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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/29/2025 has been entered.
Response to Amendment
Applicant’s amendments and remarks, filed 08/29/2025, are acknowledged. Rejections and/or objections not reiterated from previous office actions are hereby withdrawn. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 6-9, 11-26 are currently under examination.
Priority
Applicant’s claim for the benefit of priority under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) to PCT/EP2012/074181, filed 11/30/2012, which claims benefit of application #61/723,689, filed 11/07/2012, is acknowledged.
Applicant's claim for the benefit of foreign priority under 35 U.S.C. 119(a)-(d) to European Patent Application No. 11191832.2, filed on December 02, 2011, and to European Patent Application No. 11191833.0, filed on December 02, 2011, is acknowledged. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), and under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c), which papers have been placed of record in the file.
Response to Arguments
Applicant’s responses and arguments filed 08/29/2025 regarding claim rejections under 35 USC 103 have been fully considered and found not persuasive for the following reasons.
Regarding the claim rejections under 35 USC 103, Applicant has amended the independent claims for clarifying the scope of the invention with the introduction of subject matter not previously prosecuted and changing the scope of the claim therefore necessitating new grounds of rejection.
Applicant is arguing that the references of record do not teach the amended limitations within the claim.
In response the examiner is considering the argument as not persuasive and moot since it is directed to subject matter not previously prosecuted changing the scope of the claim therefore necessitating new grounds of rejection. The examiner is considering new references for addressing the amended limitations within the claim.
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 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.
Claims 1, 6-9, 12-17, 19-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ambrosini et al. (2017 IEEE Trans. Med. Imaging 36:757-768; Pub.Date 2017) in view of Piayda et al. (2018 Eur. J. Med. Res. 23: 7 pages; Pub.Date 07-31-2018) in view Dumont et al. (USPN 20150087972 A1; Pub.Date 03/06/2015; Fil.Date 07/28/2014) in view of Hansis et al. (2009 Proc. of SPIE 7258:article 72580B 11 pages; Pub.Date 2009).
Regarding independent claim 1, Ambrosini teaches a procedure/method enabling for roadmapping during catheterization (Title and abstract) for generating an image of an object of interest of a patient (Fig.1 with imaging a vascular tree of the liver of a patient), the object of interest comprising part of the vasculature of the patient (Fig.1 and p.757 col.2 last ¶ “a method to track the catheter tip inside a patient-specific contrast-enhanced 3D abdominal vasculature, obtained from peri-operative 3D Rotational Angiography (3DRA), using single-plane 2D X-ray images with no contrast agent”), the method comprising:
i) generating a three-dimensional (3D) model of the object of interest from 3D image data acquired using a 3D angiographic imaging modality (p.757 col.2 last ¶ generating “a patient-specific contrast-enhanced 3D abdominal vasculature” as in Fig.1 as obtained from a 3DRA or peri-operative 3D rotational and p.761 col.2 3rd ¶ “A 3DRA image was acquired at the beginning of each intervention when the catheter was in the common hepatic artery);
ii) obtaining contrast-enhanced x-ray image data of the object of interest over time (p.757 col.2 last ¶ “track the catheter tip inside a patient-specific contrast-enhanced 3D abdominal vasculature, obtained from peri-operative 3D Rotational Angiography (3DRA)”), […and a corresponding ECG signal over time acquired while acquiring the contrast- enhanced x-ray image data…], wherein the contrast-enhanced x-ray image data is acquired using an x-ray imaging modality with a contrast agent and an interventional device present in the contrast-enhanced x-ray image data, wherein the interventional device is used in a procedure to treat the object of interest (p.757 col.2 last ¶ “track the catheter tip inside a patient-specific contrast-enhanced 3D abdominal vasculature, obtained from peri-operative 3D Rotational Angiography (3DRA)” and “to continuously localize the catheter tip inside the 3D model of the patients vasculature during the catheterization procedures”) , and […wherein the contrast-enhanced x- ray image data covers at least one cardiac cycle of the patient …]
iii) using the 3D model of i) and the contrast-enhanced x-ray image data of ii) to generate a sequence of 3D roadmaps of the object of interest over time […that covers multiple phases of at least one cardiac cycle of the patient…], wherein the sequence of 3D roadmaps is defined in a 3D coordinate system and includes information that characterizes properties of the object of interest over time, the properties including at least one of centerlines, contours, and an image mask of the object of interest (p.759 col.2 ¶ B. Catheter Tip Tracking over time for Ambrosini teaching the use of the initial position and later real-time position of the tip of the catheter to define the 3D/2D roadmap for the guidance of the catheter via the tracking of the tip of the catheter using the registration transform to update the roadmap therefore defining a roadmap sequence in time and p.761 col.2 3rd ¶ “The 3D vessel tree centerline P in the 3DRA was extracted with a semi-automatic method based on thresholding and skeletonization” wherein the centerlines are co-registered with those intraoperative centerlines determined on non-contrast images for 3D roadmapping and catheter tip tracking p.757 col.2 last ¶ as above cited as for ) […and generating phase information that characterizes phase of the cardiac cycle of the patient for each 3D roadmap of the sequence of 3D roadmaps as determined from the ECG signal over time of ii) …];
