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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/02/2026 has been entered.
Claim Status
Claims 1-20 are pending for examination in the application filed 03/02/2026. Claims 1, 13, and 19 are currently amended.
Response to Arguments and Amendments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 11 and 12 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 11, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). Claim 12 is dependent on claim 11 and is therefore also rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7, 13-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Munch (Münch, Beat, et al. "Stripe and ring artifact removal with combined wavelet—Fourier filtering." Optics express 17.10 (2009): 8567-8591) in view of Khoury (US5987347A).
Regarding claim 1, Munch teaches a method, comprising: receiving an image ([2.3 Algorithm] This algorithm, in addition to the input image ’ima’, employs three parameters for the filtering process: the highest decomposition level L (’decNum’), the wavelet type (’wname’) and the damping factor σ (’sigma’) from Eq. 9);
performing a wavelet decomposition on image data to generate a set of wavelet coefficients ([2.3 Algorithm] The algorithm consists of three distinct parts. At lines 4-6, the wavelet decomposition is calculated by recursive splitting of the original image and of the low resolution coefficients c l l,m,n from the former decomposition level l – 1 into {c l l,m,n, c h l,m,n, c v l,m,n, c d l,m,n});
identifying a first portion of the wavelet coefficients including image artifact data, and a second portion of the wavelet coefficients not including the image artifact data ([2.2 Basic Idea a) Wavelet Filtering] Eq. 6 shows that in 2D multiresolution wavelet decomposition, the vertical details components cv are successively detached from all remaining image components. Consequently, the information from vertical stripes is exclusively condensed to c v l,m,n and to the coefficients of the finally remaining low frequency band c l L,m,n);
performing one or more 2-D Fourier transforms on the first portion of the wavelet coefficients to generate Fourier coefficients, the Fourier coefficients including the image artifact data ([2.2 Basic Idea c) Combined wavelet-FFT filtering] Subsequently, the bands containing the stripe information (e.g. c v l,m,n for vertical stripes, see portions enframed in red in the centre image of Fig.3 ) are FFT transformed to further tighten the stripe information into narrow bands (e.g. to the x̂-axis in the FFT domain in case of vertical stripes, see enframed horizontal regions in the right image of Fig.3 ). [2.2 Basic Idea b) FFT filtering] Consequently by eliminating the Fourier coefficients F(x̂,ŷ) of f(x,y) at all x̂ for ŷ = 0, the entire information arising from ideal vertical stripes will be erased);
removing the image artifact data from the Fourier coefficients generated from the one or more 2-D Fourier transforms, using a filter ([2.2 Basic Idea b) FFT filtering] Consequently by eliminating the Fourier coefficients F(x̂,ŷ) of f(x,y) at all x̂ for ŷ = 0, the entire information arising from ideal vertical stripes will be erased…For this purpose, a simple approach in the Fourier space is the application of a bandpass filter around ŷ ≈ 0. For instance, a selective damping of F(x̂,ŷ) on the x̂-axis can be obtained by multiplication of the FFT coefficients with a Gaussian function g(x̂,ŷ));
performing an inverse 2-D Fourier transform on the filtered Fourier coefficients to generate updated wavelet coefficients corresponding to the first portion (Fig. 5 Matlab code for combined wavelet-FFT stripe filtering: ln 18-19 “inverse FFT”. [2.3 Algorithm] Subsequently at lines 15-16, these coefficients are multiplied with a Gaussian function, to eliminate those close to the x̂-axis in the Fourier domain. After such damping, the coefficients ĉ′v l,m,n are transformed back to the wavelet space (line 19));
reconstructing an artifact-removed image from the updated wavelet coefficients corresponding to the first portion of the wavelet coefficients and the second portion of the wavelet coefficients ([2.2 Basic Idea c) Combined wavelet-FFT filtering] The stripe information condensed in such a way is then eliminated by multiplication with a Gaussian damping function g(x̂,ŷ) according to Eq. 9 . Finally, the destriped image f′(x,y) is reconstructed from the filtered coefficients. The result is displayed in Fig.4 on the right image);
wherein performing the wavelet decomposition on the image data further comprises: selecting a wavelet basis function for each level of N levels of the wavelet decomposition, each level corresponding to a different scale of the image; and for each level of the N levels, performing the wavelet decomposition, where a result of the wavelet decomposition includes an approximation image; wherein the approximation image from a level of the N levels is used as an input into a wavelet decomposition performed at a subsequent level rather than the image data, such that the approximation image is further decomposed at the subsequent level to extract image artifact data remaining in the approximation image at each of the N levels.
PNG
media_image1.png
162
783
media_image1.png
Greyscale
PNG
media_image2.png
165
789
media_image2.png
Greyscale
([1.1 Some considerations about wavelet and Fourier transforms] The main motivation for the decomposition of a signal into orthogonal basis functions is the deployment of the original signal information into coefficient classes that specifically group interesting structural patterns. This aspect is particularly attractive for artifacts removal techniques. In addition, orthogonal transforms often yield coefficients, which become partly small or even zero. This characteristic of wavelet and Fourier analysis makes signal decomposition especially attractive for data compression purposes…In dyadic, decimated wavelet transform, a single scale wavelet decomposition of a 1D signal f(t) consists in its fragmentation into a low and a high frequency part. This separation is perfectly reversible. The low frequency part can then be iteratively decomposed in the same way, while leaving the high frequency unchanged. After a decomposition step, the size N(l) of the resulting low and high frequency parts is halved in order to maintain the total number of coefficients constant. The next decomposition is therefore performed at half the number of coefficients but with the same filter size, i.e. at lower spatial scale, what is equivalent to filtering at double the filter size. A multiscale wavelet transform at a highest decomposition level L is therefore the subdivision of f(t) into one low frequency part, which is represented by one scaling function Φ L (t) together with its coefficients, and multiple high frequency parts, which are represented by a set of wavelet functions Ψ l (t) and their coefficients at l = 1,⋯,L different scales, yielding
PNG
media_image3.png
308
782
media_image3.png
Greyscale
PNG
media_image4.png
509
758
media_image4.png
Greyscale
Munch does not explicitly teach a method for an image processing system; performing a wavelet decomposition on image data of the medical image; and displaying the reconstructed, artifact-removed image on a display device of the image processing system.
