WO2018081492A1 - Procédé d'imagerie radiographique dynamique utilisant une décomposition en valeurs singulières - Google Patents

Procédé d'imagerie radiographique dynamique utilisant une décomposition en valeurs singulières Download PDF

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WO2018081492A1
WO2018081492A1 PCT/US2017/058654 US2017058654W WO2018081492A1 WO 2018081492 A1 WO2018081492 A1 WO 2018081492A1 US 2017058654 W US2017058654 W US 2017058654W WO 2018081492 A1 WO2018081492 A1 WO 2018081492A1
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matrix
image
images
rearranged
focal
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Hitinder S. Gurm
Jeffrey A. Fessler
Rajesh R. NADAKUDITI
Brian Moore
Saiprasad Ravishankar
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The Regents Of The University Of Michigan
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques
    • A61B6/486—Diagnostic techniques involving generating temporal series of image data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/48—Diagnostic techniques
    • A61B6/481—Diagnostic techniques involving the use of contrast agents

Definitions

  • the present disclosure relates to a method of dynamic radiograph imaging that uses singular value decomposition.
  • Radiographic imaging is ubiquitous in medicine and is used in many fashions including X-rays, computed tomography (CT) scans, and angiography. Images captured during radiography are generally enhanced to effectively illustrate a target region of interest.
  • CT computed tomography
  • angiography is a dynamic imaging technique used to assess the health of the body's vasculature. The technique can involve the use of a contrast dye/agent to help with visualizing the blood vessels and taking X-ray images of the area needed to be observed.
  • DSA digital subtraction angiography
  • an initial image i.e., a mask image
  • This initial image is then used to help subtract the non-vascular tissue from subsequent images taken after contrast dye has been injected.
  • the final image sequence i.e., final video or final dynamic images
  • the DSA technique has helped improve the diagnostic accuracy of angiography and is a key element of contemporary diagnostic and interventional radiology.
  • a method for radiographic imaging includes: radiating electromagnetic radiation (e.g., X-rays) towards an object of interest in a subject, where intensity of the electromagnetic radiation is selectable; and capturing a plurality of images of the subject over a predetermined period of time, wherein each of the plurality of images captures the object of interest and the electromagnetic radiation is maintained at a fixed intensity as the plurality of images are being captured.
  • electromagnetic radiation e.g., X-rays
  • capturing a plurality of images of the subject over a predetermined period of time wherein each of the plurality of images captures the object of interest and the electromagnetic radiation is maintained at a fixed intensity as the plurality of images are being captured.
  • a reference matrix is derived using singular value decomposition, wherein the reference matrix is indicative of background in the plurality of images.
  • the reference matrix is removed from the plurality of images to obtain a focal image sequence and the focal image sequence is displayed on a display device.
  • a contrast agent may be injected into the subject concurrently with
  • the reference matrix is derived by rearranging image data from each of the plurality of images to a single rearranged matrix having dimensions MNxT, where MxN is image data for a given image in the plurality of images, and T is the number of images in the plurality of images; performing the singular value decomposition on the rearranged matrix; and selecting the first component of the singular value decomposition as the reference matrix X re f.
  • the reference matrix may be removed from the plurality of images in one of two ways. First, the reference matrix X re f is subtracted from the rearranged matrix to yield a foreground matrix; and the foreground matrix is rearranged to obtain the focal image sequence. Alternatively, the reference matrix is rearranged to yield a reference image; and the reference image is then subtracted from each image in the plurality of images to obtain the focal image sequence.
  • image data from each image in the plurality of images is partitioned into segments. For example, one image segment may be extracted from each image and then used to construct the rearranged matrix. In another example, multiple image segments are extracted from each image and used to construct the rearranged matrix.
  • sparsifying transforms can be applied to a focal matrix, where the focal matrix is obtained by subtracting the reference matrix from the rearranged matrix.
  • the focal matrix is converted to a sparse matrix by applying a sparsifying operation.
  • the sparse matrix is then subtracted from the rearranged matric to yield a refined rearranged matrix.
  • the focal image sequence is constructed from the sparse matrix.
  • an iterative approach is employed to improve the result of the refined rearranged matrix.