iv) determining reference locations for a tip of the interventional device, wherein the reference locations correspond to the sequence of 3D roadmaps of iii) (Fig.1 with initialization of the tip position, initial tip position as reference location for the tip tracking which is performed using the tip registration as used for 3D/2D roadmap to guide the physician with the method of optimizing the initial position for the tip of the catheter, and with Ambrosini teaching also the tracking of the tip of the catheter over time using the guidance based on the roadmaps (Fig.1 with timeline/timepoint p.759 col.2 ¶ B. Catheter tip tracking and p.758 col.2 1st ¶));
v) obtaining non-contrast-enhanced x-ray image data of the object of interest over time […and a corresponding ECG signal over time acquired while acquiring the non-contrast-enhanced x-ray image data…], wherein the non-contrast-enhanced x-ray image data is acquired using an x-ray imaging modality without a contrast agent and the interventional device is present in the non-contrast-enhanced x-ray image data (Fig.1 fluorescence imaging with 2D fluoroscopic sequence with the catheter extracted from the sequence images by segmentation and p.757 col.2 last ¶ “a method to track the catheter tip inside a patient-specific contrast-enhanced 3D abdominal vasculature, obtained from peri-operative 3D Rotational Angiography (3DRA), using single-plane 2D X-ray images with no contrast agent”);
vi) determining location of the tip of the interventional device in the non-contrast-enhanced x-ray image data of v) (Fig.1 segmentation and tracking location of the tip of the catheter);
vii) selecting a particular 3D roadmap from the sequence of 3D roadmaps of iii) (Fig.1 with the tip position and registered transformation for the 3D/2D/ roadmap to guide the physician for advancing the catheter and p.761 col.1 last ¶ to col.2 1st ¶ for using the Viterbi algorithm for aligning the 3D blood vessel tree with the 2D fluoroscopy in combination with the tip position to obtain the selected roadmap), [… wherein the particular 3D roadmap is selected by matching phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data to phase of the cardiac cycle of the patient for the particular 3D roadmap, wherein the phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data is based on the ECG signal over time of v), and wherein the phase of the cardiac cycle of the patient for the particular 3D roadmap is determined from the phase information of iii) for the particular 3D roadmap…];
viii) applying a transformation function to the particular 3D roadmap of vii) to generate a resultant 2D roadmap representing an imaging plane of the non-contrast- enhanced x-ray image data of v), […wherein the transformation function is based on displacement obtained from the reference location of iv) that corresponds to the particular 3D roadmap of vii) and the location of the tip of the interventional device determined in vii)…] (p.761 col.1 last ¶ to col.2 1st ¶ for using the Viterbi algorithm for aligning the 3D blood vessel tree with the 2D fluoroscopy in combination with the tip position to obtain the selected roadmap for each location of the tip of the catheter and Fig.6 for the selection of the tip positions and p.763 col.1 ¶ C. Clinical Data with catheter being static or advanced relative to the blood vessel).
ix) overlaying a visual representation of the resultant 2D roadmap of viii) on the non-contrast-enhanced x-ray image data for display (Fig.1 with the final step of 3D tracking with overlapping the vessel projection on the fluoroscopic imaged with the catheter and Fig.8 for the optimized roadmaps to be applied as in Fig.14 for tracking the catheter)
Ambrosini does not specifically teach a corresponding ECG signal over time acquired while acquiring the contrast- enhanced x-ray image data, wherein the contrast-enhanced x- ray image data covers at least one cardiac cycle of the patient, image data and road maps that covers multiple phases of at least one cardiac cycle of the patient, and generating phase information that characterizes phase of the cardiac cycle of the patient for each 3D roadmap of the sequence of 3D roadmaps as determined from the ECG signal over time of ii), a corresponding ECG signal over time acquired while acquiring the non-contrast-enhanced x-ray image data and wherein the particular 3D roadmap is selected by matching phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data to phase of the cardiac cycle of the patient for the particular 3D roadmap, wherein the phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data is based on the ECG signal over time of v), and wherein the phase of the cardiac cycle of the patient for the particular 3D roadmap is determined from the phase information of iii) for the particular 3D roadmap, wherein the transformation function is based on displacement obtained from the reference location of iv) that corresponds to the particular 3D roadmap of vii) and the location of the tip of the interventional device determined in vii) as in claim 1.
