Systems and methods for assessing myocardial work

Abstract

Provided are methods, systems, and devices for determining a myocardial work assessment of the subject. This includes receiving, at a processor from a physiological sensor, a physiological signal of the subject; in response to an ultrasound excitation signal from an ultrasound device, receiving a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal; determining, at the processor, a myocardial stiffness metric from the plurality of cardiac ultrasound images; determining, at the processor, the myocardial work of the subject, the myocardial work determined based on a myocardial thickness metric, a myocardial strain metric, and the myocardial stiffness metric; and providing, a user interface comprising the myocardial work of the subject.

Description

SYSTEMS AND METHODS FOR ASSESSING MYOCARDIAL WORKRELATED APPLICATION

[0001] This application claims priority from United States Provisional Application No. 63 / 571 ,102, filed March 28, 2024, entitled SYSTEMS AND METHODS FOR ASSESSING MYOCARDIAL WORK, the contents of which are incorporated by reference herein.FIELD

[0002] The present embodiments related generally to the field of cardiology and more specifically to systems and methods for assessing one-beat segmental myocardial work.BACKGROUND

[0003] The assessment of myocardial work, or the workload performed by individual segments of the heart during the cardiac cycle, is integral to the diagnosis, treatment, and monitoring of various cardiovascular conditions. Myocardial work analysis provides clinicians with crucial insights into the contractile function of different regions of the myocardium, aiding in the identification of abnormalities in cardiac mechanics and function.

[0004] Cardiovascular diseases, including myocardial infarction, myocardial ischemia, cardiomyopathies, and other structural heart diseases, represent significant health burdens globally. Accurate and comprehensive assessment of myocardial function is paramount for diagnosing these conditions, planning appropriate treatment strategies, and monitoring disease progression over time. Furthermore, understanding myocardial work enables clinicians to tailor treatment approaches to individual patients, optimizing therapeutic efficacy and improving patient outcomes. By identifying areas of increased workload or dysfunction within the myocardium, healthcare providers can recommend targeted interventions, such as medications, lifestyle modifications, or interventional procedures, to address underlying cardiac issues.

[0005] In cardiac physiology, stroke work is the energy expended per cardiac beat by the ventricular chamber to propel the stroke volume into the vasculature (1 ). The reference method for assessing stroke work is based on the analysis of pressure-volume loops, whichrequires invasive measurements (2). Traditional methods for determining myocardial work include invasive catheterization methods through pressure-volume loops, involving measuring the pressure within the heart chambers and the corresponding changes in volume throughout the cardiac cycle. However, invasive catheterization procedures pose challenges and limitations. Invasive characterization of myocardial work through pressure-volume loops requires inserting catheters into the heart chambers, which carries inherent risks such as bleeding, infection, and vascular injury. Moreover, the procedure is technically demanding and requires specialized equipment and expertise, limiting its widespread use and accessibility. Additionally, pressure-volume loop analysis provides information about global cardiac function but may not accurately assess regional myocardial work or identify subtle abnormalities in myocardial mechanics.

[0006] It has been demonstrated that stroke work defined by the pressure-volume loop area is closely related to myocardial oxygen consumption (2-4) and to myocardial contractility (5). While pressure-volume loops allow for the study of global cardiac work, segmental work can also be investigated using various methods (6). First, work is defined as the product of force strength and the distance traveled in response to this force. Therefore, segmental myocardial work can be expressed as the product of myocardial contractile force and the amount of segment shortening (linear strain). Alternatively, it can be defined as the product of myocardial stress (force per unit surface area) and the change in segmental volume (3-dimensional strain) (6). While segment shortening can be measured by speckle-tracking echocardiography (STE), non-invasive assessment of myocardial stress remains challenging (7).

[0007] Measuring segmental myocardial work non-invasively is desirable for many clinical applications in adult and pediatric cardiology (8-11 ). It holds promise for aiding clinicians in identifying all stages of cardiac dysfunction and predict outcomes (12-16). Russell and colleagues introduced a non-invasive method to approximate both segmental and global myocardial work based on studying the relationship between myocardial strain as measured by STE, and blood pressure (BP) as a proxy for myocardial force (16-18). This technique has been introduced into clinical practice (12,16,18-21 ) and has been demonstrated to provide lower afterload-dependency compared with strain by STE alone toassess left ventricle (LV) systolic performance (8). Nevertheless, the use of BP as a surrogate for myocardial force has important limitations. It is indeed based on the assumption that BP adequately predicts LV pressure; and that LV systolic pressure is a reasonable surrogate for myocardial force (18). However, myocardial force development is not only determined by pressure but also by wall thickness and chamber geometry, defined by radius of curvature as defined by Laplace’s law. This is not taken into consideration in the method proposed by Russell et al (6).

[0008] Overall, conventional segmental myocardial work estimation still aligns with the findings of Sugawara et al. in their seminal work from 1985 (22): “The set of variables which expresses the work done by a small region of the ventricular wall in the greatest detail will be the stresses and strains in that region. [...] At present, there are no reliable methods to measure directly stresses in the ventricular wall. Therefore, we must estimate the stresses under a set of simplifying assumptions on the ventricular geometry and constitutive relations, using measured changes in ventricular dimensions and pressure in the ventricular cavity. [...] Since the mathematical estimation cannot be validated experimentally, it is difficult to judge which is the most realistic representation of the actual stress distribution across the ventricular wall. Until accurate and reliable methods of measuring wall stresses are developed, and the estimations of the stresses can be validated, detailed stress-strain analyses are of little practical utility.”

[0009] One common inherited cardiac disease is Hypertrophic cardiomyopathy (HCM). It is the leading cause of sudden cardiac death in children and young adults (58). Clinically, HCM is diagnosed by the presence of left ventricular hypertrophy (LVH) unexplained by another cardiac, systemic or metabolic disease (59). While sarcomere gene mutations are identified as the most prevalent cause of HCM, the phenotypic expression is extremely heterogeneous ranging from asymptomatic survival with normal life expectancy to early disease progression with end-stage heart failure or sudden cardiac death (60-63). However, the link between genotype and phenotype in HCM patients remains unclear and current risk prediction models have significant limitations(64-66), therefore there is an urgent need to develop robust diagnostic tools that allow better prediction of outcomes for this high-risk population (67).

[0010] Diastolic dysfunction is a strong predictor of major adverse cardiac effects for phenotype-positive HCM patients, but studies have also reported abnormalities in diastolic function preceding the development of LVH in genotype-positive phenotype-negative G+P- patients (60,68). Several echocardiographic parameters such as LV myocardial strain by 2D speckle tracking have shown to be quite useful in the assessment of diastolic dysfunction in phenotype-positive HCM patients, however these conventional approaches lack of sensitivity and established reference values for the pediatric population, their use for preclinical HCM population is limited (67-70).

[0011] Myocardial work, traditionally assessed by invasive catheterization methods through pressure-volume loops is more recently being estimated by non-invasive techniques for its utility to reflect cardiac performance (71-73). Russell et al (2012) showed the utilization of the regional left ventricular (LV) pressure-strain loop area, revealing a significant correlation with directly measured myocardial work (74). However, the proposed method is based on the assumption that peripheral blood pressure is an estimate for the intracavitary LV pressure which can in turn be used as a surrogate for force. Furthermore, this method does not take into account changes in force that result from individual differences in LV geometry or LV wall thickness which is an important factor for a growing pediatric population, particularly in the case of HCM patients (72).

[0012] The use of ultrasound data to determine myocardial assessments, including the use of ultrafast ultrasound devices, is a computer-based problem requiring a computer- based solution. Ultrasound devices are by their nature digital devices, and the deficiencies noted in conventional improvements indicate a desire for improved systems, methods, and devices for delivering care to patients. The problem is manifest both in the lack of functionality in conventional solutions and a lack of efficiency in analysing the data collected from a subject. Conventionally, the clinical operator (usually the doctor) uses a multitude of ultrasound parameters to quantify systolic or diastolic function. This accumulation of parameters has led us to use computer tools to analyse them, in particular using artificial intelligence. Unfortunately, to date, these tools have not proved effective in improving patient management.

[0013] There remains, therefore, a desire for improved methods, systems and devices for non-invasively determining a myocardial work assessment for a subject. This includes improvements that can provide one-beat myocardial work to quantify cardiac function using a single parameter. Logically, this may improve the efficiency of clinical operators in analysing cardiac function.SUMMARY

[0014] Provided herein are medical monitoring apparatuses, methods, and computer programs for determining stress or work as a function of time for individual myocardial segments based on strain and stiffness measurements. Compared to prior art determinations of determination of mechanical power or work for individual segments, the present embodiments are advantageous as they provide such determination solely from ultrasound measurements, by echocardiography. This may allow a fast, easy, and non- invasive determination with high temporal and spatial resolution. A number of indices for segment stress or work can be calculated which can be used as markers for the individual segment function.

[0015] In these present embodiments, we propose to overcome the intrinsic limitations of conventional approaches by introducing a direct measure of myocardial stress. Recent advancements in high frame rate ultrasound imaging provide important technical capabilities, allowing for the estimation of previously unexplored cardiac functional and structural parameters, such as myocardial stiffness assessed by shear wave elastography (SWE) (23-29). By repeatedly applying acoustic radiation force to the myocardium and capturing the corresponding shear wave velocities at various time points throughout the cardiac cycle, the present embodiments describe obtaining the variation in intrinsic myocardial stiffness during both diastole and systole (30-32). Myocardial stiffness can also be quantified by estimating the shear wave velocities naturally generated by heart movements such as valve closure or atrial contraction (called natural shear waves) (98).

[0016] Building on this innovation, the present embodiments revisit non-invasive assessment of myocardial work and demonstrate the feasibility of quantifying segmental myocardial work throughout the cardiac cycle using a comprehensive ultrasound approach. This approach may incorporate myocardial stiffness assessed by SWE (natural waves orwaves induced by acoustic radiation force), myocardial longitudinal strain evaluated by STE and myocardial thickness measured by M-Mode echocardiography. Our second objective as explained in the Examples is to compare myocardial work values between populations where variations are anticipated based on the literature (33-36). These populations include healthy volunteers, sarcomeric hypertrophic cardiomyopathy (HCM) patients, and aortic stenosis (AS) patients.

[0017] The present embodiments provide for the use of shear wave elastography so that myocardial stiffness (MS), a key parameter of cardiac function, may be assessed non- invasively (75). The inventors have previously identified a difference in diastolic myocardial stiffness between adult HCM patients and healthy controls as well as a strong correlation between MS and myocardial fibrosis estimated by MRI and with conventional echocardiographic diastolic functional parameters (76-79).

[0018] More recently the technique has been used to assess MS throughout the cardiac cycle by repeated acoustic radiation force being applied to the myocardium to quantify MS during the systolic, diastolic, and isovolumetric phases of the cardiac cycle (78). The technique was also validated with similar results in a pig model (80).

[0019] Therefore, in the Examples herein, the concept of abnormal phenotypes is revisit for HCM patients by quantitatively assessing the variation in myocardial stiffness and strain over the full cardiac cycle for a preclinical HCM (G+P-) group in comparison to sarcomeric HCM (G+P+) patients and healthy volunteer (HV). Additionally, another aim is to combine myocardial stiffness and strain measurements over the full cardiac cycle, while taking into account myocardial thickness, to compute a myocardial work index for each group.

