WO2023138690A1 - Systèmes et procédés basés sur la tomographie par impédance électrique - Google Patents

Systèmes et procédés basés sur la tomographie par impédance électrique Download PDF

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WO2023138690A1
WO2023138690A1 PCT/CN2023/073459 CN2023073459W WO2023138690A1 WO 2023138690 A1 WO2023138690 A1 WO 2023138690A1 CN 2023073459 W CN2023073459 W CN 2023073459W WO 2023138690 A1 WO2023138690 A1 WO 2023138690A1
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electric potential
subject
computer
implemented method
determining
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PCT/CN2023/073459
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English (en)
Inventor
Russell Wade CHAN
Adrien TOUBOUL
Chung San WONG
Fedi ZOUARI
Dipyaman MODAK
Pak To CHEUNG
Ho Wa LI
Pak Heng Justin CHAN
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Gense Technologies Limited
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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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30056—Liver; Hepatic

Definitions

  • the invention relates to electrical impedance tomography (EIT) based systems and methods.
  • Electrical impedance tomography is a technique that can be used for determining electrical conductivity, permittivity, and/or impedance of a body part.
  • the invention generally relates to systems and methods associated with performing of electrical impedance tomography.
  • a computer-implemented method comprising: processing electrical impedance tomography data obtained from a subject, the electrical impedance tomography data including a plurality of electric potential data sets, each electric potential data set being obtained at electrodes attached (directly or indirectly) to the subject in response to excitation signal (e.g., current) of a set frequency sequentially applied to each of the electrodes, the set frequency applied is different for different data sets and is the same of the same data set; and determining, based on the processing, whether the subject is suffering from a disease.
  • excitation signal e.g., current
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal (e.g., current) of a first frequency, a second electric potential data set associated with excitation signal (e.g., current) of a second frequency, and a third electric potential data set associated with excitation signal (e.g., current) of a third frequency.
  • a first electric potential data set associated with excitation signal e.g., current
  • a second electric potential data set associated with excitation signal e.g., current
  • a third electric potential data set associated with excitation signal e.g., current
  • the processing comprises: determining a difference between the first and second electric potential data sets to obtain a first electric potential difference data set; determining a difference between the first and third second electric potential data sets to obtain a second electric potential difference data set; applying the first and second electric potential difference data sets to a spectral unmixing model to determine parameters indicative of impact caused by tissue type i on the first and second electric potential difference data sets; and determining a value of a parameter associated with the disease based on the first and second corrected electric potential difference data sets and one or more biometric measures of the subject.
  • the first electric potential data set is used as a reference data set.
  • the reference data set may have the highest signal to noise ratio among all the data sets.
  • the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first and second electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types, ⁇ is an error term. In one example the error term is 0, in which case the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first and second electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types.
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal (e.g., current) of a first frequency, a second electric potential data set associated with excitation signal (e.g., current) of a second frequency, and a third electric potential data set associated with excitation signal (e.g., current) of a third frequency, a fourth electric potential data set associated with excitation signal (e.g., current) of a fourth frequency.
  • a first electric potential data set associated with excitation signal e.g., current
  • a second electric potential data set associated with excitation signal e.g., current
  • a third electric potential data set associated with excitation signal e.g., current
  • a fourth electric potential data set associated with excitation signal e.g., current
  • the processing comprises: determining a difference between the first and second electric potential data sets to obtain a first electric potential difference data set; determining a difference between the first and third second electric potential data sets to obtain a second electric potential difference data set; determining a difference between the first and fourth second electric potential data sets to obtain a third electric potential difference data set; applying the first, second, and third electric potential difference data sets to a spectral unmixing model to determine parameters indicative of impact caused by tissue type i on the first, second, and third electric potential difference data sets; and determining a value of a parameter associated with the disease based on the first, second, and third corrected electric potential difference data sets and one or more biometric measures of the subject.
  • the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first, second, and third electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types, ⁇ is an error term. In one example the error term is 0, in which case the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first and second electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types.
  • determining whether the subject is suffering from a disease includes comparing the determined value with a predetermined reference scale.
  • the predetermined reference scale may include predetermined values of the parameter classified according to presence or absence of the disease, and optionally, severity of the disease.
  • the first electric potential difference data set can be processed to provide a conductivity change map (e.g., average conductivity change map) of the subject.
  • the second electric potential difference data set can be processed to provide a conductivity change map (e.g., average conductivity change map) of the subject.
  • the parameter associated with the disease comprises a controlled attenuation parameter.
  • the one or more biometric measures of the subject comprises a waist circumference over height (i.e., waist circumference of the subject divided by height of the subject) measure.
  • the one or more biometric measures of the subject comprises age of the subject.
  • the one or more biometric measures of the subject comprises chest circumference of the subject.
  • the processing further comprises filtering the electric potential data sets prior to determining the differences.
  • the filtering may remove outlier (s) .
  • the computer-implemented method further comprises obtaining the electrical impedance tomography data from the subject.
  • the disease comprises a liver disease, a lung disease, a kidney disease, etc.
  • the disease comprises a fatty liver disease (e.g., nonalcoholic fatty liver disease) .
  • the computer-implemented method further comprises determining, based on the processing, a severity of the disease.
  • the computer-implemented method further comprises presenting the determination result to the user.
  • the presenting may include displaying the result to the user.
  • the result may include a “yes/no” result (as to whether the subject is suffering from a disease) and optionally a severity of the disease.
  • the subject is human being.
  • the subject is a non-human animal.