However, Piayda teaches within the same field of endeavor of generating coronary roadmaps (Title and abstract within the same concept of automatic determination of roadmaps from patient angiography applied to non-contrast imaging for surgical guidance) a real-time, dynamic coronary roadmap overlay of the coronary tree on fluoroscopy (abstract) wherein the contrast-enhanced x- ray image data covers at least one cardiac cycle of the patient, image data and road maps that covers multiple phases of at least one cardiac cycle of the patient (p.2 col.2 last ¶ “A roadmap image was automatically generated each time the interventionalist created a cine loop of angiographic images with completely contrast agent filled coronary arteries. Whether contrast was administered by a semiautomatic pump or by hand injection did not influence roadmap quality as long as the coronary tree was completely filled for three cardiac cycles” and p.3 col.1 1st ¶ “Every time a cine loop with sufficient contrast depiction of the coronary arteries in the same or a different C-arm angulation was recorded, a new mask was produced and converted into a roadmap. Those roadmaps, corresponding to the different C-arm angulations, were stored in a library and automatically presented to the interventionalist as soon as the exact stored C-arm position was obtained again, i.e., during PCI (Fig. 1)” with the cine acquisition during at least 3 cardiac cycle reading on the at least one cardiac cycle and the generation of a plurality of roadmaps covering different phases of the cardiac cycle), and since Ambrosini already teaches the tracking of the tip of the catheter over time using the guidance based on the roadmaps (Fig.1 with timeline/timepoint p.759 col.2 ¶ B. Catheter tip tracking and p.758 col.2 1st ¶) Piayda teachings would also similarly read on the determination of reference locations that correspond to the sequence of roadmaps of the object of interest over time with the selection of particular roadmap from the sequence of roadmaps over time to apply to the 3D roadmap sequence already taught by Ambrosini (Fig.1e p.3 col.1 1st ¶ “The software refers to fixed curvatures, it recognizes for example, the guiding or diagnostic catheter and attaches the stored dynamic roadmap to the tip of the catheter” recognizing the curvature signature of the catheter to fit one of the roadmaps).
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini such that the system-method further comprises wherein the contrast-enhanced x- ray image data covers at least one cardiac cycle of the patient, image data and road maps that covers multiple phases of at least one cardiac cycle of the patient, since one of ordinary skill in the art would recognize that using the imaging along the cardiac cycle to reference the imaging frames to a reference phase of the cardiac cycle and to estimate the motion of the catheter via a dynamic roadmap library was known in the art as taught by Piayda. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini and Piayda both teach the imaging with angiography and fluoroscopy of the vascular system with the presence of a catheter to overlay the fluoroscopy image of the region of interest with that of the angiography images. The motivation would have been to better track the catheter and blood vessel along the cardiac motion during the cardiac cycle, as suggested by Piayda (Fig.1 with optimal registration with optimal cardiac phase).
While Piayda teaches following the heart cycle for determining and selecting images and roadmaps (Fig.2 and p.2 col.2 last ¶), Ambrosini and Piayda do not specifically teach and generating phase information that characterizes phase of the cardiac cycle of the patient for each 3D roadmap of the sequence of 3D roadmaps as determined from the ECG signal over time of ii), a corresponding ECG signal over time acquired while acquiring the non-contrast-enhanced x-ray image data and wherein the particular 3D roadmap is selected by matching phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data to phase of the cardiac cycle of the patient for the particular 3D roadmap, wherein the phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data is based on the ECG signal over time of v), and wherein the phase of the cardiac cycle of the patient for the particular 3D roadmap is determined from the phase information of iii) for the particular 3D roadmap, wherein the transformation function is based on displacement obtained from the reference location of iv) that corresponds to the particular 3D roadmap of vii) and the location of the tip of the interventional device determined in vii) as in claim 1.
