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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 2-10, 19, and 22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 27-41 of U.S. Patent No. 11857808. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims represent an obvious broadening of the patented claims. See table below.
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Claims 11-18 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 27-41 of U.S. Patent No. 11857808 in view of Adler et al.( WO 2008086430) hereinafter Adler et al.
Regarding claims 11-13, U.S. Patent No. 11857808 teaches the claimed invention as set forth above but does not teach wherein the computing device is configured to gate delivery of the external beam ablation therapy based on electrocardiogram (ECG) signals and surface ECG electrodes configured to generate the ECG signals.
Adler et al. teaches wherein the computing device is configured to gate delivery of the external beam ablation therapy based on electrocardiogram (ECG) signals and surface ECG electrodes configured to generate the ECG signals. [0027]The motion model may be correlated to a heart signal sensor such as an electrocardiogram (ECG) or (EKG). The motion model may be derived by acquiring 3-D volumes while measuring the heart cycle signals, and the heart cycle signals may also be monitored during treatment so as to predict the position of the target tissue. [0072] The treatment plan is transmitted into the treatment system, and the patient is positioned on the treatment table. Respiratory cycle indicators such as sensors or LEDs can be placed on the chest wall of the patient to provide information (optionally via surface imaging) to the treatment system regarding chest wall motion. The treatment system processor module may predict and/or verify the motion of the target and/or surrogate structures by identifying the respiratory cycle using an intra-treatment model as described above. The patient may also have cutaneous electrocardiogram electrodes placed such that the treating physician and treatment processor module can monitor the cardiac rhythm that the patient is undergoing during treatment.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in U.S. Patent No. 11857808 wherein the computing device is configured to gate delivery of the external beam ablation therapy based on electrocardiogram (ECG) signals and surface ECG electrodes configured to generate the ECG signals as taught by Adler et al. to better track target motion.
U.S. Patent No. 11857808 as modified by Adler et al. does not teach wherein the computing device is configured to gate the delivery based on R-waves of the ECG signals
It is noted that there are a limited number of choices available to a person of ordinary skill in the art for cardiac gated imaging using the cardiac cycle and specifically the R-wave portion of the cycle. Therefore, it would have been obvious to one of ordinary skill in the art to try in U.S. Patent No. 11857808 as modified by Adler et al. gating the delivery based on R-waves of the ECG signals, with a reasonable expectation of successfully measuring the cardiac motion and delivering the treatment. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Regarding claims 14-16, U.S. Patent No. 11857808 teaches the claimed invention as set forth above but does not teach wherein the cardiac anatomical landmarks comprise a left atrium and/or left ventricle of the subject, wherein the computing device is configured to segment the left ventricle, the left atrium, and/or a left atrial appendage, and wherein the computing device is configured to identify endocardial locations near an anterior papillary muscle (APM), posterior papillary muscle (PPM), left ventricular apex (LVA), mitral valve on left side (LVMV), left aortic valve (LVAV), endocardial locations near a mitral valve (LAMV), a left atrial appendage (LAA), a left superior pulmonary vein (LSPV), a right superior pulmonary vein (RSPV), and/or an inferior.
Adler et al. teaches wherein the cardiac anatomical landmarks comprise a left atrium and/or left ventricle of the subject, wherein the computing device is configured to segment the left ventricle, the left atrium, and/or a left atrial appendage, and he computing device is configured to identify endocardial locations near an anterior papillary muscle (APM), posterior papillary muscle (PPM), left ventricular apex (LVA), mitral valve on left side (LVMV), left aortic valve (LVAV), endocardial locations near a mitral valve (LAMV), a left atrial appendage (LAA), a left superior pulmonary vein (LSPV), a right superior pulmonary vein (RSPV), and/or an inferior. Note paragraph [0042] and Claim 17, Any natural landmarks of the heart such as points, lines, surfaces and volumes in, on, and/or around the heart. The silhouette of the heart is one such example. Other examples include parts of the esophagus, the trachea, the bronchial tree, the lungs, the ribs, the diaphragm, the clavicles, the right atrium, the left atrium, the right ventricle, the left ventricle, inferior vena cava, superior vena cava, ascending aorta, descending aorta, pulmonary veins, pulmonary arteries, the heart/lung border and the blood pool. Any artificial landmarks such as one or more fiducials inserted in to the esophagus, the trachea, the bronchial tree, or a catheter placed inside the heart.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in U.S. Patent No. 11857808 wherein the cardiac anatomical landmarks comprise a left atrium and/or left ventricle of the subject, wherein the computing device is configured to segment the left ventricle, the left atrium, and/or a left atrial appendage, and wherein the computing device is configured to identify endocardial locations near an anterior papillary muscle (APM), posterior papillary muscle (PPM), left ventricular apex (LVA), mitral valve on left side (LVMV), left aortic valve (LVAV), endocardial locations near a mitral valve (LAMV), a left atrial appendage (LAA), a left superior pulmonary vein (LSPV), a right superior pulmonary vein (RSPV), and/or an inferior as taught by Adler et al. to better identify the target tissue.
