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 .
Claim Objections
Claims 2, 3, and 15 are objected to because of the following informalities:
Claim 2, uses the phrase “diffusion weight parameter.” It is suggested this phrase be rewritten to say “diffusion weight[ing] parameter” instead.
Claim 3, uses the phrase “perfusion parameter.” It is suggested this phrase be rewritten to say “perfusion weighting parameter” instead.
Claim 15, line 4: ‘in vivo pTCM’ should be ‘an in vivo prostatic tissue composition map (pTCM)’.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 9-11 and 19 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention:
Claim 9 recites the limitation "the MR signal at the voxel." There is insufficient antecedent basis for this limitation in the claim. It is suggested this phrase be rewritten to say “[an] MR signal at the voxel” instead. Claims 10 and 11 depend from claim 9 and are therefore rendered indefinite under 35 U.S.C. 112(b) given that they depend from a claim which has insufficient antecedent basis.
Claim 19 recites the limitation "for the apparent diffusion coefficients.” There is insufficient antecedent basis for this limitation in the claim. It is suggested this phrase be rewritten to say “for [] apparent diffusion coefficients” instead. Claim 19 recites the limitation "the MR signal at the voxel.” There is insufficient antecedent basis for this limitation in the claim. It is suggested this phrase be rewritten to say “[an] MR signal at the voxel” instead.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 17, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. These claims recite volume fraction maps of the ROI for each of a plurality of tissue types from the multi-parametric MR imaging data and generating from an MRI image a prostatic tissue composition map (pTCM) by transforming a multi-dimensional array of MRI data using a model to non-invasively evaluate volume fractions, ADC's, and T2's of prostate tissue components.
The limitation of determining the volume fraction maps of the ROI for each of a plurality of tissue types from the multi-parametric MR imaging data, as drafted (see claims 1 and 20); and generating from an MRI image a prostatic tissue composition map (pTCM) by transforming a multi-dimensional array of MRI data using a model to non-invasively evaluate volume fractions, ADC's, and T2's of prostate tissue components (see claim 17), which are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting by “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, the limitation of “determining” in this context encompasses the user manually calculating the volume fraction maps of the ROI for each of a plurality of tissue types from the multi-parametric MR imaging data. Similarly, but for the by “a processor” language, the limitation of “generating” in this context encompasses the user manually generating an MRI image of a prostatic tissue composition map (pTCM) by transforming a multi-dimensional array of MRI data using a model to non-invasively evaluate volume fractions, ADC's, and T2's of prostate tissue components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, these claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform determining and generating steps. The processor in these steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Further, although the claims merely mention the magnetic resonance imaging data, no actual apparatus is being positively recited where data is somehow being obtained (e.g. in the form of binary codes or printed on a paper). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Although a display is mentioned, it is merely a display which is highly generalized and commonly used. Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform both the determining and generating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. These claims are not patent eligible.
Regarding the depending claims 2-16, 18, and 19, they do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the depending claims 2-16, 18, and 19 are also found not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-8, 12-14, 16, and 20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Jerome, N. P., et al. (2016). Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging. Physics in medicine and biology, 61(24), N667–N680 (hereinafter referred to as, “Jerome”).
Regarding claim 1, Jerome discloses a non-transitory storage medium storing instructions readable and executable by at least one electronic processor (See Jerome: Page N670 (clarifying that, "all analysis was performed using in-house software developed with MATLAB")) to perform an imaging method, the method (See Jerome: Page N670 (providing that, "the ... data in Protocol 1, the signal intensity was averaged for all voxels in the ROl for each image, and this value was used in the model fitting")) comprising: obtaining multi-parametric magnetic resonance (MR) imaging data parameterized by a diffusion weighting (See Jerome: Page N674 (discussing the, "[e]stimation of the T2 values for the ... true diffusion compartments derived from the T2-IVIM model")) or perfusion weighting parameter (See Jerome: Page N676 (describing that, "the IVIM model ... allows inference of ... tissue perfusion properties")) and a magnetization relaxation parameter (See Jerome: Page N677 (providing that in, "this study we show that a complete formulation of the IVIM model, extended to allow for distinct T2 values")) for a region of interest (ROI) of a patient (See Jerome: Page N670 (clarifying that, "[i]mages were acquired … and a region of interest (ROI) was drawn covering the whole liver section")); determining volume fraction maps of the ROI for each of a plurality of tissue types from the multi-parametric MR imaging data (See Jerome: Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI") and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4); and controlling a display device to display a tissue composition map comprising or generated from the determined volume fraction maps (See Jerome: Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI") and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4).
