Patent Description:
When imaging vessels using angiography, the medical specialist operating the angiography system usually obtains angiograms providing standard vessel views or projections based on standard vessel geometry assumptions of a vessel to be imaged. To obtain these angiograms, the medical specialist has to move the C arm of an angiography system to different angiography angles with regard to a neutral position of the C arm. Determination of these angiography angles is typically performed by the medical specialist based on the standard vessel geometry assumptions and on prior experience. The angiography angles are thus not based on the individual patient and depend on the skill of the medical specialist. These angiography angles may thus lead to a generally incomplete vessel coverage and may include areas of overlap or foreshortening.

Moreover, typically some number of standard vessel views is used to obtain a vessel coverage deemed sufficient, e.g., three to five in the case of coronary arteries or one to three in the case of renal arteries. Again depending on the experience of the medical specialist, they may decide to obtain additional vessel views at further angiography angles to increase vessel coverage. However, both the standard vessel views and potentially some additional vessel views need not result in a sufficiently complete vessel coverage. Therefore, it is an objective of the present invention to provide an accurate determination of angiography angles leading to complete coverage of a vessel to be imaged using angiography.

<CIT> discloses a method for providing an optimal imaging settings for imaging a vessel. That document may be considered to disclose an angiography method for determining angiography angles, comprising the steps of: obtaining target vessel information, wherein the target vessel information defines at least one target vessel to be imaged; determining at least one angiography angle based on the target vessel information; analysing angiograms obtained using the at least one angiography angle to determine a vessel coverage of the target vessel; and based on the vessel coverage, determining additional angiography angles. The corresponding device may also be considered to be disclosed.

To achieve this objective, the present invention provides an angiography method for determining angiography angles, comprising the steps of obtaining patient information and target vessel information, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged, determining at least one angiography angle based on the patient information and the target vessel information, analyzing angiograms obtained using the at least one angiography angle to determine a vessel coverage of the target vessel and, based on the vessel coverage, determining additional angiography angles.

Further, the present invention provides an angiography device, including x-ray imaging means rotatably arranged around a patient surface, the patient surface configured to support a patient, wherein the x-ray imaging means are configured to be rotated around the patient surface according to an angiography angle, and processing means including at least one processor, the processor configured to determine angiography angles by: obtaining patient information and target vessel information, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged, determining at least one angiography angle based on the patient information and the target vessel information, analyzing angiograms obtained using the at least one angiography angle to determine a vessel coverage of the target vessel and, based on the vessel coverage, determining additional angiography angles.

Finally, the present invention provides a computer-readable storage medium, configured to store instructions, the instructions being configured to be performed by at least one processor, wherein the instructions cause the at least one processor to perform: obtaining patient information and target vessel information, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged, determining at least one angiography angle based on the patient information and the target vessel information, analyzing angiograms obtained using the at least one angiography angle to determine a vessel coverage of the target vessel and, based on the vessel coverage, determining additional angiography angles.

Embodiments of the present invention will be described with reference to the following appended drawings, in which like reference signs refer to like elements.

It should be understood that these drawings are in no way meant to limit the disclosure of the present invention. Rather, these drawings are provided to assist in understanding the invention. The person skilled in the art will readily understand that aspects of the present invention shown in one drawing may be combined with aspects in another drawing or may be omitted without departing from the scope of the present invention.

The invention generally provides a method and a system for determining angiography angles. Based on patient information and target vessel information, the method determines at least one angiography angle. The at least one angiography angle may then be provided to a medical specialist operating an angiography system or the angiography system itself to obtain angiograms based on the at least one angiography angle. The method then analyzes the angiograms to determine how complete the angiograms cover the target vessel. If the coverage is deemed incomplete, the method determines additional angiography angles, which are again provided to a medical specialist operating an angiography system or the angiography system itself to obtain angiograms. The method again analyzes the resulting angiograms with regard to vessel coverage. The method repeats the steps of determining angiography angles and analyzing resulting angiograms until the method deems the coverage of the target vessel to be complete.

The general principle of the method discussed above will now be illustrated based on <FIG>, which provides a flowchart of the method steps, in conjunction with <FIG>, which help to illustrate various aspects of the angiography method. Accordingly, <FIG> serves as a guide through the embodiments of the method of the present invention while <FIG> are used to illustrate the various actions and decisions performed and made, respectively, at various steps of the method of the present invention as well as the various expressions referred to throughout this disclosure.

<FIG> provides a flowchart of angiography method <NUM> for determining angiography angles α. An angiography angle α in the sense of the present application is the angle formed between the neutral position of a C arm of an angiography system and the imaging position of the C arm. To visualize this definition, the definition will be further explained with reference to <FIG>.

Referring to <FIG>, both show an angiography system <NUM> including a rotatable C arm <NUM>. X-ray emission means <NUM> and X-ray detection means <NUM> may be mounted on C arm <NUM>. In <FIG>, C arm <NUM> is in a neutral position P<NUM>, i.e. X-ray emission means <NUM> are located directly above a patient surface <NUM>. In <FIG>, C arm <NUM> and thereby X-ray emission means <NUM> are rotated counter-clockwise with respect to neutral position P<NUM> of C-arm <NUM> in <FIG> to a position P<NUM>. The angle between position P0 and position P1, as indicated in <FIG> is referred to as the angiography angle α.

The neutral position may also be referred to as the anterior projection (ap) since in this position, X-ray emission means <NUM> are in front of, i.e. anterior to, the patient being imaged. It will therefore be understood that the neutral position may be used as an imaging position. In such a case, angiography angle α is <NUM>°. Further, in case of a single axis angiography system, the neutral position is typically defined as shown in <FIG>. In multiple axis angiography systems, additional C arms may be present, such as a ceiling mounted C arm. In such a case, the neutral position may be defined as the position in which X-ray emission means <NUM> and X-ray detection means <NUM> are at the same level as a patient on patient surface <NUM>. Finally, angiography angle has been defined based on X-ray emission means <NUM> but may analogously be defined based on the position of X-ray detection means <NUM>.

