DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20, as originally filed, are currently pending and have been considered below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 8 and 11-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhan et al., U.S. Publication No. 2022/0313203, hereinafter, “Zhan”, and further in view of Jin et al., U.S. Publication No. 2018/0078233, hereinafter, “Jin”.
As per claim 1, Zhan discloses a system, comprising:
at least one processor and at least one storage device (Zhan, ¶0095, the processing circuitry can include computer processor circuitry having embedded and/or external non-volatile computer readable memory (e.g., RAM, SRAM, FRAM, PROM, EPROM, and/or EEPROM) that stores computer instructions (binary executable instructions and/or interpreted computer instructions) for controlling the computer processor to perform the processes described);
wherein the at least one storage device is configured to store computer instructions; and the at least one processor is configured to communicate with the at least one storage device to direct, when the computer instructions are executed (Zhan, ¶0095, the processing circuitry can include computer processor circuitry having embedded and/or external non-volatile computer readable memory (e.g., RAM, SRAM, FRAM, PROM, EPROM, and/or EEPROM) that stores computer instructions (binary executable instructions and/or interpreted computer instructions) for controlling the computer processor to perform the processes described), the system to:
obtain a plurality of sets of photon counting data, each set of the plurality of sets of photon counting data being acquired by a photon counting computed tomography (PCCT) device scanning one of a plurality of phantoms according to one or more scanning parameters (Zhan, Abstract, Various slabs of predetermined material and path lengths are placed in the photon counting CT detector and scanned using one or more stationary X-rays to obtain calibration data and parametrize the forward model);
obtain a PCCT system model (Zhan, ¶0024, the forward model for the X-ray scanner system);
calibrate the PCCT system model (Zhan, Abstract, Various slabs of predetermined material and path lengths are placed in the photon counting CT detector and scanned using one or more stationary X-rays to obtain calibration data and parametrize the forward model; Zhan, ¶0009, Disclosed is a calibration method comprising: placing at least one slab in a field of view of an X-ray scanner system, wherein the at least one slab has at least one known linear attenuation coefficient and at least one known pathlength; scanning, on the X-ray scanner system, the at least one slab with at least one X-ray tube located at plural known locations at different angles relative to the at least one slab; generating material decomposition data based on the scannings at the different angles; generating air calibration data based on at least one air scan using the at least one X-ray tube and at least one rotation speed; and calibrating a forward model for the X-ray scanner system based on the material decomposition data and the air calibration data)
Zhan does not explicitly disclose the following limitations as further recited however Jin discloses
calibrate the PCCT system model by determining, based on the plurality of sets of photon counting data, the one or more scanning parameters, and a parameter of the phantom, one or more model parameters of the PCCT system model (Jin, ¶0034, CT imaging system 10 can use a forward-model process for determining an updated spectral response model; Jin, ¶0038, A spectral response model for a certain element can be repeatedly updated for a sequence of scan angles of each scan as required for a calibration e.g. for all calibration phantoms of one or more calibration phantoms 22; Jin, ¶0029, performing one or more calibration scans can include using more than one calibration phantoms 22; Jin, ¶0031, one or more phantoms containing one or more materials may be positioned at one or more positions with the scan field of view to acquire the required calibration measurements; Jin, ¶0028, Establishing a spectral response model can include establishing tuning parameters for a spectral response model. In one aspect a spectral response model can be expressed in terms of tuning parameters … The tuning parameters of a spectral response model can be tuned with performance of a calibration process; Jin, ¶0035, system 10 can determine predicted values for calibration measurements for a certain element under current scanning conditions by establishing tuning parameters of the spectral response model and utilizing scan environment parameters in a forward model of the system; Jin, ¶0037, system 10 can establish variable parameters for modeling a current scanning environment such as parameters for modeling a calibration phantom 22 and operating parameters (operating tube voltage and operating tube current) of X-ray source 14. Parameters can model both a material of phantom 22 and an offset within the scan field of view for calibration phantom 22. At block 710, system 10 can perform a simulation (forward model) to determine predicted output values for calibration measurements; Jin, ¶0040).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Jin and Zhan because they are in the same field of endeavor. One skilled in the art would have been motivated to include the model parameters as taught by Jin in the system of Zhan in order to enable reuse and tuning of the parameters in the forward spectral response model (Jin, ¶0035).
