Patent Description:
Valvular regurgitation (also known as valvular insufficiency) is a cardiac disease characterized by the failure of the cardiac valves to close perfectly, resulting in blood flowing in the opposite direction and thereby causing regurgitation or leakage. Such valvular regurgitation/inefficiency can be experienced by one or more of the four cardiac valves; aortic valve, mitral valve, pulmonary valve, and the tricuspid valve. Valvular insufficiency/regurgitation represent the dominant functional and anatomic consequences associated with valvular heart disease, due to the higher workload and a higher pressure within the heart, increasing the chances of heart failure. Patients may experience chest pains, become quickly out of breath if they exert themselves and may suffer fainting spells as well as other symptoms. Eventually, they develop more serious complications including heart failure. There is no medication that can reverse the damage. There are several methods available to assess valvular regurgitation, such as electrocardiogram (ECG), transthoracic echocardiography (TTE), transesophageal echocardiography (TEE), magnetic resonance imaging (MRI).

Surgical valve replacement or valve repair is the standard of care in patients with severe valvular regurgitation. Surgical valve replacements/repair involves open heart surgery in which the surgeon repairs or replaces (by a mechanical or tissue valve) the patient's diseased valve. The patient is placed on a heart-lung bypass machine while the heart is stopped.

Not all patients can withstand the risks associated with surgical valve repair or replacement. Roughly one-third of all patients with severe aortic stenosis are considered too high risk for open-heart surgery (<NPL>). About <NUM>% of this group of patients die within one to two years without corrective therapies (<NPL>).

Recently, transcatheter valve replacement and transcatheter valve repair (TVR) has been developed as an alternative to the open-heart surgical approach. TVR involves a minimally invasive procedure which repairs or replaced the valve without removing the old, damaged valve. Instead, it wedges a replacement valve or repair device towards the diseased valve's place. The repair or replacement device is delivered through one of several access methods: transfemoral (in the upper leg), transapical (through the wall of the heart), subclavian (beneath the collar bone), or transcaval (from a temporary hole in the aorta near the belly button through a vein in the upper leg). As an added bonus, the recovery time is much faster for TVR patients who are typically out of hospital within three to five days. Open-heart surgery patients spend about <NUM> days in hospital and it takes a long time for their chest incision to heal.

TVR is performed minimally invasively during a catheterization procedure with the guidance of X-ray angiography and precise assessment of valvular regurgitation (VR) severity is needed during procedure when there is still a chance to avert it. Quantification of VR, typically and most commonly paravalvular, is challenging and frequently requires a multimodality assessment. TEE and x-ray angiography are the standard tools for the assessment of VR during the procedure. However, echocardiography has a low reproducibility and a low sensitivity to detect paravalvular leaks, and the Sellers' method (<NPL>) of x-ray angiography assessment is qualitative and subjective.

Within <CIT> a novel videodensitometric approach was introduced for the accurate assessment of aortic (para)valvular regurgitation during a transcatheter aortic valve implantation. This method has been extensively validated and proven to be very robust and reproducible by several authors; <NPL>, <NPL>, and by <NPL>.

The method described by <CIT> is focused on the quantitative assessment of the aortic valve, which can be considered as a two-compartment problem. However, quantification of (para)valvular regurgitation of the mitral valve or the tricuspid valve, which can be considered as a three-compartment problem, is not addressed.

Assessment of (para)valvular regurgitation for the atrioventricular valves (i.e., the mitral valve or tricuspid valve) using the method as described by <CIT> is error prone. Due to the relatively large region of the ventricle (the reference region), it is not likely to obtain homogeneous contrast filling, which hampers time-density curve assessment. Moreover, the contrast density within the ventricle will be variable by nature, this due to the change in ventricle shape and volume as a result of the cardiac cycle. Moreover, if both the atrioventricular valve and the ventricular valve suffer from (para)valvular regurgitation a two-compartment method is not sufficient.

<NPL>, discloses a method of measuring valve regurgitation.

There is thus the need to improve the assessment of valvular regurgitation such as mitral and tricuspid regurgitation by computer analysis using x-ray angiography image data.

In accordance with embodiments herein, devices, computer program products and computer implemented methods are provided for characterizing blood flow in an atrioventricular valve of the human heart, the atrioventricular valve connecting an atrium with a corresponding ventricle of the heart, the ventricle being fluidly coupled to a particular vessel that transports blood outside the ventricle, the devices, program products and methods comprising, under control of one or more computer systems configured with specific executable instructions,.

In embodiments, data representing forward blood flow from the ventricle to the particular vessel from the related time density curve based on area under the time-density curve starting from a contrast arrival frame over a number of cardiac cycles and data representing regurgitant blood flow from the ventricle to the atrium from the related time density curve based on area under the time-density curve starting from the contrast arrival frame over the number of cardiac cycles are calculated.

From the data representing forward blood flow from the ventricle to the particular vessel and the data representing regurgitant blood flow from the ventricle to the atrium a first regurgitant fraction can be calculated, particularly the first regurgitant fraction can be calculated as <MAT> wherein VentriculaValveforward flow comprises the data representing forward blood flow from the ventricle to the particular vessel, and AtrioventricularValvebackward flow comprises the data representing regurgitant blood flow from the ventricle to the atrium.