In step <NUM>, method <NUM> obtains patient information and target vessel information. The patient information defines individual medical information of a patient. In particular, the individual medical information may include general patient data and pathophysiological information. The general patient data may include information such as gender, age, height or weight. The general patient data thus enable assumptions about the individual vessel geometries and vessel positions of the patient. The pathophysiological information may include at least one diagnosis of a stenosis, an aneurism, a vasodilation and a vasoconstriction. The pathophysiological information may thus be used to further refine assumptions about the individual vessel geometries and vessel positions of the patient since they enable identifying vessel areas of interest. The target vessel information defines at least one target vessel to be imaged. The target vessel information may for example define a specific vessel segment, such as the left anterior descending artery or an entire vessel tree, such as the coronary arteries.

In step <NUM>, method <NUM> may further obtain an electrocardiogram (ECG) of the patient. Obtaining an ECG during angiography provides ECG data to method <NUM> indicating cardiac cycles and thereby data relating to the movement of the heart and the resulting movement of vessels due to the proximity of vessels to the heart and/or due to pressure changes in the vessels. Accordingly, obtaining an ECG during angiography may improve assumptions about the individual vessel geometries and vessel positions of the patient.

It should be noted that in some embodiments of the present invention, assumptions about the individual vessel geometries and vessel positions of the patient may be determined without reliance on an ECG. Accordingly, step <NUM> may be skipped in some embodiments of method <NUM>.

Step <NUM> may include step <NUM>, in which method <NUM> may select an imaging section of the ECG, which is a section of a cardiac cycle recorded in the ECG indicating a point in time for recording the angiograms. In the context of the present invention, section of a cardiac cycle refers to segments, complexes, intervals and specific points of a cardiac cycle. In other words, the ECG obtained in step <NUM> may be used to determine a point in time with regard to the cardiac cycle at which an angiogram should be recorded. For example, certain vessel segments of the coronary arteries may overlap less during specific sections of the cardiac cycle. Accordingly, recording angiograms during such sections may enable better vessel coverage in the angiograms. To further achieve better vessel coverage in the angiograms, method <NUM> may, as part of step <NUM>, further assign overlap scores to the sections of the cardiac cycle. The overlap scores may indicate the extent of overlap expected during the sections of the cardiac cycle. The imaging section may then be chosen by ranking the sections of the cardiac cycle based on the overlap scores and by choosing the section of the cardiac cycle with the overlap score indicating the lowest extent of overlap.

In some embodiments, step <NUM> may be skipped, even if step <NUM> is not skipped. For example, if the target vessel information indicates a target vessel which is not prone to overlap in angiograms due to the cardiac cycle, such as the common iliac artery, method <NUM> may skip step <NUM>.

In step <NUM>, method <NUM> may determine a vessel coverage map. The vessel coverage map may be a 2D representation of a 3D vessel segment or 3D vessel tree, as e.g., illustrated in <FIG> and <FIG>, which indicates the vessel coverage obtained from the angiograms based on the angiography angles α.

Referring to <FIG> and <FIG>, <FIG> shows a 3D frontal view 200A of a heart and of coronary arteries. <FIG> shows a 2D representation of the coronary arteries, which may be used as a vessel map. In <FIG>, the 2D representation of the coronary arteries is used as the vessel map for a vessel coverage map 200B. The segments of the coronary arteries in both <FIG> and <FIG> are based on and numbered according to the vessel segmentation as proposed by the American Heart Association (AHA) and as amended by the Society of Cardiovascular Computed Tomography (SCCT). Table <NUM> provides a short description of the respective vessel segments as well as an abbreviation for each vessel segment.

In addition to the vessel segments listed in Table <NUM>, both <FIG> and <FIG> also indicate the aortic valve <NUM>.

Vessel coverage map 200B and in particular the choice of 2D representation and segmentation in <FIG> is merely provided as an example of a coverage map. The example of <FIG> based on the AHA coronary artery segmentation classification is chosen here as it could be one possible 2D representation and vessel segmentation if the vessel coverage map is based on a predetermined vessel map. Another possible example of a predetermined vessel map would be the 2D representation and corresponding vessel segmentation for renal arteries as proposed by <NPL>".

Determining the vessel coverage map in step <NUM> may be based on the target vessel information as well as at least one of the individual medical information and a vessel map. The target vessel information defines the vessel to be imaged. Accordingly, determination of the vessel coverage map is necessarily based on the target vessel information. Further, determining the vessel coverage map may be based on the individual medical information, a vessel map, or both the individual medical information and the vessel map.

In examples of the present invention in which the vessel coverage map is based on the individual medical information the vessel coverage map may be determined by deriving the vessel geometry and the vessel segmentation from the individual medical information. For example, based on the age and weight of a patient, method <NUM> may be able to derive the vessel geometry and the vessel segmentation based on a rule set for determining respective vessel geometries and vessel segmentations. In some examples, the individual medical information may include computer tomography (CT) data, from which method <NUM> may derive the vessel coverage map.

In examples of the present invention in which the vessel coverage map is based on the vessel map, method <NUM> selects a predetermined vessel map based on the target vessel information, such as the 2D representation of the AHA coronary artery segmentation or the 2D representation of the renal artery segmentation of Lauder et.

In examples of the present invention in which the vessel coverage map is based on both the individual medical information and the vessel map, method <NUM> may select a predetermined vessel map based on the individual medical information. For example, method <NUM> may select a vessel map from a database based on the parameters included in the individual medical information. The database may e.g. include various vessel maps for the superior mesenteric artery (SMA) for patients of various ages. Method <NUM> may then determine the vessel coverage map by selecting the appropriate vessel map of the SMA based on the age of the patient as the vessel coverage map. In some embodiments, method <NUM> may also select a predetermined vessel map based on the target vessel information and then modify the predetermined vessel map based on the individual medical information to determine the vessel coverage map.

