Nuclear medicine diagnostic device and nuclear medicine image data generation method

Abstract

To generate nuclear medicine image data with good image quality.SOLUTION: A nuclear medicine diagnosis device of an embodiment comprises an acquisition unit, a determination unit, and a generation unit. The acquisition unit acquires gamma ray detection data related to an analyte. The determination unit determines the weight of a plurality of coincidences based on two or more events detected in a first time window in the gamma ray detection data. The generation unit generates first nuclear medicine image data on the basis of the weight.SELECTED DRAWING: Figure 7C

Description

[Technical Field]

[0001] Embodiments disclosed herein and in the drawings relate to a nuclear medicine diagnostic device and a method for generating nuclear medicine image data. [Background technology]

[0002] The background art provided herein is for the purpose of generally presenting the context of this disclosure. The research of the currently named inventors to the extent described in the background art section, as well as any aspects of the description that may not be recognized as prior art at the time of filing, are not expressly or implicitly recognized as prior art to this disclosure.

[0003] Positron emission tomography (PET) is a functional imaging modality that allows imaging of biochemical processes in humans or animals using radioactive tracers. In PET imaging, the tracer agent is administered to the patient (subject) by injection, inhalation, or ingestion. After administration, the drug concentrates in specific locations within the patient's body due to its physical and biomolecular properties. The actual spatial distribution of the drug, the intensity of drug accumulation areas, and the dynamics of the process from administration to final excretion are all clinically important factors.

[0004] During this process, tracers attached to the drug emit positrons. When the emitted positrons collide with electrons, an annihilation event occurs in which the positron and electron combine. In most cases, the annihilation event produces two gamma rays traveling at virtually 180 degrees apart (at 511 keV). The two gamma rays, each known as a single, are detected by a detection element to generate a pair of coincidence counts. However, the measured coincidence counts include both true coincidence counts and random coincidence counts.

[0005] In a PET scanner, a single pairing can be performed using hardware such as a coincidence counting circuit. Here, multi-photon coincidence events (i.e., three or more singles in coincidence counting) are often rejected, and only two-photon coincidence events that meet strict criteria are accepted. As the counting rate increases, the multi-photon coincidence event rate increases significantly, and simply rejecting all multi-photon coincidence events may result in a large loss of true coincidence events. Therefore, it is desirable to retain multi-photon coincidence events so as to increase the noise equivalent count rate (NECR). Both methods of completely accepting or rejecting all multi-photon coincidence events result in similar image quality, but accepting all multi-photon coincidence events also increases random events and scattered events, leading to an accompanying degradation of the image data. Therefore, a method for identifying and selecting true coincidences from a set of multiple coincidences is desired. And by identifying and selecting true coincidences from a set of multiple coincidences, it is desired to generate nuclear medicine image data with good image quality.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to generate nuclear medicine image data with good image quality. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems.

Means for Solving the Problems

[0008] The nuclear medicine diagnostic apparatus according to the embodiment includes an acquisition unit, a determination unit, and a generation unit. The acquisition unit acquires gamma-ray detection data regarding a subject. The determination unit determines the weight of each of a plurality of coincidences based on two or more events detected within a first time window in the gamma-ray detection data. The generation unit generates first nuclear medicine image data based on the weight.

Brief Description of the Drawings

[0009] [Figure 1] FIG. 1 is a diagram showing a schematic view of an exemplary detected single list combined for all detector modules over an exemplary period. [Figure 2A] FIG. 2A is a diagram showing a schematic view of an exemplary body-axis cross-sectional view of a positron emission tomography (PET) scanner according to an embodiment. [Figure 2B] FIG. 2B is a schematic view of a body-axis cross-sectional view of an exemplary PET scanner including reconstructed image data (PET image data). [Figure 2C] FIG. 2C is a diagram showing a schematic view of a body-axis cross-sectional view of an exemplary PET scanner including reconstructed image data and a first time-of-flight (TOF) kernel and a second TOF kernel. [Figure 3] FIG. 3 is a diagram showing a non-limiting example of a flowchart of a guided pairing method for determining a weighted pair within a detection window from a set of predetermined singles within the detection window according to one exemplary embodiment. [Figure 4A] FIG. 4A is a diagram showing a graph of a time difference distribution for a real central line source according to one exemplary embodiment. [Figure 4B] FIG. 4B is a diagram showing a zoom of a peak with respect to the graph of the time difference distribution shown in FIG. 4A according to one exemplary embodiment. [Figure 4C] FIG. 4C is a diagram showing a zoom of a baseline with respect to the graph of the time difference distribution shown in FIG. 4A according to one exemplary embodiment. [Figure 5A] Figure 5A shows an exemplary graph of the time difference distribution of a real central source in one exemplary embodiment. [Figure 5B] Figure 5B shows a zoom of the peak relative to the exemplary graph of the time-lag distribution in Figure 5A, according to one exemplary embodiment. [Figure 5C] Figure 5C shows a baseline zoom on an exemplary graph of the time-lag distribution in Figure 5A, according to one exemplary embodiment. [Figure 6] Figure 6 shows graphs of the mean and standard deviation of exemplary prompt events and exemplary delayed events. [Figure 7A] Figure 7A shows a non-limiting embodiment of a PET apparatus capable of carrying out the method according to the embodiment. [Figure 7B] Figure 7B shows a non-limiting embodiment of a PET apparatus that can carry out the method according to the embodiment. [Figure 7C] Figure 7C shows an example of the functional configuration of a processor according to this embodiment. [Modes for carrying out the invention]

