Motion-compensated wavelet angiography.

Abstract

Methods and systems are provided for extracting cardiac frequency angiographic phenomena of unconstrained vascular objects from angiographic examinations. In one example, a computer may acquire a series of angiographic image frames acquired at a rate faster than the cardiac frequency. Each image frame may include a plurality of pixels, each pixel may have a corresponding intensity. The computer may apply optical flow techniques to the angiographic image frames to generate a plurality of paths corresponding to the displacement of each pixel between the image frames. The computer may further generate a spatiotemporal reconstruction of the cardiac frequency angiographic phenomena based on the plurality of paths and corresponding intensities associated with each pixel of the paths, and output the spatiotemporal reconstruction of the cardiac frequency angiographic phenomena for display in one or more images.

Description

[Technical field]

[0001] The present invention relates generally to angiography, and in particular to methods and systems for the reconstruction of spatiotemporal images of moving vascular pulse waves applied to a sequence of angiographic images acquired at rates faster than cardiac frequency and involving large scale motion of vessels, tissues and / or organs at cardiac frequency. [Background technology]

[0002] The heart pumps blood to the body as a series of arterial stroke volumes. When the traveling arterial stroke volume reaches an organ's vasculature, the arterial stroke volume induces coordinated vascular movement with an expansion of the vessel diameter to accommodate the passing arterial stroke volume. Because the ventricles of the heart contract at cardiac frequency, this coordinated movement and expansion of arterial vessels occurs at cardiac frequency throughout the body.

[0003] In an angiogram, a bolus of chemical contrast agent is injected into the vascular system. This contrast agent allows the visualization of blood vessels in the body. Existing techniques are not suitable for spatiotemporal reconstruction of moving vascular pulse waves in vascular objects with large-scale motion, such as blood vessels that undergo large-scale motion due to the beating of the heart. The large-scale motion of the vascular object prevents the measurement of the movement of the hemodynamic pulse wave within the object. Such large-scale motion is too large to be ignored in an angiogram. Summary of the Invention

[0004] Embodiments of the present invention relate to methods, systems, and computer-readable media for reconstructing cardiac frequency phenomena in angiography data where vascular objects (e.g., tissues, organs, blood vessels, etc.) undergo large-scale motion. These techniques track and compensate for motion in unconstrained vascular objects and improve upon existing wavelet angiography techniques in vasculature that undergoes small-scale motion.

[0005] In this specification, the motion in an unconstrained vascular object is referred to as large-scale, wide-amplitude, or unconstrained cardiac frequency motion. The embodiments described herein can be used to track the unconstrained cardiac frequency motion of a vascular object, for example, using a motion path adjusted as a path integral. The embodiments that determine the motion path may be combined with wavelet or other time index preserving angiography techniques, thereby allowing spatiotemporal reconstruction to be performed for any physiological organ, tissue, or vasculature. This may allow, for example, spatiotemporal reconstruction of a pulse wave at a given point in a blood vessel undergoing large-scale motion.

[0006] In one aspect, a method is provided for extracting cardiac frequency angiographic phenomena of unconstrained vascular objects from an angiographic examination, the method including: acquiring, in a computer, a series of angiographic image frames acquired at a rate faster than a cardiac frequency, each angiographic image frame including a plurality of pixels, each pixel having a corresponding intensity, applying, in the computer, optical flow techniques to the angiographic image frames to generate a plurality of paths corresponding to displacements of each of the pixels between the angiographic image frames, generating, in the computer, a spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon based on the plurality of paths and the corresponding intensities associated with each of the pixels of the paths, and outputting, in the computer, the spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon for display in one or more images. The method may improve angiographic imaging by compensating for motion in vasculature.

[0007] In one example, the method further includes, for each of the angiographic image frames, recursively integrating the displacement of a given pixel in a forward and reverse time direction to generate an optical flow trajectory of the given pixel. Recursive integration may enable tracking of object motion in a vasculature.

[0008] In one example, the method further includes selecting an image frame of interest and determining the displacement of the given pixel of the image frame of interest based on the angiography image frames within a range of a number of the angiography image frames from the image frame of interest. Determining the displacement within a range of a number of image frames may allow for maintaining consistency with the frequency resolution of a mother wavelet function. In one example, the number of image frames may be five. The number five may be selected if the heart is beating at a rate of 60 beats per minute and the angiography images are acquired at 5 Hertz. In this example, determining the displacement within five frames may correspond to determining the displacement one heart beat before and one heart beat after the frame of interest. Any suitable number of image frames may be selected based on, for example, corresponding heart beats before and after the frame of interest (e.g., two heart beats before and after, two heart beats before and after, etc.).

[0009] In one example, applying the optical flow technique includes applying a dense optical flow technique that measures the optical flow of the pixels between the angiography image frames. A dense optical flow technique may allow for calculating the displacement of every pixel.

[0010] In one example, applying the optical flow technique includes applying a sparse optical flow technique, the sparse optical flow technique including tracking a limited number of object positions between the angiography image frames and interpolating movements of intervening object positions. The sparse optical flow technique may allow for calculating only the movement of key pixels.

[0011] In one example, applying the optical flow technique includes determining each of the paths based on a local coordinate system. A local coordinate system can reduce computational requirements by simplifying wavelet calculations. A local coordinate system may be defined by a particular location within a blood vessel, for example. Even if a blood vessel is undergoing large scale motion, a neighborhood of the particular location that defines a coordinate system may still be adequately represented by that coordinate system.

[0012] In one example, the method further includes providing the one or more images as a cine video sequence, which may provide a motion compensated and wavelet transformed result.

[0013] In another aspect, a system is provided that includes a communications interface configured to acquire a series of angiographic image frames acquired at a rate faster than a cardiac frequency, each angiographic image frame including a plurality of pixels, each pixel having a corresponding intensity, and one or more processors coupled to the communications interface, the one or more processors configured to apply optical flow techniques to the angiographic image frames to generate a plurality of paths corresponding to displacements of each of the pixels between the angiographic image frames, generate a spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon based on the plurality of paths and the corresponding intensities associated with each of the pixels of the paths, and output the spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon for display in one or more images. The system may improve angiographic imaging by compensating for motion in vasculature.

