JP-7857168-B2 - Image splitting apparatus, image splitting method, and magnetic resonance imaging apparatus

Inventors

• リュウ イエ
• ファン ヂェ

Assignees

• キヤノンメディカルシステムズ株式会社

Dates

Publication Date

20260512

Application Date

20220615

Priority Date

20210820

Claims (17)

1. An image splitting device for magnetic resonance imaging, An acquisition unit that acquires a positioning image including a three-dimensional image including organs or a multi-layered two-dimensional image including the organs, Based on the positioning image, a determination unit obtains a plurality of two-dimensional cross-sectional images corresponding to each of a plurality of slices arranged in the layer direction within the spatial region of the positioning image, identifies a first two-dimensional cross-sectional image in which one end of the organ in the layer direction exists and a second two-dimensional cross-sectional image in which the other end of the organ in the layer direction exists from among the plurality of two-dimensional cross-sectional images, and determines the segment defined by the position corresponding to the first two-dimensional cross-sectional image and the position corresponding to the second two-dimensional cross-sectional image as the segment in which the organ exists in the layer direction. An image splitting device comprising : a splitting unit that performs image splitting on the region of the positioning image in which the organ is located, and obtains the result of splitting the region of the organ.
2. The determination unit selects two or more two-dimensional cross-sectional images from the plurality of two-dimensional cross-sectional images based on the search algorithm, performs image segmentation on the selected two-dimensional cross-sectional images to identify a first two-dimensional cross-sectional image in which one end of the organ is located and a second two-dimensional cross-sectional image in which the other end of the organ is located. The image splitting device according to claim 1.
3. The determination unit selects the two or more cross-sectional two-dimensional images using an equally spaced selection method, a random selection method, or a selection method based on the distribution of the organs. The image splitting device according to claim 2.
4. The system further includes an optimization unit that more precisely detects and optimizes local features, which are any of a plurality of ends defining the positional range of the organ, based on the results of segmenting the organ region. The image splitting device according to claim 1.
5. The optimization unit selects the region of the local feature as the local feature region based on the division result of the organ region, and calculates and optimizes the central position of the local feature of the organ by performing three-dimensional surface detection or two-dimensional edge detection based on the selected local feature region. The image splitting device according to claim 4.
6. The local characteristic of the organ is one of the six ends that define the positional range of the organ. The image splitting device according to claim 4.
7. The aforementioned layer direction is either the head-to-foot direction, the front-to-back direction, or the left-to-right direction. The image splitting device according to claim 1.
8. The results of segmenting the organ region represent data that shows the outline, size, and location of the organ. The image splitting device according to claim 1.
9. The search algorithm is one of the following: linear search, binary search, tree-based search, or hash search. The image splitting device according to claim 3.
10. The aforementioned image segmentation process applies an image segmentation algorithm or deep learning. The image splitting device according to claim 1.
11. The system further includes a detection unit that detects the body surface area in a cross-sectional two-dimensional image based on a plurality of cross-sectional two-dimensional images acquired based on the positioning image, The optimization unit optimizes the local characteristics of the organ based on the division results of the body surface area and the organ region. The image splitting device according to claim 4.
12. The aforementioned organ is one of the following: liver, kidney, pancreas, spleen, or heart. The image splitting device according to any one of claims 1 to 11.
13. A method for image segmentation for magnetic resonance imaging, The steps include obtaining a three-dimensional image including organs or a positioning image including multiple layers of two-dimensional images including the organs, Based on the positioning image, a plurality of two-dimensional cross-sectional images are obtained corresponding to each of a plurality of slices arranged in the layer direction within the spatial region of the positioning image; a first two-dimensional cross-sectional image in which one end of the organ in the layer direction exists and a second two-dimensional cross-sectional image in which the other end of the organ in the layer direction exists are identified from among the plurality of two-dimensional cross-sectional images; and the segment defined by the position corresponding to the first two-dimensional cross-sectional image and the position corresponding to the second two-dimensional cross-sectional image is determined as the segment in which the organ exists in the layer direction. An image segmentation method comprising the steps of: performing image segmentation processing on the region of the positioning image in which the organ is located, and obtaining the segmentation result of the region of the organ.
14. The image splitting device comprises the image splitting device according to any one of claims 1 to 11. Magnetic resonance imaging device.
15. The system further includes a positioning unit for positioning the organ based on the division results of the organ's region. The magnetic resonance imaging apparatus according to claim 14.
16. The system further includes a planning unit that plans the position, orientation, and size of the scan ROI (Region of Interest) and FOV (Field of View) based on the results of segmenting the organ region. The magnetic resonance imaging apparatus according to claim 14.
17. The system further includes a rendering unit that performs three-dimensional morphological rendering of the organ based on the results of segmenting the organ region. The magnetic resonance imaging apparatus according to claim 14.