However, Dumont teaches within the same field of endeavor of dynamic imaging with overlay of angiography images and fluoroscopy images for tracking a catheter (Title, abstract and Fig.4) the use of an EKG/ECG as connected directly to the fluoroscopy system and the angiography system ([0035]) for timing each image capture for both system ([0036]) therefore for identifying the cardiac phase for each imaging capture ([0037]) teaching a corresponding ECG signal over time acquired while acquiring the contrast-enhanced x-ray image data, a corresponding ECG signal over time acquired while acquiring the non-contrast-enhanced x-ray image data and for recording the timing with reference to the cardiac cycle of the patient (Fig.1) with the angiographic sequences being aligned with particular phases of the cardiac cycle (Abstract “ For each cardiac phase, the anatomy is detected multiple times from different cardiac cycles using angiographic images. The fluoroscopic overlay for each cardiac phase is formed from a combination of angiographic candidates fit from the different cardiac cycles to the fluoroscopic image” with [0031] “The fluoroscopy and angiography frames represent the region at the same or different phases of the heart cycle” and [0099] “Where the frames of the angiographic sequence are aligned to particular phases, the frames of fluoroscopic data at those phases are used” and [0104] “The shape model before fitting, fitted candidate shape model, or combined shape model represents the anatomy and/or device at a given phase” and “For example, motion between phases in angiography may be used to predict motion and resulting location in the other phase for fluoroscopy”. Additionally, Hansis teaches within the same field of endeavor than Dumont for correcting motion for image reconstruction using time gated from ECG (Title and abstract) using a time after the reference time gating the ECG signal (¶ 2.2 Transformation parametrization, with ¶ 2.5 Periodicity assumption as related to the cardiac motion along the cardiac cycle) in order to time parametrization of the CT projection data for image reconstruction (Figs. 1, 2)therefore teaching generating phase information that characterizes phase of the cardiac cycle of the patient for each 3D roadmap of the sequence of 3D roadmaps as determined from the ECG signal over time of ii) therefore with these teaching as applied to the 3D roadmap sequence taught by Ambrosini teaching wherein the particular 3D roadmap is selected by matching phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data to phase of the cardiac cycle of the patient for the particular 3D roadmap, wherein the phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data is based on the ECG signal over time of v), and according to Dumont aligning the angiography images with local structures with the fluoroscopic images according to the timing from the cardiac phases defining the displacement of the local anatomic structures from the reconstruction from Hansis as applied to the 3D roadmap of Ambrosini teaching then wherein the phase of the cardiac cycle of the patient for the particular 3D roadmap is determined from the phase information of iii) for the particular 3D roadmap, wherein the transformation function is based on displacement obtained from the reference location of iv) that corresponds to the particular 3D roadmap of vii) and the location of the tip of the interventional device determined in vii) with the overlay of anatomy from fluoroscopic images as claimed.
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini as modified by Piayda such that system-method further comprises generating phase information that characterizes phase of the cardiac cycle of the patient for each 3D roadmap of the sequence of 3D roadmaps as determined from the ECG signal over time of ii), a corresponding ECG signal over time acquired while acquiring the non-contrast-enhanced x-ray image data and wherein the particular 3D roadmap is selected by matching phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data to phase of the cardiac cycle of the patient for the particular 3D roadmap, wherein the phase of the cardiac cycle of the patient for the non-contrast-enhanced x-ray image data is based on the ECG signal over time of v), and wherein the phase of the cardiac cycle of the patient for the particular 3D roadmap is determined from the phase information of iii) for the particular 3D roadmap, wherein the transformation function is based on displacement obtained from the reference location of iv) that corresponds to the particular 3D roadmap of vii) and the location of the tip of the interventional device determined in vii), since one of ordinary skill in the art would recognize that using the EKG signal for the cardiac cycle to time stamp each imaging capture from both the fluoroscopy system and the angiography system and to reference the imaging frames to a reference phase of the cardiac cycle and to time offset the other frames in order to estimate the motion of the catheter with the fluoroscopy images was known in the art as taught by Dumont with the reconstruction of images without the cardiac displacement according a parametrization of the projection data according to the times offset from a ECG gated reference time as taught by Hansis as to locate the 3D roadmap taught by Ambrosini. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini, Dumont, Hansis and Piayda teach the imaging with angiography and fluoroscopy of the vascular system with the presence of a catheter to overlay the fluoroscopy image of the region of interest with that of the angiography images. The motivation would have been to ideally provide a time correspondence between the two imaging modalities to correct for the motion of the heart during the cardiac cycle by improving the prediction of the anatomy position from one phase to the next phase, as suggested by Dumont (abstract).
Regarding the dependent claims 6-9, 12-17, 19-25 all the elements of these claims are instantly disclosed or fully envisioned by the combination teachings of Ambrosini, Piayda, Dumont and Hansis.