Regarding claim 2, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the at least one of a diffusion weighting or perfusion parameter includes a diffusion weighting parameter (See Jerome: Page N674 (discussing the, "[e]stimation of the T2 values for the ... true diffusion compartments derived from the T2-IVIM model")), and the diffusion weight parameter comprises one of a b-value, a diffusion time, or a diffusion gradient (See Jerome: Page N677 (clarifying that, "[e]xtending data acquisition to include multiple … b-values, combined with fitting the T2-IVIM model allows estimation of compartment T2s alongside conventional IVIM parameters")).
Regarding claim 3, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the at least one of a diffusion weighting or perfusion parameter value includes a perfusion weighting parameter (See Jerome: Page N676 (describing that, "the IVIM model ... allows inference of ... tissue perfusion properties")).
Regarding claim 4, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the at least one magnetization relaxation parameter comprises one of echo time (TE) or flip angle (See Jerome: Page N669 (clarifying that the, "inclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency")).
Regarding claim 5, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the at least one magnetization relaxation parameter value comprises echo time (TE) (See Jerome: Page N669 (clarifying that the, "inclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency")) and the diffusion weighting parameter value comprises a b-value (See Jerome: Page N677 (clarifying that, "[e]xtending data acquisition to include multiple … b-values, combined with fitting the T2-IVIM model allows estimation of compartment T2s alongside conventional IVIM parameters")).
Regarding claim 6, Jerome discloses the non-transitory storage medium of claim 5 (See above discussion), wherein: the multi-parametric magnetic resonance (MR) imaging data includes MR imaging data acquired for at least nine (TE,b-value) parameter value pairs (See Jerome: Page N667 (providing that, "[t]wo consented healthy volunteer cohorts (n = 5, 6) underwent DWI comprising multiple TE/b-value combinations (Protocol 1: TE = 62–102 ms, b = 0–250 mm−2s, 30 combinations. Protocol 2: 8 b-values 0–800 mm−2s at TE = 62 ms, with 3 additional b-values 0–50 mm−2s at TE = 80, 100 ms; scanned twice)") and Page N670 (specifying that, "Protocol 1: ... [uses thirty] b-TE combinations ... [while] Protocol 2: ... [uses a] set of 14 b-TE combinations ")), and the determining of the volume fraction maps includes solving a system of equations at each voxel for at least the volume fraction of each tissue type (See Jerome: Pages N669 (providing equations (2) and (3)), Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI"), and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4) wherein the system of equations includes, for each (TE,b-value) parameter value pair, an equation relating MR signal of the voxel acquired with the (TE,b-value) parameter value pair to a weighted sum of signal components for each tissue type (See Jerome: Pages N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")) functionally depending on relaxation time T2 (See Jerome: Page N669 (providing that the, "T2 relaxation times of both compartments are distinct and can be estimated directly from the data, and which are thus available as biomarkers in their own right, in contrast to previous methods that take an assumed value for T2 associated with the pseudo-diffusion compartment")) and apparent diffusion coefficient (ADC) of the tissue type (See Jerome: Page N668 (clarifying that while, "the apparent diffusion coefficient (ADC), conventionally derived by a two-point measurement with application of diffusion-sensitising magnetic field gradients of varying strengths (b-values) ... the diffusion decay curve in tissues is often observed to deviate from the single exponential behaviour expected by simple Gaussian diffusion ... [and the] nature and utility of this non-monoexponential signal decay observed in multiple b-value DWI ... [Furthermore, the] two-compartment intra-voxel incoherent motion (IVIM) model proposed by Le Bihan et al (1988) is a popular choice for diffusion studies in the body, with the associated pseudo-diffusion volume parameter f being a potentially useful biomarker ... [and in] the two-compartment model framework, components are commonly taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue compartments")) with each signal component weighted by the volume fraction of the tissue type (See Jerome: Pages N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")).
Regarding claim 7, Jerome discloses the non-transitory storage medium of claim 6 (See above discussion), wherein the system of equations at each voxel further includes an equation requiring that the sum of the volume fractions equals one (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time. Thus, when T(2(p)) = T(2(t)) we have f(app)(TE) = f") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")).
