WO1999038433A1 - Modele de circulation cerebrale et applications correspondantes - Google Patents

Modele de circulation cerebrale et applications correspondantes Download PDF

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Publication number
WO1999038433A1
WO1999038433A1 PCT/US1999/002276 US9902276W WO9938433A1 WO 1999038433 A1 WO1999038433 A1 WO 1999038433A1 US 9902276 W US9902276 W US 9902276W WO 9938433 A1 WO9938433 A1 WO 9938433A1
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Prior art keywords
modeling
model
vessel
cerebral
corresponding vessel
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PCT/US1999/002276
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English (en)
Inventor
Fady T. Charbel
M. E. Clark
Lewis Sadler
Noam Alperin
Francis Loth
Francis Quek
Meide Zhao
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The Board Of Trustees Of The University Of Illinois
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Application filed by The Board Of Trustees Of The University Of Illinois filed Critical The Board Of Trustees Of The University Of Illinois
Priority to CA002319119A priority Critical patent/CA2319119C/fr
Priority to JP2000529172A priority patent/JP2002501774A/ja
Priority to EP99932414A priority patent/EP1059874A4/fr
Priority to AU32858/99A priority patent/AU762822B2/en
Publication of WO1999038433A1 publication Critical patent/WO1999038433A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques
    • A61B6/484—Diagnostic techniques involving phase contrast X-ray imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0263—Measuring blood flow using NMR
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques
    • A61B6/481—Diagnostic techniques involving the use of contrast agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/507—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for determination of haemodynamic parameters, e.g. perfusion CT

Definitions

  • the field of the invention relates to blood circulation in the human body and more particularly, to blood flow within the human brain.
  • Stroke is the third leading cause of death and disability in the United States, with significant socioeconomic impact.
  • Therapeutic options for occlusive cerebrovascular diseases include a variety of reconstructive procedures such as endarterectomy, vessel transposition, bypass, angioplasty and thrombolysis; all of which share the common goal of enhancing the cerebral circulation.
  • This invention applies a computerized model of the cerebral circulation to patients with cerebrovascular diseases to simulate any of a number of procedures, thereby providing a tool for selecting the optimal procedure for any given patient.
  • the benefit of this invention provides improved patient outcome, significant cost savings and major impact on the way cerebrovascular reconstructive procedures are performed.
  • a method and apparatus are provided for modeling cerebral circulation in a living subject.
  • the method includes the steps of developing a model for living subjects in general and correcting the model to substantially conform to the overall cerebral physiology of the living subject.
  • the method further includes the step of calculating a cerebral flow of the living subject based upon the corrected model and a selected cerebral blood flow perturbation.
  • FIG. 1 is a block diagram of a modeling system for cerebral circulation in accordance with an illustrated embodiment of the invention
  • FIG. 2 is a cerebral model used by the system of FIG. 1;
  • FIG. 3 is an operator interface used by the system of FIG. 1;
  • FIG. 4 is a flow diagram used by the operator interface of FIG. 3;
  • FIG. 5 is an operator display use by the system of FIG. 1;
  • FIG. 6 is a flow chart of cerebral modeling of the system of FIG. 1;
  • FIG. 7 depicts the operator interface of FIG. 3 as it may appear during use.
  • the present invention is a practical integration of several aspects of cerebrovascular and computer research.
  • a program has been developed for computer aided neurovascular analysis and simulation that is useful for assessment and prediction of cerebral circulation.
  • the program has up to four major components: (i) a vessel extraction system from Digital Subtraction Angiography (DSA) ; (ii) a- three-dimensional phase contrast Magnetic Resonance (MR) flow measurement system and three-dimensional pulsatility visualization; (iii) a computer simulation system for cerebral circulation; and (iv) a worldwide web-based interface and window to the world.
  • DSA Digital Subtraction Angiography
  • MR Magnetic Resonance
  • the process involved in the present invention creates a virtual replica of the Circle of Willis and computes network blood flow.
  • the modeling program also allows analysis and simulation of flow and pressure within the cerebrum under the condition of a selected blood flow perturbation (e.g., a cerebral aneurysm, stenosis, bypass, other cerebrovascular disease, etc.).
