Office Action 1: Non-Final Rejection (2024-11-06)
Rejection types: 112 - Written Description/Enablement

CLAIMS:
CLAIMS

1\. A quantitative magnetic resonance imaging (qMRI) method for mapping at le ist one qMRI parameter of a biological object with an MRI system, the method comprises the following steps of:performing, by the MRI system, N scans C\_1,...,C\_N, wherein each scan C\_j, with j = 1,...,N, includes:performing at least one magnetization preparation radio frequency (RF) pulse series, each followed by Mj readout blocks, wherein two successive said magnetization preparation RF pulse series are separated by a repetition time interval TR\_j, wherein each said magnetization preparation RF pulse series is a T2-prepared inversion pulse series containing multiple pulses and characterized by an echo time, being an inter-pulse duration, TEp\_j, wherein the inter-pulse duration is configured for varying in order to obtain different T2 weightings;acquiring, by the MRI system and during each readout block, an MRI recovery signal generated by at least a part of the biological object, wherein for each said readout block R\_i,j, an MRI signal SVG {EMBED HERE} 0.160.31 is acquired by the MRI system at a different inversion time TI\_i,j, with i=1,...,Mj, and
M\_j>2;reconstructing, for and from each said MRI signal SVG {EMBED HERE} 0.160.35 an image I\_i,j of th3 part;

creating a voxel-wise signal by concatenating intensity values obtained for a same voxel for reconstructed images; and
fitting a physical model to concatenated intensity values to obtain at least one qMRI map of the at least one qMRI parameter.

2\. The qMRI method according to claim 1, wherein the echo time TEpj takes different values for each of the magnetization preparation RF pulse series of a same scan, or an identical value for all the magnetization preparation RF pulse series of the same scan, the identical value being different for each said scan.

3\. The qMRI method according to claim 1, wherein the fitting is a dictionary fitting which comprises the further steps of:performing a numerical simulation of each said scan Cj, wherein the numerical simulation is configured for generating, for each voxel, a set of simulated voxel-wise signals and, for providing, for each simulated voxel-wise signal, a value for the at least one qMRI parameter; and
reconstructing, for the at least one MRI parameter, a quantitative map representing an imaged biological part by matching, for each voxel of the quantitative map, a created voxel-wise signal of a corresponding voxel in reconstructed images and all simulated voxel-wise signals generated for the

voxel, and assigning to a considered voxel in the quantitative map the value of the at least one qMRI parameter for which a best match has been obtained.

4\. The qMRI method according to claim 3, which further comprises using a physics-based model for creating the simulated voxel-wise signal.

5\. The qMRI method according to claim 3, which further comprises acquiring an additional map for selecting the set of simulated voxel-wise signals to be matched with an acquired voxel-wise signal.

6\. The qMRI method according to claim 5, wherein the additional map is selected from the group consisting of:aB1+ map;
a B1- map;
a BC frequency offset map; and
a diffusion map.

7\. The qMRI method according to claim 1, wherein the T2-prepared inversion pulse series is adiabatic or non-adiabatic.

8\. The qMRI method according to claim 7, wherein the T2-prepared inversion pulse series is an adiabatic T2-prepared inversion pulse sequence containing two tip-down RF pulses temporally framing two refocusing adiabatic RF pulses,

wherein a time interval between a middle of a first tip-down RF pulse of the two tip-down RF pulses and a middle of a last tip-down RF pulse of hte two tip-down RF pulses is the inter-pulse duration TEp\_j.

9\. The qMRI method according to claim 8, wherein the adiabatic T2-prepared inversion pulse sequence contains a following series of temporally successive pulses including:a first pulse that is a rectangular tip-down RF pulse, followed successively by a second pulse, a third pulse, and finally a fourth pulse, wherein the second and third pulses are both hyperbolic-secant-refocusing adiabatic RF pulses, and the fourth pulse is a rectangular tip-down RF pulse.
10\. The qMRI method according to claim 1, wherein the voxel-wise signal is obtained by plotting for each voxel an intensity value of the voxel obtained in function of each couple .SVG {EMBED HERE} 0.1570.90

11\. The qMRI method according to claim 1, which further comprises selecting the at least one qMRI parameter from the group consisting of:a T1 relaxation time;
aT2 relaxation time;
aT2\* and/or T2' relaxation time;

a B1 + RF field;
a proton density;
a T1 p and/or T2p relaxation time;
a magnetization transfer parameter;
a diffusion parameter;
a perfusion parameter; and
a fat and/or water fraction parameter.