Regarding claims 17-18 , U.S. Patent No. 11857808 teaches the claimed invention as set forth above but does not teach wherein the computing device is configured to determine motion trajectories across the cardiac and/or respiratory cycles for the cardiac anatomical landmarks and wherein the computing device is configured to compute a spatial displacement in each x, y, and z directions for the cardiac anatomical landmarks.
Adler et al. teaches wherein the computing device is configured to determine motion trajectories across the cardiac and/or respiratory cycles for the cardiac anatomical landmarks. [0062] Intra-operative motion model 107 will often employ the results of the pre-treatment motion model 103, together with the movement identified in the X-rays 1 10, in ultrasound imaging 1 15, and the like (often through matching of the surrogates) so as to describe the motion of the target and the sensitive structures with respect to the physiologic wave forms 101. The pre-treatment motion model 103 may be updated based on the information obtained as the system prepares for or implements the series of radiation beams using the intra-treatment motion model 107. Motion is predicted 108 using the intra-operative motion model 107 per the physiologic wave form signals 101 , and the intra-operative motion model is validated 109 (typically by checking the predicted position and/or motion of the target or surrogate structures against the actual position and/or motion determined by the registration 106 of the most recent X-ray images 1 10, ultrasound images 1 15, and/or the like. If the model does not sufficiently accurately predict the motion and is thus not sufficiently valid, treatment may be interrupted, a new model may be built from scratch and/or the prior intra-operative model may be revised. If the model is within the desired threshold of accuracy, the treatment proceeds. [0070] A CT scan is performed using both cardiac and respiratory-gating so as to obtain a 3-D motion model corresponding to cardiac cycle movement, respiration cycle movement, and/or both. The CT data is fed into the treatment planning module, allowing a library of images to be viewed and the target volume to be identified in three dimensions. [0071] The treatment plan may be reviewed for (among other considerations) the dose, the targeted anatomy, avoidance of critical or sensitive structures near the target, or through which radiation beams should not pass, modification of treatment to the target based on consideration of the motion at the target (based on respiratory and/or cardiac cycle contributions) and/or the like.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in U.S. Patent No. 11857808 teach wherein the computing device is configured to determine motion trajectories across the cardiac and/or respiratory cycles for the cardiac anatomical landmarks and wherein the computing device is configured to compute a spatial displacement in each x, y, and z directions for the cardiac anatomical landmarks as taught by Adler et al. to better target the moving target tissue.
Regarding claim 20 , U.S. Patent No. 11857808 teaches the claimed invention as set forth above but does not teach wherein the computing device is configured to execute a motion sensing algorithm to track specific cardiac structures with unique and differentiating movements with the cardiac cycle.
Adler et al. teaches wherein [0033] A computer program then receives the location and the shape of the target and the critical structures, the prescribed doses and the geometric configuration of the radiation delivery system and computes (a) the position and orientation of the beams to be fired and (b) a contour diagram showing dose received by all voxels in the CT volume. [0049] he various modules described herein may be implemented in a single processor board of a single general purpose computer, or may be run on several different processor boards of multiple proprietary computers, with the code, data, and signals being transmitted between the processor boards using a bus, a network (such as an Ethernet, intranet, or internet), via tangible recording media, using wireless telemetry, or the like. The code may be written as a monolithic software program, but will typically comprise a variety of separate subroutines and/or programs handling differing functions in any of a wide variety of software architectures, data processing arrangements, and the like.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to include in U.S. Patent No. 11857808 wherein the computing device is configured to execute a motion sensing algorithm to track specific cardiac structures with unique and differentiating movements with the cardiac cycle as taught by Adler et al. to better target the moving target tissue.
Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 27-41 of U.S. Patent No. 11857808 in view of Adler et al.( WO 2008086430) hereinafter Adler et al. and further in view of Belt et al.( US 10251629) hereinafter Belt et al.
U.S. Patent No. 11857808 as modified by Adler et al. teaches the claimed invention as set forth above but does not teach wherein the computing device is configured to execute a machine learning algorithm by inputting multiple cardiac cycles where an electrocardiogram is used as a reference and changes from one cardiac cycle through the other to reject a particular cardiac cycle or to correct for a labeled moving part to differentiate the labeled moving part from noise or artifact.
Belt et al. teaches in the same field of endeavor an imaging system for imaging a periodically moving object. An assigning unit (18) assigns ultrasound signals like A-lines to motion phases based on a provided phase signal, wherein an ultrasound images generation unit (19) generates several ultrasound images like gated M-mode images for the different motion phases based on the ultrasound signals assigned to the respective motion phase. A selecting unit (20) is used to select an ultrasound image from the generated ultrasound images, wherein a display unit (21) displays the selected ultrasound image. The selected ultrasound image corresponds therefore to a single motion phase only such that motion artifacts in the displayed ultrasound image are reduced. The imaging system is particularly useful for, for instance, monitoring cardiac ablation procedures. Paragraph (45) teaches the use of a machine learning technique to identify the cardiac tissue.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to use in the system of U.S. Patent No. 11857808 as modified by Adler et al. a machine learning technique as taught by Belt et al. to better identify the moving cardiac tissue during both respiratory and cardiac motion cycles.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 2-12,14-20, and 22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Adler et al.( WO 2008086430) hereinafter Adler et al.
Adler et al. teaches Radiosurgical treatment of tissues of the heart to mitigate arrhythmias such as atrial fibrillation or the like. Radiosurgical targeting of the relatively rapid movement of heart tissues may be enhanced by generating a moving model volume using a time-sequence of three dimensional acquired tissue volumes. A digitally reconstructed radiograph (DRR) may be generated from the model at a desired cardiac and/or respiration motion phase and compared to an X-ray or the like taken immediately before or during treatment. When a series of radiation beams will be directed to a heart tissue to alleviate an arrhythmia, the treatment system may alter the radiation beam series in response to the type of the arrhythmia. [0054] The target will generally have motion which includes two components: respiratory motion and cardiac motion. Similarly, the surrogate structure may have two motion components: respiratory motion and cardiac motion. [0055] Referring to the individual components shown in Fig. 6, the physiological wave forms may include ECG signals and respiratory signals (including those derived from images of movement of LEDs or other surface fiducials). CT volumes 102 encompass a variety of different types of CT volumes, and may employ multiple types of CT volumes for a single patient. The CT volumes may be acquired at specific points along the cardiac cycle, respiration cycle, or the like.
[0056] Once all the desired CT volumes have been acquired, 2-D and/or 3-D image processing 1 14 of the acquired images or volumes may be employed. The image processing may include filtering, morphological filtering, mapping, gamma correction, connectivity mapping, distance mapping, order detection, ridge detection, curvature mapping, adaptive filtering, multiscale processing, multi-spectral processing, image enhancement, band pass filtering, unsharp mask filtering, top hat filtering, and/or the like. Many of the acquired volumes may include a series of discrete images at different locations, so that a wide variety of 2-D image filtering and image processing techniques may be employed on the acquired volumes.
Regarding claims 2, 19, 20 and 22, Adler et al. teaches a system for non-invasively targeting cardiac structures in external beam ablation therapy to treat cardiac arrhythmias of a subject, the system comprising a computing device configured to map targeted cardiac tissue to compensate for movements of the targeted cardiac tissue during delivery of the external beam ablation therapy by assigning cardiac anatomical landmarks, the movements resulting from respiratory and cardiac motion of the subject.