  • the modeling program currently uses the finite difference method.
  • the vessel extraction system is used for determining exact vessel diameters for use in the model.
  • an Attention-Based Model forms the basis of the vessel extraction system.
  • the vessel extraction system provides accurate and rapid measurement of vessel diameters and length at various predetermined regions of the Circle of Willis.
  • the data with the smallest vessel size resolution is currently derived from x- ray angiograms [platform: SGI Workstation, IRIS 6.2 Varsity Package (Rapidapp, Open Inventor, C++) ] .
  • Magnetic resonance imaging has developed in such a manner that useful three-dimensional data pertaining to vessel size and flow is obtainable.
  • the current source of choice of data from magnetic resonance imaging uses three-dimensional phase contrast angiographic methods to measure flow [platform: SGI Workstation, IRIS 6.2 Varsity Package (Rapidapp, Open
  • the vessel size resolution of the MRI data is on the order of a half a millimeter.
  • phase contrast magnetic resonance (MR) flow measurement system of the invention enhances the accuracy of the cross- section and flow measurements by three-dimensional localization and visualization of the vessels.
  • AIM is a selective attention-inspired interaction paradigm for the analysis of multimodal medical images (e.g., magnetic resonance angiography
  • FIG. 1 is a block diagram of a cerebral modeling system 10, generally in accordance with an illustrated embodiment of the invention.
  • the modeling system 10 generally includes a data source, 12, a central processing unit (CPU) 14, a display 18 and a keyboard 16.
  • the data source 12 may one or more medical imaging systems (e.g., MRA, MRI, XeCT, XRA, etc.). While many aspects of human attention are yet to be ascertained, the following is known: 1.
  • Attention facilitates the direction of limited cognitive resources; 2. Spatial cues (or prompts) are effective in directing the attentional "spotlight" for recognition; 3. Central semantic cues at both the feature and object levels facilitate performance in visual attention tasks; and 4. A model of what is in the attentional space of one's interlocutor is critical to maintaining effective communication.
  • AIM defines two interaction channels: a semantic context ⁇ what to look for) and a focus-of-attention FOA (where to look) .
  • the user selects the context from a menu (or a schematic diagram of the neurovascular system) and manipulates a FOA cursor (FOAC) through the data using a 2D or 3D pointing device.
  • FOAC FOA cursor
  • FIG. 2 shows the human Circle of Willis 100, including, seventy-three blood vessels. Selection of one of the vessels provides the system 10 with a context of what to look for.
  • FIG. 3 shows an interactive screen 152 that may be presented on the display 18 of the system 10.
  • the Circle of Willis 100 of FIG. 2 may be displayed as a graphical context representation in a box 152 of the screen 150.
  • An operator (not shown) of the system 10 may select a vessel (e.g., 102) using the cursor 162.
  • the operator may show the CPU 16 where to look for the vessel in the image work area 154.
  • the operator may identify the vessel 102 by placing the FOAC (i.e., the smart cursor 162) over the vessel and activating an ENTER button.
  • the AIMS processes the data in the region of the FOAC to locate and highlight entities matching the selected context in real-time as the cursor moves.
  • the user acknowledges this and the system extends the dialog by tracing the entity (e.g., the highlighted vessel) , all the while providing feedback via animated highlighting.
  • the system may trace the entity by looking for high contrast areas between adjacent pixels or groups of pixels of the image.
  • the high contrast areas may be used to identify boundary areas between vessels and surrounding tissue.
  • the system identifies the vessel within the FOAC and begins processing adjacent areas to trace the contrasted areas in three-dimensional space using the continuity of the contrasted area of a path to other parts (e.g., the respective ends) of the vessel.
  • the system uses the contrast to not only traces the vessel, but also measures a diameter of the vessel.
  • the system identifies the outer wall of the vessel by seeking the area of the greatest rate of change in contrast. A measurement may then be taken of the distance between opposing sides of the vessel. Since the system knows what it is looking for, the problem becomes one of detection of a specific entity. Hence, very specifically tuned detectors may be used. It is not critical if the detector highlights the wrong entity because the user can retarget the detector by simply moving the pointing device.