Khoury, in the same field of endeavor of image artifact removal, teaches a method for an image processing system ([Abstract] An MRI system acquires medical images that may contain streak artifacts. A filtered copy of the acquired image is compared with the acquired image to locate rows containing streak artifacts. Located rows in the acquired image are replaced with filtered rows to remove streak artifacts without affecting the rest of the acquired medical image);
performing a wavelet decomposition on image data of the medical image ([col. 4 ln. 14-22] As a first step 214 a one-dimensional (1D) discrete wavelet transform (DWT)… is performed along the row direction of the medical image);
and displaying the reconstructed, artifact-removed image on a display device of the image processing system ([col. 3 ln. 52-62] The present invention is implemented in the image processor 106 shown in FIG. 1 by carrying out the sequence of steps indicated by the flow chart in FIG. 4. As indicated at process block 200, the first step is to acquire the image using the above-described MRI system and an appropriate pulse sequence. The image is then reconstructed as indicated at process block 202 and stored in the computer system 107 as an array of pixel intensity values arranged in rows and columns. An operator may retrieve this stored image at the console 100, where it can be viewed on display 104).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Khoury to perform wavelet decomposition on a medical image using an image processing system because "The objective of this filtering step 210 is to significantly reduce, or remove the streak artifacts 206 from the image" [col. 4 ln. 8-12] since "Unfortunately, there are circumstances in which patient motion will inevitably occur and artifacts will be produced. For example, patient swallowing or subtle movements during a magnetic resonance angiography (MRA) exam of the carotid arteries using a 2D time-of-flight (TOF) pulse sequence can produce streak artifacts" [col. 1 ln. 40-45] and display the reconstructed, artifact removed image because "Another object of the invention is to enable the user to control the aggressiveness with which streak artifacts are removed. The number of rows to be replaced can be input by the user and the process will replace that number of rows starting with the most intense streaks and working down. Since the entire process only requires a few seconds to complete, the user can interactively change the number until all the objectionable streaks are removed" [col. 2 ln. 11-19].
Regarding claim 2, Munch and Khoury teach the method of claim 1. Munch further teaches wherein the result of the wavelet decomposition further includes a horizontal artifact detail image, a vertical artifact detail image, and a diagonal artifact detail image.
PNG
media_image4.png
509
758
media_image4.png
Greyscale
Regarding claim 3, Munch and Khoury teach the method of claim 1. Munch further teaches wherein a different wavelet basis function is used for each level of the N levels.
PNG
media_image1.png
162
783
media_image1.png
Greyscale
PNG
media_image2.png
165
789
media_image2.png
Greyscale
PNG
media_image5.png
249
786
media_image5.png
Greyscale
PNG
media_image6.png
377
782
media_image6.png
Greyscale
Regarding claim 4, Munch and Khoury teach the method of claim 1. Munch further teaches wherein each wavelet basis function for each level of the N levels is based on an initial wavelet basis function selected at a first level.
PNG
media_image5.png
249
786
media_image5.png
Greyscale
PNG
media_image6.png
377
782
media_image6.png
Greyscale
Regarding claim 5, Munch and Khoury teach the method of claim 1. Munch further teaches wherein N wavelet decompositions are performed to generate N coefficients at N different scales of the image, and the N coefficients are used to extract artifacts occurring at the different scales of the image ([2.2 Basic Idea c) Combined wavelet-FFT filtering] In the first step, the original image f(x,y) (e.g. the left image in Fig.3 ) is wavelet decomposed into W = {c l L,m,n, c h l,m,n, c v l,m,n, c d l,m,n},l ∈ {1,⋯,L} in order to separate the structural information into horizontal, vertical and diagonal details bands at different resolution scales).
PNG
media_image4.png
509
758
media_image4.png
Greyscale
Munch does not explicitly teach wavelet decomposition of a medical image.
Khoury teaches wavelet decomposition of a medical image ([col. 4 ln. 14-22] As a first step 214 a one-dimensional (1D) discrete wavelet transform (DWT)… is performed along the row direction of the medical image).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Khoury to perform wavelet decomposition on a medical image because "The objective of this filtering step 210 is to significantly reduce, or remove the streak artifacts 206 from the image" [col. 4 ln. 8-12] since "Unfortunately, there are circumstances in which patient motion will inevitably occur and artifacts will be produced. For example, patient swallowing or subtle movements during a magnetic resonance angiography (MRA) exam of the carotid arteries using a 2D time-of-flight (TOF) pulse sequence can produce streak artifacts" [col. 1 ln. 40-45]
Regarding claim 6, Munch and Khoury teach the method of claim 5. Munch further teaches wherein performing the wavelet decomposition further comprises performing the wavelet decomposition to remove artifacts of more than one orientation ([3.3 Separation of stripe information] Successive destriping in the horizontal and vertical directions using the proposed wavelet-FFT filter at σ → 0, L = 8 and DB42 yields image (4), which shows a high preservation of the structural information of the original Lena).