  • FIG. 1 illustrates an example of a radiographic imaging system
  • FIG. 2 is a block diagram of a controller of the imaging system
  • FIG. 3 is a flowchart of an example operation routine of the imaging system
  • FIG. 4 is a flowchart of an example routine of a dynamic image enhancer
  • FIGS. 5A, 5B, and 5C illustrate a matrix manipulation performed by the image enhancer
  • FIG. 6A is an enhanced picture of an internal structure of a patient using conventional digital subtraction angiography
  • FIG. 6B is an enhanced picture of an internal structure of a patient using the image enhancer of the present disclosure
  • FIG. 7 is a flowchart of an alternative embodiment of the image enhancer
  • FIGS 8A-8C illustrate alternative matrix manipulations performed by the image enhancer.
  • X-ray images i.e., X-ray video or fluoroscopy
  • the system further enhances the X-ray images by separating the background portion of the images (e.g., organs and bones) from a target portion that is of interest (e.g., a vascular structure).
  • the system of the present disclosure allows separation of the blood vessels (and other regions where contrast agent is present) from the background tissue.
  • the system does not require taking an initial mask image before the contrast agent is injected, as required with traditional DSA techniques, and allows for less X-ray radiation to be used for imaging.
  • X-ray radiation can be used for imaging.
  • radiation exposure to patients and healthcare providers can be reduced to meet reasonably achievable (ALARA) radiation goals, and the amount of contrast agent may be reduced while still obtaining high quality images.
  • FIG. 1 is an example embodiment of a radiographic imaging system 100 for capturing a sequence of images of the internal structures of a subject 102, such as a patient.
  • the system 100 includes an X-ray generator 104, an image detector 106, and a controller 108.
  • the X-ray generator 104 may be positioned along a first side of the subject 102, and the image detector 106 may be positioned along a second side of the subject 102 opposite to the first side.
  • the X-ray generator 104 is operable to emit X-rays toward the subject 102, such that a selected target region of the subject 102 is radiated with the X-rays.
  • the intensity of the X-ray generator 104 may be controllable by the controller 108 and an input interface (not shown) operable by a user, such as a medical professional/healthcare provider.
  • the image detector 106 may be a digital imaging device that detects the X-rays passing through the subject 102 and captures images of internal structures of the subject 102 at the selected region. In the example embodiment, the image detector 106 captures multiple images within a predetermined time period (e.g., 32 images/second). Each image is represented as M x N pixel image, where M and N are integers that are equal to or greater than 1 . It is readily understood that the image detector 106 may be configured in various suitable ways for capturing images based on detected X-rays passing through the subject 102.
  • the controller 108 may include an X-ray control module 202, an image recorder 204, and an image generator 206.
  • the X-ray control module 202 may control the X-ray generator 104 based on inputs from the user to have the generator 104 emit X-rays at a desired intensity for a designated time period.
  • the user may input desired operation parameters of the X-ray generator 104, such as position, intensity, and duration, by way of a graphical user interface displayed on a monitor 1 10 and a user interface, such as mouse or a touch screen.
  • the monitor 1 10 may be a liquid crystal display with a touch screen.
  • the image recorder 204 receives imaging data indicative of the digital images taken by the image detector 106.
  • the image generator 206 generates one or more enhanced images of a target internal structure provided in the images stored in the image recorder 204. More particularly, a given image of the selected region taken by the image detector 106 may include the target structure, which is the point of interest of the medical procedure.
  • the image generator 206 of the controller 108 separates the target structure from a background portion of the image to generate a sequence of images of the isolated target structure that are viewable on the monitor 1 10.
  • the image enhancer 208 of the present disclosure utilizes singular value decomposition (SVD) to construct enhanced images of the target structure.
  • SVD singular value decomposition
  • the image enhancer 208 derives a reference image that is indicative of the background portion using SVD.
  • the reference image may then be subtracted from the original image data to obtain a sequence of focal images that include the target structure.
  • the SVD may be used multiple times on portions of the data as described below.
  • an example operation routine of the system 100 is provided. Before the routine is executed, a patient is positioned between the X-ray generator 104 and the image detector 106. The patient may be injected with a contrast agent that can improve the visibility of the internal structures of the patient 102.