Regarding claim 8, Jerome discloses the non-transitory storage medium of claim 6 (See above discussion), wherein the system of equations at each voxel is solved for the volume fraction of each tissue type (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")) and relaxation time T2 (See Jerome: Page N669 (providing that the, "T2 relaxation times of both compartments are distinct and can be estimated directly from the data, and which are thus available as biomarkers in their own right, in contrast to previous methods that take an assumed value for T2 associated with the pseudo-diffusion compartment")) and ADC of each tissue type (See Jerome: Page N668 (clarifying that while, "the apparent diffusion coefficient (ADC), conventionally derived by a two-point measurement with application of diffusion-sensitising magnetic field gradients of varying strengths (b-values) ... the diffusion decay curve in tissues is often observed to deviate from the single exponential behaviour expected by simple Gaussian diffusion ... [and the] nature and utility of this non-monoexponential signal decay observed in multiple b-value DWI ... [Furthermore, the] two-compartment intra-voxel incoherent motion (IVIM) model proposed by Le Bihan et al (1988) is a popular choice for diffusion studies in the body, with the associated pseudo-diffusion volume parameter f being a potentially useful biomarker ... [and in] the two-compartment model framework, components are commonly taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue compartments")).
Regarding claim 12, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the determining comprises: solving a system of equations for each voxel to determine the volume fraction values for each tissue type of the plurality of tissue types (See Jerome: Pages N669 (providing equations (2) and (3)), Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI"), and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4) where the system of equations for each voxel includes an equation for each value pair of the diffusion weighting (See Jerome: Pages N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI") and Page N674 (discussing the, "[e]stimation of the T2 values for the ... true diffusion compartments derived from the T2-IVIM model")) or perfusion weighting parameter (See Jerome: Page N676 (describing that, "the IVIM model ... allows inference of ... tissue perfusion properties")) and the magnetization relaxation parameter in the multi- parametric MR imaging data (See Jerome: Page N677 (providing that in, "this study we show that a complete formulation of the IVIM model, extended to allow for distinct T2 values")).
Regarding claim 13, Jerome discloses the non-transitory storage medium of claim 12 (See above discussion), wherein the system of equations for each voxel further includes an equation requiring that the sum of the volume fractions equals one (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time. Thus, when T(2(p)) = T(2(t)) we have f(app)(TE) = f") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")).
Regarding claim 14, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the determining further includes: determining T(2) values and apparent diffusion coefficient (ADC) values for each tissue type of the plurality of tissue types (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants, referred to in this work to as T(2p) and T(2t) for pseudo- and true diffusion compartments respectively modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time ... [and that this study demonstrates] that the T2 relaxation times of both compartments are distinct and can be estimated directly from the data") and Page N668 (clarifying that, "D and D* are the true- and pseudo-diffusion coefficients")).
Regarding claim 16, Jerome discloses the non-transitory storage medium of claim 1 (See above discussion), wherein the display device is controlled to display the tissue composition map (See Jerome: Page N670 (clarifying that, "imaging was performed in free-breathing using a 1.5 T MAGNETOM Avanto clinical MR scanner (Siemens Healthcare, Erlangen, Germany)") and Page N675 (clarifying that within Fig. 4, "(a) [represents the] Calculated pseudo-diffusion fraction f maps from IVIM (left) and T2-IVIM (right) models using Protocol 2 data. The difference map (b) and histogram (c) show the reduction in f, given by T2-IVIM value minus IVIM value, observed when allowing for different T2s in the model compartments. The true-diffusion compartment T2 map is shown in (d)"); Fig. 4) comprising at least one of: display of the volume fraction map for each tissue type of the plurality of tissue types; or display of a single composition map fusing the volume fraction maps for the at least three tissue types (See Jerome: Page N670 (clarifying that, "imaging was performed in free-breathing using a 1.5 T MAGNETOM Avanto clinical MR scanner (Siemens Healthcare, Erlangen, Germany)") and Page N675 (clarifying that within Fig. 4, "(a) [represents the] Calculated pseudo-diffusion fraction f maps from IVIM (left) and T2-IVIM (right) models using Protocol 2 data. The difference map (b) and histogram (c) show the reduction in f, given by T2-IVIM value minus IVIM value, observed when allowing for different T2s in the model compartments. The true-diffusion compartment T2 map is shown in (d)"); Fig. 4).