In embodiments herein, data representing regurgitant blood flow from the particular vessel to the ventricle is calculated from the time density curve, for example based on area under the corresponding time density curve within the diastolic phase of the heart after the contrast arrival frame.

The data representing regurgitant blood flow from the particular vessel to the ventricle can be used to calculate a second regurgitant fraction, for example as <MAT> wherein VentriculaValveforward flow comprises data representing forward blood flow from the ventricle to the particular vessel, AtrioventricularValvebackward flow comprises data representing regurgitant blood flow from the ventricle to the atrium, and VentricularValvebackward flow comprises the data representing regurgitant blood flow from the particular vessel to the ventricle.

According to embodiments herein, the method may further comprise:.

The atrioventricular valves are the mitral valve located between the left atrium and the left ventricle and the tricuspid valve located between the right atrium and the right ventricle.

When the mitral valve regurgitation is assessed, the atrium corresponds to the left atrium of the heart and the region within the particular vessel corresponds to a region of the ascending aorta of the heart.

When the tricuspid valve regurgitation is assessed, the atrium corresponds to the right atrium of the heart and the region within the particular vessel corresponds to a region of the pulmonary artery of the heart.

In embodiments, the densitometric image data comprises pixel values that represent localized density of absorbed radiation due to contrast liquid over time.

In embodiments, the time-density curve represents localized density of absorbed radiation due to contrast agent by a vascular object of interest over time.

In embodiments, the image data covers a first period of time prior to injection of contrast agent into the heart and a second period of time after injection of contrast agent into the heart.

In embodiments, the contour of at least one vascular object of interest is identified in a particular image frame for example upon user input and/or automatic processes.

In embodiments, the data characterizing at least one regurgitation fraction related to the atrioventricular valve of the heart is based on conservation of mass of forward and backward flows related to the atrioventricular valve.

The time density curves are preferably normalized relative to a selected time density curve.

In embodiments, the data characterizing at least one regurgitation fraction related to the atrioventricular valve of the heart is based on difference between particular time density curves relative to a predefined phase of the heart cycle.

Embodiments also relate to a computer product directly loadable into the memory of a digital computer and comprising software code portions for performing the method according to embodiments herein when the product is run on a computer.

Embodiments also relate to a system for characterizing blood flow in an atrioventricular valve of the human heart, the atrioventricular valve connecting an atrium with a corresponding ventricle of the heart, the ventricle being fluidly coupled to a particular vessel that transports blood outside the ventricle, the system comprising at least one processor configured to execute program instructions for carrying out the method according to embodiments herein.

According to an aspect, embodiments relate to an imaging device, typically a X-ray or MRI device, more typically an X-ray device for angiography, for acquiring contrast enhanced two dimensional or three dimensional sequences of image frames, the device comprising an acquisition module for obtaining a plurality of image frames of a vessel perfused by a contrast agent, the device further comprising a processing unit configured to carry out the method according to embodiments herein for characterizing blood flow in an atrioventricular valve of the human heart.

The characteristics of the invention and the advantages derived therefrom will be more apparent from the following description of non-limiting embodiments, illustrated in the annexed drawings, in which:.

The present patent relates to methods and systems for characterization of valvular regurgitation/insufficient based on sequences of images, such as two-dimensional (2D) angiographic film of X-ray images. It will be mainly disclosed with reference to this field. Within current application the term image or image frame refers to a single image and the term image sequence refers to multiple images acquired over time and when used in relation to x-ray it comprises multiple frames covering one or more phases of the cardiac cycle.

<FIG> shows a flow chart illustrating the operations according to an embodiment of the present patent. The operations employ an imaging system capable of acquiring and processing two-dimensional image sequences of a vessel organ (or portion thereof) or other object of interest. For example, a single plane or bi-plane angiographic system can be used such to acquire the X-ray image data as those manufactured, for example, by Siemens (Artis zee Biplane) or Philips (Allura Xper FD).

<FIG> is a functional block diagram of an exemplary single plane angiographic system, which includes an angiographic imaging apparatus <NUM> that operates under commands from user interface module <NUM> and will provide data to data processing module <NUM>. The single plane angiographic imaging apparatus <NUM> captures a two-dimensional X-ray image sequence of the vessel organ of interest, for example, in the postero-anterior direction. The single plane angiographic imaging apparatus <NUM> typically includes an X-ray source and detector pair mounted on an arm of a supporting gantry. The gantry provides for positioning the arm of the X-ray source and detector at various angles with respect to a patient who is supported on a table between the X-ray source and detector. The data processing module <NUM> may be realized by a personal computer, workstation, or other computer processing system. The data processing module <NUM> processes the two-dimensional image sequence captured by the single plane angiographic imaging apparatus <NUM> to generate data as described herein. The user interface module <NUM> interacts with the user and communicates with the data processing module <NUM>. The user interface module <NUM> can include different kinds of input and output devices, such as a display screen for visual output, a touch screen for touch input, a mouse pointer or other pointing device for input, a microphone for speech input, a speaker for audio output, a keyboard and/or keypad for input, etc. The data processing module <NUM> and the user interface module <NUM> cooperate to carry out the operations of <FIG> as described below.