[0020] In a first aspect there is provided a computer-implemented method for determining a myocardial work assessment for a subject, the method comprising: receiving, at a processor from a physiological sensor, a physiological signal of the subject; in response to an ultrasound excitation signal from an ultrasound device, receiving a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal; determining, at the processor, a myocardial stiffness metric from the plurality ofcardiac ultrasound images; determining, at the processor, the myocardial work of the subject, the myocardial work determined based on a myocardial thickness metric, a myocardial strain metric, and the myocardial stiffness metric; and providing, a user interface comprising the myocardial work of the subject.

[0021] In one or more embodiments, the method may further include: determining, at the processor, the myocardial thickness metric from the plurality of cardiac ultrasound images; and determining, at the processor, the myocardial strain metric from the plurality of cardiac ultrasound images.

[0022] In one or more embodiments, the myocardial thickness metric, the myocardial strain metric, and the myocardial stiffness metric may be each assessed during one cardiac cycle.

[0023] In one or more embodiments, the myocardial thickness metric may comprise a thickness metric of a myocardial segment of the subject; the myocardial strain metric may comprise a strain metric of a myocardial segment of the subject; and the myocardial stiffness metric may comprise a stiffness metric of a myocardial segment of the subject.

[0024] In one or more embodiments, the method may further include: displaying at a display device in communication with the processor, the user interface comprising the myocardial work.

[0025] In one or more embodiments, the ultrasound device may capture the plurality of cardiac ultrasound images at a framerate of at least 100 frames per second.

[0026] In one or more embodiments, the ultrasound device may capture the plurality of cardiac ultrasound images using a focused beam.

[0027] In one or more embodiments, the physiological sensor may be one of an electrocardiogram (ECG) sensor, an electrophysiological sensor, a blood pressure sensor, and an electromyogram sensor.

[0028] In one or more embodiments, determining the myocardial stiffness metric may further comprise: delivering, using the ultrasound device, a shear wave signal to the subject; receiving, from the ultrasound device, the plurality of cardiac ultrasound images;and determining, at the processor, at least one shear wave metric or at least one transit time metric of the shear wave signal based on the plurality of cardiac ultrasound images.

[0029] In one or more embodiments, the at least one shear wave metric may comprise a velocity metric or a transit time metric during a single cardiac cycle.

[0030] In one or more embodiments, the at least one shear wave metric may be based on natural waves or waves induced by the ultrasound excitation signal.

[0031] In one or more embodiments, the myocardial work of the subject may be determined by the equation wd= f (t)E(t)dt where o represents the myocardial stress metric, s ' represents the myocardial strain metric, and o represents the myocardial stiffness metric.

[0032] In one or more embodiments, the myocardial work of the subject may be determined by the equation wd= f (t)E(t)dt where o represents the myocardial stress metric, s ' represents the myocardial strain metric, and o represents the myocardial stiffness metric multiplied by the myocardial strain.

[0033] In a second aspect there is provided a system for determining a myocardial work assessment for a subject, the system comprising: a physiological sensor for generating a physiological signal of the subject; a ultrasound device for generating an ultrasound excitation signal, and receiving in response to the ultrasound excitation signal a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal; a processor in communication with the physiological sensor and the ultrasound device, the processor configured to perform the methods described herein.

[0034] In one or more embodiments, the system may further include: a support means for supporting the physiological sensor, the ultrasound device, and the processor in proximity to the subject; an electrical storage means for powering the physiological sensor, the ultrasound device, and the processor in proximity to the subject; and wherein the support means may be wearable by the subject.

[0035] In one or more embodiments, the support means may comprise a wearable patch attached to the subject’s body.

[0036] In one or more embodiments, the system may further comprise: an output device for outputting the user interface comprising the myocardial work of the subject.

[0037] In a third aspect there is provided a device for determining a myocardial work assessment for a subject, the device comprising: a physiological sensor for generating a physiological signal of the subject; a ultrasound device for generating an ultrasound excitation signal, and receiving in response to the ultrasound excitation signal a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal; a processor in communication with the physiological sensor, and the ultrasound device, the processor configured to perform the methods described herein.

[0038] In one or more embodiments, the device may further comprise: a support means for supporting the physiological sensor, the ultrasound device, and the processor in proximity to the subject; an electrical storage means for powering the physiological sensor, the ultrasound device, and the processor in proximity to the subject; and wherein the support means is wearable by the subject.

[0039] In one or more embodiments, the support means comprises a wearable patch attached to the subject’s body.

[0040] In one or more embodiments, the device may further comprise: an output device for outputting the user interface comprising the myocardial work of the subject.DRAWINGS

[0041] A preferred embodiment of the present invention will now be described in detail with reference to the diagrams, in which:

[0042] FIG. 1 shows a system diagram in accordance with one or more embodiments.

[0043] FIG. 2 shows a device drawing of the user device of FIG. 1 in accordance with one or more embodiments.

[0044] FIG. 3 shows a method diagram for assessing one-beat segmental myocardial work in accordance with one or more embodiments.

[0045] FIG. 4 shows another method drawing of assessing one-beat segmental myocardial work in accordance with one or more embodiments.

[0046] FIG. 5 shows a system diagram and an output diagram in accordance with Example #1 .

[0047] FIG. 6 shows a summary diagram of determining myocardial work in accordance with Example #1 . Myocardial thickness, strain, and stiffness, may be recorded throughout the cardiac cycle and expressed as the same time scale. Two segment dimensions may be used to calculate the segment area of interest, strain rate is derived from strain and myocardial stress is calculated from stiffness and strain based on Hooke’s law. One-beat segmental work (W) may then be obtained by multiplying myocardial stress, strain rate and segment area. Another way to estimate myocardial work, regardless of the segment dimensions, may to use the work density, which corresponds to the area of the stress-strain loop. The stress-strain loop area may be expressed in kPa.% and reflects a one-beat work density (Wd). AVC: aortic valve closure; E : myocardial stiffness; £ : myocardial strain; h : arbitrary out-of-plane dimension; L0 : end-diastolic length of the segment; o : myocardial stress.

[0048] FIG. 7 shows myocardial stiffness, thickness, strain, and strain rate measurements throughout the cardiac cycle in the three groups in accordance with Example #1 . FIG. 7A shows myocardial stiffness of the basal antero-septal segment over time for the three groups. FIG. 7B shows wall thickness of the basal antero-septal segment is presented over time for the three groups. FIG. 7C shows longitudinal myocardial strain of the basal antero-septal segment is presented over time for the three groups. FIG. 7D shows strain rate of the basal antero-septal segment is presented over time for the three groups, it is calculated from the strain measurements and represents the velocity of myocardial deformation over time. AS: aortic stenosis; AVC: aortic valve closure; HCM: hypertrophic cardiomyopathy; HV: healthy volunteers.

[0049] FIG. 7E shows myocardial stress throughout the cardiac cycle in the three groups in accordance with Example #1 . Myocardial stress is presented over time for the three groups. AS: aortic stenosis; AVC: aortic valve closure; HCM: hypertrophic cardiomyopathy; HV: healthy volunteers.

[0050] FIG. 8 shows one-beat segmental work, contributive work, and dissipative work comparison between groups in accordance with Example #1 . FIG. 8A shows myocardial instant work is presented over time for the three groups. FIG. 8B shows one- beat segmental work is higher in AS compared with HV, and lower in HCM compared with HV. Contributive work is not statistically different between HV and HCM but higher in AS and dissipative work is not statistically different among the groups. FIG. 8C Desynchronized work is important in HCM, both in systole and diastole. (*): p<0.05; (**): p<0.01 ; (ns): non-significant difference; AS: aortic stenosis; AVC: aortic valve closure; HCM: hypertrophic cardiomyopathy; HV: healthy volunteers.

[0051] FIG. 9 shows stiffness-strain and stress-strain loops comparison between groups in accordance with Example #1 . FIG. 9A shows stiffness-strain loop area is decreased in HCM group compared with HV while there is no statistical difference between HV and AS. FIG. 9B shows stress-strain loop area is decreased in HCM group compared with HV while there is no statistical difference between HV and AS. (*): p<0.05; (**): p<0.01 ; (ns): non-significant difference; AS: aortic stenosis; AVC: aortic valve closure; HCM: hypertrophic cardiomyopathy; HV: healthy volunteers; MVC: mitral valve closure;. On histograms, values are presented as mean and standard deviation.

[0052] FIG. 10 shows a definition of segment dimensions and segmental myocardial work equation in accordance with Example #1. Segment dimensions, strain and stress are expressed as a function of time throughout the cardiac cycle to calculate instant myocardial work according to ECG timing. The volume of the basal antero-septal segment is defined by its length (L), its thickness (A) and a width (h) which is an unknown value. Myocardial stress (o) is applied to the segment volume throughout the cardiac cycle and converted to myocardial work by multiplying with strain rate and segment dimensions. L0 is the segment end-diastolic length.

[0053] FIG. 11 shows deriving one-beat segmental work from ultrasound measured parameters in accordance with Example #1 . The table provides how each physical quantity can be decomposed into a combination of other parameters that are ultimately measurable using different ultrasound techniques, in accordance with Example #1 . The dashed box highlights the parameters related to the geometrical dimensions of the myocardial segment.

[0054] FIG. 12 shows echocardiography data in accordance with Example #1. AS: aortic stenosis; AV: aortic valve; BAS: basal antero-septal segment; GLS: global longitudinal strain; HCM: hypertrophic cardiomyopathy; HV: healthy volunteers; IVS: interventricular septum; LA: left atrium; LVEDD: left ventricular end-diastolic diameter; LVEF: left ventricular ejection fraction; LVOTO: left ventricular outflow tract obstruction; SAM: systolic anterior motion of the mitral valve.

[0055] FIG. 13 shows myocardial stiffness, thickness, strain, and strain rate measurements throughout the cardiac cycle in the 4 groups in accordance with Example #2. FIG. 13A shows myocardial stiffness (MS) of the basal antero-septal segment is presented over time for the four groups. FIG. 13B shows wall thickness of the basal anteroseptal segment is presented over time for the four groups. FIG. 13C shows longitudinal myocardial strain of the basal antero-septal segment is presented over time for the four groups. FIG. 13D shows strain rate of the basal antero-septal segment is presented over time for the four groups, it is calculated from the strain measurements and represents the velocity of myocardial deformation over time. G+P-: Genotype positive Phenotype negative; G+P+: Genotype positive Phenotype positive.

[0056] FIG. 14 shows myocardial stress over the cardiac cycle for the four groups in accordance with Example #2. FIG. 14A shows myocardial stress is presented over time for the four groups. FIG. 14B shows peak myocardial stress is plotted for the 4 groups. Peak myocardial stress is statistically different for the G+P+ group in comparison to HVs, G+P- subgroup with Diastolic MS > 12 kPa. G+P-: Genotype positive Phenotype negative; G+P+: Genotype positive Phenotype positive.

[0057] FIG. 15 shows stiffness-strain and stress-strain loops in the four groups in accordance with Example #2. FIG. 15A shows myocardial Stiffness strain loops along with the corresponding area are presented for the four groups. FIG 15B shows myocardialstress strain loops along with the corresponding area are presented for the four groups. AVC: aortic valve closure; AVO: aortic valve opening; G+P-: Genotype positive Phenotype negative; G+P+: Genotype positive Phenotype positive; MVC: Mitral valve closure; MVO: Mitral valve opening; N.S: no significant difference; MS: myocardial stiffness.

