WO2022182603A1 - Transformation de radon et codes à barres de patient à homologie persistante - Google Patents

Transformation de radon et codes à barres de patient à homologie persistante Download PDF

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Publication number
WO2022182603A1
WO2022182603A1 PCT/US2022/017132 US2022017132W WO2022182603A1 WO 2022182603 A1 WO2022182603 A1 WO 2022182603A1 US 2022017132 W US2022017132 W US 2022017132W WO 2022182603 A1 WO2022182603 A1 WO 2022182603A1
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patient
code
barcode
strain
persistence
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PCT/US2022/017132
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Partho SENGUPTA
Naveena YANAMALA
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West Virginia University Board of Governors on behalf of West Virginia University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/50—Maintenance of biometric data or enrolment thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

  • a method comprises receiving imaging data associated with a patient; generating a barcode or persistence image from the imaging data; extracting feature information regarding the patient from the barcode or persistence image utilizing machine learning; and generating a patient code from the extracted feature information, wherein the feature information is retrievable from the patient code.
  • the patient code can be a QR-Code or other visual code.
  • the patient code or QR-Code can include clinical information associated with the patient.
  • the imaging data can comprise point cloud data used to generate the persistence image.
  • the persistence image can be generated using persistent homology (PH).
  • the PH can analyze the point cloud data and a sublevel set.
  • the point cloud data can be generated from a scan of the patient.
  • the scan can be an ultrasound image.
  • the barcode can be generated from a scan of the patient using a Radon transform.
  • the barcode can be based upon information produced using discrete cosine transform (DCT) analysis of content generated by the Radon transform.
  • the machine learning can comprise supervised machine learning.
  • the method can comprise storing the QR-Code in a database.
  • the method can comprise transmitting the QR-Code to a remotely located device.
  • a system comprises processing circuitry comprising a processor and memory; and a code generation application executable by the processing circuitry, where execution of the code generation application causes the processing circuitry to: generate a barcode or persistence image from imaging data associated with a patient; extract feature information regarding the patient from the barcode or persistence image utilizing machine learning; and generate a patient code from the extracted feature information, wherein the feature information is retrievable from the patient code.
  • the patient code can be a QR-code or other visual code.
  • the patient code can include clinical information associated with the patient.
  • the persistence image can be generated using persistent homology (PH).
  • the imaging data can comprise point cloud data used to generate the persistence image.
  • the barcode can be generated from a scan of the patient using a Radon transform.
  • the code generation application can cause the processing circuitry to store the patient code in a database.
  • the patient code can be transmitted to a remotely located device for storage.
  • FIG. 1 illustrates an example of a persistent homology (PH) workflow, in accordance with various embodiments of the present disclosure.
  • FIG. 2 illustrates an example of a Radon transform workflow, in accordance with various embodiments of the present disclosure.
  • FIG. 3 illustrates an example of a code generation workflow utilizing PH and Radon transform, in accordance with various embodiments of the present disclosure.
  • FIGS. 4A-4C illustrate aspects of a proof of concept of the Radon transform and PH workflow of FIG. 3, in accordance with various embodiments of the present disclosure.
  • FIGS. 4D-4K illustrate aspects of a use-case scenario of the PH workflow, in accordance with various embodiments of the present disclosure.
  • FIG. 5 is a schematic block diagram illustrating an example of a system employed for Radon transform and PH workflow for code generation, in accordance with various embodiments of the present disclosure.
  • Disclosed herein are various examples related to patient codes which may be utilized to store and retrieve data.
  • the disclosed techniques offer the ability to compress data while allowing recovery of the information.
  • the techniques can utilize persistent homology and/or Radon transform to obtain condensed information about the shape of data.
  • Informative features may be discarded when extracting only global longitudinal strain from speckle tracking echocardiography (STE) strain tracings.
  • a workflow pipeline is proposed to compute persistent homology (PH) of information such as, e.g., STE myocardial deformation curves as a dimension reduction technique and the store this information within a QR-Code.
  • PH is a method of topological data analysis (TDA) that captures information regarding the global and local “shape” of the data being analyzed.
  • TDA topological data analysis
  • the workflow provides a way to simplify the spatially and temporally complex strain curves and store only meaningful features in the QR-Code. This allows for recognition of deformation patterns associated with various pathological conditions. Further, storing this information within a QR-Code can reduce the data intensive footprint of STE, while allowing for its seamless implementation within electronic medical record systems.
