IP Library Granted Patent US 12,705,742
Granted Patent B2
US 12,705,742 · App. 18/738,796 · Granted Aug 11, 2026

Methods, systems, and devices for analyzing lung imaging data to determine collateral ventilation

Inventors: Sri Radhakrishnan (Cupertino, CA); Ryan Olivera (Granite Bay, CA)
Assignee: Pulmonx Corporation
G06T7/0012A61B6/032A61B6/50A61B6/5217G06T7/11G16H15/00G16H30/20G16H30/40G16H50/20G16H50/30G06T2207/10081G06T2207/30061G16H20/40
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Quick Facts
Patent No.
US 12,705,742
App. No.
18/738,796
Filed
Jun 10, 2024
Granted
Aug 11, 2026
Kind
B2
Art Unit
3798
USPC
600/425
Abstract

Devices, methods, and systems are provided for analyzing lung imaging data. Lung imaging data maybe analyzed to segment the lung, identify fissure locations, calculate fissure defect scores, identify adjacent lung compartments, calculate emphysema scores, calculate volumes, and calculate proximities. Collateral ventilation within a lung compartment may be determined based on the analyzed lung imaging data.

Claims (39)

1 . A method of assessing collateral ventilation in a lung of a patient, the method comprising:

analyzing computerized tomography data of the lung, wherein analyzing the computerized tomography data comprises:

segmenting tomography data representing the lung during one or more breathing cycles into segmented data representing separate lung compartments;

identifying, based on the segmented data, fissure data representing a fissure defect;

identifying, based on the segmented data and the fissure data, lung compartment data representing lung compartments adjacent to the fissure defect;

calculating, based on the lung compartment data for the one or more breathing cycles, volumes of a first lung compartment adjacent to the fissure defect and a second lung compartment adjacent to the fissure defect on an opposing side of the fissure defect during the one or more breathing cycles;

determining, based on the calculated volumes, changes in volumes of the first lung compartment adjacent to the fissure defect and the second lung compartment adjacent to the fissure defect on the opposing side of the fissure defect during the one or more breathing cycles;

determining whether collateral ventilation is present or above a threshold level in the first or second lung compartment based on the changes in volumes of the first and second lung compartments;

responsive to the collateral ventilation being not present or below the threshold level in the first or second lung compartment, generating a treatment plan comprising one or more suggested implantable devices for placement in one or more potential treatment sites based on the lung compartment data and the fissure data for the one or more breathing cycles; and

administering the treatment plan by placing at least one of the one or more suggested implantable devices into the one or more potential treatment sites.

2 . The method of claim 1 , wherein collateral ventilation is determined to be present or above the threshold level if the first and second lung compartments inflate or deflate asynchronously during breathing.

3 . The method of claim 1 , wherein the one or more suggested implantable devices comprises an endobronchial valve, clip, or plug.

4 . The method of claim 1 , wherein the one or more suggested implantable devices comprises a one-way flow control valve configured to allow airflow out of the lung compartment and prevent airflow into the lung compartment to cause lung volume reduction or reduce hyperinflation.

5 . The method of claim 1 , further comprising calculating a fissure defect score based on a size of the fissure defect.

6 . The method of claim 5 , wherein determining whether collateral ventilation is present or above the threshold level is further based on the calculated fissure defect score.

7 . The method of claim 6 , further comprising determining a degree of collateral ventilation based on the changes in volumes of the first and second lung compartments.

8 . The method of claim 1 , further comprising creating a report including the treatment plan, the report indicating lung compartments determined to have collateral ventilation or degrees of collateral ventilation for lung compartments.

9 . The method of claim 8 , wherein the one or more potential treatment sites and one or more suggested implantable devices for a potential treatment site are determined, based on the lung compartment data and the fissure data during the one or more breathing cycles, to cause lung volume reduction or reduce hyperinflation.

10 . The method of claim 9 , wherein the potential treatment site of the one or more potential treatment sites is an airway leading to the first or second lung compartment.

