CN114066947B - Image registration method and image registration device - Google Patents
Image registration method and image registration device Download PDFInfo
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- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/10116—X-ray image
Abstract
The present disclosure relates to an image registration method, comprising: shooting at n positions by using acquisition equipment to obtain n X-ray images; determining n spatial relationships of each of the n X-ray images with respect to a reference image of the n X-ray images; determining n intermediate conversion relations according to the undetermined conversion relation and the n spatial relations; respectively converting the CT images through n intermediate conversion relations to obtain n DRRs; determining a target similarity function of the n DRRs and the n X-ray images; adjusting the undetermined conversion relation through an optimization algorithm until the target similarity function reaches the maximum value; and outputting the adjusted undetermined conversion relation. According to the method, compared with a registration mode in the related technology, the method is noninvasive, n spatial relations are used as constraint conditions, the output to-be-determined conversion relation is more reasonable, and the DRR image obtained by converting the CT image according to the output to-be-determined conversion relation can be accurately registered with the X-ray image.
Description
Technical Field
The present disclosure relates to the field of medical technology, and in particular, to an image registration method, an image registration apparatus, an electronic device, and a computer-readable storage medium.
Background
In the field of surgical navigation, obtaining three-dimensional spatial information of a patient in an operation is a very critical link. At present, there are two main schemes for acquiring three-dimensional information, the first is a method for directly acquiring three-dimensional information based on intraoperative three-dimensional images; the second is to determine the three-dimensional space information of the patient based on the two-dimensional information in the operation and the three-dimensional information before the operation. In the second approach, registration fusion is most commonly performed using intraoperative two-dimensional X-ray images and preoperative CT images to obtain three-dimensional spatial information of the intraoperative patient.
Registering the two-dimensional X-ray image and the preoperative CT image, and having various implementation schemes:
based on the registration of the mark points, the mark points are driven into the human skeleton, then CT scanning is carried out, and the registration is carried out through the three-dimensional mark points in the CT and the two-dimensional mark points in the X-ray image in the operation. The mode needs to drive a marking ball into a human body, is invasive, adds one operation and is not accepted in actual clinic;
based on the registration of a single X-ray image, the registration is completed by searching characteristic points or characteristic lines and corresponding characteristic points or characteristic lines in CT through the X-ray image in the operation. In this way, since one X-ray image has only two-dimensional information, the registration result has poor accuracy in the third missing dimension;
and reconstructing the plurality of X-ray images into a three-dimensional CT image based on the registration of the plurality of images, and completing the registration by using the reconstructed CT and the preoperative CT. In this way, the X-ray image has less three-dimensional information, the reconstructed CT quality is poor, and the registration accuracy is not high.
Disclosure of Invention
The present disclosure provides an image registration method, an image registration apparatus, an electronic device, and a computer-readable storage medium to solve the disadvantages of the related art.
According to a first aspect of the embodiments of the present disclosure, an image registration method is provided, including:
shooting a target object under n positions respectively through acquisition equipment to obtain n X-ray images, wherein n is an integer greater than 1;
determining n spatial relationships of each of the n X-ray images with respect to a reference image of the n X-ray images, respectively;
determining n intermediate conversion relations according to the undetermined conversion relation and the n spatial relations;
respectively converting the CT images shot for the target object in advance through the n intermediate conversion relations to obtain n digital reconstructed radiological images DRR;
determining a target similarity function of the n DRRs and the n X-ray images;
adjusting the conversion relation to be determined through an optimization algorithm until the target similarity function reaches the maximum value;
and outputting the adjusted undetermined conversion relation.
Optionally, the optimization algorithm comprises at least one of:
gradient descent method, annealing algorithm, quasi-Newton method and L-BFGS-B algorithm.
Optionally, the harvesting device comprises a C-arm machine.
Optionally, the determining the target similarity function of the n DRRs and the n X-ray images comprises:
determining an ith two-dimensional image in the n DRRs and an ith similarity function of the ith two-dimensional image in the n DRRs and the ith X-ray image in the n X-ray images to obtain n similarity functions, wherein i is more than or equal to 1 and less than or equal to n;
and carrying out weighted summation on the n similarity functions to obtain the target similarity function.
Optionally, the similarity function comprises at least one of:
correlation coefficient function, mutual information function, mode intensity function, gradient correlation function.
