WO2008050223A2 - Procédé et système pour l'analyse automatique de structures et de pathologies de vaisseau sanguin - Google Patents

Procédé et système pour l'analyse automatique de structures et de pathologies de vaisseau sanguin Download PDF

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Publication number
WO2008050223A2
WO2008050223A2 PCT/IB2007/003197 IB2007003197W WO2008050223A2 WO 2008050223 A2 WO2008050223 A2 WO 2008050223A2 IB 2007003197 W IB2007003197 W IB 2007003197W WO 2008050223 A2 WO2008050223 A2 WO 2008050223A2
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WIPO (PCT)
Prior art keywords
identified
blood vessel
identifying
threshold
imaging data
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PCT/IB2007/003197
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English (en)
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WO2008050223A3 (fr
Inventor
Grigory Begelman
Roman Goldenberg
Shai Levanon
Shay Ohayon
Eugene Walach
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Rcadia Medical Imaging Ltd.
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Priority claimed from US11/562,875 external-priority patent/US7983459B2/en
Priority claimed from US11/562,897 external-priority patent/US7860283B2/en
Priority claimed from US11/562,771 external-priority patent/US7873194B2/en
Priority claimed from US11/562,906 external-priority patent/US7940977B2/en
Application filed by Rcadia Medical Imaging Ltd. filed Critical Rcadia Medical Imaging Ltd.
Priority to EP07825480A priority Critical patent/EP2074551A4/fr
Publication of WO2008050223A2 publication Critical patent/WO2008050223A2/fr
Publication of WO2008050223A3 publication Critical patent/WO2008050223A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20068—Projection on vertical or horizontal image axis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/031—Recognition of patterns in medical or anatomical images of internal organs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/032—Recognition of patterns in medical or anatomical images of protuberances, polyps nodules, etc.

Definitions

  • the field of the disclosure relates generally to computer systems. More specifically, the disclosure relates to automatic analysis of blood vessel structures and identification of pathologies associated with the identified blood vessels using a computer system. r
  • Chest pain is a common complaint in a hospital or clinic emergency room (ER). Evaluating and diagnosing chest pain remains an enormous challenge. The ER physician generally must quickly rule out three possible causes of the chest pain — aortic dissection (aneurysm), pulmonary embolism (PE), and myocardial infarction (coronary artery stenosis). This type of triage is known as "triple rule out.” Until recently, three different classes of diagnostic procedures were used in the ER to diagnose the three potential possibilities. Today, 64-slice multi-detector, computed tomography systems provide visualization of all three vascular beds — the heart, the lungs, and the thoraco-abdominal aorta.
  • Computed tomography combines the use of x-rays with computerized analysis of the images. Beams of x-rays are passed from a rotating device through an area of interest in a patient's body from several different angles to create cross-sectional images, which are assembled by computer into a ⁇ three-dimensional (3-D) picture of the area being studied.
  • 64-slice CT includes 64 rows of detectors, which enable the simultaneous scan of a larger cross sectional area.
  • 64-slice CT provides an inclusive set of images for evaluating the three primary potential causes of the chest pain.
  • a method and a system for automatic computerized analysis of imaging data is provided in an exemplary embodiment.
  • Coronary tree branches of the coronary artery tree may further be labeled.
  • the analyzed blood vessel may be traversed to determine a location and/or size of any pathologies.
  • a method and a system for displaying the pulmonary and coronary artery trees and/or aorta and/or pathologies detected by analyzing the imaging image data also may be provided in another exemplary embodiment.
  • the automatic computerized analysis of imaging studies can include any of the features described herein. Additionally, the automatic computerized analysis of imaging data can include any combination of the features described herein.
  • a system for defining a heart region from imaging data includes, but is not limited to, an imaging apparatus configured to generate imaging data in three dimensions and a processor operably coupled to the imaging apparatus to receive the imaging data.
  • the processor is configured to apply a first threshold to the first plane of data, wherein the first threshold is selected to eliminate a first pixel associated with air; to identify a largest first connected component from the first threshold applied data; to calculate a first center of mass of the identified largest first connected component to define a first coordinate and a second coordinate; to project the received imaging data into a second plane of the three dimensions, wherein the second plane is perpendicular to the first plane; to apply a second threshold to the second plane of data, wherein the second threshold is selected to eliminate a second pixel associated with air; to identify a largest second connected component from the second threshold applied data; and to calculate a second center of mass of the identified largest second connected component to define a third coordinate.
  • a center of a heart region is defined from the defined first coordinate, the defined second coordinate, and the defined third coordinate. The heart region is defined using a predefined offset from the center of the heart region in each of the three dimensions.
  • a device for defining a heart region from imaging data includes, but is not limited to, a memory, the memory capable of storing imaging data defined in three dimensions and a processor operably coupled to the memory to receive the imaging data.
  • the processor is configured to apply a first threshold to the first plane of data, wherein the first threshold is selected to eliminate a first pixel associated with air; to identify a largest first connected component from the first threshold applied data; to calculate a first center of mass of the identified largest first connected component to define a first coordinate and a second coordinate; to project the received imaging data into a second plane of the three dimensions, wherein the second plane is perpendicular to the first plane; to apply a second threshold to the second plane of data, wherein the second threshold is selected to eliminate a second pixel associated with air; to identify a largest second connected component from the second threshold applied data; and to calculate a second center of mass of the identified largest second connected component to define a third coordinate.
  • a center of a heart region is defined from the defined first coordinate, the defined second coordinate, and the defined third coordinate. The heart region is defined using a predefined offset from the center of the heart region in each of the three dimensions.
  • a method of defining a heart region from imaging data is provided.
  • Received imaging data is projected into a first plane.
  • a first threshold is applied to the first plane of data to eliminate data associated with air.
  • a largest first connected component is identified from the first threshold applied data.
  • a first center of mass of the identified largest first connected component is calculated to define a first coordinate and a second coordinate of the heart region.
  • the received imaging data is projected into a second plane, wherein the second plane is perpendicular to the first plane.
  • a second threshold is applied to the second plane of data to eliminate data associated with air.
  • a largest second connected component is identified from the second threshold applied data.
  • a second center of mass of the identified largest second connected component is calculated to define a third coordinate of the heart region.
  • the heart region is defined using a predefined offset from the center of the heart region in each of the three dimensions.
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of defining a heart region from imaging data.
  • a system for labeling blood vessels from imaging data includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the imaging data.
  • the processor is configured to calculate a vesselness score for a plurality of voxels of imaging data; to apply a first threshold to the calculated vesselness score of the plurality of voxels to define a first binary volume, wherein a voxel of the first binary volume is assigned a first value if it is greater than the first threshold and a second value if it is less than the first threshold; to identify a first connected component from the first binary volume; to apply a second threshold to the calculated vesselness score for the plurality of voxels to define a second binary volume, wherein a voxel of the second binary volume is assigned a third value if it is greater than the second threshold and a fourth value if it is less than the second threshold; to identify a second connected component from the second binary volume
  • a device for labeling blood vessels from imaging data includes, but is not limited to, a memory, the memory capable of storing imaging data and a processor operably coupled to the memory to receive the imaging data.
  • the processor is configured to calculate a vesselness score for a plurality of voxels of imaging data; to apply a first threshold to the calculated vesselness score of the plurality of voxels to define a first binary volume, wherein a voxel of the first binary volume is assigned a first value if it is greater than the first threshold and a second value if it is less than the first threshold; to identify a first connected component from the first binary volume; to apply a second threshold to the calculated vesselness score for the plurality of voxels to define a second binary volume, wherein a voxel of the second binary volume is assigned a third value if it is greater than the second threshold and a fourth value if it is less than the second threshold; to identify a second connected component from the
  • a method of labeling blood vessels from imaging data is provided.
  • a vesselness score is calculated for a plurality of voxels of imaging data.