In summary, step <NUM> may determine a vessel coverage map either by using a predetermined vessel map, by selecting a vessel map based on the individual medical information or by determining a vessel coverage map based on the individual medical information. The vessel coverage map may then be used by subsequent steps of method <NUM> to determine angiography angles α and to determine the vessel coverage and to determine the completeness of the vessel coverage. It should however be noted that method <NUM> may also determine the angiography angles α and the completeness of the vessel coverage without a vessel coverage map. In other words, step <NUM> may be skipped in some embodiments of the present invention.

In step <NUM>, method <NUM> may determine at least one angiography angle α. Many arteries, such as the coronary arteries shown in <FIG>, wrap at least partially around organs or other parts of the body. For example, D2 <NUM> is located lateral to the heart while dRCA <NUM> is located dorsal to the heart. Accordingly, X-ray transmission means <NUM> and X-ray detection means <NUM> need to be positioned at one or more angiography angles α enabling angiography system <NUM> to image these segments while a patient is positioned on patient surface <NUM>.

Method <NUM> determines angiography angles α based on the patient information and the target vessel information. As already discussed above, the patient information or the individual medical information defined therein, respectively, may enable method <NUM> to make assumptions about the position of vessels and the corresponding vessel geometry. For example, if the individual medical information indicates the presence of an aneurism in a specific vessel segment, method <NUM> may be able to derive a probable position of the vessel segment and a probable vessel geometry. In a further example, the individual medical information may include information on a stenosis in mRCA <NUM>, such as length, curvature and segmentation of the stenosis. The stenosis information may for example have been obtained by a CT performed before the angiography. Based on this information, method <NUM> may derive a probable position of mRCA <NUM> and a probable geometry of mRCA <NUM>. Using the probable position and the probable geometry of mRCA <NUM>, method <NUM> may determine one or more angiography angles α leading to one or more angiograms providing a clear view of mRCA <NUM> and thereby of the stenosis. Accordingly, the angiography angles α determined by method <NUM> may enable quantification of the stenosis. In yet a further example, the individual medical information may include a diagnostic ECG. The diagnostic ECG may indicate areas of the heart muscles which are under-perfused or ischemic. The heart muscles may be under-perfused or ischemic due to one or more potentially pathological vessels. This information may be used by method <NUM> to determine one or more angiography angles α leading to one or more angiograms providing a view of the one or more potentially pathological vessels. Method <NUM> may also derive a probable position of a vessel and a probable vessel geometry based on parameters included in the patient information, such as age, height or weight of a patient. For example, method <NUM> may derive, based on a database indicating various probable vessel positions and corresponding probable vessel geometries based on such parameters, one or more angiography angles α.

Method <NUM> may in some embodiments further determine angiography angles in step <NUM> based on the ECG obtained in step <NUM>. As stated above, the ECG provides ECG data to method <NUM> indicating cardiac cycles and thereby data relating to the movement of the heart. Method <NUM> may use the ECG data in step <NUM> to derive or refine the derived probable vessel positions and corresponding probable vessel geometries and thereby derive or refine the derived angiography angles α. Further, the ECG data may be used in step <NUM> to determine angiography angles α with reduced overlaps between vessels in the corresponding angiograms.

Method <NUM> may further determine the at least one angiography angle in order to optimize the resulting angiograms for various use cases, such as 2D and 3D quantitative coronary angiography (QCA), fractional flow reserve (FFR), percutaneous coronary intervention (PCI) robot-guided catheterization. For example, method <NUM> may in step <NUM> determine angiography angles leading to angiograms visualizing the pressure difference distal to a lesion and proximal to the lesion during FFR.

Method <NUM> may further determine the at least one angiography angle α based on the vessel coverage map determined in step <NUM>. As discussed above, method <NUM> may already have determined angiography angles α based on the patient information. Using the vessel coverage map, method <NUM> may be able to further refine the angiography angles α determined based on the patient information and the target vessel information. For example, based on stenosis information and the vessel coverage map 200B of <FIG>, method <NUM> may be able to determine angiography angles α by starting with angiography angles α based on the stenosis information. Method <NUM> may then continue to determine angiography angles α based on the vessel coverage map, which provides method <NUM> with information on the position of vessel segments adjacent to the vessel segment with the stenosis.

Step <NUM> may include step <NUM>, in which method <NUM> may determine, for each angiography angle α, a field of view on the vessel coverage map obtainable by the respective angiography angle α. Each angiography angle α determined in step <NUM> has a different field of view, i.e., provides a different projection, on a vessel. Using coronary angiography as an example, method <NUM> may determine in step <NUM> that an angle of <NUM>° to the left from position P<NUM> in <FIG> may have a field of view on pLAD <NUM>, mLAD <NUM>, dLAD <NUM>, D1 <NUM> and D2 <NUM>. Likewise, method <NUM> may determine in step <NUM> that an angle of <NUM>° to the right from position P<NUM> in <FIG> may have a field of view on pRCA <NUM> and mRCA <NUM>. Using vessel coverage map 200B, method <NUM> may thus determine which vessel segments of vessel coverage map 200B are expected to be covered by the fields of view obtainable by the respective angiography angles α and may thereby determine whether further angiography angles α need to be determined to provide better cover of the vessel. It will be understood that the field obtainable by the respective angiography angle α may depend on additional factors, such as a position of patient surface <NUM>, a selected magnification of X-ray detection means <NUM> and a source imager distance (SID), i.e., the distance between X-ray emission means <NUM> and X-ray detection means <NUM>, which determines a geometrical magnification factor.