  • a system comprising: one or more processors arranged (e.g., programmed) to process electrical impedance tomography data obtained from a subject, the electrical impedance tomography data including a plurality of electric potential data sets, each electric potential data set being obtained at electrodes attached (directly or indirectly) to the subject in response to excitation signal (e.g., current) of a set frequency sequentially applied to each of the electrodes, the set frequency applied is different for different data sets and is the same of the same data set; and determine, based on the processing, whether the subject is suffering from a disease.
  • excitation signal e.g., current
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal (e.g., current) of a first frequency, a second electric potential data set associated with excitation signal (e.g., current) of a second frequency, and a third electric potential data set associated with excitation signal (e.g., current) of a third frequency.
  • a first electric potential data set associated with excitation signal e.g., current
  • a second electric potential data set associated with excitation signal e.g., current
  • a third electric potential data set associated with excitation signal e.g., current
  • the one or more processors are arranged to: determine a difference between the first and second electric potential data sets to obtain a first electric potential difference data set; determine a difference between the first and third second electric potential data sets to obtain a second electric potential difference data set; apply the first and second electric potential difference data sets to a spectral unmixing model to determine parameters indicative of impact caused by tissue type i on the first and second electric potential difference data sets; and determine a value of a parameter associated with the disease based on the first and second corrected electric potential difference data sets and one or more biometric measures of the subject.
  • the first electric potential data set is used as a reference data set.
  • the reference data set may have the highest signal to noise ratio among all the data sets.
  • the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first and second electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types, ⁇ is an error term. In one example the error term is 0, in which case the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first and second electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types.
  • the system may include a memory that stores the spectral unmixing model and is operably connected with the one or more processors.
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal (e.g., current) of a first frequency, a second electric potential data set associated with excitation signal (e.g., current) of a second frequency, and a third electric potential data set associated with excitation signal (e.g., current) of a third frequency, a fourth electric potential data set associated with excitation signal (e.g., current) of a fourth frequency.
  • a first electric potential data set associated with excitation signal e.g., current
  • a second electric potential data set associated with excitation signal e.g., current
  • a third electric potential data set associated with excitation signal e.g., current
  • a fourth electric potential data set associated with excitation signal e.g., current
  • the one or more processors are arranged to: determine a difference between the first and second electric potential data sets to obtain a first electric potential difference data set; determine a difference between the first and third second electric potential data sets to obtain a second electric potential difference data set; determine a difference between the first and fourth second electric potential data sets to obtain a third electric potential difference data set; apply the first, second, and third electric potential difference data sets to a spectral unmixing model to determine parameters indicative of impact caused by tissue type i on the first, second, and third electric potential difference data sets; and determine a value of a parameter associated with the disease based on the first, second, and third corrected electric potential difference data sets and one or more biometric measures of the subject.
  • the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first, second, and third electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types, ⁇ is an error term. In one example the error term is 0, in which case the spectral unmixing model includes where ⁇ V ( ⁇ ) is the first and second electric potential difference data sets, a i is the parameter indicative of impact caused by tissue type i, ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i, M is the total number of tissue types.
  • the system may include a memory that stores the spectral unmixing model and is operably connected with the one or more processors.
  • the one or more processors are arranged to compare the determined value with a predetermined reference scale to determine whether the subject is suffering from a disease.
  • the predetermined reference scale may include predetermined values of the parameter classified according to presence or absence of the disease, and optionally, severity of the disease.
  • the one or more processors are arranged to process the electric potential difference data sets to provide a conductivity change map (e.g., average conductivity change map) of the subject.
  • a conductivity change map e.g., average conductivity change map
  • the parameter associated with the disease comprises a controlled attenuation parameter.
  • the one or more biometric measures of the subject comprises a waist circumference over height (i.e., waist circumference of the subject divided by height of the subject) measure.
  • the one or more biometric measures of the subject comprises age of the subject.
  • the one or more biometric measures of the subject comprises chest circumference of the subject.
  • the one or more processors are arranged to filter the electric potential data sets prior to determining the differences.
  • the filtering may remove outlier (s) .
  • the disease comprises a liver disease, a lung disease, a kidney disease, etc.
  • the disease comprises a fatty liver disease (e.g., nonalcoholic fatty liver disease) .
  • the one or more processors are arranged to determine, based on the processing, a severity of the disease.
  • the system further comprises an output device, such as a display, arranged to present the determination result to the user.
  • the presenting may include displaying the result to the user.
  • the result may include a “yes/no” result (as to whether the subject is suffering from a disease) and optionally a severity of the disease.
  • the subject is human being.
  • the subject is a non-human animal.
  • a non-transitory computer-readable medium storing computer instructions that, when executed by one or more processors, causes the one or more processors to perform the method of the first aspect.
  • a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the first aspect.
  • a computer-implemented method comprising: processing electrical impedance tomography data (including kidney data) obtained from a subject to determine bio-conductivity characteristic associated with a kidney of the subject; and determining, based on the determined bio-conductivity characteristic, a health state or condition of the kidney of the subject.
  • the determining comprises determining, based on the determined bio-conductivity characteristic, whether the subject is suffering from kidney disease (e.g., chronic kidney disease) .
  • kidney disease e.g., chronic kidney disease
  • the determining further comprises classifying a stage of the kidney disease (e.g., chronic kidney disease) .
  • a stage of the kidney disease e.g., chronic kidney disease
  • the determining comprises determining, based on the determined bio-conductivity characteristic, a glomerular filtration rate score or an estimated glomerular filtration rate score of the subject.