Regarding claim 6, Dumont teaches, as discussed above, of dynamic imaging with overlay of angiography images and fluoroscopy images for tracking a catheter (Title, abstract and Fig.4) the generation of an anatomic model from angiography images (Figs.3 and 5, and [0084] with model including the centerline and the edges of the blood vessel) therefore teaching the 3D model of i) includes 3D vessel centerlines and 3D surface contours representing at least one of luminal vessel surfaces, plaque, and 3D masks as claimed.
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini as modified by Piayda, Dumont and Hansis such that system-method further comprises the 3D model of i) includes 3D vessel centerlines and 3D surface contours representing at least one of luminal vessel surfaces, plaque, and 3D masks, since one of ordinary skill in the art would recognize that using an anatomical model including the centerline and the surface of the blood vessel was known in the art as taught by Dumont. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini and Dumont teach the imaging with angiography and fluoroscopy of the vascular system with the presence of a catheter to overlay the fluoroscopy image of the region of interest with that of the angiography images. The motivation would have been to ideally provide a more complete description of the size of the vessel tree to improve the prediction of the anatomy position during the cardiac phase, as suggested by Dumont (abstract).
Regarding claim 7, Ambrosini teaches that the 3D angiographic modality as 3DRA is a computed tomography modality (p.757 col.2 last CBCT) therefore teaching the 3D angiographic imaging modality of i) is selected from the group consisting of computed tomography (CT), X-ray rotational angiography, 3D Ultrasound, or magnetic resonance imaging (MRI) as claimed.
Regarding claim 8, Ambrosini and Piayda do not teach specifically processing the contrast-enhanced x-ray image data of the object of interest and corresponding ECG signal of ii) to determine a phase of the cardiac cycle of the patient for an image frame and associating the phase of the cardiac cycle to a 3D roadmap corresponding to the image frame as in claim 8.
However, as discussed above for claim 1, Dumont teach the timing stamp using the same ECG/EKG signal associated to both contrast-enhanced angiography system and non-contrast fluorography system to determine for both the same cardiac phase ([0036]) therefore for identifying the cardiac phase for each imaging capture ([0037]) and for recording the timing with reference to the cardiac cycle of the patient (Fig.1) with the angiographic sequences being aligned with particular phases of the cardiac cycle (Abstract “ For each cardiac phase, the anatomy is detected multiple times from different cardiac cycles using angiographic images. The fluoroscopic overlay for each cardiac phase is formed from a combination of angiographic candidates fit from the different cardiac cycles to the fluoroscopic image” with [0031] “The fluoroscopy and angiography frames represent the region at the same or different phases of the heart cycle”) therefore teaching processing the contrast-enhanced x-ray image data of the object of interest and corresponding ECG signal of ii) to determine a phase of the cardiac cycle of the patient for an image frame and associating the phase of the cardiac cycle to a 3D roadmap corresponding to the image frame while using the ECG signals as claimed.
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini as modified by Piayda, Dumont and Hansis such that system-method further comprises processing the contrast-enhanced x-ray image data of the object of interest and corresponding ECG signal of ii) to determine a phase of the cardiac cycle of the patient for an image frame and associating the phase of the cardiac cycle to a 3D roadmap corresponding to the image frame, since one of ordinary skill in the art would recognize that using the EKG signal for the cardiac cycle to time stamp each imaging capture from both the fluoroscopy system and the angiography system and to reference the imaging frames to a reference phase of the cardiac cycle and to time offset the other frames in order to estimate the motion of the catheter with the fluoroscopy images was known in the art as taught by Dumont. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini, Dumont and Piayda teach the imaging with angiography and fluoroscopy of the vascular system with the presence of a catheter to overlay the fluoroscopy image of the region of interest with that of the angiography images. The motivation would have been to ideally provide a time correspondence between the two imaging modalities to correct for the motion of the heart during the cardiac cycle by improving the prediction of the anatomy position from one phase to the next phase, as suggested by Dumont (abstract).
Regarding claim 9, as discussed above, Piayda teaches the use of a cine loop angiography images (Fig.1) for the generation of a library for roadmaps therefore teaching the sequence of 3D roadmaps over time are derived from the 3D model of i) and one x-ray contrast-enhanced angiographic image sequence of the object of interest acquired with a contrast agent; or the sequence of 3D roadmaps over time are derived from the 3D model of i) and two x-ray angiographic image sequences of the object of interest acquired with a contrast agent that is part of the contrast-enhanced x-ray image data of ii) as claimed.
