Image reconstruction

Abstract

A computer-implemented method (100) for generating a three-dimensional (3D) reconstructed image of a patient, the method comprising: acquiring (102) a first plurality of two-dimensional (2D) cone beam computed tomography (CBCT) projections of the patient, wherein the first plurality of 2D CBCT projections correspond to a first plurality of angular positions of a CBCT imaging source about a first center of rotation, and the patient is in a first relative position with respect to the first center of rotation; acquiring (104) a second plurality of 2D CBCT projections of the patient, wherein the second plurality of 2D CBCT projections correspond to a second plurality of angular positions of the CBCT imaging source about a second center of rotation, and the patient is in a second relative position with respect to the second center of rotation, wherein the second relative position is different from the first relative position; and performing (106) an iterative image reconstruction procedure using the first and second pluralities of 2D CBCT projections to generate a 3D reconstructed image of a volume of the patient, wherein a sub-region of the volume of the patient is represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

Description

Technical Field

[0001] Embodiments of this disclosure relate to methods, apparatus, and systems for generating three-dimensional (3D) reconstructed images of a patient using cone-beam computed tomography (CBCT) projection. More specifically, embodiments of this disclosure relate to a computer-implemented method for generating 3D reconstructed images of a patient, a method for generating 3D reconstructed images of a patient executed by an imaging system, a node, an imaging system, and a computer program product configured to perform the method for generating 3D reconstructed images of a patient. Background Technology

[0002] Radiation therapy (RT), also known as radiotherapy, uses ionizing radiation to damage or destroy unhealthy cells in the human or animal body. During treatment, ionizing radiation is directed into unhealthy cells within the body, such as tumors.

[0003] Imaging is a crucial aspect of radiotherapy (RT). Patient images are used for treatment planning, patient localization (before and during treatment), and monitoring treatment effectiveness (during and after treatment). Cone-beam computed tomography (CBCT) is a widely used imaging modality in radiotherapy applications. CBCT image acquisition involves performing a CBCT scan, in which a two-dimensional (2D) projection of the patient is obtained from a series of different projection angles (e.g., along an arc). CBCT image reconstruction involves processing the raw data from the CBCT scan into 3D reconstructed CBCT images. Example reconstruction techniques include analytical methods based on filtered back projection (FBP) (e.g., the Feldkamp-Davis-Kress (FDK) algorithm), as well as iterative methods such as Simultaneous Algebraic Reconstruction Technique (SART) and statistical iterative reconstruction.

[0004] Radiotherapy (RT) is performed according to a radiotherapy plan to apply a radiation dose distribution to a patient to achieve a range of radiotherapy goals. These goals may include, for example, a minimum radiation dose to the tumor, an upper limit on the radiation dose to healthy organs at risk of radiation damage, and an upper limit on the total radiation dose to the patient. The actual dose distribution depends on treatment parameters such as beam direction, dose per energy, number of beams, beam weighting, and exposure time. The area identified as requiring treatment (e.g., the area containing the tumor) is called the clinical target volume (CTV). The area to be treated is called the planned target volume (PTV), and is typically the CTV plus some margins added to ensure effective treatment of the CTV. Areas where the treatment dose is not ideal are called organs at risk (OARs).

[0005] Radiation therapy planning is the process of determining a radiation therapy plan to deliver an appropriate dose distribution that will meet the goals of radiation therapy. During radiation therapy planning, the patient's anatomical regions are modeled using three-dimensional images of the patient (e.g., computed tomography (CT) images or CBCT images). Image segmentation is performed to define different regions within the images (called partitions), and different goals and constraints can be defined for different partitions. The result of radiation therapy planning is the configuration and / or order in which the radiation therapy system delivers the radiation dose distribution to the patient, as instructed by the radiation therapy plan. For example, a radiation therapy plan may include parameters specifying, for example, the direction, cross-sectional shape, energy, and intensity of each radiation beam to be delivered into the patient's body. A radiation therapy plan may include a series of dose fractions of radiation therapy delivered over a predetermined time period, with each treatment delivering a specific fraction of the prescribed total dose. For example, the total radiation dose may be divided into approximately 3 to 40 fractions per day. Radiation therapy planning can be performed using a treatment planning system such as Monaco®. Radiation therapy planning may also be referred to as radiation therapy planning or treatment planning.

[0006] Image-guided radiography (IGRT) is a technique that uses intra-abdominal imaging to capture the patient's anatomy during fractionated dose administration and to accurately align the planned dose distribution with the patient's anatomy, thereby improving treatment accuracy. Cone-beam computed tomography (CBCT) is the most widely used imaging modality in IGRT.

[0007] Adaptive radiotherapy (ART) is a technique that uses imaging (e.g., CBCT imaging) to evaluate and adjust radiotherapy plans. In inter-fractional ART, images acquired during a RT (radiotherapy) phase are analyzed offline and used to adjust the radiotherapy plan for future phases (i.e., changes are made between fractions). In intra-fractional ART, images acquired during a RT phase are analyzed online (i.e., analyzed in real time during that phase) and used to adjust the radiotherapy plan for that phase (i.e., changes are made during fractions). ART can thus improve the accuracy of RT treatment, for example, by ensuring that the radiotherapy plan takes into account any changes in the patient's anatomy.

[0008] In radiotherapy applications, CBCT images are commonly used to support patient localization, treatment planning, and adaptive therapy. However, the field of view (FOV) is often insufficient to capture all anatomical structures of interest.

[0009] Therefore, there is a need to provide improved techniques for generating CBCT images.

[0010] This disclosure attempts to address these and other shortcomings encountered in the prior art. Summary of the Invention

[0011] The invention is defined in the independent claims. Optional features are listed in the dependent claims.

[0012] Embodiments of this disclosure can be implemented in digital electronic circuits, or in computer hardware, firmware, software, or in combination. Embodiments of this disclosure can be implemented as a computer program or computer program product, i.e., a computer program tangibly embodied in a non-transitory information carrier (e.g., embodied in a machine-readable storage device or a transmitted signal) for execution by or control of one or more hardware modules. The computer program can be a standalone program, a portion of a computer program, or more than one computer program, and can be written in any programming language (including compiled or interpreted languages), and can be deployed in any form (including as a standalone program or as a module, component, subroutine, or other unit suitable for a data processing environment).

[0013] This disclosure is illustrated by way of specific embodiments. Other embodiments not expressly described herein may still fall within the scope of the claims. Unless otherwise expressly or implicitly stated, the steps of the method according to the embodiments of this disclosure may be performed in different orders and still achieve the desired result. Attached Figure Description

[0014] The specific implementation scheme will now be described by way of example only, with reference to the following figures:

[0015] Figure 1 This is a flowchart of a computer-implemented method for generating 3D reconstructed images of a patient according to the present disclosure;

[0016] Figure 2 This is a flowchart of a method for generating a 3D reconstructed image of a patient, performed by an imaging system according to the present disclosure;

[0017] Figure 3 It is a schematic diagram of the technology according to this disclosure;

[0018] Figure 4 It is a schematic diagram of the technology according to this disclosure;

[0019] Figure 5A It is an image showing the reconstructed volume obtained according to the technique of this disclosure;

[0020] Figure 5B It shows an image of the reconstructed volume for comparison;

[0021] Figure 6A It is a schematic diagram of the technology according to this disclosure;

[0022] Figure 6B It is a schematic diagram of the technology according to this disclosure;

[0023] Figure 7 It is a schematic diagram of the technology according to this disclosure;

[0024] Figure 8 It is a geometric layout diagram for cone-beam projection acquisition and reconstruction;

[0025] Figure 9 This is an illustration of a cone-beam CT scanner;

[0026] Figure 10 This is a block diagram illustrating image nodes according to this disclosure;

[0027] Figure 11 This is a schematic diagram of a radiotherapy device; and

[0028] Figure 12 This is a block diagram illustrating a radiotherapy system suitable for generating 3D reconstructed images of a patient according to the present disclosure. Detailed Implementation

[0029] CBCT imaging is increasingly being used to support IGRT, adaptive radiotherapy, and larger treatment areas. Therefore, a larger CBCT FOV is required.

[0030] To address this issue, a known method is to stitch together two reconstructed volumes. In other words, two volumes are reconstructed based on two CBCT scans, and then these volumes are merged. However, this method requires a large overlap area between the two CBCT scans (i.e., the area where the patient was scanned twice) to facilitate merging, which leads to an increased radiation dose delivered to the patient in that area.