  • the use of persistent homology can address the issue of poor reproducibility of segmental strain values, by instead emphasizing the shape of the curve in the data analysis.
  • This workflow can also be used to encode other disease patterns within patient individualized QR-Codes. Examples of applications include, but are not limited to, storage of large amounts of data in a compressed format (which can be fed into machine learning pipelines), communication and/or transmission of information (intra- or extra- patient or organization), patient phenotyping, grouping, risk stratification and therapy planning, and biometric evaluation and assessment.
  • the feasibility is demonstrated using a STE strain dataset of chronic constrictive pericarditis, restrictive cardiomyopathy, and control patients. Although the technology is demonstrated with STE data, this pipeline can also be applied to other aspects of clinical datasets. This technique can be utilized to discriminate the rare disease of constrictive pericarditis from restrictive cardiomyopathy and normal patients using echocardiography image analysis.
  • STE Speckle tracking echocardiography quantifies regional cardiac function by monitoring myocardial deformation. Over the past two decades, strain imaging has gained in prevalence and shown great clinical utility. STE measures deformation in the longitudinal, circumferential, and radial axes for LV segments. This can be visualized as multiple regional strain and strain rate traces throughout the cardiac cycle.
  • GLS Global longitudinal strain
  • LV left ventricular
  • EF LV ejection fraction
  • Persistent homology is a topological data analysis tool that describes the shape of data by extracting its topological invariants.
  • the mathematical basis of PH is shown in “Computing persistent homology” by Zomorodian and Carlsson ( Discrete & Computational Geometry, 33(2), 249-274, 2005) and “Topological persistence and simplification” by Edelsbrunner et al. ( IEEE Proceedings 41st Annual Symposium on Foundations of Computer Science, pp. 454-463, 2000).
  • the pipelines for computing persistent homology and its application with machine learning are outlined in “Persistent-Homology-based Machine Learning and its Applications-A Survey” by Pun et al. (2018) and “A roadmap for the computation of persistent homology” by Otter et al. (EPJ Data Science, 6(1), 17, 2017).
  • a point cloud data set e.g., an echo strain point cloud
  • preprocessing can be performed at 104.
  • the initial step of PH is a filtration at 106 to create a series of simplicial complexes for a scale e.
  • a filtration process 106 for a point cloud data set (within a finite metric space) where a sphere of radius e is drawn around every point. At each intersection of two spheres, an edge can be drawn between the two points.
  • the filtering of the data builds a simplicial complex space, from which PH quantifies the presence of n- dimensional holes (i.e.
  • 0-dimensional holes are connected components, 1-dimensional holes are circles/loops/tunnels, and 2-dimensional holes are voids).
  • the main principle of PH is to progress through all possible values e (0 ⁇ e ⁇ ) to determine how the homology of these components change. For each structure (n-dimensional whole), the times of the birth (at what e it appears) and death (at what e it disappears) are computed.
  • the mode of filtration differs in the three different TDA based techniques applied in this pipeline: PH of point cloud data, sublevel set PH and phase space reconstructed point cloud of LV regions.
  • alpha filtration of point cloud data is used because it will be faster for the data being analyzed, which is low dimensional (3 dimensions) and in a Euclidian space.
  • the general intuition explained previously holds for the alpha complex is homotopy equivalent to the Cech complex [“Topological Data Analysis” by Zomorodian ( Advances in applied and computational topology, 70, 1-39, 2012)].
  • the alpha complex is a subcomplex of the Delunay complex, which is formed as the nerves of the Varonoi Diagram.
  • the mathematical background for the alpha filtration was introduced in “Three-dimensional alpha shapes” by Edelsbrunner and Miicke ( ACM Transactions on Graphics (TOG), 13(1), 43-72, 1994).
  • the second technique of PH is the sublevel set, which can take an input as a real valued function or image and extract topology representing the critical points within the data [“Persistent homology-a survey” by Edelsbrunner and Harer ( Contemporary Mathematics, 453, 257-282, 2008)].
  • the lower star filtration used in this process sweeps across different pixel intensity thresholds and at each scale computes the 0-dimensional homology. This process records a birth when the component is a local minimum and a death when there is a saddle point (merging of two local minimum). Additionally, by inputting a negative version of the data, lower star filtration can be performed to identify local maxima as births.
  • the third technique of PH is time-delay embedded set, which uses a non-linear dynamic signal processing approach called time-delay embedding for reconstructing a point cloud in the metric/vector space.