Note Fig. 6, paragraphs [0027], [0054] – [0056], [0027] sets forth, the invention is particularly well suited for tracking of moving tissues such as tissues of the heart and tissue structures adjacent the heart that move with the cardiac or heartbeat cycles. The invention may take advantage of structures and methods which have been developed for treating tumors, particularly those which are associated with treatments of tissue structures that move with the respiration cycle. The cardiac cycle is typically considerably faster than the respiration cycle. The invention may make use of a motion model of a tissue volume encompassing the target tissue. The motion model may be correlated to a heart signal sensor such as an electrocardiogram (ECG) or (EKG). The motion model may be derived by acquiring 3-D volumes while measuring the heart cycle signals, and the heart cycle signals may also be monitored during treatment so as to predict the position of the target tissue. Multiple models may be employed, including separation of the motion model into a cardiac cycle model and a respiration cycle model. In other embodiments, the motion model may be correlated to both cardiac and respiratory cycles. In some embodiments, a pre-treatment model may be used for planning and registration. An intra-operative model may be employed to track motion of the heart during treatment, often in response to external fiducials and/or a heart cycle signal. Paragraph [0042] – [0047], The correlation focuses on registering structures of the heart visible in the DRRs and X-rays such as: Any natural landmarks of the heart such as points, lines, surfaces and volumes in, on, and/or around the heart. The silhouette of the heart is one such example. [0047] Based on the images, a plan 44 will be prepared for treatment of the target tissue, with the plan typically comprising a series of radiation beam trajectories which intersect within the target tissue. The radiation dose within the target tissue should be at least sufficient to provide the desired effect (often comprising ablation of tissue, inhibition of contractile pathways within the heart, inhibition of arrhythmogenesis, and/or the like).
Regarding claims 3 and 8, Adler et al. teaches wherein the computing device is configured to use gating to compensate for the movements. [0063] [0063] Referring now to Figs. 5 A and 6, CT volumes may be acquired (reference numerals 58 and 102) using a variety of different approaches. A cardiac gated CT volume may be acquired at a particular phase of the EKG cycle. Two variations of cardiac gated CT may include a held- breath version and a free-breathing version, [0065] Yet another type of volume which may be acquired is the respiratory-gated CT volume. Such CT volumes may be acquired at a particular phase of the respiration cycle. and [0070] A CT scan is performed using both cardiac and respiratory-gating so as to obtain a 3-D motion model corresponding to cardiac cycle movement, respiration cycle movement, and/or both. The CT data is fed into the treatment planning module, allowing a library of images to be viewed and the target volume to be identified in three dimensions.
Regarding claim 4, Adler et al. teaches wherein the computing device is configured to use contouring to follow critical targets in the targeted cardiac tissue through subsequent treatment cycles to avoid non-targeted tissue surrounding the critical targets. [0033] Typically, the target and its surrounding tissue are first imaged using CT, resulting in a volume of data. The target volume is then delineated in this CT volume and a desired dose to the target is prescribed. Delicate or other tissue structures of concern in the vicinity of the target are also delineated and may be assigned a maximum desired dose that can be deposited at these structures. A computer program then receives the location and the shape of the target and the critical structures, the prescribed doses and the geometric configuration of the radiation delivery system and computes (a) the position and orientation of the beams to be fired and (b) a contour diagram showing dose received by all voxels in the CT volume. The radiation oncologist then reviews this data to see if the target is receiving the right dose and if structures in the vicinity receive too much dose. He or she may modify the boundaries of the target and the critical structures, along with dose received by them, to reach an acceptable treatment plan.
Regarding claim 5, Adler et al. teaches wherein the computing device is configured to map the targeted cardiac tissue using pre-procedural imaging to obtain sequential images throughout a treatment cycle, and tagging structures based on characteristics of the structures during movement in the treatment cycle. [0060] In the planning stage, the system user and/or processor defines targets, surrogates, and critical or sensitive structures using the acquired CT volumes, the DRRs, the electrograms, and/or other available input. A pre-treatment motion model 103 may be generated using the acquired CT volumes, the images of the DRRs, or other two or three dimensional information about the target and surrounding anatomy. The motion model 103 also employs the physiological wave forms 101, and most often the cardiac and/or respiratory phase information associated with each of the acquired 3-D volumes. A parametric motion model may be fitted to the data, or the raw data itself may be used so as to produce a lookup table (where the input is one or more physiological wave forms, and the output is the motion or a quantity derived from the motion such as position, velocity, acceleration, or the like for a given anatomical location in the 3-D space of the model volume or within a 2-D planar space corresponding to the DRR). The pre-treatment motion model 103 may be applied to the CT volume data to generate a new DRR. The DRRs may, for example, have a desired associated cardiac phase, respiration phase, or the like.