[0031] Some imaging systems are configured to use slip ring technology to perform multiple rotations of the imaging arm. Therefore, using these systems, helical scans can be performed, where the imaging source rotates continuously while the patient bed moves continuously along the axis of rotation of the imaging source. This produces a longer field of view (FOV) (along the axis of rotation), but requires specialized imaging equipment capable of performing more than one 360-degree rotation in the same direction (i.e., being able to continue rotating in the same direction with several 360-degree rotations). This is not possible for many radiotherapy systems (e.g., systems without slip ring technology). Furthermore, helical scans can only extend the FOV in the longitudinal direction.

[0032] This disclosure addresses these and other challenges.

[0033] In general, but not limited to, this disclosure relates to a method for expanding the available CBCT field of view. This is achieved by successively acquiring at least two CBCT acquisitions in which the patient is in different relative positions relative to the imaging device, and then performing a single reconstruction using 2D projections collected from these at least two CBCT acquisitions to generate a single reconstruction volume. Therefore, a larger field of view is achieved compared to reconstruction using only a single CBCT acquisition (where the patient is in a fixed position relative to the imaging device). Furthermore, the FOV can be expanded in any direction based on the displacement direction between the patient's different relative positions.

[0034] Figure 1 This is a flowchart illustrating a computer-implemented method 100 for generating 3D reconstructed images of a patient.

[0035] In step 102, method 100 includes acquiring a first plurality of two-dimensional (2D) CBCT projections of the patient. The first plurality of 2D CBCT projections correspond to a first plurality of angular positions of the CBCT imaging source about a first center of rotation, and the patient is in a first relative position relative to the first center of rotation. Step 102 may further include acquiring a first indication of the patient's first relative position relative to the first center of rotation.

[0036] In step 104, method 100 includes acquiring a second plurality of 2D CBCT projections of the patient. The second plurality of 2D CBCT projections correspond to a second plurality of angular positions of the CBCT imaging source around a second center of rotation, and the patient is in a second relative position relative to the second center of rotation. The second relative position differs from the first relative position. Step 104 may further include acquiring a second indication of the patient's second relative position relative to the second center of rotation.

[0037] Step 104 can be performed before, after, or substantially simultaneously with step 102.

[0038] The displacement direction from the first relative position to the second relative position can have at least one component parallel to the rotation axis of the first plurality of 2D angular projections. For example, the displacement direction can be parallel to the rotation axis, referred to herein as longitudinal displacement (reference). Figure 4 An exemplary longitudinal displacement is depicted and described.

[0039] The displacement direction from the first relative position to the second relative position can have at least a component perpendicular to the rotation axis of the first plurality of 2D angular projections. For example, the displacement direction can be transverse (perpendicular to the rotation axis in the horizontal plane), or the displacement direction can be vertical. Alternatively, the displacement direction can be a combination of longitudinal, horizontal, and transverse displacements.

[0040] The direction of displacement from the first relative position to the second relative position can be defined relative to the patient's anatomy. For example, the direction of displacement from the first relative position to the second relative position can have at least a component parallel to the patient's superior-inferior axis (i.e., the axis at the intersection of the patient's sagittal and coronal (frontal) planes). The direction of displacement from the first relative position to the second relative position can have at least a component parallel to the axis at the intersection of the patient's coronal (frontal) and transverse planes. The direction of displacement from the first relative position to the second relative position can have at least a component parallel to the axis at the intersection of the patient's sagittal and transverse planes.

[0041] One or two of the first or second 2D CBCT projections may correspond to a complete CBCT scan. A complete scan is a scan that includes multiple projections with a sufficient angular range to definitively reconstruct the complete 3D volume (e.g., the anatomical region of interest within the scan's FOV). Those skilled in the art will understand that the sufficient angular range to obtain a definitive reconstruction depends on the anatomical region to be reconstructed. For example, definitive reconstruction of many regions (e.g., larger regions such as the neck and shoulders, chest, abdomen, or pelvis) may require projections with a 360-degree angular range, while definitive reconstruction of other regions (e.g., smaller regions such as the head) may only require projections with a smaller angular range (e.g., 180 degrees plus a fan-shaped angle for anatomical regions where detector offset is not required).

[0042] In step 106, method 100 further includes performing an iterative image reconstruction process using first and second plurality of 2D CBCT projections to generate a 3D reconstructed image of the patient volume. A sub-region of the patient volume is represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections. This sub-region (the volume region projected to in both the first and second sets of projections) may be referred to as an overlap.

[0043] The iterative image reconstruction process can be performed using a first indication of the patient's first relative position relative to a first center of rotation and a second indication of the patient's second relative position relative to a second center of rotation. The first and second indications (of the first and second relative positions) can be obtained from hardware position measurements. In other words, they can correspond to (or originate from) in-situ measurements of the patient's position. This improves the accuracy of the position measurements and reduces the risk of human error (e.g., the risk of a user entering an incorrect position via a user interface). This, in turn, improves the accuracy of the reconstructed image of the patient volume generated in step 106.

[0044] The iterative image reconstruction process performed in step 106 may include generating simulations of multiple first and second 2D CBCT projections using the currently estimated 3D image of the patient volume. In the first iteration of the iterative image reconstruction process, the currently estimated 3D image of the patient volume may include an initial estimate, such as a blank image or an image generated using an analytical reconstruction method. For subsequent iterations, the currently estimated 3D image may include the output of previous iterations.

[0045] The iterative image reconstruction process performed in step 106 may further include generating an error function in the projection space that varies with the simulated 2D CBCT projection and the first and second plurality of 2D CBCT projections. For example, the error function may include a residual (the difference between the simulated projection and the measurement projection). Alternatively, the error function may include the ratio of the simulated projection to the measurement projection. The error function resides in the projection space; for example, the error function may be represented by a tensor of the same size as the tensor used to represent the measurement projection (i.e., the first and second plurality of 2D CBCT projections).

[0046] The iterative image reconstruction process performed in step 106 may also include updating the currently estimated 3D image of the patient volume based on an error function. For example, updating the currently estimated 3D image may include performing gradient descent. Gradient descent may be based on a cost function, where the cost function is based on the error function.

[0047] Performing an iterative image reconstruction process may further include applying weighting factors element-wise to the error function to generate a weighted error function. The weighting factors are defined in the projection space. The weighting factors can make one or more elements in the error function corresponding to the location representing a sub-region in the projection space less weighted than the remaining elements in the error function. For example, the weighting of all elements in the error function corresponding to the location representing a sub-region in the projection space may be less than the weighting of all the remaining elements in the error function. The location representing a sub-region in the projection space is the position where the sub-region (in the first or second plurality of 2D CBCT projections) is projected onto the detector. The element in the error function corresponding to a given location in the projection space is an element in the error function that varies with the simulated 2D CBCT projection at that given location in the projection space and the first and second plurality of 2D CBCT projections at that given location in the projection space. The weighting factors may include gradient weighting factors.

[0048] Weighting factors advantageously reduce artifacts caused by inconsistencies in sub-regions (i.e., regions scanned in two acquisitions). Inconsistencies can occur between the first and second acquisitions due to differences in the projection angle and path of X-rays. For example, inconsistencies may arise due to differences in scattering. Inconsistencies may also be caused, or alternatively, by time differences between the first and second acquisitions. For example, inconsistencies may be caused by patient movement, such as due to breathing and / or involuntary movements (such as convulsions).

[0049] Weighting factors also help explain non-spatial sampling, where more projection data is collected for a sub-region than for the rest of the patient volume. Without weighting factors, non-spatial sampling can cause overlapping regions to converge much faster than the rest of the patient volume during iterative reconstruction, leading to divergence (overshoot) in subsequent iterations. This can result in iterative reconstruction taking a long time to reach overall convergence (i.e., the entire patient volume). Weighting factors help restore uniform convergence.

[0050] In an implementation that applies a weighted factor, updating the current estimated 3D image of the patient volume based on an error function may include updating the current estimated 3D image of the patient volume based on a weighted error function.

[0051] For longitudinal displacement, the weighting factor of the element corresponding to the position in the first plurality of projections in the error function decreases as the position changes in the displacement direction, and the weighting factor of the element corresponding to the position in the second plurality of projections in the error function decreases as the position changes in the direction opposite to the displacement direction. For example, for each projection, the weighting factor of the element corresponding to the position representing the sub-region in the projection space can decrease as the position changes along the rotation axis toward the edge of the detector (i.e., a portion of the sub-region is projected onto the edge of the detector). For example, for each projection, the weighting factor can gradually decrease toward the edge of the detector.

