Image reconstruction method and device, electronic equipment, storage medium and program product

Abstract

The invention relates to the technical field of imaging, and particularly discloses an image reconstruction method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: in a proton beam delivery period, controlling detection equipment to continuously detect positron generated by proton induction so as to determine a plurality of single events; partitioning the proton beam delivery period according to the counting rates of the single events at different moments; performing coincidence operation on the plurality of single events to determine each coincidence event; based on the partition result of the proton beam delivery period, image reconstruction is carried out on the coincidence event set in each target interval, and each target interval corresponds to a single energy layer. The problem that single-radiation-field or single-energy-layer dose information cannot be accurately extracted due to PET signal overlapping caused by a multi-radiation-field delivery mode or a multi-energy-layer delivery mode is solved.

Description

Technical Field

[0001] This application relates to the field of imaging technology, and in particular to an image reconstruction method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] Proton therapy (PT) primarily utilizes proton beams to precisely irradiate tumors, achieving maximum tumor ablation and minimal damage to normal tissues. Its main advantage lies in its unique "Bragg peak" effect: protons release most of their energy at a specific depth and then rapidly decay. By precisely adjusting the maximum energy of the radiation to the lesion site during treatment, concentrated irradiation of the lesion can be achieved without causing excessive damage to normal cells. Thanks to this advantage, proton therapy is widely recognized as a key means of achieving precision medicine in radiotherapy and has become a major area of ​​development both domestically and internationally.

[0003] PET (Positron Emission Tomography) can dynamically capture transient positron-emitting nuclides (such as...) generated by the interaction of proton beams with tissues. 15 O、 11 C), thus providing three-dimensional information with high spatiotemporal resolution for range verification. However, in clinical practice, multi-field delivery modes (i.e., irradiating the tumor from different angles using multiple proton beams to improve dose distribution conformity) and multi-energy-layer delivery modes (i.e., delivering proton beams of different energies in layers within the same field to cover different depths of the tumor) cause severe overlap of PET signals. The cross-irradiation of multiple fields further exacerbates the signal aliasing, making it difficult for traditional image reconstruction algorithms to distinguish the contributions of each component. This limitation hinders the accurate extraction of dose information from single-field or single-energy-layer PET signals. Summary of the Invention

[0004] Therefore, it is necessary to provide an image reconstruction method, an image reconstruction apparatus, an electronic device, a computer-readable storage medium, and a computer program product to address the above problems.

[0005] According to a first aspect of the embodiments of this application, an image reconstruction method is provided, the image reconstruction method comprising:

[0006] During the proton beam delivery period, the detection equipment is controlled to continuously detect the positrons induced by protons in order to determine several single events;

[0007] The proton beam delivery period is divided into zones based on the count rate of the single event at different times;

[0008] Perform a conformance operation on several of the single events to determine each conformance event;

[0009] Based on the partitioning results of the proton beam delivery period, image reconstruction is performed on the set of coincidence events in each target interval, with each target interval corresponding to a single energy layer.

[0010] In one embodiment, partitioning the proton beam delivery period according to the count rate of the single event at different times includes:

[0011] Determine the times when the count rate of the single event exceeds a preset threshold, and form a first time set;

[0012] Based on the earliest and latest times in the first time set, the proton beam delivery period is divided into the background interval, the in-beam interval, and the out-of-beam interval in sequence.

[0013] The intervals that are alternately arranged within the bundle interval are respectively defined as the first count rate interval and the second count rate interval, wherein the count rate of a single event in the first count rate interval is higher than the count rate of a single event in the second count rate interval.

[0014] In one embodiment, the event data for each single event includes at least time data, location data, and energy data; the conformation operation on a plurality of single events to determine each conforming event includes:

[0015] Based on the energy data of each single event, several single events are filtered to obtain a set of single events;

[0016] Based on the time data and location data of each single event in the single event set, time matching operations and location matching operations are performed on each single event in the single event set to determine several matching events.

[0017] In one embodiment, the step of filtering a plurality of single events based on the energy data of each single event to obtain a set of single events includes:

[0018] Set energy window conditions;

[0019] From a number of individual events, select those whose energy data satisfies the energy window condition to form a set of individual events.

[0020] In one embodiment, the step of performing a time synchronization operation on each of the single events in the single event set based on the time data of each single event in the single event set to determine a plurality of synchronizing events includes:

[0021] Set time window conditions;

[0022] Perform time matching processing on two single events in a single event set that meet the time window condition to determine several matching events.

[0023] In one embodiment, the target interval is the first count rate interval.

[0024] In one embodiment, the step of performing image reconstruction on the set of coincidence events within each target interval based on the partitioning results of the proton beam delivery period includes:

[0025] For each of the first count rate intervals, a set of matching events within that first count rate interval is obtained, and image reconstruction is performed.

[0026] The reconstructed image is identified as the image of the energy layer corresponding to the first count rate interval.