[0010] The following disclosure provides many different embodiments, or examples, for carrying out different features of the subject matter provided. Specific examples of components and arrangements are described below for the sake of simplicity of this disclosure. Of course, these are merely examples and are not intended to limit the scope. For example, forming a first feature over or on a second feature in the following description may include embodiments in which the first and second features are formed in direct contact, or it may include embodiments in which additional features are formed between the first and second features, even if the first and second features are not in direct contact. Furthermore, reference numbers and / or letters may be repeated in various embodiments in this disclosure. This repetition is for the sake of simplification and clarity and does not in itself indicate relationships between the various embodiments and / or configurations described.

[0011] The order in which the different steps described herein are presented is for clarity. In general, these steps can be performed in any suitable order. Furthermore, although each of the different features, techniques, configurations, etc., of this specification may be discussed in different places in this disclosure, each of the concepts is intended to be performed independently of or in combination with one another. Thus, the present invention can be embodied and considered in many different ways.

[0012] According to one embodiment described herein, a method for guided pairing of coincidences includes applying weights to all possible coincidences in a multiphoton coincidence event. In particular, the weights to be applied can be generated based on pairs of two-photon coincidence events or based on reconstructed image data (PET image data) generated from two-photon coincidence events.

[0013] Figure 1 shows a schematic diagram of an exemplary list of detected singles, combined for all detector modules over an exemplary period. In one embodiment, a detector module can detect a single 110 within a predetermined length of time, i.e., a detection window 105. The detection window is also referred to as a time window. A single is also referred to as a single event or event. For two or more detected singles 110, a single list can be compiled that shows the detected singles 110 as a function of time. As shown, all singles 110 can be separated according to a set of detection windows 105 (105a to 105e in the example of Figure 1). For example, the single list shown may include a first detection window 105a containing one of several singles 110, a second detection window 105b containing two of several singles 110, a third detection window 105c containing three of several singles 110, a fourth detection window 105d containing four of several singles 110, and a fifth detection window 105e containing two of several singles 110. It can be understood that the predetermined time lengths of individual detection windows 105a to 105e can vary and be set according to the imaging system used (e.g., PET scanner) or based on the operator's needs / preferences. Generally, the time lengths of individual detection windows 105a to 105e can be equal to each other, for example, 12 ns each. Furthermore, it can be understood that the predetermined time lengths of individual detection windows 105a to 105e generally result in the majority of detection windows 105a to 105e containing two of the singles 110, while a minority contain one or more singles 110. However, as mentioned above, increasing the counting rate can increase the number of detection windows 105a to 105e containing three or more singles 110, thereby potentially increasing the rejection rate of some methods that attempt to pair singles. The methods described herein attempt to reduce this rejection rate, thereby enabling the generation of high-quality PET image data (reconstructed image data).PET image data is an example of nuclear medicine image data. The single list mentioned above is an example of gamma-ray detection data for a subject.

[0014] Figure 2A is a schematic diagram of an exemplary axial cross-section of a positron emission tomography (PET) scanner 200 according to an embodiment. For example, the PET apparatus according to this embodiment comprises a PET scanner 200. The PET apparatus is an example of a nuclear medicine diagnostic device. In an exemplary embodiment, the PET scanner 200 includes a ring of detection elements 205 arranged around a central axis, configured to detect electromagnetic radiation such as gamma rays. The PET scanner 200 may include additional rings of detection elements 205 arranged along the axis of the ring. Features of the additional PET scanner are shown in Figures 7A and 7B and described later. A subject to be scanned, such as a phantom or a person, can be placed at the center of the detection elements 205. The subject may include high-count radiation regions 299, such as the heart or lungs of a person, which are considered to be of high importance during imaging.

[0015] When a positron emitted from a phantom or a person collides with an electron, an annihilation event occurs, in which the positron and electron combine. In most cases, the annihilation event produces two gamma rays that travel substantially 180 degrees apart (at 511 keV). One of these gamma rays is called a single 110. To reconstruct the spatiotemporal distribution of the tracer using the principle of tomography reconstruction, each detected event is characterized for its energy (i.e., the amount of light produced), its location, and its timing. By detecting two gamma rays (i.e., two single 110s) and drawing a line between their locations (i.e., calculating the Line of Response (LOR)), the location of the original decay can be determined.