[0058] FIG. 16 shows myocardial work analysis over the full cardiac cycle for the 4 groups in accordance with Example #2. FIG. 16A shows myocardial work is presented over the full cardiac cycle for the four groups. FIG. 16B shows global, contributive, and dissipative work is presented for the four groups (left) along with the desynchronized dissipative work during systole and contributive work during diastole for the four groups. G+P-: Genotype positive Phenotype negative; G+P+: Genotype positive Phenotype positive.DESCRIPTION OF VARIOUS EMBODIMENTS

[0059] Various embodiments will now be described below to provide an example of the claimed subject matter. No example described below limits any claimed subject matter and any claimed subject matter may cover embodiments such as systems or methods that differ from those described below.

[0060] Furthermore, it will be appreciated that for simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the examples described herein. However, it will be understood by those of ordinary skill in the art that the examples described herein may be practiced without these specific details. In other instances, well- known methods, procedures, and components have not been described in detail so as not to obscure the examples described herein. Also, the description is not to be considered as limiting the scope of the examples described herein.

[0061] It should also be noted that, as used herein, the wording “and / or” is intended to represent an inclusive-or. That is, “X and / or Y” is intended to mean X or Y or both, for example. As a further example, “X, Y, and / or Z” is intended to mean X or Y or Z or any combination thereof.

[0062] It should be noted that terms of degree such as "substantially", "about" and "approximately" as used herein mean a reasonable amount of deviation of the modified term such that the end result is not significantly changed. These terms of degree may also be construed as including a deviation of the modified term if this deviation would not negate the meaning of the term it modifies.

[0063] Furthermore, the recitation of numerical ranges by endpoints herein includes all numbers and fractions subsumed within that range (e.g., 1 to 5 includes 1 , 1.5, 2, 2.75, 3, 3.90, 4, and 5). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term "about" which means a variation of up to a certain amount of the number to which reference is being made if the end result is not significantly changed.

[0064] Some elements herein may be identified by a part number, which is composed of a base number followed by an alphabetical or subscript-numerical suffix (e.g., 112a, or 112i). Multiple elements herein may be identified by part numbers that share a base number in common and that differ by their suffixes (e.g., 112i , 1122, and 112s). All elements with a common base number may be referred to collectively or generically using the base number without a suffix (e.g., 112).

[0065] The example systems and methods described herein may be implemented in hardware or software, or a combination of both. In some cases, the examples described herein may be implemented, at least in part, by using one or more computer programs, executing on one or more programmable devices comprising at least one processing element, a data storage element (including volatile and non-volatile memory and / or storage elements), and at least one communication interface. These devices may also have at least one input device (e.g., a keyboard, a mouse, a touchscreen, and the like), and at least one output device (e.g., a display screen, a printer, a wireless radio, and the like) depending on the nature of the device. For example, and without limitation, the programmable devices (referred to below as computing devices) may be a server, network appliance, embedded device, computer expansion module, a personal computer, laptop, personal data assistant, cellular telephone, smart-phone device, tablet computer, a wireless device or any other computing device capable of being configured to carry out the methods described herein.

[0066] In some examples, the communication interface may be a network communication interface. In examples in which elements are combined, the communication interface may be a software communication interface, such as those for inter-process communication (IPC). In still other examples, there may be a combination of communication interfaces implemented as hardware, software, and a combination thereof.

[0067] Program code may be applied to input data to perform the functions described herein and to generate output information. The output information is applied to one or more output devices, in known fashion.

[0068] Each program may be implemented in a high-level procedural, declarative, functional or object-oriented programming and / or scripting language, or both, to communicate with a computer system. However, the programs may be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Each such computer program may be stored on a storage media or a device (e.g., ROM, magnetic disk, optical disc) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. Examples of the system may also be considered to be implemented as a non-transitory computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein.

[0069] Furthermore, the example system, processes and methods are capable of being distributed in a computer program product comprising a computer readable medium that bears computer usable instructions for one or more processors. The medium may be provided in various forms, including one or more diskettes, compact disks, tapes, chips, wireline transmissions, satellite transmissions, internet transmission or downloads, magnetic and electronic storage media, digital and analog signals, and the like. The computer useable instructions may also be in various forms, including compiled and noncompiled code.

[0070] Various examples of systems, methods and computer programs products are described herein. Modifications and variations may be made to these examples withoutdeparting from the scope of the invention, which is limited only by the appended claims. Also, in the various user interfaces illustrated in the figures, it will be understood that the illustrated user interface text and controls are provided as examples only and are not meant to be limiting. Other suitable user interface elements may be used with alternative implementations of the systems and methods described herein.

[0071] Referring to FIG. 1 there is shown a system diagram 100 for determining a myocardial work assessment for a subject 110 in accordance with one or more embodiments. The system 100 includes a user device 102, network 104, ultrasound device 106, physiological sensor 108, and subject 110.

[0072] In a first embodiment, the myocardial work assessment is performed by a clinician (not shown) on a subject 110 using physiological sensor 108 and ultrasound device 106.

[0073] In an alternate embodiment, a wearable device 120 is disclosed including an ultrasound device 116 and a physiological sensor 118. The ultrasound device 116 may be generally provide the same functionality as the ultrasound device 106 in a wearable form. The physiological sensor 118 may generally provide the same functionality as physiological sensor 108 in a wearable form. For example, the wearable device 120 may be a patchbased device worn by the subject 110, a device attached using a chest band or chest strap to the subject 110, or another type of wearable device worn by the subject 110. The wearable device 120 may be in network communication to a user device 102 via network 104.

[0074] The user device 102 may be any two-way communication device with capabilities to communicate with other devices. A user device 102 may be a mobile device such as mobile devices running the Google® Android® operating system or Apple® iOS® operating system. The user devices 102 may be used by a user such as a subject, an administrator, clinician, or other medical professional to access a software application. The software application may send and receive data from the ultrasound device 106 and the physiological sensor 108. In one embodiment the user device 102 may send and receive data from the wearable device 120.

[0075] A user device 102 may be the personal device of a user or may be a device provided by an employer. The user device 102 may be used by an end user to access the myocardial signals and other generated myocardial metrics generated from the physiological sensor 108 and the ultrasound device 106 over network 104.

[0076] In one embodiment, the user device 102 may be a server providing a web application and a database, which may store the data and metrics from the ultrasound device 106 and physiological sensor 108. The data and metrics from the ultrasound device 106 and the physiological sensors 108 may be sent from those devices to the user device 102 over network 104, or alternatively, may be requested from the user device 102 over network 104.

[0077] Similarly, the wearable device 120 may send, or receive requests from the user device 102 for data from the ultrasound device 116 and physiological sensor 118 and other metrics generated by wearable device 120.

[0078] In either embodiment, the myocardial metrics may be generated at the ultrasound device 106 and physiological sensor 108, at the wearable device 120 including the physiological sensor 118 and ultrasound device 116, or at the user device 102. That is, the methods of FIGs. 3 and 4 may be performed in each of these three locations based on data received from the ultrasound devices 106 and 116 and the physiological sensors 108 and 118.

[0079] Network 104 may be any network or network components capable of carrying data including the Internet, Ethernet, fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network, fixed line, local area network (LAN), wide area network (WAN), a direct point-to-point connection, mobile data networks (e.g., Universal Mobile Telecommunications System (UMTS), 3GPP Long-Term Evolution Advanced (LTE Advanced), Worldwide Interoperability for Microwave Access (WiMAX), etc.) and others, including any combination of these.

[0080] The ultrasound device 106 may be a cardiovascular ultrasound system and may include a transducer 124 that may be applied to the subject’s body. The transducer 124 of the ultrasound device 106 may provide an ultrasound excitation signal to the subject110, and receive cardiac ultrasound signals in response. The ultrasound device 106 may emit an excitation signal at a range of different signal frequencies in order to generate a cardiac ultrasound signal from the subject 110 in response, for example, between 1 and 15 MHz. The ultrasound device 106 may capture the generated cardiac ultrasound signal from the subject 110 which includes encoded cardiac ultrasound images. The received cardiac ultrasound images may be captured at different framerates. For example, the received cardiac ultrasound images may be captured at different temporal resolutions, such as 100 frames per second (fps), 200 fps, 300 fps, 400 fps, 500 fps, 600 fps, 700 fps, 800 fps, 900 fps, 1000 fps, 2000 fps, or over 3000 fps. In an alternative embodiment, the received cardiac ultrasound images may be captured at less than 100 fps using a focussed beam technique. The received cardiac ultrasound images may be indexed according to the physiological signal from the physiological sensor 108.

[0081] For example, the ultrasound device 106 may be General Electric® (GE®) Vivid-E95 ultrasound system equipped with a GE® 6S-D or M5Sc-D phased-array ultrasound probe.

[0082] In another example, the ultrasound device may be a Verasonics® Vantage™ system (model #ASAO0394, Verasonics Inc., Kirkland, Washington, USA) with a phased array ultrasound probe (GE® 6S-D).

[0083] The physiological sensor 108 may be one of an electrocardiogram (ECG) sensor, an electrophysiological sensor, and an electromyogram sensor. For example, the physiological sensor may be an ECG sensor, an AccuSync® 72.

[0084] The subject 110 may be a human subject, including an infant, a child, an adolescent, or an adult. In an alternate embodiment, the subject 110 may be a non-human mammal subject such as a horse, dog, cat, etc.

[0085] The wearable device 120 includes a support means 122 for supporting the physiological sensor 118, the ultrasound device 116, and a processor (not shown) in proximity to the subject. The support means 122 may be a belt, strap, patch, adhesive strip, or another means for securably attaching the wearable device 120 to the subject 110. The ultrasound device 116 of the wearable device 120 may include a transducer (not shown) incontact with the subject 110 to provide an excitation signal and receive cardiac ultrasound signals.

[0086] The wearable device 120 is not shown to scale and may be sized as needed.

[0087] The wearable device 120 further includes an electrical storage means for powering the physiological sensor, the ultrasound device, and the processor.

[0088] Referring next to FIG. 2 there is shown a device drawing 200 of the user device 102 of FIG. 1 in accordance with one or more embodiments. The device 200 includes a communication unit 204, a display 206, a processor unit 208, a memory unit 210, an I / O unit 212, a user interface engine 214, and a power unit 216.

[0089] The communication unit 204 can include wired or wireless connection capabilities. The communication unit 204 can be used by the device 200 to communicate with other devices or computers. Communication unit 204 may communicate with a network, such as networks 104 (see FIG. 1 ).

[0090] The display 206 may be an LED or LCD based display and may be a touch sensitive user input device that supports gestures.

[0091] The processor unit 208 controls the operation of the device 200. The processor unit 208 can be any suitable processor, controller or digital signal processor that can provide sufficient processing power depending on the configuration, purposes and requirements of the device 200 as is known by those skilled in the art. For example, the processor unit 208 may be a high performance general processor. In alternative embodiments, the processor unit 208 can include more than one processor with each processor being configured to perform different dedicated tasks. The processor unit 208 may include a standard processor, such as an Intel® processor or an AMD® processor.

[0092] The processor unit 208 can also execute a user interface (Ul) engine 214 that is used to generate various Uls for a user at the device 200 including for display at display 206.