[0027] In one embodiment, the step of performing image reconstruction on the set of coincidence events within each target interval based on the partitioning results of the proton beam delivery period includes:

[0028] For each of the first count rate intervals, a set of matching events between the start time of the background interval and the end time of the first count rate interval is obtained, and image reconstruction is performed to obtain a cumulative image;

[0029] For the i-th first count rate interval, perform a difference operation on the cumulative image corresponding to the i-th first count rate interval and the cumulative image corresponding to the (i-1)-th first count rate interval to obtain the image of the energy layer corresponding to the i-th first count rate interval.

[0030] Where 1≤i≤n, i is a positive integer, and n is the number of the first count rate interval.

[0031] In one embodiment, controlling the detection device to continuously detect positrons induced by protons during the proton beam delivery period includes: using a memory pool algorithm to collect detection data.

[0032] In one embodiment, the detection device includes a positron emission tomography (PET) detector.

[0033] According to a second aspect of the embodiments of this application, an image reconstruction apparatus is provided, the image reconstruction apparatus comprising:

[0034] The detection module is used to control the detection equipment to continuously detect positrons induced by protons during the proton beam delivery period in order to determine several single events;

[0035] The partitioning module is used to partition the proton beam delivery period according to the count rate of the single event at different times;

[0036] The matching module is used to perform matching operations on several of the single events to determine each matching event;

[0037] The reconstruction module performs image reconstruction on the set of coincidence events in each target interval based on the partitioning results of the proton beam delivery period, with each target interval corresponding to a single energy layer.

[0038] In one embodiment, the partitioning module includes:

[0039] The first determining unit is used to determine each moment when the count rate of the single event exceeds a preset threshold, forming a first moment set;

[0040] The division unit is used to divide the proton beam delivery period into a background interval, an in-beam interval, and an out-of-beam interval in sequence according to the earliest and latest times in the first time set;

[0041] The second determining unit is used to determine the intervals that are alternately arranged in the bundle interval as a first count rate interval and a second count rate interval, wherein the count rate of a single event in the first count rate interval is higher than the count rate of a single event in the second count rate interval.

[0042] In one embodiment, the conformance module includes:

[0043] The filtering unit is used to filter several single events based on the energy data of each single event to obtain a set of single events;

[0044] The matching unit is used to perform time matching operations and location matching operations on each of the single events in the single event set based on the time data and location data of each single event in the single event set, so as to determine a number of matching events.

[0045] In one embodiment, the filtering unit is further configured to:

[0046] Set energy window conditions;

[0047] From a number of individual events, select those whose energy data satisfies the energy window condition to form a set of individual events.

[0048] In one embodiment, the conforming unit is further configured to:

[0049] Set time window conditions;

[0050] Perform time matching processing on two single events in a single event set that meet the time window condition to determine several matching events.

[0051] In one embodiment, the target interval is the first count rate interval.

[0052] In one embodiment, the reconstruction module includes:

[0053] The first reconstruction unit is configured to acquire a set of matching events within each of the first count rate intervals and perform image reconstruction for each of the first count rate intervals.

[0054] The third determining unit is used to determine the reconstructed image as the image of the energy layer corresponding to the first count rate interval.

[0055] In one embodiment, the reconstruction module includes:

[0056] The second reconstruction unit is used to obtain a set of coincident events between the start time of the background interval and the end time of the first count rate interval for each first count rate interval, and to perform image reconstruction to obtain a cumulative image.

[0057] The arithmetic unit is used to perform a difference operation on the cumulative image corresponding to the i-th first count rate interval and the cumulative image corresponding to the (i-1)-th first count rate interval for the i-th first count rate interval, so as to obtain the image of the energy layer corresponding to the i-th first count rate interval.

[0058] Where 1≤i≤n, i is a positive integer, and n is the number of the first count rate interval.

[0059] According to a third aspect of the embodiments of this application, an electronic device is provided, the electronic device including the image reconstruction apparatus as described above.

[0060] According to a fourth aspect of the present application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the image reconstruction method as described above.

[0061] According to a fifth aspect of the present application, a computer-readable storage medium is provided, wherein a computer program is stored on the storage medium, and when executed by a processor, the computer program implements the steps of the image reconstruction method as described above.

[0062] According to a sixth aspect of the present application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the image reconstruction method as described above.