Regarding claim 7, Munch and Khoury teach the method of claim 1. Munch further teaches wherein performing the one or more 2-D Fourier transforms on the wavelet coefficients further comprises performing the one or more 2-D Fourier transforms on the first portion of the wavelet coefficients, and not performing the one or more 2-D Fourier transforms on the second portion of the wavelet coefficients ([2.2 Basic Idea c) Combined wavelet-FFT filtering] Subsequently, the bands containing the stripe information (e.g. c v l,m,n for vertical stripes, see portions enframed in red in the centre image of Fig.3 ) are FFT transformed to further tighten the stripe information into narrow bands (e.g. to the x̂-axis in the FFT domain in case of vertical stripes, see enframed horizontal regions in the right image of Fig.3).
Regarding claim 13, Munch teaches select an initial wavelet basis function for a first level of N levels of a wavelet decomposition of image data of an image, each level corresponding to a different scale of the image; determine additional wavelet basis functions for each additional level of the N levels based on the initial wavelet basis function; for a first level of the N levels, perform the wavelet decomposition on the image data using the initial wavelet basis function to generate an approximation image, a horizontal detail image, a vertical detail image, and a diagonal detail image; for each subsequent level of the N levels, perform the wavelet decomposition on the approximation image from a previous level, rather than the image data, using a wavelet basis function of the additional wavelet basis functions, such that the approximation image is further decomposed at the subsequent level to extract image artifacts remaining in the approximation image at each of the N levels;
PNG
media_image1.png
162
783
media_image1.png
Greyscale
PNG
media_image2.png
165
789
media_image2.png
Greyscale
([1.1 Some considerations about wavelet and Fourier transforms] The main motivation for the decomposition of a signal into orthogonal basis functions is the deployment of the original signal information into coefficient classes that specifically group interesting structural patterns. This aspect is particularly attractive for artifacts removal techniques. In addition, orthogonal transforms often yield coefficients, which become partly small or even zero. This characteristic of wavelet and Fourier analysis makes signal decomposition especially attractive for data compression purposes…In dyadic, decimated wavelet transform, a single scale wavelet decomposition of a 1D signal f(t) consists in its fragmentation into a low and a high frequency part. This separation is perfectly reversible. The low frequency part can then be iteratively decomposed in the same way, while leaving the high frequency unchanged. After a decomposition step, the size N(l) of the resulting low and high frequency parts is halved in order to maintain the total number of coefficients constant. The next decomposition is therefore performed at half the number of coefficients but with the same filter size, i.e. at lower spatial scale, what is equivalent to filtering at double the filter size. A multiscale wavelet transform at a highest decomposition level L is therefore the subdivision of f(t) into one low frequency part, which is represented by one scaling function Φ L (t) together with its coefficients, and multiple high frequency parts, which are represented by a set of wavelet functions Ψ l (t) and their coefficients at l = 1,⋯,L different scales, yielding
PNG
media_image3.png
308
782
media_image3.png
Greyscale
PNG
media_image4.png
509
758
media_image4.png
Greyscale
identify a first portion of wavelet coefficients generated from the wavelet decomposition including image artifacts, and a second portion of the wavelet coefficients not including the image artifacts ([2.2 Basic Idea a) Wavelet Filtering] Eq. 6 shows that in 2D multiresolution wavelet decomposition, the vertical details components cv are successively detached from all remaining image components. Consequently, the information from vertical stripes is exclusively condensed to c v l,m,n and to the coefficients of the finally remaining low frequency band c l L,m,n);
perform one or more 2-D Fourier transforms on the first portion of the wavelet coefficients to generate Fourier coefficients, the Fourier coefficients including the image artifacts ([2.2 Basic Idea c) Combined wavelet-FFT filtering] Subsequently, the bands containing the stripe information (e.g. c v l,m,n for vertical stripes, see portions enframed in red in the centre image of Fig.3 ) are FFT transformed to further tighten the stripe information into narrow bands (e.g. to the x̂-axis in the FFT domain in case of vertical stripes, see enframed horizontal regions in the right image of Fig.3 ). [2.2 Basic Idea b) FFT filtering] Consequently by eliminating the Fourier coefficients F(x̂,ŷ) of f(x,y) at all x̂ for ŷ = 0, the entire information arising from ideal vertical stripes will be erased);
remove the image artifacts from the Fourier coefficients generated from the one or more 2-D Fourier transforms, using a filter ([2.2 Basic Idea b) FFT filtering] Consequently by eliminating the Fourier coefficients F(x̂,ŷ) of f(x,y) at all x̂ for ŷ = 0, the entire information arising from ideal vertical stripes will be erased…For this purpose, a simple approach in the Fourier space is the application of a bandpass filter around ŷ ≈ 0. For instance, a selective damping of F(x̂,ŷ) on the x̂-axis can be obtained by multiplication of the FFT coefficients with a Gaussian function g(x̂,ŷ));
perform an inverse 2-D Fourier transform on the filtered Fourier coefficients to generate updated wavelet coefficients corresponding to the first portion (Fig. 5 Matlab code for combined wavelet-FFT stripe filtering: ln 18-19 inverse FFT. [2.3 Algorithm] Subsequently at lines 15-16, these coefficients are multiplied with a Gaussian function, to eliminate those close to the x̂-axis in the Fourier domain. After such damping, the coefficients ĉ′v l,m,n are transformed back to the wavelet space (line 19));
reconstruct an artifact-removed image from the updated wavelet coefficients corresponding to the first portion of the wavelet coefficients and the second portion of the wavelet coefficients ([2.2 Basic Idea c) Combined wavelet-FFT filtering] The stripe information condensed in such a way is then eliminated by multiplication with a Gaussian damping function g(x̂,ŷ) according to Eq. 9 . Finally, the destriped image f′(x,y) is reconstructed from the filtered coefficients. The result is displayed in Fig.4 on the right image).