11 . The method of claim 8 , wherein the report comprises one or more suggested therapeutic agents to be delivered to a potential treatment site of the one or more potential treatment sites to cause lung volume reduction or reduce hyperinflation.

12 . The method of claim 11 , wherein the first or second lung compartment is determined to have collateral ventilation or a degree of collateral ventilation above the threshold level and the potential treatment site comprises one or more regions within the first or second lung compartment.

13 . The method of claim 12 , wherein the one or more suggested therapeutic agents comprises a sealant.

14 . The method of claim 8 , wherein the report comprises one or more suggested therapeutic agents to be delivered to a potential treatment site of the one or more potential treatment sites to minimize collateral ventilation between lung compartments.

15 . The method of claim 14 , wherein the first or second lung compartment is determined to have collateral ventilation or a degree of collateral ventilation above the threshold level and the potential treatment site comprises an airway leading to the fissure defect.

16 . The method of claim 15 , wherein the one or more suggested therapeutic agents comprises a sealant.

17 . A method of assessing collateral ventilation in a lung of a patient, the method comprising:

analyzing computerized tomography data of the lung, wherein analyzing the computerized tomography data comprises:

segmenting tomography data representing the lung during one or more breathing cycles into segmented data representing separate lobes;

segmenting the segmented data representing at least one of the separate lobes into sub-segmented data representing a plurality of lung segments;

identifying, based on the sub-segmented data, fissure data representing a fissure defect;

identifying, based on the sub-segmented data and the fissure data, lung segment data representing one or more lung segments of the plurality of lung segments adjacent to the fissure defect;

calculating, based on the lung segment data for the one or more breathing cycles, volumes of a first lung segment adjacent to the fissure defect and a second lung segment adjacent to the fissure defect on an opposing side of the fissure defect during the one or more breathing cycles;

determining, based on the calculated volumes, changes in volumes of the first lung segment adjacent to the fissure defect and the second lung segment adjacent to the fissure defect on the opposing side of the fissure defect during the one or more breathing cycles;

determining whether collateral ventilation is present or above a threshold level in the first or second lung segment based on the changes in volumes of the first and second lung segments;

responsive to the collateral ventilation being not present or below the threshold level in the first or second lung segment, generating a treatment plan comprising one or more suggested implantable devices for placement in one or more potential treatment sites based on the lung segment data and the fissure data for the one or more breathing cycles; and

administering the treatment plan by placing at least one of the one or more suggested implantable devices in the one or more potential treatment sites.

18 . The method of claim 17 , further comprising calculating a fissure defect score based on a size of the fissure defect, wherein determining whether collateral ventilation is present or above the threshold level is further based on the calculated fissure defect score.

19 . The method of claim 17 , wherein the one or more suggested implantable devices comprises an endobronchial valve, clip, or plug.

20 . The method of claim 17 , further comprising determining treatment success based on a comparison of the tomography data representing the lung before treatment to the tomography data representing the lung after treatment.