Regarding claim 6, Adler et al. teaches wherein the computing device is configured to map the targeted cardiac tissue by analyzing a treatment cycle using a template cycle. [0062] Intra-operative motion model 107 will often employ the results of the pre-treatment motion model 103, together with the movement identified in the X-rays 1 10, in ultrasound imaging 1 15, and the like (often through matching of the surrogates) so as to describe the motion of the target and the sensitive structures with respect to the physiologic wave forms 101 (template cycle). The pre-treatment motion model 103 may be updated based on the information obtained as the system prepares for or implements the series of radiation beams using the intra-treatment motion model 107. Motion is predicted 108 using the intra-operative motion model 107 per the physiologic wave form signals 101 , and the intra-operative motion model is validated 109 (typically by checking the predicted position and/or motion of the target or surrogate structures against the actual position and/or motion determined by the registration 106 of the most recent X-ray images 1 10, ultrasound images 1 15, and/or the like. If the model does not sufficiently accurately predict the motion and is thus not sufficiently valid, treatment may be interrupted, a new model may be built from scratch and/or the prior intra-operative model may be revised. If the model is within the desired threshold of accuracy, the treatment proceeds.
Regarding claim 7, Adler et al. teaches wherein the computing device is configured to map the targeted cardiac tissue using feedback information such that delivery of the external beam ablation therapy is configured to be varied based on the feedback information. [0031] Radiosurgery system 10 has a single source of radiation, which moves about relative to a patient. Radiosurgery system 10 includes a lightweight linear accelerator 12 mounted to a highly maneuverable robotic arm 14. An image guidance system 16 uses image registration techniques to determine the treatment site coordinates with respect to linear accelerator 12, and transmits the target coordinates to robot arm 14 which then directs a radiation beam to the treatment site. When the target moves, system 10 detects the change and corrects the beam. [0062] Intra-operative motion model 107 will often employ the results of the pre-treatment motion model 103, together with the movement identified in the X-rays 1 10, in ultrasound imaging 1 15, and the like (often through matching of the surrogates) so as to describe the motion of the target and the sensitive structures with respect to the physiologic wave forms 101. The pre-treatment motion model 103 may be updated based on the information obtained as the system prepares for or implements the series of radiation beams using the intra-treatment motion model 107. Motion is predicted 108 using the intra-operative motion model 107 per the physiologic wave form signals 101 , and the intra-operative motion model is validated 109 (typically by checking the predicted position and/or motion of the target or surrogate structures against the actual position and/or motion determined by the registration 106 of the most recent X-ray images 1 10, ultrasound images 1 15, and/or the like. If the model does not sufficiently accurately predict the motion and is thus not sufficiently valid, treatment may be interrupted, a new model may be built from scratch and/or the prior intra-operative model may be revised. If the model is within the desired threshold of accuracy, the treatment proceeds.
Regarding claims 9 and 10, Adler et al. teaches wherein the computing device is configured to map the targeted cardiac tissue in real time during delivery of the external beam ablation therapy. [0037] A preferred robot manipulator may be capable of positioning and orienting the Linac so that it follows the target due to breathing in real time. [0062] Intra-operative motion model 107 will often employ the results of the pre-treatment motion model 103, together with the movement identified in the X-rays 1 10, in ultrasound imaging 1 15, and the like (often through matching of the surrogates) so as to describe the motion of the target and the sensitive structures with respect to the physiologic wave forms 101. The pre-treatment motion model 103 may be updated based on the information obtained as the system prepares for or implements the series of radiation beams using the intra-treatment motion model 107. Motion is predicted 108 using the intra-operative motion model 107 per the physiologic wave form signals 101 , and the intra-operative motion model is validated 109 (typically by checking the predicted position and/or motion of the target or surrogate structures against the actual position and/or motion determined by the registration 106 of the most recent X-ray images 1 10, ultrasound images 1 15, and/or the like. If the model does not sufficiently accurately predict the motion and is thus not sufficiently valid, treatment may be interrupted, a new model may be built from scratch and/or the prior intra-operative model may be revised. If the model is within the desired threshold of accuracy, the treatment proceeds.