[0093] The memory unit 210 comprises software code for implementing an operating system 220, programs 222, database 224, myocardial work unit 226, Web / API Unit 228.

[0094] The memory unit 210 can include RAM, ROM, one or more hard drives, one or more flash drives or some other suitable data storage elements such as disk drives, etc. The memory unit 210 is used to store an operating system 220 and programs 222 as is commonly known by those skilled in the art.

[0095] The I / O unit 212 can include at least one of a mouse, a keyboard, a touch screen, a thumbwheel, a track-pad, a track-ball, a card-reader, an audio source, a microphone, voice recognition software and the like again depending on the particular implementation of the device 200. In some cases, some of these components can be integrated with one another.

[0096] The user interface engine 214 is configured to generate interfaces for users to configure myocardial measurements and myocardial assessments, collect data from the physiological sensor and the ultrasound device, view myocardial data from a subject, view myocardial work determinations and related notifications, view myocardial predictions, etc. The various interfaces generated by the user interface engine 214 may be displayed at display 206 or transmitted to a user device by virtue of the Web / API Unit 228 and the communication unit 204.

[0097] The power unit 216 can be any suitable power source that provides power to the device 200 such as a power adaptor or a rechargeable battery pack depending on the implementation of the device 200 as is known by those skilled in the art.

[0098] The operating system 220 may provide various basic operational processes for the device 200. For example, the operating system 220 may be a server operating system such as Ubuntu® Linux, Microsoft® Windows Server® operating system, or another operating system.

[0099] The programs 222 include various user programs. They may include several clinical applications such as electronic health records, cardiology data review applications, and other applications as known.

[0100] In one or more embodiments, the programs 222 may provide a health platform for reviewing and analysing cardiac health of the subjects.

[0101] The database 224 may be a database for storing subject information, including one or more clinicopathological values about each subject, one or more genetic indications for each subject, physiological sensor data from each subject, ultrasound data from each subject, and myocardial assessments of each subject. Each subject may have a unique identifier, and the unique identifier may reference ultrasound data (e.g. from an ultrasound device 106 or 116 in FIG. 1 ), physiological sensor data (e.g. from a physiological sensor 108 or 118 from FIG. 1), and myocardial assessments of the subject (e.g. those generated by the myocardial work unit 226). The subject database 224 may include subject information for a population of subjects, including more than 1 ,000, 10,000, or more than 100,000 subjects.

[0102] The myocardial work unit 226 may receive data from the database 224, the ultrasound device (e.g. ultrasound device 106 or 116 in FIG. 1 ), the physiological sensor (e.g. the physiological sensor 108 or 118 in FIG. 1 ), or from a user at the device 200 entering data input. The myocardial work unit 226 may operate the method of FIGs. 3 or 4 in order to generate a myocardial work assessment for a subject. The myocardial work assessment may be stored in database 224, may be displayed on display 206, or may be transmitted.

[0103] The Web / API Unit 228 may be a web-based application or Application Programming Interface (API) such as a REST (REpresentational State Transfer) API. The API may communicate in a format such as XML, JSON, or other interchange format.

[0104] The Web / API Unit 228 may receive a myocardial assessment request including data from an ultrasound device and a physiological sensor, may apply methods in FIGs. 3 and 4 to determine a myocardial assessment.

[0105] Referring next to FIG. 3 there is shown a method diagram 300 for assessing one-beat segmental myocardial work in accordance with one or more embodiments. It is understood that the method 300 may be performed by an ultrasound device (see e.g. 106 or 116 in FIG. 1 ) in communication with a physiological sensor, a wearable device (see e.g. 120) or a user device (see e.g. 102 in FIG. 1 ).

[0106] Referring to 302, a patient or subject (see e.g. subject 110 in FIG. 1 ) has ultrasound images taken using an ultrasound device 304 (see e.g. ultrasound device 106 and 116 in FIG. 1 ) and at the same time, has their physiological signal 310 (see e.g. physiological sensor 108 and 118 in FIG. 1) collected. The ultrasound images collected from the ultrasound device 304 are indexed using the physiological signal 310.

[0107] At 306 echocardiography may be performed on the subject (see e.g. 110 in FIG. 1 ) to obtain a myocardial strain metric. For example, echocardiography may be performed using GE Vivid-E95 Ultrasound system (GE Healthcare, USA) equipped with a GE 6S-D or M5Sc-D phased-array ultrasound probe. The following echocardiographic parameters may be recorded: IVS end-diastolic thickness, LV ejection fraction by Simpson’s method, indices of mitral inflow pulsed Doppler, MV annulus tissue Doppler and pulmonary veins pulsed Doppler, LA volume, LV global longitudinal strain (GLS). The presence of resting or provocable LV outflow tract obstruction (LVOTO) with peak gradient > 50 mmHg, and systolic anterior motion (SAM) of the mitral valve were recorded in the HCM group. The mean and the peak gradients of the aortic valve may be recorded for the subject.

[0108] A myocardial thickness metric may be determined. For example, a thickness of the basal antero-septal segment throughout the cardiac cycle may be measured on the parasternal short axis M-Mode acquisition using a custom-made semi-automatized graphic user interface (MATLAB, Natick, MA, USA), and based on the same ECG-based timescale than the other parameters. Myocardial thickness may thus be assessed at a plurality of different timepoints of the ECG cycle (at the beginning, middle and end of each ECG segment, see below) and a one-beat thickness vs. time curve was created for each patient. For example, the septal thickness may be assessed at 40 timepoints of the ECG cycle.

[0109] The longitudinal strain of the segment may be obtained. For example, the basal antero-septal segment strain may be obtained from the apical 3-chamber STE acquisition. Like the thickness curve acquisition, the strain values may be reported at a plurality of different timepoints of the ECG cycle using a custom-made semi-automatized graphic user interface (MATLAB, Natick, MA, USA) in order to determine a one-beat strain vs. time curve for each patient.

[0110] At 308, ultrasound acquisitions may be used to determined a myocardial stiffness metric. For example, ultrafast ultrasound acquisitions were performed during the same echocardiography examination of the subject. For example, a Verasonics Vantage system (model #ASAO0394, Verasonics Inc., Kirkland, Washington, USA) with a phased array ultrasound probe (GE 6S-D) may be used.

[0111] In one embodiment, the echocardiography and the ultrasound imaging may be performed by the same ultrasound device. In an alternate embodiment, the echocardiography and the ultrasound imaging may be performed by different devices.

[0112] The determination of myocardial stiffness metric may include using a focused ultrasound beam to generate shear waves in the myocardium and the resulting shear wave propagation may be assessed using unfocused diverging waves and tissue Doppler processing. Shear wave velocities may be measured in a segment. For example, the basal antero-septal segment in two orthogonal parasternal views (short and long axis) may be used, at 20 different timepoints of the cardiac cycle. Post-processing of the SWE data may be performed using MATLAB software (Natick, MA, USA), or another software package. A mean SW velocity with standard deviation (SD) may be determined for every time point in the cardiac cycle. The one-beat ECG trace of every patient may be divided into 20 reproducible timepoints, from a R wave to the next one, using a semi-automatized software to standardize and correct variations related to heart rate. Acquisitions throughout the cardiac cycle may be obtained using 2 sets of 10 pushes triggered by an electrocardiogram (ECG) with a 100 ms incremental delay in parasternal short and long axis views.

[0113] The collected cardiac ultrasound images may be post-processed to visualize the shear waves and compute their velocities. The entire cardiac cycle may be divided in 20 time points using the ECG wave as a reference with 8 time points in systole and 12 time points in diastole. The ECG tracing may be divided into 20 time points using an semiautomatized software to standardize and correct variations related to heart rate and RR interval. Measurements during the isovolumetric contraction (IVC) and isovolumetric relaxation (IVR) periods may be obtained at the time of mitral valve closure (MVC) and aortic valve closure (AVC) based on ultrafast B-mode cineloops

[0114] Myocardial stiffness may be estimated from the shear wave velocities using the Young’s modulus equation: E = p.c2 (with E the Young’s modulus in Pa, p the density of the medium in kg.m-3 (« 1000 kg.m-3), c the shear wave velocity in m.s-1 ). Peak systolic myocardial stiffness may be defined as the maximal value of the myocardial stiffness during the cardiac cycle. Diastolic stiffness may be defined as the averaged value of myocardial stiffness during the diastolic stiffness plateau (from the minimal to the end-diastolic value of myocardial stiffness).

[0115] It is noted that the present shear-wave analysis is distinguished from conventional methods because conventional methods only use signals to provide shearwave analysis. Present methods use signals and image analysis.Myocardial work calculation

[0116] Referring to 312, work (denoted W) is the one-beat segmental work, as the cumulative work exerted by a particular segment throughout a single cardiac cycle. For example, one segment that may be analysed may be the basal antero-septal segment. W quantifies the energy expended by a segment during its contribution to the heart’s contraction. To determine this measure, the energy consumption of the segment is assessed at each moment of the cardiac cycle, known as the instant segmental work. At any given time, this is defined as the product of the segmental stress, the cross-sectional surface area upon which this stress acts, and the change in segment length in response to this stress. These parameters may be obtained using ultrasound techniques including shear-wave elastography, M-Mode imaging, and strain imaging, respectively.Subsequently, the one-beat segmental work may be determined by integrating the instant segmental work over the entire cardiac cycle. Each physical quantity can be decomposed into a combination of other parameters as described in FIG. 11 , which may be ultimately measurable using different ultrasound techniques.

[0117] The “one-beat work density” represents the work by unit of volume. This may capture the work exerted by an infinitesimal unit of myocardial muscle at the individual fiber level. Both one-beat segmental work and one-beat work density are depicted in FIG. 6. The derivation of the one-beat segmental work and one-beat work density follow (see e.g. an example diagram of the physical model in FIG. 10).Derivation of the one-beat segmental work

[0118] The basal antero-septal segment exerts stresses on the myocardium along multiple directions: circumferential stresses in the heart short-axis view, radial stresses in trans-mural direction, and a longitudinal stress in the base-to-apex axis that contributes to the septum shortening. We only focus on this latter longitudinal contraction stress, represented in FIG. 10 (o). The total force (F) of the segment in this direction is then obtained by the product of the stress o (a force per surface unit, expressed in Pa) with the surface (A x h) on which it is applied:F = h. A. 5 Equation 1

[0119] with A the thickness of the myocardium and / ? an arbitrary length out-of-plane. We assume that during a very short period [t, t + dt], these physical values are constants.The instant segmental work 51 / 1 / of the contraction force in this period is, by definition, obtained as the product of the force with the segment’s length variation along the same longitudinal axis (dL in FIG. 10). In equation, it writes:8W = F. dL Equation 2 or 6W = h. A. G. dL Equation s

[0120] With L(t) being the length of the segment at time t and Lo the segment end- diastolic length, we can express dL and define the strain e(t) and strain rate e(t) as follows(FIG. 10): dL = L(t + dt) — L(t) Equation 4Equation 5ds(t) e(t) = Equation 6 dt dL e(t) = Equation 7Lodt

[0121] By noting E(t) the segment stiffness over time, and applying Hookes’s law for the segment, we can express the segment stress: o-(t) = E(t). e(t) Equation 8

[0122] Finally, by injecting Equation 7 and Equation 8 in Equation 3, we obtain the expression of the instant segmental work 51 / 1 / as a function of parameters measured by ultrasound (respectively, A(t) with M-Mode, E(t) with shear-wave elastography, Lo, E(t) and e(t) with strain imaging).<W(t) = Lo. h. A(t). E(t). e(t). e(t) dt Equation 9

[0123] Integrating the instant myocardial work 51 / 1 / over the full cardiac cycle, we obtain the one-beat segmental work W of the basal antero-septal segment which is the energy spent in one beat by this segment during the entire cardiac cycle (FIG. 6). Equation 10 Equation 11

[0124] Dividing by h allows us to obtain a work per unit of length, independent of the arbitrary value h, and then expressed in Joule per meter (J.rrr1).