  • a first threshold is applied to the calculated vesselness score of the plurality of voxels to define a first binary volume, wherein a voxel of the first binary volume is assigned a first value if it is greater than the first threshold and a second value if it is less than the first threshold.
  • a first connected component is identified from the first binary volume.
  • a second threshold is applied to the calculated vesselness score for the plurality of voxels to define a second binary volume, wherein a voxel of the second binary volume is assigned a third value if it is greater than the second threshold and a fourth value if it is less than the second threshold.
  • a second connected component is identified from the second binary volume. The identified first connected component is labeled with a blood vessel identifier if the first connected component intersects the identified second connected component.
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of labeling blood vessels from imaging data.
  • a system for identifying aorta exit points from imaging data includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the imaging data.
  • the processor is configured to identify an aorta object from imaging data; to calculate a vesselness score for a plurality of voxels in a ring around the identified aorta object; to apply a first threshold to the calculated vesselness score for the plurality of voxels; to identify a possible exit point based on the applied first threshold; if greater than one possible exit point is identified, to identify a closest possible exit point to the aorta object; and if greater than one closest possible exit point is identified, to identify an exit point by selecting the closest possible exit point having a maximum width.
  • a device for identifying aorta exit points from imaging data includes, but is not limited to, a memory, the memory capable of storing imaging data and a processor operably coupled to the memory to receive the imaging data.
  • the processor is configured to identify an aorta object from imaging data; to calculate a vesselness score for a plurality of voxels in a ring around the identified aorta object; to apply a first threshold to the calculated vesselness score for the plurality of voxels; to identify a possible exit point based on the applied first threshold; if greater than one possible exit point is identified, to identify a closest possible exit point to the aorta object; and if greater than one closest possible exit point is identified, to identify an exit point by selecting the closest possible exit point having a maximum width.
  • a method of identifying aorta exit points from imaging data is provided.
  • An aorta object is identified from imaging data.
  • a vesselness score is calculated for a plurality of voxels in a ring around the identified aorta object.
  • a first threshold is applied to the calculated vesselness score for the plurality of voxels.
  • a possible exit point is identified based on the applied first threshold. If greater than one possible exit point is identified, a closest possible exit point is identified to the aorta object. If greater than one closest possible exit point is identified, an exit point is identified by selecting the closest possible exit point having a maximum width.
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of identifying aorta exit points from imaging data.
  • a system for creating a blood vessel tree from imaging data includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the imaging data.
  • the processor is configured (a) to receive a blood vessel object identified from computed tomography (CT) data; (b) to select a starting point of the received blood vessel object; (c) to calculate a width map for the received blood vessel object; (d) to identify an exit point for the received blood vessel object; (e) to calculate a distance map relative to the identified exit point for the received candidate vessel object, wherein the distance map is weighted by the width values of the width map; (f) to identify an endpoint during calculation of the distance map; (g) to backtrack from the identified endpoint to the selected starting point to define a path in a vessel tree; and (h ) to repeat (g) for each identified endpoint to create a vessel tree for the blood vessel object.
  • CT computed tomography
  • a device for creating a blood vessel tree from imaging data includes, but is not limited to, a memory, the memory capable of storing imaging data and a processor operably coupled to the memory to receive the imaging data.
  • the processor is configured (a) to receive a blood vessel object identified from computed tomography (CT) data; (b) to select a starting point of the received blood vessel object; (c) to calculate a width map for the received blood vessel object; (d) to identify an exit point for the received blood vessel object; (e) to calculate a distance map relative to the identified exit point for the received candidate vessel object, wherein the distance map is weighted by the width values of the width map; (f) to identify an endpoint during calculation of the distance map; (g) to backtrack from the identified endpoint to the selected starting point to define a path in a vessel tree; and (h ) to repeat (g) for each identified endpoint to create a vessel tree for the blood vessel object.
  • CT computed tomography
  • a method of creating a blood vessel tree from imaging data includes but is not limited to, (a) receiving a blood vessel object identified from computed tomography (CT) data; (b) selecting a starting point of the received blood vessel object; (c) calculating a width map for the received blood vessel object; (d) identifying an exit point for the received blood vessel object; (e) calculating a distance map relative to the identified exit point for the received candidate vessel object, wherein the distance map is weighted by the width values of the width map; (f) identifying an endpoint during calculation of the distance map; (g) backtracking from the identified endpoint to the selected starting point to define a path in a vessel tree; and (h) repeating (g) for each identified endpoint to create a vessel tree for the blood vessel object.
  • CT computed tomography
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of creating a blood vessel tree from imaging data.
  • a method and a system for automatic computerized analysis of imaging data is provided in an exemplary embodiment.
  • Three types of pathologies, pulmonary embolism, aortic dissection, and myocardial infarction, can be identified in support of a triple rule-out procedure.
  • the major anatomical structures may be identified.
  • PE detection may be performed by analyzing the pulmonary artery tree.
  • Aortic dissection and aneurysm detection may be performed by analyzing the aorta.
  • Myocardial infarction detection may be performed by analyzing the coronary artery tree through tracking of the coronary blood vessels.
  • a system for automatically performing a triple rule-out procedure using imaging data includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the generated imaging data.
  • the processor is configured to identify the heart region from the generated imaging data; to analyze the identified heart region to detect a coronary pathology; to identify an ascending aorta object from the ascending aorta region of the imaging data; to analyze the identified ascending aorta object to detect an aortic dissection; to identify an abdominal aorta object from the thoraco-abdominal aorta region; to analyze the identified abdominal aorta object to detect an aortic dissection; to identify a left main pulmonary artery object and a right main pulmonary artery object from the pulmonary artery region of the imaging data; to analyze the identified left main pulmonary artery object and the identified right main pulmonary artery object to detect a pulmonary embolism; and to generate a report including any detected coronary pathology, any detected aortic dissection, and any detected pulmonary embolism.
  • a device for automatically performing a triple rule-out procedure using imaging data includes, but is not limited to, a memory, the memory capable of storing imaging data defined in three dimensions and a processor operably coupled to the memory to receive the imaging data.
  • the processor is configured to identify the heart region from the generated imaging data; to analyze the identified heart region to detect a coronary pathology; to identify an ascending aorta object from the ascending aorta region of the imaging data; to analyze the identified ascending aorta object to detect an aortic dissection; to identify an abdominal aorta object from the thoraco-abdominal aorta region; to analyze the identified abdominal aorta object to detect an aortic dissection; to identify a left main pulmonary artery object and a right main pulmonary artery object from the pulmonary artery region of the imaging data; to analyze the identified left main pulmonary artery object and the identified right main pulmonary artery object to detect a pulmonary embolism; and to generate a report including any detected coronary pathology, any detected aortic dissection, and any detected pulmonary embolism.
  • Imaging data is received that includes a heart region, a pulmonary artery region, an ascending aorta region, and a thoraco-abdominal aorta region of a patient.
  • the heart region, an ascending aorta object, an abdominal aorta object, a left main pulmonary artery object, and a right main pulmonary artery object are identified from the imaging data.
  • the identified heart region is analyzed to detect a coronary pathology.
  • the identified ascending aorta object and the identified abdominal aorta object are analyzed to detect an aortic dissection.
  • the identified left main pulmonary artery object and the identified right main pulmonary artery object are analyzed to detect a pulmonary embolism.
  • a report is generated that includes any detected coronary pathology, any detected aortic dissection, and/or any detected pulmonary embolism.
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of performing automatic performance of a triple rule-out procedure using imaging data.
  • a system for presenting information associated with a blood vessel to a user for assessment of the blood vessel includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the generated imaging data.
  • the processor is configured to present a two-dimensional slice of three-dimensional imaging data of a blood vessel to a user in a first user interface; to receive a blood vessel selection from the user, wherein the user selects the blood vessel through an interaction with the first user interface; to identify a blood vessel path associated with the received blood vessel selection from the three-dimensional imaging data; and to present an intensity of the selected blood vessel along the identified blood vessel path to the user for analysis of the selected blood vessel.