[0014] In another form, one or more non-transitory computer-readable storage media are provided that are encoded with instructions that, when executed by a processor, cause the processor to acquire a series of angiographic image frames obtained at a rate faster than a cardiac frequency, each angiographic image frame including a plurality of pixels, each pixel having a corresponding intensity, apply optical flow techniques to the angiographic image frames to generate a plurality of paths corresponding to displacements of each of the pixels between the angiographic image frames, generate a spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon based on the plurality of paths and the corresponding intensities associated with each of the pixels of the paths, and output the spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon for display in one or more images. The one or more non-transitory computer-readable storage media may improve angiographic imaging by compensating for motion in vasculature.

[0015] Further objects and advantages of these techniques will become apparent from the specification and drawings. [Brief description of the drawings]

[0016] [Figure 1A] FIG. 1 is a side view illustrating an example of a rotational X-ray system that may be used with embodiments of the present disclosure to obtain angiography data. [Figure 1B] FIG. 1 is a partial schematic diagram illustrating an example of a rotational X-ray system that may be used with embodiments of the present disclosure to obtain angiography data.

[0017] [Diagram 2] FIG. 1 is a schematic diagram of a computer system or information processing device that may be used with embodiments of the present disclosure.

[0018] [Diagram 3] 1 is a flowchart illustrating an exemplary data flow path for spatiotemporal reconstruction with unconstrained cardiac motion, in accordance with an embodiment of the present disclosure.

[0019] [Figure 4] 1A and 1B are angiography image frames showing an example of unconstrained vascular object motion (e.g., vascular movement) in a porcine coronary artery. (a) and (b) are successive angiography image frames in which pixel positions are tracked between frames using techniques provided herein. (c) is an overlay of (a) and (b) to show pixel displacement due to unconstrained vascular object motion (e.g., cardiac contractility).

[0020] [Diagram 5] 1 illustrates an example of an optical flow path associated with an exemplary angiography image frame for a given coronary artery, according to an embodiment of the present disclosure.

[0021] [Figure 6] 1A and 1B are example angiography image frames illustrating a wavelet angiography representation after accounting for unconstrained vascular object motion, such as coronary artery motion, in accordance with an embodiment of the present disclosure, where (a) shows the image before spatiotemporal reconstruction and (b) shows the image after spatiotemporal reconstruction.

[0022] [Figure 7] 1 is a flowchart illustrating a technique for reconstructing cardiac frequency phenomena in angiography data for an unconstrained vascular object, according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] The present technology may provide an improvement over existing techniques for angiographic imaging of unconstrained vessels, tissues and / or organs.

[0024] For example, existing angiography techniques are not suitable for spatiotemporal reconstruction of vascular pulse waves traveling through objects with unconstrained vascular motion. For example, the heart is a muscular organ flanked on the right and left by the lungs, which are soft organs that do not have the ability to constrain the heart's motion. Thus, the heart, associated blood vessels, and tissues are examples of objects with wide amplitude cardiac motion. Although existing angiography techniques are applicable to blood vessels, tissues, and / or organs with wide amplitude cardiac motion, the large scale motion of such objects creates challenges when attempting to measure the movement of hemodynamic pulse waves within the objects.

[0025] Embodiments of the present disclosure can utilize motion tracking for unconstrained vascular objects moving at cardiac frequencies. Spatiotemporal reconstruction can be applied to unconstrained cardiac moving objects by tracking the object's motion in a series of two-dimensional images and using the tracked motion to orient the spatial extent (of the images) encompassed by a wavelet transform (or other time index-preserving mathematical transform).

[0026] An angiographic image sequence consists of a set of two-dimensional image frames acquired faster than the cardiac frequency as a function of time. This three-dimensional data set therefore contains two spatial dimensions and one temporal dimension. If there is motion of blood vessels and / or other structures of interest in the angiographic image frames, a given location of a given structure will not necessarily occupy the same pixel coordinates in two adjacent or nearby temporal image frames.

[0027] According to an embodiment of the present invention, pixels can be treated as a time channel, where pixel positions may change from frame to frame in a sequence of angiographic images.

[0028] Motion tracking is performed on the image pixels to allow measurement of vascular pulses at cardiac frequencies in moving cardiovascular vessels, tissues, or organs. Images are processed based on a global coordinate system, which may be represented in an angiographic image as a three-dimensional grid. The second dimension may be defined by the horizontal and vertical rows / columns of pixels in the image, and the third dimension may be defined by the image frame number (e.g., the ordinal number of a given image frame in an angiographic image sequence).

[0029] The motion may further be tracked with respect to a local coordinate system, for example a moving vessel, organ or tissue is tracked with respect to the local coordinate system. For example, each path of an object may be determined based on the local coordinate system. A local coordinate system may be defined for each object or each key pixel in each image frame of an angiography sequence. Suppose an angiogram has a key vessel branching point 'a' in frame t1. The spatial coordinate of object 'a' in the 2D image frame is x t1a andy t1a In frame t2, object 'a' has two-dimensional coordinates x t2a andy t2b In the local coordinate system, object 'a' in image frame t1 might be at position 0 in the x dimension and position 0 in the y dimension. In image frame t2, the displacement of object 'a' may be maintained such that the position of object a remains 0 in the x dimension and 0 in the y dimension. Portions of the vessel near object a may be assigned coordinate positions relative to 'a' that remain constant from one image frame to the next.

[0030] A local coordinate system may be determined by modifying the global coordinate system based on object motion and displacement. For example, if a particular bend in a blood vessel is measured to move 5 pixels to the right between one image frame and the next, the blood flowing within that pixel may be treated as having moved the same 5 pixels in the same direction. In this sense, the blood vessel orients the local coordinate system. In particular, the local coordinate system may be translated 5 pixels to the right relative to the global coordinate system. Thus, the global coordinate system may serve as a reference coordinate system for one or more local coordinate systems.

[0031] Thus, the pixels of the image are transformed into a local coordinate system, and for the transformed pixels, a trajectory or path is determined to track the pixel's position over time. This trajectory or path is fed into a wavelet transform or other transform that preserves time indices. For the purposes of performing the wavelet transform, the angiographic image sequence may be treated as an array of x×y pixels of length t frames (e.g., an x×y array of a time signal of length t). The wavelet transform may operate on a one-dimensional sequence of numbers. Thus, the techniques described herein may enable tracking of vascular object motion even when the object motion (e.g., motion within a given pixel) is large relative to the time / frequency resolution of the selected mother wavelet (e.g., the motion is too large to be of any use). In particular, the motion of every pixel in every frame may be estimated, and the motion may be inverted (e.g., compensated). The wavelet transform may operate on data with the motion inverted. Motion may be restored in the wavelet transformed data by examining and applying the motion. Thus, after the wavelet computation is completed, the local coordinate system may be converted back to the original coordinate system.

