TECHNICAL FIELD

The present disclosure generally relates to image processing, and more particularly, a system and method for registering multi-modality images based on a registration model.

BACKGROUND

Multi-modality imaging systems may generate one or more functional images and one or more structural images. For example, a PET-CT system may generate a functional PET image and a structural CT image. One or more lesion locations may be determined accurately based on a registration of the functional image(s) and corresponding structural image(s). The images generated by a multi-modality imaging system may provide more detailed and reliable diagnostic information than that generated by a single-modality system (e.g., a CT system, a PET system, an MRI system, etc.). However, there may be an error in the reconstruction centers of a functional image and a structural image generated by a multi-modality imaging system. The error of reconstruction center may reduce registration accuracy. Therefore, it is desirable to correct the error of the reconstruction centers of the multi-modality imaging systems.

SUMMARY

According to an aspect of the present disclosure, a method is provided. The method may include one or more of the following operations. A processor may acquire a first image relating to a registration model, wherein the registration model includes a plurality of reference objects. The processor may acquire a second image relating to the registration model. The processor may determine a set of reference points based on the plurality of reference objects, the set of reference points including a plurality of first reference points, wherein the plurality of first reference points include at least two first reference points, and the distance between the at least two first reference points represents the shortest distance between two of the plurality of reference objects. The processor may determine a set of mapping data corresponding to the set of reference points in the first image and the second image, the set of mapping data reflecting a mapping relation between a first coordinate position of at least one reference point of the set of reference points in the first image and a second coordinate position of the at least one reference point in the second image. Furthermore, the processor may determine one or more registration parameters based on the set of mapping data.

According to another aspect of the present disclosure, a system is provided. The system may include at least one non-transitory computer-readable storage medium and at least one processor. The at least one storage medium may include a set of instructions. When the at least one processor execute the set of instructions, the at least one processor may be configured to perform one or more of the following operations. The at least one processor may acquire a first image relating to a registration model, wherein the registration model includes a plurality of reference objects. The at least one processor may acquire a second image relating to the registration model. The at least one processor may determine a set of reference points based on the plurality of reference objects, the set of reference points including a plurality of first reference points, wherein the plurality of first reference points includes at least two first reference points, and the distance between the at least two first reference points represents the shortest distance between two of the plurality of reference objects. The at least one processor may determine a set of mapping data corresponding to the set of reference points in the first image and the second image, the set of mapping data reflecting a mapping relation between a first coordinate position of at least one reference point of the set of reference points in the first image and a second coordinate position of the at least one reference point in the second image. The at least one processor may determine one or more registration parameters based on the set of mapping data.

According to a further aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium may include executable instructions that, when executed by at least one processor, cause the at least one processor to effectuate a method. The method may include one or more of the following operations. The computing device may acquire a second image relating to the registration model. The computing device may determine a set of reference points based on the plurality of reference objects, the set of reference points including a plurality of first reference points, wherein the plurality of first reference points includes at least two first reference points, and the distance between the at least two first reference points represents the shortest distance between two of the plurality of reference objects. The computing device may determine a set of mapping data corresponding to the set of reference points in the first image and the second image, the set of mapping data reflecting a mapping relation between a first coordinate position of at least one reference point of the set of reference points in the first image and a second coordinate position of the at least one reference point in the second image. The computing device may determine one or more registration parameters based on the set of mapping data.

According to a further aspect of the present disclosure, a system is provided. The system may have at least one processor and a storage configured to store instructions. The system may include: an acquisition module configured to acquire a first image relating to a registration model and a second image relating to the registration model, wherein the registration model includes a plurality of reference objects; a reference object determination unit configured to determine a set of reference points based on the plurality of reference objects, the set of reference points including a plurality of first reference points, wherein the plurality of first reference points include at least two first reference points, and the distance between the at least two first reference points represents the shortest distance between two of the plurality of reference objects; a mapping data determination unit configured to determine a set of mapping data corresponding to the set of reference points in the first image and the second image, the set of mapping data reflecting a mapping relation between a first coordinate position of at least one reference point of the set of reference points in the first image and a second coordinate position of the at least one reference point in the second image; and a registration parameter determination unit configured to determine one or more registration parameters based on the set of mapping data.

In some embodiments, the processor may determine a plurality of characteristic points of the plurality of reference objects. The processor may determine the plurality of reference objects in the first image based on the plurality of characteristic points. The processor may determine the plurality of reference objects in the second image based on the plurality of characteristic points.

In some embodiments, the processor may determine a plurality of characteristic points of the plurality of reference objects according to the following operations. The processor may determine a plurality of intersection points between at least three parallel planes and the plurality of reference objects. The processor may designate the plurality of intersection points as the plurality of characteristic points.

