X-ray CT apparatus, method, and storage medium

Abstract

Embodiments provide an X-ray CT apparatus, method, and storage medium capable of improving the accuracy of AEC. An X-ray CT device according to an embodiment includes a data acquisition unit, a determination unit, an acquisition unit, a generation unit, and an execution unit. The data acquisition unit acquires spiral scan data from a positioning scan performed on a first imaging subject. The determination unit determines a target noise standard deviation of a main scan performed on the first imaging object after the positioning scan. The acquisition unit acquires an attenuation-noise-dose relationship indicating a correlation among attenuation of X-rays irradiated from an X-ray source and transmitted through a second imaging subject, noise present in a reconstructed image of the second imaging subject, and a tube current value applied to the X-ray source. The generation unit generates a tube current modulation curve on the basis of the spiral scan data, the target noise standard deviation, and the attenuation-noise-dose relationship. The execution unit executes the main scan on the first imaging subject on the basis of the tube current modulation curve.

Description

[0001] Reference for relevant applications:

[0002] This application enjoys the benefit of priority to U.S. Patent Application No. 18 / 829932, filed September 10, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed in this specification and accompanying drawings relate to X-ray CT (Computed Tomography) apparatus, methods, and storage media. Background Technology

[0004] Computed tomography (CT) scans use ionizing radiation to generate images of the subject, and therefore may increase the risk of developing cancer later in life. It has been reported that CT scans result in the highest total medical radiation exposure in the United States compared to other medical imaging modalities.

[0005] In diagnostic CT imaging, depending on the protocol, different anatomical sites of the body may sometimes require exposure to different X-rays. Ideally, the subject should be scanned with the minimum possible dose while maintaining a clinically acceptable level of image quality. However, reduced dose often results in a low signal-to-noise ratio (SNR), which may affect the ability to detect specific structures or conditions.

[0006] One effective method in CT imaging for addressing this problem is the modulation of the X-ray tube current, known as Automatic Exposure Control (AEC). The purpose of AEC is to reliably reduce the radiation dose to the body while maintaining image quality by automatically optimizing the exposure of CT scans, thereby simplifying the workflow for radiologists. This strategy is widely used in routine clinical scans, across various protocols, and in anatomical fields.

[0007] Currently, almost all CT vendors offer AEC (Area-of-Care) functionality in clinical scans. With recent advancements, organ-based tube current modulation (AEC) has been introduced to mitigate radiation exposure to highly sensitive organs. However, current AEC methods typically rely on 2D radiographic images (usually one or two projection maps), thus limiting predictive accuracy. Furthermore, the size, anatomical structure, and positional deviations of the subject within the CT scanner further complicate AEC prediction. Consequently, achieving accurate AEC remains a challenging problem, particularly in scans inherent to the subject.

[0008] It is desirable to develop an AEC prediction method capable of providing more accurate and comprehensive fault image information. SUMMARY

[0009] An object of the present application is to provide an X-ray CT apparatus, method, and storage medium capable of improving the accuracy of AEC.

[0010] The X-ray CT apparatus according to the embodiment includes a data acquisition unit, a decision unit, an acquisition unit, a generation unit, and an execution unit. The data acquisition unit acquires helical scan data from a scout scan performed on a first imaging object. The decision unit decides a target noise standard deviation of a regular scan performed on the first imaging object after the scout scan. The acquisition unit acquires an attenuation-noise-dose relationship representing a correlation between attenuation of X-rays irradiated from an X-ray source and transmitted through a second imaging object, noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source. The generation unit generates a tube current modulation curve based on the helical scan data, the target noise standard deviation, and the attenuation-noise-dose relationship. The execution unit performs the regular scan on the first imaging object based on the tube current modulation curve.

[0011] EFFECTS According to the X-ray CT apparatus of the embodiment, the accuracy of AEC can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 is a block diagram showing an exemplary apparatus for performing automatic exposure control (AEC) in a computed tomography (CT) imaging system of the present embodiment.

[0013] Figure 2 is a flowchart showing an exemplary procedure for performing AEC in the CT imaging system of the present embodiment.

[0014] Figure 3 is a block diagram showing an offline attenuation-noise-dose relationship decision circuit of the present embodiment.

[0015] Figure 4 is a diagram showing an exemplary anatomical segmentation along a length direction in the present embodiment.

[0016] Figure 5A is a diagram showing an exemplary scenario for explaining generation of an image noise map from even projection data and odd projection data in the present embodiment.

[0017] Figure 5B is a diagram showing an exemplary scenario for explaining generation of an image noise map from even projection data and odd projection data in the present embodiment.

[0018] Figure 5C FIG. 1 is a diagram showing an exemplary scenario for explaining generation of an image noise map from even and odd projection data in the present embodiment.

[0019] Figure 5D FIG. 1 is a diagram showing an exemplary scenario for explaining generation of an image noise map from even and odd projection data in the present embodiment.

[0020] Figure 6 FIG. 2 is a diagram showing an exemplary 3D surface for showing a relationship among dose, image noise, and attenuation in the present embodiment.

[0021] Figure 7 FIG. 3 is a flowchart showing an exemplary procedure for deciding an attenuation-noise-dose relationship in the present embodiment.

[0022] Figure 8 FIG. 4 is a block diagram showing an offline attenuation-noise-dose relationship deciding circuit in the present embodiment.

[0023] Figure 9 FIG. 5 is a block diagram showing an online tube current modulation circuit in the present embodiment.

[0024] Figure 10 FIG. 6 is a diagram showing an exemplary tube current profile in the present embodiment.

[0025] Figure 11 FIG. 7 is a flowchart showing an exemplary procedure for performing online tube current modulation in the present embodiment.

[0026] Figure 12 FIG. 8 is a block diagram showing an overview of an exemplary CT imaging system that can incorporate the technology of the present disclosure. DETAILED DESCRIPTION

[0027] The present disclosure relates to an apparatus (X-ray CT apparatus) for performing automatic exposure control in a computed tomography (CT) imaging system including an X-ray source. The present apparatus includes a processing circuit configured to acquire helical scan data by a scout scan performed on a first imaging object, determine a target noise standard deviation (STD) of an imaging scan (a regular scan) performed on the first imaging object after the scout scan, retrieve a pre-stored attenuation-noise-dose relationship associated with an attenuation of X-rays from the X-ray source passing through a second imaging object, a noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source, generate a tube current modulation curve based on the retrieved attenuation-noise-dose relationship using the acquired helical scan data and the determined target noise STD, and perform the imaging scan on the first imaging object using the generated tube current modulation curve.

