TECHNICAL FIELD

The present invention relates to the field of cardiac wall motion classification. In particular, the present invention is directed to a computer-implemented method and an apparatus for using artificial intelligence (AI) in classifying cardiac motion based on Cardiac Magnetic Resonance (CMR) image data.

BACKGROUND

Cardiac problems account for a large portion of modern diseases.

Cardiovascular or Cardiac Magnetic Resonance (CMR) is an increasingly important diagnostic tool due to its growing utility in clinical routine. CMR may be considered as a set of magnetic resonance imaging (MRI) techniques designed to assess cardiovascular morphology, ventricular function, myocardial perfusion, tissue characterization and flow quantification. It has become the gold standard for volume quantification, cardiac wall motion assessment, and myocardial scar detection. Since MRI is a non-invasive tool and free of radiation, it is suitable for longitudinal monitoring of treatment effect and follow-up of disease evolution (Tseng et al., 2016).

The main indications for CMR are cardiomyopathies including heart failure, coronary artery disease to assess viability, myocardial stress perfusion, and the anatomical planning of cardiac interventions in congenital patients.

Compared to other techniques, CMR offers excellent spatial resolution and good contrast between tissues. In addition, CMR do not use ionizing radiation and the image quality of images acquired by CMR imaging is not affected by a patient's biotype.

Due to its characteristics, CMR is of great value to assess myocardial wall motion, which consists in the assessment of the contractile function of each segment of the left ventricle during the cardiac cycle rather than an overall classification. This is relevant in several heart diseases but assumes particular interest in ischemic heart disease where asymmetric wall motion abnormalities in the left ventricle are often present. Other diseases may also cause segmental wall motion abnormalities such as myocarditis, hypertrophic cardiomyopathy among others.

The workflow of a CMR study comprises a set of possible sequences and techniques which together provide useful diagnostic and prognostic information. As mentioned, one of the most frequent clinical indications for CMR is ischemic heart disease, one of the leading causes of death worldwide. On this setting, the CMR study is usually composed of, at least, by three studies: cine imaging, perfusion, and late-gadolinium enhancement (LGE).

Here, cine imaging typically provides short movies, which show the heart motion the cardiac cycle, allowing structure, volumes and function evaluation. One of the most important aspects of cine imaging study is the evaluation of myocardial wall motion. This dynamic assessment is performed in multiple anatomical planes that depict the heart in different views (e.g. short axis, 2-chamber long axis, 3-chamber long axis, 4-chamber long axis) in a global and segmental way, in which the motion of 17 segments of the myocardium is usually classified as one of hyperkinetic, normokinetic, hypokinetic, akinetic, dyskinetic, and aneurysmatic. The 17-segment model may be represented as a chart, table, or bipolar map, also referred to as “bulls-eye” plot (Hundley et al. 2009).

Both the perfusion and LGE studies are performed with the use of a contrast agent (e.g. gadolinium). The perfusion images may be acquired during the injection of the contrast agent and under the perfusion of a vasodilator drug that pretends to induce a mismatch of perfusion between areas with normal arteries and areas with obstructed arteries. The myocardial regions supplied by narrowed vessels have a perfusion defect that may be seen as a dark region when compared with healthy myocardium. The LGE study is performed usually 10 minutes after administration of the contrast agent and is the best exam to visualize scarring tissue (which may display as a bright region, due to the delayed washout of the contrast agent from an injured area).

The anatomical segmentation of the heart used in the following description and illustrated inFIG. 1Aand may be described as follows:

FIG. 1Ashows a four-chamber sectional view of an exemplary heart100. A longitudinal axis120(“long axis”) can be defined as any view that extends substantially parallel to the ventricle septum122. However, other definitions of this longitudinal axis120are possible, for example the electrical heart axis, or an axis extending through the apex106of the heart.

The heart can be divided into a plurality of layers, which subsequently extend substantially perpendicular to the longitudinal axis120. For example, starting from the heart apex106, the heart can be divided into a bottom layer130, an apex layer135, an apical layer140, a medial layer150, a basal layer160, and a top layer170(layer not shown). These views of the heart are referred as “short axis”.