  • the contrast agent is administered before the X-ray generator 104 emits the X-rays.
  • the contrast agent may be administered at other suitable times, such as after a predetermined number of images are taken.
  • part of the sequence of images can be enhanced before the X-ray generator is shut off for real-time display to the operator. That is, at least partial images can be presented to the operator during the imaging.
  • the controller 108 may determine whether the operation parameters for the medical procedure are received at 302.
  • the operation parameters may include, for example, the intensity of the X-ray, a time period for emitting the X-ray, and/or a position of the focal region of the subject 102 to receive the X-ray.
  • the medical professional may enter the operation parameters using a user interface (not shown).
  • the X-ray control module 202 of the controller 108 may control the X-ray generator 104 to have the X- ray generator 104 emit the X-rays at the designated intensity for the predetermined time period as indicated at 304.
  • the image generator 106 captures images (i.e., frames) of the focal region of the patient and the images (i.e., image data) are received and stored by the image recorder 202 of the controller 108.
  • the intensity of the X-ray generator 104 is maintained at the same level throughout the imaging process although intensity may vary as well.
  • the controller 108 determines if the predetermined time period identified in the operation parameters has lapsed. If the time period has lapsed, the X-ray generator 104 is deactivated and the images are enhanced at 310 using method implemented by the image enhancer 208. The enhanced images output by the image enhancer may then be displayed at 312, for example on a display. As discussed below, the output of the image enhancer may be an enhanced image data sequence (two spatial dimensions and one time dimension) that better illustrates the target region.
  • the image generator 206 of the controller 108 processes the images taken to generate one or more enhanced images that highlight the internal structure of the patient that is of interest.
  • FIG. 4 provides an example routine of the image enhancer 208 of the present disclosure.
  • the image enhancer 208 constructs a matrix X using the image data of the images taken, where the dimension of X is MNxT.
  • the controller 108 may acquire image data for T images, where each image may be represented as an MxN pixel matrix and T, M, and N are natural numbers.
  • FIG. 5A illustrates a data cube of ⁇ images.
  • the data cube is transformed into an MNxT matrix by stacking the columns of a single MxN matrix and having each new column in an MNxT matrix be image data of a subsequent image.
  • FIG. 5B illustrates image data for two images (T1 and T2), where the image data for each image is a 3x3 matrix.
  • the columns of the T1 matrix are stacked into one column to form the first column of the MNxT matrix
  • the columns of the T2 matrix are stacked into one column to form the second column of the MNxT matrix.
  • suitable methods for signal representation such as those based on tensors, may also be used, and therefore, the present disclosure should not be limited to the stacked arrangement.
  • Singular value decomposition (SVD) of matrix X is performed at 404.
  • the SVD of matrix X is provided as equation 1 in which U is an MNxMN unitary matrix, ⁇ is a diagonal MNxT matrix with non-increasing (typically decreasing) non- negative real numbers on the diagonal, and V H is a T ⁇ T unitary matrix.
  • U is a 9x9 matrix
  • ⁇ is a 9x2 matrix
  • V H is a 2 x 2 matrix.
  • T columns of U are stored and only the upper TxT portion of ⁇ is stored, as is known as the "thin" SVD.
  • the controller may extract or obtain the data matrix for the focal images (i.e., focal image sequence) that has the target internal structure by removing the reference matrix from the original image data.
  • the focal image sequence may be obtained using equation 3 in which Xf 0ca i is a MNxT data matrix of the focal image sequence.
  • an image data cube of the focal image sequence is constructed.
  • the Xfocai matrix can be unstacked to form the ⁇ ⁇ ⁇ ⁇ ⁇ image data matrix of the focal image sequence by taking each column of the MNxT matrix, which is indicative of the image data for a single image, and unstacking the column to form the M ⁇ N matrix and repeating for all T images.
  • the focal image sequence may be obtained by first unstacking the X re f matrix into an ⁇ ⁇ ⁇ ⁇ ⁇ array, and then subtracting the MxNxT reference array from the original ⁇ ⁇ ⁇ ⁇ ⁇ array to obtain the ⁇ ⁇ ⁇ ⁇ ⁇ array representing the focal image sequence, reversing the process illustrated in Fig. 5.
