System and method for aortic assessment based on kinetic energy loss

Abstract

Systems and methods for aortic assessment are disclosed. Imaging data representing a four-dimensional (4D) flow are received. Upon processing the imaging data, kinetic energy of the flow at a first location of an aorta is estimated and kinetic energy of the flow at a second location downstream of the first location is also estimated. A loss metric measuring loss of kinetic energy from the first location to the second location is computed. A prediction of risk of aortic dissection based on at least the loss metric is generated.

Description

SYSTEM AND METHOD FOR AORTIC ASSESSMENT BASED ON KINETIC ENERGY LOSSCROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application claims all benefit including priority to U.S. Provisional Patent Application No. 63 / 530,804 filed on August 4, 2023 entitled “SYSTEM AND METHOD FOR AORTIC ASSESSMENT BASED ON KINETIC ENERGY LOSS”, the entire contents of which is hereby incorporated by reference.FIELD

[0002] This disclosure relates to aortic assessment and more specifically relates to aortic assessment using magnetic resonance imaging.BACKGROUND

[0003] Aneurysms of the ascending aorta are at risk of acute type A aortic dissection (ATAAD) where an intimal tear leads to delamination of the wall layers. ATAAD is lifethreatening and those who survive to hospital require emergency surgery, which carries an 18% risk of mortality. Conversely, the mortality of elective aortic replacement is estimated to be much lower at 3.4%, with a much lower rate of morbidity as well. As such, it is beneficial to identify at-risk patients to recommend elective surgery before dissection onset. Current clinical guidelines for elective surgery are primarily based on the diameter of the aneurysm. This is despite several studies showing diameter thresholds to be insufficient in predicting dissection risk as many aneurysms below diameter cut-offs dissect, while some aneurysms above the threshold remain stable. Further, it has been shown that size provides limited insight into the microstructural changes of the tissue and its resultant biomechanical behavior. Thus, there is a need for improved methods for aortic assessment, as may be used for predicting dissection risk.SUMMARY

[0004] In accordance with an aspect, there is provided a computer-implemented method for aortic assessment. The method includes receiving imaging data representing a four-dimensional (4D) flow; upon processing the imaging data: estimating kinetic energy of the flow at a first location of an aorta; and estimating kinetic energy of the flow at a second location downstream of the first location; computing a loss metric measuring loss of kinetic energy from the first location to the second location; and generating a prediction of risk of aortic dissection based on at least the loss metric.

[0005] In this method, at least one of said estimating kinetic energy of the flow at the first location or said estimating kinetic energy of the flow at the second location may include computing per-voxel velocity using the imaging data.

[0006] In this method, at least one of said estimating kinetic energy of the flow at the first location or said estimating kinetic energy of the flow at the second location may include summing per-voxel kinetic energy over a plurality of voxels.

[0007] In this method, at least one of said estimating kinetic energy of the flow at the first location or said estimating kinetic energy of the flow at the second location may include applying an equation substantially of the form:wherein KE refers to the kinetic energy, p refers to a viscosity of the flow, vzrefers to a velocity of the / 1hvoxel of the plurality of voxels and \4oxe / refers to a volume of the voxel.

[0008] In this method, at least one of the first location and the second location may be located in an ascending aorta region.

[0009] In this method, the first location may be located proximate a sinotubular junction.

[0010] In this method, the second location may be located at a distal ascending aorta region.

[0011] In this method, the second location may be proximate an innominate artery.

[0012] In this method, the imaging data may include magnetic resonance imaging data.

[0013] In this method, the imaging data may be collected from a subject at an imaging system prior to said receiving, and the receiving may include receiving the imaging data from the imaging system.

[0014] In accordance with another aspect, there is provided a computer- implemented system for aortic assessment. The system includes at least one processor; memory in communication with the at least one processor; and software code stored in the memory. The software code when executed at the at least one processor causes the system to: receive imaging data representing a four-dimensional (4D) flow; upon processing the imaging data: estimate kinetic energy of the flow at a first location of an aorta; and estimate kinetic energy of the flow at a second location downstream of the first location; compute a loss metric measuring loss of kinetic energy from the first location to the second location; and generate a prediction of risk of aortic dissection based on at least the loss metric.

[0015] In this system, at least one of said estimating the kinetic energy of the flow at the first location or said estimating the kinetic energy of the flow at the second location may include computing per-voxel velocity using the imaging data.