Regarding claim 12, Ambrosini teaches the simulation using the model with correction for the vasculature and respiration motion (p.762 col.1 last ¶ with registration transformation for taking account of the motions).therefore reading on the operations of viii) apply a transformation to the particular roadmap in order to compensate for motion and Hansis teaches that the reconstruction via the parametric transformation to compensate for motion is estimating the distance between the centerlines in the angiographic images and the reconstructed centerlines therefore teaching that the transformation function (for compensate the cardiac motion at the considered cardiac phase) of viii) is applied to three-dimensional coordinates for centerlines or contours of the particular 3D roadmap (p.8 1st ¶).
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini as modified by Piayda, Dumont and Hansis such that system-method further comprises the transformation function of viii) is applied to three-dimensional coordinates for centerlines or contours of the particular 3D roadmap, since one of ordinary skill in the art would recognize that determining the transformation of the centerline of the blood vessels for reconstructing the centerlines according to the cardiac motion observed with the angiographic images in order to estimate the motion of the catheter with the fluoroscopy images was known in the art as taught by Dumont and as taught by Hansis. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini, Dumont, Hansis and Piayda teach the imaging with angiography and fluoroscopy of the vascular system with the presence of a catheter to overlay the fluoroscopy image of the region of interest with that of the angiography images. The motivation would have been to ideally provide an estimate of the error provided by the method to validate the method as suggested Hansis (p.8 1st ¶).
Regarding claim 13 as dependent from claim 12, as discussed for claim 12, Ambrosini teaches the compensation from the respiratory motion (p.762 col.1 last ¶ with registration transformation for taking account of the motions) while Hansis and Dumont teach the correction for the cardiac motion therefore teaching the transformation function of viii) is configured to correct for breathing motion and/or cardiac motion and/or patient motion and/or table motion as claimed.
Regarding claim 14, Ambrosini teaches the use of rigid transformations for the registration between the vessel path and the catheter from fluoroscopy images (Figs.1 and 4, p.760 col.1 last ¶ to col.2 1st ¶) teaching the transformation function of viii) comprises a rigid transformation function or a non-rigid transformation function and/or the transformation function of viii) comprises at least one of a displacement function, rotation function, or scaling function.
Regarding claim 15, Ambrosini is referring to Fig.4 for applying the registration transformation (Figs.1 and 4, p.760 col.1 last ¶ to col.2 1st ¶) which is based on the projection angles of the acquired 2D fluorography images including the catheter and its tip, therefore teaching the visual representation of the resultant 2D roadmap is generated by rendering the resultant 2D roadmap of viii) based on the viewpoint used to acquire the non-contrast-enhanced x-ray image data as claimed.
Regarding claim 16, as discussed above, Ambrosini teaches to project the 3DRA vasculature after registration onto the 2D fluorography images (Fig.14 and p.765 col.2 2nd ¶) to visualize the tracking of the tip of the catheter therefore teaching the overlaying involves a) projecting the visual representation of the resultant 2D roadmap onto the non-contrast- enhanced x-ray image data using a transparent mode, and/or b) projecting boundaries of the resultant 2D roadmap onto the non-contrast-enhanced x-ray image data as claimed.
Regarding claim 17, Ambrosini teaches the visualization of the tip of the catheter and catheter for tracking purpose as projected in the 2D view (Fig.1 display of the tip tracking from the 2D fluoroscopic images with the overlay of the roadmaps and catheter on the 2D fluoroscopic images) therefore teaching that the visual representation of the resultant 2D roadmap is configured to not obscure any instrument used to treat the object of interest.
Regarding claim 19, Ambrosini teaches the tracking of the tip of the catheter and therefore of the road mapping as performed in real time (abstract) with image frames acquired sequentially in time (p.759 col.1 3rd ¶ frame of 2D intra-operative X-ray imaging/fluoroscopic images and col.2 ¶ B. Catheter Tip Tracking) therefore teaching the operations of v) to ix) are repeated for successive frames of a live image sequence acquired without a contrast agent as claimed.
Regarding claim 20, Ambrosini teaches the interventional device as being a guiding catheter (abstract) therefore teaching the interventional device is selected from the group consisting of a guiding catheter, a guide wire, or other intraluminal device or instrument as claimed.
Regarding claim 21, Ambrosini teaches displaying the overlay of the visual representation of the transformed roadmap of the object of interest on the non-contrast-enhanced x-ray image data (Fig.1 display of the tip tracking from the 2D fluoroscopic images with the overlay of the roadmaps and catheter on the 2D fluoroscopic images).