  • AIM defines an abstraction hierarchy of contexts.
  • a context may vary in abstraction from full scene
  • an object level context may be a segment of the carotid artery
  • a feature level context may be arterial boundary model
  • a user may trace the boundary points of a vessel by hand. This provides robustness for the system for our mission- critical task in the process of patient care. For neurovascular image interpretation, this guarantees the ability to obtain the necessary vessel extraction and measurement even if the higher-level recognition algorithms fail (e.g., owing to patient pathology or data quality) .
  • the AIM model provides a system and software architecture of broad applicability. This is important because AIMS is easily extensible to accommodate new algorithms, imaging modalities and entities of interest (e.g., other brain structures such as the interhemispheric chasm of the brain, aorta walls for cardiovascular image interpretation, tumor models for digital mammogram analysis) without requiring extensive ad hoc reengineering for each domain.
  • FIG. 4 is a block diagram of the architecture characterizing the interaction model.
  • the object-oriented architecture comprises two distinct components: The user interface and the domain knowledge representation. Such modularity is important for the system 10 to be portable across platforms and display/interaction technologies (e.g., for both 2D and 3D interpretation environments) .
  • the context database maintains domain knowledge about the entities of interest (e.g., vessels of different types, and the operators necessary to extract them from the different kinds of image data) .
  • the data information maintains meta-information about the data (e.g., file name sequences, where data is stored, the resolution of each image type, etc.).
  • the Operator Selector selects the appropriate FOAC operator from the Operator Library.
  • the Process knowledge database maintains knowledge of the interpretation protocol.
  • the process knowledge database realizes the concept of dialog extension by tying it directly to medical/radiological protocol.
  • the protocol dictates an order of vessel extraction that is efficient with respect to the ordering of the XRA dataset and the content of particular viewing projections in the XRA images.
  • This protocol can be encoded seamlessly into the interaction sequence so that the system prompts the user for each succeeding vessel in the neurovasculature .
  • a protocol defini tion file pairs the order of vascular measurement with the preferred images in which the measurement may be made.
  • the system may simulate an extended dialog by prompting the user for each vessel measurement in the appropriate images . This permits the system to predict the next entity to be extracted so that the user does not have to remember the order thus reducing processing errors
  • This example also illustrates the concept of interrupting and resuming dialog streams .
  • a user may wish to interrupt the protocol for several reasons. She may wish to correct and erroneous measurement made earlier, or may want to make a measurement of opportunity out of sequence.
  • AIM permits such interruption by having the user select the context of interest. One may think of this as the user "changing the subject” .
  • AIM maintains the location of "interrupt on” and permits the user to resume the dialog (or protocol) whenever she wishes.
  • the process knowledge database actually facilitates the psycholinguistic components of discourse situatedness and repair that are essential for effective interaction.
  • FIG. 3 shows the general screen layout of the AIM interface 150 used in the interpretation of complex images.
  • the Working Area 154 is the primary area in which the operator interacts with the system in the interpretation task.
  • the Focus of Attention ( FOA) is directed by manipulating the Smart Cursor 162 over the displayed subject image in the Working Area with a pointing device (e.g., a mouse). Feedback for the interpretation process is provided in the Working Area by highlighting the objects under the Smart Cursor which satisfy the current context.
  • the FOA Status Display 158 at the bottom left of the display provides a magnified view of the area under the smart cursor as well as the processing status. In a later section, we shall see how partial processing results are presented in this display.
  • the Graphical Context Presentation 152 on the left side of the screen provides the user with the overall status of the interaction as well as the particular interpretive context .
  • this is a schematic of the neurovascular tree .
  • this may be a schematic representation of the aortic system.
  • the schematic representation provides the user with an overall state of the interpretation
  • this generic screen layout also provides for textual context identification in the System Status and Process Status boxes 156, 160.
  • the former provides general information about the selected context, and the latter details the status of the current interpretation (e.g., the dimension of the vessel being measured in the current image) .