Munch does not explicitly teach an image processing system, comprising: a processor; a non-transitory memory storing instructions that when executed, cause the processor to; wavelet decomposition of image data of a medical image; and display the reconstructed, artifact-removed image on a display device of the image processing system.
Khoury, in the same field of endeavor of image artifact removal, teaches an image processing system, comprising: a processor; a non-transitory memory storing instructions that when executed, cause the processor to: ([Abstract] An MRI system acquires medical images that may contain streak artifacts. A filtered copy of the acquired image is compared with the acquired image to locate rows containing streak artifacts. Located rows in the acquired image are replaced with filtered rows to remove streak artifacts without affecting the rest of the acquired medical image. [col. 3 ln. 36-48] The NMR signals picked up by the RF coil 152 are digitized by the transceiver module 150 and transferred to a memory module 160 in the system control 122. When the scan is completed and an entire array of data has been acquired in the memory module 160, an array processor 161 operates to Fourier transform the data into an array of image data. This image data is conveyed through the serial link 115 to the computer system 107 where it is stored in the disk memory 111. In response to commands received from the operator console 100, this image data may be archived on the tape drive 112, or it may be further processed by the image processor 106 in accordance with the present invention and conveyed to the operator console 100 and presented on the display 104);
wavelet decomposition of image data of a medical image ([col. 4 ln. 14-22] As a first step 214 a one-dimensional (1D) discrete wavelet transform (DWT)… is performed along the row direction of the medical image);
and display the reconstructed, artifact-removed image on a display device of the image processing system ([col. 3 ln. 52-62] The present invention is implemented in the image processor 106 shown in FIG. 1 by carrying out the sequence of steps indicated by the flow chart in FIG. 4. As indicated at process block 200, the first step is to acquire the image using the above-described MRI system and an appropriate pulse sequence. The image is then reconstructed as indicated at process block 202 and stored in the computer system 107 as an array of pixel intensity values arranged in rows and columns. An operator may retrieve this stored image at the console 100, where it can be viewed on display 104).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the teachings of Munch with the teachings of Khoury to perform wavelet decomposition on a medical image using an image processing system because "The objective of this filtering step 210 is to significantly reduce, or remove the streak artifacts 206 from the image" [col. 4 ln. 8-12] since "Unfortunately, there are circumstances in which patient motion will inevitably occur and artifacts will be produced. For example, patient swallowing or subtle movements during a magnetic resonance angiography (MRA) exam of the carotid arteries using a 2D time-of-flight (TOF) pulse sequence can produce streak artifacts" [col. 1 ln. 40-45] and display the reconstructed, artifact removed image because "Another object of the invention is to enable the user to control the aggressiveness with which streak artifacts are removed. The number of rows to be replaced can be input by the user and the process will replace that number of rows starting with the most intense streaks and working down. Since the entire process only requires a few seconds to complete, the user can interactively change the number until all the objectionable streaks are removed" [col. 2 ln. 11-19].
Regarding claim 14, Munch and Khoury teach the system of claim 13. Munch further teaches wherein the initial wavelet basis function is selected based on a nature of the image artifacts ([3.8 Choice of Wavelets] For the same example as in the previous section, different types and sizes of wavelets have been applied. The use of Daubechies (DB) wavelets shows visually favourable results even at low energy changes εs (Section 3.7)…In Fig.10, the energy changes in [%] after filtering with Daubechies wavelets of different size are displayed. Three images have been treated: the Lena (see Section 1), cement particles (see Section 4.1), and a sinogram (see Section 4.2). The values for the damping coefficients σ and for the highest decomposition level L have been selected as small as possible, but large enough to make visible stripe remnants disappear. This goal was achieved at damping coefficients of σ =1.5 (Lena), σ =7.0 (cement), σ =1.5 (sinogram), and for highest decomposition levels of L = 4, L = 8, L = 4, respectively. While these values have been kept constant, the wavelet filters have been varied from DB1 to DB43. For all three images, the energy changes εs are tendentially decreasing with increasing wavelet size…The largest εs occur for the cement image. This is due to (1) the large damping coefficient and (2) highest decomposition level, and (3) to the heavy stripe artifacts), and the additional wavelet basis functions are different from the initial wavelet basis function.
PNG
media_image1.png
162
783
media_image1.png
Greyscale
PNG
media_image2.png
165
789
media_image2.png
Greyscale
PNG
media_image5.png
249
786
media_image5.png
Greyscale
PNG
media_image6.png
377
782
media_image6.png
Greyscale
Regarding claim 16, Munch and Khoury teach the system of claim 13. Munch further teaches wherein performing the one or more 2-D Fourier transforms on the wavelet coefficients further comprises performing the one or more 2-D Fourier transforms on the first portion of the wavelet coefficients, and not performing the one or more 2-D Fourier transforms on the second portion of the wavelet coefficients ([2.2 Basic Idea c) Combined wavelet-FFT filtering] Subsequently, the bands containing the stripe information (e.g. c v l,m,n for vertical stripes, see portions enframed in red in the centre image of Fig.3 ) are FFT transformed to further tighten the stripe information into narrow bands (e.g. to the x̂-axis in the FFT domain in case of vertical stripes, see enframed horizontal regions in the right image of Fig.3).
