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 .
Response to Arguments
Applicant’s arguments with respect to the independent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's argument has been considered but is not persuasive as to the combined rejection. Although Hünemohr does not use the phrase 'multiple linear regression, see Eq. (3).
The amendment reciting at least one of hydrogen, carbon, nitrogen, oxygen, phosphorus, or calcium does not distinguish Hünemohr. Hünemohr expressly identifies those same six major elements and predicts their elemental mass fractions, Hünemohr, see §2.A, Eq. (3), Fig. 2.
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.
Claim(s) 1 – 4, 8 – 12 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hunemohr et al. (Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy; Med. Phys. 41 (6), June 2014) in view of Zhang et al. (Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data; Med. Phys.46(1), January (2019)) and Han et al. (A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation; Med. Phys. 43 (1), January 2016).
With regards to claim 1, Hunemohr discloses a computer-implemented method of predicting planning parameters for Monte Carlo proton radiation planning on a subject (i.e., Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy; Title) , comprising: receiving training data of tissues, wherein for each tissue, the training data include elemental compositions of a plurality of elements in the tissue, a mass density of the tissue (i.e., predicting mass density and elemental mass fractions of tissues from dual energy CT (DECT) data for Monte Carlo (MC) based dose planning; see Purpose, page 1) (Introduction) (2.A. Extraction of elemental compositions from Qe and Z eff) (2.B. Extraction of mass density from the relative electron density).
Hunemohr discloses a few relations, elemental mass fractions influence linearly (3), Mass density from relative electron density (4) and Elemental I- values taken from a reference sheet calculated with Bragg’s additivity rule (5). See below for relationships.
Hunemohr further discloses executing a linear regression model, wherein an elemental composition includes a mass fraction of one of the plurality of elements in a tissue, and the model includes that the mass fraction is a linear function; and training the model by: fitting the training data with the model (2.A. Extraction of elemental compositions from Qe and Z eff) (2.B. Extraction of mass density from the relative electron density); and adjust model parameters of the model during the fitting, wherein the trained model is configured to predict planning parameters required in a Monte Carlo proton radiation planning system in generating a proton radiation plan of a subject, wherein the planning parameters include elemental compositions and mass densities of tissues in a treatment region of the subject (Abstract) (Introduction) (2.A. Extraction of elemental compositions from Qe and Z eff) (2.B. Extraction of mass density from the relative electron density);
Hunemohr fails to expressly disclose basis material weights when a linear attenuation coefficient of the tissue is represented as a weighted sum of linear attenuation coefficients of basic materials and wherein the linear function includes the base material weights.
Han relates to a method that evaluates the accuracy and robustness of a simple, linear, separable, two-parameter model (basis vector model, BVM) in mapping proton stopping powers via dual energy computed tomography (DECT) imaging.
Han teaches basis vector models. The BVM assumes that the linear attenuation coefficient of an unknown material at location x can be represented as a linear combination of the linear attenuation coefficients of two dissimilar basis materials.
The mean excitation energy of an arbitrary tissue is given by
It was assumed that the ratio of I-values to the ICRU reference values and c1/(c1 + c2) is linearly dependent.
Han discloses applying the BVM to the estimation of proton stopping power, we hypothesized that the electron density and mean excitation energy of an arbitrary biological material can be accurately predicted by the following linear combinations where c1 and c2 were derived from DECT analysis. The linear empirical correction function was then used to update the estimation of I-values from Eq. (8). This correction function can also be generated from scanning calibration phantoms for a specific scanner (2.A. Basis vector mode) (Pages 602 – 603).
For example, CT numbers of calibration phantoms acquired by scanning at two different energies can be used to compute c1 and c2, which can then be used to estimate the uncorrected I-value of calibration phantoms from Eq. (8). Since the references of I-value can be obtained using the Bragg additivity rule given the exact elemental compositions of phantoms, the correction function was constructed by comparing reference values against uncorrected I-values with respect to c1/ (c1 + c2). Han further teaches that the linear fits for the soft and bony tissues are shown in Fig. 2. A voxel falling into the overlap region of Fig. 2 would be described as a bony tissue or soft tissue depending upon which basis pair it was assigned based on the ratio ξ (x) of its DECT image intensities. Such additions improve the computational efficiency of the overall imaging (2.A. Basis vector mode) (Pages 602 – 603) (Figure 2).
Zhang relates to experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data (Title).
Zhang teaches raw-data reconstruction of BVM weights and the electron density weighted ration and further teaches that soft and bony tissues follow different linear relations in space (Abstract) (Pages 275 – 278) (Figure 1).