Regarding claims 11-12, Adler et al. teaches wherein the computing device is configured to gate delivery of the external beam ablation therapy based on electrocardiogram (ECG) signals and surface ECG electrodes configured to generate the ECG signals. [0027]The motion model may be correlated to a heart signal sensor such as an electrocardiogram (ECG) or (EKG). The motion model may be derived by acquiring 3-D volumes while measuring the heart cycle signals, and the heart cycle signals may also be monitored during treatment so as to predict the position of the target tissue. [0072] The treatment plan is transmitted into the treatment system, and the patient is positioned on the treatment table. Respiratory cycle indicators such as sensors or LEDs can be placed on the chest wall of the patient to provide information (optionally via surface imaging) to the treatment system regarding chest wall motion. The treatment system processor module may predict and/or verify the motion of the target and/or surrogate structures by identifying the respiratory cycle using an intra-treatment model as described above. The patient may also have cutaneous electrocardiogram electrodes placed such that the treating physician and treatment processor module can monitor the cardiac rhythm that the patient is undergoing during treatment.
Regarding claims 14-16, Adler et al. teaches wherein the cardiac anatomical landmarks comprise a left atrium and/or left ventricle of the subject, wherein the computing device is configured to segment the left ventricle, the left atrium, and/or a left atrial appendage, and he computing device is configured to identify endocardial locations near an anterior papillary muscle (APM), posterior papillary muscle (PPM), left ventricular apex (LVA), mitral valve on left side (LVMV), left aortic valve (LVAV), endocardial locations near a mitral valve (LAMV), a left atrial appendage (LAA), a left superior pulmonary vein (LSPV), a right superior pulmonary vein (RSPV), and/or an inferior. Note paragraph [0042] and Claim 17, Any natural landmarks of the heart such as points, lines, surfaces and volumes in, on, and/or around the heart. The silhouette of the heart is one such example. Other examples include parts of the esophagus, the trachea, the bronchial tree, the lungs, the ribs, the diaphragm, the clavicles, the right atrium, the left atrium, the right ventricle, the left ventricle, inferior vena cava, superior vena cava, ascending aorta, descending aorta, pulmonary veins, pulmonary arteries, the heart/lung border and the blood pool. Any artificial landmarks such as one or more fiducials inserted in to the esophagus, the trachea, the bronchial tree, or a catheter placed inside the heart.
Regarding claims 17-18 , Adler et al. teaches wherein the computing device is configured to determine motion trajectories across the cardiac and/or respiratory cycles for the cardiac anatomical landmarks. [0062] Intra-operative motion model 107 will often employ the results of the pre-treatment motion model 103, together with the movement identified in the X-rays 1 10, in ultrasound imaging 1 15, and the like (often through matching of the surrogates) so as to describe the motion of the target and the sensitive structures with respect to the physiologic wave forms 101. The pre-treatment motion model 103 may be updated based on the information obtained as the system prepares for or implements the series of radiation beams using the intra-treatment motion model 107. Motion is predicted 108 using the intra-operative motion model 107 per the physiologic wave form signals 101 , and the intra-operative motion model is validated 109 (typically by checking the predicted position and/or motion of the target or surrogate structures against the actual position and/or motion determined by the registration 106 of the most recent X-ray images 1 10, ultrasound images 1 15, and/or the like. If the model does not sufficiently accurately predict the motion and is thus not sufficiently valid, treatment may be interrupted, a new model may be built from scratch and/or the prior intra-operative model may be revised. If the model is within the desired threshold of accuracy, the treatment proceeds. [0070] A CT scan is performed using both cardiac and respiratory-gating so as to obtain a 3-D motion model corresponding to cardiac cycle movement, respiration cycle movement, and/or both. The CT data is fed into the treatment planning module, allowing a library of images to be viewed and the target volume to be identified in three dimensions. [0071] The treatment plan may be reviewed for (among other considerations) the dose, the targeted anatomy, avoidance of critical or sensitive structures near the target, or through which radiation beams should not pass, modification of treatment to the target based on consideration of the motion at the target (based on respiratory and/or cardiac cycle contributions) and/or the like.