Regarding claim 22, Ambrosini does not teach the object of interest comprises part of the coronary tree, blood vessels and/or heart of the patient since Ambrosini teaches the object of interest is the liver vascular system. As discussed above, Piayda teaches the object of interest comprises part of the coronary tree, blood vessels and/or heart of the patient (Title. Abstract and Fig.1).
Regarding claim 23, Ambrosini teaches a method as set forth above wherein the method is applied with a system with different devices (p.761 col.2 3rd ¶) with computations being performed on a laptop using different algorithms as described along the publication (Fig.1 and p.762 col.1 2nd ¶) therefore, since Ambrosini is teaching the use of the laptop which teaches the use of a processor with the algorithms stored in the laptop memory as necessary to perform the method of claim 1 via instructions, teaches system for generating an image of an object of interest of a patient, the object of interest comprising part of the vasculature of the patient, the system comprising: at least one processor that, when executing program instructions stored in memory, is configured to perform the method of claim 1 as claimed.
Regarding claim 24 as dependent from claim 23, Ambrosini teaches as discussed above (p.761 col.2 3rd ¶), including “an angiographic C-arm systems (Xper Allura, Philips Healthcare, Best, the Netherlands). In total, we acquired 10 long fluoroscopic sequences (46-76 frames)” therefore reading on an imaging acquisition subsystem configured to acquire the non-contrast- enhanced x-ray image data, wherein the imaging acquisition subsystem uses an x-ray imaging modality as claimed..
Regarding claim 25 as dependent from claim 23, as discussed above, Ambrosini teaches the laptop or imaging device with a display presenting graph-images and imaging results (Figs.1, 8, 14) with also video presentations (p.765 col.2 2nd ¶) with the display as already discussed above for claim 1, therefore teaching a display subsystem configured to display the overlay of the visual representation of the transformed 3D roadmap of the object of interest on the non- contrast-enhanced x-ray image data as claimed.
Claim 11 are rejected under 35 U.S.C. 103 as being unpatentable over Ambrosini et al. (2017 IEEE Trans. Med. Imaging 36:757-768; Pub.Date 2017) in view of Piayda et al. (2018 Eur. J. Med. Res. 23: 7 pages; Pub.Date 07-31-2018) in view Dumont et al. (USPN 20150087972 A1; Pub.Date 03/06/2015; Fil.Date 07/28/2014) in view of Hansis et al. (2009 Proc. of SPIE 7258:article 72580B 11 pages; Pub.Date 2009) as applied to claim 1 and further in view of Zarkh et al. (USPN 20060074285 A1; Pub.Date 04/-6/2006; Fil.Date 09/24/2004).
Ambrosini, Piayda, Dumont and Hansis teach a method as set forth above. As discussed above for claim 1, Ambrosini teaches the generation of 3D/2D roadmaps (Fig.1) which are defining the vascular map in 3D with the tracking in time of the tip of the catheter. As discussed above, Dumont teaches, as discussed above, dynamic imaging with overlay of angiography images and fluoroscopy images for tracking a catheter (Title, abstract and Fig.4) the generation of an anatomic model from angiography images (Figs.3 and 5, and [0084] with model including the centerline and the edges of the blood vessel with information regarding the wall including lesions and their locations).
Ambrosini, Piayda, Dumont and Hansis do not specifically teach the 3D model of i) includes 3D surface contours representing luminal vessel surfaces and plaque; and the sequence of 3D roadmaps over time include information characterizing vessel contours and plaque as in claim 11.
Additionally, Zarkh teaches also within the same field of endeavor of constructing road maps for vascular system imaged by CT (Title, abstract and [0030]) with the generation of a 3D vascular arteries model including plaque ([0002]) with characterization of the plaque (abstract) therefore both Dumont and Zarkh combined teaching the 3D model of i) includes 3D surface contours representing luminal vessel surfaces and plaque; and the sequence of 3D roadmaps over time include information characterizing vessel contours and plaque as claimed.
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini as modified by Piayda, Dumont and Hansis such that system-method further comprises the 3D model of i) includes 3D vessel centerlines and 3D surface contours representing at least one of luminal vessel surfaces, plaque, and 3D masks, since one of ordinary skill in the art would recognize that using an anatomical model including the centerline and the surface of the blood vessel with information regarding vascular lesions and their locations was known in the art as taught by Dumont and since lesions such as plaque was also known in the art to be models for generating roadmaps including their classification as taught by Zarkh. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini and Dumont teach the imaging with angiography and fluoroscopy of the vascular system with the presence of a catheter to overlay the fluoroscopy image of the region of interest with that of the angiography images. The motivation would have been to ideally provide a more complete description of the size of the vessel tree and the presence of plaque as lesions with their characteristics to improve the prediction of the anatomy position and characteristics during the cardiac phase for improving the tracking and the advancement of a guide or catheter during medical procedures, as suggested by Dumont (abstract) and Zarkh ([0007]).