[0016] In one example shown in Figure 1, four of the multiple singles 110 are detected during the fourth detection window 105d. This subset is referred to herein as the “single subset” 110d. An exemplary corresponding process for spatially detecting the single subset is shown in Figure 2A. All possible combinations of pairing the single subset 110d are shown, where two pairs of single subset 110d proceeding substantially 180 degrees apart from the annihilation event are grouped together. The grouped pair of singles can be referred to as a two-photon coincidence number 210, or simply “coincidence number 210”. For example, the first detection element 205a can detect the first single subset (indicated as "a") of the single subset 110d, the second detection element 205b can detect the second single subset (indicated as "b") of the single subset 110d, the third detection element 205c can detect the third single subset (indicated as "c") of the single subset 110d, and the fourth detection element 205d can detect the fourth single subset (indicated as "d") of the single subset 110d. Therefore, to pair the single subset 110d, the combinatorial formula "N Choose 2" or NC2 can be used to determine the maximum possible number of two-photon simultaneous counts 210, where N is the number of single 110s detected in one detection window 105, and "N Choose 2" or NC2 is the number of ways (combinations) to choose 2 from N distinct items. Associated with a single subset, for all four single subsets 110d, the maximum possible number of simultaneous counts 210 is 6. In the same embodiment as shown, the six possible simultaneous counts 210 are labeled as follows:The first coincidence number 210a is the coincidence number between detection elements "a" and "b", the second coincidence number 210b is the coincidence number between detection elements "a" and "c", the third coincidence number 210c is the coincidence number between detection elements "a" and "d", the fourth coincidence number 210d is the coincidence number between detection elements "b" and "c", the fifth coincidence number 210e is the coincidence number between detection elements "b" and "d", and the sixth coincidence number 210f is the coincidence number between detection elements "c" and "d".

[0017] Conventional PET scanners cannot determine the true coincidence count (i.e., the coincidence count 210 having the true origin of the annihilation along LOR) for three or more single 110s detected in the same detection window, such as a fourth detection window 105d, leading to data rejection. Alternatively, all three or more detected single 110s may be accepted.

[0018] In an event where three or more singles 110 are detected, to retain all singles 110, the first detected single is paired with all possible subsequent detected singles that enter the detection window 105. The same pairing process is then repeated for the next detected single in the detection window 105 until all detected singles in the detection window 105 are exhausted. This process is repeated for all remaining detected singles until all detected singles in the detected single list are exhausted.

[0019] In an event where three or more single 110s are detected, the first single detected is counted along with all subsequent singles that enter the same detection window 105 in order to reject all single 110s. If it is determined that only one other single 110 exists in the detection window 105, the two single 110s are paired and determined to be one possible coincidence count. If it is determined that two or more other single 110s exist in the detection window 105, all single 110s are skipped without any pairing. This process then proceeds, starting with the first single detected in the subsequent new detection window 105. This process is repeated until all detected singles in the detected single list are exhausted.

[0020] However, completely accepting or rejecting all single 110s without attempting to determine the true coincidence count can lead to degradation of image quality and data. Therefore, this specification describes a method for determining the true coincidence count from multiple single 110 detection events while rejecting random coincidence counts, in order to improve the quality of reconstructed image data (e.g., nuclear medicine image data such as PET image data). While conventional methods of acceptance and rejection may be based solely on the timing information of single 110s, the method described herein uses a guided pairing process based on applied weights. The weights can be based on the probability that the coincidence count in question is the true coincidence count. All coincidence counts (all LORs) can be assigned weights and considered before deciding whether to accept or reject the aforementioned coincidence count, and then used in the final image reconstruction.

[0021] Referring again to Figure 2A, the single subset 110d is detected by the four detection elements 205, generating six coincidences 210a to 210f. Generally, there can be N detected singles, leading to N Choose2 (NC2) possible coincidences. In one embodiment, a weight w can be assigned to each of the six coincidences 210a to 210f. Assigning a weight w to a coincidence 210 is equivalent to assigning a weight w to the LOR corresponding to this coincidence 210. Coincidences are also called coincidences. As shown in the figure, the first coincidence 210a is assigned w1, and the second coincidence 210b is assigned w2. Furthermore, as shown in Figure 2A, w3, w4, w5, and w6 are assigned to the third, fourth, fifth, and sixth coincidence counts 210c, 210d, 210e, and 210d, respectively. To obtain the weights w1 to w6 for the six exemplary coincidence counts 210a to 210f, the number of counts p detected along each of the six coincidence counts 210 to 210f is obtained. i It is possible to determine the number of detected counts p. i This can be represented by the number of detection windows 105, which include only two single 110s that were detected.

[0022] Next, the weight w i This can be determined according to the following equation (1).

number

[0023] In equation (1), T is equal to all the weights w as shown in equation (2) below. i These are normalization coefficients that ensure the sum of the coefficients equals 1.

number

[0024] In particular, the value of p1 increases when the first coincidence count 210a passes through the high-count radiation region 299. Consequently, the weight w1 of the first coincidence count 210a increases accordingly. For example, there are 100 detection windows 105 in which only two single 110s are detected, and 90 of these 100 cases describe the case where two single 110s are detected by the first detection element 205a and the second detection element 205b. That is, the case in which the first coincidence count 210a is 90 counts. In this case, there is a majority of the detected counts that form the first coincidence count 210a, and therefore the first coincidence count 210a can have a higher weight. Empirically, the first coincidence number 210a passes through areas closer to the center of the subject and areas with high values ​​(lungs, heart, etc.), the second coincidence number 210b passes through areas farther from the center of the subject but may still pass through areas with high values ​​(lungs, etc.), and the fifth coincidence number 210e may pass through areas closer to the periphery of the subject and areas with low values ​​(shoulders, etc.). Furthermore, it can be understood that the third coincidence number 210c, the fourth coincidence number 210d, and the sixth coincidence number 210f do not pass through the subject at all. Thus, it can be inferred that, empirically, the order is determined such that w1>w2>w5>w3>w4>w6.