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.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adler et al.( WO 2008086430) hereinafter Adler et al.
Adler et al. teaches Radiosurgical treatment of tissues of the heart to mitigate arrhythmias such as atrial fibrillation or the like. Radiosurgical targeting of the relatively rapid movement of heart tissues may be enhanced by generating a moving model volume using a time-sequence of three dimensional acquired tissue volumes. A digitally reconstructed radiograph (DRR) may be generated from the model at a desired cardiac and/or respiration motion phase and compared to an X-ray or the like taken immediately before or during treatment. When a series of radiation beams will be directed to a heart tissue to alleviate an arrhythmia, the treatment system may alter the radiation beam series in response to the type of the arrhythmia. Adler et al. further teaches wherein the computing device is configured to gate delivery of the external beam ablation therapy based on electrocardiogram (ECG) signals and surface ECG electrodes configured to generate the ECG signals. Note paragraphs [0013] discusses basing the gating off of the T-wave, [0027], [0043] and [0072].
Adler et al. does not specifically teach wherein the computing device is configured to gate the delivery based on R-waves of the ECG signals.
It is noted that there are a limited number of choices available to a person of ordinary skill in the art for cardiac gated imaging using the cardiac cycle and specifically the R-wave portion of the cycle. Therefore, it would have been obvious to one of ordinary skill in the art to try in Adler et al. gating the delivery based on R-waves of the ECG signals, with a reasonable expectation of successfully measuring the cardiac motion and delivering the treatment. See KSR Int’l Co. v. Teleflex Inc., 127 S.Ct. 1727, 1742, 82 USPQ2d 1385, 1396 (2007).
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Adler et al.( WO 2008086430) hereinafter Adler et al. in view of Belt et al.( US 10251629) hereinafter Belt et al.
Adler et al. teaches Radiosurgical treatment of tissues of the heart to mitigate arrhythmias such as atrial fibrillation or the like. Radiosurgical targeting of the relatively rapid movement of heart tissues may be enhanced by generating a moving model volume using a time-sequence of three dimensional acquired tissue volumes. A digitally reconstructed radiograph (DRR) may be generated from the model at a desired cardiac and/or respiration motion phase and compared to an X-ray or the like taken immediately before or during treatment. When a series of radiation beams will be directed to a heart tissue to alleviate an arrhythmia, the treatment system may alter the radiation beam series in response to the type of the arrhythmia. Adler et al. further teaches wherein the computing device is configured to gate delivery of the external beam ablation therapy based on electrocardiogram (ECG) signals and surface ECG electrodes configured to generate the ECG signals. Note paragraphs [0013] discusses basing the gating off of the T-wave, [0027], [0043] and [0072].
Adler et al. does teach evaluating cardiac cycles and minimizing motion artifacts from both respiratory and cardiac movement cycles, [0013] Model volume will often comprise a movement model, sometimes referred to as a four dimensional (4-D) model that encompasses the target tissue. Along with the standard three dimensional tissue coordinates, the model may include movement of the tissue with time during a respiration cycle, a cardiac cycle, and/or the like. The movement model may be separated into components, such as a cardiac cycle movement model and a respiration cycle movement model. For example, a time sequence of volumes may be acquired while the patient is holding their breath so as to inhibit respiration-induced movement artifacts. The cardiac cycle movement artifacts may be minimized by selectively obtaining the volumes throughout a respiration cycle, but at a common phase of the cardiac cycle (such as the quiescent T-wave portion of the ECG cycle). [0063] By associating each CT volume with the associated phase of the respiration cycle, the time series CT volumes can be used to model respiratory-induced motion of tissue while minimizing the cardiac motion artifacts.
Adler et al. does not specifically teach computing device is configured to execute a machine learning algorithm for analyzing the cardiac cycles.
Belt et al. teaches in the same field of endeavor an imaging system for imaging a periodically moving object. An assigning unit (18) assigns ultrasound signals like A-lines to motion phases based on a provided phase signal, wherein an ultrasound images generation unit (19) generates several ultrasound images like gated M-mode images for the different motion phases based on the ultrasound signals assigned to the respective motion phase. A selecting unit (20) is used to select an ultrasound image from the generated ultrasound images, wherein a display unit (21) displays the selected ultrasound image. The selected ultrasound image corresponds therefore to a single motion phase only such that motion artifacts in the displayed ultrasound image are reduced. The imaging system is particularly useful for, for instance, monitoring cardiac ablation procedures. Paragraph (45) teaches the use of a machine learning technique to identify the cardiac tissue.