The operations of <FIG> can also be carried out by software code that is embodied in a computer product (for example, an optical disc or other form of persistent memory such as a USB drive or a network server). The software code can be directly loadable into the memory of a data processing system for carrying out the operations of <FIG>. Such data processing system can also be physically separated from the angiographic system used for acquiring the images making use of any type of data communication for getting such images as input.

In this example it is assumed that the x-ray imaging system has acquired and stored at least one two-dimensional image sequence of the vessel organ of interest (e.g., human heart or portions thereof). Any image device capable of providing two-dimensional angiographic image sequences can be used for this purpose. For example, a bi-plane or single plane angiographic system can be used such as those manufactured, for example, by Siemens (Artis zee Biplane) or Philips (Allura Xper FD).

An embodiment is now disclosed with reference to <FIG>. The therein-depicted operations can, obviously, be performed in any logical sequence and can be omitted in parts. As it is an objective of the patent to provide a select (e.g. optimal) workflow that can be used during the interventions, workflow example steps will also be referenced.

Frist an overview of the different heart valves and how they behave in the different heart states is described. The human heart has four valves as shown in <FIG>. There are two ventricular valves; the aortic valve (<NUM>) and the pulmonary valve (<NUM>), and two atrioventricular valves; the mitral valve (<NUM>) and tricuspid valve (<NUM>). During a cardiac cycle as shown in <FIG>, three main states of the heart can be identified, which are: arterial systole (<NUM>), ventricular systole (<NUM>) and relaxation period (<NUM>). <FIG> shows the different heart states with respect to the ECG signal (<NUM>) as well as an overview on the status of the valves by table <NUM>. During the arterial systole (<NUM>), the atrial valves (mitral, tricuspid) are open, and the ventricular valve (aorta, pulmonary) are closed. Blood flows from the atrial chambers in the ventricular chambers. During ventricular systole (<NUM>) the ventricles contracts and the ventricular valves are open, and the atrial valves are closed. Blood is pumped from the ventricular chambers to the lungs (through the pulmonary artery) and to the organs of the body (though the aortic artery). During the relaxation period (<NUM>), the ventricular valves are closed, and the atrial valves are open. Blood flows from the major veins (pulmonary veins and vena cava) into the atria's and from there into the ventricles. The cardiac cycle is can also be identified as two phases; systole phase and diastole phase. Systole phase includes the ventricular systole, as diastole phase includes arterial systole and relaxation period.

As can be seen in <FIG>, the workflow comprises of number of steps or operations, and the workflow will be described to detect and characterize valvular regurgitation (also called valvular insufficiency) of the valves of the heart (for the left side of the heart these are the mitral valve and aortic valve). Such valvular regurgitation is a condition in which the heart's valve does not close tightly, allowing blood to leak or flow through the valve during the period of the cardiac cycle that is should be closed tightly. If such valvular regurgitation is significant, blood movement through the patient's heart and to the rest of the patient's body is inefficient and can make the patient feel tired or out of breath. The disclosed method is not limited to the left side of the heart and can also be performed for the right side of the heart, for the tricuspid valve and pulmonary valve. For this, the patient specific x-ray image data should visualize the right chamber(s) of the heart.

The first step (<NUM>) of <FIG> involves the retrieval of the patient specific x-ray image data which consists of a sequence of angiographic x-ray image frames covering approximately one heart cycle before the injection of the contrast liquid and approximately three heart cycles after the injection of the contrast liquid. To assess mitral regurgitation, the contrast is injected into the left chamber of the heart (left ventricle), and a projection is chosen which shows the three compartments; left ventricle (LV) <NUM>, left atrium (LA) <NUM>, and ascending aorta (AO) <NUM>, with a minimum of overlap to each other as shown by <FIG>. Also illustrated by <FIG>, the aortic valve (<NUM>) which separates the LV from the AO, and the mitral valve (<NUM>) which separates the LV from the LA. Furthermore, care should be taken to avoid overlay of the descending aorta with the LA. An x-ray projection which fulfills above could be achieved by for instance a rotation of approximately RAO55 degrees and an angulation of approximately CAU30 degrees and might be depended on the specific patient anatomy.

<FIG> shows an example of a single frame within an acquired x-ray angiographic image sequence in which a replaced mitral valve (<NUM>) is visible. The frame shown is around systolic after injection of the contrast liquid. Alternatively, the patient specific projection can be obtained by means of planning on a pre-procedural three-dimensional (3D) image data sets, for instance computed tomography (CT), such as 3mensio Structural Heart software 10v1 (Pie Medical Imaging, Maastricht, the Netherlands) or Heart Navigator Software (Philips Healthcare, The Netherlands). <FIG> provides an example of obtaining the optimal projection by using the simulated angioview (disclosed by <CIT>) within the 3mensio Structural Heart software.

