Pseudo-ct image generation

Abstract

Disclosed herein is a method for generating a calibrated pseudo-CT image of at least a portion of a patient for a radiation therapy plan. The method includes obtaining radiation intensity data indicative of attenuation properties of tissue within the patient; and calibrating a first pseudo-CT image of at least a portion of the patient using the radiation intensity data to generate a calibrated pseudo-CT image.

Description

Technical Field

[0001] This invention relates to radiotherapy techniques, and more particularly to systems and methods for generating calibrated pseudo-CT images of at least a portion of a subject suitable for use in a radiotherapy plan. Background Technology

[0002] Radiation therapy can be described as the use of ionizing radiation (such as X-rays) to treat a human or animal body. Typically, radiation therapy is used to treat tumors within a patient or subject. In such treatment, ionizing radiation is used to irradiate and thus destroy or damage the cells that form part of the tumor. However, in order to apply a prescribed dose to a tumor or other target area within a subject's body, the radiation must pass through healthy tissue, thereby irradiating and potentially damaging it in the process. A general objective in this field is to minimize the dose received by healthy tissue during radiation therapy.

[0003] There are many different radiation therapy techniques that allow radiation to be applied from different angles, at different intensities, and for specific time periods. Before radiation therapy, a radiation therapy plan is created to determine how and where radiation should be applied. Typically, such a plan is created with the aid of medical imaging techniques. For example, a CT (computed tomography) scan can be performed on the patient to produce a three-dimensional image of the area to be treated. The three-dimensional image allows the treatment planner to observe and analyze the target area and identify surrounding tissues.

[0004] Different structures within a patient's body (e.g., bones, lungs, muscles, etc.) will attenuate and absorb radiation to varying degrees based on their respective densities. In other words, different tissues within the human body have different radio densities and therefore attenuate and / or absorb radiation to varying degrees. Bones are examples of tissues that are particularly radioactive or nontransmissive. Conversely, soft tissues (e.g., lung tissue) are radiotransmissive. The radio densities of various tissues can be quantified in a manner known to a technician, such as the Hounsfield scale. For radiotherapy planning, it is necessary to obtain information about the radio densities not only of the target area but also of surrounding tissues and any areas of the body through which the radiotherapy beam will pass.

[0005] Traditionally, a CT scan is performed on a patient before treatment. This provides information not only about the patient's geometry through a three-dimensional image, but also about the radiodensity of different tissues and structures within the patient's body. A CT scan typically produces a three-dimensional image composed of voxels, each assigned a CT value. Each voxel is associated with a specific location within the patient's body, and the CT values ​​of the voxels together describe the radiodensity of the tissues within the patient's body. CT values ​​are determined using CT scanning techniques and indicate the attenuation characteristics of tissues within the patient's body. CT values ​​can be expressed in Henle's units and are directly related to the electron density information needed to calculate radiation dose.

[0006] However, CT scans involve irradiating the patient from multiple angles to produce a three-dimensional image. Therefore, a drawback of CT scans is that they increase the patient's radiation dose, even before treatment begins. Furthermore, while CT scans can provide essential information about tissue density for radiotherapy planning, they offer poor soft tissue contrast. This makes it difficult for treatment planners to distinguish certain types of soft tissue. For example, tumors are difficult to see on prostate CT scans because tumors and the prostate have very similar density and attenuation characteristics, thus appearing similar, or even identical, in CT images.

[0007] In contrast, obtaining magnetic resonance (MR) images does not involve exposing the patient to ionizing radiation, and therefore does not deliver any dose to the patient. Instead, an MR scanner uses a magnetic field to excite atoms (usually hydrogen atoms) to emit radiofrequency signals, which can be detected and processed to form a three-dimensional image of the patient. MR images provide good soft tissue contrast, allowing treatment planners to better distinguish, for example, tumor tissue from prostate tissue. A disadvantage of MR scans is that they cannot indicate the attenuation characteristics of tissues within the subject's body, i.e., the patient's tissue radiodensity information, which is needed to create a radiation therapy plan.

[0008] It is now possible to combine MR and CT data to facilitate treatment planning. MR and CT images are known to be acquired independently and then aligned with each other, for example, by aligning the individual discernible locations of features of interest in the two images. Such alignment or fusion may involve rigid or deformable adjustments to the MR image to align it with the CT image, thereby producing a pair of co-aligned images. However, such alignment or registration always involves associated uncertainties or errors.

[0009] Some progress has been made in the field by creating so-called "pseudo-CT" images. Pseudo-CT images are images similar to those obtained from CT scans; that is, these images include tissue radiodensity information but are primarily or entirely derived from MR data (such as a patient's MR images).

[0010] One method for generating pseudo-CT images involves utilizing previous scan data and "atlas" images. In a typical technique using atlas images, multiple CT / MR image pairs from previous scans (either all belonging to the same patient or to different patients) are co-registered. An "average" MR image is created by combining the individual MR images from each co-registered pair. Multiple deformed CT images can be generated by analyzing the deformations required to deform the MR images of a particular MR / CT image pair to obtain the average MR atlas image and applying these same deformations to the corresponding CT images of that pair. This allows for the generation of an "average" CT image. This is the average CT atlas image. The average MR atlas image and the average CT atlas image together provide a mapping that describes how a new MR image should be deformed to generate a pseudo-CT image. For example, a new MR image can be registered to the average MR atlas image. The deformations required for this registration can be applied to the pre-existing average CT atlas image. This produces what is known as a pseudo-CT image, which has been derived from previous scan data (of other patients) and new MR images (of the patient to be treated). It is noteworthy that no new CT scan data is required, therefore no radiation dose is administered to the patient to be treated during the imaging process.

[0011] Techniques based on previous scans or training data can provide usable pseudo-CT images. However, because the pseudo-CT values ​​in the generated pseudo-CT images are based on averages obtained from multiple previous CT and MRI scans, the voxel CT values ​​in any generated pseudo-CT image are inherently inaccurate. If a pseudo-CT image contains voxels with, for example, an average error of 5% in pseudo-CT values, this can have a significant impact on treatment planning and may result in the patient receiving a higher dose of radiation than required.

[0012] Many current spoofing techniques use data from databases of previous scans in a similar manner to the methods described above; however, these previous scans typically do not use the same scanning techniques or equipment used to obtain the new "input" MR images. The prior data used to generate spoofing images is also usually obtained from a range of different patients at various times. These and other factors introduce errors into the calculation of spoofing values, so any treatment plan based on spoofing images may not be entirely optimal. Furthermore, known methods may involve performing an MRI scan to generate spoofing images, and then possibly including actual radiation therapy at a later time, allowing the patient to move between the scan and treatment locations, sometimes significantly. A patient's organs are not completely stationary within the body and may move slightly between scans and treatment, especially over the days, weeks, or even months between MRI scans and radiation therapy. This means that even if the calculated spoofing values ​​are accurate based on the patient's body at the time of the MRI scan, the three-dimensional spoofing images at the time of treatment may be less accurate.