[0016] In this system, at least one of said estimating the kinetic energy of the flow at the first location or said estimating the kinetic energy of the flow at the second location may include summing per-voxel kinetic energy over a plurality of voxels.

[0017] In this system, at least one of said estimating the kinetic energy of the flow at the first location or said estimating the kinetic energy of the flow at the second location may include applying an equation substantially of the form:wherein KE refers to the kinetic energy, p refers to a viscosity of the flow, v, refers to a velocity of the voxel and Vvoxei refers to a volume of the voxel.

[0018] In this system, at least one of the first location and the second location may be located in an ascending aorta region.

[0019] In this system, the first location may be located proximate a sinotubular junction.

[0020] In this system, the second location may be located at a distal ascending aorta region.

[0021] In this system, the second location may be proximate an innominate artery.

[0022] In this system, the imaging data may include magnetic resonance imaging data.

[0023] Many further features and combinations thereof concerning embodiments described herein will appear to those skilled in the art following a reading of the instant disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In the figures,

[0025] FIG. 1 is a schematic diagram of an aortic assessment system, in accordance with an embodiment;

[0026] FIG. 2 is a candy-cane view of an ascending aorta, in accordance with an embodiment;

[0027] FIG. 3 is a flowchart showing example operation of the aortic assessment system of FIG. 1, in accordance with an embodiment; and

[0028] FIG. 4A and FIG. 4B each is a photo of an aortic tissue sample, in accordance with an embodiment;

[0029] FIG. 5 depicts association between aortic strain and biomechanical properties, in accordance with an embodiment;

[0030] FIG. 6 depicts association between distensibility and biomechanical properties, in accordance with an embodiment;

[0031] FIG. 7 depicts association between compliance and biomechanical properties, in accordance with an embodiment;

[0032] FIG. 8 depicts association between arterial stiffness index and biomechanical properties, in accordance with an embodiment;

[0033] FIG. 9A depicts aortic pulse wave velocity in healthy imaging controls versus surgical patients, in accordance with an embodiment;

[0034] FIG. 9B depicts kinetic energy loss in healthy imaging controls versus surgical patients, in accordance with an embodiment;

[0035] FIG. 10 depicts association between aortic pulse wave velocity and biomechanical properties, in accordance with an embodiment;

[0036] FIG. 11 depicts association between kinetic energy loss and biomechanical parameters, in accordance with an embodiment; and

[0037] FIG. 12 is a schematic diagram of a computing device, in accordance with an embodiment.

[0038] These drawings depict exemplary embodiments for illustrative purposes, and variations, alternative configurations, alternative components and modifications may be made to these exemplary embodiments.DETAILED DESCRIPTION

[0039] FIG. 1 is a schematic diagram of an aortic assessment system 100, in accordance with an embodiment. As depicted, aortic assessment system 100 is interconnected with an imaging system 50 and receives therefrom imaging data representing four-dimensional (4D) flow. As detailed herein, assessment system 100 processes such imaging data to perform one or more assessments of an aorta. In some embodiments, such assessment includes assessment of viscoelastic properties of the ascending aorta. In the depicted embodiment, such assessment includes assessment of aortic dissection risk.

[0040] Conveniently, in some embodiments, such assessment may be performed ex vivo using imaging data that is collected in vivo. Conveniently, in some embodiments, such assessment may be performed non-invasively, e.g., using 4D flow data.

[0041] In the depicted embodiment, assessment system 100 uses 4D flow data to calculate kinetic energy loss in blood flow across two locations in an aorta. In particular, assessment system 100 determines kinetic energy loss from a first locationin the ascending aorta to a second location downstream of the first location. In some embodiments, the first location may be located proximate a sinotubular junction (STJ) and the second location may be located at a distal ascending aorta (DA) region. In some embodiments, the second location may be proximate the innominate artery.

[0042] Assessment system 100 computes a loss metric measuring loss of kinetic energy from the first location to the second location. Assessment system 100 generates a prediction of risk of aortic dissection based on this loss metric.

[0043] As depicted in FIG. 1 , assessment system includes an imaging interface 102, a loss estimator 104, and a predictor 106.