[0032] 1A-3 show exemplary systems or devices that may be employed to implement embodiments of the present invention. It should be understood that such systems and devices are merely illustrative of representative systems and devices, and that other hardware and software configurations may be used with embodiments of the present invention as appropriate. Thus, it is not intended that the embodiments be limited to the particular systems and devices illustrated herein, and it should be recognized that other suitable systems and devices may be employed without departing from the spirit and scope of the subject matter provided herein.

[0033] 1A and 1B, a rotating X-ray system 28 is shown that may be employed to obtain angiograms at a rate faster than the heart rate, such as by fluoroscopic angiography. In obtaining an angiogram, a chemical contrast agent may be injected into a patient positioned between the X-ray source and the detector, and the X-ray projections are captured by the X-ray detector as two-dimensional images (i.e., angiographic image frames). A series of such image frames constitutes an angiographic examination. In accordance with an embodiment of the present invention, the angiographic image frames may be acquired at a rate faster than the heart frequency, thereby enabling motion tracking and spatiotemporal reconstruction of cardiac frequency phenomena into cardiac spatial angiograms.

[0034] As shown in FIG. 1A, one example of an angiography imaging system is shown in the form of a rotating X-ray system 28. The rotating X-ray system 28 includes a base having a C-arm 30 that carries an X-ray source assembly 32 at one end and an X-ray detector array assembly 34 at the other end. The base allows the X-ray source assembly 32 and the X-ray detector array assembly 34 to be oriented at different positions and angles around a patient positioned on a table 36 while providing a physician with access to the patient. The base includes a pedestal 38 having a horizontal leg 40 that extends below the table 36 and a vertical leg 42 that extends upwardly from an end of the horizontal leg 40 spaced apart from the table 36. A support arm 44 is rotatably secured to an upper end of the vertical leg 42 for rotation about a horizontal pivot axis 46.

[0035] The horizontal pivot axis 46 coincides with the centerline of the table 36, and the support arm 44 extends radially outward from the horizontal pivot axis 46 and supports at its outer end a C-arm drive assembly 47. The C-arm 30 is slidably fixed to the C-arm drive assembly 47 and is connected to a drive motor (not shown) that slides the C-arm 30 to rotate about a C-axis 48 as indicated by arrow 50. The horizontal pivot axis 46 and the C-axis 48 intersect at a system isocenter 56 located above the table 36 and are perpendicular to each other.

[0036] An x-ray source assembly 32 is mounted at one end of the C-arm 30 and an x-ray detector array assembly 34 is mounted at the other end. The x-ray source assembly 32 emits a beam of x-rays, which is directed at the x-ray detector array assembly 34. Both assemblies 32, 34 extend radially inwardly towards a horizontal pivot axis 46 such that a central ray of the beam passes through a system isocenter 56. Thus, the central ray of the beam can be rotated about either the horizontal pivot axis 46, the C-axis 48, or both, about the system isocenter during acquisition of x-ray attenuation data from a subject positioned on the table 36.

[0037] The x-ray source assembly 32 includes an x-ray source that emits a beam of x-rays when energized. The central ray passes through a system isocenter 56 and impinges on a two-dimensional flat panel digital detector 58 housed in an x-ray detector array assembly 34. The two-dimensional flat panel digital detector 58 may be, for example, a two-dimensional array of 2048 by 2048 detector elements. Each element generates an electrical signal representative of the intensity of the impinging x-rays and therefore the attenuation of the x-rays as they pass through the patient. During a scan, the x-ray source assembly 32 and detector array assembly 34 are rotated about the system isocenter 56 to acquire x-ray attenuation projection data from different angles. In some embodiments, the detector array is capable of acquiring 50 projections or image frames per second, which is the limiting factor that determines the number of image frames that can be acquired for a given scan path and speed.

[0038] 1B, rotation of assemblies 32, 34 and operation of the x-ray source are governed by an x-ray system control mechanism 60. Control mechanism 60 includes an x-ray controller 62 that provides power and timing signals to x-ray source assembly 32. A data acquisition system (DAS) 64 within control mechanism 60 samples data from the detector elements and passes the data to an image reconstructor 65. Image reconstructor 65 receives digitized x-ray data from DAS 64 and performs high speed image reconstruction according to the methods of the present disclosure. The reconstructed image is used as an input to computer 66, which stores the image in mass storage device 69 or further processes the image. Image reconstructor 65 may be a stand-alone computer or may be integrated with computer 66.

[0039] The control mechanism 60 also includes a base motor controller 67 and a C-axis motor controller 68. In response to motion commands from the computer 66, the motor controllers 67, 68 provide power to motors within the x-ray system that cause rotation about the horizontal pivot axis 46 and the C-axis 48, respectively. The computer 66 also receives commands and scan parameters from an operator via a console 70 having a keyboard and other manually operable controls. An associated display 72 allows the operator to observe reconstructed image frames and other data from the computer 66. The operator supplied commands are used by the computer 66 under the direction of a stored program to provide control signals and information to the DAS 64, the x-ray controller 62, and the motor controllers 67, 68. The computer 66 further operates a table motor controller 74 which controls the motorized table 36 to position the patient relative to the system isocenter 56.

[0040] 2, there is shown a block diagram of a computer system or information processing device 80 (e.g., image reconstructor 65 and / or computer 66 of FIG. 1B) that may be incorporated into an angiographic imaging system, such as rotating X-ray system 28 of FIGS. 1A and 1B, providing enhanced functionality to track motion and extract cardiac frequency phenomena from angiographic data in accordance with an embodiment of the present invention, or used as a stand-alone device. Information processing device 80 may be local or remote to rotating X-ray system 28. In one example, the functionality performed by information processing device 80 may be provided as a Software-as-a-Service (SaaS) option. SaaS refers to software applications stored on one or more remote servers (e.g., in the cloud) and providing one or more services (e.g., angiographic image processing) to remote users. In one embodiment, computer system 80 includes a monitor or display 82, a computer system 84 (including a processor 86, a bus subsystem 88, a memory subsystem 90, and a disk subsystem 92), a user output device 94, a user input device 96, and a communication interface 98. Monitor 82 may include hardware and / or software elements configured to generate a visual representation or display of information. Some examples of monitor 82 may include well-known display devices such as a television monitor, a cathode ray tube (CRT), or a liquid crystal display (LCD). In some embodiments, monitor 82 may provide an input interface, such as incorporating touch screen technology.