  • This process ensures that informative dynamic invariants are preserved such that a topological analysis of the resulting point cloud data yields features that are useful for classification.
  • Such a point cloud requires a PH to be computed in a real-valued Euclidean space (e.g., using Vietoris-Rips filtration). Compared to Cech or the alpha complex, the Vietoris-Rips complex computations are much easier in higher dimensions, as Rips filtration depends only on pairwise distances.
  • the output of any form of PH is a series of birth (b) and death (d) values for each component that can be plotted in a persistence diagram.
  • This visual representation is a plot with coordinates (b, d).
  • a diagonal line is drawn through the origin because every component must be born before it dies; generally, points close to the diagonal are not as persistent while those further away are more persistent topological features.
  • the persistence, or "lifetime”, of a component is defined as b - d. In other words, the components with a larger lifetime are said to persist in the data for a longer time. While the initial intuition was that the components with the higher persistence are the most significant, it has been shown that the most significant features in classification are not always ones with the longest lifetimes Rather, the significance associated with a component’s persistence varies based on the dataset.
  • Persistence barcodes and persistence landscapes have been considered.
  • Persistence images have been introduced as a method to vectorize persistence diagrams for machine learning tasks [“Persistence images: A stable vector representation of persistent homology” by Adams et al. (The Journal of Machine Learning Research, 18(1), 218-252, 2017)].
  • the persistence image methodology has been selected because of its ability to work with a wider range of machine learning algorithms as well as the potential to select only a few discriminatory pixels to store in the limited space of a QR-Code.
  • the persistence image pipeline converts the persistence diagram to a persistence surface then a pixelated persistence image (PI) at 108.
  • First all points are converted from birth-death to birth-persistence, i.e. (b, d) to (b, d-b).
  • a weighting function can then be applied giving points that are more persistent a higher amount of intensity.
  • a Gaussian probability distribution with selected variance level is applied at each point.
  • a grid (n by n) is overlaid over the surface to form the PI with chosen resolution.
  • the pixel intensities of the PI are taken as a feature vector for machine learning and/or feature selection at 110. Vectors from different component dimensions can be concatenated into a larger vector at this stage.
  • the machine learning output can be used to generate the QR- Code at 112.
  • the Radon transform takes a function defined on the plane to a function defined on the (two-dimensional) space of lines in the plane, whose value at a particular line is equal to the line integral of the function over that line.
  • the Radon transform represents the projection data obtained as the output of a scan and can he used to reconstruct the original density from the projection data.
  • the Radon transform data is often called a sinogram because the Radon transform of an off-center point source is a sinusoid. Consequently, the Radon transform appears graphically as a number of blurred sine waves with different amplitudes and phases.
  • a scan e.g., an ultrasound image, tomography image, or other appropriate imaging scan
  • preprocessing can be performed at 204.
  • the Random transform can be applied at 206 and the Radon transform content from the image can be used to generate the barcode at 208.
  • the Radon transform content can also be further processed using a two-dimensional (2D) discrete cosine transform (DST) and/or a one-dimensional (1D) DST at 210, followed by locality sensitive discriminate analysis (LSDA) (local binary pattern analysis) at 212 [“An integrated index for identification of fatty liver disease using radon transform and discrete cosine transform features in ultrasound images” by Acharya, et al. ( Information Fusion, 31, 43-53, 2016)].
  • LSDA locality sensitive discriminate analysis
  • Ranking at 214 and classification at 216 of the processed content can provide data for the generation of the barcode at 208.
  • FIG. 3 shows an example of the Radon transform and PH code generation workflow that can be used to produce patient codes.
  • scan image data can be analyzed using the PH and/or Radon transform to extract features that can be applied to machine learning (ML) to generate identify information used to generate a QR-Code.
  • ML machine learning
  • the workflow For proof of concept, the workflow’s ability to differentiate between restrictive cardiomyopathy and chronic constrictive pericarditis was evaluated as this is a complex differential diagnosis with similar clinical presentation for both conditions.
  • FIGS. 4A-4C illustrate aspects of the proof of concept. As shown in FIG. 4A, the ultrasound scan can be converted into a sinogram using the Radon transform, which can then be converted into a barcode as illustrated in FIG. 2.
  • the alpha filtration was used to compute the PH of these strain curves for dimensions H 0 , Hi, and H 2 .
