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US-12618967-B2 - Radar-based cross-sectional image reconstruction of subject

US12618967B2US 12618967 B2US12618967 B2US 12618967B2US-12618967-B2

Abstract

One or more aspects of this disclosure relate to the usage of an impulse radio ultra-wideband (IR-UWB) radar to reconstruct a cross-sectional image of subject in a noninvasive fashion. This image is reconstructed based on the pre- and post-processing of recorded waveforms that are collected by the IR-UWB radar, after getting reflected-off the subject. Furthermore, a novel process is proposed to approximate the different tissues' dielectric constants and, accordingly, reconstruct a subject's cross-sectional image.

Inventors

  • Raghed El Bardan
  • Albert Di Rienzo
  • Dhaval Malaviya

Assignees

  • ONE HEALTH GROUP, INC.

Dates

Publication Date
20260505
Application Date
20240607

Claims (8)

  1. 1 . A process comprising: placing, at a first location of a subject, a housing comprising a radar transceiver; generating waveforms; transmitting, based on the placing at the first location and based on the generating the waveforms and via a transmit antenna of the radar transceiver, the waveforms; receiving as signals, via a receive antenna, reflections of the waveforms; processing the signals by: generating a time-delayed copy of the received signals; autocorrelating the received signals with the time-delayed copy of the received signals; determining, based on the autocorrelated signals, a k-point moving average; and blocking a direct current (DC) component by subtracting the k-point moving average from each signal of the autocorrelated signals; storing, in a storage, the autocorrelated signals with the blocked DC component; placing, at a second location of the subject, the housing; and repeating the transmitting, receiving, processing, and storing steps for signals received at the second location.
  2. 2 . The process of claim 1 , wherein the k-point moving average is applied to remove outliers and short-term fluctuations.
  3. 3 . The process of claim 1 , wherein subtracting the k-point moving average from each received signal removes clutter and static objects.
  4. 4 . An apparatus comprising: a radar transceiver configured to generate waveforms; a transmit antenna connected to the radar transceiver, wherein the transmit antenna is configured to transmit the generated waveforms; a receive antenna; a memory storing instructions; one or more processors connected to the receive antenna and configured to, when executing the instructions, cause the apparatus to: transmit, at a first time period, the generated waveforms; receive as first signals, via the receive antenna, first reflections of the waveforms; process the first signals by causing the apparatus to: generate a first time-delayed copy of the received first signals; autocorrelate the received first signals with the first time-delayed copy of the received first signals; determine, based on the first autocorrelated signals, a first k-point moving average; and block a first direct current (DC) component by subtracting the first k-point moving average from each signal of the first autocorrelated signals; store, in a storage, the first autocorrelated signals with the blocked DC component; transmit, at a second time period, the generated waveforms; receive as second signals, via the receive antenna, second reflections of the waveforms; process the second signals by causing the apparatus to: generate a second time-delayed copy of the received second signals; autocorrelate the received second signals with the second time-delayed copy of the received second signals; determine, based on the second autocorrelated signals, a second k-point moving average; and block a second direct current (DC) component by subtracting the second k-point moving average from each signal of the second autocorrelated signals; and store, in the storage, the second autocorrelated signals with the blocked DC component.
  5. 5 . The apparatus of claim 4 , wherein the first k-point moving average is applied to remove outliers and short-term fluctuations.
  6. 6 . The apparatus of claim 4 , wherein subtracting the first k-point moving average from each received first signal removes clutter and static objects.
  7. 7 . A process comprising: receiving, via a receive antenna positioned at a first location on a subject and as first signals, first reflections of waveforms; processing the first signals by: sampling the first signals as M signals in N sampling time units, wherein the N sampling time units represent N-elements in a received waveform b_i, where ∀i∈{1, 2, . . . , M}; for each M signal, determining distances at which the N-elements are sampled; and determining a reflection coefficient, Γ_(i,j), of each of the N-elements at a j-th medium boundary between mediums; determining, for each medium, a dielectric constant of the medium ∀j using a vector network analyzer; and constructing an M×N matrix, E, of the determined dielectric constants; receiving, via the receive antenna positioned at a second location on a subject and as second signals, second reflections of the waveforms; and repeating the processing for the second signals.
  8. 8 . An apparatus comprising: a radar transceiver configured to generate waveforms; a transmit antenna connected to the radar transceiver, wherein the transmit antenna is configured to transmit the generated waveforms; a receive antenna; a memory storing instructions; one or more processors connected to the receive antenna and configured to, when executing the instructions, cause the apparatus to: receive, at a first time period via the receive antenna as first signals, first reflections of the generated waveforms; process the first signals by causing the apparatus to: sample the first signals as M signals in N sampling time units, wherein the N sampling time units represent N-elements in a received waveform b_i, where ∀i∈{1, 2, . . . , M}; for each M signal, determine distances at which the N-elements are sampled; and determine a reflection coefficient, Γ_(i,j), of each of the N-elements at a j-th medium boundary between mediums; determine, for each medium, a dielectric constant of the medium ∀j using a vector network analyzer; and construct an M×N matrix, E, of the determined dielectric constants; receive, at a second time period via the receive antenna as second signals, second reflections of the generated waveforms; and repeating the process for the second signals.