Within step <NUM> of <FIG>, the contours of two objects of interest, the aorta (or AO) and the left atrium (or LA), are detected to quantify mitral valve regurgitation. Typically, a frame is selected in which the aortic valve is going to be opened but still is closed. Referring to <FIG> such a frame, further referred to as optimal frame, corresponds to end of atrial systole state and can be identified based on the ECG signal as available through the "headers" of the Digital Imaging and Communications in Medicine (DICOM) file or by visual assessment of the x-ray angiographic image sequence. Detection of specific features within the ECG signal, in our case the r-tops, can be performed as for example taught by <NPL>. The optimal frame(s) can be defined as a percentage between two consecutive r-peaks. This percentage is typically <NUM>%. When incorporating the knowledge at which frame the contrast arrives within the left ventricle, as further explain in step <NUM>, the most optimal frame which is the optimal frame in which the AO is opacified (due to the contrast liquid) can be automatically detected.

<FIG> shows the detection of the two objects in the most optimal frame by means of a manual segmentation. The contour or region of the AO, identified by <NUM>, is manually drawn covering the AO above the aortic valve (<NUM>), and the contour or region of the LA, identified by <NUM>, is manually drawn covering the left atrium. In case the selected frame does not represent the end of the atrial systole state, a reference line can be drawn which delimits the left ventricle from the left atrium within the x-ray sequence as illustrated by <FIG> (<NUM>). In case the selected frame represent the end of the atrial systole state, the motion of the mitral valve, illustrated by the arrow <NUM>, is in the most extreme location in which the contour or region of the LA does not overlap the left ventricle during the cardiac cycle. Within <FIG>, the region <NUM> within the contour or region of the segmented LA region (<NUM>) will not be taken into consideration for the quantification of the mitral regurgitation as explained by step <NUM> of <FIG>. The region <NUM> represents to the movement of the mitral valve during the cardiac cycle.

Alternatively, the contour of region of the AO and the contour or region of the LA can be superimposed from segmentation performed on the pre-procedural 3D image data sets such as CT by means of the 3mensio Structural Heart software, the Heart Navigator Software, or other pre-procedural planning software. In summary, the regions are segmented using the 3D image data and the resulting 3D segmented regions are then registered to the x-ray angiographic dataset for a selected frame as for instance described by <NPL>, or by <NPL>.

Alternatively, the contour or region of the LA can be a pre-defined shape, such as a hexagon-shape as illustrated by <FIG>. The hexagon shape is initiated by the drawing of line segment <NUM>, which result in a hexagon shape with distance <NUM>, <NUM> and <NUM> depended on the initial length of <NUM>. Afterwards, the user can change the size of the shape (<NUM>, <NUM>, <NUM> and <NUM>) by dragging on the dots. The same principle can be applied when the hexagon shape is initiated by another line segment of the circumference of the hexagon.

Within step <NUM> of <FIG>, the x-ray angiographic sequence obtained in step <NUM> is converted into a densitometric image sequence. <FIG> illustrated the creation of the densitometric image sequence using the ECG signal as available through the "headers" of the DICOM file format. The ECG signal is represented by <NUM>, and the x-ray angiographic image sequence is represented by <NUM> in which each successive frame is represented by a dot. Picture <NUM> represent a frame from the sequence in which no contrast liquid is present and picture <NUM> represents a frame from the sequence in which contrast liquid is present. The frame in which the contrast in the left ventricle arrives (further referred to as fc) is illustrated by <NUM>. This frame can be defined manually or can be automatically detected by analyzing the global intensity distribution of each frame, expressed as for instance the mean intensity, within the acquired x-ray sequence. Before the contrast is injected the global frame image intensity will be different than after the contrast is injected within the sequence. This is illustrated by <FIG> which shows the mean pixel intensity of each frame within the image sequence (<NUM>). Frames at the start of the sequence shows relatively high mean pixel intensity, whereas frames after the injection of contrast shows a relative low mean pixel intensity. This due to the lower pixel values when the x-ray radiation is absorbed by the injected contrast liquid. Within picture <NUM> of <FIG>, frame of contrast arrival (fc) is illustrated by <NUM>, which can be derived by one-dimensional signal processing techniques. For instance, the first derivative can identify the highest signal change which represent position (<NUM>), and the highest second derivative looking to the left of position <NUM> identifies the frame of contrast arrival (fc) (<NUM>). In case no ECG signal is present, the frames which represents the end diastolic phase (<NUM>), which will correspond to the r-top of the ECG, can be detected as well. Since the end diastolic phase will is defined as the phase in which the left ventricular volume is the biggest, this can be seen as a small mean pixel intensity drop (<NUM>) after the frame of contrast arrival (fc). These positions can again be identified by means of one-dimensional signal processing techniques. For instance, the local minimum can be identified by looking at the sign of the second derivative where the first derivative is closest to zero. If the second derivate is positive, we are dealing with a local minimum.