12\. The qMRI method according to claim 1, wherein the physical model uses extended-phase-graph, or Bloch-Equations, or analytical solutions.

13\. A magnetic resonance imaging system configured for carrying out the qMRI method according to claim 1.

OFFICE 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 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA  35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the phrase “magnetization preparation radio frequency (RF) pulse series” does not possess a well-established meaning in the art. It is suggested to supplement the claim language to indicate that the magnetization preparation radio frequency (RF) pulse series inverts a longitudinal magnetization while encoding T2 contrast in a subsequent TI recovery.
Regarding claim 1, it is unclear whether the inter-pulse duration is varied (such as the apparatus configured to vary the inter-pulse duration) or whether it is merely intended to define that the inter-pulse duration determines a degree of T2 weighting (implying that a change of the inter-pulse duration also changes the T2 weighting). Therefore, the claim is considered indefinite. Claims 2-13 are rejected for depending on claim 1.
Regarding claim 1, the claim discloses an “inter-pulse duration” of the T2-prepared inversion pulse series. The specification teaches that the inter-pulse duration is the time between the first and last (tip up and tip down) pulses. The claim language only states an “inter-pulse” duration which could mean a time between any of the pulses of the  T2-prepared inversion pulse series. Therefore, it is suggested to amend the claim language to correspond to the specification. Claims 2-13 are rejected for depending on claim 1.
Regarding claim 3, the claim discloses “the at least one MRI parameter”, which lacks antecedent basis. It is unclear if “the at least one MRI parameter” is the same or different from “the at least one qMRI parameter”. Claims 4-6 are rejected for depending on claim 3.
Allowable Subject Matter
Claims 1-13 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, the closest prior at is considered Nezafat (US 2023/0194641). Nezafat teaches quantitative parameter mapping using a T2 prep sequence [See Fig. 4]. However, Nezafat is at least silent in teaching “performing, by the MRI system, N scans C_1, . . . ,C_N, wherein each scan C_j, with j=1, . . . ,N, includes: performing at least one magnetization preparation radio frequency (RF) pulse series, each followed by M_j readout blocks, wherein two successive said magnetization preparation RF pulse series are separated by a repetition time interval TR_j, wherein each said magnetization preparation RF pulse series is a T2-prepared inversion pulse series containing multiple pulses and characterized by an echo time, being an inter-pulse duration, TE.sub.p_j, wherein the inter-pulse duration is configured for varying in order to obtain different T2 weightings; acquiring, by the MRI system and during each readout block, an MRI recovery signal generated by at least a part of the biological object, wherein for each said readout block R_i,j, an MRI signal S_i,j is acquired by the MRI system at a different inversion time TI_i,j, with i=1, . . . ,M_j, and M_j ≥ 2; reconstructing, for and from each said MRI signal S_i,j, an image l_i,j of the part; creating a voxel-wise signal by concatenating intensity values obtained for a same voxel for reconstructed images; and fitting a physical model to concatenated intensity values to obtain at least one qMRI map of the at least one qMRI parameter.”
See Fig. 6 of Nezafat which shows the input to the neural network, which is four T1 weighted images with corresponding inversion times. However, the pixel values of the four T1 weighted images do not seem to be concatenated. Further, the T2 weighted sequence in Fig. 4 does not teach different inversion times and multiple readouts after each T2 preparation sequence. Therefore, the claims overcome the most relevant prior art.
Conclusion