As per claim 2, Zhan and Jin discloses the system of claim 1, wherein the model parameters are related to at least one procedure of X-ray generation, X-ray absorption, energy deposition, energy resolution, charge sharing, pulse pileup effect, or energy separation in a generation process of photon counting data (Jin, ¶0037, system 10 can establish variable parameters for modeling a current scanning environment such as parameters for modeling a calibration phantom 22 and operating parameters (operating tube voltage and operating tube current) of X-ray source 14. Parameters can model both a material of phantom 22 and an offset within the scan field of view for calibration phantom 22. At block 710, system 10 can perform a simulation (forward model) to determine predicted output values for calibration measurements; Jin, ¶0042, Performing imaging can include performing material decomposition (MD) using the spectral response model for each respective element of the first through Nth elements as updated at block 116).
As per claim 3, Zhan and Jin disclose the system of claim 2, wherein the PCCT system model includes a plurality of sub-models, and the plurality of sub-models include at least one of:
an energy deposition sub-model representing a relationship between the energy deposition and the photon counting data, an energy resolution sub-model representing a relationship between the energy resolution and the photon counting data, a charge sharing sub-model representing a relationship between the charge sharing and the photon counting data, a pulse pileup sub-model representing a relationship between the pulse pileup and the photon counting data, or an energy separation sub-model representing a relationship between the energy separation and the photon counting data (Jin, ¶0026, A spectral response model 200 can include for each of several hypothetical narrow-band illumination energy levels (a narrow-band energy level ideally corresponds to a single energy level) an element's expected spectral response, for example, spectral response profile 201 for a illumination energy level, a second spectral response profile 202 for a second illumination energy level, a third spectral response profile 203 for a third illumination energy level, a fourth spectral response profile 204 for a fourth illumination energy level, a fifth spectral response profile 205 for a fifth illumination energy level, a sixth spectral response profile 206 for a sixth illumination energy level, and so forth; Zhan, ¶0049-0050, a two-step calibration method for the PCD forward model for material decomposition is applied. The method consists of two parts: 1) estimation of the flux independent weighted bin response function Swb(E) using the expectation maximization (EM) method, and 2) estimation of the pileup correction term ... which is a function of energy (E) and the measured bin counts).
As per claim 4, Zhan and Jin disclose the system of claim 1, wherein the model parameters include one or more groups, each group of the one or more groups of the model parameters corresponding to different pulse pileup orders under an energy bin (Zhan, ¶0063, a series of low flux scans on various material slabs are collected … For a CT scan, both mA and kVp need to be pre-selected before the tube is turned on. Then, the low flux weighted bin response function Swb is estimated and with the estimated Swb, the high flux slab scans are used to estimate the additional parameters in the pileup correction term ... With the estimation calibration tables of Swb and Pb for each detector pixel, the quality of the calibration is checked on a quality phantom, e.g. a uniform water phantom, or phantom with multiple inserts with uniform known materials. The image quality is assessed with predefined standards, and if it is passed, the current calibration tables are saved).
As per claim 5, Zhan and Jin disclose the system of claim 4, wherein the calibrated PCCT system model includes one or more spectrum models each of which corresponds to one of one or more energy bins, each of the one or more spectrum models representing a mapping relationship between photon counting data under the energy bin, the scanning parameter, and a parameter of a reference material through a group of model parameters corresponding to the energy bin (Jin, ¶0026, A spectral response model 200 can include for each of several hypothetical narrow-band illumination energy levels (a narrow-band energy level ideally corresponds to a single energy level) an element's expected spectral response, for example, spectral response profile 201 for a illumination energy level, a second spectral response profile 202 for a second illumination energy level, a third spectral response profile 203 for a third illumination energy level, a fourth spectral response profile 204 for a fourth illumination energy level, a fifth spectral response profile 205 for a fifth illumination energy level, a sixth spectral response profile 206 for a sixth illumination energy level, and so forth).