Assignments (3)
SECURITY INTEREST Recorded Mar 2, 2026
From: PULMONX CORPORATION
To: PERCEPTIVE CREDIT HOLDINGS V, LP, AS ADMINISTRATIVE AGENT
Reel/Frame 075012/0453 →
SECURITY INTEREST Recorded Jan 27, 2026
From: PULMONX CORPORATION
To: CANADIAN IMPERIAL BANK OF COMMERCE
Reel/Frame 074570/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 11, 2024
From: OLIVERA, RYAN; RADHAKRISHNAN, SRI
To: PULMONX CORPORATION
Reel/Frame 067693/0668 →
Continuity (3)
Division 17444336 · Aug 3, 2021
Provisional Application 63062390 · Aug 6, 2020
Related Publication 20240331154A1 · Oct 3, 2024
References Cited (118)
US 5954766A · Zadno-Azizi et al. · 1999 [cited by applicant]
US 6287290B1 · Perkins et al. · 2001 [cited by applicant]
US 6398775B1 · Perkins et al. · 2002 [cited by applicant]
US 6527761B1 · Soltesz et al. · 2003 [cited by applicant]
US 6585639B1 · Kotmel et al. · 2003 [cited by applicant]
US 6610043B1 · Ingenito · 2003 [cited by applicant]
US 6679264B1 · Deem et al. · 2004 [cited by applicant]
US 6682520B2 · Ingenito · 2004 [cited by applicant]
US 6694979B2 · Deem et al. · 2004 [cited by applicant]
US 6709401B2 · Perkins et al. · 2004 [cited by applicant]
US 6840243B2 · Deem et al. · 2005 [cited by applicant]
US 6878141B1 · Perkins et al. · 2005 [cited by applicant]
US 6941950B2 · Wilson et al. · 2005 [cited by applicant]
US 7165548B2 · Deem et al. · 2007 [cited by applicant]
US 7654998B1 · Ingenito · 2010 [cited by applicant]
US 7798147B2 · Hendricksen et al. · 2010 [cited by applicant]
US 7819908B2 · Ingenito · 2010 [cited by applicant]
US 8136526B2 · Perkins et al. · 2012 [cited by applicant]
US 8137302B2 · Aljuri et al. · 2012 [cited by applicant]
US 8445589B2 · Ingenito et al. · 2013 [cited by applicant]
US 8808194B2 · Mantri et al. · 2014 [cited by applicant]
US 9107606B2 · Radhakrishnan et al. · 2015 [cited by applicant]
US 9211181B2 · Olivera et al. · 2015 [cited by applicant]
US 9364168B2 · Mantri · 2016 [cited by applicant]
US 9592008B2 · Olivera et al. · 2017 [cited by applicant]
US 10456562B2 · Radhakrishnan et al. · 2019 [cited by applicant]
US 10478125B2 · Freitag · 2019 [cited by applicant]
US 12033320B2 · Radhakrishnan et al. · 2024 [cited by applicant]
US 20020014238A1 · Kotmel · 2002 [cited by applicant]
US 20030051733A1 · Kotmel et al. · 2003 [cited by applicant]
US 20030055331A1 · Kotmel et al. · 2003 [cited by applicant]
US 20030127090A1 · Gifford et al. · 2003 [cited by applicant]
US 20030164168A1 · Shaw · 2003 [cited by applicant]
US 20030181356A1 · Ingenito · 2003 [cited by applicant]
US 20030228344A1 · Fields et al. · 2003 [cited by applicant]
US 20040039250A1 · Tholfsen et al. · 2004 [cited by applicant]
US 20040047855A1 · Ingenito · 2004 [cited by applicant]
US 20040055606A1 · Hendricksen et al. · 2004 [cited by applicant]
US 20040074491A1 · Hendricksen et al. · 2004 [cited by applicant]
US 20040089306A1 · Hundertmark et al. · 2004 [cited by applicant]
US 20040148035A1 · Barrett et al. · 2004 [cited by applicant]
US 20050015630A1 · Yumoto et al. · 2005 [cited by applicant]
US 20050061322A1 · Freitag · 2005 [cited by applicant]
US 20050066974A1 · Fields et al. · 2005 [cited by applicant]
US 20050161048A1 · Rapacki et al. · 2005 [cited by applicant]
US 20050196344A1 · McCutcheon et al. · 2005 [cited by applicant]
US 20050244401A1 · Ingenito · 2005 [cited by applicant]