  • the determining comprises comparing the determined bio-conductivity characteristic with predetermined mapping table/curve/graph/relationship between different bio-conductivity characteristics and their respective glomerular filtration rate score or an estimated glomerular filtration rate score.
  • the determining comprises determining, based on the determined bio-conductivity characteristic, a glomerular filtration rate or an estimated glomerular filtration rate of the subject.
  • the determining comprises comparing the determined bio-conductivity characteristic with predetermined mapping table/curve/graph/relationship between different bio-conductivity characteristics and their respective glomerular filtration rate or an estimated glomerular filtration rate.
  • the processing further comprises: filtering and/or denoising the electrical impedance tomography data.
  • the processing further comprises: reconstructing EIT images associated with the kidney of the subject, each of the EIT image being associated with a respective frequency of the excitation signal; and determining respective conductivity maps based on the EIT images.
  • the electrical impedance tomography data are electric potential data obtained from electrodes attached to the subject (e.g., the upper abdominal region of the subject) .
  • the electrical impedance tomography data (including kidney data) comprises multiple sets of electric potential data each obtained for an excitation signal of a respective frequency, and wherein the frequency for the different sets are different.
  • one of the set is a reference set
  • the processing further comprises determining respective differences between the reference set and each of the other sets, to obtain multiple sets of electric potential difference data.
  • the processing further comprises determining the bio-conductivity characteristic (e.g., conductivity changes) based on the multiple sets of electric potential difference data.
  • bio-conductivity characteristic e.g., conductivity changes
  • the subject is human.
  • a system comprising one or more processors arranged (e.g., programmed) to: process electrical impedance tomography data (including kidney data) obtained from a subject to determine bio-conductivity characteristic associated with a kidney of the subject; and determine, based on the determined bio-conductivity characteristic, a health state or condition of the kidney of the subject.
  • processors arranged (e.g., programmed) to: process electrical impedance tomography data (including kidney data) obtained from a subject to determine bio-conductivity characteristic associated with a kidney of the subject; and determine, based on the determined bio-conductivity characteristic, a health state or condition of the kidney of the subject.
  • the system may further include an output device (e.g., a display) for presenting the determination result to the user.
  • an output device e.g., a display
  • the one or more processors are arranged to determine, based on the determined bio-conductivity characteristic, whether the subject is suffering from kidney disease (e.g., chronic kidney disease) .
  • kidney disease e.g., chronic kidney disease
  • the one or more processors are arranged to classify a stage of the kidney disease (e.g., chronic kidney disease) .
  • a stage of the kidney disease e.g., chronic kidney disease
  • the one or more processors are arranged to determine, based on the determined bio-conductivity characteristic, a glomerular filtration rate score or an estimated glomerular filtration rate score of the subject.
  • the one or more processors are arranged to compare the determined bio-conductivity characteristic with predetermined mapping table/curve/graph/relationship between different bio-conductivity characteristics and their respective glomerular filtration rate score or an estimated glomerular filtration rate score.
  • the one or more processors are arranged to determine, based on the determined bio-conductivity characteristic, a glomerular filtration rate or an estimated glomerular filtration rate of the subject.
  • the one or more processors are arranged to compare the determined bio-conductivity characteristic with predetermined mapping table/curve/graph/relationship between different bio-conductivity characteristics and their respective glomerular filtration rate or an estimated glomerular filtration rate.
  • the one or more processors are arranged to filter and/or de-noise the electrical impedance tomography data.
  • the one or more processors are arranged to reconstruct EIT images associated with the kidney of the subject, each of the EIT image being associated with a respective frequency of the excitation signal; and determine respective conductivity maps based on the EIT images.
  • the electrical impedance tomography data are electric potential data obtained from electrodes attached to the subject (e.g., the upper abdominal region of the subject) .
  • the electrical impedance tomography data (including kidney data) comprises multiple sets of electric potential data each obtained for an excitation signal of a respective frequency, and wherein the frequency for the different sets are different.
  • one of the set is a reference set
  • the processing further comprises determining respective differences between the reference set and each of the other sets, to obtain multiple sets of electric potential difference data.
  • the one or more processors are arranged to determine the bio-conductivity characteristic (e.g., conductivity changes) based on the multiple sets of electric potential difference data.
  • the subject is humans.
  • a non-transitory computer-readable medium storing computer instructions that, when executed by one or more processors, causes the one or more processors to perform the method of the fifth aspect.
  • a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the fifth aspect.
  • Figure 1A is a schematic diagram illustrating the steps used to predict CAP with frequency-difference EIT in one embodiment of the invention.
  • Figure 1B is a schematic diagram illustrating the data processing pipeline for predicting CAP with frequency-difference EIT in one embodiment of the invention.
  • Figure 3 is a plot of the VCTE estimated CAP and the predicted CAP using fdEIT (blue) and fdEIT with spectral unmixing (orange) method in one embodiment of the invention.
  • Figure 4 are graphs showing average CAP values across healthy population (H) and non-healthy (NH) , as classified by Fibroscan CAP: (A) the Fibroscan values, (B) Gense CAP with classic fdEIT, (C) Gense CAP with unmixed polynomial.
  • Figure 5 is a functional block diagram of a system arranged to perform the method (at least part of it) in one embodiment of the invention.
  • Figure 6A is a schematic diagram illustrating the setup, EIT data acquisition, data processing and analysis pipeline in one embodiment of the invention.