Step <NUM> may further include step <NUM>, in which method <NUM> may calculate an expected vessel coverage obtainable based on the fields of view of the angiography angles α. The expected vessel coverage may correspond to a ratio of the vessel coverage map expected to be covered by the fields of view on the vessel coverage map obtainable by the respective angiography angle α to the entire vessel coverage map. Using the preceding example of two projections at <NUM>° left and right of position P<NUM>, the ratio could for example be calculated to be approximately <NUM>% because the two projections cover <NUM> out of <NUM> segments of the coronary arteries. In another example, the ratio may be calculated in terms of the length of the vessel segments covered to the entire length of the target vessel. It will be understood that the ratio may be defined in any way which enables the determination of the expected completeness of the vessel coverage based on the angiography angles α determined in step <NUM> and its sub-steps.

Step <NUM> may further include step <NUM>, in which method <NUM> may determine angiography angles α until the calculated expected vessel coverage exceeds a vessel coverage threshold, the vessel coverage threshold indicating a ratio of the vessel coverage and the vessel coverage map. As discussed above, method <NUM> may determine an expected vessel coverage in step <NUM>, which may be compared in step <NUM> to the vessel coverage threshold to determine whether the coverage expected to be obtained based on the determined angiography angles α provides sufficiently complete coverage of the target vessel. For example, in some embodiments the coverage may be considered sufficiently complete if the expected vessel coverage of the target vessel corresponds to <NUM>% of the target vessel. In some embodiments, a higher vessel coverage may be required to consider the vessel coverage complete, such as <NUM>%. In some embodiments, a lower coverage may be sufficient to consider the vessel coverage complete, such as <NUM>%. The exact level of vessel coverage threshold may depend on the use case of the angiograms. For example, in the case of FFR, complete coverage requires coverage of both the vessel segment with the stenosis and the adjacent vessel segments in order to enable the calculation of the pressure ratio.

In summary, method <NUM> may in steps <NUM> to <NUM> determine at least one angiography angle α to obtain a vessel coverage, which is expected to be complete. Whether the coverage is expected to be complete may be determined either based on the patient information and the target vessel information alone or may rely on further aspects, like the ECG, the use case and the vessel coverage map.

In step <NUM>, method <NUM> may obtain angiograms using the at least one angiography angle α. In some embodiments, method <NUM> may directly obtain angiograms based on the at least one angiography angle determined in steps <NUM> to <NUM>. For this purpose, Method <NUM> may rotate C arm <NUM> according to the at least one angiography angle α. In some embodiments, method <NUM> may not directly obtain the angiograms. In such embodiments, method <NUM> may display the at least one angiography angle α to a medical specialist operating angiography system <NUM>, e.g., via display <NUM>. The medical specialist may then rotate C arm <NUM> according to the at least one angiography angle α, e.g., via control panel <NUM>. Accordingly, depending on the implementation of method <NUM>, step <NUM> may be skipped.

In step <NUM>, method <NUM> analyzes the angiograms obtained using the at least one angiography angle α to determine a vessel coverage of the target vessel. More precisely, method <NUM> determines in step <NUM> whether the angiograms obtained based on the at least one angiography angle α determined in step <NUM> indeed show the expected vessels or vessel segments. To this end, method <NUM> may employ image segmentation approaches, such as based on convolutional neural networks (cNN), e.g. U-Net, densely connected neural networks, deep-learning methods, graph-partitioning methods, e.g. Markoff random fields (MRF), or region-growing methods, e.g. split- and-merge segmentation, in order to identify the vessels visible in the angiogram. The image segmentation approaches may for example identify one of a centerline of a vessel and a lumen of a vessel. Any one of these image segmentation approaches may be trained on sets of angiograms with annotated centerlines or lumina or some other suitable means of training the image segmentation approaches to enable method <NUM> in step <NUM> to identify vessels in the angiograms.

Based on the identified vessels in the angiograms, method <NUM> compares the identified vessels with the vessels expected based on the at least one angiography angle α in order to determine the vessel coverage of the target vessel. For example, if the expected vessels can be identified in the vessels in the angiogram, the angiogram may be considered to provide good coverage of the vessel or vessel segment. If only a part of the expected vessels can be identified and depending on how much of the vessel can actually be identified, the angiogram may be considered to provide medium or bad coverage. The exact thresholds for good, medium and bad coverage may depend on how complete the vessel coverage needs to be for the given use case. Assuming an exemplary completeness threshold of the vessel coverage of <NUM>%, if at least <NUM>% of the expected vessels can be identified in an angiogram, the angiogram is considered to provide good coverage. If at least <NUM>% of the expected vessels can be identified in an angiogram, the angiogram may be considered to provide medium coverage. If the coverage is below <NUM>%, the angiogram may be considered to provide bad coverage. More generally speaking, good coverage may mean that the ratio of expected to identified vessels in an angiogram corresponds to the threshold set for complete vessel coverage for the use case of the angiograms. Medium coverage and bad coverage may then be defined as some thresholds below the threshold for complete vessel coverage. It should be noted that instead of three coverage levels, any other number of coverage levels may be used, depending on how detailed the coverage levels need to be distinguished.

The classification of the angiograms regarding their actual vessel coverage compared to the expected coverage may also be visualized for the medical specialist operating angiography system <NUM>. As shown in <FIG>, vessel coverage map 200B may indicate good, medium and bad coverage. In the example of <FIG>, vessel segments <NUM> to <NUM> are well covered by angiograms. Vessel segments <NUM> to <NUM>, as well as vessel segments <NUM> and <NUM> have reached medium coverage. Vessel segments <NUM> and <NUM> are still at bad coverage. Comparing these segments to 3D frontal view 200A of the heart of <FIG>, the example of coverage map 200B shows that R-PDA <NUM> and R-PLB <NUM> and thus the posterolateral part of the heart has not yet been imaged properly by angiograms. While <FIG> uses different types of shading to indicate the three coverage levels, the coverage levels may also be visualized using a traffic light system or some other visualization approach.