Once the frame of contrast arrival (fc) has been identified (<NUM>), a set of masker images is created within one cardiac cycle preceding to the frame fc, and each masker image Mi is coupled to the ECG as a percentage of the r-r interval (<NUM>). The densitometric sequence is then generated by first determine the percentage of r-r interval of the current frame and subtraction of the corresponding masker image Mi and repeating this process for all frames within the sequence. In case the percentage of r-r interval of the current frame does not match a percentage r-r interval of the masker frames Mi, the nearest neighbor masker frames Mnn can be selected. Alternatively, a new masker frame Minterpolated can be calculated by a weighted interpolation between the closest masker frames. This process generated a sequence of images in which the pixel values represent the amount of x-ray absorbed by the contrast liquid.

In case the ECG signal is not present, the method as described by the explanation of <FIG> (<NUM>) can be used to derive the ECG signal.

Alternatively, the densitometric image sequence can be created as disclose by step <NUM> of <FIG> within <CIT>.

Within step <NUM> of <FIG>, a time-density curve for the contour or region of the AO and a time density curve for the contour or region of the LA are calculated. Such time-density curves represent a time-evolution of pixel brightness of a sequence of images, and in current patent the pixel brightness values represent the density of the absorbed x-ray due to the contrast liquid and is computed from the densitometric image sequence. The time-density curve for the contour or region of the AO is computed as the mean pixel intensity within the corresponding region along the entire sequence length and are normalized against the maximum value within the contour or region of the AO. The time-density curve for the contour or region of the LA is computed as the mean pixel intensity within the corresponding region along the entire sequence length and are normalized against the maximum value within the contour or region of the LA. <FIG> shown an example of the time-density curves resulting from the analysis of the image sequence (<NUM>) for the AO region (<NUM>) and the LA region (<NUM>).

Optionally the time-density curve for the contour or region of the LA can be calculated by taking into consideration the region within the LA which becomes opacified after contrast injection. Since mitral regurgitation can result in provides high local velocity jets, it can occur that only a small region within the LA becomes opacified. To increase the accuracy of the time-density curve computed within the LA-region a method is presented which takes above into consideration. This is done by selecting a number of pixels within the contour of the LA for calculation which exceeds a certain threshold value. This threshold is defined based on a fraction of the mean opacification difference before and after contrast arrival derived within the AO region. This fraction is added to the mean opacification of the LA region before contrast arrival to define the threshold that indicates which pixels from the LA region are included, thus excluding pixels below the given threshold from analysis.

Within step <NUM> of <FIG>, data quantifying valvular regurgitation of the mitral valve is calculated. As depicted by <FIG>, during systolic the ventricular valves are open (<NUM>) and ventricular ejection takes place. When focusing on the left ventricle, during systole the aortic valve is open and an amount of blood volume within the left ventricle is ejected towards the aorta, which is called the stroke volume (SV) and identified by <NUM> within <FIG>. Any flow from the left ventricle towards the LA during the systolic phase (in which the mitral valve should be fully closed), is called regurgitant mitral flow (RMF) or backwards mitral flow as indicated by <NUM> in <FIG> and is represented by the term Mitralbackward flow (unit-less value of flow) in equation (<NUM>).

Taking into consideration the conservation law of mass, the following equation applies: <MAT>.

Within equation <NUM>, aortaforward flow is defined as the flow which is ejected from the left ventricle into the ascending aorta during the systole phase in which the aorta valve is open, mitralforward flow is defined as the flow from the left atrium towards the left ventricle during diastole phase in which the mitral valve is open, and aortabackward flow is the flow from the ascending aorta towards the left ventricle in diastole phase in which the aortic valve is closed (aortic regurgitant flow). The aortaforward flow can be derived from the time-density curve of the AO region, and is calculated by the area under such time-density curve starting from the contrast arrival (fc) over a number of cardiac cycles (Nc). Nc can be defined by the user and typically this value is <NUM>. The following equation shows how the aortaforward flow can be computed. Note that the term cycle in equation (<NUM>) defines the number of frames within the R-R interval, either extracted from the ECG signal or by means of the explanation of <FIG>.

Similarly, the mitralbackward flow can be derived from the time-density curve of the LA region by: <MAT>.

Since the time-density curves are normalized, as described by step <NUM>, the computed aortaforward flow and mitralbackward flow is expressed as unit-less value representing the flow.

A mitral regurgitation fraction, which is expressed in percentage and quantifies valvular regurgitation of the mitral valve, can be computed by: <MAT>.

When assuming that there is no aorta regurgitation, a mitral regurgitation fraction, which is expressed in percentage and quantifies valvular regurgitation of the mitral valve, can be derived by <MAT>.

As can be seen by equation <NUM>, a method is defined for quantifying atrioventricular valve regurgitation (e.g. mitral valve regurgitation) without the use of a ventricular measurement (e.g. within the left ventricle to assess mitral valve regurgitation). This approach eliminates the shortcoming in quantifying atrioventricular valve regurgitation of the method as described by <CIT>, which assume a reference region of the chamber of the valve in which the regurgitant blood originates from (e.g. for mitral regurgitation, the left ventricle). Due to the relatively large region of the ventricle (the reference region), it not likely to obtain homogeneous contrast filling, which hampers time-density curve assessment. Moreover, the contrast density within the ventricle will be variable by nature, this due to the change in ventricle shape and volume as a result of the cardiac cycle.