In some embodiments, the processor may determine the plurality of reference objects in the first image according to the following operations. The processor may acquire a first plurality of coordinate positions of the plurality of characteristic points in the first image. The processor may determine a first plurality of slopes corresponding to the plurality of reference objects based on the first plurality of coordinate positions. The processor may further determine a first location distribution of the plurality of reference objects in the first image based on the first plurality of slopes and the first plurality of coordinate positions.

In some embodiments, the processor may determine a first plurality of slopes of the plurality of reference objects according to the following operations. The processor may determine at least one slope based on every two of the plurality of characteristic points to obtain a first set of slopes. The processor may divide the first set of slopes into a plurality of groups based on values of the first set of slopes, wherein one or more slopes in each group are substantially the same. The processor may determine determining one or more target groups that have a maximum number of slopes. The processor may designate one or more values of the one or more target groups as the first plurality of slopes corresponding to the plurality of reference objects.

In some embodiments, the processor may determine a first location distribution of the plurality of reference objects in the first image according to the following operations. The processor may determine a distance between every two reference objects in the first image. The processor may determine the first location distribution of the plurality of reference objects in the first image based on the distances determined for the plurality of reference objects in the first image.

In some embodiments, the processor may determine the plurality of reference objects in the second image according to the following operations. The processor may acquire a second plurality of coordinate positions of the plurality of characteristic points in the second image. The processor may determine a second plurality of slopes corresponding to the plurality of reference objects based on the second plurality of coordinate positions. The processor may determine a second location distribution of the plurality of reference objects in the second image based on the second plurality of slopes and the second plurality of coordinate positions.

In some embodiments, the processor may determine a second plurality of slopes of the plurality of reference objects according to the following operations. The processor may determine at least one slope based on every two of the plurality of characteristic points to obtain a second set of slopes. The processor may divide the second set of slopes into a plurality of groups based on values of the second set of slopes, wherein one or more slopes in each group are substantially the same. The processor may determine one or more target groups that have a maximum number of slopes. The processor may designate one or more values of the one or more target groups as the second plurality of slopes corresponding to the plurality of reference objects.

In some embodiments, the processor may determine a second location distribution of the plurality of reference objects in the second image according to the following operations. The processor may determine a distance between every two reference objects in the second image. The processor may determine the second location distribution of the plurality of reference objects in the second image based on the distances determined for the plurality of reference objects in the second image.

In some embodiments, the processor may determine the plurality of second reference points based on the plurality of first reference points, wherein one of the plurality of second reference points and one of the plurality of first reference points that belong to a same reference object of the plurality of reference objects have a specific distance.

In some embodiments, the processor may determine a set of mapping data corresponding to the set of reference points in the first image and the second image according to the following operations. The processor may determine a first set of mapping data corresponding to the plurality of first reference points, the first set of mapping data reflecting a mapping relation between a third coordinate position of at least one first reference point of the plurality of first reference points in the first image and a fourth coordinate position of the at least one first reference point in the second image. The processor may determine a second set of mapping data corresponding to the plurality of second reference points, the second set of mapping data reflecting a mapping relation between a fifth coordinate position of at least one second reference point of the plurality of second reference points in the first image and a sixth coordinate position of the at least one second reference point in the second image.

In some embodiments, the one or more registration parameters include a rotation matrix and a translation vector.

In some embodiments, the processor may determine the one or more registration parameters by comparing a plurality of back-projection errors generated in a plurality of iterations.

In some embodiments, the plurality of iterations is performed based on a Random Sample Consensus algorithm.

In some embodiments, the plurality of reference objects include a plurality of skew lines on different planes.

In some embodiments, the registration model including the plurality of skew lines on different planes may be constructed.

In some embodiments, the first image may be a computer tomography image or a magnetic resonance image.

In some embodiments, the second image may be a positron emission tomography image.

In some embodiments, the first image may be obtained by a first imaging device, and the second image may be obtained by a second imaging device.

In some embodiments, the first image may be obtained by a first portion of a multi-modality imaging device, and the second image may be obtained by a second portion of the multi-modality imaging device.

In some embodiments, the plurality of first reference points may include a first point and a second point, and the distance between the first point and the second point represents the shortest distance between two portions of one of the plurality of reference objects.

In some embodiments, the processor may further register a third image and a fourth image based on the one or more registration parameters.

In some embodiments, the reference object determination unit may include a characteristic point determination subunit configured to determine a plurality of characteristic points of the plurality of reference objects. The reference object determination unit may be further configured to determine the plurality of reference objects in the first image based on the plurality of characteristic points and determine the plurality of reference objects in the second image based on the plurality of characteristic points.

In some embodiments, the characteristic point determination subunit may be further configured to determine a plurality of intersection points between at least three parallel planes and the plurality of reference objects, and designate the plurality of intersection points as the plurality of characteristic points.

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.