[0025] Thus, in the example shown in Figure 2A, in the single list, the weights w of each of the multiple coincidence counts 210 (the first coincidence count 210a to the sixth coincidence count 210f) based on more than two singles 110 detected within the detection window 105 i This determines the detection window 105 in which more than two single 110s are detected is an example of the first detection window. Also, in the example shown in Figure 2A, the weight w i This is determined based on the LOR defined from the two single 110s within the detection window 105 which contains only the two single 110s. Specifically, the weight w i This is determined based on the frequency of LOR defined from the two singles 110 within the detection window 105 which contains only the two singles 110. More specifically, the weight w increases as the frequency increases.i is determined.

[0026] FIG. 2B is a schematic cross-sectional view of the body axis of an exemplary PET scanner 200 including reconstructed image data (PET image data) 220. In one embodiment, the weights w1 to w6 can be determined by using the reconstructed image data 220 generated (reconstructed) by performing PET reconstruction based on a detection window 105 including only two singles 110. Random and scatter corrections can be applied. However, it is possible to exclude attenuation correction from the reconstructed image data 220. And in the example shown in FIG. 2B, the weight w i can be determined according to the following formula (3).

Equation

[0027] In Equation (3), H is the system matrix, X is the reconstructed image data 220, J is an index of the corresponding simultaneous counts 210 of the entire system (the entire PET device), and T is the normalization factor. That is, the weight w i of the simultaneous counts 210 is determined based on the forward projection of the reconstructed image 220 into the LOR of the simultaneous counts 210. In particular, it can be confirmed that the high count radiation region 299 is a region of high importance for imaging (for example, the heart of the subject), and thus it can be further confirmed that the weight values are w1>w2>w5>w3>w4>w6.

[0028] In the example shown in FIG. 2B, for example, since the degree of drug accumulation can be known from the reconstructed image data 220, the weight w i is determined so that the weight w i becomes larger for the LOR passing through the region with a higher degree of accumulation. Thus, also in the example shown in FIG. 2B, in the single list, for each of the plurality of simultaneous counts 210 (the first simultaneous count 210a to the sixth simultaneous count 210f) based on more than two singles 110 detected within the detection window 105, the weight w iThis is determined. Specifically, weights w are determined based on reconstructed image data 220 generated based on LOR defined from two single 110s. i This determines the weights w of each of the multiple simultaneous counts 210, for example, based on the intensity of the voxels that make up the reconstructed image data 220, through which each of the multiple LORs defined by more than two single counts 110 detected within the detection window 105 passes. i This is determined. Reconstructed image data 220 is an example of a second nuclear medicine image data.

[0029] Figure 2C shows a schematic cross-sectional view of an exemplary PET scanner 200, including reconstructed image data 220 and a first time-of-flight (TOF) kernel 225a and a second TOF kernel 225b. In one embodiment, the reconstructed image data 220 shown in Figure 2C is generated in a similar manner to the method for generating the reconstructed image data 220 shown in the example in Figure 2B. Random and scattering corrections can be applied. However, the reconstructed image data 220 can be made free of attenuation corrections. Weights w1 to w6 can be determined by forward projection of the reconstructed image data 220 within the range of the TOF kernels 225a to 225b for the corresponding coincidence count 210. For each possible pair in Figure 2C, there is a timing difference between the two events (single 110 detected by the two detectors "a" and "b"). When the weight w1 for the first coincidence count 210a is determined, only the image data convolved within the TOF kernel range is used in the calculation. That is, in the example shown in Figure 2C, the weight w i This can be determined according to the following equation (4).

number

[0030] In equation (4), H is the system matrix, and X TOFis the reconstructed image data 220 convolved with TOF kernels 225a-225b, j is an index of the corresponding coincidence number 210 for the entire system (entire PET apparatus), and T is the normalization coefficient. In particular, TOF kernels 225-225b can further identify the potential origin location of the annihilation event at coincidence number 210. This leads to a significant improvement in accuracy, as will be explained below.

[0031] Thus, in the example shown in Figure 2C, in the single list, the weights w of each of the multiple coincidence counts 210 (the first coincidence count 210a to the sixth coincidence count 210f) based on more than two singles 110 detected within the detection window 105 i This is determined. Specifically, weights w are determined based on reconstructed image data 220 generated based on LOR defined from two single 110s. i This determines the weights w of each of the multiple coincidence counts 210 based on the intensity of the voxels that make up the reconstructed image data 220 within the TOF kernel corresponding to each of the multiple LORs. i To decide.