  • a device for presenting information associated with a blood vessel to a user for assessment of the blood vessel includes, but is not limited to, a memory, the memory capable of storing imaging data defined in three dimensions and a processor operably coupled to the memory to receive the imaging data.
  • the processor is configured to present a two-dimensional slice of three-dimensional imaging data of a blood vessel to a user in a first user interface; to receive a blood vessel selection from the user, wherein the user selects the blood vessel through an interaction with the first user interface; to identify a blood vessel path associated with the received blood vessel selection from the three-dimensional imaging data; and to present an intensity of the selected blood vessel along the identified blood vessel path to the user for analysis of the selected blood vessel.
  • a method of presenting information associated with a blood vessel to a user for assessment of the blood vessel is provided.
  • a two-dimensional slice of three-dimensional imaging data of a blood vessel is presented to a user in a first user interface.
  • a blood vessel selection is received from the user.
  • the user selects the blood vessel through an interaction with the first user interface.
  • a blood vessel path associated with the received blood vessel selection is identified from the three-dimensional imaging data.
  • An intensity of the selected blood vessel along the identified blood vessel path is presented to the user for analysis of the selected blood vessel.
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of presenting information associated with a blood vessel to a user for assessment of the blood vessel.
  • a system for identifying a calcium and/or a soft plaque deposit in a blood vessel using imaging data of the blood vessel includes, but is not limited to, an imaging apparatus configured to generate imaging data and a processor operably coupled to the imaging apparatus to receive the generated imaging data.
  • the processor may be configured to apply a first threshold to a slice of three-dimensional imaging data of a blood vessel to define voxels above the first threshold; to identify a maximum intensity of the blood vessel from the defined voxels above the first threshold; to calculate a distance from the identified maximum intensity to a center of the blood vessel; to compare the calculated distance with a distance threshold; and if the calculated distance is greater than the distance threshold, to identify a calcium deposit.
  • the processor further may be configured to apply a second threshold to the slice to define voxels below the second threshold; if a calcium deposit is identified, to identify a soft plaque deposit from the defined voxels below the second threshold; if a calcium deposit is not identified, to determine if the defined voxels below the second threshold have a half-moon shape; and if the defined voxels below the second threshold have a half-moon shape, to identify a soft plaque deposit.
  • a device for identifying a calcium and/or a soft plaque deposit in a blood vessel using imaging data of the blood vessel includes, but is not limited to, a memory, the memory capable of storing imaging data defined in three dimensions and a processor operably coupled to the memory to receive the imaging data.
  • the processor may be configured to apply a first threshold to a slice of three-dimensional imaging data of a blood vessel to define voxels above the first threshold; to identify a maximum intensity of the blood vessel from the defined voxels above the first threshold; to calculate a distance from the identified maximum intensity to a center of the blood vessel; to compare the calculated distance with a distance threshold; and if the calculated distance is greater than the distance threshold, to identify a calcium deposit.
  • the processor further may be configured to apply a second threshold to the slice to define voxels below the second threshold; if a calcium deposit is identified, to identify a soft plaque deposit from the defined voxels below the second threshold; if a calcium deposit is not identified, to determine if the defined voxels below the second threshold have a half-moon shape; and if the defined voxels below the second threshold have a half-moon shape, to identify a soft plaque deposit.
  • a method of identifying a calcium and/or a soft plaque deposit in a blood vessel using imaging data of the blood vessel is provided.
  • a first threshold is applied to a slice of three-dimensional imaging data of a blood vessel to define voxels above the first threshold.
  • a maximum intensity of the blood vessel is identified from the defined voxels.
  • a distance from the identified maximum intensity to a center of the blood vessel is calculated and is compared with a distance threshold. If the calculated distance is greater than the distance threshold, a calcium deposit is identified.
  • a second threshold is applied to the slice to define voxels below the second threshold. If a calcium deposit is identified or if the defined voxels below the threshold have a half-moon shape, a soft plaque deposit is identified.
  • computer-readable instructions are provided that, upon execution by a processor, cause the processor to implement the operations of the method of identifying a calcium and/or a soft plaque deposit in a blood vessel using imaging data of the blood vessel.
  • FIG. 1 depicts a block diagram of an automated CT image processing system in accordance with an exemplary embodiment.
  • Figs. 2a and 2b depict a flow diagram illustrating exemplary operations performed by the automated CT image processing system of Fig. 1 in accordance with an exemplary embodiment.
  • FIG. 3 depicts a flow diagram illustrating exemplary operations performed in detecting a heart region in accordance with an exemplary embodiment.
  • FIG. 4 depicts a flow diagram illustrating exemplary operations performed in detecting a lung region in accordance with an exemplary embodiment.
  • FIGs. 5a and 5b depict a flow diagram illustrating exemplary operations performed in detecting and identifying the aorta in accordance with an exemplary embodiment.
  • FIGs. 6a and 6b depict a flow diagram illustrating exemplary operations performed in identifying coronary artery vessel exit points from the aorta in accordance with an exemplary embodiment.
  • FIGs. 7a and 7b depict a flow diagram illustrating exemplary operations performed in identifying coronary artery vessel tree in accordance with an exemplary embodiment.
  • Fig. 8 depicts a flow diagram illustrating exemplary operations performed in identifying calcium seed in accordance with an exemplary embodiment.
  • Fig. 9 depicts a flow diagram illustrating exemplary operations performed in identifying in identifying soft plaque in accordance with an exemplary embodiment.
  • Fig. 10 depicts a graph illustrating X-junction removal from a coronary artery vessel tree in accordance with an exemplary embodiment.
  • Fig. 11 depicts a first user interface of a visualization application presenting summary pathology results in accordance with an exemplary embodiment.
  • Fig. 12 depicts a second user interface of the visualization application presenting multiple views of a pathology in accordance with a first exemplary embodiment.
  • Fig. 13 depicts a third user interface of the visualization application presenting a blood vessel effective lumen area as a function of distance from the aorta in accordance with an exemplary embodiment.
  • Fig. 14 depicts a fourth user interface of the visualization application presenting multiple views of a pathology in accordance with a second exemplary embodiment.
  • Fig. 15 depicts a fifth user interface of the visualization application presenting multiple views of a pathology in accordance with a third exemplary embodiment.
  • Image processing system 100 may include a CT apparatus 101 and a computing device 102.
  • Computing device 102 may include a display 104, an input interface 106, a memory 108, a processor 110, a pathology identification application 112, and a visualization application 114.
  • CT apparatus 101 generates image data.
  • the present techniques are well-suited for use with a wide variety of medical diagnostic system modalities, including magnetic resonance imaging systems, ultrasound systems, positron emission tomography systems, nuclear medicine systems, etc.
  • the various modality systems may be of a different type, manufacture, and model.
  • computing device 102 may also include a communication interface, which provides an interface for receiving and transmitting data between devices using various protocols, transmission technologies, and media as known to those skilled in the art.
  • the communication interface may support communication using various transmission media that may be wired or wireless.
  • Display 104 presents information to a user of computing device 102 as known to those skilled in the art.
  • display 104 may be a thin film transistor display, a light emitting diode display, a liquid crystal display, or any of a variety of different displays known to those skilled in the art now or in the future.
  • Input interface 106 provides an interface for receiving information from the user for entry into computing device 102 as known to those skilled in the art.
  • Input interface 106 may use various input technologies including, but not limited to, a keyboard, a pen and touch screen, a mouse, a track ball, a touch screen, a keypad, one or more buttons, etc. to allow the user to enter information into computing device 102 or to make selections presented in a user interface displayed on display 104.
  • Input interface 106 may provide both an input and an output interface. For example, a touch screen both allows user input and presents output to the user.
  • Memory 108 is an electronic holding place or storage for information so that the information can be accessed by processor 110 as known to those skilled in the art.