[0063] The image reconstruction method provided in this application first controls the detection device to continuously detect positrons induced by protons during the proton beam delivery period, identifying several single events. Then, based on the count rate of single events at different times, the proton beam delivery period is divided into regions, and coincidence operations are performed on several single events to identify each coincidence event. Finally, for each target interval in the proton beam delivery period, image reconstruction is performed based on the set of coincidence events within that target interval, thereby obtaining reconstructed images corresponding to different target intervals, i.e., different energy layers. This solves the problem of inaccurate extraction of dose information for a single field or single energy layer due to overlapping PET signals caused by multi-field or multi-energy-layer delivery modes. It not only helps to accurately monitor the radiation dose at the tumor lesion but also supports dynamic planning and adaptive optimization of treatment plans. Attached Figure Description

[0064] Figure 1 A flowchart illustrating an image reconstruction method provided in an embodiment of this application;

[0065] Figure 2 A flowchart of step S400 in an image reconstruction method provided in an embodiment of this application;

[0066] Figure 3 This is a schematic diagram of a statistical histogram in an image reconstruction method provided in an embodiment of this application;

[0067] Figure 4 This is a schematic diagram of the beam region in an image reconstruction method provided in an embodiment of this application;

[0068] Figure 5 A flowchart of step S600 in an image reconstruction method provided in an embodiment of this application;

[0069] Figure 6 A flowchart of step S610 in an image reconstruction method provided in an embodiment of this application;

[0070] Figure 7 A flowchart of step S620 in an image reconstruction method provided in an embodiment of this application;

[0071] Figure 8 A flowchart of step S800 in an image reconstruction method provided in an embodiment of this application;

[0072] Figure 9 A flowchart of step S800 in an image reconstruction method provided in another embodiment of this application;

[0073] Figure 10 This is a schematic diagram of the structure of an image reconstruction apparatus provided in an embodiment of this application;

[0074] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0075] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application.

[0076] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0077] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0078] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0079] The following description, with reference to the accompanying drawings, illustrates some preferred embodiments of the present application. It should be noted that the following description is for illustrative purposes only and is not intended to limit the scope of protection of this application.

[0080] Rays can travel through space and matter in the form of waves or particles, releasing energy in the process. In medical applications, this property is often used to kill tumor cells. Generally speaking, forms of radiation with a mass greater than that of an electron are collectively classified as particles, such as protons and heavy ions. Currently, particle radiotherapy, with proton therapy as a typical example, has been successfully applied in the field of cancer treatment. Due to its unique advantages, this technology has become one of the most advanced radiotherapy methods, and is often figuratively referred to as a "proton knife" in clinical practice.

[0081] Proton therapy (PT), considered a last hope for cancer patients, possesses the physical characteristic of concentrated dose distribution. It primarily utilizes proton beams to precisely irradiate tumors, achieving maximum tumor ablation and minimal damage to normal tissues. Its main advantage lies in its unique "Bragg peak" effect: protons release most of their energy at a specific depth and then rapidly decay. By precisely adjusting the maximum energy of the radiation to the lesion site during treatment, concentrated irradiation can be achieved without causing excessive damage to normal cells. Thanks to this advantage, proton therapy is widely recognized as a key means of achieving precision medicine in radiotherapy and has become a major area of ​​development both domestically and internationally.

[0082] However, the clinical application of proton beam therapy is limited by range uncertainty. Furthermore, factors such as patient anatomical differences, tissue density heterogeneity, and physiological movements during treatment (e.g., respiration, organ displacement) can all cause the actual dose distribution to deviate from the planned distribution, thus threatening the safety of critical tissues and organs. To address this challenge, real-time monitoring technology has become a research focus. Among these, positron emission tomography (PET) is considered a key method for in-beam monitoring because it can non-invasively locate the distribution of positron-emitting nuclides induced by the proton beam.

[0083] PET imaging equipment can dynamically capture transient positron-emitting nuclides (such as...) generated by the interaction of proton beams with tissue. 15 O、 11C), thus providing three-dimensional information with high spatiotemporal resolution for range verification. However, in clinical practice, multi-field delivery modes (i.e., irradiating the tumor from different angles using multiple proton beams to improve dose distribution conformity) and multi-energy layer delivery modes (i.e., delivering proton beams of different energies in layers within the same field to cover different depths of the tumor) cause severe overlap in PET signals. Positrons from adjacent energy layers overlap in a single field, and the cross-irradiation of multiple fields further exacerbates the signal aliasing. This makes it difficult for traditional image reconstruction algorithms to distinguish the contributions of each component. This limitation hinders the accurate extraction of dose information from a single field or single energy layer, restricting the dynamic planning and adaptive optimization of treatment plans.

[0084] To address the aforementioned problems, this application provides an image reconstruction method, an image reconstruction apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

[0085] Reference Figure 1 In one embodiment of this application, an image reconstruction method is provided, which includes the following steps:

[0086] Step S200: During the proton beam delivery period, control the detection equipment to continuously detect the positrons induced by the protons in order to determine several single events.

[0087] The proton beam delivery period can include the period during which the proton beam is turned on and the periods before and after it. In this embodiment, the detection period of the detection device should cover the entire proton beam delivery period, thereby ensuring that the detection device can accurately detect each individual event. The detection device may include a positron emission tomography (PET) detector, which may include multiple crystal channels. Each crystal channel may include a scintillation crystal and a photoelectric conversion device coupled together. The scintillation crystal may be made of BGO, PWO, LYSO:Ce, GAGG:Ce, NaI:TI, CsI:TI, LaBr3:Ce, BaF2, etc., and the photoelectric conversion device may be made of PMT (photomultiplier tube), SiPM (silicon photomultiplier tube), etc.