According to a second aspect of the embodiments of the present disclosure, there is provided an image registration apparatus, including:
the X-ray shooting module is used for respectively shooting a target object in n positions through the acquisition equipment to obtain n X-ray images, wherein n is an integer greater than 1;
a relation determining module, configured to determine n spatial relations of each of the n X-ray images with respect to a reference image in the n X-ray images, respectively; determining n intermediate conversion relations according to the undetermined conversion relation and the n spatial relations;
an image conversion module, configured to convert, through the n intermediate conversion relationships, CT images that are taken for the target object in advance, respectively, so as to obtain n digital reconstructed radiographs DRR;
a function determination module for determining a target similarity function of the n DRRs and the n X-ray images;
the function optimization module is used for adjusting the conversion relation to be determined through an optimization algorithm until the target similarity function reaches the maximum value; and outputting the adjusted undetermined conversion relation.
Optionally, the optimization algorithm comprises at least one of:
gradient descent method, annealing algorithm, quasi-Newton method and L-BFGS-B algorithm.
Optionally, the harvesting device comprises a C-arm machine.
Optionally, the function determining module is configured to determine, from i =1 to i = n, an ith two-dimensional image of the n DRRs and an ith similarity function of the ith X-ray image of the n X-ray images to obtain n similarity functions, where 1 ≦ i ≦ n; and carrying out weighted summation on the n similarity functions to obtain the target similarity function.
Optionally, the similarity function comprises at least one of:
correlation coefficient function, mutual information function, mode intensity function, gradient correlation function.
According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute instructions to implement the method of any of the above embodiments.
According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which computer instructions are stored, and the instructions, when executed by a processor, implement the steps in the method according to any one of the above embodiments.
According to the embodiment of the disclosure, n spatial relations can be determined by shooting n X-ray images, and then the to-be-determined conversion relation can be converted first through the n spatial relations to obtain n intermediate conversion relations, and then the CT images are converted respectively through the n intermediate conversion relations to obtain n DRRs. The determined similarity function is a target similarity function of the n DRRs and the n X-ray images, and the n DRR images are obtained based on n intermediate conversion relations which are obtained based on n spatial relations, so that the n spatial relations serve as constraint conditions in the process of the conversion relation to be determined adjusted through the optimization algorithm.
Compared with a registration mode in the related technology, the method is noninvasive, n spatial relations are used as constraint conditions, the output to-be-determined conversion relation is more reasonable, and the DRR image obtained by converting the CT image according to the output to-be-determined conversion relation can be accurately registered with the X-ray image.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic flow chart diagram illustrating an image registration method in accordance with an embodiment of the present disclosure.
Fig. 2 is a schematic error diagram shown in accordance with an embodiment of the present disclosure.
Fig. 3 is a schematic flow chart diagram illustrating another image registration method in accordance with an embodiment of the present disclosure.
Fig. 4 is a schematic block diagram illustrating an image registration apparatus according to an embodiment of the present disclosure.
Fig. 5 is a schematic block diagram illustrating an electronic device in accordance with an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the disclosure, as detailed in the appended claims.
Fig. 1 is a schematic flow chart diagram illustrating an image registration method in accordance with an embodiment of the present disclosure. The method shown in this embodiment may be applied to an acquisition device for acquiring X-ray images, such as a C-arm machine, and may also be applied to an external device capable of communicating with the acquisition device, such as a processor other than a C-arm machine.
As shown in fig. 1, the image registration method may include the steps of:
in step S101, a target object is photographed at n positions by a collecting device to obtain n X-ray images, where n is an integer greater than 1;
in step S102, n spatial relationships of each of the n X-ray images with respect to a reference image in the n X-ray images are determined;
in step S103, n intermediate transformation relations are determined according to the pending transformation relation and the n spatial relations;
in step S104, converting the CT images previously taken for the target object through the n intermediate conversion relationships, respectively, to obtain n digital reconstructed radiographs DRR;
in step S105, determining a target similarity function of the n DRRs and the n X-ray images;
in step S106, the to-be-determined conversion relationship is adjusted by an optimization algorithm until the target similarity function reaches a maximum value;
in step S107, the adjusted to-be-determined conversion relationship is output.