Liu (2009) teaches that DECT material-decomposition quantities can determine elemental composition and elemental mass fractions, R4, pp. 1602-1604, Eqs. (1), (6), (12)-(13).
In view of the utility of Han/Zhang's BVM variables for directly representing attenuation, electron density, and composition-sensitive material behavior from DECT data, it would have been obvious to use those known BVM-derived variables as the predictor feature space for Hünemohr's fitted elemental-mass-fraction model, thereby predictably obtaining tissue composition and density for MC particle-therapy calculations.
With regards to claims 2, 10 and 11, Hunemohr modified discloses wherein the model includes that the mass fraction is a linear function of a weighted component ratio between the basis material weights. See the rejection of claim 1, specifically; Han equations (10) and (11); Han Figure 1; Han Page 603).
With regards to claims 3 and 16, Hunemohr modified discloses wherein the weighted component ratio is derived based on the basis material weights and electron densities of the basis materials. See the rejection of claim 1, specifically; Han (Figure 2); Zhang equation (18) (Pages 275 – 278) (Figure 1).
With regards to claims 4 and 12, Hunemohr modified discloses wherein the model includes a first submodel of a soft tissue and a second submodel of a bony tissue, and fitting the training data further comprises: bracketing materials into the soft tissue and the bony tissue based on the weighted component ratio; and applying the first submodel or the second submodel on the training data based on the bracketing. See the rejection of claim 1, specifically; Han equation (18); Zhang (Pages 275 – 278).
With regards to claim 5, wherein receiving training data of tissues further comprises normalizing the basis material weights to account for a tissue having a low mass density into the model by scaling the basis material weights with a factor.
With regards to claim 6, wherein normalizing the basis material weights further comprises scaling the basis material weights with the factor as a ratio between electron densities of the basis materials.
With regards to claim 7, wherein the model includes that the mass fraction is a linear function of the normalized basis material weights and a weighted component ratio between the normalized basis material weights.
With regards to claim 8, Hunemohr modified discloses the claimed invention according to claim 1 and also discloses a fitting step inherently compares estimates to reference density during coefficient adjustment (2.B. Extraction of mass density from the relative electron density; equation 4; page 3).
Hunemohr fails to expressly disclose wherein training the model further comprises: for each tissue, estimating the mass density of the tissue based on electron densities of the basis materials and the basis material weights; comparing the estimated mass density with the mass density of the tissue in the training data; and adjusting the model based on the comparison.
Zhang teaches to compute the proton stopping power map, the electron density and mean excitation energy are predicted foreach pixel from the two reconstructed BVM component images The electron density of a typical biological medium can be accurately estimate (2.A.2. Proton stopping power computation based on basis vector model; equation 9; page 279).
In view of the utility, to improve the computational efficiency of the overall imaging, it would have been obvious to a person of ordinary skill of the art at the time the invention was made to modify Hunemohr to include the teachings such as that taught by Zhang.
With regards to claim 9, a computer-implemented method of predicting planning parameters for Monte Carlo proton radiation planning on a subject, comprising: receiving raw data of a subject acquired using a computed tomography system; deriving basis material weights at each image voxel location based on the raw data, wherein the basis material weights are weights in expressing a linear attenuation coefficient of a tissue at the image voxel location as a weighted sum of linear attenuation coefficients of basis materials; execute a linear regression model; estimating planning parameters including elemental compositions of a plurality of elements and/or a mass density of the tissue at the image voxel location based on the basis material weights using the model; and out putting the planning parameters, wherein the planning parameters are required in a Monte Carlo proton radiation planning system in generating a proton radiation plan of the subject.
Claim(s) 5 – 7 and 13 - 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hunemohr et al. (Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy; Med. Phys. 41 (6), June 2014), Zhang et al. (Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data; Med. Phys.46(1), January (2019)) and Han et al. (A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation; Med. Phys. 43 (1), January 2016) in view of Mendonca et al. (US Pub. No. 2010/0135453 A1).
With regards to claims 5 and 13, Hunemohr discloses the claimed invention according to claims 1 and 9, and further teaches normalizes of all predicted element mass fractions (See 2.A., 2.D., 2.E., Table 1).
Hunemohr fails to disclose wherein deriving basis material weights further comprises: normalizing the basis material weights to account for a tissue having a low mass density in the model by scaling the basis material weights with a factor.
Mendonca supports ordinary use of decomposition constraints/regularization [0009]-[0011], [0039]-[0057]; (Figures 7 and 8).