  • Figure 6B is a schematic diagram illustrating data processing and analysis pipeline in one embodiment of the invention.
  • FIG. 7 shows, in one embodiment of the invention:
  • FIG. 8 shows, in one embodiment of the invention, portable EIT device discriminates CKD severities with EIT-predicted eGFR scores (eGFREIT) by bio-conductivity measurements:
  • Figure 9 is a functional block diagram of a system arranged to perform the method (at least part of it) in one embodiment of the invention.
  • Figure 10 is a flowchart illustrating a method in embodiments of the invention.
  • Figure 10 illustrates a method 1000 in embodiments of the invention.
  • the method 1000 is a computer-implemented method that generally includes obtaining EIT data in step 1002, processing the obtained EIT data in step 1004, and determining a result (e.g., a diagnostic result) in step 1006.
  • a result e.g., a diagnostic result
  • Nonalcoholic fatty liver disease also known as hepatic steatosis, is the apparition of fat around hepatocytes (liver cells) .
  • NAFLD is typically associated with sedentary lifestyle.
  • liver biopsy This technique, while useful, is invasive, relatively expensive, prone to sampling error, often painful, and might result in some severe complications.
  • Non-invasive techniques based on ultrasound-based devices and vibration-controlled transient elastography are also used to diagnose NAFLD. These techniques measure liver elasticity to infer hepatic steatosis in NAFLD quantified using the controlled attenuation parameter.
  • frequency sweeping i.e., multiple measurements each at a different frequency
  • frequency sweeping is applied to predict controlled attenuation parameter from cross-sectional EIT measurement across the liver, with both classic frequency difference and spectral unmixing model.
  • EIT Electrical impedance tomography
  • a small electrical current e.g., about 1 mA, or any other value, which does not affect normal physiology
  • electrodes e.g., on a belt
  • This electrical current induces an electric potential that is measured at each electrode.
  • a map of the conductivity inside the body is reconstructed.
  • R is the Kotre diagonal sensitivity matrix
  • ⁇ and p two regularization parameter
  • W x a matrix that incorporates prior information.
  • Tests are performed on a total of 11 human subjects (volunteers) including 3 females and 8 males, from 20 to 65 years old with a waist circumference from 71 cm to 110 cm.Individual clinical demographics and physical characteristics, including gender, BMI, age, waist circumference, height, weight, and liver disease history (if any) , are collected. Subjects with co-existing liver diseases are excluded.
  • the console consists of a power management module, with a current generator providing alternating current at frequencies ranging from 10KHz to 1MHz (other frequency ranges, e.g., in the order of GHz, are also envisaged) , a data acquisition module for potential difference measurement, and a control and output module for module coordination, data processing and cloud-server communication.
  • the position of the electrode belt targeted the upper abdominal region, as indicated by the bottom boundary of the ribcage.
  • the conductivity is reconstructed using a custom version of the pyEIT python library.
  • the changes in conductivity between two pairs of frequency (28.75kHz–20.00 kHz) and(28.75kHz–25.00 kHz) are computed by averaging the conductivity map of the difference over the region of interest (ROI) covering the whole liver.
  • ROI region of interest
  • Some embodiments of the invention provide nother method to extract more information from the frequency-difference curve.
  • the idea is based on that if the change in conductivity with respect to frequency is small, then the shape of the measured voltage over frequency is given by a linear combination of the shape of the conductivity changes over frequency.
  • the conductivity change over frequency can be approximated by its Taylor expansion:
  • the difference of potential can be approximated as follows
  • ⁇ V ( ⁇ ) is the electric potential difference data sets (except the reference set)
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types
  • ⁇ is an error term.
  • the ⁇ i ( ⁇ ) can be obtained by tabulated values and the coefficient can be estimated by classic linear mixed model methods (Table I) .
  • CAP is the true value of the controlled attenuation parameter
  • ⁇ F, i, j is the measurement error (i.e., due to sensor noise)
  • ⁇ R, j is the repetition error (the patient does not repeat exactly the same pattern) .
  • N F the number of frames acquired per repetition
  • N R the number of repetitions; in order to estimate the controlled attenuation parameter, the Monte-Carlo average over all the frames is used:
  • the controlled attenuation parameter is predicted by linear regression using as features the spatial average of the change in conductivity dC 28.75kHz-22kHz , dC 28.75kHz-25kHz and the biometric variable WoH (waist circumference over height) .
  • the first order and second order variation of V with respect to ⁇ are used to reconstruct the conductivity.
  • the ⁇ and ⁇ are estimated using least-squares verifying These are then used to reconstruct an image.
  • the average conductivity in the liver area dC ⁇ and dC ⁇ is computed and used as regressors.
  • This polynomial model provides a good approximation of the change in potential between 10kHz and 35kHz (MAPE of4%) .
  • Their performances in the predicted cap are similar to the simple difference, with an adjusted R-squared of0.914 with WoH ( Figures 3 and 4) .
  • the data suggest a strong correlation between the difference of conductivity across two frequency pairs and the controlled attenuation parameter measured by vibration-controlled transient elastography, in addition to the already observed correlation with the waist circumference over height (WoH) .
  • This correlation can be explained by the conductivity change with respect to the fat content in liver tissue, and is captured by EIT.
  • a shape prior is used to focus on the liver region.
  • the use of a self-administrable EIT device instead of a vibration-controlled transient elastography permits to have a more affordable measure with a real-time result, without needing the help of a trained professional for acquisition.