[0044] Imaging interface 102 includes a suitable combination of software and hardware configured to receive 4D flow data from imaging system 50. For example, imaging interface 102 may include a wired or wireless connection to imaging system 50 suitable for data transfer. In some embodiments, imaging interface 102 may be configured to receive imaging data from imaging system 50 in real-time or quasi realtime, e.g., during the course of an imaging procedure. In some embodiments, imaging interface 102 may be configured to receive flow data following an imaging procedure. In some embodiments, assessment system 100 may be integrated with imaging system 50. For example, assessment system 100 may be implemented as part of a suite of post processing tools integrated with imaging system 50.

[0045] In some embodiments, imaging system 50 is a magnetic resonance imaging (MRI) system. In the depicted embodiment, imaging system 50 is a Siemens Magnetom Biograph mMR (3T). Imaging system 50 may include dedicated coils. Imaging system 50 may include multiple 2D phase contrast (PC) acquisitions with retrospective ECG-gating. For temporal resolution, the number of phases may be kept to a maximum (e.g., 75-100 phases) while ensuring the imaging is completed with breath holding. Images of 6 mm thickness and 1.4-1.5 mm pixel spacing with velocity encoding (VENC) of 150 cm / s may be taken at the level of the STJ, mid-ascendingaorta and distal descending aorta at the level of the diaphragm. Steady-state free procession (SSFP) cine imaging may be performed in sagittal oblique candy cane orientation of the thoracic aorta for centerline length measurements. 4D flow imaging may be acquired with retrospective ECG-gating, using a total of 30 phases with a voxel size range of 1 .5-2.5 x 1 .5-2.5 x 2-2.5 mm3 and a VENC of 150 cm / s.

[0046] In some embodiments, imaging system 50 may be configured to utilize another suitable imaging modality, e.g., capable of providing 4D flow imaging data or providing data for obtaining voxel-wise 3D velocity data.

[0047] Loss estimator 104 computes a loss metric measuring loss of kinetic energy from the first location to the second location, such as, e.g., from the STJ to the DA. This loss metric may be referred to herein as Kinetic Energy Loss (KEL).

[0048] Loss estimator estimates KEL at desired locations (e.g., corresponding to certain levels) of the aorta by analyzing the 4D flow data.

[0049] Conventionally, the total kinetic energy (KE) of fluids can be calculated using Equation 1 :Equation 1where v is the velocity of the flow, V is the volume, and p is the viscosity of the fluid. Since loss estimator 104 is limited by the spatial resolution of the 4D flow data, the integral of Equation 1 can be approximated as a sum over the number of voxels at a level of the aorta. In the depicted embodiment, this approximation is expressed in Equation 2 wherein squares of flow velocities are summed voxel-wise:Equation 2 wherein KE again refers to the total kinetic energy, p refers to a viscosity of the flow, Vi refers to a velocity of the / 1hvoxel and Vvoxei refers to a volume of the voxel, based on a relevant imaging spatial resolution (e.g., an MRI resolution).

[0050] To obtain values for vz, three-directional velocity, measurements were taken per voxel at each of the levels of the STJ and the DA, which may be defined as oblique planes perpendicular to the direction of flow. FIG. 2 is a candy-cane view of the ascending aorta with the levels of the STJ and the DA shown in lines 202 and 204 respectively.

[0051] Loss estimator 104 computes an average value of KE over the cardiac cycle by summing the kinetic energy at each phase and dividing by the number of phases.

[0052] Loss estimator 104 computes a loss metric in the form of percent loss in kinetic energy ( / .e., KEL) from the STJ to the DA using Equation 3: Equation 3where KESTJ is the kinetic energy estimated at the ETJ (the first location), and KEDA is the kinetic energy estimated at the DA.

[0053] While some kinetic energy is lost through thermal and acoustic energy, a significant portion is lost through the aortic tissue, making it a surrogate for the biomechanical parameter energy loss as derived from biaxial tensile testing. The restis lost through increased viscous and turbulent kinetic energy loss, which is increased in aneurysmal versus normal aortas. KEL may therefore be considered a reflection of the combination of aortic tissue elastic energy loss and viscous and turbulent kinetic energy loss.

[0054] In other embodiments, the loss metric may be computed by loss estimator 104 otherwise than a percentage, e.g., as a ratio, fraction, or the like.