Regarding claim 20, Jerome discloses an imaging method, comprising: obtaining multi-parametric magnetic resonance (MR) imaging data parameterized by a diffusion weighting parameter (See Jerome: Page N674 (discussing the, "[e]stimation of the T2 values for the ... true diffusion compartments derived from the T2-IVIM model")) and a magnetization relaxation parameter (See Jerome: Page N677 (providing that in, "this study we show that a complete formulation of the IVIM model, extended to allow for distinct T2 values")) for a region of interest (ROI) of a patient (See Jerome: Page N670 (clarifying that, "[i]mages were acquired … and a region of interest (ROI) was drawn covering the whole liver section")); determining volume fraction maps of the ROI for each of a plurality of tissue types from the multi-parametric MR imaging data (See Jerome: Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI") and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4); and controlling a display device to display a tissue composition map comprising or generated from the determined volume fraction maps (See Jerome: Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI") and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4).
Claims 17 and 18 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Langer, D. L., et al. (2010). Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology, 255(2), 485–494 (hereinafter referred to as, “Langer”).
Regarding claim 17, Langer discloses a medical imaging workstation comprising a processor configured to generate from an MRI image (See Langer: Page 486 (providing that, "patients were examined with MR imaging, which was performed with a 1.5-T system (Excite HD; GE Healthcare, Milwaukee, Wis.)")) a prostatic tissue composition map (pTCM) (See Langer: Page 488 (stating that, "[p]arameter maps were generated from each MR imaging data set"); Fig. 4) by transforming a multi-dimensional array of MRI data (See Langer: Page 487 (providing that, "Dynamic contrast-enhanced MR imaging data consisted of two data sets: data obtained with multisection multiple-flip-angle fast spoiled gradient-recalled acquisition in the steady state for T1 mapping and data subsequently obtained during 50 phases of multisection fast spoiled gradient-recalled acquisition in the steady state (26 sections, temporal resolution of 10 seconds)")) using a model to non-invasively evaluate volume fractions, ADC's, and T2's of prostate tissue components (See Langer: Page 485 (clarifying that the purpose of the study is to, "investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue"), Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])"), Page 488 (clarifying that a, "model (28) with an assumed arterial input function (combined model [29]) was used to generate K(trans) and v(e ) maps"), and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")).
Regarding claim 18, Langer discloses the medical imaging workstation of claim 17 (See above discussion) wherein the tissue components comprise stroma (See Langer: Page 485 (clarifying that the purpose of the study is to, "investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue"), Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])"), Page 488 (clarifying that a, "model (28) with an assumed arterial input function (combined model [29]) was used to generate K(trans) and v(e ) maps"), and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")), epithelium (See Langer: Page 485 (clarifying that the purpose of the study is to, "investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue"), Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])"), Page 488 (clarifying that a, "model (28) with an assumed arterial input function (combined model [29]) was used to generate K(trans) and v(e ) maps"), and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space"))and lumen (See Langer: Page 485 (clarifying that the purpose of the study is to, "investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue"), Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])"), Page 488 (clarifying that a, "model (28) with an assumed arterial input function (combined model [29]) was used to generate K(trans) and v(e ) maps"), and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")).
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.
Claims 9-11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Jerome, N. P., et al. (2016). Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging. Physics in medicine and biology, 61(24), N667–N680 (hereinafter referred to as, “Jerome”) in view of Langer, D. L., et al. (2010). Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology, 255(2), 485–494 (hereinafter referred to as, “Langer”).