  • the splined data can be converted to a patient contour plot with time in the cardiac cycle as x-axis and left ventricular segment as the y- axis. In this manner the image can be thought of as a real-valued function where each pixel location corresponds to the strain value at a particular location on the ventricle at a given time.
  • Lower star filtration was used to compute sublevel set PH for dimension H 0 of strain contour plots.
  • the splined data was converted by averaging over 16/17 LV segments to represent the lateral wall, apex and interventricular septum of the left ventricle.
  • the Vietoris-Rips aka Rips-complex filtration was used to compute the PH for dimension H 0 of the strain and strain rate phase-space reconstructed LV regional signal curves.
  • the output of all input data types to PH is a set of birth and death coordinates.
  • point cloud H 0 , Hi, and H 2 , sublevel set H 0 , and phase-space-reconstructed LV regions set Ho these coordinates are used to make a persistence image for each dimension.
  • the intensity of each pixel in the persistent image is concatenated to form a vector of length 50 corresponding to the 50 pixels in the image.
  • the persistence pixel vectors for any dimension or for same dimension across different strain and strain rates can be concatenated to form a larger vector of combined features.
  • Different combinations of feature vectors are finally evaluated through PCA (Orange) and supervised machine learning (BigML/Orange).
  • FIG. 4C illustrates supervised machine learning to extract features for identifying signatures.
  • the selected features which are differentiating of ROM vs CP, ROM vs NL, and CP vs NL myocardial pathological patterns can then be encoded within a patient’s individualized patient QR-Code
  • FIGS. 4D-4K a use-case scenario of the PH workflow that harnesses both deformation patterns and global topological information of the left ventricle to characterize cardiovascular diseases is discussed with respect to FIGS. 4D-4K.
  • CP constrictive pericarditis
  • RCM restrictive cardiomyopathy
  • FIG. 4D illustrates an example of the proposed workflow.
  • a physiological signal can be pre-processed to a suitable input for persistent homology feature extraction.
  • the resulting topological features can be represented visually as an individualized patient motif produced by concatenating multiple persistent images. This motif can be directly interpreted by physician or used to develop machine learning models.
  • the pipeline can include:
  • Data Preprocessing - data is temporally and spatially normalized to an n-dimensional point cloud.
  • TDA filtration - simplicial complexes are built upon the point cloud, from which topological invariants are extracted.
  • Patient-Specific Motif - features are stored in a visual representation that can be directly interpreted by physicians/scientists or serve as input for machine learning.
  • the initial step of PH is a filtration to create a series of simplicial complexes for a scale r, where a sphere of radius r is drawn around every point. At each intersection of two spheres, an edge is drawn between the two points.
  • the filtering of data builds a simplicial complex space, from which PH quantifies the presence of n-dimensional holes i.e. 0- dimensional holes are connected components, 1-dimensional holes are circles/loops/tunnels, and 2-dimensional holes are voids.
  • n-dimensional holes i.e. 0- dimensional holes are connected components
  • 1-dimensional holes are circles/loops/tunnels
  • 2-dimensional holes are voids.
  • the main principle of PH is to progress through all possible values r (0 ⁇ r ⁇ ⁇ ) to determine how the homology of these components change. For a given filtration of a simplicial complex, determine the output (birth, death) barcode intervals, representatives for each topological feature, i.e. for each structure (n-dimensional hole), and the times of the birth (at what r it appears), and death (at what r it disappears) are computed.
  • FIG. 4E illustrates an example of the persistent homology.
  • the horizontal axis shows the filtration steps.
  • Each D-dimensional topological feature in filtration is represented by a bar that starts at the filtration step at which the feature is born and ends at the filtration step at which it dies.
  • 0-dimensional barcode each bar corresponds to a connected component
  • the length of a bar indicates how long a particular component is disconnected from other components.
  • Persistence Image To compare the homology of persistence diagrams of different patients, metrics, such as Wasserstein or Bottleneck distance, can be calculated. Persistence barcodes and persistence landscapes have been developed to represent the persistent topology within datasets. Persistence images have been introduced to vectorize persistence diagrams for machine learning tasks. The persistence image methodology was selected because of its ability to work with a broader range of machine learning algorithms and the potential to convert its feature vectors into a visual signature.
  • the persistence image (PI) pipeline converts the birth-death points to birth-persistence, i.e. ( b,d ) to ( b,d - b ). A weighting function can be applied, giving points that are more persistent a higher amount of intensity.
  • Gaussian probability distribution with selected variance level can be applied at each point.