  • Computing device 102 may have one or more memories that use the same or a different memory technology. Memory technologies include, but are not limited to, any type of RAM, any type of ROM, any type of flash memory, etc.
  • Computing device 102 also may have one or more drives that support the loading of a memory media such as a compact disk or digital video disk.
  • Processor 110 executes instructions as known to those skilled in the art.
  • the instructions may be carried out by a special purpose computer, logic circuits, or hardware circuits.
  • processor 110 may be implemented in hardware, firmware, software, or any combination of these methods.
  • execution is the process of running an application or the carrying out of the operation called for by an instruction.
  • the instructions may be written using one or more programming language, scripting language, assembly language, etc.
  • Processor 110 executes an instruction, meaning that it performs the operations called for by that instruction.
  • Processor 110 operably couples with display 104, with input interface 106, with memory 108, and with the communication interface to receive, to send, and to process information.
  • Processor 110 may retrieve a set of instructions from a permanent memory device and copy the instructions in an executable form to a temporary memory device that is generally some form of RAM.
  • Computing device 102 may include a plurality of processors that use the same or a different processing technology.
  • Pathology identification application 112 performs operations associated with analysis of blood vessel structures and with identification of pathologies associated with the analyzed blood vessels. Some or all of the operations and interfaces subsequently described may be embodied in pathology identification application 112. The operations may be implemented using hardware, firmware, software, or any combination of these methods. With reference to the exemplary embodiment of Fig. 1 , pathology identification application 112 is implemented in software stored in memory 108 and accessible by processor 110 for execution of the instructions that embody the operations of pathology identification application 112. Pathology identification application 112 may be written using one or more programming languages, assembly languages, scripting languages, etc. Pathology identification application 112 may integrate with or otherwise interact with visualization application 114.
  • Visualization application 114 performs operations associated with presentation of the blood vessel analysis and identification results to a user. Some or all of the operations and interfaces subsequently described may be embodied in visualization application 114. The operations may be implemented using hardware, firmware, software, or any combination of these methods. With reference to the exemplary embodiment of Fig. 1 , visualization application 114 is implemented in software stored in memory 108 and accessible by processor 110 for execution of the instructions that embody the operations of visualization application 114. Visualization application 114 may be written using one or more programming languages, assembly languages, scripting languages, etc. [0058] CT apparatus 101 and computing device 102 may be integrated into a single system such as a CT imaging machine. CT apparatus 101 and computing device 102 may be connected directly.
  • CT apparatus 101 may connect to computing device 102 using a cable for transmitting information between CT apparatus 101 and computing device 102.
  • CT apparatus 101 may connect to computing device 102 using a network.
  • computing device 102 is connected to a hospital computer network and a picture archive, and communication system (PACS) receives a CT study acquired on CT apparatus 101 in an ER.
  • PACS picture archive, and communication system
  • CT images are stored electronically and accessed using computing device 102.
  • CT apparatus 101 and computing device 102 may not be connected. Instead, the CT study acquired on CT apparatus 101 may be manually provided to computing device 102.
  • the CT study may be stored on electronic media such as a CD or a DVD.
  • computing device 102 may start automatic processing of the set of images that comprise the CT study.
  • CT apparatus 101 is a 64-slice multi-detector advanced CT scanner having a reconstructed slice width and inter-slice distance less than or equal to approximately 0.5 millimeters (mm), which produces standard digital imaging and communications in medicine (DICOM) images.
  • Computing device 102 may be a computer of any form factor.
  • Image processing system 100 may provide an initial classification and decision support system, which allows fast and accurate ruling out of the three major diseases associated with chest pain.
  • Image processing system 100 can be provided as a primary CT study inspection tool assisting an ER physician. Additionally, image processing system 100 can be used either to completely rule out some or all of the three diseases (in case of negative results) or as a trigger to call a radiologist and/or a cardiologist to further analyze the case. Additionally, image processing system 100 may automatically identify and segment blood vessel trees and automatically analyze each blood vessel to detect and map all relevant pathologies, including calcified and soft plaque lesions and degree of stenosis.
  • pathology identification application 112 receives CT image data.
  • the CT image data may be received from CT apparatus 101 directly or using a network.
  • the CT image data also may be received using a memory medium.
  • a heart region is identified from the received CT image data.
  • Data associated with the identified heart region is stored at computing device 102.
  • the data associated with the identified heart region includes a heart bounding box.
  • exemplary operations associated with identifying the heart region data are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting. For larger studies that may include head and neck or abdominal regions, a determination of the heart region provides a correct anatomical starting point for further segmentation. For smaller studies, a determination of the heart region reduces the size of the processed region and removes the non-relevant areas to reduce the false alarm risk. In an exemplary embodiment, the top and bottom boundaries of the heart region are cut a predetermined distance above and below the heart center.
  • a first projection into an X-Y plane is defined.
  • a positive X-axis is defined as extending out from the left side of the body.
  • a positive Y-axis is defined as extending out from the back side of the body.
  • a positive Z-axis is defined as extending out from the head of the body.
  • the first projection is defined by summing the DICOM series along the Z-axis.
  • a threshold is applied to the first projection. For example, a threshold greater than approximately zero may be applied to eliminate the negative values of air which dominate the region outside the heart region and to retain the positive Hounsfeld unit (HU) values which include the fat, blood, and bones within the heart region.
  • a first largest connect component (CC) is identified in the thresholded first projection.
  • a first center of mass of the first largest CC is determined and denoted as X 0 , Yd-
  • a second projection into a Y-Z plane is defined.
  • the second projection is defined by summing the DICOM series along the X-axis.
  • a threshold is applied to the second projection data. For example, a threshold greater than approximately zero may be applied to eliminate the negative values of air which dominate the region outside the heart region and to retain the positive HU values which include the fat, blood, and bones within the heart region.
  • a second largest CC is identified in the thresholded second projection data.
  • a second center of mass of the second largest CC is determined and denoted as Y C2 .
  • Z c A heart region center is defined as X c , Yd, Z c .
  • a heart region bounding box is defined from the heart region center and an average heart region width in each axis direction, W x , WY, WZ.
  • the defined heart region bounding box is stored at computing device 102.
  • the X-axis, Y-axis, Z-axis system centered at X 0 , Y c i, Z c defines a body coordinate system.
  • a lung region is identified from the received CT image data.
  • Data associated with the identified lung region is stored at computing device 102.
  • the data associated with the identified lung region may include a lung mask.
  • exemplary operations associated with identifying the lung region data are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting.
  • a lung threshold is applied to each slice of the DICOM series data. For example, a lung threshold of -400 HU may be applied.
  • a morphological filter is applied to the binary image.
  • the binary image is filtered using a morphological closing operation to define a lung region in the CT image data.
  • Other processes for filling holes in the image may be used as known to those skilled in the art.
  • the defined lung region is stored at computing device 102.
  • a thorax region is identified from the received CT image data. Data associated with the identified thorax region is stored at computing device 102. The thorax region may be defined as a convex hull of the lungs and the diaphragm.
  • the pulmonary arteries are identified from the received CT image data. Data associated with the identified pulmonary arteries is stored at computing device 102.
  • the received CT image data is preprocessed.
  • preprocessing may include image enhancement, smoothing, noise reduction, acquisition artifacts detection, etc.
  • image enhancement algorithms include Gaussian smoothing, median filtering, bilateral filtering, anisotropic diffusion, etc.
  • the left and right main pulmonary artery trees are defined.
  • the lumen of the left and right main pulmonary artery trees is analyzed to identify any pulmonary embolism candidates.
  • any pulmonary embolism candidates are classified.
  • possible pulmonary embolism lesions are identified.
  • an operation 222 the ascending and the visible part of the abdominal aorta are segmented.
  • an operation 224 the lumen of the abdominal aorta is segmented.
  • a 3-D geometry of the abdominal aorta is modeled.