  • the spectral unmixing method in one embodiment provides results similar to classic fdEIT (adj. R-squared of 0.914) , confirming the validity of the approach. Due to its reasonable assumptions, this approach can be applied to other organs (e.g. kidney) . A promising direction is the use tabulated/measured frequency changes instead of a polynomial basis, that could allow to more precisely target specific organs.
  • This embodiment demonstrates that multi-spectral electrical impedance tomography can predict clinical-standard controlled attenuation parameter in patients with or without nonalcoholic fatty liver disease using waist over height (WoH) biometric as complementary information.
  • This embodiment provides a novel spectral unmixing method to estimate controlled attenuation parameter from multi-spectral EIT by matching the coefficient of a functional decomposition.
  • This spectral unmixing method can be applied for processing other EIT data for diagnosing different diseases (other than liver disease) .
  • these aspects are all independent from each other (i.e., implemented separately) .
  • two or more of these aspects are implemented at the same time.
  • FIG. 5 shows an exemplary information handling system 500 that can be used as a server or another type of information processing system in one embodiment of the invention for processing EIT data.
  • the system 500 may use for implementing at least part of the method of the present invention.
  • the information handling system 500 generally comprises suitable components necessary to receive, store, and execute appropriate computer instructions, commands, or codes.
  • the main components of the information handling system 500 are a processor 502 and a memory (storage) 504.
  • the processor 502 may be formed by one or more of: CPU, MCU, controllers, logic circuits, Raspberry Pi chip, digital signal processor (DSP) , application-specific integrated circuit (ASIC) , Field-Programmable Gate Array (FPGA) , or any other digital or analog circuitry configured to interpret and/or to execute program instructions and/or to process signals and/or information and/or data.
  • the memory 504 may include one or more volatile memory (such as RAM, DRAM, SRAM) , one or more non-volatile memory (such as ROM, PROM, EPROM, EEPROM, FRAM, MRAM, FLASH, SSD, NAND, and NVDIMM) , or any of their combinations.
  • the information handling system 500 further includes one or more input devices 506.
  • input device 506 include one or more of: keyboard, mouse, stylus, image scanner, microphone, tactile/touch input device (e.g., touch sensitive screen) , image/video input device (e.g., camera) , etc.
  • output device 508 include one or more of: display (e.g., monitor, screen, projector, etc. ) , speaker, disk drive, headphone, earphone, printer, additive manufacturing machine (e.g., 3D printer) , etc.
  • the display may include a LCD display, a LED/OLED display, or any other suitable display that may or may not be touch sensitive.
  • the information handling system 500 may further include one or more disk drives 512 which may encompass one or more of: solid state drive, hard disk drive, optical drive, flash drive, magnetic tape drive, etc.
  • a suitable operating system may be installed in the information handling system 500, e.g., on the disk drive 512 or in the memory 504.
  • the memory 504 and the disk drive 512 may be operated by the processor 502.
  • the information handling system 500 also includes a communication device 510 for establishing one or more communication links (not shown) with one or more other computing devices such as servers, personal computers, terminals, tablets, phones, watches, IoT devices, or other wireless or handheld computing devices.
  • the communication device 510 may include one or more of: a modem, a Network Interface Card (NIC) , an integrated network interface, a NFC transceiver, a ZigBee transceiver, a Wi-Fi transceiver, a transceiver, a radio frequency transceiver, an optical port, an infrared port, a USB connection, or other wired or wireless communication interfaces.
  • Transceiver may be implemented by one or more devices (integrated transmitter (s) and receiver (s) , separate transmitter (s) and receiver (s) , etc. ) .
  • the communication link (s) may be wired or wireless for communicating commands, instructions, information and/or data.
  • the processor 502, the memory 504, and optionally the input device (s) 506, the output device (s) 508, the communication device 510 and the disk drives 512 are connected with each other through a bus, aPeripheral Component Interconnect (PCI) such as PCI Express, a Universal Serial Bus(USB) , an optical bus, or other like bus structure.
  • PCI Peripheral Component Interconnect
  • USB Universal Serial Bus
  • some of these components may be connected through a network such as the Internet or a cloud computing network.
  • a person skilled in the art would appreciate that the information handling system 500 shown in Figure 5 is merely exemplary and that the information handling system 500 can in other embodiments have different configurations (e.g., additional components, fewer components, etc. ) .
  • the embodiments described with reference to the Figures can be implemented as an application programming interface (API) or as a series of libraries for use by a developer or can be included within another software application, such as a terminal or computer operating system or a portable computing device operating system.
  • API application programming interface
  • program modules include routines, programs, objects, components and data files assisting in the performance of particular functions, the skilled person will understand that the functionality of the software application may be distributed across a number of routines, objects and/or components to achieve the same functionality desired herein.
  • computing system any appropriate computing system architecture may be utilized. This will include stand-alone computers, network computers, dedicated or non-dedicated hardware devices. Where the terms “computing system” and “computing device” are used, these terms are intended to include (but not limited to) any appropriate arrangement of computer or information processing hardware capable of implementing the function described.
  • group source separation may be used to improve data processing accuracy and/or efficiency.
  • group source separation utilizes every possible pair of frequency-difference data. For group source separation, grouping all the combinations of fdEIT ideally maximize the signal difference between fat and liver tissue, thus highlighted properties can be unmixed to produce a feature correlating to fat tissue.