[0013] The object of this invention is to utilize imaging technology to provide information upon which treatment planning is based, while minimizing radiation dose to the patient. This invention seeks to overcome the aforementioned and other disadvantages encountered in the prior art by providing methods and systems for improving (e.g., calibrating) pseudo-CT images used for radiotherapy planning. Summary of the Invention

[0014] The aspects of the invention are defined in the appended independent claims. Optional features are set forth in the dependent claims.

[0015] According to one aspect of the invention, a method is provided for generating a calibrated pseudo-CT image of at least a portion of a patient for radiotherapy planning. The method includes: obtaining radiation intensity data indicative of attenuation characteristics of tissues within the patient. The method further includes: calibrating a first pseudo-CT image of at least a portion of the patient using the radiation intensity data to generate the calibrated pseudo-CT image.

[0016] A pseudo-CT image can be described as a spatial map of the estimated electron density within a subject's body, where the estimated electron density is calculated or determined based on MR image data. Similarly, MR data can include spatial maps of soft tissues within a subject's body.

[0017] Generating a calibration pseudo-CT image can be described as calibrating the first pseudo-CT image.

[0018] The first pseudo-CT image can be generated based on MR data, optionally wherein the MR data is obtained by imaging the patient using an MR imager.

[0019] The method may also include: obtaining a first pseudo-CT image of at least a portion of the patient, for example by imaging the patient with an MR imager to obtain MR data, or by other means (e.g., obtaining the first pseudo-CT image from a database).

[0020] Optionally, the patient can be positioned on a patient support surface while acquiring MR data and radiation intensity data. The patient support surface can be a single, specific patient support surface (e.g., the patient support surface of an MR linear accelerator).

[0021] The first pseudo-CT image can be generated based on previously acquired MR data and previously acquired radiation intensity data. The previously acquired MR data and radiation intensity data can include MR and CT images of the patient or other patients.

[0022] The first pseudo-CT image may include multiple voxels, each corresponding voxel being associated with a pseudo-CT value, and generating a calibrated pseudo-CT image may include comparing acquired radiation intensity data with estimated radiation intensity data based on at least one pseudo-CT value. The method may include updating the individual pseudo-CT values ​​of the first pseudo-CT image based on the comparison to generate a calibrated pseudo-CT image.

[0023] The method may also include: delivering radiation from a radiation source to a patient and obtaining radiation intensity data from a radiation detector. The obtained radiation intensity data indicates the attenuation characteristics of tissues within the patient's body; for example, the data may indicate the degree to which the delivered radiation is attenuated as it passes through the patient.

[0024] Delivering radiation to a patient may also include irradiating a target area within the patient's body to deliver a dose of radiation to the target area according to a radiotherapy plan, and may also include generating a radiotherapy plan based on a first pseudo-CT image.

[0025] The method may also include: delivering a second radiation dose to the patient according to a second radiation therapy plan to deliver the second radiation dose to a target area, the second radiation therapy plan being based on calibrated pseudo-CT images.

[0026] The method may also include: delivering radiation to the patient according to a treatment plan and updating the treatment plan multiple times during the iteration process, each iteration of the iteration process including: delivering radiation to the patient according to the treatment plan to deliver a certain dose of radiation to the target area; obtaining radiation intensity data indicating the attenuation characteristics of tissues in the patient's body; updating the calibration pseudo-CT image using the radiation intensity data; and updating the treatment plan based on the updated calibration pseudo-CT image.

[0027] Optionally, the radiation intensity data may include calibrated CT images of at least a portion of the patient being imaged. The calibrated CT images have a lower resolution than the first pseudo-CT images and can be obtained via a process of administering a lower dose to the patient than a typical clinical CT scan, as described elsewhere herein.

[0028] According to another aspect, a computer-readable medium is provided that includes computer-executable instructions, which, when executed by a processor, cause the processor to perform any of the methods disclosed herein. This computer-readable medium may be a non-transient computer-readable medium.

[0029] According to another aspect, a system is provided for generating calibrated pseudo-CT images of at least a portion of a patient for radiotherapy planning. The system includes a radiation source configured and adapted to deliver radiation to the patient, and a radiation detector arranged to detect the intensity of radiation passing through the patient. The system also includes a controller (e.g., a processor) and a computer-readable medium comprising computer-executable instructions that, when executed by the controller, cause the system to perform any of the methods disclosed herein. The system may also include an MR imager, and may optionally be an MR linear accelerator.

[0030] This document also discloses a system for generating calibrated pseudo-CT images of at least a portion of a subject for radiotherapy planning. The system includes: a subject support surface; a magnetic resonance (MR) imager for acquiring MR images of the subject positioned on the subject support surface; and a radiation source and a radiation detector, the radiation source for delivering radiation to the subject positioned on the subject support surface, and the radiation detector being arranged to detect the intensity of radiation passing through the subject. The system also includes a controller and a computer-readable medium including computer-executable instructions that, when executed by the controller, cause the system to perform a method comprising: imaging the subject using the MR imager to obtain MR data; generating a pseudo-CT image based on the MR data; delivering radiation from the radiation source to the subject on the subject support surface; obtaining radiation intensity data from the radiation detector, the radiation intensity data indicating attenuation characteristics of tissues within the subject; and calibrating the pseudo-CT image using the radiation intensity data to generate a calibrated (i.e., improved) pseudo-CT image.

[0031] This article also discloses a method for generating a calibrated pseudo-CT image of at least a portion of a subject for radiotherapy planning, the method comprising: imaging the subject using an MR imager to obtain MR data; generating a pseudo-CT image of at least a portion of the subject based on the MR data; obtaining radiation intensity data indicating the attenuation characteristics of tissues within the subject; and calibrating the pseudo-CT image using the radiation intensity data to generate the calibrated pseudo-CT image. Attached Figure Description

[0032] Specific embodiments will now be described by way of example only with reference to the accompanying drawings, in which:

[0033] Figure 1 The system according to the present invention is described;

[0034] Figure 2 A flowchart describing the method of the present invention is described;

[0035] Figure 3 Describes pseudo-CT images;

[0036] Figure 4 The implementation of the method of the present invention is described;

[0037] Figure 5 A flowchart describing the iterative process according to the present invention is described; and

[0038] Figure 6 A flowchart illustrating the iterative method according to the present invention is described. Detailed Implementation

[0039] Figure 1 A schematic diagram of a system 100 or apparatus according to the present invention is described. The system is shown by way of example, and those skilled in the art will understand that other systems and apparatuses are equally capable of performing the disclosed methods. The described system includes an MR imaging apparatus 110 and a radiotherapy (RT) apparatus 120. The MR imaging apparatus 110 and the radiotherapy apparatus 120 can be operated and run in a known manner and can together form part of an MR linear accelerator. The system also includes a housing, an aperture, and a movable support surface that can be used to move a patient or other subject into the aperture at the start of an MR scan and / or radiotherapy. The MR imaging apparatus 110, the RT apparatus 120, and the subject support surface actuator 128 are communicatively coupled to a controller or processor 140. The controller 140 is also communicatively coupled to a storage device 145 including computer-executable instructions that can be executed by the controller 140 to perform the currently disclosed methods.