NEXT OFFICE 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 .
Allowable Subject Matter
Claims 1-13 allowed.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 1, the closest prior at is considered Nezafat (US 2023/0194641). Nezafat teaches quantitative parameter mapping using a T2 prep sequence [See Fig. 4]. However, Nezafat is at least silent in teaching “performing, by the MRI system, N scans C_1, . . . ,C_N, wherein each scan C_j, with j=1, . . . ,N, includes: performing at least one magnetization preparation radio frequency (RF) pulse series, each followed by M_j readout blocks, wherein two successive said magnetization preparation RF pulse series are separated by a repetition time interval TR_j, wherein each said magnetization preparation RF pulse series is a T2-prepared inversion pulse series containing multiple pulses and characterized by an echo time, being an inter-pulse duration, TEp_j, wherein the inter-pulse duration is a time between first and last pulses, and is varied by the MRI system in order to obtain different T2 weightings, the at least one magnetization preparation RF pulse series being configured for inverting a longitudinal magnetization while encoding T2 contrast in a subsequent T1 recover; acquiring, by the MRI system and during each readout block, an MRI recovery signal generated by at least a part of the biological object, wherein for each said readout block R_i,j, an MRI signal S_i,j is acquired by the MRI system at a different inversion time TI_i,j, with i=1, . . . ,M_j, and M_j ≥ 2; reconstructing, for and from each said MRI signal S_i,j, an image l_i,j of the part; creating a voxel-wise signal by concatenating intensity values obtained for a same voxel for reconstructed images; and fitting a physical model to concatenated intensity values to obtain at least one qMRI map of the at least one qMRI parameter.”
See Fig. 6 of Nezafat which shows the input to the neural network, which is four T1 weighted images with corresponding inversion times. However, the pixel values of the four T1 weighted images do not seem to be concatenated. Further, the T2 weighted sequence in Fig. 4 does not teach different inversion times and multiple readouts after each T2 preparation sequence. Therefore, the claims overcome the most relevant prior art.
Claims 2-13 are considered allowable for depending on claim 1.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US20230194641A1: Claims:
A system for quantitative parameter mapping using magnetic resonance (MR) images, the system comprising: an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images; a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and a display coupled to the quantitative parameter mapping neural network and configured to display the at least one quantitative map.
an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images;
a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and
a display coupled to the quantitative parameter mapping neural network and configured to display the at least one quantitative map.
The system according to claim 1 , wherein the plurality of weighed MR images are one of multi-contrast weighted images, Ti-weighted images, T 2 -weighted images, T 2 *-weighted images and T 1 p-weighted images.
The system according to claim 1 , wherein the at least one tissue parameter is one of T 1 , T 2 , T 2 *, or T 1 p.
The system according to claim 1 , wherein the quantitative parameter mapping neural network is a fully compensated (FC) neural network.
The system according to claim 1 , wherein the quantitative parameter mapping neural network is an encoder-decoder neural network with skip connections.
The system according to claim 1 , wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data.
The system according to claim 1 , wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.
The system according to claim 1 , wherein the at least one confounder is one of angle, heart rate, B 0 , B 1 , or off-resonance.
A method for quantitative parameter mapping using magnetic resonance (MR) images, the method comprising acquiring, using a magnetic resonance imaging (MRI) system, MR data from a subject for a plurality of weighted images using a pulse sequence; generating a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter; providing, using a processor, the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; generating, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images; and displaying, using a display, the at least one quantitative map.
acquiring, using a magnetic resonance imaging (MRI) system, MR data from a subject for a plurality of weighted images using a pulse sequence;
generating a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter;
providing, using a processor, the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter;
generating, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images; and
displaying, using a display, the at least one quantitative map.
The method according to claim 9 , wherein the plurality of weighed MR images are one of multi-contrast weighted images, T 1 -weighted images, T 2 -weighted images, T 2 *-weighted images, and T 1 p-weighted images.
The method according to claim 9 , wherein the at least one tissue parameter is one of T 1 , T 2 , T 2 *, or T 1 p.
The method according to claim 9 , wherein the quantitative parameter mapping neural network is a fully connected (FC) neural network.
The method according to claim 9 , wherein the quantitative parameter mapping neural network is an encoder-decoder neural network with skip connections.
The method according to claim 9 , wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data.
The method according to claim 7 , wherein the quantification parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.
The method according to claim 9 , wherein the at least one confounder is one of angle, heart rate, B 0 , B 1 , or off-resonance.
A magnetic resonance imaging (MRI) system comprising: a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject; a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array; and a computer system programmed to acquire MR data from a subject for a plurality of weighted images using a pulse sequence; generate a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter; provide the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and generate, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images.