  • the routine returns to the routine of FIG. 3 at 312 to display the enhanced images on the monitor 1 10. That is, the image data cube of the focal image sequence is indicative of the target structure and may be displayed on the monitor 1 10. It is to be understood that only the relevant steps of the methodology are discussed in relation to Figures 3 and 4, but that other software-implemented instructions may be needed to control and manage the overall operation of the system.
  • the radiographic imaging process of the present disclosure enhances the images taken during, for example, a CT scan or an angiography scan.
  • FIG. 6A illustrates an image (out of a sequence) that was enhanced using the conventional DSA in which a mask image is obtained by exposing the living object to high intensity X-rays.
  • FIG. 6B illustrates an image (out of a sequence) that was enhanced using the radiographic imaging process of the present disclosure in which the living object is exposed to lower intensity X-rays.
  • the image enhancer of the present disclosure provides a clear image of the target structure without the use of DSA and without exposing the patient to high intensity X-rays.
  • Figure 7 depicts an alternative routine that can be implemented by the image enhancer 208.
  • a plurality of images captured by the image detector 106 serve as input to the image enhancer 206.
  • the X-ray intensity is maintained at a fixed intensity as the plurality of images are being captured, although generating the method to accommodate varying X-ray intensity is straightforward.
  • each image segment is a matrix having dimensions P1 xP2, where P1 and P2 are greater than one and typically smaller than MxN, and one image segment is extracted from each image in the plurality of images.
  • the image segments may correspond to a region of interest in the plurality of images.
  • the image segments are then used at 702 to construct a rearranged matrix X.
  • the image segments are collected into a matrix by arranging each image segment into a column and aligning the columns from different images next to each other to complete the rearranged matrix with dimensions P1 P2xT, where T is the number of images in the plurality of images.
  • the rearranged matrix is as shown in Fig. 8A.
  • the rearranged matrix X is used to initialize an iterated matrix X L .
  • multiple image segments are extracted from each image and collected into a matrix by stacking the multiple image segments from an image on top of each other to form a column in the rearranged matrix and columns from different images are aligned next to each other to complete the rearranged matrix with dimensions KP1 P2xT, where K is the number of image segments extracted from each image, and T is the number of images in the plurality of images.
  • multiple image segments are extracted from each image but the segments are stacked next to each other to form the rearranged matrix with dimensions P1 P2xKT. An example of this arrangement is shown in Figures 8B and 8C.
  • a reference matrix X re f is derived at 704 by applying singular value decomposition to the iterated matrix X L and selecting a first component of the singular value decomposition at 705 in the same manner as described above. Again, the reference matrix is indicative of background in the plurality of images.
  • OptShrink An Algorithm for Improved Low-Rank Singal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage in IEEE Transactions on Information Theory, Vol. 60, No. 5 (May 2014) which is incorporated by reference in its entirety.
  • a focal matrix Xf 0ca i is obtained at 706 by subtracting the reference matrix X re f from the rearranged matrix X.
  • the focal matrix Xf 0ca i is then converted to a sparse matrix X spa rse- That is, a sparsifying transform is applied to the focal matrix at 708.
  • the sparse matrix X sp arse is then subtracted at 710 from the rearranged matrix X to yield a refined rearranged matrix X
  • the sparsifying transform is a simply thresholding method. Each value in the reference matrix X re f is compared to a threshold. If the value in the reference matrix X re f is less than the threshold, the value is set to zero; otherwise, the value is maintained.
  • the value of the threshold is set large enough so that the thresholding process largely eliminates noise components, but not so large that it eliminates important image details. The appropriate values depend on the level of noise in the data as determined by the X-ray source intensity and spectrum and by the thickness of the patient anatomy being imaged.
  • filtering of the focal matrix X foca i includes applying a Fourier transform. To do so, elements of the focal matrix are arranged into n x 1 matrix and a Fourier transform is applied to the n x 1 matrix. The thresholding method described above is then applied to the coefficients of the Fourier transform to create a sparse matrix. The inverse Fourier transform is then applied to the sparse matrix to obtain an n x 1 matrix containing the sparse representation of the focal matrix. Finally, the elements of the sparse n x 1 matrix are rearranged back to into a matrix having the same dimension as the focal matrix.
