023 [cited by examiner]
US 20050233328A1 · Berghs et al. · 2005 [cited by applicant]
US 20110218253A1 · Lange et al. · 2011 [cited by applicant]
US 20110313323A1 · Henderson et al. · 2011 [cited by applicant]
US 20120277572A1 · Hubbard · 2012 [cited by applicant]
US 20130150922A1 · Butson et al. · 2013 [cited by applicant]
US 20170039708A1 · Henry · 2017 [cited by examiner]
US 20180127507A1 · Greenberg et al. · 2018 [cited by applicant]
US 20190197347A1 · Okuda et al. · 2019 [cited by applicant]
US 20190248885A1 · Liddelow et al. · 2019 [cited by applicant]
US 20200100732A1 · Zizi et al. · 2020 [cited by applicant]
US 20210041518A1 · Okuda · 2021 [cited by applicant]
US 20210150671A1 · Guo · 2021 [cited by examiner]
US 20210343008A1 · Okuda et al. · 2021 [cited by applicant]
WO 2019165464A1 · 2019 [cited by applicant]
WO WO2020069509A1 · 2020 [cited by applicant]
WO 2021222529A1 · 2021 [cited by applicant]
Extended European Search Report issued in EP Application No. 21795842, 14 pages, dated Mar. 5, 2024. [cited by applicant]
Moog et al, “African Americans experience disproportionate neurodegenerative changes in the medulla and upper cervical spinal cord in early multiple sclerosis”, Multiple Sclerosis and Related Disorders, vol. 45, No. 102… [cited by applicant]
Ganiler et al, “A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies”, Diagnostic Neuroradiology, vol. 56, pp. 363-374, Mar. 4, 2014. [cited by applicant]
Tallantyre et al, “Ultra-high-field imagin distinguishes MS lesions from asymptomatic white matter lesions”, Neurology, vol. 76, pp. 534-539, Feb. 8, 2011. [cited by applicant]
Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162-173 (2018). [cited by applicant]
Wardlaw, J. M., Smith, C. & Dichgans, M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 18, 684-696 (2019). [cited by applicant]
Solomon, A. J., Naismith, R. T. & Cross, A. H. Misdiagnosis of multiple sclerosis: impact of the 2017 McDonald criteria on clinical practice. Neurology. 92, 26-33 (2019). [cited by applicant]
Sati, P. et al. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat. Rev. Neurol. 12, 7… [cited by applicant]
Hammond, K. E. et al. Quantitative in vivo magnetic resonance imaging of multiple sclerosis at 7 Tesla with sensitivity to iron. Ann. Neurol. 64, 707-713 (2008). [cited by applicant]
Solomon, A. J. et al. “Central vessel sign” on 3T FLAIR* MRI for the differentiation of multiple sclerosis from migraine. Ann. Clin. Transl. Neurol. 3, 82-87 (2016). [cited by applicant]
Sinnecker, T. et al. Evaluation of the central vein sign as a diagnostic imaging biomarker in multiple sclerosis. JAMA Neurol. 76, 1446-1456 (2019). [cited by applicant]
Absinta, M. et al. Identification of chronic active multiple sclerosis lesions on 3T MRI. AJNR Am. J. Neuroradiol. 39, 1233-1238 (2018). [cited by applicant]
Elliott, C. et al. Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions. Mult. Scler. hops ://doi.org/10.1177/13524 58518 81411 7 (2018). [cited by applicant]
Frischer, J. M. et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann. Neurol. 78, 710-721 (2015). [cited by applicant]
Dal-Bianco, A. et al. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol. 133, 25-42 (2017). [cited by applicant]
Absinta, M. et al. Association of chronic active multiple sclerosis lesions with disability in vivo. JAMA Neurol. 76, 1474-1483 (2019). [cited by applicant]
McFarland, H. F. et al. Using gadolinium-enhanced magnetic resonance imaging lesions to monitor disease activity in multiple sclerosis. Ann. Neurol. 32, 758-766 (1992). [cited by applicant]
Solomon, A. J. et al. The contemporary spectrum of multiple sclerosis misdiagnosis: a multicenter study. Neurology 87, 1393-1399 (2016). [cited by applicant]
Ziemssen, T. et al. Optimizing treatment success in multiple sclerosis. J. Neurol. 263, 1053-1065 (2016). [cited by applicant]
Sethi, V. et al. Slowly eroding lesions in multiple sclerosis. Mult. Scler. 23, 464-472 (2017). [cited by applicant]
Newton, B. D. et al. Three-dimensional shape and surface features distinguish multiple sclerosis lesions from nonspecific white matter disease. J. Neuroimaging 27, 613-619 (2017). [cited by applicant]
Sivakolundu, D. K. et al. Three-dimensional lesion phenotyping and physiologic characterization inform remyelination ability in multiple sclerosis. J. Neuroimaging 29, 605-614 (2019). [cited by applicant]
Dutta, R. et al. Mitochondrial dysfunction as a cause of axonal degeneration in multiple sclerosis patients. Ann. Neurol. 59, 478-489 (2006). [cited by applicant]
Trapp, B. D. & Stys, P. K. Virtual hypoxia and chronic necrosis of demyelinated axons in multiple sclerosis. Lancet Neurol. 8, 280-291 (2009). [cited by applicant]