  • Concept 1 generally provides, among other things:
  • a computer-implemented method comprising:
  • the electrical impedance tomography data including a plurality of electric potential data sets, each electric potential data set being obtained at electrodes attached to the subject in response to excitation signal of a set frequency sequentially applied to each of the electrodes, the set frequency applied is different for different data sets and is the same of the same data set;
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal of a first frequency, a second electric potential data set associated with excitation signal of a second frequency, and a third electric potential data set associated with excitation signal of a third frequency;
  • processing comprises:
  • determining a value of a parameter associated with the disease based on the first and second corrected electric potential difference data sets and one or more biometric measures of the subject.
  • ⁇ V ( ⁇ ) is the first and second electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types
  • ⁇ is an error term.
  • ⁇ V ( ⁇ ) is the first and second electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types.
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal of a first frequency, a second electric potential data set associated with excitation signal of a second frequency, and a third electric potential data set associated with excitation signal of a third frequency, a fourth electric potential data set associated with excitation signal of a fourth frequency;
  • processing comprises:
  • determining a value of a parameter associated with the disease based on the first, second, and third corrected electric potential difference data sets and one or more biometric measures of the subject.
  • ⁇ V ( ⁇ ) is the first, second, and third electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types
  • ⁇ is an error term.
  • ⁇ V ( ⁇ ) is the first and second electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types.
  • a system comprising:
  • processors arranged (e.g., programmed) to
  • electrical impedance tomography data obtained from a subject, the electrical impedance tomography data including a plurality of electric potential data sets, each electric potential data set being obtained at electrodes attached to the subject in response to excitation signal of a set frequency sequentially applied to each of the electrodes, the set frequency applied is different for different data sets and is the same of the same data set;
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal of a first frequency, a second electric potential data set associated with excitation signal of a second frequency, and a third electric potential data set associated with excitation signal of a third frequency;
  • processors are arranged to:
  • ⁇ V ( ⁇ ) is the first and second electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types
  • ⁇ is an error term.
  • ⁇ V ( ⁇ ) is the first and second electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types.
  • the plurality of electric potential data sets comprises, at least, a first electric potential data set associated with excitation signal of a first frequency, a second electric potential data set associated with excitation signal of a second frequency, and a third electric potential data set associated with excitation signal of a third frequency, a fourth electric potential data set associated with excitation signal of a fourth frequency;
  • processors are arranged to:
  • ⁇ V ( ⁇ ) is the first, second, and third electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types
  • ⁇ is an error term.
  • ⁇ V ( ⁇ ) is the first and second electric potential difference data sets
  • a i is the parameter indicative of impact caused by tissue type i
  • ⁇ i ( ⁇ ) is predetermined spectrum specific to tissue type i
  • M is the total number of tissue types.
  • the one or more processors are arranged to determine, based on the processing, a severity of the disease.
  • Kidney disease such as chronic kidney disease (CKD) is an escalating health problem in the global and local perspective.
  • CKD is defined as all renal abnormalities affecting kidney functions and structures which last for more than three months.
  • CKD could further progress and comorbid with hypertension, diabetes, and cardiovascular disease.
  • Conventional evaluation of CKD relies on measurement of glomerular filtration rate (GFR) in urine sample or quantifying estimated GFR (eGFR) in blood serum samples.
  • GFR glomerular filtration rate
  • eGFR estimated GFR
  • the universal method of classifying chronic kidney disease (CKD) by evaluating the eGFR calculate by the standardized serum creatinine level from the collect blood samples.
  • this mildly invasive method is susceptible to overestimation in early CKD stages which often biases by gender and muscle mass.
  • the inventors of some embodiments of the invention have realized that a non-invasive means for both early CKD detection and longitudinal CKD monitoring is eagerly awaited.
  • kidney disease progress reduced blood perfusion and restricted fluid diffusion can be observed, which resulting clinically manifests as chronic kidney disease with decreased eGFR.
  • eGFR eGFR
  • These reported renal function deteriorations could associate kidney tissue changes that lead to fundamental dielectric parameters changes such as bio-conductivity.
  • EIS electrode impedance spectroscopy
  • Some embodiments of the invention use electrical impedance tomography to assess the bio-conductivity characteristics across different CKD stages.
  • EIT Electrical Impedance Tomography
  • Conductivity of biological tissues varies according to tissue types and frequencies of applied AC current. For instance, fat tissue conductivity is known to be substantially stable across the EIT current injection frequency spectrum whereas conductivity of liver tissue significantly increases over such frequency spectrum. Hence, the fatty liver content could be characterized with frequency-difference EIT (fdEIT) .
  • fdEIT frequency-difference EIT
  • Kidney fibrosis a characteristic histopathological change in CKD, is presented as replacement of normal kidney tissue with matrices and fibrous substances which could lead to reduced electrical conductivity. Therefore, it is postulated that variation in bio-conductivity changes captured by fdEIT could reflect the pathological changes across kidney disease progression.
  • Some embodiments of the invention use EIT to determine health condition of a kidney of a subject (e.g., humans, animals) . Some embodiments of the invention classify CKD related bio-conductivity characteristics.
  • Figure 6A illustrates a setup, EIT data acquisition, data processing and analysis pipeline in one embodiment of the invention. Details of Figure 6A is described below.
  • Figure 6B shows data processing and analysis pipeline in one embodiment of the invention.
  • Figure 6B is a eGFR prediction pipeline including the group source separation.