[0028] The present disclosure also relates to a method for performing X-ray exposure control in a CT imaging system including an X-ray source. The present method includes acquiring helical scan data by a scout scan performed on a first imaging object, determining a target noise STD of an imaging scan (a regular scan) performed on the first imaging object after the scout scan, retrieving a pre-stored attenuation-noise-dose relationship associated with an attenuation of X-rays from the X-ray source passing through a second imaging object, a noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source, generating a tube current modulation curve based on the retrieved attenuation-noise-dose relationship using the acquired helical scan data and the determined target noise STD, and performing the imaging scan on the first imaging object using the generated tube current modulation curve.

[0029] The present disclosure also relates to a non-transitory computer readable medium having stored therein commands which, when executed by one or more processors, cause the one or more processors to perform the above-described method for performing X-ray exposure control in a CT imaging system including an X-ray source.

[0030] The above summary is not intended to define the present disclosure or all embodiments and / or the additional novel features thereof, but merely provides a preliminary explanation of some of the various embodiments and corresponding novel features. Additional details and / or possible points of view regarding the embodiments are further explained in the following embodiments section and corresponding drawings.

[0031] Various embodiments of the present disclosure are explained in detail with reference to the accompanying drawings, in which like numerals mean like elements.

[0032] The following disclosure provides implementations or embodiments for implementing different features of the provided subject matter. To simplify the present disclosure, the following description constitutes specific examples of elements and configurations. Of course, these are merely examples and are not intended to be limiting.

[0033] For example, the process of the description of different steps described in the present specification is prompted for easy understanding. Generally, these steps can be performed in any appropriate order. Also, different features, techniques, configurations, etc. of the present specification are sometimes discussed separately in different places of the present disclosure, but the intention is that the concepts can be performed separately or in combination with each other. Therefore, the present embodiment can be embodied and thought deeply by a plurality of different methods.

[0034] Further, in the present specification, the word "1" or the like has the meaning of "1 or more" unless otherwise specified.

[0035] The present disclosure provides a method and apparatus for improving the accuracy of automatic exploration control (AEC) and thereby improving the image quality of a computed tomography (CT) imaging system. Generally, the data of the subject that can be obtained before a normal scan is limited. The current method relies on a model using the obtained 2D subject information before acquisition, so this limitation poses a problem for accurate AEC prediction. In contrast, the method and apparatus provided by the present disclosure use an AEC prediction framework based on subject information obtained from a 3D positioning scan. By establishing an anatomically directed relationship between attenuation, image noise, and dose level, this AEC prediction framework can provide more accurate and efficient AEC prediction.

[0036] Figure 1 A block diagram showing an exemplary apparatus for performing AEC in the CT imaging system of the present embodiment. The apparatus 100 includes an offline attenuation-noise-dose relationship determination circuit 110, an attenuation-noise-dose relationship storage section 120, and an online tube current modulation circuit 130. In addition, the apparatus 100 is, for example, an X-ray CT apparatus.

[0037] The offline attenuation-noise-dose relationship determination circuit 110 collects spiral scan data from scans of a subject and / or a phantom at various dose levels (i.e., various tube current values applied to the X-ray source of the CT imaging system), and using the collected data, establishes an attenuation-noise-dose relationship for each anatomical region such as the head, head / neck, shoulder, lung, abdomen, pelvis, etc. Once established, these relationships inherent to the anatomy can be stored in the attenuation-noise-dose relationship storage section 120. In addition, the attenuation-noise-dose relationship storage section 120 is an example of a storage section.

[0038] The online tube current modulation circuit 130 acquires helical scan data generated from a 3D localization scan performed on the subject and retrieves the attenuation-noise-dose relationship stored in the attenuation-noise-dose relationship storage unit 120. The online tube current modulation circuit 130 also acquires the target noise standard deviation (STD) of a routine scan (formal scan) performed on the subject after the 3D localization scan. For example, the target noise STD can be received from the operator of the CT imaging system. Using the localization scan data and the target noise STD, the online tube current modulation circuit 130 can generate a tube current curve for each anatomical region based on the attenuation-noise-dose relationship. Then, the generated tube current curve is applied to the routine scan, enabling automatic tube current modulation.

[0039] Figure 2 A flowchart illustrating an exemplary process for implementing AEC in the CT imaging system of this embodiment. For example... Figure 2 As shown, the AEC process includes an offline section 200 for establishing the attenuation-noise-dose relationship and an online section 250 for applying the attenuation-noise-dose relationship to predict tube current modulation.

[0040] The offline process 200 begins by collecting data from a subject / phantom scan performed across a range of dose levels in step S205. In step S210, based on the collected data, attenuation-noise-dose relationships are determined for different anatomical regions. In step S215, the determined attenuation-noise-dose relationships are stored in the attenuation-noise-dose relationship storage unit 120 for use in the online process 250.

[0041] To apply the stored attenuation-noise-dose relationship to automated tube current modulation, an online procedure 250 can be performed on the subject in real time. In step S255, helical projection data is obtained from a 3D localization scan performed on the subject. In step S260, the target noise level (STD) for a routine scan (formal scan) performed on the subject is determined. In step S265, the stored attenuation-noise-dose relationship is retrieved. In step S270, using the target noise level (STD) and helical projection data, tube current curves are generated for various anatomical regions based on the attenuation-noise-dose relationship. The generated tube current curves can be applied to automated tube current modulation during routine scans of the subject.

[0042] Figure 3This is a block diagram illustrating the offline attenuation-noise-dose relationship determination circuit 110 of this embodiment. The offline attenuation-noise-dose relationship determination circuit 110 includes a subject / phantom scan data collection circuit 310, a dose information acquisition circuit 320, an anatomical pointing segmentation circuit 330, a slice-based attenuation map generation circuit 340, a slice-based image noise heatmap generation circuit 350, and a 3D attenuation-noise-dose model fitting circuit 360.

[0043] The subject / phantom scan data collection circuit 310 is an example of a collection unit that collects scan data from scans performed on the second imaging object at multiple different tube current values ​​applied to the X-ray source. Specifically, the subject / phantom scan data collection circuit 310 collects data from scans performed on one or more subjects and / or phantoms at various dose levels. The dose information acquisition circuit 320 is an example of a dose information acquisition unit that acquires dose information from the scan data, representing the tube current value applied to the X-ray source during the scan performed on the second imaging object. Specifically, the dose information acquisition circuit 320 extracts information related to the dose level used in the subject / phantom scan from the data collected by the subject / phantom scan data collection circuit 310, and sends this dose information to the 3D attenuation-noise-dose model fitting circuit 360.