[0052] Step 108 of performing the iterative image reconstruction process may include (e.g., updating the currently estimated 3D image of the patient volume may include) generating an image update in the image space. The image update may be generated based on an error function. In the example where a weighting function is applied to the error function, the image update may be generated based on a weighted error function.

[0053] Performing an iterative image reconstruction process may also include (for example, updating the currently estimated 3D image of the patient's volume may also include) applying a preconditioner element-wise to the image update. The preconditioner varies with its position in the image space.

[0054] Performing an iterative image reconstruction process may also include (e.g., the step of updating the current estimated 3D image of the patient volume may also include) updating the current estimated 3D image of the patient volume using an image update processed by a preconditioner.

[0055] The advantage of preconditioners lies in their ability to restore uniform convergence, thereby accelerating the convergence speed, especially in the case of longitudinal displacement. Therefore, 3D reconstructed images can be generated in fewer iterations.

[0056] Figure 1 An example of method 100 can be expressed mathematically as follows.

[0057] For each iteration of the iterative reconstruction process, a linear model can be assumed (e.g., ) or nonlinear models (e.g., ) to simulate ( Multiple 2D CBCT projections. It is a system projection matrix (also known as an orthographic projection operator) that describes the combination of each image pixel at each detector element. It is the Polyquant orthographic projection function:

[0058]

[0059] in ,here Is as and energy The linear decay coefficient of a piecewise linear function (the integral is the integral over the energy spectrum). Function This is the X-ray flux at a given energy. More information on the Polyquant orthogonal projection function can be found in JH Mason et al., *Physics, Medical Biology*, Vol. 62, pp. 8739–8762 (2017) (especially Equation 15), and US11915346B2. This simulation can also be further modeled to accommodate scattering (e.g., ,in (This is an estimate of the X-ray background scattering).

[0060] Can be based on notes The measurement projection set (including the first set of projections and the second set of projections) and the analog projection Generate error function .

[0061] For each iteration, the currently estimated 3D image can be updated based on the error function. For example, a gradient descent-based method can be performed as follows:

[0062]

[0063] here, It is the current estimated 3D image (e.g., represented as a tensor in image space). It is an updated estimated 3D image. It is an orthographic projection operator used to model how a volume (in image space) is projected onto a set of 2D projections (in projection space). yes transpose, It is the step size, and It is an image update based on an error function.

[0064] For the linear example, the error function can be the residual:

[0065] .

[0066] An example of the error function using the Polyquant orthographic projection function is as follows:

[0067]

[0068] in It is the Polyquant orthographic projection function. is an estimate of the X-ray background scattering, and 1 is the volume filled with one.

[0069] Another example error function that can be used for penalized weighted least squares is as follows:

[0070]

[0071] Where D is a statistical weighting vector used to reduce the weight of measures with high variance. For example, It is a common choice.

[0072] When weighting factors are applied to the error function (element by element) Then, the iterative update of the current estimate (according to an example of the present invention) becomes:

[0073]

[0074] When applying preconditioners to image updates (element-by-element) Then, the iterative update of the current estimate (according to an example of the present invention) becomes:

[0075]

[0076] When both weighting factors and preconditioners are applied, the iterative update of the current estimate (according to the example of the present invention) becomes:

[0077]

[0078] Those skilled in the art will understand that other iterative reconstruction algorithms are also possible, and this disclosure is not limited to any particular algorithm. Other examples of optimization algorithms that can be used in the techniques disclosed herein are: coordinate descent; empirical methods such as "iterative filtered back projection"; and maximum likelihood expectation maximization (MLEM). Furthermore, the algebraic reconstruction technique (ART) (or Kaczmarz algorithm) can be used to invert the linear system y = Ax.

[0079] Figure 1 The method is described for two relative positions of the patient. Those skilled in the art will understand that the same method can be applied to three (or more) relative positions of the patient by further using a third, multiple 2D projection of the patient acquired when the patient is in a third relative position to perform the image reconstruction process.

[0080] Figure 2 This is a flowchart illustrating a method 200 performed by an imaging system for generating a 3D reconstructed image of a patient. (Reference) Figure 11 and Figure 12 An exemplary imaging system is described. The imaging system may include a reference... Figure 10 Node 1000 is described. The imaging system may include radiotherapy devices, such as reference... Figure 12 The described treatment device 1250. Therefore, the imaging system can also be called a radiotherapy system. The treatment device 1250 can be, for example, a C-arm system or a ring-based system.

[0081] In step 202, method 200 includes performing a first CBCT image acquisition process, which includes acquiring a first plurality of two-dimensional 2D cone-beam computed tomography (CBCT) projections of the patient, the first plurality of 2D CBCT projections corresponding to a first plurality of angular positions of the CBCT imaging source around a first center of rotation, and the patient being in a first relative position relative to the first center of rotation. The first CBCT image acquisition process may be referred to as a first acquisition.

[0082] In step 204, method 200 includes moving one or both of the patient and the CBCT imaging source to place the patient in a second relative position with respect to a second rotation center of the CBCT imaging source.

[0083] In step 206, method 200 includes performing a second CBCT image acquisition process, which includes acquiring a second plurality of 2D CBCT projections of the patient, the second plurality of 2D CBCT projections corresponding to a second plurality of angular positions of the CBCT imaging source around a second rotation center, and the patient being in a second relative position with respect to the second rotation center. The second CBCT image acquisition process may be referred to as a second acquisition.

[0084] 2D processing, such as gain, offset, hysteresis, or scattering correction of 2D projections, can be performed online (in parallel with steps 202-206). For example, 2D processing of the first plurality of 2D projections can be performed while acquiring the first plurality of 2D projections, and 2D processing of the second plurality of 2D projections can be performed while acquiring the second plurality of 2D projections.

[0085] In step 208, method 200 includes performing an iterative image reconstruction process to generate a 3D reconstructed image of the patient volume. The iterative image reconstruction process is performed using a first plurality of 2D CBCT projections and a second plurality of 2D CBCT projections. The iterative image reconstruction process may also use a first indication of a first relative position and a second indication of a second relative position. Subregions of the patient volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections. This is referred to herein as an overlapping region.

[0086] One or both the moving patient and the CBCT imaging source can include the moving patient. Therefore, the rotation center of the CBCT imaging source remains unchanged (i.e., the first rotation center equals the second rotation center). The patient can be moved via a moving patient bed. The patient bed can be moved remotely. This allows for the execution of... Figure 2 Method 200 allows for the use of a method that eliminates the need for a human operator to enter the imaging room (e.g., manually perform step 204). This reduces the likelihood of human error and shortens the time between the first and second acquisitions. Reducing the time between acquisitions is advantageous because it lowers the possibility of unexpected patient movement between the first and second acquisitions.

[0087] During the acquisition of a second or subsequent 2D CBCT projection, the patient's position can be the starting point for providing radiotherapy. Here, patient position refers to the patient's absolute position, i.e., their location within the room (e.g., relative to a non-moving part of the radiotherapy apparatus). This prevents the need for patient movement before radiotherapy begins, thus reducing the time spent on patient imaging and treatment. This is beneficial for increasing the number of patients who can be imaged / treated using the imaging system. Moving the patient (e.g., by moving the patient bed) increases the likelihood of patient movement (i.e., unplanned movement, such as convulsions or responses to external stimuli). Therefore, reducing the number of patient movements also reduces the likelihood of accidental patient movement.

[0088] Alternatively, moving one or both of the patient and the CBCT imaging source may include moving the CBCT imaging source (and detector) while keeping the patient bed in place. As mentioned above, moving the patient (e.g., by moving the patient bed) increases the likelihood of patient disturbance and movement (i.e., unplanned movement, such as convulsions or responses to external stimuli). Therefore, by keeping the patient still and moving the imaging equipment, the likelihood of these unintended patient movements is reduced. Consequently, moving the imaging equipment can further reduce image artifacts in the resulting 3D reconstructed images of the patient volume.

[0089] The first CBCT image acquisition process may include acquiring a first plurality of 2D CBCT projections in one of a clockwise or counterclockwise direction, and the second CBCT image acquisition process may include acquiring a second plurality of 2D CBCT projections in another of a clockwise or counterclockwise direction. In other words, the first and second CBCT acquisitions can be performed in opposite directions (i.e., the imaging source rotates in opposite directions). This reduces the time required to acquire all 2D projections of the magnified FOV.

[0090] Figure 2 Method 200 can be executed in response to a single user-input command. This provides greater workflow efficiency. In particular, it reduces the risk of human error and decreases the time spent executing method 200.