Regarding claim 9, Jerome teaches the non-transitory storage medium of claim 5 (See above discussion), wherein: the multi-parametric magnetic resonance (MR) imaging data includes MR imaging data acquired for at least nine (TE,b-value) parameter value pairs (See Jerome: Page N667 (providing that, "[t]wo consented healthy volunteer cohorts (n = 5, 6) underwent DWI comprising multiple TE/b-value combinations (Protocol 1: TE = 62–102 ms, b = 0–250 mm−2s, 30 combinations. Protocol 2: 8 b-values 0–800 mm−2s at TE = 62 ms, with 3 additional b-values 0–50 mm−2s at TE = 80, 100 ms; scanned twice)") and Page N670 (specifying that, "Protocol 1: ... [uses thirty] b-TE combinations ... [while] Protocol 2: ... [uses a] set of 14 b-TE combinations")), and the determining of the volume fraction maps includes solving a system of equations at each voxel for at least the volume fraction V(T(i)) of each tissue type T(i) E {T} (See Jerome: Pages N669 (providing equations (2) and (3)), Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI"), and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4) … wherein the system of equations includes, for each (TE,b-value) parameter value pair, the equation :[EQUATION] (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time")) where S is the MR signal at the voxel (See Jerome: Page N668 (clarifying that while, "the apparent diffusion coefficient (ADC), conventionally derived by a two-point measurement with application of diffusion-sensitising magnetic field gradients of varying strengths (b-values) ... the diffusion decay curve in tissues is often observed to deviate from the single exponential behaviour expected by simple Gaussian diffusion ... [and the] nature and utility of this non-monoexponential signal decay observed in multiple b-value DWI ... [Furthermore, the] two-compartment intra-voxel incoherent motion (IVIM) model proposed by Le Bihan et al (1988) is a popular choice for diffusion studies in the body, with the associated pseudo-diffusion volume parameter f being a potentially useful biomarker ... [and in] the two-compartment model framework, components are commonly taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue compartments")), S(o) is a constant (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and] where S(0) is a scaling term")), T(E) and b are the echo time TE and b-value, respectively, of the (TE,b-value) parameter value pair (See Jerome: Page N669 (providing that the, "purpose of this prospective volunteer study is to develop and present a clinically feasible multiple b-TE measurement"), N668 (clarifying in the abbreviations that TE stands for, "Echo Time"), and Page N669 (stating that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time")) and T(2(T(i))) and ADC(T(i)) denote the relaxation time T(2) and ADC of the tissue type T(i) (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants, referred to in this work to as T(2p) and T(2t) for pseudo- and true diffusion compartments respectively modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time") and Page N668 (clarifying that, "D and D* are the true- and pseudo-diffusion coefficients")), and therefore substantially what is taught by claim 9. However, Jerome fails to teach where {T} denotes the set of the at least three tissue types. Nevertheless, Langer teaches where {T} denotes the set of the at least three tissue types (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters")).
Regarding claim 10, Jerome viewed in light of Langer teaches the non-transitory storage medium of claim 9 (See above discussion), and therefore substantially what is taught by claim 10. Furthermore, Jerome teaches wherein the system of equations at each voxel further includes the equation [EQUATION] requiring that the sum of the volume of fraction equals one (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time. Thus, when T(2(p)) = T(2(t)) we have f(app)(TE) = f") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")).
Regarding claim 11, Jerome viewed in light of Langer teaches the non-transitory storage medium of claim 9 (See above discussion), and therefore substantially what is taught by claim 11. Furthermore, Jerome teaches, wherein the system of equations at each voxel is solved for the volume fraction V(T(i)), of each tissue type (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time") and Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI")) and for the relaxation time T(2(T(i)) (See Jerome: Page N669 (providing that the, "T2 relaxation times of both compartments are distinct and can be estimated directly from the data, and which are thus available as biomarkers in their own right, in contrast to previous methods that take an assumed value for T2 associated with the pseudo-diffusion compartment")) and ADC(T(i)) of each tissue type (See Jerome: Page N668 (clarifying that while, "the apparent diffusion coefficient (ADC), conventionally derived by a two-point measurement with application of diffusion-sensitising magnetic field gradients of varying strengths (b-values) ... the diffusion decay curve in tissues is often observed to deviate from the single exponential behaviour expected by simple Gaussian diffusion ... [and the] nature and utility of this non-monoexponential signal decay observed in multiple b-value DWI ... [Furthermore, the] two-compartment intra-voxel incoherent motion (IVIM) model proposed by Le Bihan et al (1988) is a popular choice for diffusion studies in the body, with the associated pseudo-diffusion volume parameter f being a potentially useful biomarker ... [and in] the two-compartment model framework, components are commonly taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue compartments")).