[0055] Predictor 106 generates one or more predictions associated with aortic assessment, based at least on a loss metric received from loss estimator 104. In the depicted embodiment, predictor 106 generates a prediction of risk of aortic dissection based on at least the KEL received from loss estimator 104. In other embodiments, predictor 106 may generate other predictions relating to an aorta, such as various viscoelastic properties of the ascending aorta.

[0056] Predictor 106 may predict that a patient is at risk of aortic dissection by comparing the KEL against a pre-defined threshold. In the depicted embodiment, predictor 106 may predict that a patient is at risk of aortic dissection if KEL is greater than a pre-defined threshold of 80%. This threshold may be established by correlating KEL with delamination strength or another measurement associated with risk of aortic dissection. For example, it has been shown that patients who suffered acute type A aortic dissection had aortic tissue with mean delamination strength of approximately 20 N / m, which in a studied cohort corresponds to a KEL threshold of 80%.

[0057] In other embodiments, this threshold may vary. In some embodiments, this threshold may be 60% or another suitable threshold. In some embodiments, this threshold may be a threshold between 60% and 80%. In some embodiments, predictor 106 may generate a likelihood metric indicating a likelihood of aortic dissection.

[0058] In some embodiments, predictor 106 may generate its prediction using KEL in combination with one or more other clinical variables for risk-assessment in patientswith aortic disease. In some embodiments, predictor 106 may generate its prediction using KEL in combination with one or more other imaging-derived metrics such as, for example, aortic pulse wave velocity (aPWV).

[0059] In the literature, energy loss of the ascending aorta has been linked to disease seventy and propensity for aortic dissection. In the present disclosure, it has been observed that as blood loses energy as it travels along the aorta, the majority is lost as kinetic energy. Conveniently, in some embodiments, assessment system 100 is able to perform the assessment disclosed herein without estimating or otherwise measuring total energy.

[0060] Each of Imaging interface 102, loss estimator 104, and predictor 106 may be implemented using a suitable combination of software and hardware components. Software components may be implemented using conventional programming languages such as Java, J#, C, C++, C#, Perl, Visual Basic, Ruby, Scala, etc. Software components may be in the form of one or more executable programs, scripts, routines, statically / dynamically linkable libraries, or the like.

[0061] The operation of assessment system 100 is further described with reference to the flowchart depicted in FIG. 3. Assessment system 100 performs the example operations depicted at blocks 300 and onward, in accordance with an embodiment.

[0062] Prior to block 302, imaging is performed on a subject using imaging system 50.

[0063] At block 302, assessment system 100 receives imaging data representing 4D flow. In embodiments in which imaging system 50 is an MRI system, the imaging data received by assessment system 100 includes MRI data.

[0064] At block 304, assessment system 100 processes the imaging data and upon such processing, estimates kinetic energy of the flow at a first location of an aorta atblock 306 and estimates kinetic energy of the flow at a second location downstream of the first location at block 308.

[0065] At block 310, assessment system 100 computes a loss metric measuring loss of kinetic energy from the first location to the second location.

[0066] At block 312, assessment system 100 generates a prediction of risk of aortic dissection based on at least the loss metric. This prediction may be provided to a clinician for interpretation and further assessment (e.g., to evaluate suitability for elective surgery). In some embodiments, the prediction may be provided to a clinician via a computer interface. In some embodiments, the prediction may be provided in combination with at least some of the imaging data. In some embodiments, the prediction may be provided to a downstream computer system for further automated analysis and / or assessment.

[0067] It should be understood that steps of one or more of the blocks depicted in FIG. 3 may be performed in a different sequence or in an interleaved or iterative manner. Further, variations of the steps, omission or substitution of various steps, or additional steps may be considered.Biomechanical Parameters

[0068] Medial degeneration has been shown to correlate with various aortic tissue biomechanical parameters. These include delamination strength, a biomechanical parameter derived by peel testing the aortic wall, which simulates an aortic dissection event. Lower delamination strength is found in aortas that have suffered aortic dissection when compared to healthy aortas. Modulus of elasticity, also known as aortic stiffness, is another biomechanical parameter, and one that has been found to be increased in diseased tissue. In addition, aneurysmal tissue exhibits reduced performance in returning stored mechanical energy back to blood flow (the Windkessel function). Energy loss, which describes the hysteresis between the loading andunloading cycles of a stress-strain curve, has been previously found to negatively correlate with elastin content, and importantly, to delamination strength as well.