[0091] Figure 2 Method 200 may also include references Figure 1 Method 100 describes any one or more of the steps and features.

[0092] Figure 2 The method is described for two relative positions of the patient. Those skilled in the art will understand that the same method can be applied to three (or more) relative positions of the patient, by performing a third CBCT image acquisition procedure with the patient in a third relative position and further using a third plurality of 2D projections of the patient acquired during the third CBCT image acquisition procedure to perform an image reconstruction procedure. Using CBCT projections (compared to, for example, fan-beam CT) allows for the capture of a larger anatomical region in each projection, thus enabling the capture of a larger anatomical region using only two patient positions (see reference below). Figure 5A (As shown).

[0093] Figure 3 This is a schematic diagram of an exemplary CBCT imaging setup.

[0094] Patient 302 lies on patient bed 304, with imaging source 306 and imaging detector 308 positioned opposite each other, and patient 302 located between them. During CBCT image acquisition, imaging source 306 and imaging detector 308 rotate around a center of rotation, as... Figure 3 The directional vectors inside and outside the page are shown. The region of interest for patient 302 is located (e.g., around or near) the center of rotation. Figure 3 The imaging source 306' and imaging detector 308' are further shown after being rotated 180 degrees.

[0095] The field of view 310 is determined by the volume scanned by the detectors 308 and 308' as they rotate around the patient 302. For example, the field of view 310 of a 40x40 cm detector at a distance of 1.6 m from the source (without further collimation) is a cylindrical volume (approximately) 20 cm in length with a conical 'endcap', resulting in a maximum longitudinal length of 26 cm for the field of view. (Data outside the conical endcap cylindrical FOV is insufficient for reconstruction and is therefore masked, thus forming a conical endcap cylindrical FOV).

[0096] According to the specific implementation of the technology disclosed in this paper, the second FOV is obtained by longitudinally translating the patient 302 (i.e., parallel to the rotation axis of the imaging system), such as... Figure 4 As shown.

[0097] Figure 4 This is a schematic diagram of a specific implementation of the technology disclosed herein, wherein the patient 302 moves longitudinally relative to the imaging source and the detector.

[0098] Figure 4 The initial position of patient 302 is shown (as shown in the image). Figure 3 (As shown). The first CBCT acquisition is performed while the patient is in this first position 302.

[0099] Figure 4 The new position of the patient at 302' after longitudinal translation is further shown. A second CBCT acquisition is performed with the patient in this second position 302'.

[0100] Figure 4 Two fields of view (FOV) corresponding to these two CBCT acquisitions are depicted: the imaging device's FOV 310 when the patient is in the initial position 302 and the imaging device's field of view 310' when the patient is in the new position 302'. Both fields of view 310 and 310' are depicted relative to the patient in the new position 302' to show the position of the two FOVs relative to each other and relative to the patient 302'. Therefore, it can be seen that the new field of view 310' captures different regions of the patient's anatomy, including, for example, the patient's neck. Figure 4 As shown, the patient is selected for translation (302 to 302') so that the two visual fields 310' and 310' overlap.

[0101] Figure 5A The reconstructed volume is obtained by executing a method according to a specific implementation of the technology disclosed herein. This method is referenced from... Figure 1 and Figure 2 Exemplary implementations of methods 100 and 200 are described.

[0102] The first CBCT scan is performed with the patient in the first position, for example, as Figure 3 The depicted (and referenced) Figure 3 (Describe it).

[0103] like Figure 4 As shown (and referenced) Figure 4 (Description provided) The patient was moved to a second position by moving the patient bed longitudinally. A second CBCT scan was performed while the patient was in the second position.

[0104] A single iterative image reconstruction process is performed using projections acquired in the first and second CBCT scans to generate a 3D reconstructed image 502 of the patient volume. Figure 5A A 2D view of the generated 3D image is shown.

[0105] For reference Figure 1 The iterative image reconstruction process is performed using a first plurality of 2D CBCT projections and a second plurality of 2D CBCT projections in a single iteration. Subregions of the patient volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

[0106] The reconstruction method used is an improvement on the Polyquant iterative reconstruction method described in patent US11915346B2. (See references) Figure 1 The update equation described, in the case of simultaneous application of weighting factors and preconditioners:

[0107]

[0108] In the example of this invention, the element-wise error function is:

[0109]

[0110] Weighting factor For sine functions:

[0111]

[0112] in It is the width of the gradient applied (in projection space), and This refers to the position relative to the edge of the detector (on the detector in projection space). Those skilled in the art will understand that other functions can also be used for the error function. For example, in other examples, a linear function, a step function, or other gradual functions may be used. (Based on the geometry of the imaging beam) is calculated as the detector region onto which the sub-region (i.e., the overlapping region) is projected. For a cone-beam setup, this distance can be estimated as:

[0113]

[0114] in It is the distance from the source to the detector. It is the distance from the source to the center of rotation. It is the width of the detector, and This is the translation distance (i.e., bed offset). It should be noted that in the case of parallel beams, this gradient width will be... .

[0115] In this example, the element-wise preconditioner is:

[0116]

[0117] Where 1 is a volume filled with one.

[0118] This example demonstrates the benefits of the techniques disclosed herein. It should be noted that although this example uses longitudinal examination bed movement and a Polyquant orthographic projection model, the beneficial effects of this disclosure can be achieved by using examination bed movement in any direction and by using alternative orthographic projection models.

[0119] In comparison, Figure 5B The reconstructed volumetric image generated by volumetric stitching (described below) is shown.

[0120] Use from reference Figure 5A The projection obtained from the first CBCT scan is used to perform a first CBCT reconstruction to generate a first reconstruction volume.

[0121] Use from reference Figure 5A The projection obtained from the second CBCT scan is used to perform a second CBCT reconstruction to generate a second reconstruction volume.

[0122] The first and second reconstructed volumes are combined using post-reconstruction volume stitching (which adds the first and second reconstructed volumes and normalizes the spatial overlap). Figure 5B A 2D view of the generated 3D image 504 is shown.

[0123] like Figure 5B As shown, existing volumetric splicing techniques fail to provide a complete reconstructed volume at the connection between the first and second reconstructed volumes. The gap at the connection (such as...) Figure 5B (As shown) reflects Figure 3The conical end cap cylindrical FOV is shown. These gaps correspond to areas that were not adequately sampled for reliable reconstruction in a single scan (these areas are removed through a masking operation, resulting in the conical end cap cylindrical FOV).

[0124] By increasing the overlap between the first and second reconstruction volumes, volume stitching methods can achieve a more complete reconstruction volume; however, this reduces the total field of view of the stitched image and increases the dose to the patient in the overlapping area. Conversely, by implementing the technique of this disclosure, the projection data from the two scans are merged before reconstruction, thus providing sufficient sampling for reliable reconstruction. Figure 5B Missing areas (e.g.) Figure 5A (As shown).

[0125] like Figure 5A and Figure 5B As shown, the technique disclosed herein achieves a more complete reconstructed volume than volumetric stitching methods. The overlap between the projections used to generate each image 502, 504 is the same, but the gap in image 202 indicates that a larger overlap is required to achieve a complete volume. In other words, the technique disclosed herein requires less overlap and therefore provides a larger FOV than existing techniques.

[0126] The technology disclosed herein is also faster. The reconstruction time for generating image 502 is 10 seconds, while the reconstruction time for generating image 504 is approximately 20 seconds (10 seconds + 10 seconds).

[0127] Finally, the technique used to generate image 504 provides flexibility for motion between two scans by creating a smooth fusion between the two acquisitions using a gradient weighting factor.

[0128] Figure 6A This is a schematic diagram of the CBCT imaging setup viewed downwards (in the lateral direction) along the rotation axis of the imaging system.

[0129] Patient 602 is lying on patient bed 604. The imaging system includes an imaging source 606 and an imaging detector 608.

[0130] The lateral FOV (i.e., the effective diameter of the 'scan volume') depends on the lateral positioning of the detector relative to the central beam axis (i.e., the detector offset).

[0131] exist Figure 6A In this configuration, detector 608 is positioned opposite imaging source 606 with no detector offset, resulting in the lateral field of view 610 shown. For example, for a 40x40 cm detector located 1.6 m from the source (without further collimation), its lateral field of view 610 is a (roughly) cylindrical volume with a diameter of approximately 26 cm.

[0132] Figure 6BThis is a schematic diagram of a CBCT imaging setup with detector offset in the lateral dimension.