US 20060004305A1 · George et al. · 2006 [cited by applicant]
US 20060020347A1 · Barrett et al. · 2006 [cited by applicant]
US 20060030863A1 · Fields et al. · 2006 [cited by applicant]
US 20060076023A1 · Rapacki et al. · 2006 [cited by applicant]
US 20060107956A1 · Hendricksen et al. · 2006 [cited by applicant]
US 20060135947A1 · Soltesz et al. · 2006 [cited by applicant]
US 20060162731A1 · Wondka et al. · 2006 [cited by applicant]
US 20060264772A1 · Aljuri · 2006 [cited by examiner]
US 20070005083A1 · Sabanathan et al. · 2007 [cited by applicant]
US 20070043350A1 · Soltesz et al. · 2007 [cited by applicant]
US 20070110813A1 · Ingenito et al. · 2007 [cited by applicant]
US 20070142742A1 · Aljuri et al. · 2007 [cited by applicant]
US 20070186932A1 · Wondka et al. · 2007 [cited by applicant]
US 20070186933A1 · Domingo et al. · 2007 [cited by applicant]
US 20070203396A1 · McCutcheon et al. · 2007 [cited by applicant]
US 20070225747A1 · Perkins et al. · 2007 [cited by applicant]
US 20080009760A1 · Wibowo et al. · 2008 [cited by applicant]
US 20080051719A1 · Nair et al. · 2008 [cited by applicant]
US 20080072914A1 · Hendricksen et al. · 2008 [cited by applicant]
US 20080086107A1 · Roschak · 2008 [cited by applicant]
US 20080115787A1 · Ingenito · 2008 [cited by applicant]
US 20080221582A1 · Gia et al. · 2008 [cited by applicant]
US 20080221703A1 · Que et al. · 2008 [cited by applicant]
US 20080228130A1 · Aljuri et al. · 2008 [cited by applicant]
US 20080228137A1 · Aljuri et al. · 2008 [cited by applicant]
US 20080249503A1 · Fields et al. · 2008 [cited by applicant]
US 20080261884A1 · Tsai et al. · 2008 [cited by applicant]
US 20080281352A1 · Ingenito et al. · 2008 [cited by applicant]
US 20090241964A1 · Aljuri et al. · 2009 [cited by applicant]
US 20090255537A1 · Shaw et al. · 2009 [cited by applicant]
US 20100036361A1 · Nguyen et al. · 2010 [cited by applicant]
US 20100040538A1 · Ingenito et al. · 2010 [cited by applicant]
US 20100158795A1 · Aljuri et al. · 2010 [cited by applicant]
US 20110087122A1 · Aljuri et al. · 2011 [cited by applicant]
US 20110270116A1 · Freitag et al. · 2011 [cited by applicant]
US 20110295141A1 · Radhakrishnan et al. · 2011 [cited by applicant]
US 20120150027A1 · Mantri et al. · 2012 [cited by applicant]
US 20130317293A1 · Olivera et al. · 2013 [cited by applicant]
US 20140107396A1 · Freitag · 2014 [cited by applicant]
US 20140315175A1 · Nguyen et al. · 2014 [cited by applicant]
US 20150238270A1 · Raffy · 2015 [cited by examiner]
US 20150294462A1 · Yin et al. · 2015 [cited by applicant]
US 20150342610A1 · Radhakrishnan et al. · 2015 [cited by applicant]
US 20160328850A1 · Yin et al. · 2016 [cited by applicant]
US 20160367259A1 · Radhakrishnan et al. · 2016 [cited by applicant]
US 20170224301A1 · Radhakrishnan · 2017 [cited by examiner]
US 20180092731A1 · Radhakrishnan et al. · 2018 [cited by applicant]
US 20200037958A1 · Freitag · 2020 [cited by applicant]
US 20200222120A1 · Culala · 2020 [cited by applicant]
US 20220007962A1 · Radhakrishnan et al. · 2022 [cited by applicant]
US 20220007967A1 · Radhakrishnan et al. · 2022 [cited by applicant]
JP 2009542374A · 2009 [cited by applicant]
JP 2019507618A · 2019 [cited by applicant]
Klinder , et al. , “Lobar fissure detection using line enhancing filters” , SPIE Medical Imaging , Mar. 13, 2013 , 8 pages. [cited by applicant]