[0044] The anatomically-oriented segmentation circuit 330 is one example of a segmentation unit that performs anatomically-based segmentation on an image reconstructed from scan data. Specifically, the anatomically-oriented segmentation circuit 330 receives scan data from the subject / phantom scan data collection circuit 310, performs anatomically-oriented segmentation based on the received scan data, and sends the segmentation results to the 3D attenuation-noise-dose model fitting circuit 360.

[0045] As mentioned above, different anatomical locations on the body sometimes require different exposure levels. Furthermore, variations in anatomical structure and size lead to different attenuations, potentially affecting the noise STD. To account for this difference across anatomical regions, the AEC framework provided in this disclosure employs an anatomically oriented segmentation method. This method improves the accuracy of AEC predictions by corresponding to the unique characteristics of different anatomical locations.

[0046] In one embodiment of this disclosure, deep learning techniques are used in anatomically-oriented segmentation. For example, a neural network including three-dimensional convolutions can learn segmentation through supervised learning. This neural network can be mounted using typical U-networks and other suitable network architectures. For example, the anatomically-oriented segmentation circuit 330 generates a reconstructed image by reconstructing scan data, inputs this reconstructed image into the neural network, and obtains segmentation labels representing the respective anatomical structures of the second imaging objects from the output of the neural network as the result of anatomically-based segmentation, thereby performing anatomically-based segmentation on the image reconstructed from the scan data.

[0047] Here, the training dataset can include image volumes from helical scans performed on various subjects and phantoms. The training objective of the neural network is to obtain segmented labels corresponding to different anatomical sites. Manual segmentation can be used to distinguish various anatomical regions. The loss function can be selected based on the optimized training results. For example, the anatomical pointing segmentation circuit 330 obtains a set of training images for training the neural network. For each image in this set of training images, a set of segmentation labels representing anatomical structures is obtained through manual segmentation. The neural network is trained based on the set of training images and the set of segmentation labels.

[0048] Figure 4 This represents an exemplary segmentation generated using CT images of a body phantom along its length. Key regions can include, for example, the head / neck, shoulders, lungs, abdomen, and pelvis.

[0049] Return to Figure 3 The slice-based attenuation map generation circuit 340 is one example of an attenuation map generation unit. Based on the scan data, it generates an attenuation map related to the second imaging object for each slice. Specifically, the slice-based attenuation map generation circuit 340 receives scan data from the subject / phantom scan data collection circuit 310, generates an attenuation map for each slice using the received data, and sends the generated attenuation map to the 3D attenuation-noise-dose model fitting circuit 360.

[0050] Various methods for generating attenuation maps per slice based on raw CT projection data can be utilized, including, but not limited to, analytical reconstruction methods. Furthermore, weighting methods such as Parker weights can be used to address potential data redundancy in the raw projection. The attenuation map is generated in the form of 2D reconstructed slices. Each pixel within a 2D reconstructed slice represents the measured linear attenuation coefficient (μ) of the corresponding voxel within the subject / phantom.

[0051] The slice-based image noise thermal map generation circuit 350 is an example of a thermal map generation unit that generates an image noise thermal map for each slice based on scan data. Specifically, the slice-based image noise thermal map generation circuit 350 receives scan data from the subject / phantom scan data collection circuit 310, generates an image noise thermal map for each slice, and sends the image noise thermal map to the 3D attenuation-noise-dose model fitting circuit 360.

[0052] Various methods can be used to obtain image noise, such as deriving image noise from reconstructed images or from noise present in projected data.

[0053] In one implementation, CT projection data is grouped into odd-numbered projections and even-numbered projections. The groups of odd-numbered projections can be used to reconstruct an image. Figure 5A As shown), the even-numbered projections can be used to reconstruct another image corresponding to the same slice (as shown). Figure 5B (As shown). By subtracting one of the two reconstructed images from the other, it is possible to obtain... Figure 5C The difference image shown is then transformed into a noise heatmap inherent to the slice, representing the distribution of noise within the slice. Figure 5D (As shown).

[0054] In the above implementation, a difference image was obtained from two images reconstructed using even-numbered and odd-numbered projections, but other grouping methods for the projection data can also be implemented. For example, by randomly selecting one view from each consecutive pair of views and assigning it to the first group, and assigning the other view of the pair to the second group, it is possible to derive two non-overlapping projection groups. In another example, it is even permissible to allow a certain amount of view overlap between the two groups.

[0055] return Figure 3 The 3D attenuation-noise-dose model fitting circuit 360 is an example of a determination unit that determines the attenuation-noise-dose relationship based on scan data. Specifically, the 3D attenuation-noise-dose model fitting circuit 360 determines the coefficients inherent to the anatomy of the 3D surface model by performing model fitting based on segmentation, dose information, attenuation maps, and image noise heatmaps, where the segmentation is based on anatomy. For example, the 3D attenuation-noise-dose model fitting circuit 360 establishes a 3D attenuation-noise-dose model for each anatomical region segmented by the anatomy-oriented segmentation circuit 330 using received dose information, attenuation maps, and image noise heatmaps. Here, the coefficients of the 3D model are determined by model fitting to represent the correlation or relationship between attenuation, image noise, and dose levels specific to the anatomical region. These coefficients derived from the model fitting can be stored in the attenuation-noise-dose relationship storage unit 120 for use in real-time AEC prediction.

[0056] Figure 6 This represents an illustrative 3D surface used to illustrate the relationship between dose, image noise, and attenuation in this embodiment. Compared to conventional methods that modulate tube current based solely on noise distribution, the prediction accuracy of AEC is improved by incorporating the additional dimension of attenuation.

[0057] Figure 7 This is a flowchart illustrating an exemplary process 700 for determining the attenuation-noise-dose relationship in this embodiment. In step S710, projection data generated from helical scans of a subject and / or phantom for different dose levels is received. In step S720, anatomically oriented image segmentation is performed based on the received helical projection data to obtain anatomical regions. In step S730, an attenuation map is generated for each slice based on the received helical projection data. In step S740, dose information is obtained from the scan data. In step S750, an image noise heatmap is generated for each slice based on the received helical projection data. In step S760, for each anatomical region, the coefficients of a 3D attenuation-noise-dose model are determined by model fitting based on the dose information, the attenuation map, and the image noise heatmap for each anatomical region. These 3D model coefficients can be saved for use between in-line current modulation processes 250.