Regarding claim 17, Munch and Khoury teach the system of claim 16. Munch further teaches wherein the first portion of wavelet coefficients on which the 2-D Fourier transform is performed includes artifact data of one orientation at each of the N levels ([3.2. Tight condensation of stripe information] The first innovative feature characterizing the high performance of the proposed wavelet-FFT filter is the tight condensation of vertical (or horizontal) stripes into strictly isolated ĉ v l,m,n subsets. This characteristic is required to achieve the requirements (a) - (c) in Section 1, and is visualized in Fig. 6 . The image to the left shows a pattern consisting exclusively of perfect vertical colored stripes. The centre image displays the resulting wavelet coefficients after a multiscale decomposition up to level L = 4 obtained using the Haar wavelet (DB1). The entire image information is now condensed in the vertical details band c v l,m,n and the remaining low pass band c l L,m,n. The latter can be further decomposed until the stripe characteristics disappear, or due to the successive downsampling, until some few coefficients which are containing the overall background offset is remaining only).
Regarding claim 18, Munch and Khoury teach the system of claim 16. Munch further teaches wherein the first portion of wavelet coefficients on which the 2-D Fourier transform is performed includes artifact data of more than one orientation at each of the N levels ([3.3. Separation of stripe information] The destriping procedure, i.e. Fourier filtering and coefficient damping, is applied only to some selected detail bands of the wavelet coefficients. This is the key reason for the high performance of this wavelet-FFT filter in removing stripe artifact. In fact, in this way, each processed detail band holds structural information within a bounded scale only therefore enabling stripe filtering to be optimized to the specific scale. In addition, a large number of detail bands remains completely unchanged guaranteeing the preservation of all structural features outside the detail bands affected by stripe artifacts…The power of the strict discrimination between stripe information and other structures is demonstrated in the extreme example in Fig.8 . The Lena image (1) was first scaled by a value of ≈ 1/50 in order to further fade out the original structures relative to the stripes. An image consisting only of colored horizontal and vertical stripes (2) was then added to it. In the resulting image (3), the stripe noise completely covers all other information. The image corruption is so strong that the Lena or any other structure in the original image cannot be visually recognized. Successive destriping in the horizontal and vertical directions using the proposed wavelet-FFT filter at σ → 0, L = 8 and DB42 yields image (4), which shows a high preservation of the structural information of the original Lena).
Claims 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Munch in view of Khoury and Guo (US20230305126A1).
Regarding claim 11, Munch and Khoury teach the method of claim 1. Munch does not explicitly teach wherein the medical image is an ultrasound image obtained by any beamforming method such as Retrospective Transmit Beamforming (RTB), Synthetic Transmit Beamforming (STB), or a different beamforming method.
Guo, in the same field of endeavor of ultrasound image analysis, teaches wherein the medical image is an ultrasound image obtained by any beamforming method such as Retrospective Transmit Beamforming (RTB), Synthetic Transmit Beamforming (STB), or a different beamforming method ([0056] In an embodiment, an ultrasound beamforming method is provided. [0183] Image decomposition methods for decomposing the initial ultrasound images in this embodiment include, but are not limited to, singular value decomposition, wavelet transform decomposition, pyramid decomposition, empirical mode decomposition, and the like. Each characteristic sub-image includes feature information of the initial ultrasound image, such as high-frequency information, low-frequency information, and noise information).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Guo to use an ultrasound obtained by a beamforming method because "In diagnostic ultrasound, a clinical diagnosis is according to an ultrasound image of a tissue, and therefore quality of the ultrasound image will directly affect accuracy of the diagnosis. Ultrasound beamforming is one of the most critical steps in an ultrasound imaging system. Quality of ultrasound beamforming will directly determine quality of a final ultrasound image" [0002].
Regarding claim 12, Munch, Khoury, and Guo teach the method of claim 11. Munch does not explicitly teach wherein the ultrasound image is one of a B- mode ultrasound image, a color Doppler or spectral Doppler ultrasound image, and an elastography image.
Guo teaches wherein the ultrasound image is one of a B- mode ultrasound image, a color Doppler or spectral Doppler ultrasound image, and an elastography image ([0093] After an echo signal is received by the receiver circuit 320 and processed by a subsequent module and corresponding algorithm, a B-mode image reflecting an anatomical structure of the target tissue, a C-mode image reflecting the anatomical structure of the target tissue and blood flow information, and a D-mode image reflecting a Doppler spectrum image are generated).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Guo to have the ultrasound image be one of a B- mode ultrasound image, a color Doppler or spectral Doppler ultrasound image, and an elastography image because "The transmitting sequence is used to control some or all of the plurality of array elements to transmit an ultrasound wave to a biological tissue. Parameters of the transmitting sequence include positions of the array elements, a quantity of the array elements, and transmission parameters of the ultrasound beam (such as amplitude, frequency, times of transmissions, transmission interval, transmission angle, waveform, and focusing position). In some cases, the transmitter circuit 310 is further configured to delay a phase of the transmitted beam, such that different transmitting array elements transmit ultrasound waves at different times, and ultrasound beams transmitted can be focused in a predetermined region of interest. The parameters of the transmitting sequence may vary depending on different working modes, such as B image mode, C image mode, and D image mode (Doppler mode).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Munch in view of Khoury and Yang (W. Z. Yang, Z. H. Liu, S. L. Wang and S. K. Lu, "Destriping Line-Scan Color Image in Transform Domain," 2015 International Conference on Computer Science and Applications (CSA), Wuhan, China, 2015, pp. 90-94).
