TECHNICAL FIELD

The present disclosure generally relates to medical imaging, and in particular, to systems and methods for determining target images that correspond to at least one cardiac cycle of an object's heart.

BACKGROUND

Cardiac cine images include a plurality of successive images of a region of interest (ROI) of a patient's heart that are acquired during a period of time when the heart relaxes and contracts repeatedly. The cardiac cine images are arranged in a temporal order and reflect the cardiac motion during the period of time. In the process of acquiring cardiac cine images, electrocardiograph (ECG) is usually used to monitor the cardiac motion and provide synchronization (gating) signal to an imaging system. Although ECG gating is considered the clinical standard for cardiac imaging, it is still problematic in several aspects. For example, firstly, cardiac imaging with ECG gating requires the patient to hold his/her breath for a long time. Secondly, the ECG signals could be unstable for some patients (e.g., patient with arrhythmia, hairy chest, or abnormal chest and cardiovascular geometry) and even difficult to obtain for some special applications (e.g., fetus cardiac scan). Cardiac imaging without ECG gating, however, cannot provide cardiac information such as which images correspond to the same cardiac cycle, or which images correspond to the start or end of cardiac diastole or cardiac systole, etc., while such information is important for a doctor to diagnose the heart's condition and disease. However, determining the cardiac information by analyzing the plurality of images with the naked eyes and experience of the doctor is not only time consuming but also often inaccurate. Therefore, it is desirable to provide methods and/or systems for automatic cardiac imaging to overcome the aforementioned problems.

SUMMARY

According to a first aspect of the present disclosure, a system for medical imaging may include one or more storage devices and one or more processors configured to communicate with the one or more storage devices. The one or more storage devices may include a set of instructions. When the one or more processors executing the set of instructions, the one or more processors may be directed to perform one or more of the following operations. The one or more processors may obtain a plurality of successive images of a region of interest (ROI) including at least a portion of an object's heart. The plurality of successive images may be based on imaging data acquired from the ROI by a scanner without electrocardiography (ECG) gating. The plurality of successive images may be related to one or more cardiac cycles of the object's heart. The one or more processors may automatically determine, in the plurality of successive images, target images that correspond to at least one of the one or more cardiac cycles of the object's heart.

In some embodiments, for at least one of the target images, the one or more processors may determine corresponding time information based on a temporal resolution of the plurality of successive images.

In some embodiments, to automatically determine, in the plurality of successive images, the target images that correspond to the at least one of the one or more cardiac cycles of the object's heart, the one or more processors may determine a reference image based on the plurality of successive images, the reference image relating to cardiac diastole of the object's heart. The one or more processors may determine correlations between the reference image and at least multiple of the plurality of successive images. The one or more processors may determine, in the plurality of successive images, diastolic images relating to the cardiac diastole based on the correlations. The one or more processors may determine two or more terminal images in the diastolic images. The one or more processors may determine the target images by including images between two neighboring terminal images of the two or more terminal images in the plurality of successive images.

In some embodiments, to determine the reference image based on the plurality of successive images, the one or more processors may divide the plurality of successive images into two or more first groups. The one or more processors may select one of the two or more first groups. A count of images in the selected first group may be greatest among the two or more first groups. The one or more processors may determine a median image based on the images of the selected first group. A pixel value of each pixel in the median image may be a median of pixel values of corresponding pixels of the images in the selected first group. The one or more processors may determine the reference image based on the median image.

In some embodiments, the plurality of successive images may be divided into the two or more first groups using K-means clustering.

In some embodiments, to determine the reference image based on the median image, the one or more processors may determine the median image as the reference image.

In some embodiments, to determine the reference image based on the median image, the one or more processors may determine a similarity degree between the median image and at least one of the images in the selected first group by comparing the pixel value of each pixel of the median image with the pixel value of the corresponding pixel in the at least one of the images in the selected first group. The one or more processors may determine one of the images in the selected first group as the reference image based on the similarity degree.

In some embodiments, to determine, in the plurality of successive images, the diastolic images relating to the cardiac diastole based on the correlations, the one or more processors may determine a median of the correlations of the at least multiple of the plurality of successive images. The one or more processors may determine the diastolic images based on the median of the correlations. The correlations of the diastolic images may be larger than the median of the correlations of the at least multiple of the plurality of successive images.

In some embodiments, to determine the two or more terminal images in the diastolic images, the one or more processors may divide the diastolic images into two or more second groups using K-means clustering. The one or more processors may determine a number for each of the diastolic images based on a time order of the diastolic images. In each of the two or more second groups, the one or more processors may determine the image the number of which is a median of the numbers of the diastolic images in the second group as the terminal image.

In some embodiments, to automatically determine, in the plurality of successive images, the target images that correspond to the at least one of the one or more cardiac cycles of the object's heart, the one or more processors may determine the target images using machine learning.

In some embodiments, to determine the target images using machine learning, the one or more processors may determine two or more terminal images in the plurality of successive images based on a first trained machine learning model. The one or more processors may determine the target images by including images between two neighboring terminal images of the two or more terminal images in the plurality of successive images.

In some embodiments, to determine the target images using machine learning, the one or more processors may determine diastolic images relating to cardiac diastole of the object's heart or systolic images relating to cardiac systole of the object's heart in the plurality of successive images using a second trained machine learning model. The one or more processors may determine two or more terminal images in the diastolic images or the systolic images. The one or more processors may determine the target images by including images between two neighboring terminal images of the two or more terminal images in the plurality of successive images.

In some embodiments, the plurality of successive images may be generated based on MR data using compressed sensing.

According to another aspect of the present disclosure, a method for medical imaging may include one or more of the following operations. One or more processors may obtain a plurality of successive images of a region of interest (ROI) including at least a portion of an object's heart. The plurality of successive images may be based on imaging data acquired from the ROI by a scanner without electrocardiography (ECG) gating. The plurality of successive images may be related to one or more cardiac cycles of the object's heart. The one or more processors may automatically determine, in the plurality of successive images, target images that correspond to at least one of the one or more cardiac cycles of the object's heart.

According to yet another aspect of the present disclosure, a system for medical imaging may include an image obtaining module configured to obtain a plurality of successive images of a region of interest (ROI) including at least a portion of an object's heart. The plurality of successive images may be based on imaging data acquired from the ROI by a scanner without electrocardiography (ECG) gating. The plurality of successive images may be related to one or more cardiac cycles of the object's heart. The system may also include an image selecting module configured to automatically determine, in the plurality of successive images, target images that correspond to at least one of the one or more cardiac cycles of the object's heart.

According to yet another aspect of the present disclosure, a non-transitory computer readable medium may comprise at least one set of instructions for medical imaging. The at least one set of instructions may be executed by one or more processors of a computing device. The one or more processors may obtain a plurality of successive images of a region of interest (ROI) including at least a portion of an object's heart. The plurality of successive images may be based on imaging data acquired from the ROI by a scanner without electrocardiography (ECG) gating. The plurality of successive images may be related to one or more cardiac cycles of the object's heart. The one or more processors may automatically determine, in the plurality of successive images, target images that correspond to at least one of the one or more cardiac cycles of the object's heart.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities, and combinations set forth in the detailed examples discussed below.