To further improve identification of vessels visible in the angiograms obtained using the at least one angiography angle α, method <NUM> may in some embodiments further be able to determine the presence and the position of a contrast medium catheter in the angiograms. Like the centerlines and the lumina of vessels, the contrast medium catheter may be detected in step <NUM> using image segmentation approaches such as cNNs, MRFs or any of the aforementioned image segmentation approaches, trained on angiograms including annotated contrast medium catheters. For example, a cNN may be used as an image-to-image network trained on pairs of angiograms and angiograms with annotated contrast medium catheter wires, which outputs contrast medium catheter wire heatmaps. Based on the contrast medium catheter wire heatmaps, method <NUM> may trace the centerline of the contrast medium catheter wire. The traced contrast medium catheter wire may be used by method <NUM> to determine a 3D model of the contrast medium catheter wire. The 3D model of the contrast medium catheter wire may then be projected onto a 2D vessel map, such as vessel coverage map 200B, in order to determine the vessel coverage. Accordingly, the contrast medium catheter position may be used to further identify vessels in the angiograms obtained using the at least one angiography angle α.

Step <NUM> may include step <NUM>, in which method <NUM> detecting, within the angiograms, distorted image areas. The distorted image areas include at least one of overlap and foreshortening. Referring to <FIG>, overlap may for example occur between pLAD <NUM>, pCx <NUM> and RI <NUM>. Foreshortening, i.e., appearing shorter than in reality, may in particular occur for longer vessel segments, such as dLAD <NUM>. To detect the distorted image areas, method <NUM> may employ image segmentation approaches, e.g. cNNs, densely connected neural networks, deep-learning methods, graph-partitioning methods or region-growing methods For example, any one of these approaches may be used as an image-to-image network to predict a heatmap indicating areas of overlap or of foreshortening. These image-to-image networks may be trained for example on synthetic angiograms generated by computer tomography angiography (CTA). More precisely, CTA scans may be used to derive a 2D projection of a 3D CTA scan with identified vessel centerlines and/or segmentations, which is then used to train the image-to-image networks.

Step <NUM> may further include step <NUM>, in which method <NUM> detects, within the angiograms, pathological vessel segments indicative of a pathophysiological condition. The pathophysiological condition may be one of a stenosis, an aneurism, a vasodilation and a vasoconstriction. In one example, method <NUM> may detect pathological vessel segments in the angiograms for example based on neural networks, such as ResNet-<NUM>, ResNet-<NUM>, Inception ResNet, NASNet or MobileNet trained on annotated angiograms of patients suffering from any one of the above-mentioned pathophysiological conditions. The identified pathological vessel segments may then be used to refine the at least one angiography angle. In a further example, method <NUM> may detect pathological vessel segments based on automatic lumen segmentation, i.e., the radius profile extracted along the centerlines of the lumina may be used by method <NUM> to identify pathological vessel segments.

Step <NUM> may further include step <NUM>, in which method <NUM> may compare the vessel coverage with the expected vessel coverage calculated in step <NUM>. In other words, method <NUM> may determine in step <NUM> if the vessel coverage achieved with the angiograms obtained using the at least one angiography angle α corresponds to the expected vessel coverage. The result of this comparison is then used in step <NUM>, which will be discussed below.

In summary, method <NUM> analyzes the angiograms obtained using the at least one angiography angle α to identify the vessels visible in the angiograms and to thereby identify how well the angiograms cover the target vessel. In addition, the analysis of the angiograms may identify aspects, such as pathological vessel segments, which may be used to refine the at least one angiography angle α.

In step <NUM>, method <NUM> may detect vessel segments in the angiograms obtained using the at least one angiography angle α. In other words, method <NUM> may identify explicitly which vessel segments are visible in the angiograms using the at least one angiography angle α. For example, method <NUM> may identify OM2 <NUM>, L-PDA <NUM> and PLB-L <NUM> in an angiogram. By contrast, method <NUM> identifies visible vessels in step <NUM> but does not identify individual vessel segments. It will be understood, of course, that in some embodiments of the present invention step <NUM> may be integrated into step <NUM>, i.e., the identification of vessels in step <NUM> may entail an identification of the vessel segments. Method <NUM> may identify the vessel segments in step <NUM> using a learning-based image to-image network trained on pairs of angiograms and corresponding pixel-wise coronary segment annotations. The pixel-wise coronary segment annotations may have been provided by medical specialists or may have been derived from synthetic angiograms obtained by e.g., CTA.

In step <NUM>, method <NUM> may rank the angiograms obtained using the at least one angiography angle α. The ranking criteria may include at least one of an overlap detection, a foreshortening detection, a detection of pathological vessel segments, and image quality. For example, angiograms identified as including distorted image areas in step <NUM>, which may include areas of overlap and of foreshortening, may be ranked lower than angiograms not including such distorted areas. Angiograms including pathological vessel segments, as e.g. detected in step <NUM>, may be ranked higher than angiograms not including such segments given that these angiograms include important diagnostic information. Angiograms with poor image quality, such as angiograms obtained with little contrast applied during imaging or angiograms including noise may be ranked lower than angiograms with good image quality. More generally speaking, the ranking may be indicative of a diagnostic quality, i.e., how well the angiograms may be used for a diagnosis of a patient. Accordingly, the angiograms obtained using the at least one angiography angle α may be ranked based on their diagnostic quality, as informed by the above ranking criteria.

Method <NUM> may, based on the ranking determined in step <NUM>, decide that some angiograms obtained using the at least one angiography angle α, need to be obtained again. This decision may for example be based on a diagnostic quality score. For example, the angiograms may be assigned points for each of the ranking criteria. If the resulting score of an angiogram is below a diagnostic quality threshold, method <NUM> may determine that the angiogram need to be obtained again.