[0069] For clinical translation, measurements of aortic biomechanics based on data collected in vivo are necessary. Unfortunately, such measurements of aortic biomechanics have not be correlated with clinically-relevant endpoints such as aortic dissection, nor have they been validated against ex-vivo mechanical testing gold standards. Thus, in vivo magnetic resonance imaging (MRI)-based and ex-vivo mechanical testing-based measurements of aortic biomechanics were compared. Several parameters have been suggested for measurement of vessel stiffness using data collected in vivo: distensibility, compliance, arterial stiffness index (ASI), and aortic pulse wave velocity (aPWV).ExperimentPatient Population

[0070] A total of 26 patients with ascending aortic aneurysms scheduled for elective surgery underwent pre-operative magnetic resonance imaging (MRI), e.g., via imaging system 50, and had ascending aortic tissue samples excised during surgery. Four healthy volunteers without aortic disease also underwent MRI.Mechanical testing: Biaxial tensile testing (energy loss and tangent modulus of elasticity)

[0071] Excised tissue was stored in Ringer’s lactate solution on ice during transportation (maximum 10 minutes) and at 4°C until testing. Mechanical testing was performed within 24 hours of resection. The modulus of elasticity (E) and energy loss (AUL) of the tissue specimens was measured through biaxial tensile testing using a BioTester (CellScale). Ascending aortic tissue samples excised during aortic replacement surgery were collected as complete rings, as shown in FIG. 4A and cutinto 14 mm x 14 mm squares 302 (FIG. 4B) from the outer curvature (OC). In the case more than one 14 mm x 14 mm sample could be taken, all samples were tested and the mean modulus of elasticity and energy loss was used for analysis. The thickness of the samples and a reference glass slide was imaged using a high-magnification zoom lens (Navitar) and measured with Imaged. Samples were submerged in 37°C Ringer’s lactate solution and subject to 10 precondition cycles and 3 analyzed cycles of 25% equibiaxial strain. Stress-strain curves generated were analyzed using an inhouse MATLAB script to quantify the tangent modulus of elasticity (E) at 10% strain and energy loss (AUL) in the circumferential and longitudinal directions.Mechanical testing: Peel testing (delamination strength)

[0072] Aortic dissection risk is represented by delamination strength (Sd) measured as the average force required to peel the layers of the aortic wall. Tissues at higher risk of aortic dissection are expected to exhibit lower delamination strengths. Ascending aortic tissue samples collected during surgery were cut into 6 mm x 30 mm strips 304 from the OC adjacent to the 14 mm x 14 mm sample 302 taken for tensile testing, as shown in FIG. 4B. A small incision was introduced with a scalpel to the medial layer and the tissue was peeled manually to leave approximately 14 mm of intact tissue for testing. The strip was subsequently submerged in 37 C Ringer’s lactate solution and subject to uniaxial peel testing using the BioTester. The average force required per unit area to delaminate the tissue determined the Sd.Magnetic Resonance Imaging

[0073] MRI imaging was conducted using imaging system 50 configured as a magnetic resonance imaging system. MRI data representing four-dimensional (4D) flow was collected.Derivation of in vivo biomechanical parameters

[0074] MRI-derived surrogates for aortic stiffness include strain as defined by change in cross-sectional area of the vessel over the cardiac cycle divided by its minimal area. Related parameters that were evaluated are distensibility (D), compliance (C) and arterial stiffness index (AS I), each defined in Table 1 . Imaged was used to manually measure aortic cross-sectional area in peak systole and diastole.

[0075] Table 1 : Distensibility, compliance and arterial stiffness index equations. As= peak systolic area, Ad= peak diastolic area, Ps= end systolic blood pressure, Pd= end diastolic blood pressure.

[0076] An alternative to the strain-based approach to estimating arterial stiffness is to use pulse-wave velocity (PWV), which measures the speed with which a pulse wave propagates along the length of a vessel. It is related to stiffness through the Moens- Korteweg equation. In vivo, it is measured using equation 4, where AL = length between sampling sites and TT = transit time. Equation 4

[0077] To measure aortic pulse wave velocity (aPWV), the centerline length between the sinotubular junction (STJ) and the distal descending aorta (DDA) was measured. CVi 42 was used for flow analysis of the 2D phase contrast series at thesetwo pre-defined levels. The TT was then calculated by measuring the arrival time of the pulse wave from the STJ to the DDA.Results

[0078] Strain (FIG. 5) and distensibility (FIG. 6) correlated with circumferential energy loss and weakly with longitudinal energy loss and delamination strength. Compliance correlated only with circumferential energy loss (FIG. 7). Arterial stiffness index (ASI) correlated significantly with each of the biomechanical parameters evaluated, as shown in FIG. 8.