  • a portable EIT system consist of five main modules: power management module to provide constant power supply to all other electronic modules through the power socket or the Li-ion battery, current generation module to generate AC of 1 mApp and a voltage amplitude of 1 Vpp, a signal distribution and readout module to introduce the generated current to the subject via the electrodes (e.g., 16-electrode belt) using a set of CMOS multiplexers, a data acquisition module is the analog front-end that acquires, measures, amplifies the differential voltage from the electrodes, and a control and output module consists of an analog-to-digital converter (ADC) , an MCU for programing device outputs matching the desired measurement paradigm.
  • ADC analog-to-digital converter
  • ex-vivo EIT is first performed on fresh pig kidneys. EIT measurements are performed with saline water phantom (0.9%physiologic saline solution) with 16 silver electrodes. Current stimulation is induced at multiple frequencies ranging from 14kHz to 200 kHz. EIT measurement is performed with empty water phantom with 33 frames per seconds (fps) to verify the functionality of the portable EIT system. Then, EIT measurements are repeated with putting fresh pig kidneys into water phantom.
  • fps frames per seconds
  • EIT measurements For in-vivo EIT measurements, 98 subjects including both healthy volunteers and CKD patients are enrolled. CKD stages are classified with the extracted eGFR scores from their blood serum samples [Stage 1 CKD (eGFR>90) ; Stage 2 CKD (eGFR: 61-90); Stage 3 CKD (eGFR: 31-60) ; Stage 4 CKD (eGFR: 15-30) and Stage 5 CKD (eGFR ⁇ 15) ] . EIT measurements are performed at 33 fps with two current injection frequencies (33.6 kHz and 100 kHz) using a portable EIT system and customized electrode belt consists of sixteen equally spaced gel-electrodes. The electrode belt is circumferentially positioned on the upper abdominal region. The contactless of gel-electrodes is then manually checked before starting the EIT measurement, indicating by the low electrode-skin contact impedance. Subjects are asked to stay still and breathe normally throughout the EIT measurements.
  • an alternating current is injected sequentially between all adjacent electrode pairs and the potential differences are measured across other 13 adjacent electrode pairs.
  • a data frame consisting of 208 (16 ⁇ 13) differential voltage measurements is acquired at a rate of 33 frames per second.
  • Individual data frames are denoised by setting outlier voltage values above and below system thresholds to the corresponding value in the reference data frame.
  • Mean conductivity map is computed for each set of time-series images at each frequency. Frequency differencing is then performed by subtracting the conductivity maps at 33.6 kHz and 100 kHz. Kidney related conductivity values are then extracted from region of interest around kidney regions near bottom one-third of the reconstructed frequency differencing conductivity map. Conductivity changes between frequencies are computed and further regressed with individual biometrics. Predict eGFR values are further computed from the measured conductivity changes and compared with the standard eGFR scores to evaluate its robustness on classifying CKD stages.
  • Group source separation may be used to isolate signals from different internal tissues out of the reconstructed images of each individual. Frequency differences conductivity images at all contrast frequencies are used for the group source separation. Healthy subjects’ data are used as reference group to assist in isolating the signals because the electrical responses from healthy subjects are more consistent in comparison to including all subjects from a variety of CKD stages.
  • the source of the kidney signal is determined from the group separation result. From this group result, the individual kidney source is extracted. After the group source separation, the signal from the kidneys is the strongest amongst all other signals in the extracted kidney image component.
  • the region of interest (ROI) i.e., the kidneys, is then extracted from the individual source. After the kidney signal and the ROIs are extracted, EIT related features are generated, including but not limited to the mean conductivity within the ROIs, mean conductivity outside the ROIs, and the ratio between them. The EIT related features can then be further processed, based on the above, to perform eGFR prediction (and optionally stage classification) .
  • ex-vivo EIT is first performed on fresh pig kidneys immersed in a water phantom as shown in Figure 7A.
  • the reconstructed images with empty water phantom reference showed the kidney contour (Figure 7B) , demonstrating the capability of portable EIT device to detect and localize the pig kidney inside physiologic saline medium.
  • frequency differencing conductivity maps are computed at multiple frequencies 10 kHz EIT images ( Figure 7C) .
  • the measured conductivity of the pig kidney is increased with increasing current simulation frequency, demonstrating the increased conductivity of kidney tissue over frequency change.
  • the fdEIT images suggest the optimal stimulation frequencies for kidney imaging range from 28 kHz to 100 kHz. This result verified the feasibility of applying fdEIT in-vivo to measure regional bio-conductivity characteristics on human subjects.
  • the detected mean bio-conductivity changes are-3.9, -67.5, -133.5, -212.6, and-294.7 (a.u. ) in patients with CKD Stage 1, 2, 3, 4 and 5, respectively ( Figure 7A) .
  • the trend analysis shows that patients with more advanced stages of CKD show significant reduction in bio-conductivity.
  • the results highlight the potential of portable EIT device on predicting the eGFR scores and potentially beneficial to early screening or chronic treatment monitoring of CKD.
  • This embodiment discloses a non-invasive approach for CKD evaluation by examining the bio-conductivity characteristics. It has been found that subjects with later CKD stages showed lower eGFR and significant greater conductivity decrease. This aligned with the previous findings reporting reduced renal blood perfusion, restricted fluid diffusion, presence of fibrotic tubules and tubular atrophy during kidney disease progression. These renal functional deterioration and kidney structural changes could lead to the observed conductivity decreases.
  • This embodiment illustrates the bio-conductivity characteristics of different CKD stages with portable frequency differencing EIT device.
  • a significant correlation between EIT-predicted eGFR by captured bio-conductivity changes and standard eGFR are shown.
  • Such renal function assessments with portable EIT device demonstrated the potential to ameliorate the detection and classification of CKD into a portable, accessible, self-administrable home-based process.