In step <NUM>, method <NUM> determines, based on the vessel coverage, additional angiography angles α. More precisely, based on the vessel coverage determined in step <NUM>, method <NUM> determines whether to repeat steps <NUM> to <NUM> to determine additional angiography angles in order to increase the vessel coverage already obtained with the angiograms based on the at least one angiography angle α. Determining additional angiography angles includes both determining further angiography angles as well as modifying the at least one angiography angle α. Step <NUM> is therefore shown in the flowchart of <FIG> as an arrow splitting into two and returning either to step <NUM> or continuing to the termination of method <NUM>.

Since the at least one angiography angle α has been determined in step <NUM> with the expectation to achieve complete vessel coverage as required for the given use case, method <NUM> determines in step <NUM> to repeat steps <NUM> to <NUM> if the expected vessel coverage has not been achieved. This is further shown by the vessel coverage threshold discussed above with regard to steps <NUM>, <NUM> and <NUM>. Method <NUM> calculates the expected vessel coverage in step <NUM> and continues to determine angiography angles α in step <NUM> until the expected vessel coverage exceeds the vessel coverage threshold. In step <NUM>, method <NUM> determines whether the actual vessel coverage exceeds the vessel coverage threshold. If the actual vessel coverage as determined in step <NUM> is below the vessel coverage threshold, i.e. if the actual vessel coverage obtained using the at least one angiography angle α does not provide complete coverage of the target vessel as required by the use case, method <NUM> determines in step <NUM> to return to step <NUM> in order to determine additional angiography angles to increase the vessel coverage. Repeating step <NUM> is based on the analysis of the angiograms performed in step <NUM>, as will be discussed in the following.

As discussed above, method <NUM> identifies vessels visible in the angiograms obtained using the at least one angiography angle α and compares the identified vessels with the expected vessels. Based on this comparison, method <NUM> may, when repeating step <NUM>, determine that the previously determined at least one angiography angle α needs to be modified. For example, method <NUM> may have originally determined in step <NUM> that an angiography angle of <NUM>° to the right from position P<NUM> in <FIG> may lead to an angiogram showing pRCA <NUM> and mRCA <NUM>. However, upon analysis in step <NUM>, method <NUM> determined that the angiogram taken at this angle shows pRCA <NUM> and mRCA <NUM> only partially (e.g., the section where both vessel segments connect, which is furthest to the right, as shown in <FIG>). Accordingly, upon repeating step <NUM>, method <NUM> may modify the angle to e.g., <NUM>° in order to improve the coverage of pRCA <NUM> and mRCA <NUM>. Of course, method <NUM> may also determine in step <NUM>, based on the analysis in step <NUM>, to determine entirely new angiography angles instead of or in addition to modifying the at least one angiography angle α.

Further, method <NUM> may, when repeating step <NUM>, modify the at least one angiography angle α based on the distorted image areas detected in step <NUM>. For example, modifying the at least one angiography angle may reduce or remove an overlap between vessels. The same applies to the reduction or removal of foreshortening of vessels.

Further, method <NUM> may, when repeating step <NUM>, modify the at least one angiography angle α or may determine additional angiography angles based on the pathological vessel segment detected in step <NUM>. For example, method <NUM> may determine additional angiography angles to obtain angiograms covering vessel segments adjacent to the pathological vessel segment detected in step <NUM> in order to perform FFR. In a further example, method <NUM> may modify the at least one angiography angle α which lead to obtaining the angiogram of the pathological vessel segment in order to provide an improved angiogram of the pathological vessel segment, e.g. to obtain more complete coverage of the pathological vessel segment. To this end, method <NUM> may employ a machine learning algorithm to determine modified or further angiography angles based on the pathological vessel segment. The machine learning algorithm may have been trained on annotated angiograms of pathological vessel segments and a corresponding rule set determining how to determine additional or modified angiography angles. The rule set may be a dataset, on which the machine learning algorithm performs a single nearest neighbor search. The machine learning algorithm may also rely on single view human avatar reconstruction, i.e., a 3D reconstruction of the vessel from the angiogram showing the pathological segment. In a further example, method <NUM> may, based on the pathological vessel segment detected in step <NUM>, determine additional angiography angles by mapping the angiograms obtained using the at least one angiography angle α onto latent code with a neural network, such as mixture density networks or cNNs. The latent code may be combined with a code obtained from the pathological vessel segment using an embedding layer. The combined code may be passed through a multilayer perceptron (MLP) decoder, which then outputs one ore more additional angiography angles. In this example, the neural network may be trained with pairs of angiograms and sets of optimal next angiography angles.

Further, method <NUM> may, when repeating step <NUM>, modify the at least one angiography angle α or may determine additional angiography angles based on the identified vessel segments in step <NUM>. For example, if method <NUM> identifies mLAD <NUM> in an angiogram in step <NUM>, method <NUM> may determine, when repeating step <NUM>, additional angiography angles to obtain coverage of pLAD <NUM> and dLAD <NUM>.

Finally, method <NUM> may, when repeating step <NUM>, modify the at least one angiography angle α or may determine additional angiography angles based on the ranking of the angiograms determined in step <NUM>. For example, method <NUM> may, when repeating step <NUM>, determine additional angiography angles to obtain angiograms with higher rankings, i.e., higher diagnostic scores, or may modify the at least one angiography angle α to increase the diagnostic score of angiograms obtained using the at least one modified angiography angel α.

In summary, if at step <NUM> method <NUM> determines that the vessel coverage is not complete for the given use case, repeats step <NUM> to determine additional angiography angles, which includes modifying the at least one angiography angle α, and step <NUM> to analyze angiograms obtained using the additional angiography angles. Method <NUM> continues repeating steps <NUM> and <NUM> until method <NUM> determines at step <NUM> that the vessel coverage is complete for the given use case. It will be understood that method <NUM> will likewise repeat the sub-steps of step <NUM> and step <NUM> as well as step <NUM> and step <NUM> if these steps are implemented. Method <NUM> thus determines angiography angles and analyzes angiograms obtained using these angiography angles until method <NUM> determines that the vessel coverage of the target vessel is complete for the given use case.