Optionally, data quantifying aortic valve regurgitation (which involves aortabackward flow) can be incorporated into the calculation. For this, an additional region needs to be defined, as illustrated by <NUM> within <FIG>. The additional region (<NUM>) represents the left ventricular outflow track (LVOT), which is further identified as LVOT region. The LVOT region is the part within the left ventricle just below the aortic valve. Within <FIG>, <NUM> represents the AO region and <NUM> represents the LA region, similar to <FIG>. This additional region can be defined by the user or automatically detected by image processing methods for instance as described within this patent.

The extraction of data representing aortabackward flow is further illustrated by <FIG>. Within <FIG>, <NUM> represent the time-density curve of the AO region (<NUM>) and the LVOT region (<NUM>). Note that the time-density normalization is now normalized against the time-density curve with the highest values. In this case the LVOT time-density curve is chosen for normalization. This also means that the time-density curve of the LA region will be normalized against the time-density curve with the highest values and in this particular example this will also be the LVOT time-density curve. The marker depicted by <NUM> represents the frame of contrast arrival (fc) and the marker depicted by <NUM> represents the frame in which the injection of the contrast liquid stops. The duration of contrast injection is known upfront to the procedure and this frame can be calculated by using the frame rate, duration of contrast injection and fc. The markers between <NUM> identifies the R-R interval in frames. Furthermore, for illustration purpose the R-R cycle identified by <NUM> shows no aortic regurgitation, as the R-R cycle identified by <NUM> shows the behavior of the time-density curve of the AO region and the LVOT region in case aortic regurgitation would be present. From the moment the systolic phase is finalized, in which the aortic valve is closed, the LVOT time-density curve will increase incase aortic regurgitation is present. The aortabackward flow can be determined as the area between the LVOT time-density curve and the AO time-density curve within the diastolic phase (<NUM>) and is visualized in <FIG> by <NUM>. Now the mitral regurgitation fraction, which is expressed in percentage and quantifies valvular regurgitation of the mitral valve, can be computed by <MAT>.

Furthermore, an aorta regurgitation fraction, which is expressed in percentage and quantifies aortic valve regurgitation, can be computed by dividing the area identified by <NUM> by the area identified by <NUM>. The area <NUM> can be calculated as the area between the time-density curve for the LVOT region and the time-density curve for the AO region over the diastolic phase <NUM>. The area <NUM> can be calculated as the area under the time-density curve for the AO region over the diastolic phase <NUM>.

Alternatively, regurgitation of the mitral valve can be quantified by data expressed in absolute values, such as milliliter, instead of a percentage value only. For this the left ventricle needs to be segmented in at least the end systolic frame and the end diastolic frame. Such a segmentation can be performed manually, semi-automatically, or automatic as for instance available in CAAS Workstation <NUM> - LVA workflow (Pie Medical Imaging, the Netherlands). <FIG> shows a cut from the LVA workflow of the CAAS Workstation. Picture <NUM> shows the LV segmented in end diastolic phase and <NUM> shows the LV segmented in end systolic phase. Using the Simpson method, the LV volume can be calculated as for instance taught by <NPL>. From both the end diastolic LV volume (EDV) and end systolic LV volume (ESV), the stroke volume (SV) can be calculated by: <MAT>.

Other left ventricle parameters can then be calculated as well, for instance the left ventricle ejection fraction, cardiac output, as well as region wall motion like the centerline model, radial model, slager model, etc..

Next, regurgitation of the mitral valve can be expressed by data in absolute value, for instance in milliliters, by: <MAT>.

The same is true for quantifying regurgitation of the aortic valve by means of the equation: <MAT>.

In <FIG>, picture <NUM> shows an example in which the regurgitation within the left atrium is visualized by means of a 2D color map. This 2D color map is derived by combining the results of the LA time-density curve (<NUM>) with its spatial position related to the left atrium. The regurgitant flow through the mitral valve and/or the aortic valve can, in fact, be visualized by means of a color map obtained by the time intensity curves and/or parameters related to such time intensity curves. This can be achieved, for example, by computing within each pixel within the left atrium, the integral of the time density curve scaled to the maximum value within the AO time-density curve. This way of presenting the outcome of the method of the present invention is very powerful. Within <FIG>, picture <NUM>, zero regurgitation as derived within a pixel is represented by the color blue, in which high regurgitation as derived within a pixel is represented by a red color in case the color scaling goes from blue (min value) to red (maximum value). Regurgitation maps can, in fact, be displayed overlaid, for example by varying the opacity, with the input images or with related images, either registered or not, such as those obtained from a different imaging modality, thus providing an immediate overview of insufficiency. Regurgitation maps can be presented statically, for example, as the total integral of the time density curve scaled to the reference or dynamically, for example, by displaying the sub integral of the time density curve related to the frame being viewed. The representation of time-density curves as color maps can obviously be achieved independently from any parameter determination and thus numeric indication of insufficiency according to the present invention.