As per claim 6, Zhan and Jin disclose the system of claim 5, wherein calibrating the PCCT system model based on the plurality of sets of photon counting data including determining a group of model parameters corresponding to an energy bin according to operations includes: for one of the different pulse pileup orders under the energy bin, determining an initial value of a model parameter among the group of model parameters corresponding to the pulse pileup order; and determining a target value of the model parameter based on the initial value of the model parameter corresponding to the pulse pileup order (Zhan, ¶0061, The calibration tables are updated from time to time based on the system/detector performance variations. This can also be designed as an iterative procedure. If the image quality is not good enough on a quality check phantom, this calibration process is repeated with the updated calibration tables from the last iteration as the initial guess; Zhan, ¶0063, the low flux weighted bin response function Swb is estimated and with the estimated Swb, the high flux slab scans are used to estimate the additional parameters in the pileup correction term Pb. With the estimation calibration tables of Swb and Pb for each detector pixel, the quality of the calibration is checked on a quality phantom, e.g. a uniform water phantom, or phantom with multiple inserts with uniform known materials. The image quality is assessed with predefined standards, and if it is passed, the current calibration tables are saved and then used for the following patient/object scans data processing. Otherwise, the procedure goes through the first three steps again using the last iteration of Swb and Pb as the initial guess. Here, commonly examined standards are: image CT number accuracy, uniformity, spatial resolution, noise and artifacts).
As per claim 8, Zhan and Jin disclose the system of claim 6, wherein the determining an initial value of a model parameter corresponding to the pulse pileup order includes: determining, based on a reference set of photon counting data obtained under a scanning parameter satisfying a condition, a reference model parameter; and determining, based on the reference model parameter, the initial value of the model parameter corresponding to the pulse pileup order (Zhan, ¶0063, the low flux weighted bin response function Swb is estimated and with the estimated Swb, the high flux slab scans are used to estimate the additional parameters in the pileup correction term Pb. With the estimation calibration tables of Swb and Pb for each detector pixel, the quality of the calibration is checked on a quality phantom, e.g. a uniform water phantom, or phantom with multiple inserts with uniform known materials. The image quality is assessed with predefined standards, and if it is passed, the current calibration tables are saved and then used for the following patient/object scans data processing. Otherwise, the procedure goes through the first three steps again using the last iteration of Swb and Pb as the initial guess. Here, commonly examined standards are: image CT number accuracy, uniformity, spatial resolution, noise and artifacts).
As per claim 11, Zhan discloses a system, comprising: at least one processor and at least one storage device; wherein the at least one storage device is configured to store computer instructions; and the at least one processor is configured to communicate with the at least one storage device to direct, when the computer instructions are executed (Zhan, ¶0095, the processing circuitry can include computer processor circuitry having embedded and/or external non-volatile computer readable memory (e.g., RAM, SRAM, FRAM, PROM, EPROM, and/or EEPROM) that stores computer instructions (binary executable instructions and/or interpreted computer instructions) for controlling the computer processor to perform the processes described), the system to:
obtain a set of photon counting data acquired by a PCCT device scanning a target subject according to a target scanning parameter (Zhan, ¶0062, The high level workflow of the above process is demonstrated in FIG. 2. Steps 1) to 4) represent the calibration workflow, and steps 5) to 8) demonstrate how the calibration tables are used in the operational scans of patients/objects to produce spectral image);
obtain a calibrated PCCT system model representing a mapping relationship between photon counting data, a scanning parameter, and one or more material parameters of one or more reference materials (Zhan, ¶0018, generate material decomposition data based on the scannings at the different angles; generate air calibration data based on at least one air scan using the at least one X-ray tube and at least one rotation speed; and calibrate a forward model for the X-ray scanner system based on the material decomposition data and the air calibration data);
determine, based on the set of photon counting data and the target scanning parameter, one or more target material parameters via the calibrated PCCT system model (Zhan, ¶0018, generate material decomposition data based on the scannings at the different angles; generate air calibration data based on at least one air scan using the at least one X-ray tube and at least one rotation speed; and calibrate a forward model for the X-ray scanner system based on the material decomposition data and the air calibration data).