Regarding claim 8, Munch and Khoury teach the method of claim 1. Munch does not explicitly teach wherein the filter is a notch filter.
Yang, in the same field of endeavor of image artifact removal, teaches wherein the filter is a notch filter ([pg. 93 para 2-3] We employ a Gaussian function H(u,v) as the filter (equation 11). The value of sigma controls the width of the filter in v-direction. After processed by the band-stop filter H(u,v), the filtered Fourier coefficients G(u,v) were obtained by using the formula (4)).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Yang to use a notch filter because "In the Fourier spectrum, the vertical details are within the center of a horizontal narrow band area (i.e., Fig.4 (c) in the red circled part). An appropriate filtering function can effectively remove the stripes in the vertical details" [pg. 93 para. 2].
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Munch in view of Guo and Yang.
Regarding claim 19, Munch teaches a method, comprising: acquiring an image ([2.3 Algorithm] This algorithm, in addition to the input image ’ima’, employs three parameters for the filtering process: the highest decomposition level L (’decNum’), the wavelet type (’wname’) and the damping factor σ (’sigma’) from Eq. 9);
performing a wavelet decomposition of the image to generate a set of wavelet coefficients ([2.3 Algorithm] The algorithm consists of three distinct parts. At lines 4-6, the wavelet decomposition is calculated by recursive splitting of the original image and of the low resolution coefficients c l l,m,n from the former decomposition level l – 1 into {c l l,m,n, c h l,m,n, c v l,m,n, c d l,m,n});
performing one or more 2-D Fourier transforms on selected wavelet coefficients of the set of wavelet coefficients to generate a set of Fourier coefficients, the selected wavelet coefficients including image artifact data ([2.2 Basic Idea c) Combined wavelet-FFT filtering] Subsequently, the bands containing the stripe information (e.g. c v l,m,n for vertical stripes, see portions enframed in red in the centre image of Fig.3 ) are FFT transformed to further tighten the stripe information into narrow bands (e.g. to the x̂-axis in the FFT domain in case of vertical stripes, see enframed horizontal regions in the right image of Fig.3 ). [2.2 Basic Idea b) FFT filtering] Consequently by eliminating the Fourier coefficients F(x̂,ŷ) of f(x,y) at all x̂ for ŷ = 0, the entire information arising from ideal vertical stripes will be erased);
removing the image artifact data from the Fourier coefficients, using a filter ([2.2 Basic Idea b) FFT filtering] Consequently by eliminating the Fourier coefficients F(x̂,ŷ) of f(x,y) at all x̂ for ŷ = 0, the entire information arising from ideal vertical stripes will be erased…For this purpose, a simple approach in the Fourier space is the application of a bandpass filter around ŷ ≈ 0. For instance, a selective damping of F(x̂,ŷ) on the x̂-axis can be obtained by multiplication of the FFT coefficients with a Gaussian function g(x̂,ŷ));
regenerating the selected wavelet coefficients from the set of Fourier coefficients with the image artifact data removed, using inverse 2-D Fourier transforms (Fig. 5 Matlab code for combined wavelet-FFT stripe filtering: ln 18-19 inverse FFT. [2.3 Algorithm] Subsequently at lines 15-16, these coefficients are multiplied with a Gaussian function, to eliminate those close to the x̂-axis in the Fourier domain. After such damping, the coefficients ĉ′v l,m,n are transformed back to the wavelet space (line 19));
reconstructing an artifact-removed image using the regenerated wavelet coefficients ([2.2 Basic Idea c) Combined wavelet-FFT filtering] The stripe information condensed in such a way is then eliminated by multiplication with a Gaussian damping function g(x̂,ŷ) according to Eq. 9 . Finally, the destriped image f′(x,y) is reconstructed from the filtered coefficients. The result is displayed in Fig.4 on the right image);
wherein performing the wavelet decomposition of the image data to generate the set of wavelet coefficients further comprises: selecting a wavelet basis function for each level of N levels of the wavelet decomposition, each level corresponding to a different scale of the image; and for each level of the N levels, performing the wavelet decomposition, where a result of the wavelet decomposition includes an approximation image; wherein the approximation image from a level of the N levels is used as an input into a wavelet decomposition performed at a subsequent level, and the image data is not used as an input into the wavelet decomposition performed at the subsequent level, such that the approximation image is further decomposed at the subsequent level to extract image artifact data remaining in the approximation image at each of the N levels.