[0125] For consistency of the units, we chose the sign of dL so that it is negative if the segment contracts. In this way, we make sure to obtain a positive work if the contraction stress o actually manages to shorten the segment (o<0,dL<0, so W>0).

[0126] Finally, we define the work density 6wdby removing all terms in the expression of the instant segment work 5W that are dependent of geometrical dimensions, namely Lo, h and A(t).<5wd(t) = E(t)~ . E(t~). E(t)~ dt Equation 12Or written differently:<5wd(t) = <7(t). e(t). dt Equation 13

[0127] Ultimately, the one-beat work density wdis obtained by integrating over the full cardiac cycle :Equation 14Equation 15

[0128] This index is expressed in kPa.%. Noticeably, Equation 15 is also the expression of the area within the o =parametric curve, according to Green’s theorem (16). In other words, the one-beat work density is the area of the stress-strain loop.

[0129] In addition to the two time-averaged variables, the instant value of work throughout the cardiac cycle (FIGs. 10 and 6) may be determined. By convention, the axes orientations may be selected so that a positive work corresponds to segment contraction. Consequently, the positive segment of the instant work curve represents the actual contraction of the segment during systole and is termed "contributive work", as it contributes to the pumping action of the heart. Conversely, the negative segment of the instant work curve, occurring physiologically in early diastole, signifies the work expended by the segment that does not contribute to segment shortening, as the resultant forces lead to segment lengthening. This negative work is termed “dissipative work” and can be viewed as the energy expended to resist the stretching of the segment. Instances where dissipative work occurs during systole or contributive work during diastole are considered as desynchronized work. The timing of aortic valve closure (AVC) may be determined from the cine-echo loop and utilized to distinguish between systole and diastole using the same time scale based on the ECG segmentation described earlier.

[0130] At 314, the myocardial assessment, including any of the determined myocardial work of the subject, the myocardial thickness metric, the myocardial strain metric, and the myocardial stiffness metric may be displayed to a user (or the subject), for example in a user interface such as those generated by Ul interface engine 214 (see e.g. FIG. 2).

[0131]

[0132] NTD: Re: analysis of shear wave vs. image analysis. Conventional solutions only use signals to provide SW analysis. OV uses signal -> image in order to provide analysis and analyze it.

[0133] NTD: Re: analysis of same signal used to identify strain, stiffness and thickness. (RF) signal used to provide info from human body --> use the signals in order to get --> using displacement information can identify velocity which is related to stiffness. Strain -> each particle displacement

[0134] NTD: Re: claim 4 can assess myocardial work everywhere - there are 17 segments in each ventricle. Anatomical definition but stay vague and incorporate features for multiple segments. Keep these concepts in every segment and not just in one. In terms of methods, can do it anywhere.

[0135] Referring next to FIG. 4 there is shown another method drawing 400 of assessing one-beat segmental myocardial work in accordance with one or more embodiments.

[0136] At 402, receiving, at a processor from a physiological sensor, a physiological signal of the subject.

[0137] At 404, in response to an ultrasound excitation signal from an ultrasound device, receiving a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal.

[0138] At 406, determining, at the processor, a myocardial stiffness metric from the plurality of cardiac ultrasound images.

[0139] At 408, determining, at the processor, the myocardial work of the subject, the myocardial work determined based on a myocardial thickness metric, a myocardial strain metric, and the myocardial stiffness metric.

[0140] At 410, providing, a user interface comprising the myocardial work of the subject.

[0141] Optionally, the method may further include: determining, at the processor, the myocardial thickness metric from the plurality of cardiac ultrasound images; and determining, at the processor, the myocardial strain metric from the plurality of cardiac ultrasound images.

[0142] Optionally, the myocardial thickness metric, the myocardial strain metric, and the myocardial stiffness metric may be each assessed during one cardiac cycle.

[0143] Optionally, the myocardial thickness metric may comprise a thickness metric of a myocardial segment of the subject; the myocardial strain metric may comprise a strain metric of a myocardial segment of the subject; and the myocardial stiffness metric may comprise a stiffness metric of a myocardial segment of the subject.

[0144] Optionally, the method may further comprise, displaying at a display device in communication with the processor, the user interface comprising the myocardial work.

[0145] Optionally, the ultrasound device may capture the plurality of cardiac ultrasound images at a framerate of at least 100 frames per second.

[0146] Optionally, the ultrasound device may capture the plurality of cardiac ultrasound images using a focused beam.

[0147] Optionally, the physiological sensor may be one of an electrocardiogram (ECG) sensor, an electrophysiological sensor, and an electromyogram sensor.

[0148] Optionally, the determining the myocardial stiffness metric may further comprise: delivering, using the ultrasound device, a shear wave signal to the subject; receiving, from the ultrasound device, the plurality of cardiac ultrasound images; and determining, at the processor, at least one shear wave metric or at least one transit time metric of the shear wave signal based on the plurality of cardiac ultrasound images.

[0149] Optionally, the at least one shear wave metric may comprise a velocity metric or a transit time metric during a single cardiac cycle.

[0150] Optionally, the at least one shear wave metric may be based on natural waves or waves induced by the ultrasound excitation signal.

[0151] Optionally, the myocardial work of the subject may be determined by the equation wd= f (t)c(t)dt where a represents the myocardial stress metric, E represents the myocardial strain metric, and a represents the myocardial stiffness metric.

[0152] Optionally, the myocardial work of the subject may be determined by the equation wd= f (t)c(t)dt where a represents the myocardial stress metric, E represents the myocardial strain metric, and a represents the myocardial stiffness metric multiplied by the myocardial strain.

[0153] The present invention has been described here by way of example only. Various modification and variations may be made to these exemplary embodiments without departing from the spirit and scope of the invention, which is limited only by the appended claims.EXAMPLESExample 1: Toward non-invasive assessment of myocardial work using myocardial stiffness and strain: a human pilot studyMethodsPopulation and study design

[0154] Between January 2021 and December 2022, patients from less than 18 years of age with HCM or with severe AS, and age-matched healthy volunteers (HV) were prospectively enrolled at The Hospital for Sick Children, Toronto, Ontario, Canada.Demographic and clinical data were anonymously collected from medical charts and recorded in a dedicated online database. Inclusion criteria for HCM group were LV hypertrophy (LVH) defined as a Z-score of interventricular septum (IVS) > +2.5 in the absence of family history, or > +2 in the presence of a positive family history, positive genetic testing for sarcomeric HCM, or no detectable metabolic, syndromic or neuromuscular cause for HCM (37). Inclusion criteria for AS group were severe aortic valve stenosis defined as mean gradient > 40mm Hg by Doppler-echocardiography and normal LV ejection fraction. Inclusion criteria for HV group were absence of current or previous history of congenital or acquired heart disease and normal echocardiogram.

[0155] This study was approved by The Hospital for Sick Children Research Ethics Board (REB #1000074747 and #1000070089) and all subjects or their legal guardians provided informed written consent.Myocardial work calculation

[0156] One-beat segmental work is defined as, denoted as W, as the cumulative work exerted by the basal antero-septal segment throughout a single cardiac cycle. Essentially, this quantifies the energy expended by this segment during its contribution to the heart’s contraction. To derive this measure, energy consumption of the segment at each moment of the cardiac cycle was assessed, known as the instant segmental work. At any given time, this is defined as the product of the segmental stress, the cross-sectional surface area upon which this stress acts, and the change in segment length in response to this stress. These parameters can be obtained using ultrasound techniques: shear-wave elastography, M-Mode imaging, and strain imaging, respectively. Subsequently, the one- beat segmental work is determined by integrating the instant segmental work over the entire cardiac cycle. Table 1 , outlines how each physical quantity can be decomposed into a combination of other parameters that are ultimately measurable using different ultrasound techniques.

[0157] The “one-beat work density” represents the work by unit of volume. This concept captures the work exerted by an infinitesimal unit of myocardial muscle at the individual fiber level. Both one-beat segmental work and one-beat work density are depicted in Figure 6. The rigorous mathematical derivation of the one-beat segmental work and one-beat work density are described above in the detailed description, with a diagram of the physical model (Figure 10).

[0158] In addition to the two time-averaged variables, the intricacies of the instant value of work throughout the cardiac cycle are delved into (Figure 11 and Figure 6). By convention, the axes orientations are selected so that a positive work corresponds to segment contraction. Consequently, the positive segment of the instant work curve represents the actual contraction of the segment during systole and is termed "contributive work", as it contributes to the pumping action of the heart. Conversely, the negative segment of the instant work curve, occurring physiologically in early diastole, signifies thework expended by the segment that does not contribute to segment shortening, as the resultant forces lead to segment lengthening. This negative work is termed “dissipative work” and can be viewed as the energy expended to resist the stretching of the segment. Instances where dissipative work occurs during systole or contributive work during diastole are considered as desynchronized work. The timing of aortic valve closure (AVC) was determined from the cine-echo loop and utilized to distinguish between systole and diastole using the same time scale based on the ECG segmentation described earlier.Echocardiography

[0159] Echocardiography was performed using GE Vivid-E95 Ultrasound system (GE Healthcare, USA) equipped with a GE 6S-D or M5Sc-D phased-array ultrasound probe. The following echocardiographic parameters were recorded in all groups: IVS end- diastolic thickness, LV ejection fraction by Simpson’s method, indices of mitral inflow pulsed Doppler, MV annulus tissue Doppler and pulmonary veins pulsed Doppler, LA volume, LV global longitudinal strain (GLS). The presence of resting or provocable LV outflow tract obstruction (LVOTO) with peak gradient > 50 mmHg, and systolic anterior motion (SAM) of the mitral valve were recorded in the HCM group. The mean and the peak gradients of the aortic valve were recorded in the AS group.

[0160] Thickness of the basal antero-septal segment throughout the cardiac cycle was measured on the parasternal short axis M-Mode acquisition using a custom-made semi-automatized graphic user interface (MATLAB, Natick, MA, USA), and based on the same ECG-based timescale than the other parameters. Septal thickness was thus assessed at 40 different timepoints of the ECG cycle (at the beginning, middle and end of each ECG segment, see below) and a one-beat thickness vs. time curve was created for each patient.

[0161] The longitudinal strain of the basal antero-septal segment was obtained on the apical 3-chamber STE acquisition. Like the thickness curve acquisition, the strain values were reported at forty different timepoints of the ECG cycle using a custom-madesemi-automatized graphic user interface (MATLAB, Natick, MA, USA) creating a one-beat strain vs. time curve for each patient.Ultra fast Ultrasound Imaging

[0162] Ultrafast ultrasound acquisitions were performed during the same echocardiography examination using a Verasonics Vantage system (model #ASAO0394, Verasonics Inc., Kirkland, Washington, USA) with a phased array ultrasound probe (GE 6S- D).