[0133] Detector 608' is offset relative to imaging source 606, and its lateral field of view 610' is greater than Figure 6A The setup shown achieves a lateral FOV of 610. For example, this is for a detector located 1.6 m from the source (without further collimation) with a 190 mm detector offset (e.g., ...). Figure 6B The 40x40 cm detector (shown) has a lateral field of view 610' that is approximately cylindrical in diameter, with a diameter of about 51.4 cm.

[0134] like Figure 7 As shown, the FOV can be further extended using the techniques disclosed herein.

[0135] Figure 7 This is a schematic diagram of a CBCT imaging setup in the horizontal dimension according to the present disclosure.

[0136] Figure 7 The initial position of patient 702 lying on patient bed 704 is shown. The first CBCT acquisition is performed with the patient in this first position 702 and detector 708 in an offset position relative to imaging source 706. Figure 7 The positions of source 706' and detector 708' after a 180-degree rotation are also shown.

[0137] The patient is moved laterally to position 702' (by moving the patient bed 704'). A second CBCT acquisition is performed with the patient in this second position 702'. The FOV 710' of the second CBCT acquisition is shown relative to the patient in the second position 702'. The FOV 710 of the first CBCT acquisition is also shown relative to the patient in the second position 702'.

[0138] The patient's entire transverse cross-section is captured within the combined field of view (FOV). Therefore, a larger transverse FOV is achieved using the reconstruction technique disclosed herein.

[0139] Figure 8The geometry of a typical cone-beam projection acquisition setup is shown, which can be used for image acquisition and image reconstruction techniques according to the techniques disclosed herein. For simplicity, only the xy-plane is shown; skilled readers will understand that the beam also extends to the z-axis perpendicular to the xy-plane. In this example, the acquisition device captures projection data (projections) of an object 802 (e.g., a patient). An X-ray source 804 generates and emits X-rays 806 to the object 802. In CBCT, X-rays can be considered as beams of light emitted from a point source. Detectors (or multiple detectors) 808 capture projections, which are a set of line integrals along the path radiated from the source 804. Multiple projections of the image can be acquired from different angles by rotating the source 804 and detectors 808 around the center of the image 810. In this example, the source 804 and detectors 808 can be rotated in an arc along an orbital path 812 (referred to herein as the scan arc or CBCT scan arc). In this way, the xy-plane can rotate around the origin (or the center of image 810) (clockwise or counterclockwise) to maintain the relative positional relationship between the light source 804 and the detector 808 while traversing the orbital path 812. Of course, other configurations of the imaging system are possible; for example, the light source can be configured to rotate, and a full circle of detectors can be configured to capture the projection, or multiple light sources can be arranged circumferentially around a full circle of detectors.

[0140] The attenuation of the intensity of rays passing through object 802 can be measured by processing the signal received from detector 808. By performing a series of projection measurements on object 802 at different projection angles, a sine curve can be constructed from the projection data, mapping the spatial dimension of the detector array to the dimension of the projection angle. The intensity attenuation caused by a specific volume within the object will be depicted as a sine wave along the spatial dimension of the detector perpendicular to the system's rotation axis. The sine wave amplitude corresponding to object volumes farther from the rotation center is greater than that corresponding to object volumes closer to the rotation center. The phase of each sine wave in the sine curve corresponds to its relative angular position with respect to the rotation axis. An image can be reconstructed by performing image reconstruction techniques (such as the inverse Radon transform) on the projection data in the sine curve, where the reconstructed image corresponds to a cross-sectional slice or volume of object 802. Detector 808 may include underlying detector elements (such as pixels or pixel boxes) rather than a single active region. The measured ray intensity can then be considered as a function of the underlying intensity signal acquired on the detector elements of the detector. This has the advantage of reducing the influence of random noise, which can interfere with the intensity measured on a single detector element (especially low intensities). For example, the measured radiation intensity can be the average (mean, mode, or median) of the intensity of the individual detector elements at the bottom layer.

[0141] Figure 9The geometry of a cone-beam projection acquisition setup incorporated into a medical CT scanner 900 is shown. A radiation source 904 within the CT scanner (not depicted) emits an X-ray beam that passes through a patient supported on an examination table. A detector 908 captures the attenuated X-rays. The radiation source 904 and detector 908 can be configured to rotate within the CT scanner gantry to acquire projection measurements at a range of different projection angles.

[0142] CBCT image acquisition protocols typically involve acquiring CBCT projections at equidistant locations along the scan arc with constant intensity and energy. However, as... Figure 9 As shown, the angular intervals between measurement orientations need not be equal. For example, according to the embodiments disclosed herein, higher density projection angles can be used to improve the image quality of reconstructed CBCT images and prioritize image quality at locations within the patient's body where RTP is sensitive to anatomical changes. For the remainder of the rotation, a set of densely sampled projections within a small sub-arc can be combined with projections of lower sampling density to obtain improved image quality in some desired areas while minimizing imaging dose. Francien G. Bossema's paper, "Optimization of Projection Angle Selection in Computed Tomography" (Master's Thesis in Mathematics, Leiden University), provides information on optimizing projection angle selection in computed tomography.

[0143] Image reconstruction methods can take into account the curvature within the gantry (or the gantry itself). For example, in one implementation, curvature can be considered when determining the location where the isocentric ray strikes the detector. The method can then determine the intensity at the isocentric location through interpolation.

[0144] review Figure 2 The method, in which the first and second image acquisition processes of steps 202 and 206 can be performed using the methods described above, can be used in conjunction with the methods described above. Figure 8 and Figure 9 The relevant settings will be used to execute this.

[0145] Figure 10 This is a block diagram illustrating an example node 1000 according to an example of this disclosure, the management node being able to implement... Figure 1 Method 100 is shown. For example, node 1000 can implement this after receiving appropriate instructions from computer program 1050. Figure 1 Method 100.

[0146] refer to Figure 10 Node 1000 includes a processor or processing circuitry 1002, a memory 1004, and may include an interface 1006. The processing circuitry 1002 is operable to perform the functions described in the preceding reference. Figure 1The discussion covers some or all of the steps of method 100. Memory 1004 may contain elements that can be executed by processing circuitry 1002 to enable node 1000 to perform... Figure 1 Instructions for some or all of the steps of method 100 shown. These instructions may also include instructions for performing one or more telecommunications and / or data communication protocols. The instructions may be stored in the form of computer program 1050. In some examples, processor or processing circuitry 1002 may include one or more microprocessors or microcontrollers, and other digital hardware that may include digital signal processors (DSPs), application-specific digital logic, etc. Processor or processing circuitry 1002 may be implemented by any type of integrated circuit such as application-specific integrated circuit (ASIC), field-programmable gate array (FPGA). Processor or processing circuitry 1002 may include a graphics processing unit (GPU). Memory 1004 may include one or more types of memory suitable for the processor, such as read-only memory (ROM), random access memory, cache memory, flash memory devices, optical storage devices, solid-state drives, hard disk drives, etc.

[0147] In some of the examples described above, node 1000 can be incorporated into an imaging system, and examples of this disclosure also provide imaging systems that include the nodes described above. The imaging system can be included (or incorporated into) a radiotherapy device. The imaging system can be used for image-guided RT and / or ART.

[0148] Figure 11 A radiotherapy apparatus suitable for performing radiotherapy and / or image acquisition is described. The radiotherapy apparatus is an example of the imaging system described herein (e.g., configured to perform...). Figure 2 Method 200 imaging system).

[0149] A cross-section through the radiotherapy device 1100 includes a radiation head 1110 and a beam receiving device (detector) 1102, both of which are attached to a rack 1104. The radiation head 1110 includes a radiation source 1112 that emits a radiation beam 1106. The radiation head 1110 also includes a beam shaping device 1118 that controls the size and shape of the radiation field associated with the beam.

[0150] The beam receiving device 1102 is configured to receive radiation emitted from the radiating head 1110 for the purpose of absorbing and / or measuring the radiated beam. In the view shown, the radiating head 1110 and the beam receiving device 1102 are placed radially opposite each other.

[0151] The rack 1104 is rotatable and supports the radiator 1110 and beam receiver 1102, allowing them to rotate about a rotation axis 1108 that coincides with the patient's longitudinal axis. The rack provides rotation of the radiator 1110 and beam receiver 1102 in a plane perpendicular to the patient's longitudinal axis (e.g., the sagittal plane). Three rack orientations can be defined. , , , making The direction is perpendicular to the frame rotation axis. The direction extends from the point on the gantry corresponding to the radiator head 1110 towards the rotation axis of the gantry. Therefore, from the patient's reference frame, The direction rotates as the frame rotates.