  • the pulldown menu items 164 on the top of the screen permit alteration of the system parameters and selection of data sets on which to operate.
  • FIG. 7 shows an example of the AIM interface 150 as it may appear during the interpretation of XRA images of the neurovascular system.
  • the XRA image is displayed in the Working Area 154. This area is scrollable, so any size of XRA image can be displayed. The user can increase or decrease the size of the image at any time by choosing appropriate selection item from the menubar 164.
  • the Focus of Attention (FOA) is represented by the box 162 shown in the image and is directed by the mouse.
  • 11 is provided in the Graphical Context Presentation 152. It is shown in the upper left corner of the screen to provide object level context to the system in a graphical fashion.
  • the information about this vessel is displayed in the window 160 in the lower right corner of the screen. This information contains the vessel number (used as an index) , the vessel name, the width and any comment written by the user about the measurement process of this vessel. If no measurement has been taken for this vessel, the vessel, the vessel has the default width stored in the database. The vessels are highlighted in different colors to give feedback on the selection and measurement processes.
  • the FOA Status Display 158 at the bottom left of the screen displays the processing status of the smart cursor. It shows a magnified view of vessel boundary found in the FOA and locates the cross-section where the measurement is being made. Partial results of the computation and the process parameters are also shown in this area.
  • FIG. 5 shows an additional window 177 that facilitates the easy use of the system 10.
  • every patient's XRA images are stored on the storage device in a separated directory.
  • the system 10 creates thumbnail images of the original images, as shown in the window
  • vessel size and location may be obtained from MRA, MRI, XeCT or XRA data.
  • the dimensional information derived from the data may be enhanced using MRI operating in the Doppler mode.
  • Doppler MRI allows volumetric blood flow to be determined by measuring blood velocity across a cross- section of each vessel.
  • the enhancement of the accuracy and vessel size resolution arises from the three-dimensional reconstruction of the vessels that currently uses an interpolation scheme that is constrained by a piecewise smooth volumetric flow equation.
  • the flow data is corroborated by transcranial Doppler measurements at predetermined vessel intervals.
  • the flow velocity has also been found to vary as a result of vasoreactivity .
  • Benchmark values for cerebral autoregulation have been established and is continuously refined.
  • the data analysis system supports both automatic and interactive extraction of the vessel cross-sections.
  • a color coding scheme facilitates visualization and user interaction with the data to reduce the inter-user variability.
  • a user can view, move and freely rotate a three-dimensional picture of the
  • the user can also place a plane anywhere in the three-dimensional picture, then view the two-dimensional cross-section of the cerebrovascular network 100 at that location.
  • the vessels are clearly discernible in the cross- section, with color coding to denote flow into and out of the planar cross-section.
  • Three-dimensional pulsatility of blood flow can be animated and visualized in the currently-developed user interface 150.
  • a vessel can be selected by the user for detailed and graphical analysis of the flow through the vessel cross-section that animatedly shows the pulsatile flow changes with time .
  • the computer simulation system 10 for cerebral circulation employs the outputs from both the vessel extraction system and the three-dimensional phase contrast MR flow measurement system to calibrate, customize and drive the cerebral circulation model [platform: PC (Pentium) , Windows95/NT or DOS, Lahey Fortran 77] .
  • the model is reconfigurable to account for person-to-person variability in the cerebrovascular network.
  • the overall number of vascular segments in the flow model can be increased or decreased as needed to form a customized model for each patient.
  • the computer simulation system is flexible and accommodates empirical observations of measurements from the x-ray and magnetic resonance angiograms as well as from direct measurements of flow using flowmeters during surgery, and transcranial Doppler (TCD) at selected sites.
  • TCD transcranial Doppler
  • the parameters of the model can be adapted as validation of the model requires in "normal" subjects and subjects with cerebrovascular diseases.
  • the computer model is a one-dimensional, explicit, finite difference algorithm based on a conservation of mass equation, a Navier-Stokes momentum equation, and an equation of state relating local pressure to local size of artery [Khufahl & Clark, ASME J. of Biomech . Eng. 107:112-122 (1985)].