Therefore, It would have been obvious to one of ordinary skill in the art at the time of the invention to use in the system of Adler et al. a machine learning technique as taught by Belt et al. to better identify the moving cardiac tissue during both respiratory and cardiac motion cycles.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cline et al.( US 5433199) teaches A 4D image data set of a living subject is obtained by non-invasive imaging means representing three dimensions and time in relation to the subject's cardiac cycle. In order to determine various vascular parameters, it is useful to segment the image data set into internal structures defined as having the same tissue types contiguous locations. To accomplish this, a gradient data set is constructed from the image data set indicating the magnitude of spatial changes in the image data set. A plurality of locations are selected in `training` along with corresponding data values in the image and gradient data sets. These data values are plotted against each other to construct a scatter plot then processed to determine a bivariate statistical probability distribution. The remaining data values are then assigned a tissue type based upon their plot on the bivariate statistical probability distribution. Contiguous locations having the same tissue type assignment are identified as a solid structure. These solid structures may be the internal volume of cardiac chambers. Since these volumes may be accurately measured over the cardiac cycle, vascular functionality, such as ejection fraction, and cardiac output may be measured.
Schneider et al.( US 9412044) teaches A method (10) for respiratory motion compensation by applying principle component analysis (PCA) on cardiac imaging samples obtained using 2D/3D registration of a pre-operative 3D segmentation of the coronary arteries. (8) The present invention obviates the aforementioned problems by providing a method of motion compensation for cardiac imaging of a patient, comprising the steps of: a. obtaining a pre-operative volumetric image dataset of a cardiac target site for the patient; b. obtaining an intra-operative image of the target site; c. performing a registration of the pre-operative volumetric dataset with the intra-operative image; d. incorporating a model of the respiratory motion made by the patient's heart into the registration to compensate for the respiratory motion; and e. adjusting the registrations for the intra-operative image and subsequent intra-operative images using the motion model.
BERT et al.( DE 102007045879) teaches method for determining an effective radiation dose distribution when irradiating a movable target volume (102) having a plurality of volume elements, comprising the following steps: acquiring first position data of the plurality of volume elements in a first movement phase of the movable target volume (102); Acquiring further position data of the plurality of volume elements in at least one further movement phase of the movable target volume (102); Determining transformation parameters by transforming the first position data into the further position data; during the irradiation of a raster point, recording the current movement phase of the movable target volume (102); Assigning raster points (222), during the irradiation of which the movable target volume (102) was in the first movement phase, to a first sub-irradiation plan; Assigning further raster points (222a), during the irradiation of which the movable target volume (102) was in the at least one further movement phase, to at least one further sub-irradiation plan; and determining the effective dose for at least one of the plurality of volume elements, in each case from contributions of the raster points (222, 222a) of the first sub-irradiation plan and the at least one further sub-irradiation plan, using the transformation parameters.
Sauer et al.( US 7853308) teaches system and method for positioning a patient for radiotherapy is provided. The method comprises: acquiring a first x-ray image sequence of a target inside the patient at a first angle; acquiring first respiratory signals of the patient while acquiring the first x-ray image sequence; acquiring a second x-ray image sequence of the target at a second angle; acquiring second respiratory signals of the patient while acquiring the second x-ray image sequence; synchronizing the first and second x-ray image sequences with the first and second respiratory signals to form synchronized first and second x-ray image sequences; identifying the target in the synchronized first and second x-ray image sequences; and determining three-dimensional (3D) positions of the target through time in the synchronized first and second x-ray image sequences.
BRINKS et al.( WO 2008127368) teaches motion tracking apparatus has treatment scanner (108) that acquires projection data indicative of an object that includes a treatment target and the target is subject to motion during a treatment session. The motion modeler (116) uses the projection data acquired during the treatment session to model the motion of the target. The treatment device (114) treats the target during the treatment session and varies the spatial location of the applied treatment as a function of the motion model.
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/BRIAN L CASLER/Primary Examiner, Art Unit 3791
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