In one embodiment, the target object may be a patient in need of surgery, including but not limited to humans and animals. For a target object, a CT (Computed Tomography) image may be taken before an operation, and the CT image is a three-dimensional CT image. Then, the above steps can be executed in the operation, the pose of the acquisition equipment is adjusted, the target object is shot, and the target object is shot in n poses respectively to obtain n X-ray images.
N is an integer greater than 1, a larger value n may be selected to improve registration accuracy, but since the X-ray image taken has a certain damage to the patient, n is not suitable to be set to be larger, for example, n may be 2 in the case where the requirement on registration accuracy is not very high, and for example, n may be 5 in the case where the requirement on registration accuracy is very high. The value of n may be set as needed, and the disclosure is not limited.
After obtaining the n X-ray images, one of the n X-ray images may be used as a reference image, and then n spatial relationships with respect to the reference image in the n X-ray images are determined for each of the n X-ray images.
Wherein the spatial relationship may be represented by 6 parameters, of which 3 parameters represent translations in three spatial directions and the other 3 parameters represent rotation angles in three spatial directions.
For the purpose of registration, the disclosed embodiments finally obtain a relationship to be transformed, which may also be referred to as a spatial transform to be determined. The to-be-determined transformation relationship may be represented by 6 parameters, wherein 3 parameters represent translation in three spatial directions, and the other 3 parameters represent rotation angles in three spatial directions.
The undetermined transformation relationship may be adjusted in the registration process, and in this embodiment, n intermediate transformation relationships may be determined according to the undetermined transformation relationship and the n spatial relationships, and then the CT images taken for the target object in advance are transformed respectively through the n intermediate transformation relationships, so as to obtain n digital reconstructed radiographs DRR (digital reconstructed Radiograph). Where the CT image is a three-dimensional image and the resulting DRR image is a two-dimensional image, the three-dimensional image is converted to a two-dimensional image for registration with the X-ray image.
The specific registration mode may be to determine a target similarity function between n DRRs and n X-ray images, and then adjust the to-be-determined conversion relationship through an optimization algorithm until the target similarity function reaches a maximum value. When the target similarity function reaches the maximum value, it can be determined that the DRR image obtained according to the adjusted undetermined conversion relation is registered with the X-ray image, and then the adjusted undetermined conversion relation can be output. Of course, the DRR obtained by converting the CT image according to the adjusted undetermined conversion relationship may also be output as needed.
According to the embodiment of the disclosure, n spatial relationships can be determined by shooting n X-ray images, and then the relationship to be converted can be converted first through the n spatial relationships to obtain n intermediate conversion relationships, and then the CT images are converted respectively through the n intermediate conversion relationships to obtain n DRRs. The similarity function thus determined is a target similarity function of the n DRRs and the n X-ray images, and since the n DRR images are obtained based on the n intermediate transformation relationships, which are obtained based on the n spatial relationships, the n spatial relationships serve as constraint conditions in the process of the to-be-determined transformation relationship adjusted by the optimization algorithm.
Compared with a registration mode in the related technology, the method is noninvasive, n spatial relations are used as constraint conditions, the output to-be-determined conversion relation is more reasonable, and the DRR image obtained by converting the CT image according to the output to-be-determined conversion relation can be accurately registered with the X-ray image.
Fig. 2 is a schematic error diagram shown in accordance with an embodiment of the present disclosure.
In the related art, based on the registration of an X-ray image a and a CT image, the error range of the registration is, for example, the error range a in fig. 2. Taking n =2 as an example, that is, 2X-ray images, X-ray image a and X-ray image B are obtained by shooting, and if X-ray image a is registered with CT image alone, the error is error range a, and if X-ray image B is registered with CT image alone, the error is error range B, and according to the embodiment of the present disclosure, the spatial relationship between X-ray image a and X-ray image B with respect to the reference image (for example, X-ray image a) is used as a constraint condition, so that the obtained error is the intersection of error range a and error range B, and as shown in fig. 2, the error is obviously reduced with respect to both error range a and error range B.
Optionally, the optimization algorithm comprises at least one of:
gradient descent method, annealing algorithm, quasi-Newton method and L-BFGS-B algorithm.
It should be noted that the optimization algorithm can be selected according to the requirement, and is not limited to the above algorithm.