[0040] The subject support surface is configured to move between a first position generally outside the aperture and a second position generally inside the aperture. In the first position, a patient or subject can mount the patient support surface. The support surface and the patient can then move to the second position inside the aperture to image the patient via MR imager 110 and / or to image or treat the patient using RT device 120. The support surface can move to the second position inside the aperture, where the method of the present invention can be performed. The movement of the patient support surface is achieved and controlled by a subject support surface actuator 128, which can be described as an actuation mechanism. The actuation mechanism is configured to move the subject support surface in a direction parallel to and defined by the central axis of the aperture. The terms “subject” and “patient” are used interchangeably herein, such that the subject support surface can also be described as a patient support surface. The subject support surface can also be referred to as a movable or adjustable examination table or worktable.

[0041] MR imaging apparatus 110 is configured to acquire images of a subject positioned (i.e., located on) a subject support surface. MR imaging apparatus 110 may also be referred to as an MR imager. MR imaging apparatus 110 can be, for example, a conventional MR imaging apparatus 110 that operates in a known manner to acquire MR data. MR imaging technicians will understand that such an MR imaging apparatus 110 may include a main magnet 112, one or more gradient coils 114, one or more receiving coils 116, and an RF pulse applicator 118. The operation of MR imaging apparatus 110 is controlled by a controller.

[0042] In an example MR imaging apparatus 110 that can be used in the methods of the present invention, a main magnet 112 has coils surrounding an aperture and is configured to generate a main magnetic field according to standard operation of the MR imager / MR imaging apparatus 110. Gradient coils 114 are also positioned around the aperture such that the coils of the main magnet 112, the gradient coils 114, and the aperture share a common central axis. The gradient coils 114 are positioned and configured to generate a magnetic field with a gradient. Typically, the gradient coils 114 are axially spaced around the aperture. The generated magnetic field has a position-varying intensity, causing hydrogen protons in the patient's body to interact with the generated magnetic field in a manner that varies according to their position. This allows the MRI signal to be spatially encoded. The MR imaging apparatus 110 also includes a radio frequency (RF) pulse applicator 118, also known as an RF scanner, which is configured to emit radio frequency pulses of a specific frequency that interact with the hydrogen protons as they precess around the magnetic field. The RF pulses impart energy (i.e., excite) the hydrogen protons, and when the hydrogen protons relax from this excited state, an MR signal is generated. The MR imaging apparatus 110 also includes a receiving coil 116 configured to receive (i.e., detect) MR signals. The total generated magnetic field is affected when protons relax and / or decay from their excitation, misaligned state. These changes can be detected as induced currents by (one or more) the receiving coils 116. This induced current can then be processed according to known techniques to extract frequency and phase information. While reference has been made to the receiving coil 116, in some embodiments, the system includes a radio frequency system comprising a single coil that both transmits radio signals and receives reflected signals. Alternatively, the system may also include dedicated transmit and receive coils and / or multi-element phased array coils.

[0043] RT device 120 is configured to direct radiation to a patient. RT device 120 includes one or more radiation sources 122 and a radiation detector 124. RT device 120 also includes a collimator 126 adapted and configured to collimate the radiation emitted by the one or more radiation sources 122. Radiation sources 122 are adapted and configured to deliver radiation to a patient positioned on a patient support surface. The one or more radiation sources 122 may include a linear accelerator for generating a radiotherapy beam. The one or more radiation sources 122 may additionally include radiation sources suitable for imaging. MR imaging device 110 and RT device 120 may together form an MR linear accelerator system.

[0044] Typically, the radiation detector 124 is positioned relative to the diameter of the radiation source 122. The radiation detector 124 is adapted and configured to generate radiation intensity data. Specifically, the radiation detector 124 is positioned and configured to detect the intensity of radiation passing through the subject. The radiation detector 124 can also be described as a radiation detection device. The detector may include multiple component detectors, each measuring the radiation intensity at a specific point on the detector. The RT device 120 may include the imaging radiation source 122 and various radiation sources that can be used for treatment. For example, in addition to the treatment beam, the device may include a CBCT (cone-beam CT) device for imaging purposes. In one example, the radiation detector 124 includes an imaging panel (e.g., an "MV" panel) opposite a mega-voltage (MV) radiation beam source, and the MV panel can be used to image the MV radiation beam after it has passed through the patient.

[0045] The RT device 120 also includes an RT actuator 128 (e.g., an actuation mechanism) configured to move the radiation source 122 and the radiation detector 124 relative to the patient support surface. The radiation source 122 and the radiation detector 124 are positioned diametrically opposed to each other to allow the detector to capture and detect radiation emitted by the source. The RT actuation mechanism is configured to rotate the RT mechanism about an aperture axis to allow radiation to be delivered to the patient from a variety of different angles in a generally acceptable manner. The shape of the beam can be varied, and this variation can be controlled, for example, by a collimator 126. The collimator 126 may be a multi-leaf collimator 126 configured to adjust the beam shape according to the radiotherapy plan to adapt to a specific clinical technique.

[0046] The controller is a computer, processor, or other processing device. The controller may be formed from several discrete processors; for example, the controller may include: an MR imaging device processor that controls the MR imaging device 110; an RT device processor that controls the operation of the RT device 120; and a subject support surface processor that controls the operation and actuation of the subject support surface. The controller is communicatively coupled to a memory (i.e., a computer-readable medium). The methods described herein may be embodied on a computer-readable medium, which may be a non-transient computer-readable medium. The computer-readable medium may carry computer-readable instructions arranged to be executed on the controller to cause the controller to perform any or all of the methods described herein.

[0047] As used herein, the term "computer-readable medium" refers to any medium that stores data and / or instructions for causing a processor to operate in a particular manner. Such storage media can include non-volatile media and / or volatile media. Non-volatile media can include, for example, optical discs or magnetic disks. Volatile media can include dynamic memory. Exemplary forms of storage media include floppy disks, floppy disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage media, CD-ROMs (Read-Only Optical Disc Repository), any other optical data storage media, any physical medium having a pattern of one or more holes, RAM (Random Access Memory), PROMs (Programmable Read-Only Memory), EPROMs (Erasable Programmable Read-Only Memory), flash EPROMs, NVRAMs (Non-Volatile RAM), and any other memory chips or cartridges.

[0048] Figure 2 This is a flowchart illustrating a method according to the present invention. The method can be described as a method for generating improved (e.g., calibrated) pseudo-CT images of at least a portion of a subject for radiotherapy planning.