  • the modeled aorta is compared to a hypothesized "normal" abdominal aorta to identify deviations from the hypothesized "normal" abdominal aorta.
  • suspicious locations are detected and analyzed to identify dissections and aneurysms.
  • the aorta is identified.
  • the aorta is detected in the first imaging slice of the heart region bounding box based, for example, on intensity and shape properties including circularity, compactness, and area.
  • the remainder of the aorta is identified by moving from slice to slice and looking for a similar 2-D object in each slice.
  • Data associated with the identified aorta is stored at computing device 102.
  • the data associated with the identified aorta includes an aorta shape and boundary in the identified heart region. It is assumed that the heart region bounding box detected on the previous step includes the aorta exit from the heart and that the cross section of the aorta in the upper slice of the heart region is approximately circular.
  • a first slice is selected from the heart region data.
  • an aorta threshold is applied to the first slice of the DICOM series data. For example, a lung threshold of 200 HU may be used.
  • a morphological filter is applied to the binary image.
  • the binary image is filtered using a series of morphological filtering operators including an opening operator using a first parameter.
  • one or more CCs are identified.
  • a compactness of each identified CC is determined.
  • identified CCs having a determined compactness that exceeds a compactness threshold are eliminated from further consideration.
  • the compactness threshold is 0.75.
  • a size of each identified connected component is determined.
  • identified CCs having a size that exceeds a maximum size threshold or that is below a minimum size threshold are eliminated from further consideration.
  • the maximum size threshold is 10,000 pixels.
  • the minimum size threshold is 1 ,000 pixels.
  • a determination is made concerning whether or not any identified CCs remain for consideration. If identified CCs remain for consideration, processing continues at an operation 518. If no identified CCs remain for consideration, processing continues at an operation 520. In operation 518, an initial aorta candidate is selected from the remaining CCs. For example, if a plurality of identified CCs remain for consideration, the largest candidate CC that is foremost in the body is selected as the initial aorta candidate.
  • a next slice is selected from the heart region data.
  • the aorta threshold is applied to the next slice of the DICOM series data.
  • the morphological filter is applied to the binary image.
  • one or more CCs are identified.
  • a compactness of each identified CC is determined.
  • the identified CCs having a determined compactness that exceeds the compactness threshold are eliminated from further consideration.
  • a size of each identified connected component is determined.
  • the identified CCs having a size that exceeds the maximum size threshold or that is below the minimum size threshold are eliminated from further consideration.
  • a determination is made concerning whether or not any identified CCs remain for consideration in the current slice. If identified CCs remain for consideration, processing continues at an operation 538. If no identified CCs remain for consideration, processing continues at operation 520.
  • the identified CCs from the current slice are compared with the aorta candidate object(s) created from the previous slice(s).
  • a determination is made concerning whether or not any identified CCs match CCs identified from the previous slices.
  • the matched CC is assigned to the aorta candidate object. For example, if a center of a CC is closer than twenty pixels to the center of an aorta candidate object, the CC may be identified as matched with the aorta candidate object.
  • a new aorta candidate object is created based on the CC.
  • an operation 546 a determination is made concerning whether or not all of the slices have been processed. If slices remain, processing continues at operation 520. If no slices remain, in an operation 548, aorta candidate objects are eliminated based on length. For example, aorta candidate objects that persist for less than 20 slices may be removed from further consideration.
  • an aorta object is selected from the remaining aorta candidate objects. For example, the aorta candidate object closest to the upper left corner of the image may be selected as the aorta object.
  • a bounding box is defined around the selected aorta object to identify a region in which the aorta is located in the CT image data.
  • the selected aorta object is stored at computing device 102.
  • exit points of the coronary arteries from the aorta are identified by evaluating all structures connected to the aorta object which look like a vessel.
  • the direction of the vessel near a link point with the aorta object should be roughly perpendicular to an aorta centerline.
  • the left and right coronary arteries are expected to exit from the aorta object in a certain direction relative to the body coordinate system. If there are several exit point candidates, the exit point candidate which leads to a larger blood vessel tree is selected. It is assumed that the selected aorta object includes the points where the left and right coronary trees connect with the aorta.
  • Imaging slices including the aorta bounding box and a mask of the aorta detected at a previous slice are processed to detect the aorta at the current slice.
  • a first slice is selected from the aorta object.
  • regions are segmented based on a segmentation threshold at the detected aorta edges.
  • the segmentation threshold may be calculated from the median value of pixels of the smoothed image at the detected edges.
  • a ring of pre-defined radius is defined around the aorta edges detected on the previous slice and the edges are found in the ring on the current slice. Small edges are removed from further consideration.
  • the segmentation threshold may be selected adaptively. For example, if no edges are found in the ring using the calculated segmentation threshold, the calculated segmentation threshold is reduced by half, and the procedure is repeated. If no edges are found using the lowered segmentation threshold, the calculated segmentation threshold is used for subsequent slices.
  • the segmented image is post-processed. For example, small segmented objects are removed, possible vessels are removed from the segmented aorta candidates, and the segmented aorta candidates are intersected with the aorta detected in the previous slice.
  • the aorta candidates are validated by ensuring that there is at least one candidate that intersected the aorta detected in the previous slice and by ensuring that the aorta does not grow too fast. For example, if the aorta size in both a previous and a current slice is larger than 1500 pixels, the size growth ratio may be limited to 1.4.
  • the aorta candidates are selected.
  • CCs with a small intersection with the previously detected aorta are removed from consideration, and the upper-left-most candidate is chosen if a plurality of aorta candidates exist in the current slice.
  • the compactness of the selected aorta candidate is checked to ensure that the candidate is not compact. If the aorta candidate is not compact, the aorta search window is limited for the next slice. If the aorta candidate is compact, the whole image is used to search for the aorta in the next slice.
  • a bounding box for the aorta is calculated.
  • the bounding box size may be fixed and only the position of the bounding box updated to compensate for aorta movement. If the aorta candidate is compact, the bounding box may be attached to the upper left side of the aorta.
  • a vesselness score is calculated for each voxel of the aorta object.
  • the vesselness score can be determined using a vesselness function.
  • a vesselness function is a widely used function based on the analysis of Hessian eigen values.
  • a good description of an exemplary vesselness function can be found for example in Frangi, A. F., Niessen, W. J., Vincken, K. L. and Viergever, M. A., 1998, "Multiscale Vessel Enhancement Filtering", MICCAI'98, LNCS 1496, pp. 130-137.
  • a vesselness threshold is applied to the calculated vesselness score to identify possible exit points based on the HU value of a ring around the aorta object. Pixels in a ring around the detected aorta are grouped into CCs, which are analyzed to choose the most probable candidates for coronary tree exit points.
  • possible exit points are filtered to remove false candidates. For example, the possible exit points may be filtered based on a size of the CC corresponding to the possible exit, a location of the CC relative to the aorta, an incident angle of the CC relative to the aorta, etc.
  • a determination is made concerning whether or not any exit points are left.
  • a CC is identified for each exit point included in the possible exit points list.
  • a volume is calculated for each exit point CC (EPCC).
  • EPCC exit point CC
  • any EPCC having a volume below a volume threshold is eliminated from further consideration as an exit point.
  • the volume threshold may be 1500 voxels.
  • a maximum width of each EPCC is calculated.
  • any EPCC having a maximum width below a width threshold is eliminated from further consideration as an exit point.
  • the width threshold may be 2 mm.
  • a distance to the aorta is calculated for each EPCC.
  • the EPCC having a minimum distance to the aorta is selected.
  • a determination is made concerning whether or not a plurality of EPCCs remain. If a plurality of EPCCs remain, processing continues at an operation 644. If a plurality of EPCCs do not remain, processing continues at an operation 646.
  • the EPCC having a maximum width is selected from the plurality of EPCCs remaining.
  • the exit point is identified from the selected EPCCs.