Zhan does not explicitly disclose the following limitations as further recited however Jin discloses
determine, based on the set of photon counting data and the target scanning parameter of the target subject, one or more target material parameters corresponding to the target subject via the calibrated PCCT system model (Jin, ¶0042, FIG. 3, method 100 subsequent to block 116 can include performing imaging of an object such a human subject. Performing imaging can include performing material decomposition (MD) using the spectral response model for each respective element of the first through Nth elements as updated at block 116. MD can be performed by system 10 for improving CT imaging performance. System 10 can be activated to perform a CT scan and responsively imaging system 10 can output a CT scan image using determined MD information. In one embodiment, system 10 can output MD information in the form of basis material projections, e.g. water and iodine projections, which can be used by image reconstructor 34 to perform image reconstruction. In one embodiment performance of imaging can include performance of MD and/or performance of image reconstruction … [Equation 1]); and
obtain, based on the one or more target material parameters corresponding to the subject, one or more reconstructed images related to the target subject (Jin, ¶0042, FIG. 3, method 100 subsequent to block 116 can include performing imaging of an object such a human subject. Performing imaging can include performing material decomposition (MD) using the spectral response model for each respective element of the first through Nth elements as updated at block 116. MD can be performed by system 10 for improving CT imaging performance. System 10 can be activated to perform a CT scan and responsively imaging system 10 can output a CT scan image using determined MD information. In one embodiment, system 10 can output MD information in the form of basis material projections, e.g. water and iodine projections, which can be used by image reconstructor 34 to perform image reconstruction. In one embodiment performance of imaging can include performance of MD and/or performance of image reconstruction).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Jin and Zhan because they are in the same field of endeavor. One skilled in the art would have been motivated to include the model parameters for image reconstruction as taught by Jin in the system of Zhan in order to enable reuse and tuning of the parameters in the forward spectral response model (Jin, ¶0035).
As per claim 12, Zhan and Jin disclose the system of claim 11, wherein the determining, based on the set of photon counting data and the target scanning parameter of the target subject, the target material parameters corresponding to the target subject through the calibrated PCCT system model includes:
determining, based on the set of photon counting data, the target scanning parameter of the subject, and attenuation coefficients of the one or more reference materials corresponding to the target subject, target thicknesses of the one or more reference materials corresponding to the target subject through the calibrated PCCT system mode (Jin, ¶0043, One example of a function that can be used to perform material decomposition (MD) is the following. [Equation 1] where (A) is a vector of area density estimates for the collection of basis materials, λi is the photon counts in the ith energy bin ... The calculation of λi ... is based on the forward model of the CT imaging system ... [Equation 3] ... where R(E,E′) is the element-by-element calibrated spectral response function; Ti-1 and Ti are the energy thresholds of the ith energy bin. The attenuation coefficient μ(x,E) is defined as [Equation 4] where aa(x) is the density distribution and fa(E) is the mass attenuation coefficient, M is the total number of basis materials).
As per claim 13, Zhan and Jin disclose the system of claim 12, wherein the obtaining, based on the target material parameters of the reference materials corresponding to the target subject, reconstructed images related to the target subject includes: determining, based on the target thicknesses of the one or more reference materials, a material line integral of each of the reference materials corresponding to the target subject; and generating, based on the material line integral of each of the reference materials corresponding to the target subject, a material decomposition image of each of the reference materials corresponding to the target subject (Jin, ¶0043, One example of a function that can be used to perform material decomposition (MD) is the following. [Equation 1] where (A) is a vector of area density estimates for the collection of basis materials, λi is the photon counts in the ith energy bin ... The calculation of λi ... is based on the forward model of the CT imaging system ... [Equation 3] ... where R(E,E′) is the element-by-element calibrated spectral response function; Ti-1 and Ti are the energy thresholds of the ith energy bin. The attenuation coefficient μ(x,E) is defined as [Equation 4] where aa(x) is the density distribution and fa(E) is the mass attenuation coefficient, M is the total number of basis materials ... Thus the line integral in Eq. 2 can be written as [Equation 5]; Jin, ¶0042, System 10 can be activated to perform a CT scan and responsively imaging system 10 can output a CT scan image using determined MD information. In one embodiment, system 10 can output MD information in the form of basis material projections, e.g. water and iodine projections, which can be used by image reconstructor 34 to perform image reconstruction. In one embodiment performance of imaging can include performance of MD and/or performance of image reconstruction).