[0058] exist Figure 3 as well as Figure 7 In the illustrated implementation, coefficients of the 3D surface model are determined for each anatomical region to represent the correlation or relationship between attenuation, image noise, and dose level. Alternatively, a lookup table can be created for each anatomical region to represent such correlations or relationships. For example, the lookup table includes multiple entries that associate the anatomical region with its respective attenuation, noise, and dose level.

[0059] Figure 8 This is a block diagram illustrating the offline attenuation-noise-dose relationship determination circuit 110 of this embodiment. The offline attenuation-noise-dose relationship determination circuit 110 includes a subject / phantom scan data collection circuit 310, a dose information acquisition circuit 320, an anatomical pointing segmentation circuit 330, a slice-based attenuation map generation circuit 340, a slice-based image noise heatmap generation circuit 350, and an attenuation-noise-dose lookup table construction circuit 860. Figure 8 The structure and function of the subject / phantom scanning data collection circuit 310, dose information acquisition circuit 320, anatomical pointing segmentation circuit 330, slice-based attenuation map generation circuit 340, and slice-based image noise heatmap generation circuit 350 are described in relation to... Figure 3 The corresponding constituent elements are the same.

[0060] The attenuation-noise-dose lookup table construction circuit 860 is an example of a lookup table creation unit. It creates an anatomically specific lookup table based on anatomical segmentation, dose information, attenuation maps, and image noise heatmaps. Specifically, the attenuation-noise-dose lookup table construction circuit 860 receives dose information obtained by the dose information acquisition circuit 320, anatomical regions segmented by the anatomical pointing segmentation circuit 330, attenuation maps generated by the slice-based attenuation map generation circuit 340, and image noise heatmaps generated by the slice-based image noise heatmap generation circuit 350. Using the received data, the attenuation-noise-dose lookup table construction circuit 860 constructs an anatomically specific lookup table representing the correlation between attenuation, image noise, and dose. Furthermore, the lookup tables constructed for each anatomical region can be stored in the storage unit 120 for application in real-time AEC prediction.

[0061] Furthermore, the lookup table can be expanded to include more dimensions. For example, it can include anatomical regions, attenuation, voltage and current of the X-ray source, wedge, and helix spacing, but it is not limited to these. It can integrate various factors that affect image noise into the lookup table, resulting in a more comprehensive representation of the imaging environment.

[0062] Figure 9 This is a block diagram showing the online tube current modulation circuit 130 of this embodiment. The online tube current modulation circuit 130 includes a 3D attenuation-noise-dose model coefficient retrieval circuit 910, a spiral projection data receiving circuit 920, a target noise STD determination circuit 930, an anatomical pointing segmentation circuit 940, a slice-based attenuation map generation circuit 950, and a tube current curve determination circuit 960.

[0063] The 3D attenuation-noise-dose model coefficient retrieval circuit 910 is an example of an acquisition unit. It acquires an attenuation-noise-dose relationship, which represents the correlation between the attenuation of X-rays irradiated from an X-ray source and passing through a second imaging object, the noise present in the reconstructed image of the second imaging object, and the tube current value applied to the X-ray source. For example, the 3D attenuation-noise-dose model coefficient retrieval circuit 910 retrieves 3D model coefficients for various anatomical regions from the attenuation-noise-dose relationship storage unit 120 and sends these coefficients to the tube current curve determination circuit 960.

[0064] The spiral projection data receiving circuit 920 is an example of a data acquisition unit, and acquires spiral scan data from a positioning scan performed on a first imaging object. Specifically, the spiral projection data receiving circuit 920 receives spiral projection data generated from a 3D positioning scan of the subject, and sends the received data to an anatomical pointing segmentation circuit 940 and a slice-based attenuation map generation circuit 950.

[0065] The target noise standard deviation (STD) determination circuit 930 is one example of a determination unit that determines the target noise standard deviation of the formal scan performed on the first imaging object after the positioning scan. Specifically, the target noise STD determination circuit 930 determines the target noise STD for the normal scan (formal scan) performed on the subject after the positioning scan and sends the target noise STD to the tube current profile determination circuit 960. For example, the target noise STD can be determined based on the protocol of the normal scan, or it can be determined by the operator of the CT scanner.

[0066] The anatomical pointing segmentation circuit 940 is one example of a segmentation unit that performs anatomical-based segmentation on an image reconstructed from helical scan data. Specifically, the anatomical pointing segmentation circuit 940 uses the received helical scan data to perform segmentation based on anatomical regions and sends the segmentation result to the tube current profile determination circuit 960. Figure 3 and Figure 8 Similarly, the anatomically pointed segmentation circuit 940 can be implemented through a trained neural network.

[0067] The slice-based attenuation map generation circuit 950 is an example of an attenuation map generation unit. It uses helical scan data to generate an attenuation map related to the first imaging object for each slice. Specifically, the slice-based attenuation map generation circuit 950 generates an attenuation map for each slice based on the received helical scan data and sends the generated attenuation map to the tube current profile determination circuit 960. Figure 3 as well as Figure 8 Similarly, the slice-based attenuation map generation circuit 340 and the slice-based attenuation map generation circuit 950 can generate attenuation maps through analytical reconstruction of the original projection data. Furthermore, by applying a weighted method, potential data redundancy within the projection data can be handled.

[0068] The tube current curve determination circuit 960, as an example of the generation unit, generates a tube current modulation curve based on helical scan data, target noise standard deviation, and attenuation-noise-dose relationship. Specifically, the tube current curve determination circuit 960 generates an anatomically specific tube current modulation curve based on a 3D surface model with coefficients inherent to the determined anatomy, using an attenuation map related to the first imaging object and the target noise standard deviation. For example, the tube current curve determination circuit 960 uses model coefficients received from the 3D attenuation-noise-dose model coefficient retrieval circuit 910, and determines the tube current curve for various anatomical regions based on the received attenuation map and target noise STD.

[0069] Figure 10This section shows an exemplary tube current curve in an axial section of the lung region in this embodiment. The tube current value can be modulated based on a specific tube current curve selected for that region. The distance of the tube current curve from the center represents the intensity of the tube current. Considering that tube current curve 101 is shorter from the center than tube current curve 102, selecting tube current curve 101 can reduce the X-ray radiation applied to the lung region. Furthermore, according to tube current curve 101, the tube current reaches its maximum value in the transverse direction and its minimum value in the anterior-posterior (AP) direction.