Kuhnigk , et al. , “Lung Lobe Segmentation by Anatomy-guided 3D Watershed Transform” , Proceedings of SPIE—The International Society for Optical Engineering 5032 , May 15, 2003 , pp. 1482-1490. [cited by applicant]
Kuhnigk , et al. , “New Tools for Computer Assistance in Thoracic CT. Part 1. Functional Analysis of Lungs, Lung Lobes, and Bronchopulmonary Segments” , RadioGraphics, vol. 25, No. 2 , Mar. 2005 , pp. 525-536. [cited by applicant]
Lassen , et al. , “Automatic Segmentation of Lung Lobes in CT Images Based on Fissures Vessels, and Bronchi”, IEEE Transactions on Medical Imaging, vol. 32, No. 2 , Feb. 2013 , pp. 210-222. [cited by applicant]
Lassen , et al. , “Interactive Lung Lobe Segmentation and Correction in Tomographic Images” , Proceedings of the SPIE, Medical Imaging, Computer-Aided Diagnosis, vol. 79631 , Mar. 8, 2011. [cited by applicant]
Lassen , et al. , “Lung and Lung Lobe Segmentation Methods at Fraunhofer MEVIS” , Lobe and Lung Analysis (LOLA11) , 2011 , 15 pages. [cited by applicant]
Qian , et al. , “Elastic Contour Model-based Analysis of Structural Deformations: Toward Timeseq Uenced Regional Lung Parenchymal Analysis” , Medical Imaging 1996: Physiology and Function from Multidimensional Images, v… [cited by applicant]
Reinhardt , et al. , “3D Pulmonary CT Image Registration With a Standard Lung Atlas” , Proceedings vol. 3978, Medical Imaging 2000: Physiology and Function from Multidimensional Images , Apr. 20, 2000. [cited by applicant]
Reinhardt , et al. , “Detection of Lung Lobar Fissures Using Fuzzy Logic” , Physiology and Function from Multidimensional Images , May 20, 1999. [cited by applicant]
Reinhardt , et al. , “Pulmonary Imaging and Analysis” , Handbook of Medical Imaging, vol. 2 , Jun. 14, 2000 , pp. 1005-1060. [cited by applicant]
Revel , et al. , “Automated Lobar Quantification of Emphysema in Patients With Severe COPD” , European Radiology, vol. 18, No. 12 , Dec. 2008 , pp. 2723-2730. [cited by applicant]
Schmidt-Richberg , et al. , “Evaluation of Algorithms for Lung Fissure Segmentation in CT Images” , Bildverarbeitung Für Die Medizin 2012 , Mar. 16, 2012. [cited by applicant]
Sluimer , et al. , “Toward Automated Segmentation of the Pathological Lung in CT” , IEEE Transactions on Medical Imaging, vols. 24, No. 8 , Aug. 2005 , pp. 1025-1038. [cited by applicant]
Wiemker , et al. , “Unsupervised Extraction of the Pulmonary Interlobar Fissures From High Resolution Thoracic CT Data”, International Congress Series, vol. 1281 , May 2005 , pp. 1121-1126. [cited by applicant]
Xiao , et al. , “Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter” , IEEE Transactions on Medical Imaging, vol. 35, No. 6 , Jun. 2016 , pp. 1488-1500. [cited by applicant]
Zhang , et al. , “Atlas-Driven Lung Lobe Segmentation in Volumetric X-Ray CT Images” , IEEE Transactions on Medical Imaging, vol. 25, No. 1 , Jan. 2006 , pp. 1-16. [cited by applicant]
Zhang , et al. , “Lung Lobe Segmentation by Graph Search With 3D Shape Constraints” , Proceedings of the SPIE, vol. 4321 , May 2001 , pp. 204-215. [cited by applicant]
Schuhmann et al., “Computed Tomography Predictors of Response to Endobronchial Valve Lung Reduction Treatment. Comparison with Chartis”, American Journal of Respiratory and Critical Care Medicine, vol. 191, No. 7, Apr. … [cited by applicant]