As per claim 14, Zhan and Jin disclose the system of claim 11, wherein the determining, based on the set of photon counting data and the target scanning parameter of the target subject, the target material parameters corresponding to the target subject through the calibrated PCCT system model includes: determining, based on the set of photon counting data, the target scanning parameter of the subject, and attenuation coefficients of the one or more reference materials corresponding to the target subject, a target material line integral corresponding to the target subject through the calibrated PCCT system model (Jin, ¶0043, One example of a function that can be used to perform material decomposition (MD) is the following. [Equation 1] where (A) is a vector of area density estimates for the collection of basis materials, λi is the photon counts in the ith energy bin ... The calculation of λi ... is based on the forward model of the CT imaging system ... [Equation 3] ... where R(E,E′) is the element-by-element calibrated spectral response function; Ti-1 and Ti are the energy thresholds of the ith energy bin. The attenuation coefficient μ(x,E) is defined as [Equation 4] where aa(x) is the density distribution and fa(E) is the mass attenuation coefficient, M is the total number of basis materials ... Thus the line integral in Eq. 2 can be written as [Equation 5]; Jin, ¶0042, System 10 can be activated to perform a CT scan and responsively imaging system 10 can output a CT scan image using determined MD information. In one embodiment, system 10 can output MD information in the form of basis material projections, e.g. water and iodine projections, which can be used by image reconstructor 34 to perform image reconstruction. In one embodiment performance of imaging can include performance of MD and/or performance of image reconstruction).
As per claim 15, Zhan and Jin disclose the system of claim 14, wherein the obtaining, based on the target material parameters corresponding to the subject, one or more reconstructed images related to the target subject includes: determining, based on the target material line integral corresponding to the target subject, material line integrals of the one or more reference materials corresponding to the target subject; and reconstructing a material decomposition image of the target subject based on one of the material line integrals of the one or more reference materials corresponding to the target subject (Jin, ¶0043, One example of a function that can be used to perform material decomposition (MD) is the following. [Equation 1] where (A) is a vector of area density estimates for the collection of basis materials, λi is the photon counts in the ith energy bin ... The calculation of λi ... is based on the forward model of the CT imaging system ... [Equation 3] ... where R(E,E′) is the element-by-element calibrated spectral response function; Ti-1 and Ti are the energy thresholds of the ith energy bin. The attenuation coefficient μ(x,E) is defined as [Equation 4] where aa(x) is the density distribution and fa(E) is the mass attenuation coefficient, M is the total number of basis materials ... Thus the line integral in Eq. 2 can be written as [Equation 5] … where [Equation 6] refers to the material density integral which is the definition of material area density; Jin, ¶0042, System 10 can be activated to perform a CT scan and responsively imaging system 10 can output a CT scan image using determined MD information. In one embodiment, system 10 can output MD information in the form of basis material projections, e.g. water and iodine projections, which can be used by image reconstructor 34 to perform image reconstruction. In one embodiment performance of imaging can include performance of MD and/or performance of image reconstruction).
As per claim 16, Zhan and Jin disclose the system of claim 11, wherein the determining, based on the set of photon counting data and the target scanning parameter of the target subject, the target material parameters corresponding to the target subject through the calibrated PCCT system model includes: constructing, based on the calibrated PCCT system model, a comparison table; and determining, based on the comparison table, the set of photon counting data, and the target scanning parameter of the target subject, the target material parameters corresponding to the target subject (Jin, ¶0036, At block 720, system 10 can compare predicted and actual calibration measurements and at decision block 726 system 10 can determine if a predicted value for calibration measurements for a current element matches actual calibration measurements as output at block 714. For system 10, determining that there is a match at block 726 system 10 can apply at least one matching criteria. According to the matching criteria in one embodiment a predicted value for a calibration measurement need not be identical to an actual calibration measurement but can resemble, e.g., be statistically similar to an actual calibration measurement. If system 10 at block 726 determines that there is a match between a predicted value for calibration measurements and actual calibration measurements, system 10 at block 730 can select the spectral response model yielding the match as the updated calibration spectral response model for the current element; Jin, ¶0045, Comparing FIGS. 4A and 4B, updated spectral response models 200 can be differentiated between elements to permit accurate imaging that can compensate for the fact that different elements may have different and non-uniform characteristics; Zhan, ¶0061, The calibration tables are updated from time to time based on the system/detector performance variations. This can also be designed as an iterative procedure. If the image quality is not good enough on a quality check phantom, this calibration process is repeated with the updated calibration tables from the last iteration as the initial guess).