[0079] Aortic pulse wave velocity (aPWV) was significantly lower in control patients without aortic disease as compared with patients with aortic aneurysms undergoing elective aortic surgery. FIG. 9A shows aortic PWV in healthy imaging controls versus surgical patients (mean ± 1 SD. P-value calculated using 2-sided Welch’s t-test). Moreover, it demonstrated significant correlations across all biomechanical parameters we evaluated. Higher aPWV was associated with decreased E, increased AUi_ and decreased Sd, as shown in FIG. 10.

[0080] Kinetic energy loss (KEL) was significantly higher in patients with aortic aneurysms than compared to control patients, in line with expectations. FIG. 9B shows KEL in healthy imaging controls versus surgical patients (mean ± 1 SD. P-value calculated using 2-sided Welch’s t-test).

[0081] As shown in FIG. 11 , when compared with ex vivo biomechanical parameters, KEL demonstrated very strong correlation with delamination strength Sd where higher KEL was associated with lower Sd. It also demonstrated statistically significant positive correlations with circumferential energy loss.

[0082] In each of FIG. 5, FIG. 6, FIG. 7, FIG. 8, FIG. 9, FIG. 10, and FIG. 11 respective biomechanical parameters are shown for an outer curvature (OC). The depicted R2and p-values were calculated using simple linear regression.

[0083] FIG. 12 is a schematic diagram of a computing device 1200 which may be used to implement aortic assessment system 100.

[0084] As depicted, computing device 1200 includes at least one processor 1202, memory 1204, at least one I / O interface 1206, and at least one network interface 1208.

[0085] Each processor 1202 may be, for example, any type of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or any combination thereof.

[0086] Memory 1204 may include a suitable combination of any type of computer memory that is located either internally or externally such as, for example, randomaccess memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM) or the like.

[0087] Each I / O interface 1206 enables computing device 1200 to interconnect with one or more input devices, such as a keyboard, mouse, camera, touch screen and a microphone, or with one or more output devices such as a display screen and a speaker.

[0088] Each network interface 1208 enables computing device 1200 to communicate with other components, to exchange data with other components, to access and connect to network resources, to serve applications, and perform other computing applications by connecting to a network (or multiple networks) capable ofcarrying data including the Internet, Ethernet, plain old telephone service (POTS) line, public switch telephone network (PSTN), integrated services digital network (ISDN), digital subscriber line (DSL), coaxial cable, fiber optics, satellite, mobile, wireless (e.g. Wi-Fi, WiMAX), SS7 signaling network, fixed line, local area network, wide area network, and others, including any combination of these.

[0089] For simplicity only, one computing device 1200 is shown but assessment system 100 may include multiple computing devices 1200. The computing devices 1200 may be the same or different types of devices. The computing devices 1200 may be connected in various ways including directly coupled, indirectly coupled via a network, and distributed over a wide geographic area and connected via a network (which may be referred to as “cloud computing”).

[0090] For example, a computing device 1200 may be a server, network appliance, set-top box, embedded device, computer expansion module, personal computer, laptop, personal data assistant, cellular telephone, smartphone device, LIMPC tablets, video display terminal, gaming console, or any other computing device capable of being configured to carry out the methods described herein.

[0091] The foregoing discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.

[0092] The embodiments of the devices, systems and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory ornon-volatile memory or other data storage elements or a combination thereof), and at least one communication interface.

[0093] Program code is 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 some embodiments, the communication interface may be a network communication interface. In embodiments in which elements may be combined, the communication interface may be a software communication interface, such as those for inter-process communication. In still other embodiments, there may be a combination of communication interfaces implemented as hardware, software, and combination thereof.

[0094] Throughout the foregoing discussion, numerous references will be made regarding servers, services, interfaces, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer readable tangible, non- transitory medium. For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions.