  • This rearranged sparse matrix Xsparse is likewise subtracted at 710 from the rearranged matrix X to yield a refined rearranged matrix X L 2- [50] While two examples of sparsifying transforms have been presented, one skilled in the art will readily understand that other types of sparsifying transforms (including learned transforms) can be applied to the focal matrix to yield the sparse matrix X spa rse-
  • the sparse component of the LASSI algorithm is one example algorithm which may be applied and is described in Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging in IEEE Transactions on Medical Imaging, Vol. 36, No. 5 (January 2017) which is incorporated by reference in its entirety. It is also understood that sparsifying operation can be applied with or without the partitioning steps.
  • LASSI Low-Rank and Adaptive Sparse Signal
  • _2 is compared at 71 1 to the starting point, i.e., rearranged matrix X
  • the matrices are compared using a Frobenius norm or an elementwise norm of the matrices.
  • the process continues at 704 by applying singular value decomposition to the refined rearranged matrix X
  • Other comparison methods are also contemplated by this disclosure. The processing steps are repeated until the difference between the matrices is less than the threshold.
  • the threshold is chosen small enough that the final converged image sequence is sufficiently close to the image sequence that would be obtained if more iterations were used, but not so small that an impractically large number of iterations are needed.
  • an upper limit on the number of iterations will also be imposed at 71 1 to ensure that the image sequence is constructed sufficiently quickly based on the capabilities of the computing device.
  • an image data cube of the focal image sequence is constructed at 712 from the sparse matrix X sp arse-
  • the image data cube of the focal image sequence may in turn be displayed on the monitor 1 10. It is to be understood that only the relevant steps of the methodology are discussed in relation to Figure 7, but that other software- implemented instructions may be needed to control and manage the overall operation of the system.
  • the radiographic imaging process of the present disclosure does not require exposing the patient to a high intensity X-rays to generate a mask image and, instead, produces a higher quality image with less radiation.
  • the process utilizes information from all of the frames to produce a dynamic + static decomposition that reveals greater dynamic details at higher signal to noise ratios than the conventional method.
  • the radiographic imaging process of the present disclosure requires lower doses of radiation and, possibly, contrast agent while maintaining clinically acceptable image quality.
  • the process can be incorporated into contemporary imaging systems, and does not require new and expensive systems.
  • the process may also improve the safety and quality of imaging using other imaging modalities, such as CO 2 angiography which, while avoiding the use of iodinated contrast media, are associated with a considerable increase in exposure to radiation.
  • the direction of an arrow generally demonstrates the flow of information (such as data or instructions) that is of interest to the illustration.
  • information such as data or instructions
  • the arrow may point from element A to element B. This unidirectional arrow does not imply that no other information is transmitted from element B to element A.
  • element B may send requests for, or receipt acknowledgements of, the information to element A.
  • module or the term “controller” may be replaced with the term “circuit.”
  • the term “module” or “controller” may refer to, be part of, or include: an Application Specific Integrated Circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on-chip.
  • ASIC Application Specific Integrated Circuit
  • FPGA field programmable gate array
  • the module may include one or more interface circuits.
  • the interface circuits may include wired or wireless interfaces that are connected to a local area network (LAN), the Internet, a wide area network (WAN), or combinations thereof.
  • LAN local area network
  • WAN wide area network
  • the functionality of any given module of the present disclosure may be distributed among multiple modules that are connected via interface circuits. For example, multiple modules may allow load balancing.
  • a server (also known as remote, or cloud) module may accomplish some functionality on behalf of a client module.
  • code may include software, firmware, and/or microcode, and may refer to programs, routines, functions, classes, data structures, and/or objects.
  • shared processor circuit encompasses a single processor circuit that executes some or all code from multiple modules.
  • group processor circuit encompasses a processor circuit that, in combination with additional processor circuits, executes some or all code from one or more modules. References to multiple processor circuits encompass multiple processor circuits on discrete dies, multiple processor circuits on a single die, multiple cores of a single processor circuit, multiple threads of a single processor circuit, or a combination of the above.
  • shared memory circuit encompasses a single memory circuit that stores some or all code from multiple modules.