Neuropathology Group. Medical Research Council Cognitive F and Aging S. Pathological correlates of late-onset dementia in a multicentre, community-based population in England and Wales. Neuropathology Group of the Medic… [cited by applicant]
Hoogeveen, E. S. et al. MRI evaluation of the relationship between carotid artery endothelial shear stress and brain white matter lesions in migraine. J. Cereb. Blood Flow Metab. https ://doi.org/10.1177/02716 78X19 857… [cited by applicant]
Fernando, M. S. et al. White matter lesions in an unselected cohort of the elderly: molecular pathology suggests origin from chronic hypoperfusion injury. Stroke 37, 1391-1398 (2006). [cited by applicant]
Van Veluw, S. J. et al. Different microvascular alterations underlie microbleeds and microinfarcts. Ann. Neurol. 86, 279-292 (2019). [cited by applicant]
Trapp, B. D. et al. Axonal transection in the lesions of multiple sclerosis. N. Engl. J. Med. 338, 278-285 (1998). [cited by applicant]
Chang, A., Tourtellotte, W. W., Rudick, R. & Trapp, B. D. Premyelinating oligodendrocytes in chronic lesions of multiple sclerosis. N. Engl. J. Med. 346, 165173 (2002). [cited by applicant]
Brown, R. B., Traylor, M., Burgess, S., Sawcer, S. & Markus, H. S. Do cerebral small vessel disease and multiple sclerosis share common mechanisms of white matter injury?. Stroke https ://doi.org/10.1161/STROK EAHA1 180… [cited by applicant]
Elliott, C. et al. Chronic white matter lesion activity predicts clinical progression in primary progressive multiple sclerosis. Brain 142, 2787-2799 (2019). [cited by applicant]
Lebrun, C. et al. Unexpected multiple sclerosis: follow-up of 30 patients with magnetic resonance imaging and clinical conversion profile. J. Neurol. Neurosurg. Psychiatry 79, 195-198 (2008). [cited by applicant]
Okuda, D. T. et al. Incidental MRI anomalies suggestive of multiple sclerosis: the radiologically isolated syndrome. Neurology 72, 800-805 (2009). [cited by applicant]
Lebrun-Frenay, C. et al. Radiologically isolated syndrome: 10-year risk estimate of a clinical event. Ann. Neurol. https ://doi.org/10.1002/ana.25799 (2020). [cited by applicant]
Hansen, M. R. et al. Post-gadolinium 3-dimensional spatial, surface, and structural characteristics of glioblastomas differentiate pseudoprogression from true tumor progression. J. Neurooncol. 139, 731-738 (2018). [cited by applicant]
Nyul, L. G., Udupa, J. K. & Zhang, X. New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 19, 143-150 (2000). [cited by applicant]
Caselles, V., Kimmel, R. & Sapiro, G. Geodesic active contours. Int. J. Comput. Vision 22, 61-79 (1997). [cited by applicant]
Stan Development Team. RStan: the R interface to Stan. R package version 2.19.2. https ://mc-stan.org/. (2019). [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2021/029835, issued Oct. 27, 2022. [cited by applicant]
Written Opinion for International Application No. PCT/US2021/029835, mailed Sep. 8, 2021. [cited by applicant]
International Search Report for International Application No. PCT/US2021/029835, mailed Sep. 8, 2021. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2021/028898, mailed Nov. 10, 2022, 07 Pages. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2021/029835, mailed Nov. 10, 2022, 07 Pages. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2021/028898, mailed Aug. 23, 2021, 08 Pages. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2021/029835, mailed Sep. 8, 2021, 08 Pages. [cited by applicant]
Office Action for European Patent Application No. 21795842.0, mailed Apr. 14, 2025, 9 pages. [cited by applicant]
Reich et al., “Multiparametric magnetic resonance imaging analysis of the corticospinal tract in multiple sclerosis” NeuroImage 2007, 38, pp. 271-279. [cited by applicant]
Sivakolundu et al., “Bold signal within and around white matter lesions distinguishes multiple sclerosis and non-specific white matter disease: a three dimensional approach”, J. Neural., 267(10), 2888-2896, 2020. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2020/052452, mailed Feb. 16, 2023, 6 Pages. [cited by applicant]
International Search Report and Written Opinion for International Application No. PCT/US2020/052452, mailed Jun. 22, 2022, 08 Pages. [cited by applicant]
Extended European Search Report issued in Corresponding European Application No. 21797087.0, dated Apr. 18, 2024. [cited by applicant]
Calloni et al., “Multiparametric MR imaging of Parkinsonisms at 3 tesla: Its role in the differentiation of idiopathic Parkinson's disease versus atypical Parkinsonian disorders” European Journal of Radiology 2018, 109,… [cited by applicant]