As per claim 17, Zhan and Jin disclose the system of claim 16, wherein the determining, based on the comparison table, the set of photon counting data, and the target scanning parameter of the target subject, the target material parameters corresponding to the target subject includes: determining, based on the set of photon counting data and the target scanning parameter, first photon counting data and second photon counting data from the comparison table; determining, based on the first photon counting data and the second photon counting data, a first material parameter and a second material parameter corresponding to the target subject, respectively; and determining, based on the first material parameter and the second material parameter, a target material parameter corresponding to the target subject via interpolation processing (Zhan, ¶0009, Disclosed is a calibration method comprising: placing at least one slab in a field of view of an X-ray scanner system, wherein the at least one slab has at least one known linear attenuation coefficient and at least one known pathlength; scanning, on the X-ray scanner system, the at least one slab with at least one X-ray tube located at plural known locations at different angles relative to the at least one slab; generating material decomposition data based on the scannings at the different angles; generating air calibration data based on at least one air scan using the at least one X-ray tube and at least one rotation speed; and calibrating a forward model for the X-ray scanner system based on the material decomposition data and the air calibration data; Zhan, ¶0011, the material decomposition data includes a weighted bin response and a pulse pileup correction term; Zhan, ¶0029, FIG. 2 shows a material decomposition calibration and processing workflow; Zhan, ¶0060, Once Swb(E) is estimated from the calibration at each tube voltage (kVp) setting for each detector pixel, it is saved as a software calibration table on the system. It will be used as an input to further estimate the pileup correction term ... Both tables are then used for the material decomposition in object/patient scans to estimate the basis material path lengths; Zhan, ¶0078, From the scans, in next step S630, material decomposition calibration processing is performed to produce decomposition calibration tables, as mentioned above. Furthermore, in S640, air scans are performed at a range of rotation speeds to produce air calibration tables. In S650, patient/object scans (at a known rotation speed) can be gathered. The decomposition calibration tables, air calibration tables, and patient/object scans can then be used in S660 for phantom/object scan processing and utilize a calibrated forward model).
As per claim 18, Zhan and Jin disclose the system of claim 11, wherein the determining, based on the set of photon counting data and the target scanning parameter of the target subject, the target material parameters corresponding to the target subject through the calibrated PCCT system model includes: obtaining the target material parameters corresponding to the target subject by inputting the set of photon counting data and the target scanning parameter of the target subject into the calibrated PCCT system model (Zhan, ¶0078, From the scans, in next step S630, material decomposition calibration processing is performed to produce decomposition calibration tables, as mentioned above. Furthermore, in S640, air scans are performed at a range of rotation speeds to produce air calibration tables. In S650, patient/object scans (at a known rotation speed) can be gathered. The decomposition calibration tables, air calibration tables, and patient/object scans can then be used in S660 for phantom/object scan processing and utilize a calibrated forward model).
As per claim 19, Zhan and Jim disclose the system of claim 11, wherein the obtaining a calibrated PCCT system model includes:
obtaining a plurality of sets of photon counting data, each set of the plurality of sets of photon counting data being acquired by the PCCT device scanning one of a plurality of phantoms according to one or more scanning parameters (Zhan, Abstract, Various slabs of predetermined material and path lengths are placed in the photon counting CT detector and scanned using one or more stationary X-rays to obtain calibration data and parametrize the forward model; Jin, ¶0029, performing one or more calibration scans can include using more than one calibration phantoms 22; Jin, ¶0031, one or more phantoms containing one or more materials may be positioned at one or more positions with the scan field of view to acquire the required calibration measurements);
obtaining a PCCT system model (Zhan, ¶0024, the forward model for the X-ray scanner system; Jin, ¶0034, CT imaging system 10 can use a forward-model process for determining an updated spectral response model; Jin, ¶0038, A spectral response model for a certain element can be repeatedly updated for a sequence of scan angles of each scan as required for a calibration e.g. for all calibration phantoms of one or more calibration phantoms 22); and
calibrating the PCCT system model by determining, based on the plurality of sets of photon counting data, the one or more scanning parameters, and a parameter of the phantom, one or more model parameters of the PCCT system model (Jin, ¶0029, performing one or more calibration scans can include using more than one calibration phantoms 22; Jin, ¶0031, one or more phantoms containing one or more materials may be positioned at one or more positions with the scan field of view to acquire the required calibration measurements; Jin, ¶0028, Establishing a spectral response model can include establishing tuning parameters for a spectral response model. In one aspect a spectral response model can be expressed in terms of tuning parameters … The tuning parameters of a spectral response model can be tuned with performance of a calibration process; Jin, ¶0035, system 10 can determine predicted values for calibration measurements for a certain element under current scanning conditions by establishing tuning parameters of the spectral response model and utilizing scan environment parameters in a forward model of the system; Jin, ¶0037, system 10 can establish variable parameters for modeling a current scanning environment such as parameters for modeling a calibration phantom 22 and operating parameters (operating tube voltage and operating tube current) of X-ray source 14. Parameters can model both a material of phantom 22 and an offset within the scan field of view for calibration phantom 22. At block 710, system 10 can perform a simulation (forward model) to determine predicted output values for calibration measurements; Jin, ¶0040).