[0163] The shear wave imaging technique used in this study has previously been published (27,29,32,38). Briefly, a focused ultrasound beam was used to generate shear waves in the myocardium and the resulting shear wave propagation was assessed using unfocused diverging waves and tissue Doppler processing. Shear wave velocities were measured in the basal antero-septal segment in two orthogonal parasternal views (short and long axis), at 20 different timepoints of the cardiac cycle. Post-processing of the SWE data was performed using MATLAB software (Natick, MA, USA) and a mean SW velocity with standard deviation (SD) was calculated for every time point in the cardiac cycle. The one-beat ECG trace of every patient was divided into 20 reproducible timepoints, from a R wave to the next one, using a semi-automatized software to standardize and correct variations related to heart rate.

[0164] Myocardial stiffness was estimated from the shear wave velocities using the Young’s modulus equation: E = p.c2(with E the Young’s modulus in Pa, p the density of the medium in kg.rrr3(« 1000 kg.rrr3), c the shear wave velocity in m.s’1). Peak systolic myocardial stiffness was defined as the maximal value of the myocardial stiffness during the cardiac cycle. Diastolic stiffness was defined as the averaged value of myocardial stiffness during the diastolic stiffness plateau (from the minimal to the end-diastolic value of myocardial stiffness).Statistics

[0165] Continuous variables were presented as mean and standard deviation (SD) when the distribution could be reasonably considered normal and as median with 25thto 75thpercentile, when the distribution was skewed. Categorial data were expressed ascounts and percentages. Given the wide heterogeneity of the HCM group in terms of myocardial stiffness and work values, Welch’s t-test was used to compare the groups. A value of p <0.05 was considered significant. Statistical analyses were performed using R 4.0.1 and GraphPad Prism 9.5.1.ResultsDemographic and clinical characteristics

[0166] Forty-five patients were included in the study including 20 patients in the HV group (mean age = 9.8 ± 5.3 years, sex ratio = 0.55), 20 in the HCM group (mean age = 10.0 ± 6.1 years, sex ratio = 0.65) and 5 in the AS group (mean age = 5.3 ± 4.3 years of age, sex ratio = 0.80). The patients’ weight and body surface area were similar between HV (43.6 ± 31 .6 kg and 1 .24 ± 0.6 m2, respectively) and HCM (40.8 ± 26.3 kg and 1 .20 ± 0.54 m2, respectively) but lower in AS group (28.6 ± 23.4 kg and 0.88 ± 0.53 m2, respectively).

[0167] All HCM patients had predominantly septal hypertrophy. A pathogenic sarcomeric gene variant was identified in 15 patients (75%).Conventional imaging findings

[0168] Echocardiography results are summarized in Figure 12. Not enough direct measures of systolic pulmonary pressure were available to be reported, but all patients in HV and AS groups and most patients in HCM group have indirect signs of normal pulmonary pressure. No greater than mild aortic regurgitation was observed in the AS group.Myocardial stiffness, strain, and thickness

[0169] The variations of myocardial stiffness, myocardial strain, myocardial strain rate and wall thickness of the basal antero-septal segment are presented throughout the cardiac cycle in Figure 7A-D. The peak systolic stiffness was higher in the AS group (64.8 ± 6.4 kPa) compared to HV (46.7 ± 14.3 kPa), p<0.01 and not statistically different between HCM (62.3 ± 56.8 kPa) and HV, p=0.25. The diastolic myocardial stiffness was higher in HCM group (25.8 ± 26.9 kPa) compared to HV (5.9 ± 1 .8 kPa), p<0.01 , and not statistically different between HV and AS patients (7.8 ± 2.2 kPa), p=0.13.Myocardial stress

[0170] The variation of myocardial stress is presented throughout the cardiac cycle in Figure 7E. The negative peak myocardial stress was lower in the HCM (-4.1 ± 4.6) compared to HV (-8.0 ± 3.3 kPa), p<0.01 and higher in AS (-12.3 ± 1 .7 kPa) than in HV, p<0.01.Myocardial work

[0171] Comparison of one-beat segmental work, contributive work and dissipative work between groups are presented in Figure 8 (panel A and B), as well as quantification of desynchronized work (panel C).

[0172] The values of the stress-strain loop area and stiffness-strain loop area in each group are summarized in Figure 9.Discussion

[0173] In this study, the technical feasibility of calculating segmental myocardial work based on measurements of myocardial stiffness, strain, and thickness was demonstrated. Applying this technique to healthy children, HCM and AS pediatric patients, it was observed that one-beat myocardial work of the basal antero-septal segment is higher in AS and considerably reduced in HCM, compared with healthy controls.Methodological considerations

[0174] The rationale of the method combining stiffness and strain assessments relies on Hooke's law. The main advantages of this approach are: 1 ) to represent the intrinsic constraint occurring within each myocardial segment, providing an individualized estimation of stress for each analyzed segment, 2) to consider the geometry of the myocardial segment and its variation during the cardiac cycle, 3) to provide a work measure in unit of consumed energy (J) per distance (m). Moreover, studies have demonstrated excellent intra- and inter-operator reproducibility in strain and stiffness measurement (26,27,39).

[0175] The presented method was not directly compared to the pressure-strain loop technique for different reasons. First, to date, the pressure-strain loop technique is not considered the gold standard for myocardial work assessment (17,40-42). Second, thepressure-strain technique is not well established in children (15,43). Third, there is currently no evidence supporting the notion that blood pressure serves as a reliable surrogate for local myocardial stress within the septal wall, particularly given the unknown precise geometry of the chamber. Consequently, comparing the stress values derived from this model with the studies’ localized estimates may result in discrepancies that are challenging to interpret.

[0176] Stiffness-based myocardial work calculation, despite its promising potential, comes with several limitations. First, the technique has not yet reached the stage of routine clinical usability due to the required ultrafast ultrasound system (28), and still requires an off-line phase of analysis for shear wave velocity measurement. Second, it relies on assumptions regarding application of Young's modulus and Hooke's law equations in a one-dimensional tensor (i.e. one direction) in the myocardium, which is neither a homogeneous nor a continuous medium (44). This may introduce uncertainties in the accuracy of the measurements. This limitation may soon be addressed with the rapid advancements in volumetric ultrafast ultrasound. Nevertheless, Hooke's law has previously been employed to analyze different biological tissues, including vessels, and has been regarded as a plausible model for simplifying the study of cardiovascular biomechanics (45-47). Finally, only one cardiac segment is assessed in this feasibility study, and due to inherent imaging limitations, only two dimensions of the segment volume in the equation were incorporated.Myocardial stiffness and work in cardiac physiology

[0177] Non-invasive assessment of myocardial work in humans has paved the way for a better understanding of cardiac mechanics and metabolism. In this study, myocardial work assessed by stiffness-strain data increases in the setting of aortic valve stenosis. Increased stiffness and preserved strain in AS patients was observed. This phenomenon can be attributed to effective ventricular-arterial coupling which compensates for left ventricular pressure overload (48). Higher systolic myocardial stiffness in hypercontractile ventricles was already observed in preclinical studies that have demonstrated the relationship between systolic myocardial stiffness and contractility (30,49). These results support the hypothesis that higher systolic myocardial stiffness does not necessarily implytissue remodeling and / or myocardial fibrosis but may be only the reflect of contractile function adaptation (30,31 ,49).

[0178] These results also provide valuable insights into the analysis of stress-strain and stiffness-strain loops analysis. It was showed that stiffness-strain loop area evolves in the same way as the stress-strain loop area, with the same magnitude of difference between groups. As the strain-stress loop area represents the one-beat work density (Figure 10), this implies that the stiffness-strain loop area could serve as an additional index of myocardial work per unit of volume, regardless of the segment’s dimensions. Nevertheless, these findings indicate that when comparing AS versus HV, one-beat segmental myocardial work demonstrates superior discriminatory ability compared to geometry-independent stress-strain or stiffness-strain loop area (Figure 8 and 9). This means that the higher one-beat segmental work observed in AS is not solely attributable to augmented work at the fiber level (i.e. the work density); rather, it is also associated with the adaptive hypertrophy of the analyzed segment. Consequently, this results in a further increase of the work when considering the segment's geometry. In contrast, for HCM, the diminished fiber work is such that the incorporation of segment geometry has a comparatively minimal impact on the overall work values.Clinical impact

[0179] These findings corroborate results already observed in adults for both HCM (12,33,34) and degenerative AS (35,36). Sarcomeric HCM is expected to exhibit heightened myocardial work in its initial stages, transitioning to reduced work in later stages alongside structural tissue disruptions (33,34,50). This study’s HCM cohort predominantly comprises individuals with severe myocardial damage, wherein a significant reduction in work within the basal anteroseptal segment is observed. This reduction is primarily attributed to severe strain reduction in hypertrophied segments, likely due to a high degree of fiber disarray (51 ,52). Similar findings have been reported in adult HCM patients using regional pressure-strain loop area analysis, which was also associated with septal dyssynchrony (53,54). On the other hand, AS patients with LV hypertrophy and preserved systolic function despite a high gradient, exhibit increased myocardial work. This increase is attributed to the adaptive remodeling of the myocardium at the chamber (LV mass andgeometry), the segment (increased one-beat segmental work) and the fiber (increased work density) levels.

[0180] By integrating 2D geometrical variations of the segment throughout the cardiac cycle, this study’s technique proves particularly relevant in ventricles characterized by extreme dimensions and / or pathological wall thickness-to-radius ratios (8,9).Nevertheless, the application of this technique is not well-established in children with only two recent studies incorporating pediatric subjects aged 3-4 years (15,43). The authors of these studies conclude that non-invasive myocardial work using the commercially available software package (Automated Functional Imaging; EchoPAC V.203, GE) is feasible in children, albeit with higher intra- and inter-observer variability compared to adults (15). Additionally, operators may encounter challenges in blood pressure measurement, especially in the youngest children (55). In observations, it was found that the study’s method is particularly well-suited for children, imposing no additional constraints on image acquisition compared to standard ultrasound.Clinical perspectives

[0181] The perspectives of this work hold promise but the method requires further validation to solidify its clinical relevance. One important avenue for validation involves comparing this technique against invasive pressure-volume loop area measurements. This comparison would help elucidate the relationship between chamber-level and segmentlevel assessments, providing insights into the reliability and accuracy of the method. Additionally, integrating in silico models into this research could offer valuable perspectives. These computational models can simulate various physiological conditions and pathologies, providing the ability to explore the performance of this technique in a controlled environment (56,57).