Regarding claim 15, Jerome teaches the non-transitory storage medium of claim 1 (See above discussion), and therefore substantially what is taught by claim 15. However, Jerome fails to teach wherein the plurality of tissue types includes a stromal tissue type, an epithelial tissue type, and a lumen tissue type; wherein the volume fractions for the stromal tissue type, the epithelial tissue type, and the lumen tissue type are measured non-invasively by in vivo pTCM and compared with quantitative analysis of whole mount hematoxylin and eosin stained tissue. Nevertheless, Langer teaches wherein the plurality of tissue types includes a stromal tissue type (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters")), an epithelial tissue type (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters")), and a lumen tissue type (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters")); wherein the volume fractions for the stromal tissue type (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")), the epithelial tissue type (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")), and the lumen tissue type (See Langer: Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) are measured non-invasively by in vivo pTCM (See Langer: Page 485 (clarifying that the purpose of the study is to, "investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue"), Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])"), and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) and compared with quantitative analysis of whole mount hematoxylin and eosin stained tissue (See Langer: Page 485 (providing that, "[w]hole-mount hematoxylin-eosin-stained sections were generated and digitized at histologic resolution. Percentage areas of tissue components (nuclei, cytoplasm, stroma, luminal space) were measured by using image segmentation. Corresponding regions on MR images and histologic specimens were defined by using anatomically defined segments in peripheral zone (PZ) and central gland tissue. Cancer and normal PZ regions were identified at histopathologic analysis. Each MR parameter–histologic tissue component pair was assessed by using linear mixed-effects models, and cancer versus normal PZ values were compared by using nonparametric tests")).
The teachings of Jerome and the teachings of Langer are considered to be analogous to the claimed invention because they are in the same field of medical imaging analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the instant application to combine what is taught by the teachings of Jerome and the teachings of Langer, to provide for what is taught by claims 9-11 and 15, since Jerome provides on Page N678 that the, "T2-IVIM scheme has the potential to provide data in a more controlled setting to further our understanding of a potentially important source of variation affecting f estimates in different pathologies. In addition, accounting for known influence of acquisition parameters will increase the reliability of cross-scanner comparisons."
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Langer, D. L., et al. (2010). Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology, 255(2), 485–494 (hereinafter referred to as, “Langer”) in view of Jerome, N. P., et al. (2016). Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging. Physics in medicine and biology, 61(24), N667–N680 (hereinafter referred to as, “Jerome”).
Regarding claim 19, Langer teaches the medical imaging workstation of claim 18 (See above discussion), and therefore substantially what is taught by claim 19. While Jerome teaches wherein: the multi-dimensional array of MRI data is parameterized by echo time T(E ) (See Jerome: Page N669 (clarifying that the, "inclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency")) and b-value (See Jerome: Page N677 (clarifying that, "[e]xtending data acquisition to include multiple … b-values, combined with fitting the T2-IVIM model allows estimation of compartment T2s alongside conventional IVIM parameters")) and comprises MR imaging data acquired for at least nine (T(E),b-value) parameter value pairs (See Jerome: Page N667 (providing that, "[t]wo consented healthy volunteer cohorts (n = 5, 6) underwent DWI comprising multiple TE/b-value combinations (Protocol 1: TE = 62–102 ms, b = 0–250 mm−2s, 30 combinations. Protocol 2: 8 b-values 0–800 mm−2s at TE = 62 ms, with 3 additional b-values 0–50 mm−2s at TE = 80, 100 ms; scanned twice)") and Page N670 (specifying that, "Protocol 1: ... [uses thirty] b-TE combinations ... [while] Protocol 2: ... [uses a] set of 14 b-TE combinations")) … by solving a system of equations at each voxel for the volume fractions (See Jerome: Pages N669 (providing equations (2) and (3)), Pages N673-N674 (stating that a, "typical example of f maps from the two models, from voxel-by-voxel model fitting, is presented in figure 4(a); the smaller f observed in the T2-IVIM fitting is consistent in the liver parenchyma and well-visualised as a difference map, and by the histogram off differences (figures 4(b) and (c)). Vascular features are retained in the T2-IVIM f map, showing the ability of the model to deal with varying tissue composition through the ROI"), and N679 (clarifying that, "the extended model ... [provides] a more accurate picture of the pseudo-diffusion volume fraction"); Fig. 4) … and for the relaxation times (See Jerome: Page N669 (providing that the, "T2 relaxation times of both compartments are distinct and can be estimated directly from the data, and which are thus available as biomarkers in their own right, in contrast to previous methods that take an assumed value for T2 associated with the pseudo-diffusion compartment")) … and for the apparent diffusion coefficients (See Jerome: Page N668 (clarifying that while, "the apparent diffusion coefficient (ADC), conventionally derived by a two-point