[0152] The radiotherapy apparatus 1100 also includes a support surface or examination table 1120 on which the subject (or patient) is supported during radiotherapy or image acquisition. The radiation head 1110 is configured to rotate about a rotation axis 1108, such that the radiation head 1110 directs radiation toward the subject from various angles around the subject to distribute the radiation dose received by healthy tissue over a larger area of ​​healthy tissue while establishing a prescribed radiation dose at the target area.

[0153] The radiotherapy device 1100 is configured to apply a radiation beam toward a radiation isocenter, which is located approximately on the rotation axis 1108 at the center of the gantry 1104, regardless of the placement angle of the radiation head 1110.

[0154] The dimensions of the rotatable frame 1104 and the radiant head 1110 are designed to allow for the presence of a central hole 1122. The central hole 1122 provides an opening sufficient to allow the subject to be positioned without accidentally contacting the radiant head 1110 or other mechanical components as the frame rotates around the subject.

[0155] A radiation head 1110 emits a radiation beam 1106 along a beam axis 1124 (or radiation axis or beam path), wherein the beam axis 1124 defines the direction of radiation emitted by the radiation head 1110. The radiation beam 1106 is incident on a beam receiving device 1102, which may include at least one of a beam blocker and / or a radiation detector. The beam receiving device 1102 is attached to a frame 1104 on the diameter-opposite side of the radiation head 1110 to attenuate and / or detect the radiation beam after it has passed through the subject.

[0156] The radiation beam axis 1124 can be defined, for example, as the center or point of maximum intensity of the radiation beam 1106.

[0157] Beam shaping device 1118 limits the diffusion range of the radiated beam 1106. Beam shaping device 1118 is configured to adjust the shape and / or size of the radiation field generated by the radiation source. Beam shaping device 1118 achieves this by defining a variable-shaped aperture (also referred to as a window or opening) to collimate the radiated beam 1106 into a selected cross-sectional shape. In this example, beam shaping device 1118 may be provided by a combination of a diaphragm and an MLC. Beam shaping device 1118 may also be referred to as a beam corrector.

[0158] The radiotherapy device 1100 can be configured to provide two radiotherapy modes: coplanar and non-coplanar (also referred to as tilted). In coplanar treatment, radiation is emitted in a plane perpendicular to the rotation axis of the radiation head 1110. In non-coplanar treatment, radiation is emitted at an angle not perpendicular to the rotation axis. To apply coplanar and non-coplanar treatment, the radiation head 1110 can be moved between at least two positions, one where radiation is emitted in a plane perpendicular to the rotation axis (coplanar configuration) and the other where radiation is emitted in a plane not perpendicular to the rotation axis (non-coplanar configuration).

[0159] In a coplanar configuration, the radiator 1110 is positioned to rotate about a rotation axis in a first plane. In a non-coplanar configuration, the radiator is tilted relative to the first plane, such that the radiation field generated by the radiator is oriented at an angle relative to both the first plane and the rotation axis. In a non-coplanar configuration, the radiator 1110 is positioned to rotate in a corresponding second plane that is parallel to and offset from the first plane. The radiation beam is emitted at an angle relative to the second plane, and therefore, as the radiator rotates, the beam sweeps out in a cone shape.

[0160] In one configuration, when the radiotherapy equipment is in both coplanar and non-coplanar modes, the beam receiving device 1102 can be held in the same position relative to the rotatable gantry. Therefore, the beam receiving device 1102 is configured to rotate about a rotation axis in the same plane in both coplanar and non-coplanar modes. This can be the same plane as the plane in which the radiation head rotates. In an alternative configuration, the beam receiving device 1101 can also be rotated.

[0161] The beam shaping device 1110 is configured to reduce the spread of the radiation field in a non-coplanar configuration compared to a coplanar configuration.

[0162] The radiotherapy apparatus 1100 includes a controller 1130, which is programmed to control a radiation source 1112, a beam receiving device 1106, and a gantry 1102. The controller 1130 can perform functions or operations such as radiotherapy planning, treatment execution, image acquisition, image processing, motion tracking, motion management, and / or other tasks involved in the radiotherapy process.

[0163] The controller 1130 is programmed to control various components of the device 1100, such as the rack 1104, the radiator 1110, the beam receiver 1102, and the support surface 1120, in order to acquire projection data (i.e., the projected image) suitable for image reconstruction.

[0164] The hardware components of controller 1130 may include one or more computers (e.g., general-purpose computers, workstations, servers, terminals, portable / mobile devices, etc.); processors (e.g., central processing units (CPUs), graphics processing units (GPUs), microprocessors, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), dedicated or specially designed processors, etc.); memory / storage devices, such as memory (e.g., read-only memory (ROM), random access memory (RAM), flash memory, hard disk drives, optical disks, solid-state drives (SSDs), etc.); input devices (e.g., keyboards, mice, touchscreens, microphones, buttons, knobs, trackballs, joysticks, handles, controllers, etc.); output devices (e.g., displays, printers, speakers, vibration devices, etc.); circuitry; printed circuit boards (PCBs); or other suitable hardware. The software components of controller 1130 may include operating device software, application software, etc.

[0165] The radiator 1110 can be connected to a head actuator 1114, which is configured to actuate the radiator 1110, for example, between a coplanar configuration and one or more non-coplanar configurations, or, for example, in response to the detection of bending, to actuate the radiation source 1112 and / or detector 1102. This may involve translation and rotation of the radiator 1110 relative to the rack. In some implementations, the head actuator may include a curved guide along which the radiator 1110 can move to adjust the position and angle of the radiator 1110. A controller 1130 can control the configuration of the radiator 1130 via the head actuator 1114.

[0166] The beam shaping apparatus 1118 includes a shaping actuator 1116. The shaping actuator is configured to control the position of one or more elements in the beam shaping apparatus 1118 to shape the radiated beam 1106. In some implementations, the beam shaping apparatus 1116 includes an MLC (Multi-Layer Cylinder), and the shaping actuator 1116 includes means for actuating blades of the MLC. The beam shaping apparatus 1118 may further include a diaphragm, and the shaping actuator 1116 may include means for actuating a diaphragm block. A controller 1130 may control the beam shaping apparatus 1118 via the shaping actuator 1116.

[0167] Figure 12A block diagram of one implementation of an imaging system 1200 is shown, which includes a computing system 1210 within which a set of instructions can be executed to cause the computing system 1210 to perform any or more methods discussed herein.

[0168] The computing system 1210 should be considered as including any number or set of machines, such as computing devices, that individually or collectively execute a set (or more sets) of instructions to perform any one or more methods discussed herein. That is, the hardware and / or software may be housed in a single computing device or distributed across multiple computing devices within the computing system. In some implementations, one or more elements of the computing system may be connected (e.g., networked to) other machines, such as a local area network (LAN), intranet, extranet, or the Internet. One or more elements of the computing system may operate as a server or client computer in a client-server network environment, or act as a peer-to-peer computer in a peer-to-peer (or distributed) network environment. One or more elements of the computing system may be a personal computer (PC), tablet computer, set-top box (STB), personal digital assistant (PDA), cellular phone, network appliance, server, network router, switch, or bridge, or any machine capable of executing a set of instructions (executed sequentially or otherwise) specifying the actions to be taken by that machine.

[0169] The computing system 1210 includes controller circuitry 1211 and memory 1213 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM), such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM). Memory 1213 may include static memory (e.g., flash memory, static random access memory (SRAM), etc.) and / or auxiliary memory (e.g., data storage devices), which communicate with each other via a bus (not shown).

[0170] Controller circuit 1211 represents one or more general-purpose processors, such as microprocessors, central processing units, accelerated processing units, etc. More specifically, controller circuit 1211 may include complex instruction set computing (CISC) microprocessors, reduced instruction set computing (RISC) microprocessors, very long instruction word (VLIW) microprocessors, processors implementing other instruction sets, or processors implementing combinations of instruction sets. Controller circuit 1211 may also include one or more special-purpose processing devices, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. One or more processors of the controller circuit may have a multi-core design. Controller circuit 1211 is configured to execute processing logic for performing the operations and steps discussed herein.

[0171] The computing system 1210 may further include network interface circuitry 1215. The computing system 1210 may be communicatively coupled to input device 1220 and / or output device 1230 via input / output circuitry 1217. In some implementations, input device 1220 and / or output device 1230 may be elements of the computing system 1210. Input device 1220 may include alphanumeric input devices (e.g., keyboard or touchscreen), cursor control devices (e.g., mouse or touchscreen), audio devices (such as microphones), and / or haptic input devices. Output device 1230 may include audio devices (such as speakers), video display units (e.g., liquid crystal display (LCD) or cathode ray tube (CRT)), and / or haptic output devices. In some implementations, input device 1220 and output device 1230 may be provided as a single device or separate devices.