[0182] Ultimately, a potential future application lies in acquiring elastography data encompassing the entire heart. This feasibility arises from the ability to apply acoustic radiation force and track shear waves across all cardiac segments. When combined with strain measurements, this technique has the potential to offer comprehensive assessments of myocardial work for all ventricular and atrial segments, encompassing total heart myocardial work. This aspect holds particular relevance for clinical applications.Conclusion

[0183] This study demonstrated the technical feasibility of calculating one-beat segmental myocardial work based on measurements of myocardial stiffness, strain and thickness, throughout the cardiac cycle in humans. This method holds potential for clinical applications in diagnosing cardiac dysfunction and therapeutic monitoring. Future prospective studies will play a crucial role in establishing correlations between myocardial work results and clinical outcomes.Example 2: Revisiting the concept of” phenotype” in pediatric hypertrophic cardiomyopathy using myocardial stiffness and strain variations assessed by ultra fast ultrasound imagingMethodsPopulation and design

[0184] Patients from 0 to 18 years of age with G+P+ along with an age-matched population of G+P-patients and HVs were prospectively included at The Hospital for Sick Children, Toronto, Ontario, Canada. The inclusion criteria for the G+P+ were the presence of LV hypertrophy (LVH) defined as a Z-score of interventricular septum (IVS) > +2.5 in the absence of family history, or > +2 in case of family history, with morphological or genetic arguments for sarcomeric HCM and exclusion of other pediatric HCM causes (80). The inclusion criteria for the G+P- group was based on the identification of a known genetic mutation that causes sarcomeric HCM (59). Inclusion criteria for the HV group were good general health status, absence of current or previous history of congenital or acquired heart disease, and normal echocardiogram. All subjects gave informed written consent, and the study was approved by The Hospital for Sick Children Research Ethics Board (REB # 1000074747).Conventional echocardiographyEquipment

[0185] A full echocardiographic study was performed using GE Vivid-E95 Ultrasound system (GE Healthcare, USA) equipped with a 6S-D or M5Sc-D phased-array probe.Thickness measurement

[0186] The thickness of the basal antero-septal segment was measured on the parasternal short axis M-Mode acquisition, throughout the cardiac cycle. During the off-line analysis, the septal thickness values were reported at forty different timepoints of the ECG cycle using a semi-automatized graphic user interface, custom-created in MATLAB software (California, USA), and according to the same ECG-based timescale than the other parameters. The one-beat thickness vs. time curve was thus drawn for each patient and used as is for the myocardial work calculation step.Segmental strain measurement

[0187] The strain of the basal antero-septal segment was obtained on the 3-chamber STE acquisition, throughout the cardiac cycle. In a similar way to the thickness curve acquisition, the strain values were reported at forty different timepoints of the ECG cycle using a semi-automatized graphic user interface, custom-created in MATLAB software (California, USA) and according to the same ECG-based timescale than the other parameters. The one-beat strain vs. time curve was thus drawn for each patient and used as is for the myocardial work calculation step.Myocardial stiffness assessment by shear wave cardiac elastography

[0188] The SWE used in this study has previously been described (81 )(82). Ultrafast acquisitions for SWE were performed using the Verasonics Vantage systems (Vantage 256, Verasonics Inc., Kirkland, Washington) with a phased array ultrasound probe (GE 6S- D).

[0189] As illustrated in Figure 13C, an acoustic radiation force induced by a focused ultrasound beam was used to generate shear waves in the myocardial tissue and its propagation velocity was assessed using UUI with diverging waves. SW velocities were measured in the anteroseptal basal segment of the heart in two orthogonal parasternal views (short and long axis) as shown in Figure 13B. Post-processing of the SW Imaging data was performed in MATLAB (R2019a, The MathWorks Inc., Natick, MA, USA) and a mean SW velocity with standard deviation (SD) was calculated for every time point in the cardiac cycle.

[0190] Acquisitions throughout the cardiac cycle were obtained using 2 sets of 10 pushes triggered by an electrocardiogram (ECG) with a 100 ms incremental delay in parasternal short and long axis views (total of 20 acquisitions in short axis and 20 acquisitions in long axis per participant), shown in Figure 13B-C. Each push was followed by a 20ms UUI acquisition at 3320 frame per second. The collected frames were then postprocessed to visualize the shear waves and compute their velocities. The entire cardiac cycle was divided in 20 time points using the ECG wave as a reference with 8 time points in systole and 12 time points in diastole. The ECG tracing of every patient was divided into 20 time points using a semi-automatized software to standardize and correct variations related to heart rate and RR interval. Measurements during the isovolumetric contraction (IVC) and isovolumetric relaxation (IVR) periods were obtained at the time of mitral valve closure (MVC) and aortic valve closure (AVC) based on ultrafast B-mode cineloops. Acoustic output strictly complied with the FDA Track 3 recommendations (Ml<1 .9, Tl<3, ISPTA<720mW / cm2 and ISPPA<190W / cm2)(26).

[0191] According to previous works, abnormal mean diastolic myocardial stiffness (DMS) was defined as > 12 kPa (by applying Young’s modulus) in this current study (75,76,84).Myocardial Stiffness-Strain and Stress-Strain loops

[0192] Using ECG correlation, temporal loops were obtained combining stiffness and strain results or stress and strain results. Area of each loop was extracted using a semiautomatized graphic user interface, custom -created in MATLAB software (California, USA).Statistical analysis

[0193] The normality of the distribution of the quantitative variables of interest was established with the Shapiro Wilks test. Descriptive statistics was used for quantitative variables. To measure the central tendency we used means, for variation we used ranges and standard deviation. For qualitative variables proportions were used. For the inferential statistics, the Student's T test was used for scalar variables of independent samples. To determine correlation, Pearson coefficient was obtained for normally distributed data and Spearman Coefficient for non-normally distributed data. The Chi2 test was used tocompare proportions of dichotomous categorical variables. A value of p<0.05 was considered significant. One-way ANOVA was used to compare the quantitative variables for multiple groups. All statistical analyses were performed using MATLAB (version R2020b) and PRISM (2022).ResultsPopulation Characteristics

[0194] An age-matched population of 20 HV (mean age = 11.1 ± 4.5 years), 20 G+P+ (mean age = 11 ,6±5.3) and 20 G+P- (mean age = 11.1 ± 4.8 years) were included in the study (total = 60 participants). All G+P+ had predominantly septal hypertrophy. The demographic data of the population study are summarized in Table 1 . There is no significant difference in the weight and body surface area of the 3 groups (p> 0.05). For G+P+ patients, 7 / 20 (35%) patients were under cardiac medications, and all these patients were taking b-blockers. One G+P+ patient (1 / 20) was also taking amiodarone and one G+P+ patient was also taking calcium blockers.Conventional Echocardiography

[0195] Echocardiography results are summarized in Table 1 .Myocardial stiffness and strain

[0196] The variations throughout the cardiac cycle of myocardial stiffness, myocardial strain, myocardial strain rate and wall thickness of the basal antero-septal segment are presented in Figure 13. The results for myocardial stiffness (Figure 13a) show that there is no significant difference between the peak systolic stiffness between the HVs (48.7 ± 3.8 kPa), G+P+ (63.7 ± 22.8 kPa) or G+P- (44.3 ± 6.9 kPa), (p value = 0.13). However, the mean diastolic myocardial stiffness is significantly higher in G+P+ (21 .0 ± 4.9 kPa) compared to the HVs (7.15 ± 0.72 kPa). Within the G+P- group, participants were classified into two subgroups based on their observed mean diastolic stiffness: those with mean diastolic stiffness >12kPa HCM (n=9; 14.9 ± 1.4 kPa) and those with mean diastolic stiffness <12kPa (n=11 ; 7.66 ± 1 .2 kPa).

[0197] As shown in Figure 13c, the peak systolic strain shows no significant difference between the HVs and G+P- subgroups (p value = 0.41). However, the G+P+ group has significantly lower peak systolic strain (p value <0.01) compared to both the HVs and G+P- subgroups as shown in Figure 13b. Similar trend is showed for strain rate among the 4 groups shown in Figure 13d.Myocardial stress

[0198] As illustrated in Figure 14, the peak systolic myocardial stress is similar between the HVs and G+P- subgroups (p value = 0.46). However, G+P+ group has significantly lower peak systolic myocardial stress compared to HVs (p value <0.01 ) and G+P- subgroups (p value <0.01) as shown in Figure 13b.Myocardial Stiffness-Strain loops and Stress-Strain loops

[0199] The stiffness vs strain loops and stress vs strain loops for all groups are shown in Figure 15. The area of stiffness vs strain loop is similar for HVs and G+P- subgroup with normal diastolic stiffness (p value = 0.31 ) but significantly lower in G+P+ and G+P- subgroup with abnormal diastolic stiffness (p value < 0.01 ) (Figure 15a). Similar trend is observed for the myocardial stress vs strain loops for all 4 groups (Figure 15b).Myocardial work

[0200] Figure 16a. shows the variation in myocardial work throughout the cardiac cycle all groups. The comparison of global myocardial work, contributive work and dissipative work (Figure 16b.) shows that there is an overall significant reduction in the global work for G+P+ group which is not seen for G+P- subgroups. Desynchrony in work during systole and diastole was observed for G+P+ patients but was also observed for G+P- subgroup with abnormal Diastolic stiffness during diastole (Figure 16b. right panel). For G+P+, desynchronized work was quantified during systole as dissipative work of 17.3 ± 28.9 pJ / mm (45% of global work) and contributive work of 15.3 ± 18.0 pJ / mm (40% of the total work) during diastole. For G+P- with abnormal diastolic myocardial stiffness, desynchronized work was quantified during diastole as contributive work of 5.6 ± 3.2 pJ / mm (5% of the total work).Discussion

[0201] In the present investigation, it was showed that using exclusively myocardial thickness as parameter to discriminate normal and abnormal phenotype for G+ patients seems to be inadequate. Indeed, when analyzing myocardial stiffness and myocardial work, G+P- presented heterogenous results. For a total of 20 G+P- patients, some of them (n=11 ) presented myocardial stiffness and work results comparable to HV but others (n=9) showed significant differences compared to HV. Similarly, G+P+ showed significant abnormal myocardial stiffness, stress, and work. Although these parameters were not correlated with clinical outcomes in the study due its design, these results pave the way for an improved screening in patients with sarcomeric HCM mutations.

[0202] Traditionally, the quantification of myocardial work has relied on invasive measures, primarily the pressure-volume loop area, which provides major information about myocardial and cardiac performances. In recent years, non-invasive methods using pressure-deformation loop area have emerged which exhibit linear correlations between myocardial work and oxygen consumption as patient-friendly alternatives, revolutionizing the assessment of cardiac work. Russell et al. (2012) presented a novel clinical method for quantifying regional left ventricular pressure-strain loop area, highlighting its potential as a non-invasive index of myocardial work (73). In comparison, the utility of myocardial strain alone has shown little success as strain values can vary significantly depending on the child's age, size, and growth rate, making it challenging to establish definitive thresholds for pathological changes (68,85,86). By the Hooke’s law, the non-invasive assessment of myocardial work using myocardial stress, stain, and thickness allows us to integrate the geometric variations of the septal segment throughout the cardiac cycle which is key for a hypertrophic cardiomyopathy population and also for the growing healthy control group. Contrary to ventricular pressure estimates, myocardial stress over the full cardiac cycle captures the full spectrum of intrinsic constraint imposed on the myocardium (71 ,87). This difference is particularly important for cardiomyopathy assessment as it takes into account both the wall thickness and the myocardial strain (71 ,88).