[0070] Figure 11 This is a flowchart illustrating an exemplary process for performing online tube current modulation according to this embodiment. In step S1110, helical projection data generated from a 3D localization scan of the subject is received. In step S1120, anatomically directed image segmentation is performed on the image reconstructed based on the helical projection data. In step S1130, an attenuation map is generated for each slice based on the helical projection data. In step S1140, a target noise level (STD) is determined for a typical scan performed on the subject. In step S1150, coefficients of the 3D attenuation-noise-dose model determined for various anatomical regions are retrieved. In step S1160, tube current curves are determined for different anatomical regions based on the 3D attenuation-noise-dose model, the target noise level (STD), and the attenuation map.

[0071] Figure 9 as well as Figure 11 The illustrated implementation shows a scenario where a 3D surface model is used to represent the relationship between attenuation, image noise, and dose. Alternatively, in-line tube current modulation can be based on... Figure 8 The illustrated implementation utilizes an anatomically inherent lookup table. Using subject information from 3D localization scans, based on an anatomically oriented 3D surface model or lookup table, the AEC prediction framework of this disclosure achieves more accurate AEC predictions than previous methods using 2D radiographic images.

[0072] Figure 12 This is a schematic block diagram of the X-ray CT apparatus or scanner according to this embodiment. Figure 12 As shown in the side view, the imaging stand 1250 also includes an X-ray tube 1251, an annular frame 1252, and a multi-row or two-dimensional array X-ray detector 1253. The X-ray tube 1251 and X-ray detector 1253 are mounted to radially sandwich a subject OBJ supported on the annular frame 1252, which is rotatable about a rotation axis RA. The rotation unit 1257 rotates the annular frame 1252 at a high speed of 0.4 seconds per revolution as the subject OBJ moves along the axis RA inward or outward from the page shown in the illustration.

[0073] Hereinafter, embodiments of the X-ray CT apparatus of this disclosure will be described with reference to the accompanying drawings. Furthermore, X-ray CT apparatuses include various types such as rotating / rotational type apparatuses that include an X-ray tube and an X-ray detector rotating together around the subject being examined, and fixed / rotational type apparatuses where multiple detector elements are arranged in a ring or plane and only the X-ray tube rotates around the subject being examined. This disclosure can be applied to any type. Here, the rotating / rotational type, which is currently the mainstream, is illustrated.

[0074] The multi-slice X-ray CT apparatus also includes a high-voltage generator 1259, which generates a tube voltage applied to the X-ray tube 1251 via a collector ring 1258 to cause the X-ray tube 1251 to generate X-rays. The X-rays irradiate the subject OBJ, the cross-sectional area of ​​which is represented by a circle. For example, the X-ray tube 1251 has an average X-ray energy in the first scan that is lower than the average X-ray energy in the second scan. This allows for more than two scans corresponding to different X-ray energies. An X-ray detector 1253 is located on the opposite side of the X-ray tube 1251, across the subject OBJ, to detect the irradiating X-rays propagating through it. The X-ray detector 1253 also includes various detection elements or units.

[0075] The X-ray CT apparatus also includes other devices for processing the detection signals from the X-ray detector 1253. The data acquisition circuit or data acquisition system (DAS) 1254 converts the signals output from the X-ray detector 1253 of each channel into voltage signals, amplifies the signals, and then converts the signals into digital signals. The X-ray detector 1253 and DAS 1254 are configured to process a predetermined total number of projections per rotation (TPPR).

[0076] The aforementioned data is transmitted via a non-contact data transmitter 1255 to a pre-processing device 1256 housed in a control console external to the imaging stand 1250. The pre-processing device 1256 performs specific corrections, such as sensitivity correction, related to the raw data. A memory 1262 stores the resulting data, also known as projection data, before the reconstruction processing. The memory 1262, along with the reconstruction device 1264, input device 1265, and display 1266, is connected to the system controller 1260 via a data / control bus 1261. The system controller 1260 controls a current regulator 1263 that limits the current to a level sufficient to drive the CT system. The system controller 1260 is an example of an actuator that performs a formal scan of the first imaging target based on a tube current modulation curve.

[0077] In various generations of CT scanning systems, the detectors rotate and / or remain fixed relative to the subject. In one embodiment, the aforementioned CT system may be an example of a system combining third-generation and fourth-generation geometry. In a third-generation system, the X-ray tube 1251 and X-ray detector 1253 are radially mounted on an annular frame 1252, rotating around the subject OBJ as the annular frame 1252 rotates about the rotation axis RA. In a fourth-generation geometry system, the detectors are fixedly positioned around the subject, and the X-ray tube rotates around the subject. In an alternative embodiment, the imaging stage 1250 has multiple detectors mounted on the annular frame 1252, supported by C-arms and supports.

[0078] The memory 1262 is capable of storing measured values ​​representing the radiation irradiance of the X-rays emitted by the X-ray detector 1253. Furthermore, the memory 1262 is capable of storing dedicated programs for performing CT image reconstruction, material identification, and motion estimation and motion compensation methods, including those described in this specification.

[0079] The reconstruction device 1264 is capable of performing the methods described in this specification. Furthermore, the reconstruction device 1264 is capable of performing pre-reconstruction image processing such as volume rendering processing and image difference processing as needed.

[0080] The pre-reconstruction processing of the projection data performed by the pre-processing device 1256 can include, for example, detector calibration, correction for detector nonlinearity and polarity effects.

[0081] The post-reconstruction processing performed by the reconstruction device 1264 can, as needed, include image filtering and smoothing, volume rendering, and image differencing. The image reconstruction process can be performed using filtered backprojection, successive approximation image reconstruction, or probabilistic image reconstruction. The reconstruction device 1264 can use memory to store, for example, projection data, reconstructed images, calibration data and parameters, and computer programs.

[0082] The reconfigurable device 1264 can include a CPU (processing circuit) that can be executed as discrete logic gates, such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other Complex Programmable Logic Device (CPLD). The installation of the FPGA or CPLD can be coded using VDHL, Verilog, or other hardware description languages. This coding can be directly stored in the electronic memory within the FPGA or CPLD, or it can be stored as a separate electronic memory. Furthermore, the memory 1262 can be non-volatile, such as ROM, EPROM, EEPROM, or flash memory. The memory 1262 can also be a volatile memory such as static RAM or dynamic RAM, or a processor such as a microcontroller or microprocessor can be used to manage the electronic memory and manage the interaction between the FPGA or CPLD and the memory.