  • group memory circuit encompasses a memory circuit that, in combination with additional memories, stores some or all code from one or more modules.
  • the term memory circuit is a subset of the term computer-readable medium.
  • the term computer-readable medium does not encompass transitory electrical or electromagnetic signals propagating through a medium (such as on a carrier wave); the term computer-readable medium may therefore be considered tangible and non-transitory.
  • Non-limiting examples of a non-transitory computer- readable medium are nonvolatile memory circuits (such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).
  • nonvolatile memory circuits such as a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit
  • volatile memory circuits such as a static random access memory circuit or a dynamic random access memory circuit
  • magnetic storage media such as an analog or digital magnetic tape or a hard disk drive
  • optical storage media such as a CD, a DVD, or a Blu-ray Disc
  • the apparatuses and methods described in this application may be partially or fully implemented by a special purpose computer created by configuring a general purpose computer to execute one or more particular functions embodied in computer programs.
  • the functional blocks and flowchart elements described above serve as software specifications, which can be translated into the computer programs by the routine work of a skilled technician or programmer.
  • the computer programs include processor-executable instructions that are stored on at least one non-transitory computer-readable medium.
  • the computer programs may also include or rely on stored data.
  • the computer programs may encompass a basic input/output system (BIOS) that interacts with hardware of the special purpose computer, device drivers that interact with particular devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, etc.
  • BIOS basic input/output system
  • the computer programs may include: (i) descriptive text to be parsed, such as HTML (hypertext markup language), XML (extensible markup language), or JSON (JavaScript Object Notation) (ii) assembly code, (iii) object code generated from source code by a compiler, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc.
  • source code may be written using syntax from languages including C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCamI, Javascript®, HTML5 (Hypertext Markup Language 5th revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB, SIMULINK, Julia, and Python®.
  • languages including C, C++, C#, Objective-C, Swift, Haskell, Go, SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCamI, Javascript®, HTML5 (Hypertext Markup Language 5th revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB,

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Abstract

L'invention concerne une technique de traitement vidéo qui sépare efficacement la partie stationnaire d'une vidéo de la partie dynamique de la vidéo. Cette technique repose sur une décomposition en valeurs singulières pour isoler la partie stationnaire de la vidéo de la partie dynamique de la vidéo. Le résultat est une image de meilleure qualité avec un sujet exposé à significativement moins de rayonnement et/ou d'agent de contraste.
PCT/US2017/058654 2016-10-28 2017-10-27 Procédé d'imagerie radiographique dynamique utilisant une décomposition en valeurs singulières WO2018081492A1 (fr)

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US20080051648A1 (en) * 2006-08-25 2008-02-28 Suri Jasjit S Medical image enhancement system
US20100158341A1 (en) * 2008-12-18 2010-06-24 Siemens Medical Solutions Usa, Inc. System for Automatically Generating a Mask for Digital Subtraction Angiography
US20100259550A1 (en) * 2009-04-10 2010-10-14 Siemens Medical Solutions Usa, Inc. Method of Visualization of Contrast Intensity Change Over Time in a DSA Image
US20110305378A1 (en) * 2008-08-13 2011-12-15 Koninklijke Philips Electronics N.V. Mask construction for cardiac subtraction
US20150327783A1 (en) * 2012-11-29 2015-11-19 The Trustees Of The University Of Pennsylvania Noninvasive 4-d time-resolved dynamic magnetic resonance angiography

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080051648A1 (en) * 2006-08-25 2008-02-28 Suri Jasjit S Medical image enhancement system
US20110305378A1 (en) * 2008-08-13 2011-12-15 Koninklijke Philips Electronics N.V. Mask construction for cardiac subtraction
US20100158341A1 (en) * 2008-12-18 2010-06-24 Siemens Medical Solutions Usa, Inc. System for Automatically Generating a Mask for Digital Subtraction Angiography
US20100259550A1 (en) * 2009-04-10 2010-10-14 Siemens Medical Solutions Usa, Inc. Method of Visualization of Contrast Intensity Change Over Time in a DSA Image
US20150327783A1 (en) * 2012-11-29 2015-11-19 The Trustees Of The University Of Pennsylvania Noninvasive 4-d time-resolved dynamic magnetic resonance angiography

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