[0049] In step 210, MR data is obtained. This may include imaging the subject using an MR imager to obtain MR data. MR data is associated with or includes MR images. In other words, MR data may be MR images, or the data may be in a form and type that allows for the construction of MR images using known techniques. In some implementations of the disclosed method, MR data is obtained by scanning the patient using an MR imager while the patient is positioned on a patient support surface. MR data may be received via other known methods, such as via email or by retrieving MR data from memory following MR scans of the patient at different times and / or different locations.

[0050] In step 220, a pseudo-CT image is generated based on the MR data. In other words, a pseudo-CT image is generated using the MR data. Pseudo-CT images can be created using known techniques, such as volume density coverage techniques, voxel-based techniques, and / or atlas-based techniques. Those skilled in the art will be familiar with these techniques, but a non-exhaustive and brief overview of some known methods will be described with helpful background information. Volume density coverage techniques are methods for generating datasets that can be used to calculate dose from MR images. Volume density coverage is applied to the entire patient volume in the MR image. For example, known segmentation techniques are used to divide tissues in the MR image into different categories (e.g., soft tissue, bone, and air), and an electron density value or Hounsfield Unit (HU) value is assigned to each category. In another example, if a region of the MR image is determined to be associated with the patient's liver, specific predetermined pseudo-CT values ​​are assigned to the individual voxels in that region of the image. Therefore, it should be understood that volume density techniques cannot account for any variations in attenuation characteristics within a specific tissue.

[0051] Voxel-based techniques are an alternative category of methods that can be used to create pseudo-CT images. In typical voxel-based techniques, the MR intensity of a voxel in an MR image is used to assign a HU value. These methods typically utilize a predetermined mapping between MR intensity and HU values. Such a mapping can be created by analyzing co-registered CT and MR image pairs in a database. The MR intensity of a specific voxel in an MR image can be compared to the HU value of the corresponding voxel from the same patient in a CT image. This mapping can be created by comparing co-registered MRI and CT images, and it can be used in conjunction with linear interpolation techniques to allow assigning HU values ​​to a 3D mesh of voxels based on MR intensity in the MR image, thereby creating pseudo-CT images. Machine learning techniques are particularly well-suited for creating these mappings if suitable training data (e.g., a database of co-registered MRI and CT images) is provided.

[0052] Figure 2 Step 220 of the flowchart can be referenced. Figure 3 To explain, Figure 3 A simplified schematic diagram of a pseudo-CT image 310 of patient 320 is shown. For simplicity, in Figure 3 Only a two-dimensional pseudo-CT image 310 is described. The pseudo-CT image comprises multiple voxels. Voxels form a three-dimensional mesh representing at least a portion of the patient's body. As those skilled in the art will understand, voxel size depends on multiple factors, and voxel size determines the resolution of the pseudo-CT image 310. Each voxel in the pseudo-CT image 310 is associated with a corresponding pseudo-CT value. Figure 3Four representative voxels are depicted in the pseudo-CT image 310. Each voxel has an assigned pseudo-CT value, described in the figure as μ1', μ2', μ3', and μ4'. Each pseudo-CT value is intended to describe (i.e., indicate) the radioactivity at a specific point within the patient's body. Therefore, the pseudo-CT value also signifies an indication of the attenuation characteristics of tissues within the patient's body.

[0053] In other words, each pseudo-CT value is an estimate or approximation of the CT value determined when the patient will undergo a CT scan. However, the determined pseudo-CT values ​​may be inaccurate. The inaccuracy of the determined pseudo-CT values ​​can affect the RT treatment plan. This may result in a prescribed radiation dose that is higher than the required dose, or in a situation where the optimal dose of radiation is not delivered to the tumor.

[0054] exist Figure 2 Step 230 involves obtaining radiation intensity data. This radiation intensity data can be described as transmission data or radiation transmission data. To obtain the radiation intensity data, radiation source 122 transmits radiation through the patient and measures the intensity of the radiation after its transmission through the patient in a manner known to those skilled in the art and generally described elsewhere herein. Finally, in step 240, the radiation intensity data is used to generate a calibrated or improved pseudo-CT image. As used herein, improving the pseudo-CT image means creating a new pseudo-CT image with more accurate pseudo-CT values. Thus, the pseudo-CT image is improved. Another way to consider this improvement in the pseudo-CT image is to calibrate it using the radiation intensity data.

[0055] The method of the present invention is advantageous for several reasons. It retains the advantages of existing pseudo-CT techniques. A full CT scan is not required, thus reducing the total radiation dose to the patient. Furthermore, according to the method disclosed in the present invention, more accurate pseudo-CT images are obtained by using pseudo-CT image calibration / improvement techniques. More accurate pseudo-CT images have more accurate pseudo-CT values, and providing such improved pseudo-CT images means that treatment planning can be more accurate, thereby ensuring that the radiation dose can be better targeted to the target area and reducing the dose to surrounding tissues.

[0056] Technicians will understand that these steps do not need to be followed. Figure 2The steps are performed in the order shown. For example, the order of steps 210 and 230 can be reversed. Alternatively, steps 210 and 230 can be performed simultaneously in a suitably configured system (e.g., an MR linear accelerator). This contrasts with existing known methods, in which MR imaging, subsequent CT scans, and subsequent RT treatment are performed at different times. In existing methods, imaging and treatment can also be performed on different patient support surfaces depending on which machine (e.g., an MR imager, a CT scanner, or RT device 120) is used. These methods can be problematic because the distribution of tissues within the patient's body can vary significantly between scans and treatments. The properties of the tissues can also vary. For example, variations in the amount and location of gas in the patient's intestines or fluid in the patient's lungs can lead to differences in tissue distribution between the scan and the actual treatment time. Treatment planned based on images acquired at different times and in different patient positions may result in less accurate RT treatment.

[0057] In an exemplary implementation of this method, the subject is imaged in step 210 while the patient is on the same patient support surface, and radiation intensity data is obtained in step 230. In other words, the patient is positioned approximately in the same location and has approximately the same tissue geometry when the MR image is obtained and when the radiation intensity data is obtained.

[0058] In some implementations, steps 210 and 230 are performed simultaneously. This function ensures that when radiation intensity data is acquired, the spatial distribution of tissue in the MR image matches the spatial distribution of the tissue. Embodiments in which MR and radiation data are acquired approximately simultaneously and / or when the patient is in approximately the same position are advantageous because the resulting images will have more similar geometric characteristics, thus facilitating registration of the acquired data and images. Moreover, when MR and radiation intensity data are acquired simultaneously, it means that various types of data represent the patient in a specific geometry (i.e., position), which means that the effects of conscious or unconscious patient movement can be mitigated, which could otherwise introduce inaccuracies into the calibration of spurious CT images.

[0059] Examples of treatments performed while the patient is on the same surface are particularly advantageous. In these techniques, the patient is imaged from the same location as the patient being treated. Furthermore, these techniques ensure that the image represents the geometry within the patient's body at the time of treatment (e.g., tissue distribution). This, in turn, ensures more accurate and efficient treatment planning.