As per claim 20, Zhan and Jin disclose the system of claim 11, wherein the set of photon counting data includes a plurality of subsets of photon counting data corresponding to different energy bins (Jin, ¶0026, A spectral response model 200 can include for each of several hypothetical narrow-band illumination energy levels (a narrow-band energy level ideally corresponds to a single energy level) an element's expected spectral response, for example, spectral response profile 201 for a illumination energy level, a second spectral response profile 202 for a second illumination energy level, a third spectral response profile 203 for a third illumination energy level, a fourth spectral response profile 204 for a fourth illumination energy level, a fifth spectral response profile 205 for a fifth illumination energy level, a sixth spectral response profile 206 for a sixth illumination energy level, and so forth); and
the obtaining, based on the one or more target material parameters corresponding to the subject, one or more reconstructed images related to the target subject includes: determining, based on the plurality of subsets of photon counting data, a material line integral of each of the reference materials under the different energy bins; determining, based on the material line integrals of the reference materials under the different energy bins, projection data of at least one energy bin; and obtaining, based on the projection data of the at least one energy bin, a material decomposition image corresponding to the target subject (Jin, ¶0043, One example of a function that can be used to perform material decomposition (MD) is the following. [Equation 1] where (A) is a vector of area density estimates for the collection of basis materials, λi is the photon counts in the ith energy bin ... The calculation of λi ... is based on the forward model of the CT imaging system ... [Equation 3] ... where R(E,E′) is the element-by-element calibrated spectral response function; Ti-1 and Ti are the energy thresholds of the ith energy bin. The attenuation coefficient μ(x,E) is defined as [Equation 4] where aa(x) is the density distribution and fa(E) is the mass attenuation coefficient, M is the total number of basis materials ... Thus the line integral in Eq. 2 can be written as [Equation 5] … where [Equation 6] refers to the material density integral which is the definition of material area density; Jin, ¶0042, System 10 can be activated to perform a CT scan and responsively imaging system 10 can output a CT scan image using determined MD information. In one embodiment, system 10 can output MD information in the form of basis material projections, e.g. water and iodine projections, which can be used by image reconstructor 34 to perform image reconstruction. In one embodiment performance of imaging can include performance of MD and/or performance of image reconstruction; Zhan, ¶0067).
Allowable Subject Matter
Claims 7, 9 and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claims 7, 9 and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims because while the prior art discloses generating calibration models including model parameters the prior art does not disclose the limitations, “wherein the system performs an optimization of the model parameter based on the initial value of the model parameter corresponding to the pulse pileup order to determine the target value of the model parameter, and the optimization includes performing an iterative process including multiple iterations, in each iteration, determining, based on a portion of a set of photon counting data under the energy bin, the parameter of the phantom and the one or more scanning parameters corresponding to the set of photon counting data, photon counting estimation through the spectrum model including the initial value of the model parameter; constructing, based on an error between the portion of the set of the photon counting data under the energy bin and the photon counting estimation, a target optimization function; updating the initial value of the model parameter based on the target optimization function to obtain an updated value of the model parameter that is designated as the initial value of the model parameter in a next iteration; and in response to determining that a termination condition is satisfied, determining an updated value of the model parameter as the target value of the model parameter,” “wherein the determining, based on the reference model parameter, the initial value of the model parameter corresponding to the pulse pileup order includes: in response to determining that the pulse pileup order is zero, determining the initial value of the model parameter corresponding to the pulse pileup order by normalizing the reference model parameter based on a reference tube current” and “wherein the determining, based on the reference model parameter, the initial value of the model parameter corresponding to the pulse pileup order includes: in response to determining that the pulse pileup order is non-zero, determining an initial value of the model parameter corresponding to a pulse pileup order of zero by normalizing the reference model parameter based on a reference tube current; and determining, based on the initial value of the model parameter corresponding to the pulse pileup order being of zero, the initial value of the model parameter corresponding to the pulse pileup order being of non-zero” as recited in claims 7, 9 and 10.
Conclusion
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668
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