  • Figure 9 shows an exemplary information handling system 900 that can be used as a server or another type of information processing system in one embodiment of the invention for processing EIT data.
  • the system 900 may use for implementing at least part of the method of the present invention.
  • the information handling system 900 generally comprises suitable components necessary to receive, store, and execute appropriate computer instructions, commands, or codes.
  • the main components of the information handling system 900 are a processor 902 and a memory (storage) 904.
  • the processor 902 may be formed by one or more of: CPU, MCU, controllers, logic circuits, Raspberry Pi chip, digital signal processor (DSP) , application-specific integrated circuit (ASIC) , Field-Programmable Gate Array (FPGA) , or any other digital or analog circuitry configured to interpret and/or to execute program instructions and/or to process signals and/or information and/or data.
  • the memory 904 may include one or more volatile memory (such as RAM, DRAM, SRAM) , one or more non-volatile memory (such as ROM, PROM, EPROM, EEPROM, FRAM, MRAM, FLASH, SSD, NAND, and NVDIMM) , or any of their combinations.
  • the information handling system 900 further includes one or more input devices 906.
  • input device 906 include one or more of: keyboard, mouse, stylus, image scanner, microphone, tactile/touch input device (e.g., touch sensitive screen) , image/video input device (e.g., camera) , etc.
  • output device 908 include one or more of: display (e.g., monitor, screen, projector, etc. ) , speaker, disk drive, headphone, earphone, printer, additive manufacturing machine (e.g., 3D printer) , etc.
  • the display may include a LCD display, a LED/OLED display, or any other suitable display that may or may not be touch sensitive.
  • the information handling system 900 may further include one or more disk drives 912 which may encompass one or more of: solid state drive, hard disk drive, optical drive, flash drive, magnetic tape drive, etc.
  • a suitable operating system may be installed in the information handling system 900, e.g., on the disk drive 912 or in the memory 904.
  • the memory 904 and the disk drive 912 may be operated by the processor 902.
  • the information handling system 900 also includes a communication device 910 for establishing one or more communication links (not shown) with one or more other computing devices such as servers, personal computers, terminals, tablets, phones, watches, IoT devices, or other wireless or handheld computing devices.
  • the communication device 910 may include one or more of: a modem, a Network Interface Card (NIC) , an integrated network interface, a NFC transceiver, a ZigBee transceiver, a Wi-Fi transceiver, a transceiver, a radio frequency transceiver, an optical port, an infrared port, a USB connection, or other wired or wireless communication interfaces.
  • Transceiver may be implemented by one or more devices (integrated transmitter (s) and receiver (s) , separate transmitter (s) and receiver (s) , etc. ) .
  • the communication link (s) may be wired or wireless for communicating commands, instructions, information and/or data.
  • the processor 902, the memory 904, and optionally the input device (s) 906, the output device (s) 908, the communication device 910 and the disk drives 912 are connected with each other through a bus, aPeripheral Component Interconnect (PCI) such as PCI Express, a Universal Serial Bus (USB) , an optical bus, or other like bus structure.
  • PCI Peripheral Component Interconnect
  • USB Universal Serial Bus
  • some of these components may be connected through a network such as the Internet or a cloud computing network.
  • a person skilled in the art would appreciate that the information handling system 900 shown in Figure 9 is merely exemplary and that the information handling system 900 can in other embodiments have different configurations (e.g., additional components, fewer components, etc. ) .
  • the embodiments described with reference to the Figures can be implemented as an application programming interface (API) or as a series of libraries for use by a developer or can be included within another software application, such as a terminal or computer operating system or a portable computing device operating system.
  • API application programming interface
  • program modules include routines, programs, objects, components and data files assisting in the performance of particular functions, the skilled person will understand that the functionality of the software application may be distributed across a number of routines, objects and/or components to achieve the same functionality desired herein.
  • computing system any appropriate computing system architecture may be utilized. This will include stand-alone computers, network computers, dedicated or non-dedicated hardware devices. Where the terms “computing system” and “computing device” are used, these terms are intended to include (but not limited to) any appropriate arrangement of computer or information processing hardware capable of implementing the function described.
  • a computer-implemented method comprising:
  • determining comprises determining, based on the determined bio-conductivity characteristic, whether the subject is suffering from kidney disease (e.g., chronic kidney disease) .
  • kidney disease e.g., chronic kidney disease
  • processing further comprises: filtering and/or denoising the electrical impedance tomography data.
  • the electrical impedance tomography data (including kidney data) comprises multiple sets of electric potential data each obtained for an excitation signal of a respective frequency, and wherein the frequency for the different sets are different.
  • a system comprising:
  • processors arranged (e.g., programmed) to:
  • the electrical impedance tomography data (including kidney data) comprises multiple sets of electric potential data each obtained for an excitation signal of a respective frequency, and wherein the frequency for the different sets are different.

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Abstract

L'invention concerne un procédé mis en œuvre par ordinateur associé à la réalisation d'une tomographie par impédance électrique. Le procédé peut généralement consister à traiter des données de tomographie par impédance électrique obtenues à partir d'un sujet et à déterminer, sur la base du traitement, (i) si le sujet est atteint d'une maladie et/ou (ii) un état pathologique ou une pathologie d'un organe ou d'une partie corporelle du sujet.
PCT/CN2023/073459 2022-01-24 2023-01-24 Systèmes et procédés basés sur la tomographie par impédance électrique WO2023138690A1 (fr)

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