[0060] Therefore, the disclosed method may further include the following steps: positioning the subject on the patient support surface when the support surface is in a first position outside the hole, and actuating the support surface to a second position inside the hole. Once the patient and the support surface are inside the hole, at least the following steps can be performed. Figure 2Steps 210 and 230. In some embodiments, MR data and radiation intensity data are acquired simultaneously or overlapping in time. After steps 210 and 230 are completed, the support surface is actuated to the first position again, thereby allowing the patient to leave the support surface.

[0061] The method of applying radiation to the patient in step 230 will vary depending on how the pseudo-CT image is calibrated in step 240. Many implementations are possible, and some will be described in detail below.

[0062] In a first exemplary implementation of the disclosed method, in step 230, a single instance of radiation is directed at the patient to obtain data, which is then used to improve (i.e., calibrate) the pseudo-CT image. The obtained radiation intensity data can be described as normalized data or calibration data. In one example, radiation source 122 is configured to emit a low-dose radiation beam (e.g., an MV radiation beam). In this example, a single low-dose radiation beam passes through the patient, and the accumulated MV beam absorption is measured along the path of the beam using radiation detector 124. Radiation source 122 emits radiation in a single direction and / or at a single projection angle to obtain radiation intensity data indicative of the attenuation characteristics of tissues within the patient's body. "Low dose" can be achieved by applying radiation only for a very short period of time (e.g., significantly shorter than the length of time for which a prescribed dose of radiation will be delivered to the target area).

[0063] Figure 4 This is a simplified diagram based on the first implementation method. Figure 4 The schematic RT apparatus described includes a radiation source 402, a collimator 404, and a detector 406. The radiation source 402 directs radiation toward the patient in a first direction and at a first projection angle relative to the patient. The radiation may be in the form of a parallel radiation beam, and / or in an implementation using a cone-beam computed tomography (CBCT) apparatus, it may be in a cone shape. The radiation source 402 emits radiation with an initial intensity I0 or an initial intensity distribution. Typically, the radiation is also emitted at a specific frequency. In other words, the radiation consists of monochromatic photons. In a simple example, the initial radiation beam may consist of N monochromatic photons emitted per unit time, and the radiation detector 406 may detect N-ΔN photons per unit time. The reduction in the number of photons ΔN is due to the attenuation of the radiation within the patient's body. Thus, radiation data (e.g., radiation intensity data) indicative of the attenuation characteristics of tissues within the patient's body are obtained.

[0064] and Figure 3 Similarly, for the sake of simplicity, in Figure 4Only two dimensions are shown. The different tissues of the patient are schematically represented as simple rectangles with representative attenuation values ​​μ1, μ2, μ3, and μ4. If a full CT scan of the patient is performed, μ1, μ2, μ3, and μ4 are the CT values ​​that would be assigned to the described regions of the patient's body. As radiation passes through different regions of tissue within the patient's body, the attenuation of the radiation is cumulative. Detector 406 measures the intensity of the radiation passing through the patient and transmits the intensity data to controller or processor 408.

[0065] In this first implementation, in step 240, the obtained radiation intensity data is used to improve the existing pseudo-CT image. (Reference) Figure 4 A simplified diagram should be understood, whereby radiation passing through two regions of tissue with attenuation coefficients μ1 and μ2 will be attenuated depending on the amounts of both μ1 and μ2. Therefore, the detected intensity I1 at the detector can be used in conjunction with knowledge of the original radiation intensity I0 to determine the cumulative attenuation value indicating μ1+μ2. Similarly, the cumulative attenuation value indicating μ3+μ4 can be determined. The cumulative attenuation value indicates the reduction in beam intensity after passing through the tissue of the patient's body. Multiple cumulative attenuation values ​​can be determined in this way, for example, by guiding the radiation beam through the patient in a specific direction. In other words, cumulative attenuation information is obtained using a single projection angle. Referring to μ1+μ2, i.e., using the additive relationship between the coefficients. However, those skilled in the art should understand that the specific relationship between the detected radiation intensity I1, the original radiation intensity I0, and the attenuation characteristics of the various regions of the patient's body will depend on the symbols and definitions used. Regardless of the symbols, those skilled in the art will know how to generate standard CT images.

[0066] The cumulative attenuation information obtained from radiation intensity data can be compared with the pseudo-CT values ​​of pseudo-CT images. This is provided as an illustrative example and reference. Figure 3 A schematic pseudo-CT image can be used to compare μ1'+μ2' (i.e., the cumulative attenuation value suggested or predicted by the pseudo-CT values ​​of the pseudo-CT image) with μ1+μ2 (i.e., the measured or obtained cumulative attenuation value obtained from radiation intensity data). Radiation intensity information obtained from a single projection angle can be used as standardized data for calibrating the entire pseudo-CT image. Based on this comparison, the pseudo-CT values ​​can be updated to better reflect the characteristics of the patient's body. In other words, the estimated attenuation values ​​assigned to individual voxels in the pseudo-CT image can be updated based on the obtained radiation intensity data to produce a calibrated (or in other words, improved) pseudo-CT image that better reflects the characteristics of the tissues within the patient's body.

[0067] As described above, a pseudo-CT image comprises multiple voxels, each associated with a pseudo-CT value. The pseudo-CT value can be described as an estimate indicating electron density. Therefore, calibration can be described as including: comparing the estimated electron density information in the pseudo-CT image with obtained electron density information, and updating the individual estimated electron density values ​​of the pseudo-CT image based on this comparison to produce a calibrated pseudo-CT image. In other words, the initial pseudo-CT image comprises multiple voxels, each associated with a pseudo-CT value. The initial pseudo-CT image can be calibrated by comparing the obtained radiation intensity data with the pseudo-CT values, and based on this comparison, the individual pseudo-CT values ​​of the pseudo-CT image can be updated to produce a calibrated pseudo-CT image.

[0068] To update pseudo-CT values ​​based on radiation intensity data, numerous mathematical methods can be used. For example, interpolation methods can be used to update pseudo-CT values, where the obtained data informs the interpolation method. Similarly, the obtained data can be used to inform one of several possible curve-fitting techniques aimed at achieving a "best fit" between the cumulative attenuation value determined based on the estimated pseudo-CT values ​​and the measured cumulative attenuation value determined from the radiation intensity data.

[0069] In one example, when comparing the cumulative attenuation information obtained from radiation intensity data with the estimated cumulative attenuation information obtained from pseudo-CT images, the pseudo-CT values ​​are determined to be 5% too low on average based on the measured data. In a simple example, the individual pseudo-CT values ​​of the pseudo-CT images can be increased by 5% to produce more accurate (i.e., improved) pseudo-CT images.