[0172] In some implementations, the computing system 1210 may include image processing circuitry 1219. Image processing circuitry 1219 may be configured to process image data 1280 (e.g., images, imaging data), such as medical images acquired from one or more imaging data sources, treatment device 1250, and / or image acquisition device 1240. Image processing circuitry 1219 may be configured to process or preprocess image data. For example, image processing circuitry 1219 may convert received image data into a specific format, size, resolution, etc. In some implementations, image processing circuitry 1219 may be combined with controller circuitry 1211.

[0173] In some implementations, the imaging system 1200 may include an image acquisition device 1240 and / or a treatment device 1250. The image acquisition device 1240 and the treatment device 1250 may be provided as a single device. In some implementations, the image acquisition device 1240 and the treatment device 1250 are a combined radiotherapy device capable of delivering therapeutic radiation and acquiring imaging data. In some implementations, the treatment device 1250 is configured to, for example, perform imaging in addition to providing treatment and / or perform imaging during treatment. The treatment device 1250 includes the main radiation delivery component of the radiotherapy system.

[0174] Image acquisition device 1240 can be configured to output image data 1280, which is accessible to computing system 1210. Treatment device 1250 can be configured to output treatment data 1260, which is accessible to computing system 1210.

[0175] The computing system 1210 can be configured to access or acquire treatment data 1260, planning data 1270, and / or image data 1280. Treatment data 1260 can be acquired from an internal data source (e.g., from memory 1213) or from an external data source (such as treatment device 1250 or an external database). Planning data 1270 can be acquired from memory 1213 and / or from an external source (such as a planning database). Planning data 1270 may include information acquired from one or more of image acquisition device 1240 and treatment device 1250.

[0176] A "hardware component" is a tangible (e.g., non-transitory) physical component (e.g., a group or more processors) capable of performing certain operations and which can be configured or arranged in some physical manner. A hardware component may include dedicated circuitry or logic permanently configured to perform certain operations. A hardware component may include dedicated processors, such as FPGAs or ASICs. A hardware component may also include programmable logic or circuitry temporarily configured by software to perform certain operations.

[0177] Furthermore, these modules and components can be implemented as firmware or functional circuitry within a hardware device. Further, modules and components can be implemented using any combination of hardware devices and software components, or solely using software (e.g., code stored or otherwise embodied in a machine-readable medium or transmission medium).

[0178] Unless otherwise specifically indicated, as will be apparent from the following discussion, it should be understood that throughout the description, the use of terms such as “acquire,” “execute,” “receive,” “determine,” “compare,” “realize,” “maintain,” “identify,” etc., refers to the actions and processes of a computer system or similar electronic computing device that manipulates data represented as physical (electronic) quantities in the registers and memories of the computer system and transforms that data into other data similarly represented as physical quantities in the computer system’s memory or registers or other such information storage, transmission, or display devices.

[0179] It should be understood that the above description is intended to be illustrative and not restrictive. Many other implementations will be apparent to those skilled in the art upon reading and understanding the above description. Although this disclosure has been described with reference to specific exemplary implementations, it should be recognized that this disclosure is not limited to the described implementations but can be implemented with modifications and variations within the spirit and scope of the appended claims. Therefore, this specification and drawings are to be regarded as illustrative and not restrictive. Consequently, the scope of this disclosure should be determined by reference to the appended claims and the full scope of their equivalents.

[0180] The following are some numbered statements listing the embodiments of this disclosure.

[0181] 1. A computer-implemented method (100) for generating a three-dimensional 3D reconstructed image of a patient, the method comprising:

[0182] (102) Obtain the first plurality of two-dimensional 2D cone-beam computed tomography (CBCT) projections of the patient, wherein the first plurality of 2D CBCT projections correspond to the first plurality of angular positions of the CBCT imaging source around the first rotation center, and the patient is in a first relative position relative to the first rotation center;

[0183] Acquire (104) a second plurality of 2D CBCT projections of the patient, wherein the second plurality of 2D CBCT projections correspond to a second plurality of angular positions of the CBCT imaging source around a second rotation center, and the patient is in a second relative position relative to the second rotation center, wherein the second relative position is different from the first relative position; and

[0184] An iterative image reconstruction process (106) is performed using the first and second plurality of 2D CBCT projections to generate a 3D reconstructed image of the patient’s volume, wherein sub-regions of the patient’s volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

[0185] 2. The method as described in embodiment 1, wherein one or both of the first plurality of 2D CBCT projections and the second plurality of 2DCBCT projections correspond to a complete CBCT scan.

[0186] 3. The method as described in embodiment 1 or 2, wherein performing the iterative image reconstruction process (106) comprises:

[0187] Simulations of the first and second plurality of 2D CBCT projections are generated using the currently estimated 3D image of the patient volume;

[0188] An error function is generated in the projection space, the error function varying with the simulated 2D CBCT projection and the first and second plurality of 2D CBCT projections; and

[0189] The current estimated 3D image of the patient's volume is updated based on the error function.

[0190] 4. The method of embodiment 3, wherein performing the iterative image reconstruction process of (106) further includes:

[0191] A weighted error function is generated by applying a weighting factor to each element of the error function, wherein one or more elements in the error function corresponding to the positions representing the sub-regions in the projection space are weighted less than the weights of the remaining elements in the error function.

[0192] 5. The method of embodiment 4, wherein updating the current estimated 3D image of the patient volume based on the error function includes updating the current estimated 3D image of the patient volume based on the weighted error function.

[0193] 6. The method of any one of embodiments 3 to 5, wherein the displacement direction from the first relative position to the second relative position is parallel to the rotation axis of the first and second plurality of 2D angular projections.

[0194] 7. The method of embodiment 6 as described in embodiment 4 or 5, wherein for the elements in the error function corresponding to the positions in the first plurality of projections, the weighting factor decreases as the position changes in the displacement direction, and for the elements in the error function corresponding to the positions in the second plurality of projections, the weighting factor decreases as the position changes in the direction opposite to the displacement direction.

[0195] 8. The method of any one of embodiments 1 to 5, wherein the displacement direction from the first relative position to the second relative position has at least a component parallel to the rotation axis of the first plurality of 2D angular projections.

[0196] 9. The method as described in any one of embodiments 1 to 5 or 8, wherein the displacement direction from the first relative position to the second relative position has at least a component perpendicular to the rotation axis of the first plurality of 2D angular projections.

[0197] 10. The method as described in any of the preceding embodiments, wherein performing the iterative image reconstruction process described in (106) includes:

[0198] Generate image updates in the image space;

[0199] The image update applies a preconditioner element-wise, wherein the preconditioner varies with its position in the image space; and

[0200] The current estimated 3D image of the patient volume is updated using an image update processed by preconditioners.

[0201] 11. The method as described in any of the preceding embodiments, wherein prior to performing the iterative image reconstruction process described in (106), the method further comprises:

[0202] A first indication of the first relative position and a second indication of the second relative position are obtained from hardware position measurements for use in performing the iterative image reconstruction process (106).

[0203] 12. A computer-implemented method (200) for generating a three-dimensional 3D reconstructed image of a patient, performed by an imaging system, the method comprising:

[0204] Perform (202) a first CBCT image acquisition process, the first CBCT image acquisition process includes acquiring a first plurality of two-dimensional 2D cone-beam computed tomography (CBCT) projections of the patient, the first plurality of 2D CBCT projections corresponding to a first plurality of angular positions of the CBCT imaging source around a first rotation center, and the patient being in a first relative position relative to the first rotation center.

[0205] Move (204) one or both of the patient and the CBCT imaging source so that the patient is in a second relative position with respect to a second rotation center of the CBCT imaging source;

[0206] Perform (206) a second CBCT image acquisition process, the second CBCT image acquisition process including acquiring a second plurality of 2D CBCT projections of the patient, the second plurality of 2D CBCT projections corresponding to a second plurality of angular positions of the CBCT imaging source around the second rotation center, and the patient being in a second relative position relative to the second rotation center; and

[0207] An iterative image reconstruction process (208) is performed to generate a 3D reconstructed image of the patient’s volume, wherein the iterative image reconstruction process is performed using a first plurality of 2D CBCT projections and a second plurality of 2D CBCT projections, wherein sub-regions of the patient’s volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

[0208] 13. The method of embodiment 12, wherein moving (204) one or both of the patient and the CBCT imaging source comprises moving the patient by moving the patient bed.

