WO2019071128A1 - Évaluation d'imagerie médicale de la masse ventriculaire gauche - Google Patents

Évaluation d'imagerie médicale de la masse ventriculaire gauche Download PDF

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WO2019071128A1
WO2019071128A1 PCT/US2018/054611 US2018054611W WO2019071128A1 WO 2019071128 A1 WO2019071128 A1 WO 2019071128A1 US 2018054611 W US2018054611 W US 2018054611W WO 2019071128 A1 WO2019071128 A1 WO 2019071128A1
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left ventricular
lvid
lvl
value
mass
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PCT/US2018/054611
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English (en)
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Sunil Krishna VASIREDDI
Prasongchai SATTAYAPRASERT
Ashish Aneja
Najmul I. SIDDIQI
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Mhs Care-Innovation Llc
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
    • A61B5/1075—Measuring physical dimensions, e.g. size of the entire body or parts thereof for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer
    • 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
    • A61B6/503—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 the heart
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
    • A61B2576/023—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the heart
    • 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
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Definitions

  • the present disclosure is directed to methods and systems for determining left ventricular mass.
  • the methods and systems are configured to compute left ventricular mass using a paraboloidal model volume of the left ventricle of a human heart.
  • the present disclosure is anticipated for use with echocardiograms for predicting the presence of a risk of cardiac hypertrophy, but is amenable for use with other medical imaging modalities as well.
  • FIG. 1 is a parasternal long axis cross-sectional view of a human heart 10.
  • the left ventricle 12 is one of the four chambers of the heart and is located below the left atrium 20.
  • the left ventricle 12 (“LV") is separated from the right ventricle 14 by the interventricular septum 18.
  • the left ventricle 12 pumps oxygen-rich blood to the rest of the body. As a result, the walls of the left ventricle are the thickest of the heart's chambers.
  • left ventricular hypertrophy may be caused by high blood pressure and narrowing of the aortic valve, and causes thickening and hardening of the muscle tissue that makes up the left ventricle wall 16.
  • Left ventricular hypertrophy is an important factor in determining the risk of cardiovascular disease.
  • Increased left ventricular mass (LVM) also portends a higher risk of sudden cardiac death in patients with hypertension and congestive heart failure.
  • LVM left ventricular mass
  • Cardiac Magnetic Resonance (“CMR”) imaging (known as a Cardiac MRI) is considered the gold-standard test for measuring the left ventricular mass (LVM) because CMR does not depend upon geometric assumptions used with two-dimensional echocardiography.
  • the CMR procedure can take over an hour and is expensive to perform.
  • the CMR is also limited by a relative lack of widespread availability, patient claustrophobia, patient size, the presence of metallic objects, the presence of currently implanted devices, patients being unable to lie flat for a prolonged time period, issues with cardiac gating when a rhythm abnormality is present, and the lack of conclusive safety for use with pregnant women.
  • the CMR is a computationally intensive approach that is highly dependent on image quality, which can have diminished quality in up to 40% of cases.
  • Three-dimensional echocardiography is the next best non-invasive test for measuring LVM.
  • accuracy depends on image quality and patient cooperation.
  • three-dimensional echocardiography is not widely available, is computationally intensive, and is operator dependent.
  • Two-dimensional echocardiography remains the most widely used tool for measuring the LVM.
  • Two-dimensional echocardiography is relatively inexpensive and is widely available.
  • Two-dimensional echocardiography relies on ultrasound imaging technology to view the heart and is dependent on image quality and patient cooperation. It also makes geometric assumptions which do not apply uniformly to all patients, which leads to frequent inaccuracies in measurements. Particularly, this procedure is known to overestimate the size of the heart.
  • the present disclosure relates to methods and systems for better estimation / measurement of left ventricular mass. Rather than using the conventional ellipsoidal model, a paraboloidal model is used instead to estimate the left ventricular mass (LVM).
  • LVM left ventricular mass
  • Also disclosed herein are medical imaging systems for measuring left ventricular mass the system comprising an imaging device and a processor, the processor being configured to compute the left ventricular mass using a paraboloidal model volume.
  • a has a value of about 0.3
  • b has a value of about 2
  • c has a value of about 21 .
  • the imaging device transmits information to the processor, and the processor is configured to determine a measurement characterizing the left ventricle, the measurement being selected from the group consisting of an interventricular septal thickness; a left ventricular end-diastolic dimension; a posterior wall thickness; a left ventricular length; and combinations thereof.
  • the processor is configured to determine a measurement characterizing the left ventricle, the measurement being selected from the group consisting of an interventricular septal thickness; a left ventricular end-diastolic dimension; a posterior wall thickness; a left ventricular length; and combinations thereof.