Method <NUM> may also be used to train medical specialists to better determine angiography angles manually. In such implementations of method <NUM>, method <NUM> may determine the angiography angles as discussed above and may then compare them to the angiography angles chosen by the medical specialist. Such a training may lead to a reduced absorbed dose and to a reduced use of contrast agent. Further, method <NUM> may also output explanations for the reasons specific angiography angles have been determined to improve the training of medical specialists.

As briefly discussed above, <FIG> show exemplary angiography system <NUM>. In <FIG>, angiography system is in neutral position P<NUM>. In <FIG>, angiography system <NUM> is in a rotated position P<NUM>. As discussed above, the angle between the two positions is referred to as the angiography angle α. Angiography system <NUM> includes C arm <NUM>, on which X-ray emission means <NUM> and X-ray detection means <NUM> may be mounted. C arm <NUM> and thereby X-ray emission means <NUM> and X-ray detection means <NUM> are positioned to center around patient surface <NUM>. X-ray emission means <NUM> may emit X-rays which may penetrate through a patient positioned on patient surface <NUM>. X-ray detection means <NUM> detects the X -rays emitted from X-ray emission means <NUM>. When a patient on patient surface <NUM> is injected with a radio-opaque contrast agent into the patient's vessels, some of the X-rays emitted by X-ray emission means <NUM> are absorbed by the radio-opaque contrast agent, leading X-ray detection means <NUM> to detect an image of the vessels filled with the radio-opaque contrast agent, i.e. an angiogram. X-ray emission means <NUM> and X-ray detection means <NUM> may also collectively be referred to as x-ray imaging means.

C arm <NUM> may be coupled to C arm rotation unit <NUM>. C arm rotation unit <NUM> may be any motorized means configured to rotate C arm <NUM> according to the at least one angiography angle α determined by method <NUM>. C arm rotation unit <NUM> may be attached to and controlled by C arm control until <NUM>. C arm control unit <NUM> may be any kind of circuitry capable of controlling C arm <NUM>. For example, C arm control unit <NUM> may include computing device <NUM> of <FIG> or may be configured to interface with C computing device <NUM>.

Angiography system <NUM> may further include a control panel <NUM> mounted onto a side surface of patient surface support <NUM>. Control panel <NUM> may be used to control C arm <NUM> in embodiments of the present invention in which method <NUM> displays the at least one angiography angle α to the medical specialist instead of operating C arm <NUM> directly via method <NUM>. does not show any connections between control panel <NUM> and C arm <NUM> to simplify the depiction of exemplary angiography system <NUM>. In some embodiments, the connection may be wireless. In some embodiments, the connection may be wired and may e.g., be integrated into the ceiling of the room where angiography system <NUM> is located.

Angiography system <NUM> may finally also include a display <NUM>. Display <NUM> may be used to display information to the medical specialist, such as vessel coverage map 200B or the at least one angiography angle α.

<FIG> shows a computing device <NUM> configured to perform method <NUM>. Computing device <NUM> may include a processor <NUM>, a graphics processing unit (GPU) <NUM>, a memory <NUM>, a bus <NUM>, a storage <NUM>, a removable storage <NUM>, an angiography system control interface <NUM> and a communications interface <NUM>.

Processor <NUM> may be any kind of single-core or multi-core processing unit employing a reduced instruction set (RISC) or a complex instruction set (CISC). Exemplary RISC processing units include ARM based cores or RISC V based cores. Exemplary CISC processing units include x86 based cores or x86-<NUM> based cores. Processor <NUM> may perform instructions causing computing device <NUM> to perform method <NUM>. Processor <NUM> may be directly coupled to any of the components of computing device <NUM> or may be directly coupled to memory <NUM>, GPU <NUM> and bus <NUM>.

GPU <NUM> may be any kind of processing unit optimized for processing graphics related instructions or more generally for parallel processing of instructions. As such, GPU <NUM> may perform part or all of method <NUM> to enable fast parallel processing of instructions relating to method <NUM>. It should be noted that in some embodiments, processor <NUM> may determine that GPU <NUM> need not perform instructions relating to method <NUM>. GPU <NUM> may be directly coupled to any of the components of computing device <NUM> or may be directly coupled to processor <NUM> and memory <NUM>. GPU <NUM> may also be coupled to a display, such as display <NUM> of angiography system <NUM>, via connection 420C. In some embodiments, GPU <NUM> may also be coupled to bus <NUM>.

Memory <NUM> may be any kind of fast storage enabling processor <NUM> and GPU <NUM> to store instructions for fast retrieval during processing of the instructions well as to cache and buffer data. Memory <NUM> may be a unified memory coupled to both processor <NUM> and GPU <NUM> enabling allocation of memory <NUM> to processor <NUM> and GPU <NUM> as needed. Alternatively, processor <NUM> and GPU <NUM> may be coupled to separate processor memory 430a and GPU memory 430b.

Storage <NUM> may be a storage device enabling storage of program instructions and other data. For example, storage <NUM> may be a hard disk drive (HDD), a solid state disk (SSD) or some other type of non-volatile memory. Storage <NUM> may for example store the instructions of method <NUM> as well as the e.g. the angiograms obtained using the at least one angiography angle α.

Removable storage <NUM> may be a storage device which can be removably coupled with computing device <NUM>. Examples include a digital versatile disc (DVD), a compact disc (CD), a Universal Serial Bus (USB) storage device, such as an external SSD, or a magnetic tape. Removable storage <NUM> may for example be used to provide the patient information and the target vessel information to computing device <NUM> and thereby to method <NUM> or to store the angiograms. It should be noted that removable storage <NUM> may also store other data, such as instructions of method <NUM>, or may be omitted.