Description

RELATED APPLICATION This application claims priority to U. S. application Ser. No. 17/576,151, filed Jan. 14, 2022, now U.S. Pat. No. 12,038,850, which claims priority to U.S. application Ser. No. 16/410,474, filed May 13, 2019, now U.S. Pat. No. 11,226,411, which claims priority to U.S. application No. 62/670,396, filed May 11, 2018. The contents of these applications are expressly incorporated herein by reference in their entirety for all purposes. BACKGROUND In 2002, the Federal Communications Commission (FCC) authorized the unlicensed use of ultra-wideband (UWB) technology in the frequency range from 3.1 to 10.6 GHz (ET Docket 98-153, First Report and Order 02-48), using an adequate wideband signal format with a low equivalent isotropically radiated power (EIRP) level ($−41.3$ dBm/MHz). Since then, UWB technology has attracted growing interest across many different verticals and fields, e.g., wireless communications and a diverse set of radar sensor applications. UWB systems can be categorized into two classes: i) multi-band orthogonal frequency division multiplexing (MB-OFDM) UWB, and ii) impulse radio UWB (IR-UWB). The former class is primarily used for applications that support exceedingly high data rates such as video streaming, and is beyond the scope of this work. However, this class is not compliant with energy-constrained applications, given that high performance electronics are required to operate an MB-OFDM radio. On the other hand, IR-UWB may be purposed to accommodate low-power consumption and low-complexity. Furthermore, an IR-UWB radar is characterized by: i) higher penetration capabilities, ii) robustness to interference and multipath, and iii) high precision ranging. The aforementioned characteristics of the latter class have motivated both the research community and the industry to explore using IR-UWB radars in energy-constrained, short-range wireless health applications. Some have investigated the use of UWB microwave imaging for the detection of several diseases inside a human body. Others have studied how a Ground-Penetrating Radar (GPR) can help in modeling and evaluating the dielectric constants of different geologic materials. Yet others have proposed wearable microwave head imaging for stroke and cancer detection, as well as a compact and lightweight radio-frequency (RF) switching system for the first, respectively. However, those approaches focused on evaluating the antennae performance in their work. Furthermore, others have evaluated the fundamental performance of antennae positioning for microwave imaging applications. Nevertheless, those approaches primarily focused on measuring the total field on the S21 port and controlling the robotic arm using a camera. Further, some have used GPRs to calculate the dielectric constants of various asphalts from time intervals and amplitudes while others have modeled dielectric constant values of geologic materials to aid GPRs in data collection and interpretation. However, these dielectric constant modeling approaches are not feasible when used to model tissues, bones, organs, and fluids given their diversity. SUMMARY Systems and methods are disclosed in which a cross-section of a subject may be modeled. Radar may be used to reconstruct a cross-sectional image of a subject in a noninvasive fashion. The systems and method may be used to detect early signs of an illness or a disease, and preventing potential health risks that are tightly coupled with inferences drawn from such images. The images may be reconstructed following an estimation process in which the different dielectric constants that constitute the subject are approximated. One or more processes that operates on IR-UWB radar (or any radar in general) signals reflected off the subject at different depths may be used. One or more images may be reconstructed based on the pre- and post-processing of recorded waveforms that are collected by a radar (e.g., a IR-UWB radar), after being reflected by structures at different depths. Further, processes are described to approximate the dielectric constants of different tissues, organs, bones, and fluids. The inferences drawn from this estimation process provide the information to reconstruct the subject's cross-sectional image. One or more arrays of dielectric constants may be obtained and sorted by proximity to the radar. A clustering method may be applied to identify the different parts of the torso or other body structure and reconstruct the image. The resulting mapping may be in grayscale or color. One or more aspects of the disclosed process may be implemented in hardware devices, or in a general purpose computer programmed with instructions based on the described process. Additional aspects, configurations, embodiments and examples are described in more detail below. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS Certain specific configurations of a modeling system and components thereof, are described below with reference to