[0088] When a proton enters the human body, it is induced to produce a positron. This positron annihilates with negative electrons in the body, generating a pair of photons with opposite directions and the same energy. These two photons can be captured by different detection positions of a detection device. When a detector captures a photon, energy is deposited within the detector's crystal channel, ultimately generating a scintillation pulse during photoelectric conversion. In this embodiment, the above process can be considered a single event occurring within a single crystal channel. The event data for a single event can include at least energy data, position data, and time data. Energy data refers to the energy value of the scintillation pulse, position data refers to the channel code corresponding to the scintillation crystal that captured the event, and time data refers to the time the photon was captured.

[0089] The above method can be used to identify several individual events during the proton beam delivery period, as well as the event data for each individual event, for subsequent analysis.

[0090] Step S400: Divide the proton beam delivery period into zones based on the count rate of order events at different times.

[0091] That is, the number of single events detected by the detector at different times during the proton beam delivery period can be obtained, i.e., the count rate of single events at different times. Considering that the count rate of single events will increase explosively during proton beam delivery and drop sharply before or after the proton beam delivery ends, this embodiment follows the above pattern and divides the proton beam delivery period into zones based on the count rate of single events at different times. This allows for the division of time intervals corresponding to before, during, and after proton beam delivery. At the same time, the distribution pattern of the count rate of single events can be used to further divide the intervals where different energy layers are located, thereby separating the data of different energy layers. This facilitates the subsequent reconstruction of images of different energy layers and avoids the problem of inaccurate dose information extraction caused by signal overlap.

[0092] Step S600: Perform a conformance operation on several single events to determine each conformance event.

[0093] After obtaining several individual events and their event data, the individual events can be processed to determine each matching event.

[0094] Specifically, the two single events that occur at different detection locations on a detection device when a positron annihilates with a negative electron in the human body, producing a pair of photons, can be called a coincidence event. In actual detection, multiple single events may be detected, and among these, multiple coincidence events or interference events may exist. Therefore, after identifying all detected single events, coincidence can be performed on each single event to obtain matching events. Generally, coincidence processing can include energy coincidence and time coincidence; that is, energy coincidence and time coincidence can be performed based on the energy and time data of each single event. Two single events whose energy and time both meet the corresponding conditions can be considered a coincidence event.

[0095] Step S800: Based on the partitioning results of the proton beam delivery period, image reconstruction is performed on the set of events that meet the criteria in each target interval, with each target interval corresponding to a single energy layer.

[0096] Specifically, several target intervals can be determined from the various intervals of the proton beam delivery period obtained from the aforementioned partitioning. In this embodiment, different target intervals correspond to different energy layers. Determining different target intervals is equivalent to determining different energy layers, thus achieving energy layer separation. After determining several target intervals, a set of several matching events corresponding to each target interval can be obtained, and image reconstruction can be performed accordingly. This yields different images corresponding to different target intervals, i.e., different energy layers.

[0097] The image reconstruction method provided in this application first controls the detection device to continuously detect positrons induced by protons during the proton beam delivery period, identifying several single events. Then, based on the count rate of single events at different times, the proton beam delivery period is divided into regions, and coincidence operations are performed on several single events to identify each coincidence event. Finally, for each target interval in the proton beam delivery period, image reconstruction is performed based on the set of coincidence events within that target interval, thereby obtaining reconstructed images corresponding to different target intervals, i.e., different energy layers. This solves the problem of inaccurate extraction of dose information for a single field or single energy layer due to overlapping PET signals caused by multi-field delivery modes and multi-energy layer delivery modes. It not only helps to accurately monitor the radiation dose at the tumor lesion but also supports dynamic planning and adaptive optimization of treatment plans.

[0098] Reference Figure 2 In one embodiment of this application, step S400, which involves dividing the proton beam delivery period into zones based on the count rate of order events at different times, may further include the following steps:

[0099] Step S410: Determine the times when the count rate of a single event exceeds a preset threshold, and form a first time set.

[0100] In this embodiment, all single events detected by the detection device can be arranged in ascending order of time, thereby determining the total number of single events at each moment, i.e., the single event count rate. A threshold can be preset for the single event count rate, and the moments when the single event count rate exceeds the preset threshold can be counted. For ease of description, the set of the counted moments is defined as the first moment set in this embodiment.

[0101] The preset threshold value can be set according to actual needs. In a specific example, a statistical histogram can be drawn based on the time data of each single event and the count rate of order events at different times (see reference). Figure 3 The horizontal axis of the statistical histogram represents time, which can be understood as the time of a single event or the time corresponding to the proton beam delivery period. The vertical axis represents the count rate of a single event. Specifically, a specific count rate can be determined as a preset threshold based on the plotted statistical histogram, or any value between 30% and 50% of the maximum count rate in the statistical histogram can be determined as the preset threshold.

[0102] Step S420: Based on the earliest and latest times in the first time set, the proton beam delivery period is divided into the background interval, the in-beam interval, and the out-of-beam interval.