[0083] Alternatively, the CPU within the reconfigurable device 1264 can execute a computer program comprising a set of computer-readable instructions that perform the functions described herein, the program being stored in any of the aforementioned non-transient electronic memory and / or hard disk drive, CD, DVD, flash drive, or other known storage media. Furthermore, the computer-readable instructions may also be provided by utility applications, background daemons, or components of an operating system, or combinations thereof, and can be executed in cooperation with processors such as Intel Xenon or AMD Opteron processors, as well as processors of Microsoft OS, UNIX, Solaris, LINUX, Apple OS, macOS, and other operating systems known to those skilled in the art. Furthermore, the CPU can be configured as multiple processors operating in parallel to execute commands.

[0084] In one embodiment, the reconstructed image can be displayed on a display 1266. The display 1266 can be an LCD display, a CRT display, a plasma display, an OLED, an LED, or other displays known in the art.

[0085] The memory 1262 can be configured as a hard disk drive, CD-ROM drive, DVD drive, flash memory drive, RAM, ROM, or other electronic storage device known in the art.

[0086] Based on the foregoing teachings, various modifications and variations of the embodiments described herein can be made. Therefore, it should be understood that within the scope of the claims, this application can be implemented by methods other than those specifically described herein. The invention is not limited to the embodiments described herein. In particular, in variations not illustrated, features of the illustrated embodiments can be combined with each other.

[0087] Alternatively, the embodiments of this disclosure may also be described as follows.

[0088] (1) An apparatus (X-ray CT apparatus) for performing automatic exposure control in a computed tomography (CT) imaging system including an X-ray source, comprising a processing circuit configured to: acquire helical scan data from a positioning scan performed on a first imaging object; determine a target noise standard deviation (STD) for an imaging scan (formal scan) performed on the first imaging object after the positioning scan; retrieve a pre-stored attenuation-noise-dose relationship associated with attenuation of X-rays from an X-ray source passing through a second imaging object, noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source; generate a tube current modulation curve based on the retrieved attenuation-noise-dose relationship using the acquired helical scan data and the determined target noise STD; and perform an imaging scan on the first imaging object using the generated tube current modulation curve.

[0089] (2) In the apparatus described in (1), the processing circuit is further configured to: collect scan data from a scan performed on a second imaging object under multiple different tube current values ​​applied to an X-ray source, determine a specific attenuation-noise-dose relationship based on the collected scan data, and store the determined attenuation-noise-dose relationship as a pre-stored attenuation-noise-dose relationship.

[0090] (3) In the apparatus described in (2), the determined attenuation-noise-dose relationship is a 3D surface model representing the correlation between attenuation, noise, and tube current values. The processing circuit is further configured to: obtain dose information representing the tube current value applied to the X-ray source during a scan of the second imaging object from the collected scan data; perform anatomical-based segmentation on the image reconstructed based on the collected scan data; generate an attenuation map related to the second imaging object for each slice based on the collected scan data; generate an image noise thermogram for each slice based on the collected scan data; and, based on the anatomical-based segmentation, use the obtained dose information and the second imaging object... The generated attenuation map and generated image noise heatmap related to the object are used to determine the anatomically inherent coefficients of the 3D surface model by performing model fitting. The 3D surface model with the determined anatomically inherent coefficients is retrieved. Anatomically based segmentation is performed on the image reconstructed based on the acquired helical scan data. Attenuation map related to the first imaging object is generated for each slice using the acquired helical scan data. Based on the retrieved 3D surface model with the determined anatomically inherent coefficients, the generated attenuation map related to the first imaging object and the determined target noise STD are used to generate the anatomically inherent tube current curve.

[0091] (4) In the apparatus described in (3), the processing circuit is further configured to: perform reconstruction by using the collected scan data to generate a reconstructed image; input the reconstructed image into a neural network; and obtain segmentation labels representing the anatomical structures of the respective second imaging objects from the output of the neural network as the result of anatomical-based segmentation, thereby performing anatomical-based segmentation on the image reconstructed based on the collected scan data.

[0092] (5) In the apparatus described in (4), the processing circuit is further configured to: obtain a set of training images for training a neural network; obtain a group of segmentation labels for each specific image in the set of training images by manual segmentation of the specific images, the segmentation labels representing the respective anatomical structures, and train the neural network based on the set of training images and the group of segmentation labels.

[0093] (6) In the apparatus described in (3), the processing circuit is further configured to generate an attenuation map relating to the second imaging object by performing analytical reconstruction based on the collected scan data to obtain a 2D reconstruction slice as the generated attenuation map, wherein the pixels in the obtained 2D reconstruction slice represent the linear attenuation coefficients of voxels in the second imaging object.

[0094] (7) In the apparatus described in (3), the processing circuit is further configured to: divide scan data collected from a specific scan into a first set of projection data and a second set of projection data for each specific scan performed on the second imaging object; reconstruct a first image based on the first set of projection data; reconstruct a second image based on the second set of projection data; perform a subtraction for generating a differential image based on the first image and the second image; and generate a noise heatmap based on the generated differential image, wherein the generated noise heatmap represents the distribution of noise within a slice reconstructed for the specific scan, thereby generating an image noise heatmap based on the collected scan data.

[0095] (8) In the apparatus described in (7), the processing circuit is further configured to divide the scan data collected from a specific scan into a first group including odd-numbered projection data and a second group including even-numbered projection data.

[0096] (9) In the apparatus described in (2), the unfolded attenuation-noise-dose relationship is a lookup table representing the correlation between attenuation, noise, and tube current values. The processing circuit is further configured to: obtain dose information representing the tube current value applied to the X-ray source during the scan performed on the second imaging object from the collected scan data; perform anatomical-based segmentation on the image reconstructed based on the collected scan data; generate an attenuation map related to the second imaging object for each slice based on the collected scan data; generate an image noise heatmap for each slice based on the collected scan data; and perform anatomical-based segmentation... Using the acquired dose information, the generated attenuation map related to the second imaging object, and the generated image noise heatmap, an anatomically-inherent lookup table is created. The created anatomically-inherent lookup table is retrieved, and anatomically-based segmentation is performed on the image reconstructed from the acquired helical scan data. Using the acquired helical scan data, an attenuation map related to the first imaging object is generated for each slice. Based on the retrieved anatomically-inherent lookup table, using the generated attenuation map related to the first imaging object and the determined target noise STD, an anatomically-inherent tube current modulation curve is generated.