  • a grid of n x n can be overlaid over the surface to form the PI with chosen resolution.
  • the pixel intensities of the PI can be taken as a feature vector for machine learning and feature selection.
  • Vectors from different component dimensions can be concatenated into a disease pattern motif for both visualization and storage.
  • FIG. 4F illustrates an overview of the use-case scenario.
  • Three regions were defined in each the mid short axis (1A) and apical four chamber view (1B).
  • the average regional curves (1C) for each strain parameter were calculated and transformed using phase space reconstruction (1D). This served as the input for persistent homology filtration (2A).
  • the resulting birth and death coordinates for dimension 0 were converted to a vector form for machine learning using persistent image methodology (2B).
  • Pre-processing From the 4ch STE analysis, longitudinal strain (LS), longitudinal strain rate (LSR), radial strain (RS), and radial strain rate (RSR) were obtained. Due to the different image frame times and heart rates, each patient had a varying number of time points over one cardiac cycle from which measurements were recorded.
  • Patient-Specific Motif The workflow produced a visual representation indicative of the initial input that can be interpreted directly by physicians/scientists while also being capable of feeding into downstream machine learning tasks.
  • the patient-specific motifs that are generated showcase the general trends of the disease conditions while still maintaining individual patient characteristics, allowing the patients to be followed up over time and monitored for cardiac functions changes via their unique visual signature.
  • Average Strain Pattern Motifs Referring now to FIG. 4G, shown are the average strain pattern motifs for each cardiac condition.
  • the output visual signature for persistent homology workflow was a heatmap-like motif with x-axis corresponding to strain or strain rate in longitudinal, radial, and circumferential directions; y-axis corresponding to persistence pixel position in the resulting 1 by 50 vector for each combination of wall region and strain measurement; and z-color corresponding to the pixel intensity calculated through persistent image vectorization.
  • the average strain motifs for constrictive pericarditis (A), restrictive cardiomyopathy (B), and normal/control patients (C) are shown to demonstrate group defining patterns.
  • CP constrictive pericarditis
  • RCM restrictive cardiomyopathy
  • LS longitudinal strain
  • LSR longitudinal strain rate
  • RS radial strain
  • RSR radial strain rate
  • CS circumferential strain
  • CSR circumferential strain rate.
  • Machine Learning Classifiers To determine if the features extracted through the pipeline helped distinguish the cardiac conditions, three binary class classifiers were developed for CP vs. RCM, CP vs. normal, and RCM vs. normal. Next, the data were split into an 80% training and 20% testing set using replicable deterministic sampling. Finally, the performance of these models was compared with a baseline performance achieved by logistic regression models using average peak longitudinal strain from the 4Ch view. This peak value approximates the global longitudinal strain that clinicians typically extract from cardiac strain imaging data.
  • FIG. 4H illustrates an example of binary classifier receiver operating curves.
  • the persistent homology workflow outperformed or matched the performance metrics of the GLS model for RCM vs. CP (A), RCM vs. NL (B), and CP vs. NL (C).
  • CP constrictive pericarditis
  • RCM restrictive cardiomyopathy
  • NL normal
  • GLS global longitudinal strain
  • TDA topological data analysis.
  • a multi-class classifier was created to discriminate between all conditions; the average across all classes AUC, sensitivity (Sn), and specificity (Sp) were improved in comparison to the baseline model.
  • FIG. 4I illustrates the Shapley additive explanations for the multi-class model.
  • the Shapley plot presents the top ten important features responsible for the multi-class machine learning model to output its predictions that discriminated each cardiac condition from the other two, i.e., CP from RCM and Normal (A), RCM from CP and Normal (B), and Normal from CP and RCM (C).
  • the features identified from this interpretable artificial intelligence tool can correspond to specific regions in the patient specific motif that contribute to disease stratification visually.
  • CP constrictive pericarditis
  • RCM restrictive cardiomyopathy
  • NL normal
  • LS longitudinal strain
  • LSR longitudinal strain rate
  • RS radial strain
  • RSR radial strain rate
  • CS circumferential strain
  • CSR circumferential strain rate.
  • FIG. 4J shows the original phase reconstruction point clouds for septal longitudinal strain analysis.
  • Representative point clouds are provided for CP, RCM, and Normal groups.
  • CP constrictive pericarditis
  • RCM restrictive cardiomyopathy. It can be observed that multiple RCM patients have a much tighter trajectory of longitudinal strain, while CP and normal patients tend to have much broader loop patterns.