[0103] Understandably, because proton beam delivery is periodic, meaning single events generally occur within the same time period, the various moments in the first time set typically fall within the same time period of the proton beam delivery cycle. Based on this, the proton beam delivery cycle can be sequentially divided into background intervals according to the earliest and latest moments in the first time set. Figure 3 Background interval in the middle), in the bundle interval ( Figure 3 Beam-On interval and off-beam interval (in the context) Figure 3 The Beam-off interval is defined as follows: the background interval is the interval before the earliest time in the first time set, corresponding to the time period during which the proton beam has not yet been delivered; the in-beam interval is the interval between the earliest and latest time in the first time set, corresponding to the time period during which the proton beam is delivered; and the out-of-beam interval is the interval after the latest time in the first time set, corresponding to the time period during which the proton beam delivery has ended.

[0104] Step S430: The intervals that are alternately arranged in the bundle interval are respectively defined as the first count rate interval and the second count rate interval. The count rate of a single event in the first count rate interval is higher than the count rate of a single event in the second count rate interval.

[0105] When the proton beam is delivered using a multi-field or multi-energy-layer delivery method, multiple energy layers often exist. This means that relatively high count rate peaks and relatively low count rate valleys typically alternate within the beam interval. Based on this pattern, two adjacent intervals alternating within the beam interval can be defined as the first count rate interval (corresponding to a relatively high count rate peak) and the second count rate interval (corresponding to a relatively low count rate peak), respectively. In this embodiment, a single first count rate interval can be associated with a single energy layer interval, thereby achieving the separation of data from different energy layers. Figure 4 This is a schematic diagram of the bundle interval. Figure 4 The intervals containing the green areas are the first count rate intervals, and the intervals corresponding to the white areas adjacent to the green areas are the second count rate intervals.

[0106] Reference Figure 5 In one embodiment of this application, step S600, which involves performing a conformation operation on several single events to determine each conformation event, may further include the following steps:

[0107] Step S610: Based on the energy data of each single event, filter several single events to obtain a set of single events.

[0108] That is, individual events can be filtered based on their energy data, and the selected events form a set of individual events. Specifically, refer to... Figure 6 Step S610 may further include the following steps:

[0109] Step S611: Set energy window conditions.

[0110] Step S612: From a number of single events, select the single events whose energy data meets the energy window conditions to form a set of single events.

[0111] Setting an energy window condition specifically refers to setting an energy range. In this embodiment, the energy range can be set based on the energy of the photons produced by the annihilation of positrons and negative electrons in the human body. For example, in a specific example, the energy range can be set to 350 keV to 650 keV.

[0112] If the energy data of a single event falls within the energy range, it can be considered that the energy data of the single event meets the energy window condition. If the energy data of a single event exceeds the energy range, it can be considered that the energy data of the single event does not meet the energy window condition. Finally, the single events whose energy data meets the energy window condition can be summarized to obtain the single event set.

[0113] Step S620: Based on the time data and location data of each single event in the single event set, perform time matching operation and location matching operation on each single event in the single event set to determine several matching events.

[0114] For the set of single events obtained after energy matching, time matching can be performed on each single event in the set. Specifically, refer to... Figure 7 Step S620 may further include the following steps:

[0115] Step S621: Set time window conditions.

[0116] Step S622: Perform time conformity processing on two single events in the single event set that meet the time window condition to determine several conforming events.

[0117] Specifically, each single event in the single event set can be designated as a master event. For each master event, a time window extending in the opposite direction of the time axis is set. Single events whose time data falls within this time window are compared with the master events for time synchronization. If a synchronization is successful, the two are considered a pair of synchronized events, thus identifying several synchronized events. These synchronized events can be arranged in ascending order of the master events' time. The size of the time window can be fixed or determined based on prior knowledge. The specific method for time synchronization processing can employ existing time synchronization algorithms, which will not be elaborated upon here.

[0118] In one embodiment of this application, the target interval is a first count rate interval. As mentioned above, a single first count rate interval generally corresponds to a single energy layer interval. Therefore, the first count rate interval can be determined as the target interval. There are generally multiple first count rate intervals. Therefore, in this embodiment, there are generally multiple target intervals.

[0119] Reference Figure 8 In one embodiment of this application, step S800, which involves reconstructing images of the set of events within each target interval based on the partitioning results of the proton beam delivery period, may further include the following steps:

[0120] Step S810: For each first count rate interval, obtain the set of matching events within the first count rate interval and perform image reconstruction.

[0121] Step S820: Determine the reconstructed image as the image of the energy layer corresponding to the first count rate interval.

[0122] That is, for each first count rate interval, image reconstruction can be performed based on the set of matching events within that first count rate interval. The reconstructed image is the image of the energy layer corresponding to that first count rate interval. The image reconstruction algorithm can employ the OSEM-ToF (Ordered Subset Expectation Maximization with Time-of-Flight) algorithm, an iterative image reconstruction algorithm that incorporates time-of-flight (ToF) information for PET data processing. Time-of-flight refers to the time difference between the arrival of two photons generated by positron annihilation at the detector, which can be used to estimate the location of the event.