While this structure is not limiting to the invention, this division has been proven to reduce complexity while still permitting a sufficient spatial resolution of the relevant regions of the heart muscle.

The heart is divided into two ventricles, namely, a left ventricle102and a right ventricle104, which are connected to a left and a right atrium, respectively, such that physiologically, blood flows from the atria to the respective ventricles via a left valve (mitral valve) and a right valve (tricuspid valve). While the left ventricle102is responsible for the systemic circulation, the right ventricle104is responsible for the pulmonary circulation. The muscle tissue of the left ventricle is thicker than that of the right ventricle and the following description will be based mainly of an assessment of the left ventricle. However, it will be clear to a person skilled in the art that the methods, systems and apparatuses disclosed herein can also be used for assessing the right ventricle.

In order to depict the cardiac motion in a reproducible, standardized way, the muscle tissue of the left ventricle may be illustrated as a “wall motion bulls-eye”190, as illustrated inFIG. 1C.

FIG. 1Bis a drawing for better understanding of the representation of the bulls-eye190.FIG. 1Bdepicts sections of a basal layer160, a medial layer150, and an apical layer140in a view essentially along the longitudinal axis120. The views inFIG. 1Bshow sections through the left ventricular muscle102and the right ventricular muscle104.

Each layer140,150,160can be further divided into a plurality of segments, namely, each of the circular rings depicting the left ventricle102can then be divided into a plurality of segments along a circumferential direction. For example, the basal ring and the medial ring may each be divided in six segments 1-6 and 7-12, respectively, namely, an anterior segment 1, 7, an anteroseptal segment 2, 8, an inferoseptal segment 3, 9, an inferior segment 4, 10, an inferolateral segment 5, 11, and an anterolateral segment 6, 12. The apical ring may be divided into four segments, namely an anterior13, a septal14, an inferior15, and a lateral segment 16, such that the basal160, medial150, and apical140levels can be divided into a total of 16 segments. Stacking these 16 levels as concentric rings, wherein the basal layer160forms the outermost ring161, the medial layer150forms the next ring151in a radially inward direction, and the apical layer140forms the subsequent next ring141in a radially inward direction, and adding a 17th segment131in the middle, which represents the anatomical apex of the heart, results in the “wall motion bulls-eye”, which allows for depicting the segments of the left ventricular muscle tissue that are segmented both along a direction essentially parallel to the longitudinal heart axis and along a circumferential direction of the ventricle in a standardized 2-dimensional diagram, which is shown inFIG. 1C. This segmentation is routinely used in the clinical field.

FIG. 2exemplifies a classification for a patient model. InFIG. 2, the bulls-eye diagram is rotated, and segments 1-7 are annotated, which correspond to the segments 1-7 ofFIG. 1C. The remaining segments 8-17 have been omitted fromFIG. 2, but the spatial relation of the segments is identical to the one depicted inFIG. 2C. InFIG. 2, for example, segments 1, 2, 5, 6, 7, and 8 are classified as class “2”, segments 11, 12, 13, 14, 15, 16, and 17 have been classified as class “1”, segments 4 and 10 have been classified as class “3”, and segments 3 and 9 have been classified as class “4”.

For example, the classes (also referred to as movement features below) may correspond to the movement of each segment during the heart cycle. The classes “1” to “4” above can correspond, for example, to the classification: “1”: normal, “2”: hypokinetic, “3”: akinetic, “4”: dyskinetic, and “5”: aneurysmatic.

While a classification as “normal” implies a physiological contraction movement of the portion of the heart muscle corresponding to the respective segment, a classification as “akinetic” implies an absence of contraction of that segment.

Characterizing the heart motion according to the above-described classification system allows for graphically illustrating the heart muscle function in a standardized pattern.

In order to assess and classify myocardial wall motion for each segment 1-17, the heart action has to be monitored for a certain time period, preferably at least over the course of a complete heart cycle. Additionally, for each point in time, images of the heart are acquired for a plurality of sections along the longitudinal axis of the heart. In other words, for each section across the longitudinal axis of the heart, a time-series of images is acquired, such that a plurality of image sequences for each section are obtained. This method is referred to as “cine imaging” of the heart, as explained above.