Claim 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ambrosini et al. (2017 IEEE Trans. Med. Imaging 36:757-768; Pub.Date 2017) in view of Piayda et al. (2018 Eur. J. Med. Res. 23: 7 pages; Pub.Date 07-31-2018) in view Dumont et al. (USPN 20150087972 A1; Pub.Date 03/06/2015; Fil.Date 07/28/2014) in view of Hansis et al. (2009 Proc. of SPIE 7258:article 72580B 11 pages; Pub.Date 2009) as applied to claim 1 and further in view of Zand et al. (For. Pat. CA 2604563 A1; Pub.Date 10/26/2006; Fil.Date 04/14/2006).
Ambrosini, Piayda, Dumont and Hansis teach a method as set forth above.
Ambrosini, Piayda, Dumont and Hansis do not specifically teach the non-contrast-enhanced x-ray image data of v) is derived by subtraction of a baseline image as in claim 18.
However, Zand teaches within the same field of endeavor of medical devices for analyzing data from systems detecting tissues (Title and abstract) the common practice for analyzing fluorescence response (Fig.9 and [0092]) with subtracting the values of baseline in absence of excitation light from the fluorescence values to determine the more precise fluorescence response from the tissue therefore teaching since Ambrosini is analyzing fluorography image data that the non-contrast-enhanced x-ray image data of v) is derived by subtraction of a baseline image as claimed.
Therefore it would have been obvious for a person of ordinary skill in the art before the time of filling the invention to have adapted the system-method of Ambrosini as modified by Piayda, Dumont and Hansis such that system-method further comprises the non-contrast-enhanced x-ray image data is derived by subtraction of a baseline image, since one of ordinary skill in the art would recognize that when analyzing fluorescence data, subtracting a baseline value without excitation light from the fluorescence value under excitation light was common practice and known in the art as taught by Zand. One of ordinary skill in the art would have expected that this modification could have been made with predictable results since Ambrosini and Zand teach analyzing the fluoroscopy data acquired from tissues. The motivation would have been to ideally provide more precise fluoroscopic images by eliminating background noises, as suggested by Zand (Fig. 9 and [0092]).
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Ambrosini et al. (2017 IEEE Trans. Med. Imaging 36:757-768; Pub.Date 2017) in view of Piayda et al. (2018 Eur. J. Med. Res. 23: 7 pages; Pub.Date 07-31-2018) in view Dumont et al. (USPN 20150087972 A1; Pub.Date 03/06/2015; Fil.Date 07/28/2014) in view of Hansis et al. (2009 Proc. of SPIE 7258:article 72580B 11 pages; Pub.Date 2009) as applied to claim 1 and further in view of Florent et al. (USPN 20100049038 A1; Pub.Date 02/25/2010; Fil.Date 02/25/2008).
Ambrosini teaches a system with at least a processor for implementing the method of claim 1 as set forth above by Ambrosini, Piayda, Dumont and Hansis for claim 1 and claim 23, wherein the method is applied with a system with different devices (p.761 col.2 3rd ¶) with computations being performed on a laptop ( Ambrosini Fig.1 and p.762 col.1 2nd ¶) which implicitly teach at least one processor that, when executing program instructions stored in memory, is configured to perform a method similar to the method in claim 1 since the use of a processor and a memory for storing the software is conventional and well-known in the art as evidenced by Florent teaching performing cardiac roadmapping and imaging a catheter within the roadmap with angiography images and fluoroscopy images (Title and abstract and Fig.2) with at least one processor ([0072] processor 51) with the software stored on a computer readable medium ([0032]) in order to provide instruction to the processor to perform the method of processing angiography image data for roadmapping and fluoroscopy for tracking the catheter with matching the roadmaps (Figs. 2 and 3). Therefore Ambrosini teaches implicitly with the computer readable medium storing the software with instructions when executed will render the processor to perform the method as claimed in claim 1 therefore non-transitory program storage device tangibly embodying a program of instructions that are executable on a machine to perform the operations of claim 1 for generating an image of an object of interest of a patient, wherein the object of interest comprises part of the vasculature of the patient.
Conclusion
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/PATRICK M MEHL/Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798