  • a coronary artery vessel tree is defined. For a traversed section of the blood vessel tree, a set of end points is identified, and an attempt is made to continue tracking beyond the end point in the direction of the corresponding tree branch. If an additional tree segment is detected, it is connected to the traversed tree, and the processing continues recursively. The process is finished when no branch can be continued.
  • the stopping condition may result in connecting a wrong structure to the coronary tree (e.g. a vein or some debris in a noisy CT study).
  • the successfully tracked vessels are identified and those which remain to be detected are identified. For example, which blood vessels are to be segmented (e.g.
  • RCA LM, LAD, LCX and others
  • a maximum vessel length to track e.g. 5 cm from the aorta
  • a minimum blood vessel diameter to continue tracking also may be defined as inputs to the process.
  • the location of some blood vessels may be based on anatomical landmarks. For example, the RCA goes in the right atrio-ventricular plane. These anatomical landmarks, which may be collated through an anatomical priors processing operation, allow false structures in the "wrong" places to be discarded and support the location of lost branches in the "right” places.
  • a graph representation of the segmented vessel tree can be built from the identified end points and bifurcation points. Graph nodes are the end points and the bifurcation points. The edges are segments of a vessel centerline between the nodes.
  • exemplary operations associated with defining the coronary artery vessel tree are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting.
  • the vesselness score data is received.
  • a first binary volume is defined for a first threshold.
  • the first binary volume includes a '1' for each voxel that exceeds the first threshold and a '0' for each voxel that does not exceed the first threshold.
  • a second binary volume is defined for a second threshold.
  • the second binary volume includes a T for each voxel that exceeds the second threshold and a '0' for each voxel that does not exceed the second threshold.
  • the second threshold has a higher HU value than the first threshold.
  • one or more CCs in the first binary volume that intersect voxels from the second binary volume are selected as the one or more vessel CCs (VCCs). In an exemplary embodiment, intersection may be determined based on a spatial proximity between the CCs.
  • the first threshold and the second threshold are selected based on a statistical analysis of the input data such that the amount of voxels above the first threshold is approximately 0.15% of the total number of voxels in the volume and such that the amount of voxels above the second threshold is approximately 0.45% of the total number of voxels.
  • the selected VCCs are labeled in the first binary volume.
  • a starting point or root is selected for a first VCC.
  • a width map is calculated for the first VCC.
  • the width map includes the width of the VCC or the inverse distance from any point in the VCC to the boundary of the VCC. Thus, small values are near the centerline of the VCC and larger values are at the edges of the VCC.
  • the exit point of the selected VCC is identified in the binary volume.
  • the right coronary artery tree has a single exit point. Additionally, the left main artery tree has a single exit point.
  • a distance map is calculated for the first VCC.
  • the distance is calculated from any point in the VCC to the identified exit point.
  • the distance map includes the calculated distance weighted by the width to ensure that the minimal path follows the vessel centerline.
  • candidate endpoints are identified. For example, during the calculation of the weighted distance map, one or more candidate endpoints may be saved. The candidate endpoints are voxels, which did not update any of their neighbors during the distance map calculation.
  • non-local maxima candidate endpoints are filtered. Thus, the candidate endpoints are scanned and only local maxima with respect to the distance from the root over a given window are kept. This process eliminates candidates that are not true blob vessel end points.
  • an identifier for the candidate endpoint is created.
  • a new vertex is added to a symbolic graph of the vessel tree.
  • a first candidate endpoint is backtracked to the root to create graph edges.
  • An auxiliary volume is used to mark voxels that have already been visited.
  • the back-tracking may be a gradient descent iterative process (the gradient is in the distance field). Because the weighted distance map contains a single global minimum (the root), convergence is guaranteed. The method used to define the distance map ensures that the backtracking will be along the centerline or close to it.
  • all visited voxels in the auxiliary volume are marked with the current vertex identifier during the backtracking.
  • an operation 730 a determination is made concerning whether or not a root is reached. If a root is reached, processing continues at an operation 732. If a root is not reached, processing continues at an operation 734. In operation 732, a new edge and vessel path are defined based on the backtracking. Processing continues at an operation 740. In an operation 734, a determination is made concerning whether or not an already visited voxel is reached. If an already visited voxel is reached, processing continues at an operation 736. If an already visited voxel is not reached, processing continues at an operation 726 to continue the backtracking to the endpoint. In an operation 736, a new edge and a new bifurcation vertex are defined. In an operation 738, the new bifurcation vertex is connected to the currently backtracked path, and the new edge and the new bifurcation vertex are added to the vessel tree.
  • processing continues at an operation 746.
  • short branches are removed based on the rationale that they do not contribute to the analysis because important findings are usually located at the major blood vessels, which are thick and elongated. Therefore, in an exemplary embodiment, graph edges which lead to endpoints that are less than a length threshold are removed.
  • An exemplary length threshold is 5 mm.
  • x-junctions are removed to eliminate veins. Veins are usually faint and spatially close to the arteries. X-junctions are defined as two very close bifurcation points. For example, close bifurcation points may be less than approximately 3 mm from each other.
  • a bifurcation may be two VCCs intersecting at angles between approximately 70 degrees and approximately 110 degrees. Additionally, a bifurcation may be two VCCs intersecting at angles approximately equal to 90 degrees. The sub-tree which remains is the one which has the closest direction to the edge arriving from the aorta.
  • a vessel tree 1000 includes a first vessel path 1002, a second vessel path 1004, and a third vessel path 1006.
  • First vessel path 1002 and second vessel path 1004 form a first x-junction 1008.
  • First vessel path 1002 has the closest direction to the edge arriving from the aorta and is selected to remain in the vessel tree.
  • Second vessel path 1004 is removed.
  • First vessel path 1002 and third vessel path 1004 form a second x-junction 1010.
  • first vessel path 1002 has the closest direction to the edge arriving from the aorta and is selected to remain in the vessel tree.
  • Third vessel path 1006 is removed.
  • single entry, single exit point vertices are removed. These vertices are created when one of the endpoints is recursively continued. The vertex is removed, and the two edges are joined to a single path.
  • the defined coronary artery vessel tree is labeled.
  • a list of graph edges may be assigned to each blood vessel tracked by analyzing the relative section positions and locations relative to detected anatomical heart landmarks.
  • the blood vessel tree represented by a centerline for each blood vessel segment is stored at computing device 102.
  • a radius of each blood vessel is determined.
  • a blood vessel centerline is determined.
  • "Sausages" of blood vessels are obtained from the coronary artery vessel tree. Each "sausage” includes axial blood vessel cross-sections taken perpendicular to the blood vessel direction. Initially, a blood vessel center is presumed to be at the center of each section. Either “stretched” or “curved” blood vessels can be used. Any blood vessel radius and center line estimation method can be used. In an exemplary embodiment, low pass post-filtering between consecutive cross-sections is performed. Because a blood vessel may be surrounded by tissue having similar attenuation values, indirect indicators may be used to define the blood vessel edge.
  • a blood vessel center consisting of a number of pixels can be defined, in particular when a cross section is elongated. In an exemplary embodiment, the blood vessel center is reduced to a single pixel.
  • an operation 250 areas of calcium are identified in each blood vessel. Any high precision calcium identification method can be used.
  • a cross section based analysis aimed at location of the calcium seeds is performed, and a volume based analysis aimed at removal of spurious seeds created by the "salt noise" and by the growing of valid seeds into the correct calcium area is performed.
  • exemplary operations associated with identifying the areas of calcium, if any, in each blood vessel are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting.
  • a first slice is selected from the heart region data.
  • a calcium threshold is applied to the first slice.
  • the calcium threshold is 150 intensity levels above the blood vessel lumen level. Adaptive threshold values taking into account expected lumen values are used.
  • a morphological filter is applied to the thresholded first slice based on the non-concentric nature of the calcium deposits. Empirical observations indicate that calcium tends to appear close to the blood vessel borders.