[0041] The computer system 84 may include well-known computer components such as one or more central processing units (CPUs), memory or storage devices, graphics processing units (GPUs), communication systems, interface cards, and the like. As shown in FIG. 2, the computer system 84 may include one or more processors 86 that communicate with a number of peripheral devices via a bus subsystem 88. The processor 86 may include a commercially available central processing unit or the like. The bus subsystem 88 may include mechanisms for allowing the various components and subsystems of the computer system 84 to communicate with each other as intended. Although the bus subsystem 88 is shown diagrammatically as a single bus, alternative embodiments of the bus subsystem may utilize multiple bus subsystems. The peripheral devices in communication with the processor 86 may include a memory subsystem 90, a disk subsystem 92, a user output device 94, a user input device 96, a communication interface 98, and the like.

[0042] Processor 86 may be implemented using one or more analog and / or digital electrical or electronic components, and may include a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), programmable logic, and / or other analog and / or digital circuit elements configured to perform various functions described herein, such as by executing instructions stored in memory subsystem 90 and / or disk subsystem 92 or another computer program product.

[0043] The memory subsystem 90 and the disk subsystem 92 are examples of physical storage media configured to store data. The memory subsystem 90 may include a number of memories including random access memory (RAM), which volatilely stores program code, instructions, and data during program execution, and read-only memory (ROM), where fixed program code, instructions, and data are stored. The disk subsystem 92 may include a number of file storage systems that provide persistent (non-volatile) storage for programs and data. Other types of physical storage media include floppy disks, removable hard disks, compact disk read-only memory (CD-ROM), digital video disk (DVD), optical storage media such as barcodes, semiconductor memories such as flash memory, read-only memory (ROM), battery-backed volatile memory, networked storage devices, and the like. The memory subsystem 90 and the disk subsystem 92 may be configured to store programming and data configurations that provide the functionality or features of the techniques described herein, such as the motion tracking system 120 and the spatiotemporal reconstruction system 130 (see FIG. 3). Software code modules and / or processor instructions that implement or provide functionality when executed by processor 86 may be stored in memory subsystem 90 and disk subsystem 92. Memory subsystem 90 may be a non-transitory computer-readable storage medium.

[0044] User input devices 96 may include hardware and / or software elements configured to receive input from a user for processing by components of computer system 80. User input devices may include all possible types of devices and mechanisms for inputting information to computer system 84. These may include keyboards, keypads, touch screens, touch interfaces integrated into displays, audio input devices such as microphones and voice recognition systems, and / or other types of input devices. In various embodiments, user input devices 96 may include computer mice, trackballs, trackpads, joysticks, wireless remotes, drawing tablets, voice command systems, eye tracking systems, and the like. In some embodiments, user input devices 96 are configured to allow a user to select or interact with objects, icons, text, and the like, which may be displayed on monitor 82 via commands, actions, or gestures, such as, for example, clicking a button.

[0045] User output devices 94 may include hardware and / or software elements configured to output information to a user from components of computer system 80. User output devices may include all possible types of devices and mechanisms for outputting information from computer 84. These may include displays (e.g., monitor 82), printers, touch or force feedback devices, audio output devices, etc.

[0046] Communications interface 98 may include hardware and / or software elements configured to provide one-way or two-way communications with other devices. For example, communications interface 98 may provide an interface between computer system 84 and other communications networks and devices, such as via an Internet connection.

[0047] 3 shows a data flow diagram in which angiography data 110 is obtained from a rotational x-ray system 28 at a rate faster than the heart rate. The angiography data is provided to a motion tracking system 120, which processes the angiography data to generate optical flow paths that are provided to a spatiotemporal reconstruction system 130. Once the spatiotemporal reconstruction system 130 receives the optical flow paths, a spatiotemporal reconstruction is performed on the unconstrained vascular object. These embodiments are described in further detail throughout this application.

[0048] According to an exemplary embodiment of the present invention, angiographic data (including angiographic image frames) may be acquired. In addition to acquiring angiographic image frames, additional cardiac signals / data may be simultaneously acquired to serve as cross-correlation targets for purposes of performing spatiotemporal reconstruction of vascular pulse waves based on the techniques provided herein. For example, the additional cardiac signals / data may serve as reference cardiac signals for phase indexing of pixels in angiographic projections. Exemplary apparatus for acquiring / providing reference cardiac signals using such pulse oximetry systems and / or devices / systems in the form of echocardiogram / electrocardiogram (EKG) systems or devices. In an exemplary embodiment, output from such devices (e.g., EKG devices) may be communicated to computer system 84 via a communication interface.

[0049] [Motion Tracking] According to the present embodiment, optical flow techniques are utilized in conjunction with wavelet and other time index preserving transform angiography techniques to account for unconstrained cardiac frequency motion and improve performance in constrained vessels and tissues (e.g., the brain, etc.).

[0050] Optical flow techniques address the movement of objects from one image frame to the next. An object (a collection of pixels) moves from one frame to the next, and the displacement of the object is determined, for example, by measuring delta x (change relative to the x-axis) and delta y (change relative to the y-axis). Based on the delta x and delta y values, the local coordinate system may be warped relative to the global coordinate system. The local coordinate system may be used to reflect the movement of pixels along a path, and the intensities of the pixels along the path may be obtained and provided to a wavelet or other transform. Exemplary embodiments of the invention may utilize optical flow techniques to convert a path represented by a simple line (which may be interpolated) provided to the transform into a variable path consisting of the location of blood vessels and other structures per angiographic image frame. That is, optical flow techniques may be used to track the movement of objects from one frame to the next.

[0051] To represent the optical flow technique, we introduce the following notation: The two spatial dimensions are denoted as x and y, and the time dimension is denoted as t. The intensity of an angiographic image at location x, y, t is denoted as A(x, y, t). Angiographic image frames are represented at discrete time locations t i For a particular object, such as a particular location in a blood vessel, the image is acquired in frame t i At the position x,y in the next angiographic frame t i+1 The displacement of the object at is Δ x ,Δ y Therefore, in terms of optical flow notation, position A(x,y,t i ) transitions as follows: A(x,y,t i ) →A(x+Δ x,i+1 ,y+Δ y,i+1 ,t i+1 ) The next frame t i+2 Now, the object's position transitions as follows: A(x+Δ x,i+1 ,y+Δ y,i+1 ,t i+1 ) →A(x+Δ x,i+1 +Δ x,i+2 ,y+Δ y,i+1 +Δ y,i+2 ,t i+2 )

[0052] Each subsequent frame is reported as a change in position by the optical flow calculation. The net position at a distance of k frames is t i From i+k The term "recursive" refers to the fact that the optical flow displacement of the position after the transition is used as the next displacement, and these frame-wise displacements are used to construct a trajectory / path. This trajectory / path may be piecewise, not necessarily following a straight line. Thus, the motion adjusted trajectory, given by the complex continuous wavelet transform of f(t), is obtained from the integration of the optical flow computation along the path. The Gabor wavelet equation ψ(t) spans isotropically in the positive and negative time directions. Thus, the trajectory of the path integral is computed as an ordered concatenation of the optical flow integrals in both the positive t and negative t directions.