[0032] In one embodiment, for FIG. 2B, since the first coincidence count 210a appears to pass through the high count radiation region 299, the weight w1 is greater than the weight w2. Similarly for FIG. 2C, the first TOF kernel 225a shown in FIG. 2C provides information that further clarifies that the potential origin of the annihilation event of the first coincidence count 210a is actually located outside the high count radiation region 299. Further, the first TOF kernel 225a is actually only partially located within the human body in the reconstructed image data 220 and is further partially located completely outside the body. In contrast, the second TOF kernel 225b is located within the human body and can pass approximately through the center of mass. Without TOF information, the relative weights would be w1>w2>w5, and the number of counts detected along the first coincidence count 210a is greater compared to the second coincidence count 210b. That is, in a non-TOF scenario, the sum of the voxel intensities passing through each of the plurality of coincidence counts 210a, 210b, 210e can be calculated. Then from this image data, since the first coincidence count 210a has a very high count activity from the high count radiation region 299 (e.g., the heart), it can be determined that the sum of the voxel intensities of the first coincidence count 210a is greater than the sum of the second coincidence count 210b and the sum of the fifth coincidence count 210e. Instead, when introducing the first TOF kernel 225a and the second TOF kernel 225b (and any other necessary TOF kernels 225 for any other coincidence counts 210), the timing difference reveals that the origin location of the first coincidence count 210a would have been mostly outside the body. Further, the sum of the voxel intensities only within the first TOF kernel 225a or the second TOF kernel 225b can be summed. Thus, if the voxel intensity within the second TOF kernel 225b is greater than the voxel intensity within the first TOF kernel 225a, the weight becomes larger, and conversely, it can be determined that w1<w5<w2 is true. That is, the weights are determined such that w1<w5<w2.

[0033] All weights w iOnce assigned to each of multiple simultaneous counts 210 (multiple LORs), the weights can be used for guided pairing or reconfiguration.

[0034] In one embodiment, only one paired coincidence count 210 can be accepted for each detection window 105. Here, the coincidence count 210 with the highest weight w is paired, and all other coincidence counts 210 are rejected. For example, in the example in Figure 2A, w1>w2>w5>w3>w4>w6. Therefore, only the first coincidence count 210a with weight w1 is retained, and the other coincidence counts 210b~210f are rejected. Then, reconstructed image data (PET image data) is generated based on the weight w1. Specifically, reconstructed image data is generated based on the first coincidence count 210a with weight w1. Thus, in one embodiment, reconstructed image data is generated using the coincidence count (LOR) with the largest weight among a plurality of coincidence counts 210 (a plurality of LORs). The reconstructed image data generated in this way is an example of first nuclear medicine image data.

[0035] In another embodiment, each simultaneous count 210 is weighted w i It is held by the possibility of, or 1-w i It is rejected due to the possibility that 0 <w i < 1. For example, the first coincidence number 210a can have a weight w1 of 0.75. To determine whether to accept or reject the first coincidence number 210a, a number with a uniform distribution between 0 and 1 can be generated. If the generated number is less than or equal to 0.75, the event for the first coincidence number 210a is accepted; otherwise, the event is rejected. For example, a number greater than 0 and less than 1 can be randomly generated, and the generated number and its weight w i Compare the two. The number of generated items is the weight w i If the following conditions are met, then weight w i It accepts the assigned coincidence number 210 (LOR). On the other hand, the generated number has weight w i If it is greater, weight w iThe system rejects the assigned coincidence count of 210 (LOR).

[0036] Note that weight i With a probability corresponding to the weight w i The system may accept or reject the assigned coincidence number 210 (LOR). For example, if the weight w1 is 0.3, the system may accept the first coincidence number 210a (LOR corresponding to the first coincidence number 210a) to which weight w1 is assigned with a probability of 30% ((w1 × 100)%), and reject the first coincidence number 210a (LOR corresponding to the first coincidence number 210a) to which weight w1 is assigned with a probability of 70% (100 - (w1 × 100)%).

[0037] Thus, in another embodiment, weight w is selected from among multiple simultaneous counts 210 (multiple LORs). i Reconstructed image data is generated using a corresponding coincidence count of 210 (LOR). The reconstructed image data generated in this way is also an example of the first nuclear medicine image data.

[0038] In yet another embodiment, all possible simultaneous counts 210 have their corresponding weights w i It can be saved together with the weight w. During sinogram reconstruction, all possible simultaneous counts 210 correspond to the weight w of the sinogram. i It can be multiplied by the weight w for each of the 210 identical clock counts during list mode reconstruction. i Maintain the weight w during iterative reconstruction. i This can be used as a correction factor.

[0039] Thus, in yet another embodiment, weight w i Using this as a correction factor, reconstructed image data is generated. The reconstructed image data generated in this way is also an example of the first nuclear medicine image data.

[0040] Thus, according to the various embodiments described above, weight w i Reconstructed image data is generated using an appropriate coincidence count of 210 (LOR) according to the result, and weight wi This is used as a correction factor to generate reconstructed image data. Therefore, it is possible to generate reconstructed image data with good image quality.

[0041] Figure 3 shows a non-limiting embodiment of a flowchart of a guided pairing method 300 that determines weighted pairs within a detection window from a predetermined set of singles within the detection window, according to one exemplary embodiment.

[0042] In step 305, the PET apparatus (PET system) detects at least two singles 110 within the detection window 105. In the embodiment, if one single 110 is detected within the detection window 105, there are no other singles 110 to pair with the detected single 110, and therefore the data is not accepted. If at least two singles 110 are detected, at least two singles 110 can be paired.