In other embodiments, one or more machine learning systems or other forms of computer-based artificial intelligence can be trained or otherwise configured to detect one or more objects of interest (e.g., contours of relevant regions of the heart organ) and/or extract data characterizing valvular regurgitation of one or more valves of the heart organ directly from the image sequence. The machine learning system(s) can be embodied by one or more artificial neural networks, decision trees, support vector machines, and/or Bayesian networks. The machine learning system(s) can be trained by supervised learning involving of a set of training data, unsupervised learning, or semi-supervised learning.

The disclosed method is not limited to the left side of the heart and can also be performed for the right side of the heart. For this, the patient specific x-ray image data should visualize the right chamber(s) of the heart, the right ventricle and right atrium. Mirroring the method to the right side of the heart, the following is considered. The mitral valve is replaced by the tricuspid valve, and the aortic valve is replaced by the pulmonary valve. Furthermore, the left atrium is replaced by the right atrium, the left ventricle is replaced by the right ventricle, and the left ventricular outflow track from the left ventricle to the ascending aorta is replaced by the right ventricular outflow track from the right ventricle to the pulmonary artery. The volumes used to calculate the stroke volume of equation <NUM> represents the ED and ES volume of the right ventricle, as for instance taught by <NPL>.

Operations can be performed by processor unit on a standalone system, or a semi-standalone system which is connected to the X-ray cinefluorograpic system (<FIG>) or any other image system to acquire two-dimensional angiographic image sequences. <FIG> illustrates an example of a high-level block diagram of an x-ray cinefluorograpic system. In this block diagram an example is shown on how embodiments could integrate in such a system.

Portions of the system (as defined by various functional blocks) may be implemented with dedicated hardware, analog and / or digital circuitry, and / or one or more processors operating program instructions stored in memory.

The X-ray system of <FIG> includes an X-ray tubes <NUM> with a high voltage generator <NUM> that generates an X-ray beam <NUM>. The high voltage generator <NUM> controls and delivers power to the X-ray tube <NUM>. The high voltage generator <NUM> applies a high voltage across the vacuum gap between the cathode and the rotating anode of the X-ray tube <NUM>. Due to the voltage applied to the X-ray tube <NUM>, electron transfer occurs from the cathode to the anode of the X-ray tube <NUM> resulting in X-ray photon-generating effect also called Bremsstrahlung. The generated photons form an X-ray beam <NUM> directed to the image detector <NUM>.

An X-ray beam <NUM> comprises of photons with a spectrum of energies that range up to a maximum determined by among others the voltage and current submitted to the X-ray tube <NUM>. The X-ray beam <NUM> then passes through the patient <NUM> that lies on an adjustable table <NUM>. The X-ray photons of the X-ray beam <NUM> penetrate the tissue of the patient to a varying degree. Different structures in the patient <NUM> absorb different fractions of the radiation, modulating the beam intensity. The modulated X-ray beam <NUM>' that exits from the patient <NUM> is detected by the image detector <NUM> that is located opposite of the X-ray tube. This image detector <NUM> can either be an indirect or a direct detection system.

In case of an indirect detection system, the image detector <NUM> comprises of a vacuum tube (the X-ray image intensifier) that converts the X-ray exit beam <NUM>' into an amplified visible light image. This amplified visible light image is then transmitted to a visible light image receptor such as a digital video camera for image display and recording. This results in a digital image signal.

In case of a direct detection system, the image detector <NUM> comprises of a flat panel detector. The flat panel detector directly converts the X-ray exit beam <NUM>' into a digital image signal. The digital image signal resulting from the image detector <NUM> is passed through a digital image processing unit <NUM>. The digital image processing unit <NUM> converts the digital image signal from <NUM> into a corrected X-ray image (for instance inverted and/or contrast enhanced) in a standard image file format for instance DICOM. The corrected X-ray image can then be stored on a hard drive <NUM>.

Furthermore, the X-ray system of <FIG> comprises of a C-arm <NUM>. The C-arm holds the X-ray tube <NUM> and the image detector <NUM> in such a manner that the patient <NUM> and the adjustable table <NUM> lie between the X-ray tube <NUM> and the image detector <NUM>. The C-arm can be moved (rotated and angulated) to a desired position to acquire a certain projection in a controlled manner using the C-arm control <NUM>. The C-arm control allows for manual or automatic input for adjustment of the C-arm in the desired position for the X-ray recording at a certain projection.

The X-ray system of <FIG> can either be a single plane or a bi-plane imaging system. In case of a bi-plane imaging system, multiple C-arms <NUM> are present each consisting of an X-ray tube <NUM>, an image detector <NUM> and a C-arm control <NUM>.

Additionally, the adjustable table <NUM> can be moved using the table control <NUM>. The adjustable table <NUM> can be moved along the x, y and z axis as well as tilted around a certain point.

Furthermore, a measuring unit <NUM> is present in the X-ray system. This measuring unit contains information regarding the patient, for instance information regarding ECG, aortic pressure, biomarkers, and/or height, length etc..