[0053] Consider an example where an object is displaced by dx1 in the x dimension and dy1 in the y dimension between frames t1 and t2, and displaced by dx2 in the x dimension and dy2 in the y dimension between frames t2 and t3. The displacement between frames t1 and t3 can be obtained by integrating the displacement between frames t1 and t2 and the displacement between frames t2 and t3. In particular, the integrated displacement in the x dimension is dx1+dx2, and the integrated displacement in the y dimension is dy1+dy2. This process can be repeated for all image frames.

[0054] Thus, for each image frame, the displacement of a given pixel can be recursively integrated in forward and reverse time directions to generate the optical flow trajectory of the given pixel. The use of path integrals, such as wavelet path integrals, can enable compensation of motion in unconstrained vascular objects. For example, the techniques described herein can compensate for motion aliasing due to cardiac expansion and contraction, making a beating heart appear stationary / not moving in the processed angiogram images.

[0055] As image frames move away from the center of interest, motion tracking tends to drift and become less accurate. This is because the motion trajectory is based on the integral of the displacement between frames. While truly random errors may be corrected with motion tracking over many frames, minor biases in tracking may be magnified by integrating the estimated frame-by-frame displacement into the path trajectory. Thus, when performing positive or negative motion tracking, a subset of frames may be considered with respect to a designated frame that includes a designated object or a central object. The designated object or central object may be a pixel. For example, for a designated frame, 3-5 frames may be processed in the positive and negative directions relative to the designated pixel (e.g., the calculation may become more inaccurate as more distant frames are considered). In one example, an image frame of interest may be selected and the displacement for a given pixel in the image frame of interest may be determined based on image frames that are within a range of several (e.g., 5) image frames from the image frame of interest. Once the motion is determined for the designated frame, the next frame may be selected as the designated frame and the process may be repeated. In this way, motion determination may be performed along the angiogram time series, for example in a "window" approach.

[0056] Any suitable optical flow technique may be used to measure the motion of an object from one image frame to the next. Various techniques may have comparative advantages and disadvantages. A suitable technique for a given scenario may be useful in utilizing the object's motion to identify cardiac frequency activity by wavelet angiography.

[0057] To measure object motion, sparse optical flow techniques may be used. Sparse optical flow techniques allow only key pixel motion to be calculated. Pixel motion between key pixels is interpolated from the motion of nearby key pixels. Key pixels may be selected according to a criterion such that corresponding key pixels represent the same part of the same object on adjacent image frames. Examples of key pixel criteria may be edges or edge intersections. Sparse optical flow techniques include, but are not necessarily limited to, the method described in Lucas and Kanade, “An Iterative Image Registration Techniques with an Application to Stereo Vision”, Proceedings DARPA Image Understanding Workshop, April 1981, pp. 121-130 (incorporated herein by reference), Harris Corner Adaptation described in Harris and Stephens, “A Combined Corner and Edge Detector”, Alvey Vision Conference, 1988 (incorporated herein by reference), and / or derivatives of these methods.

[0058] In sparse optical flow techniques, a limited number of object positions are tracked between image frames and the movement of intervening object positions is interpolated. This may involve assuming an affine or fractional linear transformation between the two images. These techniques involve applying a geometric transformation to successive images by using common object positions on each image.

[0059] One example of a sparse optical flow technique relies on identifying consistent features in two image frames and involves computing a warping that allows mapping of features in one image frame to the next. This is described, for example, in Shi and Tomasi, "Good Features to Track," IEEE Conference on Computer Vision and Pattern Recognition, June 1994 (incorporated herein by reference). Examples of consistent features are corners and edges, which may be identified by any suitable mathematical technique. Warping may be performed by an interpolation method, such as linear or cubic spline interpolation.

[0060] In other embodiments, dense optical flow techniques may be used that measure the optical flow of all pixels between image frames. Examples include, but are not limited to, variational methods derived from the use of the Lagrange equation, such as those described in Horn and Schunck, "Determining Optical Flow", Artificial Intelligence 17, 1981, 185-203. Horn and Schunck describe a method that assumes that the brightness of pixels is constant from one image frame to the next and determines the spatial distribution of pixels between one image frame and the next. Changes in the spatial distribution of pixels can be interpreted as representing object motion. Other approaches include the dense optical flow technique described by Farnebaeck (ae with an umlaut), “Very High Accuracy Velocity Estimation using Orientation Tensors, Parametric Motion, and Simultaneous Segmentation of the Motion Field”, Proceedings Eighth IEEE International Conference on Computer Vision, July 2001 (incorporated herein by reference), which is based on the generation of local coordinate polynomials convenient for describing the spatial transformation between image frames. Still other approaches are based on local transforms such as the Hilbert transform that generate monogenic signals, which may be generalized to two dimensions to form the Riesz transform. The Riesz transform and related transforms occurring in the frequency domain may be employed to estimate the local structure tensor of tissue. This may be used to estimate the optical flow of biological objects from one angiographic image frame to the next. Additionally, there are directional integral transforms such as shearlets, ringlets, and curvelets that may be utilized for optical flow information. Any of these approaches may be suitable for use with the techniques provided herein.

[0061] Yet another approach may involve techniques involving deformable registration, such as the use of convolutional networks and other deep learning / machine learning / artificial intelligence techniques that may be employed to estimate optical flow and, in particular, calculate the motion of an object from one image frame to the next. A suitable example of a mathematical deformable registration may be diffeomorphism, which is derived from the field of topology. An example of a suitable open source software package for diffeomorphism is the Python DiPy library. This information, along with the brightness of the object, may be combined to estimate the object's motion from one frame to the next. Some implementations of diffeomorphism assume that the tracked object is segmented in each image frame of interest. The diffeomorphism may then be calculated between the segmented options from one image frame to the next. One example of a suitable mathematical method for segmenting vascular structures and angiographic images is the Frangi filter described in Frangi et al., "Multiscale vessel enhancement filtering," Medical Image Computing and Computer-Assisted Intervention, 1998, which is incorporated herein by reference.