[0097] (10) A method for performing X-ray exposure control in a computed tomography (CT) imaging system including an X-ray source, comprising the steps of: acquiring helical scan data from a positioning scan performed on a first imaging object; determining a target noise standard deviation (STD) for an imaging scan performed on the first imaging object after the positioning scan; retrieving a pre-stored attenuation-noise-dose relationship associated with attenuation of X-rays from an X-ray source passing through a second imaging object, noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source; generating a tube current modulation curve based on the retrieved attenuation-noise-dose relationship using the acquired helical scan data and the determined target noise STD; and performing an imaging scan on the first imaging object using the generated tube current modulation curve.

[0098] (11) The method described in (10) further includes the steps of: collecting scan data from scans performed on a second imaging object at multiple different tube current values ​​applied to an X-ray source; unfolding a specific attenuation-noise-dose relationship based on the collected scan data; and storing the unfolded attenuation-noise-dose relationship as a pre-stored attenuation-noise-dose relationship.

[0099] (12) In the method described in (11), the unfolded attenuation-noise-dose relationship is a 3D surface model representing the correlation between attenuation, noise, and tube current values. The unfolding step further includes the following steps: obtaining dose information from the collected scan data representing the tube current value applied to the X-ray source during the scan performed on the second imaging object; performing anatomical-based segmentation on the image reconstructed based on the collected scan data; generating an attenuation map related to the second imaging object per slice based on the collected scan data; generating an image noise heatmap per slice based on the collected scan data; and, based on the anatomical-based segmentation, using the obtained dose information, the generated attenuation map related to the second imaging object, and the generated attenuation map... The steps of image noise heatmap, performing model fitting to determine the anatomically inherent coefficients of the 3D surface model, and retrieving pre-stored attenuation-noise-dose relationships also include retrieving the 3D surface model with the determined anatomically inherent coefficients. The steps of generating tube current modulation predictions also include: performing anatomically based segmentation on the image reconstructed from the acquired helical scan data; and using the acquired helical scan data, generating an attenuation map associated with the first imaging object for each slice; and generating an anatomically inherent tube current curve based on the retrieved 3D surface model with the determined anatomically inherent coefficients, using the generated attenuation map associated with the first imaging object and the determined target noise STD.

[0100] (13) In the method described in (12), the step of performing anatomical-based segmentation on the image reconstructed based on the collected scan data further includes the following steps: performing reconstruction using the collected scan data to generate a reconstructed image; inputting the reconstructed image into a neural network; and obtaining segmentation labels representing the anatomical structures of the respective second imaging objects from the output of the neural network as the result of anatomical-based segmentation.

[0101] (14) The method described in (13) further includes the following steps: obtaining a set of training images for training a neural network; obtaining a group of segmentation labels for each specific image in the set of training images by manually segmenting the specific images, the segmentation labels representing the respective anatomical structures; and training a neural network based on the set of training images and the group of segmentation labels.

[0102] (15) In the method described in (12), the step of generating an attenuation map related to the second imaging object further includes the following steps: performing analytical reconstruction based on the collected scan data to obtain a 2D reconstruction slice as the generated attenuation map, wherein the pixels in the obtained 2D reconstruction slice represent the linear attenuation coefficients of voxels in the second imaging object.

[0103] (16) In the method described in (12), the step of generating an image noise heatmap based on the collected scan data further includes the following steps: for each specific scan performed on the second imaging object, segmenting the scan data collected from the specific scan into a first set of projection data and a second set of projection data; reconstructing a first image based on the first set of projection data; reconstructing a second image based on the second set of projection data; performing a subtraction to generate a difference image based on the first image and the second image; and generating a noise heatmap based on the generated difference image, the generated noise heatmap representing the distribution of noise within a slice reconstructed for the specific scan.

[0104] (17) In the method described in (16), the segmentation step further includes the following step: segmenting the scan data collected from a particular scan into a first group including odd-numbered projection data and a second group including even-numbered projection data.

[0105] (18) In the method described in (11), the expanded attenuation-noise-dose relationship is a lookup table representing the correlation between attenuation, noise, and tube current values. The expansion step includes the following steps: obtaining dose information from the collected scan data representing the tube current value applied to the X-ray source during the scan performed on the second imaging object; performing anatomical-based segmentation on the image reconstructed based on the collected scan data; generating an attenuation map related to the second imaging object for each slice based on the collected scan data; generating an image noise thermogram for each slice based on the collected scan data; and using the obtained dose information and the second imaging object based on the anatomical-based segmentation. The steps of creating an anatomically-inherent lookup table using the generated attenuation map and the generated image noise heatmap, and retrieving the pre-stored attenuation-noise-dose relationship, also include retrieving the created anatomically-inherent lookup table. The steps of generating the tube current modulation curve also include: performing anatomically-based segmentation on the image reconstructed from the acquired helical scan data; generating an attenuation map related to the first imaging object for each slice using the acquired helical scan data; and generating an anatomically-inherent tube current curve based on the retrieved anatomically-inherent lookup table, using the generated attenuation map related to the first imaging object and the determined target noise STD.

[0106] (19) A non-transitory computer-readable medium internally storing commands to cause one or more processors to perform a method for performing X-ray exposure control in a computed tomography (CT) imaging system including an X-ray source, the method comprising the steps of: acquiring helical scan data from a positioning scan performed on a first imaging object; determining a target noise standard deviation (STD) for an imaging scan performed on the first imaging object after the positioning scan; retrieving a pre-stored attenuation-noise-dose relationship associated with attenuation of X-rays from the X-ray source passing through a second imaging object, noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source; generating a tube current modulation curve based on the retrieved attenuation-noise-dose relationship using the acquired helical scan data and the determined target noise STD; and performing an imaging scan on the first imaging object using the generated tube current modulation curve.

[0107] (20) In the non-transitory computer-readable medium of (19), the method further includes the steps of: collecting scan data from scans performed on a second imaging object at multiple different tube current values ​​applied to an X-ray source; expanding a specific attenuation-noise-dose relationship based on the collected scan data; and storing the expanded attenuation-noise-dose relationship as a pre-stored attenuation-noise-dose relationship.