Storage <NUM> and removable storage <NUM> may be coupled to processor <NUM> via bus <NUM>. Bus <NUM> may be any kind of bus system enabling processor <NUM> and optionally GPU <NUM> to communicate with storage device <NUM> and removable storage <NUM>. Bus <NUM> may for example be a Peripheral Component Interconnect express (PCIe) bus or a Serial AT Attachment (SATA) bus.

Angiography system control interface <NUM> may enable computing device <NUM> to interface with angiography system <NUM> via connection 470C to control C arm <NUM> in accordance with method <NUM>. For example, angiography system control interface <NUM> may be dedicated logic circuitry configured to control rotation of C arm <NUM>. In some embodiments, angiography system control interface <NUM> may be C arm control unit <NUM>. In some embodiments, angiography system control interface <NUM> may also be omitted and computing device <NUM> interfaces with angiography device <NUM> solely via communications interface <NUM>. In such embodiments, processor <NUM> may control C arm directly via communications interface <NUM>.

Communications interface <NUM> may enable computing device <NUM> to interface with external devices, either directly or via network, via connection 480C. Communications interface <NUM> may for example enable computing device <NUM> to couple to a wired or wireless network, such as Ethernet, Wifi, a Controller Area Network (CAN) bus or any bus system appropriate in medical systems. For example, computing device <NUM> may be coupled with angiography system <NUM> via connection 480C in order to receive angiograms or to transmit coverage map 200B and the at least one angiography angle α. Communications interface may also be a USB port or a serial port to enable direct communication with an external device.

As stated above, computing device <NUM> may be integrated with angiography system <NUM>. For example, computing device <NUM> may be integrated with C arm control unit <NUM> or may be placed inside patient surface support <NUM>.

The invention may further be illustrated by the following examples.

In an example, an angiography method for determining angiography angles comprises the steps of: obtaining patient information and target vessel information, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged; determining at least one angiography angle based on the patient information and the target vessel information; analyzing angiograms obtained using the at least one angiography angle to determine a vessel coverage of the target vessel; and based on the vessel coverage, determining additional angiography angles.

In an example, the individual medical information may include general patient data and pathophysiological information.

In an example, determining the at least one angiography angle may be further based on the pathophysiological information, wherein the pathophysiological information may include at least one diagnosis of a stenosis, an aneurism, a vasodilation and a vasoconstriction.

In an example, analyzing the angiograms obtained using the at least one angiography angle may include detecting, within the angiograms, distorted image areas, the distorted image areas including at least one of overlap and foreshortening; and determining the additional angiography angles may include modifying the at least one angiography angle to reduce the distorted image areas.

In an example, analyzing the angiograms obtained using the at least one angiography angle may include detecting, within the angiograms, pathological vessel segments indicative of a pathophysiological condition; and determining the additional angiography angles may include determining angiography angles having a field of view covering the pathological vessel segments.

In an example, the method may further comprise obtaining an electrocardiogram (ECG) of the patient, and wherein the ECG may at least be used to: further determine the at least one angiography angle; and select an imaging section of the ECG, the imaging section being a section of a cardiac cycle recorded in the ECG indicating a point in time for recording the angiograms.

In an example, the method may further comprise determining a vessel coverage map based on the target vessel information as well as at least one of the individual medical information and a vessel map, wherein, if the vessel coverage map is based on both the individual medical information and the vessel map, the vessel map may be selected based on the individual medical information.

In an example, determining the at least one angiography angle may further be based on the vessel coverage map.

In an example, determining the at least one angiography angle based on the vessel coverage map may include: determining, for each angiography angle, a field of view on the vessel coverage map obtainable by the respective angiography angle; calculating an expected vessel coverage obtainable based on the fields of view of the angiography angles; and determining angiography angles until the calculated expected vessel coverage exceeds a vessel coverage threshold, the vessel coverage threshold indicating a ratio of the vessel coverage and the vessel coverage map.

In an example, analyzing the angiograms obtained using the at least one angiography angle to determine a vessel coverage of the target vessel may include comparing the vessel coverage with the expected vessel coverage; and determining the additional angiography angles based on the vessel coverage may include determining angiography angles increasing the vessel coverage if the vessel coverage is below the vessel coverage threshold.

In an example, the method may further comprise detecting vessel segments in the angiograms obtained using the at least one angiography angle and determining additional angiography angles may further be based on the detected vessel segments.

In an example, the method may further comprise: ranking the angiograms obtained using the at least one angiography angle based on at least one of overlap detection, foreshortening detection, detection of pathological vessel segments and image quality, wherein determining additional angiography angles may further be based on the ranking.

In an example, the method may further comprise obtaining angiograms using the at least one angiography angle.

In an example, an angiography device includes x-ray imaging means rotatably arranged around a patient surface, the patient surface configured to support a patient, wherein the x-ray imaging means are configured to be rotated around the patient surface according to an angiography angle; and processing means including at least one processor, the processor configured to determine angiography angles according to the angiography method of any one of the preceding examples.

In an example, a computer-readable storage medium is configured to store instructions, the instructions being configured to be performed by at least one processor, wherein the instructions cause the at least one processor to perform the method of any one of the preceding examples.

Claim 1:
Angiography method (<NUM>) for determining angiography angles (α), comprising the steps of:
obtaining (<NUM>) patient information and target vessel information, wherein the patient information defines individual medical information of a patient and wherein the target vessel information defines at least one target vessel to be imaged;
determining (<NUM>) at least one angiography angle (α) based on the patient information and the target vessel information;
analyzing (<NUM>) angiograms obtained using the at least one angiography angle (α) to determine a vessel coverage of the target vessel; and
based on the vessel coverage, determining (<NUM>) additional angiography angles (α).