[0123] Reference Figure 9 In another alternative embodiment of this application, step S800, which is to perform image reconstruction on the set of matching events in each target interval based on the partitioning results of the proton beam delivery period, may further include the following steps:

[0124] Step S830: For each first count rate interval, obtain the set of coincident events between the start time of the background interval and the end time of the first count rate interval, and perform image reconstruction to obtain the cumulative image.

[0125] Step S840: For the i-th first count rate interval, perform a difference operation on the cumulative image corresponding to the i-th first count rate interval and the cumulative image corresponding to the (i-1)-th first count rate interval to obtain the image of the energy layer corresponding to the i-th first count rate interval. Where 1 ≤ i ≤ n, i is a positive integer, and n is the number of first count rate intervals.

[0126] Unlike the aforementioned method of directly reconstructing images from the set of coincidence events in the first count rate interval, in this embodiment, the cumulative images up to the end of each first count rate interval during the proton beam delivery period can be reconstructed first. Then, the difference method is used to perform a difference operation on two adjacent cumulative images, and the image after the difference operation is used as the image of the energy layer corresponding to each first count rate interval.

[0127] Assume the background interval is 0 to t1, and the beam interval is t1 to t2. 2n The beam separation interval is t 2n ~t end The first count rate intervals within the bundle interval are t1~t2, t3~t4, ...,t 2n-1 ~t 2n The second count rate intervals within the beam interval are t2~t3, t4~t5, …,t 2n-2 ~t 2n-1 .

[0128] We can select 0 to t2, 0 to t4, ..., 0 to t 2n For each interval containing a set of matching events, image reconstruction is performed to obtain a cumulative image sequence I1, I2, ..., I... n Then perform the difference operation using the following formula:

[0129]

[0130] Among them, L i I represents the image of the i-th first count rate interval (i.e., energy layer) obtained by the difference operation, and I0 represents the background image, i.e., the reconstructed image of the coincidence event in the interval 0 to t1.

[0131] In one embodiment of this application, step S200, which is during the proton beam delivery period, controlling the detection device to continuously detect the positrons induced by the protons, may include: using a memory pool algorithm to collect detection data.

[0132] Specifically, multiple contiguous memory segments can be allocated in the memory, and each memory segment can be controlled to be read or written in a cyclic manner.

[0133] Taking a memory pool with 8 memory segments as an example, probe data is written sequentially starting from memory segment 0. After each memory segment is written, it is marked as readable. The pointer pWriting for writing memory segments will then point to the next memory segment. Then the pointer pReadable for reading memory segments will point to the memory segment that has been written. If the last memory segment (i.e., memory segment 7) is written, the pointer pWriting will point to the first memory segment (memory segment 0).

[0134] The pointer pReading also reads the probe data sequentially starting from memory segment 0. If pReading is not equal to pReadable, it means that there is still a memory segment to be read. At this time, the probe data can be processed directly. After processing, the memory segment is marked as writable, and the pointer pReading points to the next memory segment. If pReading is equal to pReadable, it means that there is no memory to be read, and we need to wait for the pointer pReadable to step forward.

[0135] The memory pool algorithm described above can efficiently manage memory read and write operations during the acquisition of probe data, ensuring that event data of all single events can be collected stably and quickly during the proton beam delivery period.

[0136] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0137] Based on the same inventive concept, another embodiment of this application provides an image reconstruction apparatus for implementing the image reconstruction method described above. The solution provided by this image reconstruction apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more image reconstruction apparatus embodiments provided below can be found in the limitations of the image reconstruction method described above, and will not be repeated here.

[0138] Reference Figure 10 The image reconstruction apparatus provided in this embodiment includes a detection module 200, a partitioning module 400, a matching module 600, and a reconstruction module 800. Among them,

[0139] The detection module 200 is used to control the detection equipment to continuously detect the positrons induced by protons during the proton beam delivery period in order to determine several single events.

[0140] The partitioning module 400 is used to partition the proton beam delivery period according to the count rate of order events at different times;

[0141] The conformance module 600 is used to perform conformance operations on several single events to determine each conformance event;

[0142] The reconstruction module 800, based on the partitioning results of the proton beam delivery period, performs image reconstruction on the set of events that match each target interval, with each target interval corresponding to a single energy layer.

[0143] In one embodiment of this application, the partitioning module 400 includes:

[0144] The first determining unit is used to determine the times when the count rate of a single event exceeds a preset threshold, forming a first time set;

[0145] The division unit is used to divide the proton beam delivery period into the background interval, the in-beam interval, and the out-of-beam interval according to the earliest and latest times in the first time set.

[0146] The second determining unit is used to determine the intervals that are alternately arranged in the bundle interval as the first count rate interval and the second count rate interval, respectively, wherein the count rate of a single event in the first count rate interval is higher than the count rate of a single event in the second count rate interval.