Han discloses normalizing, see 2.A.
Zhang disclose normalizing (page 275).
In view of the utility, to improve the computational efficiency of the overall imaging, it would have been obvious to a person of ordinary skill of the art at the time the invention was made to modify Hunemohr to include the teachings such as that taught by Mendonca.
With regards to claims 6 and 14, Hunemohr discloses the claimed invention according to claims 1 and 9, and further teaches normalizes of all predicted element mass fractions (See 2.A., 2.D., 2.E., Table 1).
Hunemohr fails to disclose wherein normalizing the basis material weights further comprises scaling the basis material weights with the factor as a ratio between electron density of the basis materials.
Han discloses normalizing, see 2.A.
Zhang disclose normalizing (page 275).
Mendonca supports ordinary use of decomposition constraints/regularization [0009]-[0011], [0039]-[0057]; (Figures 7 and 8).
In view of the utility, to improve the computational efficiency of the overall imaging, it would have been obvious to a person of ordinary skill of the art at the time the invention was made to modify Hunemohr to include the teachings such as that taught by Mendonca.
With regards to claims 7 and 15, Hunemohr discloses the claimed invention according to claims 1 and 9, and further teaches normalizes of all predicted element mass fractions (See 2.A., 2.D., 2.E., Table 1).
Hunemohr fails to disclose wherein each elemental composition includes a mass fraction of one of the plurality of elements, and the model includes that the mass fraction is a linear function of the normalized basis material weights and a weighted component ratio between the normalized basis material weights.
Han discloses normalizing, see 2.A.
Zhang disclose normalizing (page 275).
Mendonca supports ordinary use of decomposition constraints/regularization [0009]-[0011], [0039]-[0057]; (Figures 7 and 8).
In view of the utility, to improve the computational efficiency of the overall imaging, it would have been obvious to a person of ordinary skill of the art at the time the invention was made to modify Hunemohr to include the teachings such as that taught by Mendonca
Claim(s) 17 - 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hunemohr et al. (Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy; Med. Phys. 41 (6), June 2014), Zhang et al. (Experimental implementation of a joint statistical image reconstruction method for proton stopping power mapping from dual-energy CT data; Med. Phys.46(1), January (2019)) and Han et al. (A linear, separable two-parameter model for dual energy CT imaging of proton stopping power computation; Med. Phys. 43 (1), January 2016) in view of Charyyev et al. (Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy. Br J Radiol. 2022 Jan 1;95(1129):20210644) doi: 10.1259/bjr.20210644. Epub 2021 Oct 28. PMID: 34709948; PMCID: PMC8722254 and Liu et al. (Machine learning based oxygen and carbon concentration derivation using dual-energy CT for PET-based dose verification in proton therapy. Med Phys. 2022; 49: 3347–3360). https://doi.org/10.1002/mp.1558.
With regards to claim 17, Hunemohr discloses the claimed invention according to claims 1 and 9, but fails to expressly disclose the learning architecture added by claim 17.
Charyyev discloses Dual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interac tions. This work presents a learning- based method to synthesize DECT images from SECT image for proton radiotherapy (Abstract).
Figure 1. The systematic flowchart of the proposed method. The first row shows the training stage. The second row shows the prediction stage for a new patient. HECT, high energy CT; LECT, low energy CT, SECT, single energy CT; sHECT, synthetic high energy CT (Page 3-6 and 11) (Figures 1, 2 and 8).
With regards to claim 18, wherein an elemental composition includes a mass fraction of one of the plurality of elements, and the model includes that the mass fraction is a function of the basis material weights and a weighted component ratio.
Liu discloses proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g. C and O) wherein, machine learning framework for deriving oxygen and carbon. Liu teaches a machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Two machine learning models, UNet and ResNet, are able to utilize spatial correlation and obtain accurate carbon and oxygen concentration. As such, Liu lays a foundation to apply the proposed approach to clinical images (Abstract).
In view of the utility, to improve the computational efficiency of the overall imaging with learning-based architecture, it would have been obvious to a person of ordinary skill of the art at the time the invention was made to modify Hunemohr to include the teachings such as that taught by Charyyev and Liu.
With regards to claim 18, see the rejection of claim 3.
With regards to claim 19, see the rejection of claim 4.
With regards to claim 20, see the rejections of claims 5-7
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DJURA MALEVIC whose telephone number is (571)272-5975. The examiner can normally be reached M-F (9-5).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Uzma Alam can be reached at 571.272.3995. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DJURA MALEVIC/Examiner, Art Unit 2884
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
Prosecution Insights