[0070] In another example, the initial pseudo-CT image is segmented into image regions associated with different tissues or tissue types (e.g., water, bone, and air). Therefore, each voxel in the pseudo-CT image is labeled or annotated with the tissue type it is believed to represent. Segmentation techniques (including computer-based segmentation techniques) are well known to those skilled in the art and will not be discussed further. In step 240, this information can be included as a factor in the calibration process to selectively update the pseudo-CT values ​​of the pseudo-CT image based on the tissue type represented by a particular voxel. For example, when obtaining radiation intensity data that includes cumulative attenuation information, it can be determined that radiation attenuating primarily through bone tissue is attenuated to a level that is well consistent with the attenuation expected based on the pseudo-CT image. However, the segmented pseudo-CT or MRI image indicates that radiation passes through a relatively large amount of soft tissue, resulting in less accurate cumulative attenuation information. Using knowledge of the radiation beam path, a spatial map of the patient's body provided by the MR data and / or pseudo-CT image, and image segmentation techniques, it can be determined how the pseudo-CT values ​​of the pseudo-CT image should be updated / adjusted based on tissue type. In other words, voxels associated with different tissue types can be updated based on the tissue type they represent, thus the disclosed method allows pseudo-CT images to be updated based on different tissue types within a patient.

[0071] This is particularly useful for lung tissue. A particular disadvantage of current pseudo-CT techniques is that they cannot assign accurate pseudo-CT values ​​to lung tissue, partly because lung tissue is non-uniform and also because lung tissue cannot produce a strong MR signal due to the low proton density in the lungs. Incorporating segmentation techniques into the method described above means that, in step 240, more accurate pseudo-CT values ​​can be assigned to voxels associated with the lung tissue in the patient's body.

[0072] As described above, the obtained radiation intensity data allows for improved accuracy of existing spoofed CT images while minimizing the radiation dose delivered to the patient. Of course, while a “single” beam, angle, or direction is referenced, it should be understood that a small number of beams can be used to achieve the goal of obtaining radiation intensity data to improve spoofed CT images while ensuring that the patient's radiation exposure is less than that delivered by a full CT scan. This results in more accurate spoofed CT images while delivering a radiation dose to the patient that is significantly lower than that of a full CT scan.

[0073] In a second implementation of the disclosed method, the radiation used to obtain radiation intensity data in step 230 is the radiation to be used in RT treatment. In other words, the treatment beam is used to obtain radiation intensity data.

[0074] The degree to which tissue attenuates radiation, and correspondingly, the tissue's sensitivity to radiation, depends on the characteristics of the applied radiation. Specifically, the attenuation characteristics of tissue depend on the frequency of the applied radiation. If pseudo-CT values ​​are improved based on a first type of radiation with first characteristics (e.g., X-rays with a first frequency), the resulting improved pseudo-CT image may not accurately describe how tissues within the patient's body will interact with a treatment beam having characteristics different from those of X-rays. For example, when the treatment beam has a second, different frequency. Therefore, using radiation intensity data obtained using the treatment beam to improve pseudo-CT images allows for the generation of pseudo-CT images that better describe how the treatment beam will interact with the patient's body.

[0075] As is known to those skilled in the art, radiotherapy typically involves applying radiation to a patient from multiple angles to accumulate a specific dose in a target area while minimizing the radiation dose to surrounding healthy tissue. Therefore, RT treatment may involve applying multiple consecutive beams of radiation, each with different characteristics (e.g., different beam shapes, radiation frequencies, intensities, and application angles). For example, multiple therapeutic radiation beams may be applied at different angles and intensities, each beam delivering a specific portion of a predetermined dose to the target area according to the treatment plan. Thus, radiation is delivered to the subject to deliver a total cumulative dose of radiation to the target area from multiple therapeutic beams according to the radiotherapy plan.

[0076] In the second implementation, treatment can be planned based on an initial (i.e., uncalibrated) pseudo-CT image. In this implementation, the application of the therapeutic radiation beam serves two purposes: first, to deliver a specific dose of radiation to the target area according to the treatment plan; and second, to obtain radiation intensity information, which can be used to refine the pseudo-CT image to facilitate the planning of the remaining radiotherapy. After the therapeutic radiation beam has been applied to the patient, radiation intensity data has been obtained, and a portion of the predetermined dose has been delivered according to the treatment plan, the pseudo-CT image can be calibrated or refined using the methods described above with respect to the first implementation or elsewhere herein.

[0077] In one example, the method may include: initiating treatment using a treatment plan created using an initial pseudo-CT image; delivering a first RT beam; obtaining radiation intensity data (e.g., by analyzing an image generated from the radiation intensity data); and considering whether the density values ​​obtained from the radiation intensity data match (i.e., conform) the density of the same region in the pseudo-CT image. If they do not match or conform to the estimated values ​​of the pseudo-CT values, the values ​​of the pseudo-CT image can be modified. The new density values ​​can then be used to deliver the next treatment beam, and the process can be repeated.

[0078] Taking this example further, in the further disclosed method, an iterative process is used to continuously improve and enhance the accuracy of pseudo-CT images. The iterative process involves delivering radiation to the subject according to the treatment plan and updating the treatment plan multiple times. (Reference) Figure 5 It describes a flowchart of the iterative process according to the present invention.

[0079] In step 510, an initial radiotherapy plan is generated based on the initial pseudo-CT image. This process may include using the pseudo-CT image to determine the angle, intensity, and delivery method of the radiotherapy beam. For example, a dose calculation algorithm can use the pseudo-CT values ​​of voxels from the pseudo-CT image to determine the optimal angle for applying radiation. Treatment simulation can be performed using computer software that can be used for the geometric, radiological, and dosimetric aspects of the planned treatment. The treatment plan may require many different shapes of radiation delivered by beams of varying orientations and intensities. Technicians will be familiar with radiotherapy planning, and further details are not required here.

[0080] In step 520, radiation is delivered to the subject according to the initial radiotherapy plan. The delivered radiation may be, for example, a single beam of specific intensity, or a beam with a specific intensity distribution, applied from a specific angle for a predetermined duration. These factors are determined according to the initial treatment plan. The radiation dose received by the target area, depending in part on the accuracy of the initial pseudo-CT image, is equal to the radiation dose that should be delivered by the first treatment beam as specified in the initial treatment plan. For example, the delivered dose may be a portion of the total dose to be delivered to the target area. In step 530, radiation intensity data is obtained. When the treatment beam is applied to the subject, i.e., when the radiation is delivered to the subject, the radiation detector obtains radiation intensity data.

[0081] In step 540, the pseudo-CT image is improved / calibrated using the techniques disclosed herein using radiation intensity data, and in step 550, the treatment plan is updated based on the improved pseudo-CT image. For example, a dose calculation algorithm can use the improved pseudo-CT values ​​to determine the characteristics of the next radiation beam to be applied to the patient. The updated treatment plan is improved because it is based on a more accurate image of the subject (i.e., a pseudo-CT image including more accurate pseudo-CT values). In step 560, radiation is delivered to the patient according to the updated treatment plan. The characteristics of this therapeutic radiation are determined based on the more accurate pseudo-CT image and are therefore better optimized to further minimize the radiation dose to healthy tissue. Radiation intensity data is also obtained as the radiation passes through the patient's body.