PNG
media_image1.png
162
783
media_image1.png
Greyscale
PNG
media_image2.png
165
789
media_image2.png
Greyscale
([1.1 Some considerations about wavelet and Fourier transforms] The main motivation for the decomposition of a signal into orthogonal basis functions is the deployment of the original signal information into coefficient classes that specifically group interesting structural patterns. This aspect is particularly attractive for artifacts removal techniques. In addition, orthogonal transforms often yield coefficients, which become partly small or even zero. This characteristic of wavelet and Fourier analysis makes signal decomposition especially attractive for data compression purposes…In dyadic, decimated wavelet transform, a single scale wavelet decomposition of a 1D signal f(t) consists in its fragmentation into a low and a high frequency part. This separation is perfectly reversible. The low frequency part can then be iteratively decomposed in the same way, while leaving the high frequency unchanged. After a decomposition step, the size N(l) of the resulting low and high frequency parts is halved in order to maintain the total number of coefficients constant. The next decomposition is therefore performed at half the number of coefficients but with the same filter size, i.e. at lower spatial scale, what is equivalent to filtering at double the filter size. A multiscale wavelet transform at a highest decomposition level L is therefore the subdivision of f(t) into one low frequency part, which is represented by one scaling function Φ L (t) together with its coefficients, and multiple high frequency parts, which are represented by a set of wavelet functions Ψ l (t) and their coefficients at l = 1,⋯,L different scales, yielding
PNG
media_image3.png
308
782
media_image3.png
Greyscale
PNG
media_image4.png
509
758
media_image4.png
Greyscale
Munch does not explicitly teach a method for an ultrasound system; acquiring an ultrasound image via a probe of the ultrasound system during a scan of a subject; performing a wavelet decomposition of the ultrasound image; and displaying the reconstructed, artifact-removed image on a display device of the ultrasound system during the scan.
Guo, in the same field of endeavor of ultrasound image analysis, teaches a method for an ultrasound system; acquiring an ultrasound image via a probe of the ultrasound system during a scan of a subject ([Abstract] Embodiments of the disclosure provide an ultrasound beamforming method and device. [0116] In this embodiment, an ultrasonic probe may be used to transmit an ultrasound wave to the target tissue and receive an ultrasonic echo returned by the target tissue, to obtain the channel data of the target tissue in real time);
performing a wavelet decomposition of the ultrasound image ([0045] performing wavelet decomposition on each initial ultrasound image using a same wavelet base and decomposition level, and determining a characteristic sub-image for representing an overall structural feature, a characteristic sub-image for representing a local detail feature in a horizontal direction, a characteristic sub-image for representing a local detail feature in a vertical direction, and a characteristic sub-image for representing a local detail feature in a diagonal direction at each decomposition level, to obtain the characteristic sub-images of the initial ultrasound images. [0046] In an embodiment, fusing the obtained characteristic sub-images to obtain the fused ultrasound image. [0141] When the fusion is performed according to image characteristic, weights of high-frequency information for representing an image detail feature and low-frequency information for representing an overall structural feature of the image may be increased, and a weight for representing noise information of the image may be reduced);
and displaying the reconstructed, artifact-removed image on a display device of the ultrasound system during the scan ([0143] Further, the fused ultrasound image of the target tissue and the initial ultrasound images may be displayed in the display interface simultaneously or sequentially, so that the user can selectively view or compare as required. [0124] In a real-time imaging application scenario, the at least two different ultrasound beamforming methods may be used to process the same channel data in parallel, so as to improve a processing speed and meet the need of real-time imaging).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Guo to acquire an ultrasound image, perform wavelet decomposition on it, and display the reconstructed artifact removed image because "When the fusion is performed by means of adaptive calculation, the fusion coefficients will adaptively change depending on the obtained image data, so as to retain the advantages of the beamforming methods to the greatest extent and obtain a high-quality fused ultrasound image…When the fusion is performed according to image characteristic, weights of high-frequency information for representing an image detail feature and low-frequency information for representing an overall structural feature of the image may be increased, and a weight for representing noise information of the image may be reduced" [0141] and "Displaying the ultrasound images obtained using the different beamforming methods in the display interface makes it possible for the user to observe the target tissue from different perspectives and grasp more information about the target tissue, which facilitates a more comprehensive and accurate grasp of the situation of the target tissue" [0132].
Munch does not explicitly teach removing the image artifact data from the set of Fourier coefficients using a notch filter.
Yang, in the same field of endeavor of image artifact removal, teaches removing the image artifact data from the set of Fourier coefficients using a notch filter ([pg. 93 para 2-3] We employ a Gaussian function H(u,v) as the filter (equation 11). The value of sigma controls the width of the filter in v-direction. After processed by the band-stop filter H(u,v), the filtered Fourier coefficients G(u,v) were obtained by using the formula (4)).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Yang to use a notch filter because "In the Fourier spectrum, the vertical details are within the center of a horizontal narrow band area (i.e., Fig.4 (c) in the red circled part). An appropriate filtering function can effectively remove the stripes in the vertical details" [pg. 93 para. 2].
Regarding claim 20, Munch, Guo, and Yang teach the method of claim 19. Munch further teaches wherein the one or more 2-D Fourier transforms are not performed on the entire set of wavelet coefficients ([2.2 Basic Idea c) Combined wavelet-FFT filtering] Subsequently, the bands containing the stripe information (e.g. c v l,m,n for vertical stripes, see portions enframed in red in the centre image of Fig.3 ) are FFT transformed to further tighten the stripe information into narrow bands (e.g. to the x̂-axis in the FFT domain in case of vertical stripes, see enframed horizontal regions in the right image of Fig.3).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Munch in view of Khoury and Schmid (US20170322071A1).
Regarding claim 9, Munch and Khoury teach the method of claim 1. Munch does not explicitly teach wherein the method is applied to the medical image "on-the-fly" at a time of acquisition, and the artifact-removed image is displayed on the display device in real time.