  • the arterial networks contain vessel loops (as well as many branchings) , the pressure and flow nodes are staggered throughout the model. Each vessel is divided into many segments; the flow nodes are located at segment ends, the pressure nodes at segment centers. Any multi-vessel network configuration can be specified solely from the data file.
  • the model is forced by one or more pressure or flow signatures at appropriate locations.
  • a pressure-time signature at the root of the aorta obtained from prototype measurements or angiographic data, serves as the forcing function. Velocities at certain points in the network as determined by transcranial Doppler measurement are also integrated.
  • FIG. 6 the model of the system 10 is first initialized 202 with initialized pressures and flows at all points in all vessels. A cross-sectional area is calculated 202 for all points using the current pressure.
  • the mass balance is determined 204 by calculating pressures at all points except the pressures at vessel junction centers.
  • An inlet forcing function is invoked 206 to update pressure and flow sources.
  • the internal junction boundary conditions may be evaluated 208 by calculating flows at all junctions and the pressures at all junction centers.
  • a momentum balance may then be determined 210 by calculating the flows at all points except the flows at junctions.
  • the pressures at all junction centers may be used as special forcing functions to introduce internal flow sources .
  • a set of terminal boundary conditions may be determined 212 by calculating flows at the last nodes in all efferent vessels.
  • a current time value is incremented 214 and the incremented time is compared 216 with a modeling period. If the incremented time is less than the modeling period, the process 200 repeats. If not, the process terminates.
  • a baseline vessel network 100 is used in the current model, including the Circle of Willis, ophthalmic arteries and other natural anastomoses.
  • the number of vessels can rise as needed.
  • segments stenosed (perturbed) by any specified amount can be placed in any number of vessels .
  • Aneurysms can also be simulated at various sites in the network. The results of any of a number of surgical procedures may be accurately predicted based upon the model results of the system 10.
  • a worldwide web-based interface and window to the world is available to allow users access to the interactive and user-friendly program from remote sites throughout the Internet.
  • the system for computer aided neurovascular analysis and simulation is usefully applied to model and analyze various neurosurgical conditions.
  • the volume of brain fed can be calculated. First, the total mass of the brain can be determined. Then, the portion fed can be determined as a percentage of the determined total. Knowing the mass of brain fed by a particular artery allows the volume of blood necessary to feed that mass to be determined. Those early models were general, using assumptions of the elasticity of the blood vessels, the viscosity of the blood, and the vasculature arrangement of a normal patient's brain circulatory system.
  • the present invention is a refined model that is capable of being adapted to specific patients. Once the volume of blood necessary has been determined, the model is calibrated to a particular patient.
  • Deviations of the arterial structure of the blood supply of the patient's brain from the general model are identified from the angiograms .
  • An x-ray angiogram (XR angiogram) of the patient's brain is used to determine the diameter of the blood vessels.
  • Magnetic Resonance imaging angiography is then used to determine an actual blood flow in the brain. Missing or additional arterial segments may be identified and used to adjust the model. A knowledge of the actual arterial structure and actual blood flows can be used to customize the model to the actual patient.
  • An empirical study of user variability using the three-dimensional phase contrast MR angiographic flow measurement system was conducted. The study implemented an end-to-end system. The study tested the repeatability of measurements by having ten users select the vessel cross-section from the interpolated color
  • the simulation of cerebral circulation presents a range of challenging fluid dynamic problems, including: modeling the non-Newtonian properties of blood; dealing with "physiological" unsteady pulsatile flow; modeling the elasticity of vessel walls; and modeling moving boundaries caused by vessel wall elasticity.
  • the cerebral circulation network is an interconnected three dimensional arterial network, the question arises of how the curvature of the artery is modeled.
  • the asymmetric and three dimensional characteristic of bifurcations are also very important issues .
  • Computer simulation started with models of the dog's cerebral circulation system. Clark et al . , Acta Neurol . Scandinav. , 43:189-204 (1967), built a computer model for one-dimensional, linear, steady laminar flow and compared the result with an engineering model built by the same group [Himwich et al . , Archive of Neurology,
  • the cerebral circulation network of a human is more complex than the that of an animal . Considerations to be addressed included the number of arteries it was necessary to simulate, the selection of those arteries, and the configuration of the chosen arteries to represent the functionality of the Circle of Willis.