[0082] In step 570, it is determined whether the stopping criterion has been met. If the stopping criterion has not been met, the obtained radiation intensity data is used to improve the pseudo-CT image, and steps 540, 550, and 560 are repeated until the stopping criterion is met. When the stopping criterion is met, the iteration process ends in step 570.

[0083] Stopping criteria can take many forms. A stopping criterion can be whether a predetermined or dynamically determined threshold has been reached. For example, a stopping criterion could be whether a predetermined number of iterations has been performed. Stopping criteria can be related to, associated with, or based on the treatment plan. For example, a stopping criterion could include determining whether the total cumulative dose has been applied to the target area. Thus, a stopping criterion could simply be "Is treatment complete?". Alternatively, a stopping criterion can be related to, associated with, or based on the accuracy of pseudo-CT values ​​in pseudo-CT images. For example, determining whether a stopping criterion has been met could include determining that the pseudo-CT values ​​have reached a specific level of accuracy, or are otherwise sufficiently accurate. For example, a pseudo-CT value can be considered sufficiently accurate after determining that the value has not changed by more than a certain percentage amount in previous iterations or during a set number of previous iterations. Thus, the method could include determining that the pseudo-CT images are accurate enough to optimize treatment, and that further improvement of the pseudo-CT images is not required. This iterative approach allows for efficient treatment while ensuring that the treatment plan is optimized.

[0084] Using an iterative approach, the dose applied to the patient by each corresponding treatment beam is continuously updated based on increasingly accurate images of the patient. Since radiation intensity data is obtained from the treatment radiation beam that will be delivered anyway, the dose to the patient does not increase beyond existing methods that base RT treatment on pseudo-CT images. Furthermore, the treatment can become increasingly refined and precise, ensuring that the radiation dose to healthy tissue is minimized while the optimal dose is delivered to the target area.

[0085] In an alternative embodiment, instead of obtaining MR data in step 210 and specifically generating a pseudo-CT image using, for example, an MR imaging apparatus in step 220, the pseudo-CT image can be obtained and / or received in any manner and from any source. Receiving a pseudo-CT image may include retrieving it from memory. Currently disclosed calibration methods can be used to improve / calibrate any pseudo-CT image, and therefore those skilled in the art will understand that the method according to the invention can be performed by the following steps: receiving a pseudo-CT image of at least a portion of a subject, which is generated based on MR data associated with the subject's MR image; obtaining radiation intensity data indicating the attenuation characteristics of tissues within the subject; and calibrating the pseudo-CT image using the radiation intensity data to generate a calibrated pseudo-CT image.

[0086] In a third implementation of the currently disclosed method, the radiation intensity data obtained in step 230 can be a coarse CT scan. A coarse CT scan is not a “full” or typical CT scan. Instead, fewer beams and / or fewer projection angles are used. In one example, 10-beam and low-dose MV CBCT images are obtained. The resulting coarse CT image may have a diameter of approximately 1 cm. 3 The voxel size is limited, resulting in a resolution that would make its use in dosing planning impractical. However, coarse CT scans can be used to calibrate spurious CT images, and the radiation dose applied to the patient is much smaller compared to a full CT scan.

[0087] In one example, image reconstruction techniques are used after a small amount of cone beam is applied to the patient to create a reconstructed, coarse CT image. Therefore, the radiation intensity data in this example may include CT projection data. Image reconstruction techniques are used to generate tomographic images from projection data acquired at different angles around the patient. As is known to those skilled in the art, the type of reconstruction technique used affects image quality and may therefore affect the radiation dose typically calculated based on the image, which is then used to plan the treatment. Generally, it is desirable to achieve the highest possible spatial resolution for a given radiation dose. Suitable reconstruction techniques include those utilizing the Feldkamp algorithm and the Simultaneous Algebraic Reconstruction Technique (SART). SART is particularly useful, for example, in the current situation, where limited projection data is available. Various related SART-based techniques can also be used, such as OS-SART, FA-SART, VW-OS-SART, SARTF, etc. These and other reconstruction techniques are generally known to those skilled in the art.

[0088] When generating coarse, low-resolution CT images, they can be compared with higher-resolution pseudo-CT images to improve the accuracy of the pseudo-CT values ​​in the pseudo-CT images.

[0089] In a further disclosed method, pseudo-CT images can be improved based on additional MR data. MR data is acquired during radiotherapy, and in some examples, MR data is acquired continuously during treatment. Updating pseudo-CT images based on MR data acquired during radiotherapy means that the treatment plan can be updated to account for changes in patient geometry during treatment. This further disclosed method can be performed independently of other methods and implementations disclosed herein, but can also be performed in combination with and / or together with any other methods and implementations described herein to provide a particularly effective method for improving pseudo-CT images.

[0090] Figure 6A flowchart illustrating an iterative method according to the invention is described. In step 610, MR data is acquired in a manner known to those skilled in the art and described elsewhere herein. For example, an MR image of the patient is obtained. Simultaneously with acquiring the initial MR image, the patient is in an initial position on a patient support surface. In this initial position, for example, the patient's limbs may have an initial orientation, and fluids and gases, as well as other tissues within the patient's body, may be arranged in a specific manner. A pseudo-CT image is generated based on the MR data using known techniques.

[0091] In step 620, an initial radiotherapy plan is generated. The treatment plan is generated at least in part based on the initial pseudo-CT image. For example, pseudo-CT values ​​can be input into the dose calculation algorithm in a similar or identical manner to when the pseudo-CT image is actually a real CT image. In step 630, radiation is delivered to the subject according to the initial treatment plan. As described elsewhere herein, the initial treatment plan may require that this initial radiation delivery include an initial treatment beam delivered at a specific angle, shape, frequency, and intensity. This initial radiation delivery applies a portion of the total prescribed dose to the target area.

[0092] As described in the background section of this application, CT images are commonly used in existing methods to assist in planning radiotherapy. However, in existing methods, there is inevitably a time delay between obtaining CT images, planning treatment, and applying the treatment beam according to the treatment plan. During this time delay, the patient may have moved, meaning that treatment radiation is applied based on a pseudo-CT image that no longer fully describes the patient's geometry on the support surface at the time of treatment. For example, the patient's limbs may have moved to a different orientation. Patient movement can include conscious movement (e.g., displacement of weight on the support surface and slight movement of the patient's limbs) for comfort. Patient movement can also be caused by subconscious movement (e.g., those caused by biological processes such as breathing and the movement of gases and fluids in the body). This can result in suboptimal doses being applied to healthy tissue and target areas as part of the treatment.

[0093] In step 640, additional MR data is obtained. For example, additional MR images are obtained. In step 650, the additional MR data is used to improve the pseudo-CT image. In other words, in this step, the initial pseudo-CT image is improved based on the additional MR data. For example, the geometry of the pseudo-CT image can be adjusted based on the geometry of the additional MR image. In this way, the pseudo-CT image is updated to better reflect the patient's geometry during treatment, and radiotherapy is planned based on more accurate images.