  • these measurements can be manually determined and inputted into the equation.
  • the processor is configured to transmit the left ventricular mass to a display device in communication with the processor.
  • the processor may also be configured to: compare the left ventricular mass to a threshold; and in response to the left ventricular mass meeting the threshold, generate an alert.
  • the imaging device may be selected from the group consisting of: an ultrasonic transducer probe; a cardiac magnetic resonance machine; an x-ray machine; and a computed tomography machine.
  • FIG. 1 is a parasternal long axis cross-sectional view of a human heart, showing where measurements are made that are used in conventional equations for determining the LV mass.
  • FIG. 2 is a schematic diagram of a medical imaging system for measuring LV mass according to one embodiment of the disclosure.
  • FIG. 3 is a parasternal long axis cross-sectional view of a human heart, showing where measurements are made that are used in the new equations for determining the LV mass.
  • FIG. 4 is a chart comparing the LV mass (Y-axis) determined according to cardiac magnetic resonance (CMR), conventional LV mass equation (ASE), and the new paraboloid model based LV mass equation (PMV) for 148 different subjects.
  • the y-axis is in grams, and runs from 0 to 450 in increments of 50. The values were plotted in ascending CMR mass.
  • FIG. 5 is a chart comparing the LV mass (Y-axis) determined according to cardiac magnetic resonance (CMR), conventional LV mass equation (ASE), and the new paraboloid model equations (PMV) for 148 different subjects used for validating the PMV equation.
  • the y-axis is in grams, and runs from 0 to 600 in increments of 100. The values were plotted in ascending CMR mass.
  • the term “about” can be used to include any numerical value that can vary without changing the basic function of that value. When used with a range, “about” also discloses the range defined by the absolute values of the two endpoints, e.g. "about 2 to about 4" also discloses the range “from 2 to 4.” The term “about” may refer to plus or minus 10% of the indicated number.
  • LVM left ventricular mass
  • g 0.8 ⁇ 1.04[([LVEDD + IVSd + PWd] 3 - LVEDD 3 )] ⁇ + 0.6 (1 )
  • LVEDD the LV-end-diastolic dimension (mm)
  • IVSd the interventricular septal thickness at end-diastole (mm)
  • PWd the posterior wall thickness at end-diastole (mm). See FIG. 1 , which indicates where these measurements are made, as recognized by those of ordinary skill in the art.
  • the conventional approach models the left ventricle as a prolated ellipsoid.
  • the relative wall thickness (RWT) may be compared against a threshold value for further classifying the left ventricular mass increase as either being a concentric hypertrophy (RWT > 0.42) or eccentric hypertrophy (RWT ⁇ 0.42).
  • Equation (1 ) is used with linear echocardiography to estimate LVM.
  • the LVM and the left ventricular volume are of great clinical value as they can independently predict the risk of adverse cardiac events and premature death.
  • traditional 2D echocardiography measurements to estimate the LVM are highly variable, with an inter-observer variability of 37% and intra-observer variability of 19%. They also correlate poorly to the true LVM.
  • Linear echocardiography methods are usually preferred because 2D echocardiography imaging is more complex, requires measurement of more parameters, and requires better images to make accurate measurements.
  • FIG. 2 is a schematic diagram of a medical imaging system 100 including an LVM computing device 102, a medical imaging device 104, and a user interface 106 having a display 108.
  • These devices 102-106 may be linked together by communication links, referred to herein as a network. In another embodiment, these devices 102-106 may be physically embodied in a single unit. It is also contemplated that the computing device 102 may be located elsewhere on a network to which the medical imaging device 104 is connected, such as on a central server, a networked computer, or the like, or distributed throughout the network or otherwise accessible thereto.
  • the computing device 102 illustrated in FIG. 2 includes a controller 110 that is configured to perform algorithms for determining a paraboloidal volume and mass of a left ventricle.
  • the controller 110 includes a digital signal processor 112, which controls the overall operation of the computing device 102 by execution of processing instructions that are stored in a memory 114 connected to the processor 112.
  • the memory 114 may represent any type of tangible computer readable medium such as random access memory (RAM), read only memory (ROM), magnetic disk or tape, optical disk, flash memory, or holographic memory. In one embodiment, the memory 114 comprises a combination of random access memory and read only memory.
  • the processor 112 can be variously embodied, such as by a single-core processor, a dual-core processor (or more generally by a multiple-core processor), a digital processor and cooperating math coprocessor, a digital controller, or the like.
  • the processor 112 in addition to controlling the operation of the computing device 102, executes instructions stored in memory 114 for performing the algorithms set forth herein. In some embodiments, the processor 112 and memory 114 may be physically combined in a single chip.