[0062] A suitable example of a deep learning framework may be the VoxelMorph open source software package described in Balakrishnan et al., “VoxelMorph: A Learning Framework for Deformable Medical Image Registration”, arXiv:1809.05231 [cs.CV], September 1, 2019 (incorporated herein by reference). VoxelMorph employs a deep learning neural network to compute diffeomorphisms between objects in each image frame. Another suitable example of a deep learning framework may employ a neural network to interpolate cardiac angiograms at higher video frame rates from cardiac angiograms captured at lower rates. Software packages such as the Depth-Aware video frame INterpolation (DAIN) network model described in Bao et al., “Depth-Aware Video Frame Interpolation,” IEEE Conference on Computer Vision and Pattern Recognition, 2019 (incorporated herein by reference) may use various convolutional neural networks for video frame interpolation.

[0063] In other embodiments, steerable filters may be employed to detect local orientation in an image. This approach may be used to estimate motion trajectories. Steerable filters include convolution kernels and may be used for image enhancement and feature extraction. For example, if different objects are moving in different directions, a given image frame may have a set of steerable filters corresponding to the different objects in the image. Each motion direction may be represented by a steerable filter that encodes and filters the specific motion direction. In yet other examples, any suitable supervised or unsupervised machine learning model may be used (e.g., mathematical / statistical models, classifiers, feedforward, recurrent or other neural networks, etc.).

[0064] The above list includes examples of optical flow techniques that can be adapted for the purpose of motion-compensated wavelet or time index-preserving angiography. The embodiments in the present application are not necessarily limited to the optical flow techniques listed above, but may include any suitable optical flow technique. For example, the image displacement function (ImageDisplacements[]) provided by the Mathematica® environment, described in Wolfram Research, “ImageDisplacements”, Wolfram Language function, https: / / reference.wolfram.com / language / ref / ImageDisplacements.html, 2016 (incorporated herein by reference), may be employed to perform wavelet angiography with motion compensation by optical flow techniques.

[0065] Additionally, multiple objects may be tracked, each with its own local coordinate system. The motion of all objects may be inverted (in a bookkeeping sense) for wavelet calculations. In one example, the motion of every pixel may be estimated. In sparse optical flow techniques, the motion of key pixels that may be computationally efficient to track may be measured, and the motion of pixels between the key pixels may be interpolated. In dense optical flow techniques, the motion of every pixel may be measured. Deep learning systems based on AI object recognition may use a similar approach to sparse optical flow techniques. Some deep learning methods may be based on object recognition and / or training on pixel-wise motion, which may be similar to dense optical flow techniques.

[0066] [Wavelet angiography / spatiotemporal reconstruction] Wavelet angiography uses the complex-valued wavelet transform to generate a spatiotemporal reconstruction of cardiac frequency phenomena in angiograms acquired at rates faster than the cardiac frequency. The wavelet transform is based on line integrals.

[0067] For a signal f(t) and a mother wavelet ψ, the complex continuous wavelet transform of f(t) is given by the following equation:

number

number

[0068] As discussed above, cardiac frequency signals in angiograms are utilized to increase the sensitivity of angiographic imaging to arterial and venous anatomies. This allows for identification of circulatory changes and pathological patterns, such as vascular occlusions and other blood flow conditions, at lower x-ray doses and / or lower intravascular contrast volumes. Furthermore, arterial and venous anatomies may be separated without the need to manipulate and insert catheters into the distal arterial tree. Coherence in cardiac frequencies between circulatory subsystems may be utilized to allow anatomical identification of arterial and venous anatomies at lower x-ray doses and lower intravascular contrast volumes.

[0069] In carrying out the methods described herein, angiographic data may be recorded using digital detection devices, such as those commercially available as part of scanning devices available from manufacturers such as Philips and Siemens. The digital data is then imported into computer memory. After importing the angiogram into computer memory (in the absence of motion aliasing), a spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon can be obtained. In one example, the spatiotemporal reconstruction may be performed according to the techniques described in U.S. Patent No. 10,123,761, issued November 13, 2018, U.S. Patent Application No. 16 / 784,125, filed February 6, 2020, U.S. Patent Application No. 16 / 784,073, filed February 6, 2020, U.S. Patent Application No. 16 / 813,513, filed March 9, 2020, U.S. Patent Application No. 16 / 832,695, filed March 27, 2020, and / or U.S. Patent Application No. 16 / 841,247, filed April 6, 2020. These documents are incorporated herein by reference.

[0070] The angiography data may be imported into a computer memory and reformatted in a processor in the memory to give an array of time signals. A complex-valued wavelet transform is applied by the processor to each pixel-wise time signal to obtain an array of wavelet transforms. The pixel-wise wavelet transforms are filtered by the processor against cardiac frequency. This is done by setting to zero all wavelet coefficients that do not correspond to the cardiac wavelet scale (in the field of wavelets, this term corresponds to the concept of cardiac frequency). The pixel-wise wavelet transformed data is inverse wavelet transformed by the processor back to the time domain and reformatted in the computer memory into pixels. Each data element (voxel) of this three-dimensional grid is complex-valued.

[0071] Each frame may be rendered by the processor as an image having a luminance-hue color model to represent the complex data in each pixel. The cardiac frequency magnitude is represented as luminance and the phase is represented as hue. The q images may be rendered by the processor as a motion cine or may be saved by the processor as a video file format. For example, one or more images may be provided as a cine video sequence. In one example, object motion may be applied in reverse to every pixel in every image to create a new video sequence in which all objects appear to be stationary. This video may be processed with wavelet calculations to identify cardiac frequency phenomena. The object motion may then be applied forward to restore the object motion to the wavelet transformed result.

[0072] If the wavelet transform has a frequency resolution of, say, 10 image frames, then the motion may only be calculated for a neighborhood of 10 image frames. This may be repeated for every image frame. If there is error drift in the motion estimate, the error due to drift may be limited to the drift that may occur in those 10 frames, as opposed to the aggregate drift from hundreds of frames of an angiography sequence.

[0073] Any suitable transform that can operate on complex numbers that preserve the time index after transformation to the frequency domain and that can extract a spatiotemporal reconstruction of the cardiac frequency angiography phenomenon is contemplated for use with the present technique.