Description

The embodiments disclosed herein and in the drawings relate to an image splitting apparatus, an image splitting method, and a magnetic resonance imaging apparatus. Magnetic resonance imaging (MRI) is a type of diagnostic imaging technique widely used in current clinical practice. MRI uses high-frequency signals at the Larmor frequency to induce magnetic excitation, causing the atomic nuclei of a subject located in a static magnetic field to rotate. Images are then reconstructed based on the nuclear magnetic resonance (NMR) signals generated by this magnetic excitation. Clinically, for example, in abdominal (hepatobiliary and pancreatic) magnetic resonance imaging, it is necessary to provide image information of digestive organs such as the liver, gallbladder, and pancreas. Therefore, to scan the complex array of organs in the abdomen, a series of scans are required, sometimes involving contrast-enhanced scans with the injection of contrast agents. Planning this series of scans is often done manually by technicians based on their individual skills and experience, making it time-consuming and difficult to guarantee the consistency and accuracy of scan locations, sometimes requiring rescans. Therefore, automating scan planning in abdominal magnetic resonance imaging has high clinical value. Conventional automated scan planning techniques in magnetic resonance imaging (MRI) include methods that utilize structural symmetry to automatically plan the head, spine, knee joints, and shoulder joints. However, this method, which relies on structural symmetry, cannot be applied to asymmetrical abdominal organs, making automated planning of the abdomen impossible. Furthermore, for example, conventional methods for positioning the liver region use the position of the lower rib edge instead of the lower edge of the liver; however, this method cannot accurately represent the position of the lower edge of the liver. Furthermore, in multi-organ abdominal scans, it is necessary to plan the scan by referring to information on the location and size of multiple organs. For example, in the liver region, information such as the location of the diaphragm at the upper edge of the liver, and the locations of the pancreas and common bile duct are required. Moreover, accuracy, standardization, and speed are required in abdominal scan planning to achieve efficient and even automated magnetic resonance imaging (MRI) scans. Since the patient is placed inside the MRI machine during the scan, the scan time must be kept as short as possible. Therefore, scan planning has high time requirements, demanding detection and automated planning within seconds, or even real-time. Furthermore, the scan planning requires speed in segmenting and positioning the location and size of each organ. However, conventional techniques applied to the division and positioning of the location and size of individual abdominal organs require a long time to image an entire organ (e.g., the entire liver). Therefore, current algorithms for dividing the location and size of individual organs cannot meet the aforementioned requirements. U.S. Patent No. 8,693,760Specification of Chinese Patent No. 105678746Japanese Patent Publication No. 2020-109614Japanese Patent Publication No. 2012-115434Japanese Patent Publication No. 2007-111123 Figure 1 is a schematic diagram showing an example of the configuration of an image splitting apparatus for magnetic resonance imaging according to the first embodiment.Figure 2 is a flowchart showing an example of the operation of an image splitting apparatus for magnetic resonance imaging according to the first embodiment.Figure 3 is a schematic diagram showing an example in which the temporary positioning unit of the image division device according to the first embodiment selects a two-dimensional cross-sectional image.Figure 4 is a schematic diagram showing an example of image splitting processing performed by the splitting unit of the image splitting device according to the first embodiment on a two-dimensional cross-sectional image within a segment.Figure 5 is a schematic diagram showing another example in which the temporary positioning unit of the image division device according to the first embodiment selects a two-dimensional cross-sectional image.Figure 6 is a schematic diagram showing an example of the configuration of an image splitting device for magnetic resonance imaging according to the second embodiment.Figure 7 is a flowchart showing an example of the operation of an image splitting apparatus for magnetic resonance imaging according to the second embodiment.Figure 8 is a schematic diagram showing an example of a local feature that the optimization unit of the image division device according to the second embodiment performs optimization processing on.Figure 9 is a schematic diagram showing another example of a local feature that the optimization unit of the image division device according to the second embodiment performs opti