More specifically, several images can be acquired along a longitudinal axis over a period of time. As a result, a plurality of subsequent images is acquired that depicts the heart in a series of subsequent cross-sections, which depict the heart muscle, and, particularly, the left ventricle, as annular cross-sections. Each of these images will be referred to in the following as a “slice”.

Conventionally, the wall movement of each segment of the wall motion bulls-eye may be evaluated by a human operator, such as a physician, by visually assessing the images obtained by cine imaging.

LIST OF REFERENCES

Tseng et al., Introduction to Cardiovascular Magnetic Resonance: Technical Principles and Clinical Applications, Acta Cardiol Sinica 2016; 32:129-144.Schulz-Menger et al., Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for Cardiovascular Magnetic Resonance (SCMR) Board of Trustees Task Force on Standardized Post Processing, Journal of Cardiovascular Magnetic Resonance 2013, 15:35.Kramer et al., Standardized cardiovascular magnetic resonance (CMR) protocols 2013 update, Journal of Cardiovascular Magnetic Resonance 2013, 15:91.Hundley et al., Society for Cardiovascular Magnetic Resonance guidelines for reporting cardiovascular magnetic resonance examinations, Journal of Cardiovascular Magnetic Resonance 2009, 11:5.

SUMMARY OF THE INVENTION

Technical Problem

In the conventional methods of classifying the cardiac motion based on CMR cine imaging, the visual analysis (e.g. cine evaluation) is highly subjective and requires a long time (for example, in the range of 30-60 minutes). Therefore, it is an object of the present invention to provide a methodology by which the classification of the cardiac motion becomes available in a significantly reduced amount of time.

Solution

The present invention addresses the above technical problems.

In particular, in view of the limitations discussed above, the present inventors have devised, in accordance with a first example aspect herein, a computer-implemented method for providing a cardiac motion classification based on Cardiac Magnetic Resonance (CMR) image data, wherein the CMR image data comprise a plurality of image frames, I(x, y, z, t), acquired for respective two-dimensional slices in at least one longitudinal direction, z, of the heart and for a plurality of times, t, the method including: a myocardium segmentation step of inputting the plurality of image frames into two or more trained neural networks, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; a slice classification step of assigning each of the plurality of image frames in each slice, z, to an anatomic layer of the heart; a movement feature extraction and classification step of, for each of the masks and the corresponding anatomic layers, extracting a movement feature of the heart and classifying the movement feature into one of a number of pre-determined movement features; and an associating step of associating the classified movement feature with the corresponding layer for the cardiac motion classification.

The present inventors have further devised, in accordance with a second example aspect herein, an apparatus for providing a cardiac motion classification based on Cardiac Magnetic Resonance (CMR) image data, the apparatus comprising: a processing system including one or more processors; and a memory that stores executable instructions, that, when executed by the processing system, performs operations, the operations executing a computer-implemented method for providing a cardiac motion classification based on Cardiac Magnetic Resonance (CMR) image data, wherein the CMR image data comprise a plurality of image frames, I(x, y, z, t), acquired for respective two-dimensional slices in at least one longitudinal direction, z, of the heart and for a plurality of times, t, the method including: a myocardium segmentation step of inputting the plurality of image frames into two or more trained neural networks, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; a slice classification step of assigning each of the plurality of image frames in each slice, z, to an anatomic layer of the heart; a movement feature extraction and classification step of, for each of the masks and the corresponding anatomic layers, extracting a movement feature of the heart and classifying the movement feature into one of a number of pre-determined movement features; and an associating step of associating the classified movement feature with the corresponding layer for the cardiac motion classification.

The present inventors have further devised, in accordance with a third example aspect herein, a computer program which, when executed by a computer, causes the computer to perform the computer-implemented method according to the first example aspect herein.

The present inventors have further devised, in accordance with a fourth example aspect herein, a non-transitory computer-readable storage medium storing a computer program in accordance with the third aspect.

The present inventors have further devised, in accordance with a fifth example aspect herein, a signal carrying a computer program in accordance with the third aspect.

Effects

The above computer-implemented method and the above apparatus are implemented to use artificial intelligence (AI) models in classifying cardiac wall motion based on CMR image data and improve the reliability and reduce the time conventionally needed for the assessment or classification of cardiac wall motion.