[0094] In step 650, additional MR data is used to improve the pseudo-CT image. This may include examining the radiation delivered to the patient and applying corrections and improvements to align with the patient's current geometry. For example, a patient's breathing will move the patient's chest cavity and lung tissue. This movement may mean that points in spaces previously occupied by the patient's lung tissue are now occupied by the patient's ribs.

[0095] In step 660, the treatment plan is updated based on the improved pseudo-CT images. For example, the treatment plan is updated to reflect the updated geometry of the patient in the images. In step 670, radiation is delivered to the patient according to the updated treatment plan. For example, additional MR images may indicate that the gas in the patient's lungs or intestines has shifted relative to the rest of the patient's body compared to the initial MR images used to prepare the initial treatment plan, thereby allowing for a more optimized angle for the next treatment beam of radiotherapy.

[0096] In step 680, it is determined whether the stopping criteria are met. Step 680 may include determining whether treatment has been stopped. Step 680 is optional, and in some examples, steps 640 through 670 are performed consecutively until treatment is completed.

[0097] If the stopping criteria have been met or satisfied, or if it is determined in step 680 that the iterative process should stop, then the process ends in box 690.

[0098] If the process continues, then in step 640 additional MR data is obtained, in step 650 the additional MR data is used to improve the current iteration of the pseudo-CT images, and in step 660 the treatment plan is updated based on the updated pseudo-CT images. In step 670, additional radiation is delivered to the patient based on (i.e., according to) the updated treatment plan.

[0099] It should be understood that the disclosed method allows for dynamic updating of pseudo-CT images and treatment plans as the patient, and, for example, fluids and gases within the patient's body, move during treatment. In this way, the method ensures that the pseudo-CT accurately depicts the patient's geometry during treatment. A particular advantage of this method is that the patient's breathing can be taken into account during treatment, allowing for more accurate and efficient delivery of the prescribed dose. When the patient inhales, gas is carried into the lungs, and tissues within the patient's body shift as the lungs expand. Continuously acquired additional MR data allows for tracking this change, and changes in tissue position can be accounted for during radiotherapy.

[0100] In a particularly advantageous, publicly available example, the combination Figure 5 and Figure 6 The method enables the pseudo-CT value of the pseudo-CT image to be continuously improved based on radiation intensity data obtained from each treatment beam, and the geometry of the pseudo-CT to be continuously updated based on MR data acquired during treatment.

[0101] The above implementations and arrangements have been described by way of example only, and should be considered in all respects as illustrative rather than restrictive. It should be understood that variations can be made to the described implementations and arrangements without departing from the scope of the invention.

Claims

1. A system for generating calibrated pseudo-CT images of at least a portion of a patient for radiotherapy planning, the system comprising: A radiation source and a radiation detector, wherein the radiation source is configured to generate a radiotherapy beam for delivering radiation to the patient, and the radiation detector is arranged to detect the intensity of radiation passing through the patient; Controller; as well as A computer-readable medium comprising computer-executable instructions that, when executed by the controller, cause the system to: Obtain a first pseudo-CT image of at least a portion of the patient; Using the radiotherapy beam, radiation intensity data indicating the attenuation characteristics of the tissues within the patient are obtained; as well as The first pseudo-CT image of at least a portion of the patient is calibrated using the radiation intensity data to generate the calibrated pseudo-CT image.

2. The system according to claim 1, characterized in that, The first pseudo-CT image is generated based on MR data, which is obtained by imaging the patient using an MR imager.

3. The system according to claim 2, characterized in that, Obtaining the first pseudo-CT image includes: imaging the patient using the MR imager to obtain the MR data.

4. The system according to claim 3, characterized in that, When the MR data and radiation intensity data are obtained, the patient is positioned on a patient support surface.

5. The system according to any one of the preceding claims, characterized in that, The first pseudo-CT image is also generated based on MR data and radiation intensity data obtained prior to the generation of the first pseudo-CT image.

6. The system according to any one of claims 1 to 4, characterized in that, The first pseudo-CT image includes multiple voxels, each corresponding voxel being associated with a pseudo-CT value, and wherein, Generating the calibration pseudo-CT image includes: comparing the obtained radiation intensity data with estimated radiation intensity data based on at least one of the pseudo-CT values; and updating the respective pseudo-CT values ​​of the first pseudo-CT image based on the comparison to generate the calibration pseudo-CT image.

7. The system according to any one of claims 1 to 4, characterized in that, The computer-executable instructions also cause the system to: deliver radiation from the radiation source to the patient and obtain radiation intensity data from the radiation detector.

8. The system according to claim 7, characterized in that, Delivering the radiation to the patient further includes irradiating a target area within the patient's body to deliver a dose of radiation to the target area in accordance with a radiotherapy plan.

9. The system according to claim 8, characterized in that, The computer-executable instructions also cause the system to generate the radiotherapy plan based on the first pseudo-CT image.

10. The system according to claim 8, characterized in that, The computer-executable instructions also enable the system to: A second radiation dose is delivered to the patient according to a second radiotherapy plan, which is based on the calibrated pseudo-CT image.

11. The system according to any one of claims 1 to 4, characterized in that, The computer-executable instructions further cause the system to: deliver radiation to the patient according to a treatment plan and update the treatment plan multiple times during the iterative process, each iteration of the iterative process including: Radiation is delivered to the patient according to the treatment plan to deliver a specific dose of radiation to the target area; Obtain radiation intensity data that indicates the attenuation characteristics of the tissues within the patient; Update the calibrated pseudo-CT image using the radiation intensity data; and The treatment plan is updated based on the updated calibrated pseudo-CT images.

12. The system according to any one of claims 1 to 4, characterized in that, The radiation intensity data includes at least a portion of the calibrated CT images of the patient, the calibrated CT images having a lower resolution than the first pseudo-CT image.

13. The system according to any one of claims 1 to 4, characterized in that, The first pseudo-CT image includes multiple voxels, and each corresponding voxel is associated with a pseudo-CT value and tissue type; and, The process of generating the calibration pseudo-CT image further includes updating the pseudo-CT value of each voxel based on the radiation intensity data and the tissue type.

14. The system according to claim 1, characterized in that, Also includes: An MR imager, which is configured to acquire MR data.

15. The system according to claim 14, characterized in that, It also includes a patient support surface, wherein the MR imager is configured to acquire the MR data, and the radiation detector is configured to detect the intensity of radiation passing through the patient when the patient is positioned on the patient support surface.

16. A computer-readable medium for use in a system according to any one of claims 1 to 15, comprising computer-executable instructions, which, when executed by a processor, cause the processor to perform: Obtain at least a portion of the patient's first pseudo-CT image; Using a radiation therapy beam, obtain radiation intensity data that indicates the attenuation characteristics of the tissues within the patient; as well as The first pseudo-CT image of at least a portion of the patient is calibrated using the radiation intensity data to generate a calibrated pseudo-CT image.

Images