  • Cerebral circulation in humans is not consistent between individuals.
  • the number of arteries, and parameters such as artery length and diameter differ from person to person, limiting the utility of standard data for individual cases.
  • a successful model must be
  • the difficulty of obtaining direct measurements in humans limits the ability to accumulate parameter information to refine the model.
  • FIG. 2 is the schematic drawing of the 73 vessel model of Clark et al . After adding naturally occurring anastomoses, the total number of vessels increased to eighty-five. When artificial anastomoses were imposed, the number of vessels increased to eighty-seven.
  • Duros et al . Neurological Res . , 13:217-223 (1991), built a model that not only contained the cerebral arteries but also the human body main supply arteries .
  • the Duros et al . model was used to simulate the rupture condition of aneurysm.
  • the main differences between the various models are how they define the problem (e.g. whether the flow is steady or pulsatile, whether the vessel wall is rigid or elastic, and whether a linear or non-linear governing equation is used to describe the flow field) and how they solve the problem (e.g. whether an analytical method or numerical method is used) . Defini tion of the Problem .
  • R is the pipe radius and 1 is the pipe length.
  • a computer model can be divided into two categories .
  • the first type of models applied the linear governing equation and rigid vessel assumption.
  • the solution is given by the Hagen- Poiseuille formula.
  • the solution is given by the Wormersley model.
  • models of this type include Clark et al . Acta Neurol . Scandinav. , 43:189-204 (1967), and Hillen et al . , J “ . Biomechanics, 21:807-814 (1988). Because some of the electrical models [Roller and Clark, J. Biomechanics, 2:244-251 (1969); Helal, Comput . Bio . Med. , 24:103-118 (1994)] were derived from the Hagen-Poiseuille formula, they belong to this type.
  • the other type is non-linear, pulsatile flow and distensible vessel models.
  • models of this type include Kufahl & Clark [Kufahl and Clark, J. Biomechanical Engineering, 107:112-122 (1985); Clark et al., Neurological Research, 11:217-230 (1989); Kufahl, Ph . D. thesis (Univ. of Illinois, Urbana: 1980)] Hillen et al. [Hillen et al . , J. Biomechanics, 15:441-448 (1982); Hillen et al . , J. Biomechanics, 19:187-194 (1986)] and Duros et al . , Neurological Research, 13:217- 223 (1991) .
  • FD finite-difference
  • FE finite-element
  • FV finite volume
  • Cerebral network models usually contain hundreds of arteries and tens of bifurcations, which makes the use the finite element approach difficult using presently available computer equipment.
  • To apply the finite element method in the network simulation powerful computers are needed. With advances in computation technology, the finite element method can be applied in cerebral network simulation.
  • the present invention currently incorporates the finite difference approach. Application .
  • Stroke is the second leading cause of death in the United States, as well as in most western countries.
  • cerebral revascularization e.g. angioplasty, endarterectomy, byphase and embolectomy
  • the procedure of choice for cerebral revascularization e.g. angioplasty, endarterectomy, byphase and embolectomy
  • each particular patient is at least in theory the one that restores cerebral blood flow.
  • each patient has a unique dimensional structure. As a result, any surgical treatment can have different effects on different patients.
  • Computer models can simulate the cerebral circulation under "normal” conditions. Computer models can also be used to predict the results of potential treatment procedures. However, there is no system currently available which forms a comprehensive model customized to the patient or which allows a user to perturb that model at will.
  • Hillen et al . J “ . Biomechanics , 15:441-448 (1982), built a non-linear one dimensional model to study the functional significance of the Circle of Willis.
  • the model consisted of two afferent and two efferent arteries connected by the posterior communicating artery. Hillen et al . found that in normal cases, the flow in the posterior communicating artery was towards the posterior cerebral artery, and that the flow direction in posterior communicating artery depended on the ratio of peripheral resistance. Raising the ratio significantly would change the flow direction, therefore Hillen et al . postulated the formation of a dead point where flow in the posterior communicating artery approaches zero .