[0074] 4(a) and 4(b) show two consecutive angiographic image frames 400A and 400B (one frame apart), where angiographic image frame 400A and angiographic image frame 400B are selected from a porcine coronary angiogram acquired at 30 Hz (faster than the cardiac frequency). The same locations on the same coronary artery (called the left anterior descending artery), labeled coronary artery location 2 and coronary artery location 4, are shown on angiographic image frames 400A and 400B, respectively. The spatial displacement of the same coronary artery location between angiographic image frames 400A and 400B indicates the magnitude of the motion. FIG. 4(c) shows an overlay 400C of angiographic image frames 400A and 400B. Pixel 6 in the overlay 400C has been offset to indicate the motion that has occurred from angiographic image frame 400A to angiographic image frame 400B.

[0075] FIG. 5 shows an example of an optical flow path of a selected pixel 9 for a particular image frame 500 of an optical angiogram. In this example, the image frame 500 has a particular pixel of interest in a blood vessel, which we will call the optical flow pixel 9. An optical flow trajectory 8 is shown extending in both time directions from the optical flow pixel 9. The optical flow trajectory 8 represents the path of the same pixel 9 as the heart is beating. This trajectory represents the integral path of a wavelet transform (e.g., a Gabor wavelet transform). It shows the trajectory of motion throughout the angiogram sequence as a bolus of contrast agent is injected and then dissipates. Although the optical flow pixel 9 moves at the cardiac frequency, this pixel will not follow exactly the same trajectory from one heartbeat to the next, as there may be variations in myocardial contractions and errors due to motion tracking. The optical flow path is the path along which the wavelet transform and inverse transform are performed to generate the spatiotemporal reconstruction.

[0076] This process is repeated, for example, for each angiogram image frame to generate a series of motion adjusted Gabor wavelet transforms, which may be filtered at cardiac frequencies and inverse transformed, where only the wavelet scales corresponding to cardiac frequencies are retained.

[0077] Each pixel grid location x, y, t is returned as complex-valued data, which may be rendered as an image using any suitable scheme, such as a luminance-hue model (e.g., the magnitude of the complex values ​​is rendered as luminance and the cardiac frequency phase is rendered as hue), etc. In one example, one or more of the techniques described in U.S. Patent No. 10,123,761 may be utilized to render the image.

[0078] An angiographic image is an image intensity number on a discrete grid according to the number of pixels in the horizontal x and vertical y directions. However, optical flow trajectories may return object position changes as fractional pixel values. Therefore, in one embodiment, the discrete angiographic image grid may be converted into a spline-interpolated data quantity that allows access to fractional pixel values ​​by interpolation.

[0079] An example of a motion compensated wavelet angiogram transform is shown in Figures 6(a)-6(b). A raw angiogram image frame 600A (Figure 6(a)) is shown on the left, and the result after motion processing and spatio-temporal reconstruction is shown in wavelet angiogram image frame 600B (Figure 6(b)) on the right. Matching pixels 12 represent the same location on the same vessel, as labeled.

[0080] FIG. 7 is a flow chart of operations for acquiring and processing data involving large scale cardiac motion, according to one embodiment of the techniques described herein. In operation 710, a series of angiographic image frames are acquired from an angiographic examination at a rate faster than the cardiac frequency. For example, the angiographic data image frames may be acquired using a rotational x-ray angiography system such as that shown in FIGS. 1A and 1B and acquired or received by a computer in the angiography system or by a stand-alone computer. The angiographic image frames may include a time series of angiographic image frames. Each image frame may be associated with a particular time t and may include a number of pixels. Each pixel is located at an xy location within the image frame and may have a corresponding brightness or intensity.

[0081] In operation 720, optical flow techniques are applied to the angiography image frames to generate multiple paths corresponding to the displacement of each pixel between image frames. For example, motion tracking may be performed on the image pixels by a computer to enable measurement of vascular pulses at cardiac frequencies in moving cardiac vessels, tissues, or organs. The images may represent a global coordinate system, and motion may be tracked with respect to a local coordinate system. For example, moving vessels, organs, or tissues may be tracked with respect to a local coordinate system. Thus, the pixels of the image may be transformed to the local coordinate system, and for the transformed pixels, a trajectory or path may be determined to track the position of the pixel over time.

[0082] In operation 730, a spatiotemporal reconstruction of the cardiac frequency angiography phenomenon is generated by the computer based on the multiple paths and the corresponding intensities associated with each pixel of the paths. For example, the multiple trajectories or paths generated by the computer in operation 720 may be fed into a wavelet transform or other transform that preserves the time index. The pixel-wise transform may then be filtered by the computer to cardiac frequency. This may be done by setting all transform coefficients that do not correspond to the cardiac wavelet scale (in the field of wavelets, this term corresponds to the concept of cardiac frequency) to zero. The pixel-wise transformed data may then be transformed back to the time domain by the computer and reformatted into pixels in computer memory. Each data element (voxel) of this three-dimensional grid may be complex-valued.

[0083] In operation 740, the spatiotemporal reconstruction of the cardiac frequency angiography phenomenon is output for display in one or more images. For example, each frame may be rendered by a computer as an image having a luminance-hue color model to represent the complex data in each pixel. The magnitude of the cardiac frequency may be represented as luminance and the phase as hue. The images may be displayed on a screen, saved as image files, and / or printed. The q images may be rendered by a computer as a motion cine or saved by a processor as a video file format.

[0084] The techniques described herein are not necessarily limited to cardiac applications. Other applications may include optical angiography. For example, optical angiography of the retina in awake patients may capture saccadic eye movements. The techniques described herein may be used to compensate for this type of movement. Other sources of motion that may be compensated for by this technique include, but are not necessarily limited to, respiratory motion, spontaneous voluntary or involuntary movements. These techniques may be applied to any suitable object / body part (e.g., blood vessels, organs, etc.) that undergoes large-scale or unconstrained movement.

[0085] The techniques described herein may be performed in conjunction with the administration of an effective amount of a contrast agent to visualize the object. In cases where motion determination via an optical flow path involves both forward and reverse motion, these techniques may be suitable for measuring object motion in intermediate image frames (e.g., image frames surrounded by other image frames in an image sequence). However, it will be appreciated that the techniques described herein may also be suitable for measuring object motion at the beginning and end of an image sequence, and may be used with or without a contrast agent.

[0086] The present invention may include methods, systems, apparatus, and / or computer program products at any level of technical detail in which they may be integrated. The computer program product may include a computer readable storage medium having computer readable program instructions for causing a processor to carry out aspects of the present invention.