Optionally, the harvesting device comprises a C-arm machine.
The acquisition device is not limited to the C-arm machine described above as long as it can acquire an X-ray image during surgery.
Fig. 3 is a schematic flow chart diagram illustrating another image registration method in accordance with an embodiment of the present disclosure. As shown in fig. 3, the determining the target similarity function of the n DRRs and the n X-ray images includes:
in step S1051, from i =1 to i = n, determining the ith two-dimensional image of the n DRRs and the ith similarity function of the ith X-ray image of the n X-ray images to obtain n similarity functions, wherein i is greater than or equal to 1 and less than or equal to n;
in step S1052, the n similarity functions are weighted and summed to obtain the target similarity function.
In one embodiment, n similarity functions may be obtained based on n DRRs and n X-ray images, and for the n similarity functions, the target similarity function may be obtained by a weighted summation, or by other methods, such as calculating a mean value, calculating a root mean square, and the like.
Optionally, the similarity function comprises at least one of:
correlation coefficient function, mutual information function, mode intensity function, gradient correlation function.
It should be noted that the similarity function can be selected according to the need, and is not limited to the above function.
The present disclosure also proposes embodiments related to an image registration apparatus, corresponding to the embodiments of the image registration method described above.
Fig. 4 is a schematic block diagram illustrating an image registration apparatus according to an embodiment of the present disclosure. The apparatus shown in this embodiment may be applied to an acquisition device for acquiring X-ray images, such as a C-arm machine, and may also be applied to an external device capable of communicating with the acquisition device, such as a processor other than the C-arm machine.
As shown in fig. 4, the image registration apparatus may include:
the X-ray shooting module 101 is used for respectively shooting a target object in n positions through the acquisition equipment to obtain n X-ray images, wherein n is an integer greater than 1;
a relation determining module 102, configured to determine n spatial relations of each of the n X-ray images with respect to a reference image in the n X-ray images; determining n intermediate conversion relations according to the undetermined conversion relation and the n spatial relations;
an image conversion module 103, configured to convert, through the n intermediate conversion relationships, CT images that are taken of the target object in advance, respectively, so as to obtain n digital reconstructed radiographs DRR;
a function determining module 104 for determining a target similarity function between the n DRRs and the n X-ray images;
a function optimization module 105, configured to adjust the to-be-determined conversion relationship through an optimization algorithm until the target similarity function reaches a maximum value; and outputting the adjusted undetermined conversion relation.
Optionally, the optimization algorithm comprises at least one of:
gradient descent method, annealing algorithm, quasi-Newton method and L-BFGS-B algorithm.
Optionally, the harvesting device comprises a C-arm machine.
Optionally, the function determining module is configured to determine, from i =1 to i = n, an ith two-dimensional image of the n DRRs and an ith similarity function of the ith X-ray image of the n X-ray images to obtain n similarity functions, where 1 ≦ i ≦ n; and carrying out weighted summation on the n similarity functions to obtain the target similarity function.
Optionally, the similarity function comprises at least one of:
correlation coefficient function, mutual information function, mode intensity function, gradient correlation function.
An embodiment of the present disclosure also provides an electronic device, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute instructions to implement the method of any of the above embodiments.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method according to any of the above embodiments.
Fig. 5 is a schematic block diagram of an electronic device shown in accordance with an embodiment of the present disclosure. Embodiments of the image registration apparatus of the present disclosure may be applied on an electronic device. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. The software implementation is taken as an example, and is formed by reading corresponding computer program instructions in the nonvolatile memory into the memory for operation through the processor of the device where the software implementation is located as a logical means. From a hardware level, as shown in fig. 5, which is a hardware structure diagram of a device in which the image registration apparatus of the present disclosure is located, in addition to the processor, the network interface, the memory, and the nonvolatile memory shown in fig. 5, the device in which the apparatus is located in the embodiment may also generally include other hardware, such as a forwarding chip responsible for processing a packet, and the like; the device may also be a distributed device in terms of hardware structure, and may include multiple interface cards to facilitate expansion of message processing at the hardware level.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the disclosure. One of ordinary skill in the art can understand and implement without inventive effort.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice in the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (12)
1. An image registration method, comprising:
shooting a target object under n positions respectively through acquisition equipment to obtain n X-ray images, wherein n is an integer greater than 1;
determining n spatial relationships of each of the n X-ray images with respect to a reference image of the n X-ray images, respectively;
determining n intermediate transformation relations according to the pending transformation relation and the n spatial relations, including: converting the undetermined conversion relation through the n spatial relations to obtain n intermediate conversion relations;
respectively converting the CT images shot for the target object in advance through the n intermediate conversion relations to obtain n digital reconstructed radiological images DRR;
determining a target similarity function of the n DRRs and the n X-ray images;
adjusting the conversion relation to be determined through an optimization algorithm until the target similarity function reaches the maximum value;
and outputting the adjusted undetermined conversion relation.