  • the processor 112 may store any one or a combination of modules including an image buffering module 116, a left ventricular mass (“LVM”) calculator 122, a classifier 124, and an output module 126.
  • the image buffering module 116 of the processor acquires a medical image and processes the image to quantify / determine characteristics of a heart captured in the image. Particularly, there is no limitation made herein on the level of processing performed on the image.
  • module 116 can detect a left ventricle in a region of interest ("ROI") of the image, and further process the ROI to estimate the left ventricular wall thicknesses (i.e., interventricular septal thickness and posterior wall thickness) and cavity dimensions (i.e., LV length and LV end-diastolic dimension) ("the LV dataset").
  • ROI region of interest
  • the LV dataset can be automatically determined, or can be manually measured by an operator based on an image produced by the device, and the values then manually inputted by the operator.
  • FIG. 3 is a partial cross-sectional view of a human heart, where like numbers are used to represent like and corresponding parts to FIG. 1.
  • FIG. 3 illustrates the LV dataset that is generated by the present system.
  • the interventricular septal thickness IVS is measured across the interventricular septum 18;
  • the left ventricular end-diastolic dimension LVID is measured across the left ventricle cavity 12;
  • the posterior wall thickness PW is measured across the left ventricle wall 16;
  • the left ventricular length LVL is measured along the left ventricle cavity 12.
  • the LV dataset can be generated automatically by the image buffering module 116.
  • the image buffering module 116 can be omitted altogether, and the computing device 102 can instead receive the LV dataset as a manual input from a graphic user interface.
  • the computing device 102 is configured to receive the LV dataset from the medical imaging device 104.
  • the LV dataset may be input from any suitable source, such as a workstation, a database, and a memory storage device, such as a disk, or the like, that is in communication with the controller containing the processor 112 and memory 114.
  • the present computing device 102 is contemplated for use with known medical imaging devices, which provide data on the heart to the computing device 102, which can process the data to form an image or video of the heart and display the image / video to a user via a user interface 106 including a display.
  • the computing device 102 allows the user to manually designate the start and end points for the desired region that represent the LV wall thicknesses and dimensions, for example via the interface 106.
  • the computing device 102 is configured to compute the distance between the start and end points.
  • the LV dataset can be transmitted to a storage device 126 in communication with the computing device 102 for later processing.
  • the LVM calculator applies the elliptical paraboloidal model volume to an algorithm for computing the left ventricular mass (LVM).
  • the algorithm is represented by the equation:
  • LVM a ⁇ [((IV S + LVID + PW) 2 ⁇ (LVL + PW)) - [(LVID) 2 ⁇ LVL] + b ⁇ [LVID ⁇ IVS ⁇
  • a has a positive value less than one; b and c each have a positive value; and c is at least 5 times greater than b.
  • a has a value from zero to one (0 to 1 ), and in more specific embodiments has a value of 0.3.
  • c is at least 5 times greater than b.
  • a has a value of about 0.3; b has a value of about 2; and c has a value of about 21 .
  • the IVS, LVID, PW, and LVL are generally measured at the leaflet tip level in the parasternal long axis view.
  • the PW can alternatively be measured at the leaflet tip level in the best available short axis (SAX) view.
  • the computed LV mass can be applied to a classifier 124, which compares the LV mass to at least one predetermined threshold criteria.
  • the threshold criteria is based on the cardiovascular condition that is being tested for diagnosis on the subject. In the illustrative example, such condition can include the risk of hypertrophy in the tested subject, but there is no limitation made herein to the condition.
  • the classifier can apply the LV dataset to Equation (2) to compute a relative wall thickness, and compare the RWT to a predetermined threshold value.
  • the predetermined threshold value is 0.42, but this is not limited to any one value.
  • the classifier can output a decision regarding a concentric hypertrophy (RWT > 0.42) or eccentric hypertrophy (RVVT ⁇ 0.42).
  • the classifier can output a decision regarding a risk or no-risk of cardiovascular hypertrophy using the LVM.
  • the classifier can output a decision regarding the risk of other cardiovascular conditions, or a prediction value (e.g., a percent risk value) associated with a risk of the subject being diagnosed with such condition.
  • the classifier 124 generates a decision associated with the LV mass, and this decision is indicative of a risk or the presence of the condition.
  • the output module 126 transmits at least one of the paraboloidal volume, the LV mass, and or the decision to the user interface 106, which can be incorporated in the computing device 102 itself or in a remote user device.
  • the LVM can also be combined with other information to determine the cardiac risk present in a patient.