[0108] According to at least one of the embodiments described above, the accuracy of AEC can be improved.

[0109] Several embodiments have been described, but these embodiments are given by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other ways, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the scope of the invention as described in the claims and its equivalents.

Claims

1. An X-ray CT apparatus, wherein, Possessing: a data acquisition unit that acquires helical scan data from a positioning scan performed on a first imaging object; a decision unit that decides a target noise standard deviation of a regular scan performed on the first imaging object after the positioning scan; an acquisition unit that acquires an attenuation-noise-dose relationship representing a correlation between attenuation of X-rays irradiated from an X-ray source and transmitted through a second imaging object, noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source; a generation unit that generates a tube current modulation curve based on the helical scan data, the target noise standard deviation, and the attenuation-noise-dose relationship; and an execution unit that performs the regular scan on the first imaging object based on the tube current modulation curve. Further possessing:

2. The X-ray CT apparatus according to claim 1, wherein a collection unit that collects scan data from scans performed on the second imaging object under a plurality of different tube current values applied to the X-ray source; a determination unit that determines the attenuation-noise-dose relationship based on the scan data; and a storage unit that stores the determined attenuation-noise-dose relationship.

3. The X-ray CT apparatus according to claim 2, wherein the determined attenuation-noise-dose relationship is a 3D surface model representing a correlation between the attenuation, the noise, and the tube current value, the X-ray CT apparatus further possesses: a dose information acquisition unit that acquires dose information representing the tube current value applied to the X-ray source in the scans performed on the second imaging object from the scan data; a segmentation unit that performs anatomy-based segmentation on an image reconstructed from the scan data; an attenuation map generation unit that generates, per slice, an attenuation map related to the second imaging object based on the scan data; and a heat map generation unit that generates, per slice, an image noise heat map based on the scan data, the determination unit decides a coefficient inherent to anatomy of the 3D surface model by performing model fitting based on segmentation, the dose information, the attenuation map, and the image noise heat map, the segmentation being based on the anatomy, the segmentation unit performs the anatomy-based segmentation on an image reconstructed from the helical scan data, the attenuation map generation unit generates, per slice, an attenuation map related to the first imaging object using the helical scan data, the generation unit generates an anatomy-inherent tube current modulation curve based on the 3D surface model having the decided coefficient inherent to the anatomy, using the attenuation map related to the first imaging object and the target noise standard deviation. the segmentation unit generates a reconstructed image by using reconstruction using the scan data, inputs the reconstructed image to a neural network, acquires, as a result of the anatomy-based segmentation, segmentation labels representing respective anatomical configurations of the second imaging object from an output of the neural network, and thereby performs the anatomy-based segmentation on the image reconstructed from the scan data. ​ 4. The X-ray CT apparatus according to claim 3, wherein ​ 5. The X-ray CT apparatus according to claim 4, wherein The dividing section acquires a set of training images for training the neural network, acquires, for each image of the set of training images, a group of segmentation labels respectively representing anatomical structures by manual segmentation, and trains the neural network based on the set of training images and the group of segmentation labels.

6. The X-ray CT apparatus according to claim 3, wherein The attenuation map generating section generates the attenuation map related to the second imaging object by performing analytical reconstruction for acquiring 2D reconstructed slices, each pixel of which represents a linear attenuation coefficient of a voxel within the second imaging object, based on the scan data.

7. The X-ray CT apparatus according to claim 3, wherein The heat map generating section performs the following processing for each scan performed on the second imaging object: divides scan data collected from the scan into a first group of projection data and a second group of projection data, reconstructs a first image based on the first group of projection data, reconstructs a second image based on the second group of projection data, generates a difference image of the first image and the second image, based on the difference image, generates a noise heat map representing a distribution of noise within a slice reconstructed from the scan, thereby generating the image noise heat map based on the scan data.

8. The X-ray CT apparatus according to claim 7, wherein The heat map generating section divides the scan data collected by the scan into a first group including odd-numbered projection data and a second group including even-numbered projection data.

9. The X-ray CT apparatus according to claim 2, wherein The determined attenuation-noise-dose relationship is a look-up table representing a correlation between the attenuation, the noise, and the tube current value, The X-ray CT apparatus further comprises: a dose information acquiring section that acquires dose information from the scan data, the dose information representing the tube current value applied to the X-ray source in the scan performed on the second imaging object; a dividing section that performs anatomy-based division on an image reconstructed from the scan data; an attenuation map generating section that generates, on a per-slice basis, an attenuation map related to the second imaging object based on the scan data; a heat map generating section that generates, on a per-slice basis, an image noise heat map based on the scan data; and a look-up table making section that makes an anatomy-specific look-up table based on division, the dose information, the attenuation map, and the image noise heat map, the division being based on the anatomy, the dividing section performs anatomy-based division on an image reconstructed from the helical scan data, the attenuation map generating section generates, on a per-slice basis, an attenuation map related to the first imaging object using the helical scan data, the generating section generates, based on the look-up table, an anatomy-specific tube current modulation curve from the attenuation map related to the first imaging object and the target noise standard deviation. comprising the steps of:

10. A method for performing X-ray exposure control in an X-ray CT apparatus comprising an X-ray source, wherein, acquiring helical scan data from a positioning scan performed on a first imaging object; deciding a target noise standard deviation of a formal scan performed on the first imaging object after the positioning scan; ​ acquiring an attenuation-noise-dose relationship representing a correlation between an attenuation of X-rays irradiated from the X-ray source and transmitted through a second imaging object, a noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source; generating a tube current modulation curve based on the helical scan data, the target noise standard deviation, and the attenuation-noise-dose relationship; and performing the formal scan on the first imaging object based on the tube current modulation curve.

11. A storage medium, non-transitorily storing a program, wherein, The program causes a computer that controls an X-ray CT apparatus including an X-ray source and performs X-ray exposure control to perform each of the following processes: acquiring helical scan data from a positioning scan performed on a first imaging object; determining a target noise standard deviation of a formal scan performed on the first imaging object after the positioning scan; acquiring an attenuation-noise-dose relationship representing a correlation between an attenuation of X-rays irradiated from the X-ray source and transmitted through a second imaging object, a noise present in a reconstructed image of the second imaging object, and a tube current value applied to the X-ray source; generating a tube current modulation curve based on the helical scan data, the target noise standard deviation, and the attenuation-noise-dose relationship; and performing the formal scan on the first imaging object based on the tube current modulation curve.

Images