[0147] In one embodiment of this application, the conforming module 600 includes:

[0148] The filtering unit is used to filter several single events based on the energy data of each single event to obtain a set of single events;

[0149] The matching unit is used to perform time matching operations and location matching operations on each single event in the single event set based on the time data and location data of each single event in the single event set, so as to determine a number of matching events.

[0150] In one embodiment of this application, the filtering unit is further configured as follows:

[0151] Set energy window conditions;

[0152] From a number of individual events, select those whose energy data meets the energy window conditions to form a set of individual events.

[0153] In one embodiment of this application, the conforming unit is further configured as follows:

[0154] Set time window conditions;

[0155] Perform time matching processing on two single events in a single event set that meet the time window condition to determine several matching events.

[0156] In one embodiment of this application, the target interval is a first count rate interval.

[0157] In one embodiment of this application, the reconstruction module 800 includes:

[0158] The first reconstruction unit is used to obtain a set of matching events within each first count rate interval and perform image reconstruction for each first count rate interval.

[0159] The third determining unit is used to determine the reconstructed image as the image of the energy layer corresponding to the first count rate interval.

[0160] In one embodiment of this application, the reconstruction module 800 includes:

[0161] The second reconstruction unit is used to obtain a set of coincident events between the start time of the background interval and the end time of the first count rate interval for each first count rate interval, and to perform image reconstruction to obtain a cumulative image.

[0162] The arithmetic unit is used to perform a difference operation on the cumulative image corresponding to the i-th first count rate interval and the cumulative image corresponding to the (i-1)-th first count rate interval to obtain the image of the energy layer corresponding to the i-th first count rate interval.

[0163] Where 1≤i≤n, i is a positive integer, and n is the number of the first count rate interval.

[0164] The image reconstruction apparatus provided in this application first controls the detection device to continuously detect positrons induced by protons during the proton beam delivery period, identifying several single events. Then, based on the count rate of single events at different times, the proton beam delivery period is divided into regions, and coincidence operations are performed on several single events to identify each coincidence event. Finally, for each target interval in the proton beam delivery period, image reconstruction is performed based on the set of coincidence events within that target interval, thereby obtaining reconstructed images corresponding to different target intervals, i.e., different energy layers. This solves the problem of inaccurate extraction of dose information for a single field or single energy layer due to overlapping PET signals caused by multi-field delivery modes and multi-energy layer delivery modes. It not only helps to accurately monitor the radiation dose at the tumor lesion but also supports dynamic planning and adaptive optimization of treatment plans.

[0165] Each module in the aforementioned image reconstruction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independent of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the operations corresponding to each module.

[0166] In one embodiment, an electronic device is provided that may include any components for implementing the image reconstruction apparatus described in the foregoing embodiments of this application. For example, the electronic device may be implemented using hardware, software programs, firmware, or a combination thereof.

[0167] In one embodiment, an electronic device is provided, including a memory and a processor, the memory storing a computer program executable on the processor, which, when executing the computer program, implements the steps in the above-described method embodiments.

[0168] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 11As shown, the electronic device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores various types of data involved in the image reconstruction method. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements an image reconstruction method.

[0169] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0170] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0171] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0172] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0173] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0174] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An image reconstruction method, characterized in that, The image reconstruction method includes: During the proton beam delivery period, the detection equipment is controlled to continuously detect the positrons induced by protons in order to determine several single events; The proton beam delivery period is divided into zones based on the count rate of the single event at different times; Perform a conformance operation on several of the single events to determine each conformance event; Based on the partitioning results of the proton beam delivery period, image reconstruction is performed on the set of coincidence events in each target interval, with each target interval corresponding to a single energy layer.

2. The image reconstruction method according to claim 1, characterized in that, The step of partitioning the proton beam delivery period according to the count rate of the single event at different times includes: Determine the times when the count rate of the single event exceeds a preset threshold, and form a first time set; Based on the earliest and latest times in the first time set, the proton beam delivery period is divided into the background interval, the in-beam interval, and the out-of-beam interval in sequence. The intervals that are alternately arranged within the bundle interval are respectively defined as the first count rate interval and the second count rate interval, wherein the count rate of a single event in the first count rate interval is higher than the count rate of a single event in the second count rate interval.

3. The image reconstruction method according to claim 1, characterized in that, The event data for each single event includes at least time data, location data, and energy data; the process of performing a coincidence operation on several single events to determine each coincident event includes: Based on the energy data of each single event, several single events are filtered to obtain a set of single events; Based on the time data and location data of each single event in the single event set, time matching operations and location matching operations are performed on each single event in the single event set to determine several matching events.

4. The image reconstruction method according to claim 3, characterized in that, The process of filtering several single events based on their energy data to obtain a set of single events includes: Set energy window conditions; From a number of individual events, select those whose energy data satisfies the energy window condition to form a set of individual events.