  • Such other information may include a patient history of heart failure, hypertension, diabetes, hyperlipidemia, coronary artery disease, myocardial infarction, or arrhythmias.
  • Embodiments are contemplated wherein the equations discussed herein can be stored in a single module or as multiple modules embodied in the different devices.
  • the software modules as used herein, are intended to encompass any collection or set of instructions executable by the computing device 102 or other digital system so as to configure the computer or other digital system to perform the task that is the intent of the software.
  • the computing device 102 also includes one or more communication interfaces 130, such as network interfaces, for communicating with external devices.
  • the communication interfaces 130 may include, for example, a modem, a router, a cable, and and/or Ethernet port, etc.
  • the communication interfaces 130 are configured to send or receive data, such as video and/or medical image data 132 from imaging device 104 and the LV mass 134.
  • the LV computing device 102 may include one or more special purpose or general purpose computing devices, such as a server computer or digital front end (DFE), or any other computing device capable of executing instructions for performing the exemplary algorithms.
  • FIG. 2 further illustrates the computing device 102 connected to a medical imaging device 104 for generating and/or receiving medical image or frame data 132 in electronic format.
  • the medical imaging device 104 is a two- dimensional echocardiogram.
  • the disclosure is not limited to any one imaging modality.
  • the imaging device can be an ultrasonic transducer probe; a cardiac magnetic resonance machine; an x-ray machine; or a computed tomography machine.
  • Ultrasound is a medical imaging technique that uses frequencies typically between 1 megahertz (MHz) and 18 MHz.
  • An ultrasonic transducer probe usually includes one or more piezoelectric transducers encased in a plastic housing.
  • ultrasound operates by generating sound waves from the transducer(s), then transmitting the high-frequency sound waves into the body near the heart.
  • the sound waves may be focused either by the shape of the transducer(s), a lens in front of the transducer(s), or by phased array techniques. This focusing produces arc-shaped sound waves from the face of the transducer.
  • the waves travel into the body and comes into focus at a desired depth.
  • the sound waves travel until hitting a boundary between tissues (e.g.
  • the reflected sound waves vibrate the transducer, and the transducer turns the vibrations into electrical pulses that travel to the computing device 102, where they are processed and transformed into a digital image.
  • Controls for the transducer probe allow the operator to set / change properties of the ultrasonic sound waves, such as frequency, duration, and scanning mode.
  • Cardiac magnetic resonance imaging (cardiac MRI, or CMR imaging) is a painless imaging test that uses radio waves and magnets to capture information about the heart.
  • the patient is inserted into a long, narrow tube in the MRI machine that provides a magnetic field.
  • the human body is made up of different elements, most of which are also magnetic.
  • the magnetic field of the patient's body reacts with the magnetic field to transmit a faint radio signal.
  • the MRI machine reads the radio signal and converts it to electronic data that is sent to the computing device 102 for processing.
  • a contrast dye to highlight blood flow might be used.
  • X-rays have a frequency of 30 petahertz to 30 exahertz.
  • An X-ray tube generates a beam of X-rays, which is aimed at the patient.
  • the X-rays that pass through the patient are filtered through a device called a grid or X-ray filter, to reduce scatter, and strike digital sensors that converts the signals generated into digital information, which is transmitted to the computing device 102.
  • an X-ray tube opposite an X-ray detector (or detectors) in a ring-shaped apparatus rotate around a patient, producing data that can be used to produce a computer-generated cross-sectional image (tomogram) by the computing device.
  • CT is acquired in the axial plane, with coronal and sagittal images produced by computer reconstruction.
  • Other forms of CT include positron emission tomography, single-photon emission computed tomography, and ultrasound computer tomography.
  • the system 100 includes a storage device 126 that is part of or in communication with the computing device 102.
  • the computing device 102 can be in communication with a server (not shown) that includes a processing device and memory, such as storage device 126, or has access to a storage device 126, for storing look-up tables (LUTs) that associates LV masses or thresholds with the types of hypertrophy and/or other cardiovascular conditions.
  • a server not shown
  • LUTs look-up tables
  • the system includes a user interface 106 in communication with the LV computing device 102.
  • the user interface 106 can include a computer belonging to a healthcare provider.
  • the user interface 106 can include an input device, such as a keyboard or touch or writable screen, for receiving input, and/or a cursor control device, such as a mouse, trackball, or the like, for communicating user input information and command selections.
  • the GUI can further include a display 108 for displaying information including the output regarding the LV mass and/or a diagnosis.
  • the system 100 may include one or more computing devices 102, such as a PC, such as a desktop, a healthcare oriented tablet PC, a server computer, laptop, combinations thereof, or any other computing device capable of executing the instructions for performing the exemplary algorithms.