[0203] From a clinical perspective, these results identify a subgroup in the G+P- group with abnormal myocardial stiffness and work which is clinically considered normal owing to normal septal thickness in accordance with the American College ofCardiology / American Heart Association guidelines on pediatric HCM (2). However, several studies have highlighted the substantial variability in phenotypic expression, even among individuals with the same genetic mutation (9,89). The incomplete penetrance of HCM- associated genetic mutations underscores the multifactorial nature of the disease and advancement in cardiac imaging has now become crucial for the integration of structural and functional information in addition to molecular analysis to provide the basis of new therapeutic interventions (90). Several echocardiographic parameters such as septal E / peak mitral annular velocity have shown to have a correlation with adverse events for HCM patients in adults (91 ). However, not only are normal reference values for echocardiographic parameters for a pediatric population lacking, but discrepancies between criteria within individuals prevent further classification and result in poor interobserver agreement (69,92). Previous studies done have demonstrated myocardial stiffness as a key parameter for the assessment of diastolic dysfunction in a pediatric population, both for healthy volunteers and HCM patients (74,77,93). In this recent study the importance of considering ventricular geometry when assessing myocardial stiffness was demonstrated, particularly myocardial thickness for the HCM population.

[0204] Subsequently, to see how the viscoelastic property of the myocardial tissue translate towards its dynamic function of actively contracting and relaxing during systole and diastole, myocardial stiffness was combined with myocardial strain to compute myocardial stress and subsequently myocardial work using Hook’s law. The results of this study show a significant reduction in myocardial work index for G+P+ patients compared to HVs, which is consistent with the results of similar studies that use pressure-strain loops for work assessment to hypothesize myocardial fibrosis in HCM patients (70). Furthermore, this study was able to quantify a significant degree of desynchrony in segmental work during systole and diastole for the G+P+ population. Dyssynchronous contraction in the absence of intra / interventricular conduction defects on the ECG is common in patients with HCM, especially if they have significant septal hypertrophy (94, 95). However, it was interesting to observe a small degree of desynchrony in segmental work during diastole for the G+P- subgroup with abnormal diastolic stiffness which may have the potential to be anew parameter for risk stratification of G+P- patients (86). These preliminary results pave the way for further longitudinal studies in correlation with clinical outcomes.Limitations

[0205] An important limitation of this study was the relatively small sample size of 20 subjects per group which made it difficult to compare results for specific genotypes. Further studies with large cohort numbers could help correlate results with specific gene mutations. Additionally, the assessment was performed for only the antero-basal septal segment which limits our ability to generalize results for other segments of the myocardium.Furthermore, pressure-volume loop area measurements were unable to be obtained for comparison with these results due to the highly invasive procedure of cardiac catheterization. Lastly from a methodological perspective, the myocardial strain assessment does not take into consideration the changes in these parameters along the circumferential tensor owing to the anisotropy of the myocardial tissue (96,97).Conclusion

[0206] In this study a new approach to estimating myocardial work by measuring myocardial stiffness over the full cardiac cycle in combination with myocardial strain and thickness was demonstrated. These results show that myocardial work assessment using stiffness and strain data has the potential to identify abnormal phenotype for patients with G+P- profile. Using these parameters, the definition of normal phenotype is this population could be optimized and better individualized.Table 1LVEF (Simpson) (%) 62.3 + 4.5 67.9 + 6.3* 65.3 + 4.3 64.1 + 3.0 < 0.01Strain ABS (%)-17.7 + 4.7 -19.3 + 3.7 < 0.01GLS (%) -20.2 + 1.5 -14.9 + 4.2* -21.2 + 2.4 -21.4 + 1.9 < 0.01MVE (m / s) 97.6 + 16.9 77 + 24.2* 97.9 + 14 103.4 + 4.7 0.02MVA (m / s) 45.7 + 13.4 45.4 + 14.0* 48.7 + 10 46.5 + 17 < 0.01MVE / A 2.3 + 0.9 1.8 + 0.68 2.1 + 0.4 2.5 + 0.9 0.05MVdt (ms) 135 + 53.3 164.6 + 45.2 132.9 + 36 150.9 + 51 0.09IVRT (ms) 70.1 + 12.5 67.7+ 21.4 67.3 + 7.1 81.4 + 10.1 0.13Mve' (cm / s) 18 + 3.3 8.6 + 4.6* 17.1 + 2.4 19.5 + 4.2 0.01Mva' (cm / s) 6.8 + 2.0 5.4 + 1.8 7.1 + 2.2 7.3 + 1.8 0.47MVE / e' 5.6 + 1.2 10.8 + 4.8* 5.8 + 0.8 5.6 + 1.6 0.03IVSe' (cm / s) 14 + 2.0 6.3 + 3.3 14.1 + 1.7 13.9 + 2.8 0.15IVSa' (cm / s) 6.4 + 1.6 6.7 + 2.7 5.9 + 1.6 5.8 + 0.8 0.1IVSE / e' 7.1 + 1.2 15.2+ 3.6* 7 + 1.2 7.8 + 2.3 < 0.01E / e' average 6.4 + 1.1 13+ 3.1* 6.4 + 0.9 6.7 + 1.9 < 0.01LA Area (4-chamber) (cm2) 10.9 + 3.7 16.9 + 6.1* 12.6 + 3.9 10.8 + 3.4 < 0.01LA Volume (mL) 24.9 + 12.3 48.6 + 8.1* 30 + 9.0 23.1 + 10.4 < 0.01LA Volume Index (mL / m2) 17.5 + 5.1 33.9 + 18.6 * 21.1 + 5.1 18.1 + 5.2 < 0.01Legend. BMI: body mass index; BSA: body surface area; DBP: diastolic blood pressure;RR: interval between 2 subsequent R waves; IVSD: interventricular septum diameter;LVEDd: left ventricular end-diastolic diameter; LVEDV: left ventricular end-diastolic volume; LVESV: left ventricular end-systolic volume; LVEF: left ventricular ejection fraction; ABS:Antero basal septal segment; GLS: global longitudinal strain; MVE = Mitral value E-wave velocity; MVA = Mitral value A-wave velocity; MVE / A = Mitral value E / A ratio; MVe’ = Mitral annulus tissue Doppler e’ velocity; MVa’ = Mitral annulus tissue Doppler a’ velocity; MVs’ = Mitral annulus tissue Doppler s’ velocity; MVE / e’ = Mitral valve E / e’ ratio; IVRT = Isovolumetric relaxation time; E-wave DT = E-wave deceleration time; IVS e’ = Septal annulus tissue Doppler e’ velocity; IVS a’ = Septal annulus tissue Doppler a’ velocity; IVS s’ = Septal annulus tissue Doppler s’ velocity; IVS E / e’ = Septal annulus tissue Doppler E / e’ ratio; LA Area = Left Atrial Area; LA Length = Left Atrial Length; HCM: hypertrophic cardiomyopathy; HV: healthy volunteers; IVS: interventricular septum; LA: left atrium; DMS: Diastolic Myocardial Stiffness. * represents significant difference between this group and all the remaining groups.REFERENCES1. 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Claims

CLAIMS:1 . A computer-implemented method for determining a myocardial work assessment for a subject, the method comprising:- receiving, at a processor from a physiological sensor, a physiological signal of the subject;- in response to an ultrasound excitation signal from an ultrasound device, receiving a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal;- determining, at the processor, a myocardial stiffness metric from the plurality of cardiac ultrasound images;- determining, at the processor, the myocardial work of the subject, the myocardial work determined based on a myocardial thickness metric, a myocardial strain metric, and the myocardial stiffness metric; and- providing, a user interface comprising the myocardial work of the subject.

2. The method of claim 1 further comprising: determining, at the processor, the myocardial thickness metric from the plurality of cardiac ultrasound images; and determining, at the processor, the myocardial strain metric from the plurality of cardiac ultrasound images.

3. The method of claim 1 or claim 2 wherein the myocardial thickness metric, the myocardial strain metric, and the myocardial stiffness metric are each assessed during one cardiac cycle.

4. The method of any one of claims 1 to 3 wherein: the myocardial thickness metric comprises a thickness metric of a myocardial segment of the subject;the myocardial strain metric comprises a strain metric of a myocardial segment of the subject; and the myocardial stiffness metric comprises a stiffness metric of a myocardial segment of the subject.

5. The method of any one of claims 1 to 4 further comprising, displaying at a display device in communication with the processor, the user interface comprising the myocardial work.

6. The method of any one of claims 1 to 5 wherein the ultrasound device captures the plurality of cardiac ultrasound images at a framerate of at least 100 frames per second.

7. The method of any one of claims 1 to 6 wherein the ultrasound device captures the plurality of cardiac ultrasound images using a focused beam.

8. The method of any one of claims 1 to 7, wherein the physiological sensor is one of an electrocardiogram (ECG) sensor, an electrophysiological sensor, and an electromyogram sensor.

9. The method of any one of claims 1 to 8, wherein determining the myocardial stiffness metric further comprises:- delivering, using the ultrasound device, a shear wave signal to the subject;- receiving, from the ultrasound device, the plurality of cardiac ultrasound images; and- determining, at the processor, at least one shear wave metric or at least one transit time metric of the shear wave signal based on the plurality of cardiac ultrasound images.

10. The method of claim 9 wherein the at least one shear wave metric comprises a velocity metric or a transit time metric during a single cardiac cycle.11 . The method of claim 9 or claim 10 wherein the at least one shear wave metric is based on natural waves or waves induced by the ultrasound excitation signal.

12. The method of any one of claims 1 to 11 , wherein the myocardial work of the subject is determined by the equation wd= f (t) (t)dt where a represents the myocardial stress metric, E represents the myocardial strain metric, and a represents the myocardial stiffness metric.

13. The method of any one of claims 1 to 11 , wherein the myocardial work of the subject is determined by the equation wd= f (t) (t)dt where a represents the myocardial stress metric, E represents the myocardial strain metric, and a represents the myocardial stiffness metric multiplied by the myocardial strain.

14. A system for determining a myocardial work assessment for a subject, the system comprising:- a physiological sensor for generating a physiological signal of the subject;- a ultrasound device for generating an ultrasound excitation signal, and receiving in response to the ultrasound excitation signal a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal;- a processor in communication with the physiological sensor and the ultrasound device, the processor configured to perform the method of any one of claims 1 to 12.

15. The system of claim 14 further comprising:- a support means for supporting the physiological sensor, the ultrasound device, and the processor in proximity to the subject;- an electrical storage means for powering the physiological sensor, the ultrasound device, and the processor in proximity to the subject; and- wherein the support means is wearable by the subject.

16. The system of claim 15 wherein the support means comprises a wearable patch attached to the subject’s body.

17. The system of any one of claims 14 to 16 further comprising:- an output device for outputting the user interface comprising the myocardial work of the subject.

18. A device for determining a myocardial work assessment for a subject, the device comprising:- a physiological sensor for generating a physiological signal of the subject;- a ultrasound device for generating an ultrasound excitation signal, and receiving in response to the ultrasound excitation signal a plurality of cardiac ultrasound signals of the subject, the plurality of cardiac ultrasound signals encoding a plurality of cardiac ultrasound images indexed based on the physiological signal;- a processor in communication with the physiological sensor, and the ultrasound device, the processor configured to perform the method of any one of claims 1 to 12.

19. The device of claim 18, further comprising:- a support means for supporting the physiological sensor, the ultrasound device, and the processor in proximity to the subject;- an electrical storage means for powering the physiological sensor, the ultrasound device, and the processor in proximity to the subject; and- wherein the support means is wearable by the subject.

20. The device of claim 19 wherein the support means comprises a wearable patch attached to the subject’s body.21 . The device of any one of claims 18 to 20, further comprising: an output device for outputting the user interface comprising the myocardial work of the subject.

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