[0095] The technical solution of embodiments may be in the form of a software product. The software product may be stored in a non-volatile or non-transitory storage medium, which may be a compact disk read-only memory (CD-ROM), a USB flash disk, or a removable hard disk. The software product includes a number of instructions that enable a computer device (personal computer, server, or network device) to execute the methods provided by the embodiments.

[0096] The embodiments described herein are implemented by physical computer hardware, including computing devices, servers, receivers, transmitters, processors,memory, displays, and networks. The embodiments described herein provide useful physical machines and particularly configured computer hardware arrangements.

[0097] Of course, the above described embodiments are intended to be illustrative only and in no way limiting. The described embodiments are susceptible to many modifications of form, arrangement of parts, details and order of operation. The disclosure is intended to encompass all such modification within its scope, as defined by the claims.

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method for aortic assessment, the method comprising: receiving imaging data representing a four-dimensional (4D) flow; upon processing the imaging data: estimating kinetic energy of the flow at a first location of an aorta; and estimating kinetic energy of the flow at a second location downstream of the first location; computing a loss metric measuring loss of kinetic energy from the first location to the second location; and generating a prediction of risk of aortic dissection based on at least the loss metric.

2. The computer-implemented method of claim 1 , wherein at least one of said estimating kinetic energy of the flow at the first location or said estimating kinetic energy of the flow at the second location includes computing per-voxel velocity using the imaging data.

3. The computer-implemented method of claim 2, wherein at least one of said estimating kinetic energy of the flow at the first location or said estimating kinetic energy of the flow at the second location includes summing per-voxel kinetic energy over a plurality of voxels.

4. The computer-implemented method of claim 3, wherein at least one of said estimating kinetic energy of the flow at the first location or said estimatingkinetic energy of the flow at the second location includes applying an equation substantially of the form:wherein KE refers to the kinetic energy, p refers to a viscosity of the flow, vzrefers to a velocity of the voxel of the plurality of voxels and Vvoxei refers to a volume of that voxel.

5. The computer-implemented method of claim 1 , wherein at least one of the first location and the second location are located in an ascending aorta region.

6. The computer-implemented method of claim 5, wherein the first location is located proximate a sinotubular junction.

7. The computer-implemented method of claim 6, wherein the second location is located at a distal ascending aorta region.

8. The computer-implemented method of claim 7, wherein the second location is proximate an innominate artery.

9. The computer-implemented method of claim 1 , wherein the imaging data includes magnetic resonance imaging data.

10. The computer-implemented method of claim 1 , wherein the imaging data is collected from a subject at an imaging system prior to said receiving, and said receiving includes receiving the imaging data from the imaging system.

11. A computer-implemented system for aortic assessment, the system comprising: at least one processor;memory in communication with the at least one processor; software code stored in the memory, which when executed at the at least one processor causes the system to: receive imaging data representing a four-dimensional (4D) flow; upon processing the imaging data: estimate kinetic energy of the flow at a first location of an aorta; and estimate kinetic energy of the flow at a second location downstream of the first location; compute a loss metric measuring loss of kinetic energy from the first location to the second location; and generate a prediction of risk of aortic dissection based on at least the loss metric.

12. The computer-implemented system of claim 11 , wherein at least one of said estimating the kinetic energy of the flow at the first location or said estimating the kinetic energy of the flow at the second location includes computing per- voxel velocity using the imaging data.

13. The computer-implemented system of claim 12, wherein at least one of said estimating the kinetic energy of the flow at the first location or said estimating the kinetic energy of the flow at the second location includes summing per- voxel kinetic energy over a plurality of voxels.

14. The computer-implemented system of claim 13, wherein said at least one of said estimating the kinetic energy of the flow at the first location or saidestimating the kinetic energy of the flow at the second location includes applying an equation substantially of the form:wherein KE refers to the kinetic energy, p refers to a viscosity of the flow, v, refers to a velocity of the voxel and Vvoxei refers to a volume of the voxel.

15. The computer-implemented system of claim 11 , wherein at least one of the first location and the second location are located in an ascending aorta region.

16. The computer-implemented system of claim 15, wherein the first location is located proximate a sinotubular junction.

17. The computer-implemented system of claim 16, wherein the second location is located at a distal ascending aorta region.

18. The computer-implemented system of claim 17, wherein the second location is proximate an innominate artery.

19. The computer-implemented system of claim 11 , wherein the imaging data includes magnetic resonance imaging data.

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