  • the memory 114 may represent any type of non-transitory computer readable medium such as random access memory (RAM), read only memory (ROM), magnetic disk or tape, optical disk, flash memory, or holographic memory. In one embodiment, the memory 114 comprises a combination of a random access memory and read only memory. In some embodiments, the processor 112 and memory 114 may be combined in a single chip. Memory 114 stores instructions for performing the exemplary computations as well as the processed data.
  • module as used herein is intended to encompass such instructions stored in storage medium such as RAM, a hard disk, optical disk, or so forth, and is also intended to encompass so-called “firmware” that is software stored on a ROM or so forth.
  • Such software may be organized in various ways, and may include software components organized as libraries, Internet-based programs stored on a remote server or so forth, source code, interpretive code, object code, directly executable code, and so forth. It is contemplated that the software may invoke system-level code or calls to other software residing on a server (not shown) or other location to perform certain functions.
  • the various components of the computing device 102 may be all connected by a bus 136 (FIG. 2)
  • a two-dimensional echocardiographic calculation of left ventricular mass was performed on 148 patients using the standard method employing conventional equation (1 ) promulgated by the American Society of Echocardiography (ASE).
  • a CMR was also performed on the same 148 patients.
  • the estimations based from the echocardiogram produced a 95% confidence interval, compared to the gold standard CMR, with a mean difference in the range between 50 to 60 grams, and with pairwise comparison of echo and MRI in the same individual patients there was an error of 60 grams +/- 40 grams, with an overall correlation coefficient of 0.72-0.8.
  • a linear echocardiographic calculation of left ventricular mass was also performed on the 148 patients using the paraboloid model disclosed herein and employing equation (3).
  • the estimations based from the echocardiogram produced a 95% confidence interval, compared to the CMR, with a significantly reduced mean difference to within 1 gram, and with pairwise comparison of echo and MRI in the same individual patients there was an error of 25 grams +/- 20 grams, with an overall correlation coefficient of 0.74-0.87.
  • the values were plotted after arranging them in ascending order by CMR mass. Again, this figure shows the LV mass using the PMV equation (3) with these values is consistently closer to the CMR mass than the LV mass produced using ASE. Results for the validation are shown in Table B below: Table B.
  • the new equation improves the accuracy and precision of the estimated LV mass for linear echocardiography to within 1 gram in the population and within 20 grams for a given individual as determined by the gold standard CMR imaging, but with much lower cost and much greater availability. This is much more accurate than the conventional equations which model the heart as a prolate ellipsoid, shown here in the same patients to have an error of 60 grams +/- 40 grams.
  • new equation (3) represents a significant reduction in absolute error by 30% and relative error by 60%. This equation can be readily incorporated into current echocardiogram machines, interpretation software, and online calculators for clinical practice. It is also noted that for the dataset of 148 patients used to produce FIG.
  • the LV wall parameters input into the formula were imaged by multiple technologists and were manually measured by at least 3 different cardiologists. This adds validity to the practicality and reliability of using equation (3), because inter-observer and intra-observer variability is usually a major limiting factor in the accuracy of echocardiogram derived measurements.
  • Equation (3) provides for a cheaper, more practical, and reliable (between multiple observers) measurement of LV wall volume due to an improved geometric model. By multiplying LV volume with density of the cardiac tissue, the LVM can be obtained.

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Abstract

La présente invention concerne des procédés et des systèmes de mesure de la masse ventriculaire gauche en effectuant une estimation géométrique paraboloïde d'un ventricule gauche d'un sujet à l'aide des valeurs d'entrée caractérisant le ventricule gauche. Ces valeurs sont acquises à partir de modalités d'imagerie, telles qu'un échocardiogramme, un MRI cardiaque, un balayage CAT, etc. Le système émet en outre une détermination concernant le risque d'hypertrophie cardiovasculaire. Le système applique la masse ventriculaire gauche à un dispositif de classification, qui émet une décision concernant le risque, ou un diagnostic, d'hypertrophie cardiaque sur la base des résultats seuils.
PCT/US2018/054611 2017-10-06 2018-10-05 Évaluation d'imagerie médicale de la masse ventriculaire gauche WO2019071128A1 (fr)

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DEVEREUX, R. ET AL.: "Echocardiographic Determination of Left Ventricular Mass in Man. Anatomic Validation of the Method", CIRCULATION, vol. 55, no. 4, April 1977 (1977-04-01), pages 613 - 618, XP055588545, ISSN: 0009-7322, DOI: 10.1161/01.CIR.55.4.613 *

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