[0043] In step 310, the PET device determines whether it detected three or more single 110s within the detection window 105 in step 305. If it is determined in step 310 that it did not detect three or more single 110s within the detection window 105 in step 305 (step 310: No), then two single 110s were detected in step 305. Therefore, the two single 110s can be paired, and there are no other possible pairings available. For this reason, in step 395, one coincidence count 395 is derived. Then, in step 312, PET reconstruction is performed based on the one possible coincidence count 395, and reconstructed image data 397 is generated.

[0044] On the other hand, in step 310, if it is determined in step 305 that three or more single 110s have been detected within the detection window 105 (step 310: Yes), then in step 315, all possible pairings of the single 110s are performed to determine all possible coincidence counts 210 from the set of single 110s for the detection window 105, and in step 398, all possible multiple coincidence counts are derived. The maximum number of possible coincidence counts 210 can be determined using NChoose2, where N is the number of detected single 110s within the detection window 105. For example, as shown in Figures 2A to 2C, 4Choose2 is used to derive six possible coincidence counts 210 for the four detected single 110s in detection elements 205a to 205d.

[0045] In step 325, the weights w for each of the multiple simultaneous counts 210 (multiple LORs) i This generates a weight w. i This can be based on the number of detected counts or the sum of voxel intensities along the same count of 210. Furthermore, the weight w i This can be based on the forward projection of PET image reconstruction with a simultaneous count of 210. Also, for example, weight w i This can be based on the forward projection of the reconstructed image within the TOF kernel 225 with a simultaneous count of 210.

[0046] In step 330, a single guided pairing can be performed based on the weighted coincidence counts 210 to obtain weighted pairs 399. The weighted pairs 399 can then be used when generating the updated reconstructed image. In one embodiment, all possible coincidence counts 210 are their corresponding weights w i It can be saved together with the weight w. During sinogram reconstruction, all possible simultaneous counts 210 correspond to the weight w of the sinogram. i It can be multiplied by the weight w for each of the 210 identical clock counts during list mode reconstruction. i Maintain the weight w during iterative reconstruction. iThis can be used as a correction factor.

[0047] To obtain quantitative data from PET, the sum of true and scattered coincidence counts can be determined by estimating and subtracting random coincidence counts from the measurement data of each LOR. The number of random coincidence counts detected as delayed coincidence counts will, on average, be equal to the number of random coincidence counts in the prompt coincidence count sinogram. By correcting for random coincidence counts, delayed coincidence counts are subtracted from the prompt coincidence count sinogram when they occur. More precisely, using the mean values, P=T+S+R and D=R, and the correction for random coincidence counts is T+S=PD, where P, T, S, R, and D are the number (or rate) of prompt coincidence counts, true coincidence counts, scattered coincidence counts, random coincidence counts, and delayed coincidence counts. This allows for accurate correction of random coincidence counts, but increases the statistical noise in the net (prompt)-(delayed) coincidence count sinogram.

[0048] To test the effectiveness of the above-described method and verify that the prompts and delays have the same distribution for random events (also referred to as "random"), the time difference distribution was tested using a real central line source. The prompt can be understood to mean prompt coincidence events or events detected within detection window 105. The delay can be understood to mean delayed coincidence events where detection window 105 is greatly extended compared to the detection window 105 of the detected prompt coincidence event. In particular, the probability of random events detected within the detection window (|t1 - t2| < tc, where t1 and t1 are times for two singles and tc is the detection window size) is the same as for random events within the delayed detection window (|t1 - (t2 + td)| < tc), where td is a user-defined parameter that describes the fixed time difference between two singles. For the real central line source, a graph of the time difference distribution according to one exemplary embodiment is shown in FIG. 4A. FIG. 4B is a diagram showing a zoom of the peak for the graph of the time difference distribution shown in FIG. 4A according to one exemplary embodiment. FIG. 4C is a diagram showing a zoom of the baseline for the graph of the time difference distribution shown in FIG. 4A according to one exemplary embodiment. Note that FIG. 4A is presented to show baseline data for accepting all coincidences (see "with mcoin") or rejecting all coincidences (see "without mcoin") and does not include the results of guided pairing.

[0049] By using the disclosed guided pairing method, more true events are generated and randomness is reduced. Data showing the effectiveness of the disclosed method is shown in FIGS. 5A - 5C, including the baseline data of FIGS. 4A - 4C.

[0050] Figure 5A shows an exemplary graph of the time-lag distribution of a real central source according to one exemplary embodiment. Figure 5B shows a peak zoom on the exemplary graph of the time-lag distribution in Figure 5A according to one exemplary embodiment. Figure 5C shows a baseline zoom on the exemplary graph of the time-lag distribution in Figure 5A according to one exemplary embodiment. In the method described above, guided pairing yields the same amount of “prompts” (i.e., true pairs) compared to retaining all coincidences, thus demonstrating that guided pairing can find true pairs within coincidences. Furthermore, the amount of prompts by rejecting all coincidences is far less than when retaining all coincidences or both of the described guided pairing methods. On the other hand, the guided pairing method is less random compared to retaining all coincidences.