[0209] 14. The method of embodiment 13, wherein the patient bed is remotely moved.

[0210] 15. The method as described in embodiment 13 or 14, wherein the patient’s position during the acquisition of the second plurality of 2D CBCT projections is the starting position for delivering radiotherapy to the patient.

[0211] 16. The method of embodiment 15, wherein moving (204) one or both of the patient and the CBCT imaging source includes moving the CBCT imaging source while keeping the position of the patient bed supporting the patient unchanged.

[0212] 17. The method of any one of embodiments 12 to 16, wherein performing (202) the first CBCT image acquisition process includes acquiring the first plurality of 2D CBCT projections in one of a clockwise and counterclockwise manner, and wherein performing (206) the second CBCT image acquisition process includes acquiring the second plurality of 2D CBCT projections in another of a clockwise and counterclockwise manner.

[0213] 18. The method of any one of embodiments 12 to 17, wherein the method is performed in response to a single user input command.

[0214] 19. A node (1000) for generating a three-dimensional 3D reconstructed image of a patient, the node comprising: a memory (1004) storing computer-executable instructions; and processing circuitry (1002) configured to cause the node to:

[0215] Acquire a first plurality of two-dimensional (2D) cone-beam computed tomography (CBCT) projections of the patient, wherein the first plurality of 2D CBCT projections correspond to a CBCT imaging source at a first plurality of angular positions around a first rotation center, and the patient is at a first relative position relative to the first rotation center;

[0216] Acquire a second plurality of 2D CBCT projections of the patient, wherein the second plurality of 2D CBCT projections correspond to a second plurality of angular positions of the CBCT imaging source around a second rotation center, and the patient is in a second relative position relative to the second rotation center, wherein the second relative position is different from the first relative position; and

[0217] An iterative image reconstruction process is performed using the first and second plurality of 2D CBCT projections to generate a 3D reconstructed image of the patient's volume, wherein sub-regions of the patient's volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

[0218] 20. The node (1000) as described in embodiment 19, wherein the processing circuitry is further configured to cause the node to perform the method according to any one of embodiments 2 to 11.

[0219] 21. An imaging system (1200) for generating three-dimensional 3D reconstructed CBCT images of a patient, the imaging system comprising processing circuitry (1219) configured to cause the imaging system to:

[0220] Perform a first CBCT image acquisition process, which includes acquiring a first plurality of two-dimensional 2D cone-beam computed tomography (CBCT) projections of the patient, the first plurality of 2D CBCT projections corresponding to a first plurality of angular positions of the CBCT imaging source around a first rotation center, and the patient being in a first relative position relative to the first rotation center.

[0221] Move one or both of the patient and the CBCT imaging source so that the patient is in a second relative position with respect to a second rotation center of the CBCT imaging source;

[0222] Perform a second CBCT image acquisition process, which includes acquiring a second plurality of 2D CBCT projections of the patient, the second plurality of 2D CBCT projections corresponding to a second plurality of angular positions of the CBCT imaging source around the second rotation center, and the patient being in a second relative position relative to the second rotation center; and

[0223] An iterative image reconstruction process is performed to generate a 3D reconstructed image of the patient's volume, wherein the iterative image reconstruction process is performed using a first plurality of 2D CBCT projections and a second plurality of 2D CBCT projections, wherein sub-regions of the patient's volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

[0224] 22. The imaging system (1200) of embodiment 21, wherein the processing circuit (1219) is further configured to cause the imaging system to perform the method according to any one of embodiments 13 to 18.

[0225] 23. The imaging system (1200) as described in embodiment 21 or 22, wherein the imaging system includes a radiotherapy device (1250).

[0226] 24. A computer program product comprising a computer-readable medium embodying computer-readable code configured to cause the computer or processor, when executed by a suitable computer or processor, to perform the method as described in any one of embodiments 1 to 11.

Claims

1. A computer-implemented method (100) for generating a three-dimensional 3D reconstructed image of a patient, the method comprising: (102) Obtain the first plurality of two-dimensional 2D cone-beam computed tomography (CBCT) projections of the patient, wherein the first plurality of 2D CBCT projections correspond to the first plurality of angular positions of the CBCT imaging source around the first rotation center, and the patient is in a first relative position relative to the first rotation center; (104) Acquire a second plurality of 2D CBCT projections of the patient, wherein the second plurality of 2D CBCT projections correspond to a second plurality of angular positions of the CBCT imaging source around a second rotation center, and the patient is in a second relative position relative to the second rotation center, wherein the second relative position is different from the first relative position; as well as An iterative image reconstruction process (106) is performed using the first and second plurality of 2D CBCT projections to generate a 3D reconstructed image of the patient’s volume, wherein sub-regions of the patient’s volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

2. The method of claim 1, wherein one or both of the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections correspond to a complete CBCT scan.

3. The method of claim 1 or 2, wherein performing the iterative image reconstruction process (106) comprises: Simulations of the first and second plurality of 2D CBCT projections are generated using the currently estimated 3D image of the patient volume; An error function is generated in the projection space, which varies with the simulated 2D CBCT projection and the first and second plurality of 2D CBCT projections; as well as The current estimated 3D image of the patient's volume is updated based on the error function.

4. The method of claim 3, wherein performing the iterative image reconstruction process (106) further comprises: A weighted error function is generated by applying a weighting factor to each element of the error function, wherein one or more elements in the error function corresponding to the positions representing the sub-regions in the projection space are weighted less than the weights of the remaining elements in the error function.

5. The method of claim 4, wherein updating the current estimated 3D image of the patient volume based on the error function includes updating the current estimated 3D image of the patient volume based on the weighted error function.

6. The method of any one of claims 3 to 5, wherein the displacement direction from the first relative position to the second relative position is parallel to the rotation axis of the first and second plurality of 2D angular projections.

7. The method of claim 6 when referring to claim 4 or 5, wherein for the elements in the error function corresponding to the positions in the first plurality of projections, the weighting factor decreases as the position changes in the displacement direction, and for the elements in the error function corresponding to the positions in the second plurality of projections, the weighting factor decreases as the position changes in the direction opposite to the displacement direction.

8. The method of any one of claims 1 to 5, wherein the displacement direction from the first relative position to the second relative position has at least a component parallel to the rotation axis of the first plurality of 2D angular projections.

9. The method of any one of claims 1 to 5 or 8, wherein the displacement direction from the first relative position to the second relative position has at least a component perpendicular to the rotation axis of the first plurality of 2D angular projections.

10. The method of any of the preceding claims, wherein performing the iterative image reconstruction process (106) comprises: Generate image updates in the image space; The image update is applied element-wise with a preconditioner, wherein the preconditioner varies with its position in the image space; as well as The current estimated 3D image of the patient volume is updated using an image update processed by preconditioners.

11. The method of any of the preceding claims, wherein prior to performing the iterative image reconstruction process of (106), the method further comprises: A first indication of the first relative position and a second indication of the second relative position are obtained from hardware position measurements for use in performing the iterative image reconstruction process (106).

12. A node (1000) for generating a three-dimensional 3D reconstructed image of a patient, the node comprising: Memory (1004) that stores computer-executable instructions; and processing circuitry (1002), the processing circuitry being configured to cause the node to: Acquire a first plurality of two-dimensional (2D) cone-beam computed tomography (CBCT) projections of the patient, wherein the first plurality of 2D CBCT projections correspond to a CBCT imaging source at a first plurality of angular positions around a first rotation center, and the patient is at a first relative position relative to the first rotation center; Acquire a second plurality of 2D CBCT projections of the patient, wherein the second plurality of 2D CBCT projections correspond to a second plurality of angular positions of the CBCT imaging source around a second rotation center, and the patient is in a second relative position relative to the second rotation center, wherein the second relative position is different from the first relative position; and An iterative image reconstruction process is performed using the first and second plurality of 2D CBCT projections to generate a 3D reconstructed image of the patient's volume, wherein sub-regions of the patient's volume are represented in both the first plurality of 2D CBCT projections and the second plurality of 2D CBCT projections.

13. The node (1000) of claim 12, wherein the processing circuitry is further configured to cause the node to perform the method of any one of claims 2 to 11.

14. A computer program product comprising a computer-readable medium embodying computer-readable code configured to cause the computer or processor, when executed by a suitable computer or processor, to perform the method as described in any one of claims 1 to 11.

Images