  • model 73 representing the basic circle and served as a benchmark.
  • model 85 the total number of vessels increased to 85.
  • the Duros model differed from the other models in that the model not only contained cerebral arteries, but also contained 30 main supply arteries to different organs of the human body.
  • the aneurysm was balloon-shaped, elastically tapered with zero distal flow and was placed at the junction of the internal carotid, the anterior cerebral and the middle cerebral arteries. To simulate the condition where a rupture may happen, several parameters were adjusted: all terminal vessels' resistance values were enlarged by a factor of 10; two 80 percent stenoses were placed in the middle cerebral artery and anterior cerebral artery. Systemic pressure was increased to 150 mm Hg to represent hypertension. The compliance coefficient of the aneurysm was set to 1.5 to represent a stiff wall condition.
  • Duros et al . focused on the pressure propagation inside the aneurysm and found that the pressure did not change with the neck diameter, but the pressure peak value increased with increasing the sack diameter. In order to achieve a high pressure (310 mm Hg) which may trigger the rupture of the aneurysm, hypertension, increased number of reflecting sites in both the near and far fields, and arteriosclerotic arteries were needed .
  • the computational model of cerebral circulation of the present invention is a safer alternative to the balloon occlusion test (BOT) .
  • BOT balloon occlusion test
  • the model used by the system 10 was used to create a virtual replica of the Circle of Willis and compute network blood flow using the finite difference method. To evaluate the ability of the model to identify patients who tolerate permanent carotid occlusion, the difference in ipsilateral computed middle cerebral artery flow between patients passing and failing the balloon occlusion test was determined prospectively.
  • CFD computational fluid dynamics
  • the numerical model of the cerebral circulation was utilized along with the data from other supplemental diagnostic modalities to evaluate cerebrocirculatory collateral function during the balloon occlusion test (BOT) .
  • Case example A 49 year old female displayed diplopia and headache. Upon investigation, she was found to have a large right cavernous internal carotid artery (ICA) aneurysm. A computer analysis of her cerebral blood flow was done to assess her cerebral circulation, focusing mainly the total middle cerebral artery flow. The numerical value of the total middle cerebral artery blood flow with and without occlusion of the ipsilateral ICA was predicted by the computer flow to be almost the same.
  • ICA internal carotid artery
  • the patient Underwent balloon occlusion of the ICA on the right side, just distal to the aneurysms.
  • the patient displayed good flow in both cerebral hemispheres with minimal changes in total computed middle cerebral artery flow.
  • Temporary balloon occlusion is invasive and adds risk to the evaluation of patients who may already be at risk for infarction.
  • the numerical model of the cerebral circulation is employed as an aid in the evaluation of patients for permanent internal carotid occlusion.
  • Pulsatile pressure and flow in normal and diseased vessels can also be simulated. Modeling of network flow with anatomical variations, bypasses, or lepto-meningeal collateral vessels is also possible.
  • this model was utilized to simulate changes in the middle cerebral artery blood flow before and after the balloon occlusion. If the blood flow is decreased in a region of the Circle of Willis after occlusion of the carotid artery, the simulation shows red at the region receiving decreased flow, and displays numbers corresponding to the magnitude of the decrease. If the blood flow is increased in a region of the Circle of Willis after occlusion, the simulation shows green at the region receiving increased flow, and denotes the magnitude. From the simulation output, the user can estimate whether the patient will tolerate the occlusion (or would pass the balloon occlusion test) .
  • the simplest blood vessel wall model is a rigid tube.
  • vessel wall elasticity has an important effect on the blood flow wave propagation.
  • the vessel wall is composed of three distinct layers, an intima, a media and an adventitia. Each of these layers has a unique function.
  • the vessel wall is not only elastic but also viscoelastic.