[0051] In particular, guided pairing yields a similar peak height to retaining all coincidence counts in Figure 5B, while also yielding a lower baseline compared to retaining all coincidence counts in Figure 5C. Therefore, this larger difference between peak and baseline indicates a higher signal-to-noise ratio by using the guided pairing method compared to systems that use methods that accept or reject all coincidence counts. Furthermore, the improvements in data quality described above do not require a significant increase in computational power. However, it should be emphasized that there may be a computational offset, as rejecting some coincidence counts results in fewer pairs for reconstruction, thus leading to a reduction in reconstruction time. Thus, as shown herein, the methods and systems disclosed herein offer advantages over current imaging systems, including improved accuracy in identifying true coincidence counts, improved SNR and data quality, and faster processing and reconstruction times.

[0052] Figure 6 shows graphs of the mean and standard deviation of exemplary prompt and delayed events. To test whether bias was introduced into the guided paired data, the mean and standard deviation were calculated for prompt and delayed events within two windows, as shown by the dashed boxes in Figure 6. The two windows contained 200 data points, and N pp =3167±59 (prompt) and N de = 3124 ± 57 (delay) is obtained as the mean ± standard deviation. From this, it can be expressed as shown in equations (5) and (6) below.

number

number

[0053] In the formula, N pp This is the average number of prompt points, N de ΔN is the average number of delay points, ΔN is the difference between the average number of prompt points and the average number of delay points, and σ(ΔN) is the standard deviation of ΔN. In particular, since ΔN < σ(ΔN), the two tail regions within the black box do not have a significant difference, and therefore no bias is introduced.

[0054] Figures 7A and 7B show non-limiting embodiments of a PET apparatus 700 capable of carrying out method 300 according to the embodiment. The PET apparatus 700 comprises a detector ring containing a number of gamma-ray detectors (GRDs) (e.g., GRD1, GRD2 to GRDN), each configured as a rectangular detector module. According to one embodiment, the detector ring contains 40 GRDs. In another embodiment, there are 48 GRDs, and a larger number of GRDs is used to produce a larger bore size for the PET apparatus 700. The PET apparatus 700 is an example of a nuclear medicine diagnostic device.

[0055] Each GRD may include a two-dimensional array of individual detector crystals that absorb gamma rays and emit scintillation photons. These scintillation photons can be detected by a two-dimensional array of photomultiplier tubes (PMTs), also located within the GRD. A light guide can be positioned between the array of detector crystals and the PMTs.

[0056] Alternatively, scintillation photons can be detected by an array of silicon photomultipliers (SiPMs), with each individual detector crystal having a corresponding SiPM.

[0057] Each photodetector (e.g., PMT or SiPM) can generate an analog signal indicating when a scintillation event occurred, as well as the energy of the gamma ray that generated the detection event. Furthermore, photons emitted from one detector crystal can be detected by two or more photodetectors, and based on the analog signals generated by each photodetector, the detector crystal corresponding to the detection event can be determined, for example, using Anger logic and crystal decoding.

[0058] Figure 7B shows a schematic diagram of a PET scanner system having a gamma-ray photon counting detector (GRD) arranged to detect gamma rays emitted from the subject's obstetric blob (OBJ). The GRD can measure the timing, position, and energy corresponding to the detection of each gamma ray. In one embodiment, the gamma-ray detector is arranged in a ring shape, as shown in Figures 7A and 7B. The detector crystal can be a scintillator crystal having individual scintillator elements arranged in a two-dimensional array, and the scintillator elements can be any known scintillator material. The PMTs can be arranged so that light from each scintillator element is detected by multiple PMTs, enabling Anger arithmetic and crystal decoding of the scintillation event.

[0059] Figure 7B shows an example of the configuration of the PET apparatus 700, where the subject OBJ to be imaged is placed on the bed 716, and the GRDN from the GRD module GRD1 is arranged circumferentially around the subject OBJ and the bed 716. The GRD is fixedly connected to an annular component 720 which is fixedly connected to the gantry 740. The gantry 740 of the PET apparatus 700 includes an open opening through which the subject OBJ and the bed 716 can pass, and the GRD can detect gamma rays emitted in the opposite direction from the subject OBJ due to an annihilation event, and the timing and energy information can be used to determine the coincidence of gamma ray pairs.

[0060] Figure 7B also shows the circuitry and hardware for acquiring, storing, processing, and distributing gamma-ray detection data. The PET apparatus 700 includes, as such circuitry and hardware, a processor 770, a network controller 774, a memory 778, and a Data Acquisition System (DAS) 776. The PET apparatus 700 also includes data channels that route detection measurement results from the GRD to the DAS 776, processor 770, memory 778, and network controller 774. The DAS 776 can control the acquisition, digitization, and routing of detection data from the detector. In one embodiment, the DAS 776 controls the movement of the patient bed 716. The processor 770 performs functions including reconstruction of reconstructed image data from the detection data (e.g., the single list described above), pre-reconstruction processing of the detection data, and post-reconstruction processing of the image data.