[0087] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device or to an external computer or storage device over a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include conductive transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device may receive the computer-readable program instructions from the network and forward the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0088] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0089] These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing device to produce a machine, whereby the instructions, executed by the processor of the computer or other programmable data processing device, create means for performing the functions / operations specified in the flowchart and / or block diagram blocks. These computer-readable program instructions may be stored on a computer-readable storage medium capable of directing a computer, programmable data processing device, and / or other device to function in a particular manner. Thus, the computer-readable storage medium on which the instructions are stored constitutes an article of manufacture including instructions implementing aspects of the functions / operations specified in the flowchart and / or block diagram blocks.

[0090] The computer readable program instructions may be loaded into a computer, other programmable data processing apparatus, or other device and executed on the computer, other programmable apparatus, or other device to generate a sequence of operational steps to generate a computer-implemented process, such that the instructions executing on the computer, other programmable apparatus, or other device perform the functions / operations specified in the flowchart and / or block diagram blocks.

[0091] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions, which may include one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart diagrams, as well as combinations of blocks in the block diagrams and / or flowchart diagrams, may be implemented by a special purpose hardware-based system that executes the specified functions or operations, or executes a combination of special purpose hardware and computer instructions.

[0092] The above description is intended to teach those skilled in the art how to practice the subject matter of this application, and is not intended to detail all obvious modifications and variations that may become apparent to those skilled in the art upon reading the description. However, all such obvious modifications and variations are intended to be included within the scope of the invention as defined by the claims. The claims are intended to encompass the elements and steps in any order effective to fulfill the purpose intended therein, unless the context specifically indicates to the contrary.

Claims

1. 1. A method for extracting cardiac frequency angiographic phenomena of unconstrained vascular objects from an angiographic examination, comprising: acquiring, in a computer, a series of angiographic image frames acquired at a rate faster than a cardiac frequency, each said angiographic image frame comprising a plurality of pixels, each said pixel having a corresponding intensity; applying an optical flow technique to the angiographic image frames to generate a plurality of paths corresponding to displacements of each of the pixels between the angiographic image frames, the applying of the optical flow technique including, for each of the angiographic image frames, recursively integrating the displacements of a given of the pixels in a forward and reverse time direction to generate an optical flow trajectory for the given of the pixels; generating, at the computer, a spatio-temporal reconstruction of the cardiac frequency angiographic phenomenon based on the plurality of paths and the corresponding intensities associated with the pixels of each of the paths; outputting the spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon for display in one or more images; The method includes:

2. Select the image frame of interest, determining the displacement of a given pixel of the image frame of interest based on the angiography image frames within a range of a number of the angiography image frames from the image frame of interest; The method of claim 1 further comprising:

3. Applying the optical flow technique applying a dense optical flow technique to measure optical flow of the plurality of pixels between the angiographic image frames; The method of claim 1.

4. Applying the optical flow technique applying a sparse optical flow technique, the sparse optical flow technique comprising: Tracking a limited number of object locations between said angiographic image frames; and interpolating the intervening object position movements. The method of claim 1.

5. Applying the optical flow technique determining each said path based on a local coordinate system; The method of claim 1.

6. The method of claim 1 , further comprising providing the one or more images as a cine video sequence.

7. 1. A system comprising: a communications interface configured to acquire a series of angiographic image frames acquired at a rate faster than a cardiac frequency, each said angiographic image frame comprising a plurality of pixels, each said pixel having a corresponding intensity; one or more processors coupled to a communication interface; the one or more processors: applying an optical flow technique to the angiographic image frames to generate a plurality of paths corresponding to displacements of each of the pixels between the angiographic image frames, the applying of the optical flow technique including, for each of the angiographic image frames, recursively integrating the displacements of a given of the pixels in a forward and reverse time direction to generate an optical flow trajectory for the given of the pixels; generating a spatiotemporal reconstruction of a cardiac frequency angiographic phenomenon based on the plurality of paths and the corresponding intensities associated with the pixels of each of the paths; Outputting the spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon for display in one or more images. The system is configured as follows:

8. the one or more processors: Select the image frame of interest, and determining the displacement of the given pixel of the image frame of interest based on the angiography image frames within a range of a number of the angiography image frames from the image frame of interest. The system of claim 7.

9. the one or more processors: and applying a dense optical flow technique to measure an optical flow of the plurality of pixels between the angiography image frames. The system of claim 7.

10. the one or more processors: and further configured to apply a sparse optical flow technique; The sparse optical flow technique comprises: Tracking a limited number of object locations between said angiographic image frames; and interpolating the intervening object position movements. The system of claim 7.

11. the one or more processors: and further configured to determine each of the paths based on a local coordinate system. The system of claim 7.

12. the one or more processors: and further configured to provide the one or more images as a cine video sequence. The system of claim 7.

13. One or more non-transitory computer-readable storage media encoded with instructions that, when executed by a processor, cause the processor to: acquiring a series of angiographic image frames acquired at a rate faster than a cardiac frequency, each said angiographic image frame comprising a plurality of pixels, each said pixel having a corresponding intensity; applying an optical flow technique to the angiographic image frames to generate a plurality of paths corresponding to displacements of each of the pixels between the angiographic image frames, the applying of the optical flow technique including, for each of the angiographic image frames, recursively integrating the displacements of a given of the pixels in a forward and reverse time direction to generate an optical flow trajectory for the given of the pixels; generating a spatiotemporal reconstruction of a cardiac frequency angiographic phenomenon based on the plurality of paths and the corresponding intensities associated with the pixels of each of the paths; outputting the spatiotemporal reconstruction of the cardiac frequency angiographic phenomenon for display in one or more images; One or more non-transitory computer-readable storage media that perform the steps of:

14. The instructions cause the processor to: Select the image frame of interest, determining the displacement of a given pixel of the image frame of interest based on the angiography image frames within a range of a number of the angiography image frames from the image frame of interest; The one or more non-transitory computer-readable storage media of claim 13, further comprising:

15. The instructions cause the processor to: applying a dense optical flow technique to measure optical flow of the plurality of pixels between the angiographic image frames; The one or more non-transitory computer-readable storage media of claim 13, further comprising:

16. The instructions cause the processor to: Further applying sparse optical flow techniques; The sparse optical flow technique comprises: Tracking a limited number of object locations between said angiographic image frames; and interpolating the intervening object position movements.

14. One or more non-transitory computer-readable storage media as recited in claim 13.

17. The instructions cause the processor to: and providing the one or more images as a cine video sequence.

14. One or more non-transitory computer-readable storage media as recited in claim 13.

Images