measurement with application of diffusion-sensitising magnetic field gradients of varying strengths (b-values) ... the diffusion decay curve in tissues is often observed to deviate from the single exponential behaviour expected by simple Gaussian diffusion ... [and the] nature and utility of this non-monoexponential signal decay observed in multiple b-value DWI ... [Furthermore, the] two-compartment intra-voxel incoherent motion (IVIM) model proposed by Le Bihan et al (1988) is a popular choice for diffusion studies in the body, with the associated pseudo-diffusion volume parameter f being a potentially useful biomarker ... [and in] the two-compartment model framework, components are commonly taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue compartments")) … wherein the system of equations includes, for each (T(E),b-value) parameter value pair, the equation: [EQUATION] (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time")) Where S is the MR signal at the voxel (See Jerome: Page N668 (clarifying that while, "the apparent diffusion coefficient (ADC), conventionally derived by a two-point measurement with application of diffusion-sensitising magnetic field gradients of varying strengths (b-values) ... the diffusion decay curve in tissues is often observed to deviate from the single exponential behaviour expected by simple Gaussian diffusion ... [and the] nature and utility of this non-monoexponential signal decay observed in multiple b-value DWI ... [Furthermore, the] two-compartment intra-voxel incoherent motion (IVIM) model proposed by Le Bihan et al (1988) is a popular choice for diffusion studies in the body, with the associated pseudo-diffusion volume parameter f being a potentially useful biomarker ... [and in] the two-compartment model framework, components are commonly taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue compartments")), S(o) is a constant (See Jerome: Page N669 (clarifying that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and] where S(0) is a scaling term")), and T(E) and b are the echo time T(E) and b-value, respectively, of the (T(E),b-value) parameter value pair (See Jerome: Page N669 (providing that the, "purpose of this prospective volunteer study is to develop and present a clinically feasible multiple b-TE measurement"), N668 (clarifying in the abbreviations that TE stands for, "Echo Time"), and Page N669 (stating that the, "[i]nclusion of distinct transverse relaxation constants ... modifies the standard IVIM model for echo time dependency (equation (2)): ... [Equation (2) and where an] apparent pseudo-diffusion volume fraction can be defined using equation (2) by taking a weighted combination of the terms scaling the b-value dependent exponentials, that is: ... [Equation (3) which] gives the pseudo-diffusion volume fraction that would be estimated using equation (1) for a given set of parameters and echo time")). However, Jerome fails to teach wherein the pTCM map is generated … V(stroma), V(epithelium), and V(lumen) of the stroma, epithelium, and lumen respectively … T(2(stroma)), T(2(epithelium)), and T(2(lumen)) of the stroma, epithelium, and lumen respectively … ADC(stroma), ADC(epithelium), and ADC(lumen) of the stroma, epithelium, and lumen respectively. Nevertheless, Langer teaches wherein the pTCM map is generated (See Langer: Page 488 (stating that, "[p]arameter maps were generated from each MR imaging data set"); Fig. 4) … V(stroma), V(epithelium), and V(lumen) (See Langer : Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) … of the stroma, epithelium, and lumen respectively (See Langer : Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of v(e ) in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) … T(2(stroma)), T(2(epithelium)), and T(2(lumen)) (See Langer : Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of T2 in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) of the stroma, epithelium, and lumen respectively (See Langer : Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of T2 in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) … ADC(stroma), ADC(epithelium), and ADC(lumen) (See Langer : Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of ADC in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")) of the stroma, epithelium, and lumen respectively (See Langer : Page 486 (stating that the study, "investigated the relationships between various tissue components (percentages of nuclei, epithelial cytoplasm, stroma, and luminal space) and in vivo MR imaging parameters (ADC, T2, volume transfer constant [K(trans)], extravascular extracellular volume fraction [v(e )])") and Page 490, Table 3 (showing the measurement category of ADC in the "MR Measurement" column and results for the measurements pertaining to various tissue types including "Cytoplasm," "Stroma," and "Luminal Space")).
The teachings of Langer and the teachings of Jerome are considered to be analogous to the claimed invention because they are in the same field of medical imaging analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the instant application to combine what is taught by the teachings of Langer and the teachings of Jerome, to provide for what is taught by claim 19, since Jerome provides on Page N678 that the, "T2-IVIM scheme has the potential to provide data in a more controlled setting to further our understanding of a potentially important source of variation affecting f estimates in different pathologies. In addition, accounting for known influence of acquisition parameters will increase the reliability of cross-scanner comparisons."
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). 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.
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/MICHAEL T ROZANSKI/Primary Examiner, Art Unit 3797
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