  • the vessel elasticity was modeled by a relationship between blood vessel cross-sectional area and local pressure. Raines proposed a widely used model :
  • a ⁇ x) A 0 ⁇ x)[ ⁇ + C (p - p fl )+ C 0 ⁇ p - p 0 f ⁇
  • Chang and Tarbell simulated pulsatile flow in the aortic arch and found that the secondary flow was nearly as large as the axial flow component, the secondary flow was complex with up to seven vortices, peak axial and highest r.m.s. wall shear stress were found at the inside wall. Also the axial-flow direction was reversed at the inside wall.
  • Chang and Tarbel simulated flow in the coronary artery using the same method of Chang and Tarbel. They found that flow was quasi-steady under resting
  • Perktold et al analyzed flow in the carotid siphon and left main coronary artery. They found that the maximum secondary flow velocities were on the order of three to four percent of the maximum axial velocity and the secondary flow has an important influence on the wall shear stress distribution.

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Abstract

La présente invention concerne un procédé et un appareil de modélisation de la circulation cérébrale chez un sujet vivant. Ce procédé consiste à réaliser (200) un modèle pour des sujets vivants en général et à le corriger de manière à ce qu'il corresponde quasiment à la physiologie cérébrale générale du sujet vivant. Ce procédé concerne par ailleurs le calcul du débit cérébral du sujet, lequel calcul repose sur le modèle corrigé et sur une perturbation sélectionnée du débit sanguin cérébral.
PCT/US1999/002276 1998-02-03 1999-02-03 Modele de circulation cerebrale et applications correspondantes WO1999038433A1 (fr)

Priority Applications (4)

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CA002319119A CA2319119C (fr) 1998-02-03 1999-02-03 Modele de circulation cerebrale et applications correspondantes
JP2000529172A JP2002501774A (ja) 1998-02-03 1999-02-03 脳循環モデルと用途
EP99932414A EP1059874A4 (fr) 1998-02-03 1999-02-03 Modele de circulation cerebrale et applications correspondantes
AU32858/99A AU762822B2 (en) 1998-02-03 1999-02-03 Cerebral circulation model and applications

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US7358098P 1998-02-03 1998-02-03
US60/073,580 1998-02-03

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US8157742B2 (en) 2010-08-12 2012-04-17 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US8200466B2 (en) 2008-07-21 2012-06-12 The Board Of Trustees Of The Leland Stanford Junior University Method for tuning patient-specific cardiovascular simulations
US8249815B2 (en) 2010-08-12 2012-08-21 Heartflow, Inc. Method and system for patient-specific modeling of blood flow
US8548778B1 (en) 2012-05-14 2013-10-01 Heartflow, Inc. Method and system for providing information from a patient-specific model of blood flow
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US9962106B2 (en) 2015-02-13 2018-05-08 Toshiba Medical Systems Corporation Medical image processing apparatus and medical image processing method
US10354050B2 (en) 2009-03-17 2019-07-16 The Board Of Trustees Of Leland Stanford Junior University Image processing method for determining patient-specific cardiovascular information
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US7739090B2 (en) 1998-02-03 2010-06-15 University Of Illinois, Board Of Trustees Method and system for 3D blood vessel localization
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EP1222627A4 (fr) * 1999-09-20 2003-07-16 Univ Illinois Modele de circulation et applications
EP2028608A3 (fr) * 1999-09-20 2009-03-04 The Board Of Trustees Of The University Of Illinois Modèle de circulation et applications
US6860266B2 (en) 2000-11-03 2005-03-01 Dartmouth-Hitchcock Clinic Physiological object displays
WO2002071933A2 (fr) * 2001-01-23 2002-09-19 Alliance Pharmaceutical Corp. Affichages d'objets physiologiques
WO2002071933A3 (fr) * 2001-01-23 2003-10-30 Alliance Pharma Affichages d'objets physiologiques
US8062224B2 (en) 2004-10-28 2011-11-22 Uab Vittamed Method and apparatus for non-invasive continuous monitoring of cerebrovascular autoregulation state
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EP1059874A4 (fr) 2003-05-07
JP2002501774A (ja) 2002-01-22
CA2319119A1 (fr) 1999-08-05
EP1059874A1 (fr) 2000-12-20
AU762822B2 (en) 2003-07-03

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