  • a maximum intensity in a given cross-section is identified as a possible location of a Calcium seed.
  • a distance from the center to the maximum intensity is calculated.
  • a determination is made concerning whether or not the calculated distance exceeds a calcium distance threshold.
  • the calcium distance threshold is based on a comparison with an estimated radius value. If the distance does not exceed the calcium distance threshold, processing continues in an operation 814. If the distance does exceed the calcium distance threshold, processing continues in an operation 812. In operation 812, an area of the calcium seed is calculated. In operation 814, a determination is made concerning whether or not any vessels remain for processing. If vessels remain, processing continues at operation 808. If no vessels remain, processing continues at an operation 816. In operation 816, a determination concerning whether or not any slices remain for processing is performed. If no slices remain, processing continues at an operation 820. If slices remain, processing continues at an operation 818. In operation 818, the next slice is selected from the heart region data and processing continues at operation 802.
  • a volume of any identified calcium seed(s) is calculated based on the area calculated for each slice and the number of slices over which the identified calcium seed(s) extends. If a calcium seed is identified, it also is extended to the surrounding high intensity areas providing that no "spill to the center" occurs. An extent of the calcium seed may be determined based on a threshold. For example, lumen intensities exceeding approximately 650 HU may be considered to be calcified plaque or part of the Calcium seed.
  • an operation 252 areas of soft plaque are identified in each blood vessel by the low intensity inside the blood vessel area. Any high precision soft plaque identification method can be used. With reference to Fig. 9, exemplary operations associated with identifying the areas of soft plaque, if any, in each blood vessel are described in accordance with an exemplary embodiment. Additional, fewer, or different operations may be performed, depending on the embodiment. The order of presentation of the operations is not intended to be limiting.
  • a first slice is selected from the heart region data.
  • a soft plaque threshold is applied to the first slice. In an exemplary embodiment, the soft plaque threshold is between approximately 50 HU and approximately 200 HU.
  • Adaptive threshold values taking into account expected lumen values are used in an exemplary embodiment.
  • a determination is made concerning whether or not calcium is present.
  • the presence of calcium may indicate the presence of the frequent figure "8" shaped pattern.
  • calcium is located in one of the ovals of the "8".
  • a lumen is located in the other oval of the "8", and soft plaque connects the two ovals. If calcium is present, processing continues at an operation 906. If calcium is not present, processing continues at an operation 908. In operation 906, a soft plaque area is identified and processing continues at an operation 911.
  • a volume of any identified soft plaque area(s) is calculated based on the area calculated for each slice and the number of slices over which the identified soft plaque area(s) extends.
  • a volume of any identified calcium seed(s) is updated to include areas between the calcium seed and the blood vessel border and between the calcium and soft plaque areas to compensate for natural intensity low passing that may have occurred during the CT image acquisition.
  • a severity of any obstructions identified containing soft plaque or calcium is calculated. Any obstruction computation method can be used.
  • a total obstruction ratio is calculated as a ratio of the total calcium and soft plaque areas divided by the total blood vessel area excluding the border area.
  • an obstruction is identified to be severe if the total obstruction ratio exceeds 50% for at least two consecutive cross sections. An examining physician may be allowed to control the threshold to achieve a system sensitivity matching their clinical requirements.
  • the cross section images may appear reasonably normal.
  • pathology must be identified based on the analysis of global variations.
  • global filters are applied to identify pathologies. For example, a first filter may be applied to identify a rapid decrease in the blood vessel radius. A second filter may be applied to identify a rapid decrease in the lumen intensity. A third filter may be applied to identify a rapid increase in the lumen intensity.
  • the decisions from the series of filters may be cumulative. As a result, it is sufficient if a pathology is identified through use of one of the three filters.
  • the filters may use the values of blood vessel radius and luminance as computed above.
  • First user interface 1100 may include a header portion 1102.
  • Header portion 1102 may include patient data, study data, and/or acquisition data.
  • data displayed in header portion 1102 may be obtained from a header of the DICOM data.
  • First user interface 1100 further may include a blood vessel list portion 1104.
  • Blood vessel list portion 1104 may include a list of the blood vessels in the created blood vessel tree.
  • Displayed next to a name identifying each blood vessel may be information related to each blood vessel including a lumen status, a total number of lesions, a number of calcium lesions, and a number of soft plaque lesions.
  • the lumen status may indicate "normal” or a percentage of blockage that may be a percentage range. If a plurality of lesions are present, the range may indicate the maximum blockage range.
  • a maximum volume and Agatston score may be displayed for the calcium lesions.
  • a maximum volume and score also may be displayed for the soft plaque lesions.
  • User selection of a blood vessel 1106 in blood vessel list portion 1104 may cause display of a detailed description of the lesions associated with the selected blood vessel in a detail portion 1108.
  • Detail portion 1108 may include a list of the lesions. For each lesion, a segment name, a lesion type, a degree of stenosis value, a volume, a distance from the aorta, a distance from the blood vessel origin, an eccentricity, a degree of positive remodeling, and a morphological regularity may be shown.
  • First user interface 1100 further may include a totals portion 1110.
  • Totals portion 1110 may include summary data associated with a degree of stenosis, lesions, the number of stents, etc.
  • a visualization of the blood vessels is provided to a user based on the processes described with reference to Figs. 2-9.
  • a second user interface 1200 of visualization application 114 in accordance with a first exemplary embodiment is shown.
  • four simultaneous views of the same pathology may be shown to facilitate a correct diagnosis with each view presented in a different area of second user interface 1200.
  • Each view may be created using a variety of graphical user interface techniques in a common window, in separate windows, or in any combination of windows.
  • Second user interface 1200 may include a first axial slice viewer 1202, a first 3-D coronary vessel map 1204, a first stretched blood vessel image 1206, and an intra-vascular ultrasound (IVUS) type view 1208.
  • First axial slice viewer 1202 presents intensity levels from a slice of imaging data. The intensity levels may be indicated in color or gray-scale. For example, first axial slice viewer 1202 may indicate an identified pathology 1203 in red. First axial slice viewer 1202 may present the slice of imaging data in a top left area of second user interface 1200.
  • First 3-D coronary vessel map 1204 provides a view of the blood vessel tree synchronized with first axial slice viewer 1202 to indicate the identified pathology 1203.
  • First 3-D coronary vessel map 1204 may be presented in a top right area of second user interface 1200 and may include a 3-D grid to identify the length of the blood vessels in the blood vessel tree in each direction. Selecting an area of first stretched blood vessel image 1206 may cause image rotation of first 3-D coronary vessel map 1204 around its axis to facilitate a correct 3-D perception of the blood vessel structure.
  • First 3-D coronary vessel map 1204 may be synchronized with first axial slice viewer 1202 to distinguish the selected blood vessel from the remaining blood vessels in the blood vessel tree.
  • First 3-D coronary vessel map 1204 may be rotated using an input interface as known to those skilled in the art.
  • Indicators may be provided in first 3-D coronary vessel map 1204 to indicate end points and bifurcations. For example, end points may be indicated using green circles and bifurcations may be indicated using red circles.
  • First stretched blood vessel image 1206 includes a vertical bar which denotes a location of the slice displayed in first axial slice viewer 1202 in a stretched view of a selected blood vessel.
  • First stretched blood vessel image 1206 may be located in a bottom left area of second user interface 1200.
  • the physician can superimpose corresponding plaque areas. For example, soft plaque may be indicated in red and calcified plaque indicated in blue. If desired, the physician can invoke an edit mode and correct automatic results.
  • a third user interface of visualization application 114 graphically displays a lumen area of each blood vessel to clearly identify all stenosis lesions and to allow an evaluation of their severity.
  • the graphical display includes a distance from the aorta on the X-axis and a lumen area on the Y-axis.
  • a first curve 1300 indicates a normal blood vessel lumen.