  • the persistent homology workflow detects that in RCM patients, a fully formed connected component in dimension 0 forms at lower radius values; on the other hand, CP and normal patients have separate components persist into larger radius values that would be required to create a connected simplicial complex around the data points.
  • RCM patients have a restrained septal longitudinal strain compared to CP and normal patients.
  • a similar observation was identified for CP patients who do not exhibit much activity in Wall 1 circumferential strain greater than pixel 13, indicating a reduced circumferential strain in this region compared to RCM and normal patients.
  • a slightly different pattern is discovered regarding the radial apical strain rate in CP patients.
  • phase space reconstruction point clouds show the original phase reconstruction point clouds for apical radial strain analysis.
  • Representative point clouds are provided for CP, RCM, and Normal groups.
  • CP constrictive pericarditis
  • RCM restrictive cardiomyopathy. This may suggest one of the compensatory mechanisms present in CP patients is an increase in apical radial strain rate.
  • the other informative features can also be confirmed and investigated by referring to the original phase space reconstruction point clouds.
  • topological data extraction of segmental strain analysis has been harnessed to develop a better stratification tool for cardiovascular diseases.
  • a more holistic assessment of left ventricular function can be obtained through echocardiography alone.
  • a workflow was developed for the use-case model that can accurately stratify uncommon cardiovascular diseases in a small patient cohort.
  • the structural and functional data can be represented as a persistent image that can be displayed as a motif, allowing for clinical assessment of the processed data.
  • TDA topological data analysis
  • Strain analysis can be segmentally divided into anatomically unique locations that comprise the entirety of the left ventricular myocardium.
  • the 48 (short-axis view) and 49 (apical four-chamber view) segmental strain parameters were combined into three functional groupings that included the anterior septal, inferior septal, and lateral wall in short axis and the lateral wall, apex, and interventricular septum in apical four chamber view.
  • Analysis of segmental strain waveforms as aggregates instead of individual segments can remove the stochastic nature that analysis of each separate segment would precipitate. Instead, grouping by functional domains allows for averaging curves through a more physiologically relevant manner, specifically regarding the contractile nature and ultrastructural properties of cardiomyocytes within the myocardium.
  • each computing device 300 includes processing circuitry comprising at least one processor circuit, for example, having a processor 303 and a memory 306, both of which are coupled to a local interface 309.
  • each computing device 300 may comprise, for example, at least one server computer or like device.
  • the local interface 309 may comprise, for example, a data bus with an accompanying address/control bus or other bus structure as can be appreciated.
  • the computing device 300 can include one or more network interfaces 310.
  • the network interface 310 may comprise, for example, a wireless transmitter, a wireless transceiver, and a wireless receiver.
  • the network interface 310 can communicate to a remote computing device using a Bluetooth protocol.
  • Bluetooth protocol As one skilled in the art can appreciate, other wireless protocols may be used in the various embodiments of the present disclosure.
  • Stored in the memory 306 are both data and several components that are executable by the processor 303.
  • stored in the memory 306 and executable by the processor 303 are code generation program 315, application program 318, and potentially other applications.
  • Also stored in the memory 306 may be a data store 312 and other data.
  • an operating system may be stored in the memory 306 and executable by the processor 303.
  • executable means a program file that is in a form that can ultimately be run by the processor 303.
  • Examples of executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory 306 and run by the processor 303, source code that may be expressed in proper format such as object code that is capable of being loaded into a random access portion of the memory 306 and executed by the processor 303, or source code that may be interpreted by another executable program to generate instructions in a random access portion of the memory 306 to be executed by the processor 303, etc.
  • An executable program may be stored in any portion or component of the memory 306 including, for example, random access memory (RAM), read-only memory (ROM), hard drive, solid-state drive, USB flash drive, memory card, optical disc such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components.
  • RAM random access memory
  • ROM read-only memory
  • hard drive solid-state drive
  • USB flash drive USB flash drive
  • memory card such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components.
  • CD compact disc
  • DVD digital versatile disc
  • the memory 306 is defined herein as including both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power.
  • the memory 306 may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory components, or a combination of any two or more of these memory components.
  • the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices.
  • the ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable readonly memory (EEPROM), or other like memory device.
  • the processor 303 may represent multiple processors 303 and/or multiple processor cores and the memory 306 may represent multiple memories 306 that operate in parallel processing circuits, respectively.