5. The image reconstruction method according to claim 3, characterized in that, The step of performing a time synchronization operation on each of the single events in the single event set based on the time data of each single event in the single event set to determine a plurality of synchronizing events includes: Set time window conditions; Perform time matching processing on two single events in a single event set that meet the time window condition to determine several matching events.

6. The image reconstruction method according to claim 2, characterized in that, The target interval is the first count rate interval.

7. The image reconstruction method according to claim 6, characterized in that, The process of image reconstruction for the set of coincidence events within each target interval, based on the partitioning results of the proton beam delivery period, includes: For each of the first count rate intervals, a set of matching events within that first count rate interval is obtained, and image reconstruction is performed. The reconstructed image is identified as the image of the energy layer corresponding to the first count rate interval.

8. The image reconstruction method according to claim 6, characterized in that, The process of image reconstruction for the set of coincidence events within each target interval, based on the partitioning results of the proton beam delivery period, includes: For each of the first count rate intervals, a set of matching events between the start time of the background interval and the end time of the first count rate interval is obtained, and image reconstruction is performed to obtain a cumulative image; For the i-th first count rate interval, perform a difference operation on the cumulative image corresponding to the i-th first count rate interval and the cumulative image corresponding to the (i-1)-th first count rate interval to obtain the image of the energy layer corresponding to the i-th first count rate interval. Where 1≤i≤n, i is a positive integer, and n is the number of the first count rate interval.

9. The image reconstruction method according to claim 1, characterized in that, During the proton beam delivery period, controlling the detection equipment to continuously detect positrons induced by protons includes: using a memory pool algorithm to collect detection data.

10. The image reconstruction method according to claim 1, characterized in that, The detection equipment includes a positron emission tomography (PET) detector.

11. An image reconstruction apparatus, characterized in that, The image reconstruction apparatus includes: The detection module is used to control the detection equipment to continuously detect positrons induced by protons during the proton beam delivery period in order to determine several single events; The partitioning module is used to partition the proton beam delivery period according to the count rate of the single event at different times; The matching module is used to perform matching operations on several of the single events to determine each matching event; The reconstruction module performs image reconstruction on the set of coincidence events in each target interval based on the partitioning results of the proton beam delivery period, with each target interval corresponding to a single energy layer.

12. The image reconstruction apparatus according to claim 11, characterized in that, The partitioning module includes: The first determining unit is used to determine each moment when the count rate of the single event exceeds a preset threshold, forming a first moment set; The division unit is used to divide the proton beam delivery period into a background interval, an in-beam interval, and an out-of-beam interval in sequence according to the earliest and latest times in the first time set; The second determining unit is used to determine the intervals that are alternately arranged in the bundle interval as a first count rate interval and a second count rate interval, wherein the count rate of a single event in the first count rate interval is higher than the count rate of a single event in the second count rate interval.

13. The image reconstruction apparatus according to claim 11, characterized in that, The conformance module includes: The filtering unit is used to filter several single events based on the energy data of each single event to obtain a set of single events; The matching unit is used to perform time matching operations and location matching operations on each of the single events in the single event set based on the time data and location data of each single event in the single event set, so as to determine a number of matching events.

14. The image reconstruction apparatus according to claim 13, characterized in that, The filtering unit is further configured to: Set energy window conditions; From a number of individual events, select those whose energy data satisfies the energy window condition to form a set of individual events.

15. The image reconstruction apparatus according to claim 13, characterized in that, The conforming unit is further configured as follows: Set time window conditions; Perform time matching processing on two single events in a single event set that meet the time window condition to determine several matching events.

16. The image reconstruction apparatus according to claim 12, characterized in that, The target interval is the first count rate interval.

17. The image reconstruction apparatus according to claim 16, characterized in that, The reconstruction module includes: The first reconstruction unit is configured to acquire a set of matching events within each of the first count rate intervals and perform image reconstruction for each of the first count rate intervals. The third determining unit is used to determine the reconstructed image as the image of the energy layer corresponding to the first count rate interval.

18. The image reconstruction apparatus according to claim 16, characterized in that, The reconstruction module includes: The second reconstruction unit is used to obtain a set of coincident events between the start time of the background interval and the end time of the first count rate interval for each first count rate interval, and to perform image reconstruction to obtain a cumulative image. The arithmetic unit is used to perform a difference operation on the cumulative image corresponding to the i-th first count rate interval and the cumulative image corresponding to the (i-1)-th first count rate interval for the i-th first count rate interval, so as to obtain the image of the energy layer corresponding to the i-th first count rate interval. Where 1≤i≤n, i is a positive integer, and n is the number of the first count rate interval.

19. An electronic device, characterized in that, The electronic device includes the image reconstruction apparatus as described in any one of claims 11 to 18.

20. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the image reconstruction method as described in any one of claims 1 to 10.

21. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the image reconstruction method as described in any one of claims 1 to 10.

22. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the image reconstruction method according to any one of claims 1-10.

Images