Schmid, in the same field of endeavor of image artifact removal, teaches wherein the method is applied to the medical image “on-the-fly” at a time of acquisition, and the artifact-removed image is displayed on the display device in real time ([0065] The processing (step 230) may remove additional artifacts that can be identified in the reconstructed images, and in any event creates a short envelope image (232) and a long envelope image (234). In an embodiment, the short and long envelope images (232, 234) are used to generate parametric images (step 240) process. The generate parametric images (step 240) process outputs an oxygenation map (250), a hemoglobin map (255) and a masked oxygenation map (260). In an embodiment, any or all of the three maps are coregistered with, and overlaid on an ultrasound image (step 265). A display can be provided for display of one or more of the displayable images displayed in steps 270, 275, 280, 285, 290 and 295. [0125] In an embodiment, where a “live” display is provided, an estimate can be formed using a plurality of prior frames. In an embodiment, where a “live” display is provided, an estimate can be formed using a plurality of prior spatially distinct frames. In an embodiment, where a “live” display is provided and the system comprises a satisfactory frame rate, the estimate can comprise past frames and future frames, provided that the display is delayed in time to permit the use of such frames. Thus, for example, where the frame rate for a given laser is, e.g., 5 frames per second, and the display is about one second behind real time, it may be possible to use four or five “future” frames to estimate the interframe persistent artifacts in the current frame).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Schmid to use a medical image processing system to display artifact-removed images in real time because this "would provide the operator the ability to vary parameters used in processing, when processing or viewing optoacoustic images" [0066].
Regarding claim 10, Munch and Khoury teach the method of claim 1. Munch does not explicitly teach wherein the medical image is generated at a first time of acquisition and stored in the image processing system, and the method is applied to the medical image at a second, later time to remove the image artifact data prior to viewing the artifact-removed image on the display device.
Schmid, in the same field of endeavor of image artifact removal, teaches wherein the medical image is generated at a first time of acquisition and stored in the image processing system ([0058] In an embodiment, the sinogram, along with other data recorded relating to the use of the optoacoustic device, may be recorded in a laser optic movie file or LOM. The LOM is not, as the name would suggest, a movie file, but rather, the LOM is a collection of recorded data that may be recorded in group of related files, or more preferably, in a single data file. One consideration for the format of the LOM is the differing and likely asynchronous processes that generate data requiring storage in the LOM. In an embodiment, the LOM can be used to store a variety of information concerning the use of the optoacoustic device, including, without limitation, the long and short optoacoustic sinograms, ultrasound frames, configuration data, annotations made by a user, or at a later time, an audio and/or video recording made during the use of the optoacoustic device and information concerning version information as reported by the optoacoustic system and its software), and the method is applied to the medical image at a second, later time to remove the image artifact data prior to viewing the artifact-removed image on the display device ([0126] In an embodiment where a display output is provided after a reading is complete, all of the spatially distinct frames (or all of the spatially distinct frames of a give wavelength) can be used in creating a post-reading estimate of interframe persistent artifacts, and then entire reading can be reconstructed and output with the post-reading estimate of interframe persistent artifacts eliminated from the sinograms prior to their reconstruction).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the method of Munch with the teachings of Schmid to use a medical image processing system to store image data and process it later because "the playback mode further allows the user to apply different filters, tuned to find different physiological features, to the same stored data. The playback mode further allows a different reconstruction algorithm to be used during playback or export. One useful parameter that can be changed in the playback mode is the ROI depth, described above. It can also be configured to allow the interframe persistent artifact reduction" [0413].
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Munch in view of Khoury and Dixit (Dixit, Ankur, and Shefali Agarwal. "Vertical stripe correction in Hyperion image using wavelet transformation and singular value decomposition (SVD)." Geocarto International 37.10 (2022): 3033-3050).
Regarding claim 15, Munch and Khoury teach the system of claim 14. Munch further teaches wherein a first set of wavelets and decompositions is applied to the image data to detect artifacts at a first orientation; a second set of wavelets and decompositions is applied to the image data to detect artifacts at a second orientation ([3.3 Separation of stripe information] Successive destriping in the horizontal and vertical directions using the proposed wavelet-FFT filter at σ → 0, L = 8 and DB42 yields image (4), which shows a high preservation of the structural information of the original Lena).
Munch does not explicitly teach a third set of wavelets and decompositions is applied to the image data to detect artifacts at a third orientation.
Dixit, in the same field of endeavor of image artifact removal, teaches a third set of wavelets and decompositions is applied to the image data to detect artifacts at a third orientation ([Conclusion] In this study we have proposed a method to destripe the hyperspectral image. We have proposed to correct these artifacts using a novel approach using wavelet and Singular Value Decomposition. We have identified the stripes in the bands of hyperspectral image and if it is present then it applies the correction method. In this proposed method, we have first decomposed the striped band into three components that are horizontal, vertical and diagonal using wavelet decomposition… Considering one band at a time and then performing the correction on the frequency domain, makes it more generic that can be applied to any other dataset containing stripes. Not only vertical, this method can remove horizontal as well as diagonal stripes also, if present. One needs to select the concerned components (horizontal, vertical or diagonal) where the stripe is dominant).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time that the invention was made to modify the teachings of Munch with the teachings of Dixit to apply a third set of wavelets and decompositions to detect artifacts at a third orientation because "In first step presence of striping is identified in the band. If it is not present in the band then it stops and check for the next band. The bands which are identified as affected of striping noise are implicated with striping removal step, the second step of destriping algorithm" [Methodology pg. 4-5].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jacqueline R Zak whose telephone number is (571) 272-4077. The examiner can normally be reached M-F 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571) 270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JACQUELINE R ZAK/Examiner, Art Unit 2666
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
Prosecution Insights