  • the local interface 309 may be an appropriate network that facilitates communication between any two of the multiple processors 303, between any processor 303 and any of the memories 306, or between any two of the memories 306, etc.
  • the local interface 309 may comprise additional systems designed to coordinate this communication, including, for example, performing load balancing.
  • the processor 303 may be of electrical or of some other available construction.
  • code generation program 315 and the application program 318, and other various systems described herein may be embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software/general purpose hardware and dedicated hardware. If embodied in dedicated hardware, each can be implemented as a circuit or state machine that employs any one of or a combination of a number of technologies. These technologies may include, but are not limited to, discrete logic circuits having logic gates for implementing various logic functions upon an application of one or more data signals, application specific integrated circuits (ASICs) having appropriate logic gates, field-programmable gate arrays (FPGAs), or other components, etc. Such technologies are generally well known by those skilled in the art and, consequently, are not described in detail herein.
  • any logic or application described herein, including the code generation program 315 and the application program 318, that comprises software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system such as, for example, a processor 303 in a computer system or other system.
  • the logic may comprise, for example, statements including instructions and declarations that can be fetched from the computer-readable medium and executed by the instruction execution system.
  • a "computer-readable medium" can be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with the instruction execution system.
  • the computer-readable medium can comprise any one of many physical media such as, for example, magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium may be a random access memory (RAM) including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM).
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • MRAM magnetic random access memory
  • the computer-readable medium may be a read-only memory (ROM), a programmable readonly memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device.
  • ROM read-only memory
  • PROM programmable readonly memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • any logic or application described herein, including the code generation program 315 and the application program 318, may be implemented and structured in a variety of ways.
  • one or more applications described may be implemented as modules or components of a single application.
  • separate applications can be executed for the PH and Radon transform workflows as illustrated in FIGS. 1-3.
  • one or more applications described herein may be executed in shared or separate computing devices or a combination thereof.
  • a plurality of the applications described herein may execute in the same computing device 300, or in multiple computing devices in the same computing environment.
  • terms such as “application,” “service,” “system,” “engine,” “module,” and so on may be interchangeable and are not intended to be limiting.
  • GLS has a simplicity since it is viewed as a single value; on the other hand, a series of strain curves is cognitively demanding to process. Simplifying a series of segmental strain curves to its meaningful information while preserving each patient’s unique identity is not trivial.
  • the proof of concept workflow provides a way to simplify the spatially and temporally complex strain curves and store only meaningful features in a QR-Code. Moreover, by using persistent homology the issue of poor reproducibility of segmental strain values can be addressed, by instead emphasizing the shape of the curve in the data analysis. This workflow can be used to encode other disease patterns within patient individualized QR-Codes.
  • ratios, concentrations, amounts, and other numerical data may be expressed herein in a range format. It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited.
  • a concentration range of “about 0.1% to about 5%” should be interpreted to include not only the explicitly recited concentration of about 0.1 wt% to about 5 wt%, but also include individual concentrations (e.gf., 1%, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.5%, 1.1%, 2.2%, 3.3%, and 4.4%) within the indicated range.
  • the term “about” can include traditional rounding according to significant figures of numerical values.
  • the phrase “about ‘x’ to ‘y’” includes “about ‘x’ to about ‘y’” ⁇

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Abstract

Divers exemples concernent des codes de patient qui peuvent être utilisés pour stocker et extraire des données compressées. Dans un exemple, un procédé consiste à recevoir des données d'imagerie associées à un patient ; à générer un code à barres ou une image de persistance à partir des données d'imagerie ; à extraire des informations de caractéristiques concernant le patient à partir du code à barres ou de l'image de persistance en utilisant un apprentissage machine ; et à générer un code de patient à partir des informations de caractéristiques extraites, les informations de caractéristiques pouvant être extraites du code. Le code patient peut être un code visuel tel que, par exemple, un code QR. Dans un autre exemple, un système comprend un circuit de traitement et une application de génération de code qui amène les circuits de traitement à générer un code à barres ou une image de persistance à partir de données d'imagerie associées à un patient ; à extraire des informations de caractéristiques concernant le patient à partir du code à barres ou de l'image de persistance en utilisant un apprentissage machine ; et à générer un code de patient à partir des informations de caractéristiques extraites.
PCT/US2022/017132 2021-02-23 2022-02-21 Transformation de radon et codes à barres de patient à homologie persistante WO2022182603A1 (fr)

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