A general unit <NUM> is also present in the X-ray system. This general unit <NUM> can be used to interact with the C-arm control <NUM>, the table control <NUM>, the digital image processing unit <NUM>, and the measuring unit <NUM>.

An embodiment is implemented by the X-ray system of <FIG> as follows. A clinician or other user acquires at least two X-ray angiographic image sequences of a patient <NUM> by using the C-arm control <NUM> to move the C-arm <NUM> to a desired position relative to the patient <NUM>. The patient <NUM> lies on the adjustable table <NUM> that has been moved by the user to a certain position using the table control <NUM>.

The X-ray image sequences are then generated using the high voltage generator <NUM>, the X-ray tube <NUM>, the image detector <NUM> and the digital image processing unit <NUM> as described above. These images are then stored on the hard drive <NUM>. Using these X-ray image sequences, the general processing unit <NUM> performs the methods as described by present patent, as for instance as described by <FIG> using the information of the measuring unit <NUM>, the digital image processing unit <NUM>, C-arm control unit <NUM> and the table control unit <NUM>.

There have been described and illustrated herein several embodiments of a method and apparatus for restoring missing information regarding the order and the flow direction of the velocity components. While particular embodiments of the invention have been described, it is not intended that the invention be limited thereto. For example, the data processing operations can be performed offline on images stored in digital storage, such as a PACS commonly used in the medical imaging arts.

The embodiments described herein may include a variety of data stores and other memory and storage media as discussed above. These can reside in a variety of locations, such as on a storage medium local to (and/or resident in) one or more of the computers or remote from any or all of the computers across the network. In a particular set of embodiments, the information may reside in a storage-area network ("SAN") familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers, servers or other network devices may be stored locally and/or remotely, as appropriate. Where a system includes computerized devices, each device can include hardware elements that may be electrically coupled via a bus, the elements including, for example, at least one central processing unit ("CPU" or "processor"), at least one input device (e.g., a mouse, keyboard, controller, touch screen or keypad) and at least one output device (e.g., a display device, printer or speaker). Such a system may also include one or more storage devices, such as disk drives, optical storage devices and solid-state storage devices such as random access memory ("RAM") or read-only memory ("ROM"), as well as removable media devices, memory cards, flash cards, etc..

Such devices also can include a computer-readable storage media reader, a communications device (e.g., a modem, a network card (wireless or wired), an infrared communication device, etc.) and working memory as described above. The computer-readable storage media reader can be connected with, or configured to receive, a computer-readable storage medium, representing remote, local, fixed and/or removable storage devices as well as storage media for temporarily and/or more permanently containing, storing, transmitting and retrieving computer-readable information. The system and various devices also typically will include a number of software applications, modules, services, or other elements located within at least one working memory device, including an operating system and application programs, such as a client application or web browser. It should be appreciated that alternate embodiments may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets) or both.

Various embodiments may further include receiving, sending, or storing instructions and/or data implemented in accordance with the foregoing description upon a computer-readable medium. Storage media and computer readable media for containing code, or portions of code, can include any appropriate media known or used in the art, including storage media and communication media, such as, but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information such as computer readable instructions, data structures, program modules or other data, including RAM, ROM, Electrically Erasable Programmable Read-Only Memory ("EEPROM"), flash memory or other memory technology, Compact Disc Read-Only Memory ("CD-ROM"), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other medium which can be used to store the desired information and which can be accessed by the system device. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.

The use of the terms "a" and "an" and "the" and similar referents in the context of describing the disclosed embodiments (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including" and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to,") unless otherwise noted. The term "connected," when unmodified and referring to physical connections, is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein and each separate value is incorporated into the specification as if it were individually recited herein. The use of the term "set" (e.g., "a set of items") or "subset" unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unless otherwise noted or contradicted by context, the term "subset" of a corresponding set does not necessarily denote a proper subset of the corresponding set, but the subset and the corresponding set may be equal.

Operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. Processes described herein (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof. The code may be stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable storage medium may be non-transitory.

Claim 1:
A method for characterizing blood flow in an atrioventricular valve (<NUM>) of the human heart, the atrioventricular valve connecting an atrium (<NUM>) with a corresponding ventricle (<NUM>) of the heart, the ventricle being fluidly coupled to a particular vessel (<NUM>) that transports blood outside the ventricle (<NUM>), the method comprising:
a) obtaining image data of the heart, for example angiographic x-ray image frames;
b) identifying a contour (<NUM>) of the atrium (<NUM>) within the image data;
c) identifying a contour (<NUM>) of a region within the particular vessel (<NUM>) within the image data;
d) converting the image data to densitometric image data;
e) calculating a time-density curve for the atrium from the contour of the atrium (<NUM>) and the densitometric image data;
f) calculating a time-density curve for the region of the particular vessel (<NUM>) from the contour of the region of the vessel and the densitometric image data; and
g) generating data representing at least one regurgitation fraction related to the atrioventricular valve (<NUM>) of the heart from the time-density curves.