2. The method of claim 1, wherein the optimization algorithm comprises at least one of:
gradient descent method, annealing algorithm, quasi-Newton method and L-BFGS-B algorithm.
3. The method of claim 1, wherein the collection device comprises a C-arm machine.
4. The method of any of claims 1 to 3, wherein said determining the target similarity function of the n DRRs to the n X-ray images comprises:
determining an ith two-dimensional image in the n DRRs and an ith similarity function of the ith two-dimensional image in the n DRRs and the ith X-ray image in the n X-ray images to obtain n similarity functions, wherein i is more than or equal to 1 and less than or equal to n;
and carrying out weighted summation on the n similarity functions to obtain the target similarity function.
5. The method of claim 4, wherein the similarity function comprises at least one of:
correlation coefficient function, mutual information function, mode intensity function, gradient correlation function.
6. An image registration apparatus, comprising:
the X-ray shooting module is used for respectively shooting a target object in n positions through the acquisition equipment to obtain n X-ray images, wherein n is an integer greater than 1;
a relation determining module, configured to determine n spatial relations of each of the n X-ray images with respect to a reference image in the n X-ray images; and determining n intermediate transformation relations according to the undetermined transformation relation and the n spatial relations, including: converting the undetermined conversion relation through the n spatial relations to obtain n intermediate conversion relations;
an image conversion module, configured to convert, through the n intermediate conversion relationships, CT images that are taken of the target object in advance, respectively, so as to obtain n digital reconstructed radiographs DRR;
a function determination module for determining a target similarity function of the n DRRs and the n X-ray images;
the function optimization module is used for adjusting the conversion relation to be determined through an optimization algorithm until the target similarity function reaches the maximum value; and outputting the adjusted undetermined conversion relation.
7. The apparatus of claim 6, wherein the optimization algorithm comprises at least one of:
gradient descent method, annealing algorithm, quasi-Newton method and L-BFGS-B algorithm.
8. The apparatus of claim 6, wherein the collection device comprises a C-arm machine.
9. The apparatus of any of claims 6 to 8, wherein said function determining module is configured to determine an i-th two-dimensional image of said n DRRs and an i-th similarity function of an i-th X-ray image of said n X-ray images from i =1 to i = n to obtain n similarity functions, wherein 1 ≦ i ≦ n; and carrying out weighted summation on the n similarity functions to obtain the target similarity function.
10. The apparatus of claim 9, wherein the similarity function comprises at least one of:
correlation coefficient function, mutual information function, mode intensity function, gradient correlation function.
11. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute instructions to implement the method of any one of claims 1 to 5.
12. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of any one of claims 1 to 5.
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| CN103065322A (en) * | 2013-01-10 | 2013-04-24 | 合肥超安医疗科技有限公司 | Two dimensional (2D) and three dimensional (3D) medical image registration method based on double-X-ray imaging |
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| CN110946654A (en) * | 2019-12-23 | 2020-04-03 | 中国科学院合肥物质科学研究院 | Bone surgery navigation system based on multimode image fusion |
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| CN103065322A (en) * | 2013-01-10 | 2013-04-24 | 合肥超安医疗科技有限公司 | Two dimensional (2D) and three dimensional (3D) medical image registration method based on double-X-ray imaging |
| CN110009669A (en) * | 2019-03-22 | 2019-07-12 | 电子科技大学 | A kind of 3D/2D medical image registration method based on deeply study |
| CN110400357A (en) * | 2019-07-05 | 2019-11-01 | 北京航空航天大学 | A kind of 4D-CBCT method for reconstructing based on the constraint of motion perception image |
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