[0061] The processor 770 can be configured to perform various steps of the method 300 described herein, variations thereof, and other various processes described above. Figure 7C is a diagram showing an example of the functional configuration of the processor 770 according to an embodiment. As shown in Figure 7C, the processor 770 includes an acquisition function 770a, a determination function 770b, and a generation function 770c. Each of the processing functions of the processor 770, the acquisition function 770a, the determination function 770b, and the generation function 770c, is recorded in the memory 778 of the PET device 700 in the form of a program that can be executed by a computer. The processor 770 is a processor that reads each program from the memory 778 and executes each program that has been read, thereby realizing each processing function corresponding to each program. In other words, the processor 770 in the state in which each program has been read has each of the processing functions shown in the processor 770 in Figure 7C.

[0062] The acquisition function 770a generates the single list described above from the detection measurement results from the GRD collected by the DAS776. In this way, the acquisition function 770a acquires the single list. The acquisition function 770a is an example of an acquisition unit.

[0063] The decision function 770b uses the single list obtained by the acquisition function 770a to determine the weight w i Determine the weight w i The various processes described above are executed to assign the same count 210 (LOR). The decision function 770b is an example of a decision unit.

[0064] The generation function 770c uses weight w i The above-mentioned processes for generating reconstructed image data are performed using this method. Generation function 770c is an example of a generation unit.

[0065] In this explanation, the term "processor" refers to circuits such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), Application Specific Integrated Circuit (ASIC), or programmable logic device (e.g., Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), or Field Programmable Gate Array (FPGA)). The processor 770 functions by reading a program stored in memory 778 and executing the read program. Alternatively, instead of storing the program in memory 778, the processor 770 may be configured to directly incorporate the program into its circuitry. In this case, the processor 770 functions by reading and executing the program incorporated into its circuitry.

[0066] The memory 778 can be a hard disk drive, CD-ROM drive, DVD drive, flash drive, RAM, ROM, or any other electronic storage device known in the art.

[0067] Network controllers 774, such as Intel Ethernet® PRO network interface cards from Intel Corporation in the United States, can interface between various parts of the PET imaging system. Furthermore, network controller 774 can also interface with an external network. As can be understood, this external network can be a public network such as the Internet, or a private network such as a LAN or WAN network, or any combination thereof, and may also include a PSTN or ISDN subnetwork. The external network can also be wired, such as an Ethernet network, or wireless, such as a cellular network, including EDGE, 3G, 4G, and 5G wireless cellular systems. The wireless network can be WiFi, Bluetooth®, or any other known wireless communication format.

[0068] The preceding description has included specific details, such as the particular shape of the processing system, as well as descriptions of the various components and processes used therein. However, it should be understood that the technology of this specification may be carried out in other embodiments that deviate from these specific details, and such details are for illustrative purposes only and are not limiting. The embodiments disclosed herein have been described with reference to the accompanying drawings. Similarly, for illustrative purposes, specific figures, materials, and configurations have been described to provide a complete understanding. Nevertheless, embodiments may be carried out without such specific details. Components having substantially the same functional structure are represented by similar reference letters, and therefore redundant descriptions may be omitted.

[0069] Various techniques have been described as multiple separate operations to aid in understanding various embodiments. The order of description should not be interpreted as meaning that these operations are necessarily order-dependent. In fact, these operations do not have to be performed in the order presented. The described operations may be performed in a different order than in the embodiments described. Additional embodiments may perform various additional operations and / or omit the described operations.

[0070] According to at least one embodiment described above, it is possible to generate nuclear medicine image data with good image quality.

[0071] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0072] 700 PET equipment 770 processor 770a Acquisition function 770b Decision Function 770c generation function

Claims

1. An acquisition unit that acquires gamma-ray detection data related to the subject, A determination unit determines the weight of each of the multiple coincidences based on more than two events detected within a first time window in the gamma-ray detection data, based on the frequency of LOR corresponding to each of the multiple coincidences, which is defined by the two events included in a second time window containing only two detected events. A generation unit that generates first nuclear medicine image data based on the aforementioned weights, Equipped with, The determination unit determines the weight of each of the plurality of coincidences based on the frequency of the LOR for each coincidence, in a nuclear medicine diagnostic device.

2. The nuclear medicine diagnostic apparatus according to claim 1, wherein the determination unit determines the weight such that it increases as the frequency increases.

3. The nuclear medicine diagnostic apparatus according to claim 1, wherein the generation unit generates the first nuclear medicine image data using the LOR with the largest weight among a plurality of LORs defined by more than two events.

4. The nuclear medicine diagnostic apparatus according to claim 1, wherein the generation unit generates the first nuclear medicine image data using LORs corresponding to the weights from among a plurality of LORs defined by more than two events.

5. The nuclear medicine diagnostic apparatus according to claim 1, wherein the generation unit generates the first nuclear medicine image data using the weight as a correction coefficient.

6. We obtained gamma-ray detection data for the subject, In the gamma-ray detection data, the weight of each of the multiple coincidences based on more than two events detected within a first time window is determined based on the frequency of LOR corresponding to each of the multiple coincidences, which is defined by the two events included in a second time window containing only two detected events. This includes generating nuclear medicine image data based on the aforementioned weights, A method for generating nuclear medicine image data, wherein determining the weight of each of the plurality of coincidences includes determining the weight of each of the plurality of coincidences based on the frequency of the LOR for each of the coincidences.

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