  • a second curve 1302 indicates a stenosis due to calcified plaque.
  • a third curve 1304 indicates a stenosis due to soft plaque.
  • Fourth user interface 1400 may include a second axial slice viewer 1402, a second 3-D coronary vessel map 1404, a second stretched blood vessel image 1406, and a pathology report type view 1408.
  • Second axial slice viewer 1402 may include an axial slice of the imaging data presented in a top left area of second user interface 1400. provides a current location on the 3-D coronary vessel map synchronized with second axial slice viewer 1402.
  • Second 3-D coronary vessel map 1404 may be presented in a top right area of second user interface 1400 and may include a 3-D grid to identify the length of the blood vessels in the blood vessel tree in each direction. Selecting an area of second 3-D coronary vessel map 1404 may cause image rotation around its axis facilitating a correct 3-D perception of the blood vessel structure.
  • Second stretched blood vessel image 1406 includes a vertical bar which denotes a location of the slice displayed in second axial slice viewer 1402 in a stretched view of a selected blood vessel. Second stretched blood vessel image 1406 may be presented in a bottom left area of second user interface 1400.
  • Pathology report type view 1408 may contain a pathology list 1409 of detected pathologies based on the processes described with reference to Figs. 2-9.
  • the pathologies may include soft plaque, calcified plaque, and mixed plaque regions.
  • the location and stenosis level may be included for each pathology in pathology list 1409.
  • Selecting a pathology 1410 from pathology list 1409 of pathology report type view 1408 may cause a synchronized display of pathology 1410 in second axial slice viewer 1402, second 3-D coronary vessel map 1404, and second stretched blood vessel image 1406.
  • second axial slice viewer 1402 includes a first pathology indicator 1412, which indicates the location of pathology 1410 in second axial slice viewer 1402.
  • Second 3-D coronary vessel map 1404 includes a second pathology indicator 1414, which indicates the location of pathology 1410 in the 3-D coronary artery tree view.
  • Second stretched blood vessel image 1406 includes a first point 1416 and a second point 1418, which indicate the location of pathology 1410 in second stretched blood vessel image 1406.
  • a fifth user interface 1500 of visualization application 114 in accordance with a third exemplary embodiment is shown.
  • Fifth user interface 1500 may include a third axial slice viewer 1502 and a third stretched blood vessel image 1504.
  • Third axial slice viewer 1502 is synchronized with third stretched blood vessel image 1504.
  • Third axial slice 1502 presents intensity levels from an axial slice of imaging data. The intensity levels may be indicated in color or gray-scale.
  • third axial slice viewer 1502 may indicate an identified pathology 1506 in red.
  • Third stretched blood vessel image 1504 presents intensity levels of a blood vessel selected from third axial slice viewer 1502 and shown in stretched form.
  • Third stretched blood vessel image 1504 may includes a vertical bar 1508 which denotes a location of the slice presented in third axial slice viewer 1502.
  • third stretched blood vessel image 1504 shows a stretched presentation of the appropriate vessel.
  • third axial slice viewer 1502 displays the appropriate slice of the patient study.
  • fifth user interface 1500 may initially include third axial slice viewer 1502.
  • third axial slice viewer 1502 When the user selects an artery from third axial slice viewer 1502, the selected blood vessel is presented in third stretched blood vessel image 1504 with vertical bar 1508 denoting the location of the slice presented in third axial slice viewer 1502.
  • Execution of one or more of the processes described with reference to Figs. 2-9 may be performed after selection of the blood vessel to identify the stretched blood vessel presented in third stretched blood vessel image 1504.
  • using a single "click" the user may trigger a determination of all relevant segments of the blood vessel from the slices and reconstruct the stretched blood vessel for presentation in third stretched blood vessel image 1504.
  • Fifth user interface 1500 further may include an axial presentation only button 1510, a new study selection button 1512, a save current screen button 1514, and an exit program button 1516.
  • User selection of axial presentation only button 1510 causes stretched blood vessel image 1504 to be removed from fifth user interface 1500.
  • User selection of new study selection button 1512 causes presentation of a selection window that allows the user to select a new patient study for analysis.
  • User selection of save current screen button 1514 causes presentation of a save window that allows the user to select a location and a name for a file to which the contents of fifth user interface 1500 are saved for review, for printing, for sending with a message, etc.
  • User selection of exit program button 1516 may cause fifth user interface 1500 to close.

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  • Engineering & Computer Science (AREA)
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  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Geometry (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Ultra Sonic Daignosis Equipment (AREA)

Abstract

L'invention concerne un procédé de définition d'une région du cœur à partir de données d'imagerie. Les données d'imagerie reçues sont projetées sur un premier plan. Un premier seuil est appliqué au premier plan de données pour éliminer des données associées à l'air. Un premier composant connecté le plus grand est identifié à partir du premier seuil appliqué aux données. Un premier centre de masse du premier composant connecté le plus grand identifié est calculé pour définir une première coordonnée et une seconde coordonnée de la région du cœur. Les données d'imagerie reçues sont projetées sur un second plan, le second plan étant perpendiculaire au premier plan. Un second seuil est appliqué au second plan de données pour éliminer les données associées à l'air. Un second composant connecté le plus grand est identifié à partir du second seuil appliqué aux données. Un second centre de masse du second composant connecté le plus grand identifié est calculé pour définir une troisième cordonnée de la région du cœur.
PCT/IB2007/003197 2006-10-25 2007-10-24 Procédé et système pour l'analyse automatique de structures et de pathologies de vaisseau sanguin WO2008050223A2 (fr)

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US86291206P 2006-10-25 2006-10-25
US60/862,912 2006-10-25
US11/562,897 2006-11-22
US11/562,875 US7983459B2 (en) 2006-10-25 2006-11-22 Creating a blood vessel tree from imaging data
US11/562,771 2006-11-22
US11/562,897 US7860283B2 (en) 2006-10-25 2006-11-22 Method and system for the presentation of blood vessel structures and identified pathologies
US11/562,771 US7873194B2 (en) 2006-10-25 2006-11-22 Method and system for automatic analysis of blood vessel structures and pathologies in support of a triple rule-out procedure
US11/562,875 2006-11-22
US11/562,906 US7940977B2 (en) 2006-10-25 2006-11-22 Method and system for automatic analysis of blood vessel structures to identify calcium or soft plaque pathologies
US11/562,906 2006-11-22

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CN113096778A (zh) * 2019-12-23 2021-07-09 宏达国际电子股份有限公司 影像处理方法、系统及非暂态计算机可读存储介质
WO2022164374A1 (fr) * 2021-02-01 2022-08-04 Kahraman Ali Teymur Mesure automatisée de paramètres morphométriques et géométriques de grands vaisseaux dans l'angiographie pulmonaire par tomodensitométrie

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CN104143184A (zh) * 2013-05-10 2014-11-12 上海联影医疗科技有限公司 一种肺部分割的方法
CN104143184B (zh) * 2013-05-10 2017-12-22 上海联影医疗科技有限公司 一种肺部分割的方法
US10282844B2 (en) 2015-05-05 2019-05-07 Shanghai United Imaging Healthcare Co., Ltd. System and method for image segmentation
US10482602B2 (en) 2015-05-05 2019-11-19 Shanghai United Imaging Healthcare Co., Ltd. System and method for image segmentation
CN113096778A (zh) * 2019-12-23 2021-07-09 宏达国际电子股份有限公司 影像处理方法、系统及非暂态计算机可读存储介质
CN113096778B (zh) * 2019-12-23 2024-05-28 宏达国际电子股份有限公司 影像处理方法、系统及非暂态计算机可读存储介质
WO2022164374A1 (fr) * 2021-02-01 2022-08-04 Kahraman Ali Teymur Mesure automatisée de paramètres morphométriques et géométriques de grands vaisseaux dans l'angiographie pulmonaire par tomodensitométrie

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