a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject;
a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field;
a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array; and
a computer system programmed to acquire MR data from a subject for a plurality of weighted images using a pulse sequence; generate a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter; provide the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and generate, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images.
acquire MR data from a subject for a plurality of weighted images using a pulse sequence;
generate a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter;
provide the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and
generate, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images.
The MRI system according to claim 17 , wherein the quantitative parameter mapping neural network is a fully connected (FC) neural network.
The MRI system according to claim 17 , wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data.
The MRI system according to claim 17 , wherein the quantification parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.
Ref US20230194641: Claims:
1. A system for quantitative parameter mapping using magnetic resonance (MR) images, the system comprising:
an input for receiving a plurality of weighted MR images of a subject and a corresponding at least one imaging parameter for the plurality of weighted MR images; a quantitative parameter mapping neural network coupled to the input and configured to estimate at least one tissue parameter and generate at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and a display coupled to the quantitative parameter mapping neural network and configured to display the at least one quantitative map.
2. The system according to claim 1, wherein the plurality of weighed MR images are one of multi-contrast weighted images, Ti-weighted images, T2-weighted images, T2*-weighted images and T1p-weighted images.
3. The system according to claim 1, wherein the at least one tissue parameter is one of T1, T2, T2*, or T1p.
4. The system according to claim 1, wherein the quantitative parameter mapping neural network is a fully compensated (FC) neural network.
5. The system according to claim 1, wherein the quantitative parameter mapping neural network is an encoder-decoder neural network with skip connections.
6. The system according to claim 1, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data.
7. The system according to claim 1, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.
8. The system according to claim 1, wherein the at least one confounder is one of angle, heart rate, B0, B1, or off-resonance.
9. A method for quantitative parameter mapping using magnetic resonance (MR) images, the method comprising
acquiring, using a magnetic resonance imaging (MRI) system, MR data from a subject for a plurality of weighted images using a pulse sequence; generating a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter; providing, using a processor, the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; generating, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images; and displaying, using a display, the at least one quantitative map.
10. The method according to claim 9, wherein the plurality of weighed MR images are one of multi-contrast weighted images, T1-weighted images, T2-weighted images, T2*-weighted images, and T1p-weighted images.
11. The method according to claim 9, wherein the at least one tissue parameter is one of T1, T2, T2*, or T1p.
12. The method according to claim 9, wherein the quantitative parameter mapping neural network is a fully connected (FC) neural network.
13. The method according to claim 9, wherein the quantitative parameter mapping neural network is an encoder-decoder neural network with skip connections.
14. The method according to claim 9, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data.
15. The method according to claim 7, wherein the quantification parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.
16. The method according to claim 9, wherein the at least one confounder is one of angle, heart rate, B0, B1, or off-resonance.
17. A magnetic resonance imaging (MRI) system comprising:
a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject; a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array; and a computer system programmed to 
acquire MR data from a subject for a plurality of weighted images using a pulse sequence;
generate a plurality of weighted MR images of a subject from the acquired MR data, each of the plurality of weighted MR images having a corresponding at least one imaging parameter;
provide the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images to a quantitative parameter mapping neural network, wherein the quantitative parameter mapping neural network is trained using a set of training data utilizing at least one confounder for the quantification of the at least one tissue parameter; and
generate, using the quantitative parameter mapping neural network, an estimate of at least one tissue parameter and at least one quantitative map for the at least one tissue parameter based on the plurality of weighted MR images of the subject and the corresponding at least one imaging parameter for the plurality of weighted MR images.
18. The MRI system according to claim 17, wherein the quantitative parameter mapping neural network is a fully connected (FC) neural network.
19. The MRI system according to claim 17, wherein the quantitative parameter mapping neural network is trained using a set of training data comprising a combination of native and post-contrast MR data.
20. The MRI system according to claim 17, wherein the quantification parameter mapping neural network is trained using a set of training data comprising a combination of numerically simulated MR data and MR phantom data.