WO2004010381A1 - Gestion par l'image - Google Patents

Gestion par l'image Download PDF

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Publication number
WO2004010381A1
WO2004010381A1 PCT/GB2003/003249 GB0303249W WO2004010381A1 WO 2004010381 A1 WO2004010381 A1 WO 2004010381A1 GB 0303249 W GB0303249 W GB 0303249W WO 2004010381 A1 WO2004010381 A1 WO 2004010381A1
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Prior art keywords
image
ray
parameters
control
algorithms
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PCT/GB2003/003249
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English (en)
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WO2004010381B1 (fr
Inventor
Edward James Morton
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The University Of Surrey
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Priority to AU2003246969A priority Critical patent/AU2003246969A1/en
Publication of WO2004010381A1 publication Critical patent/WO2004010381A1/fr
Publication of WO2004010381B1 publication Critical patent/WO2004010381B1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • 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
    • A61B6/54—Control of apparatus or devices for radiation diagnosis
    • A61B6/542—Control of apparatus or devices for radiation diagnosis involving control of exposure
    • 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
    • A61B6/56—Details of data transmission or power supply, e.g. use of slip rings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G06T5/94—Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Definitions

  • This invention relates to control systems employing images produced by radiation-generating imaging plant or equipment. It is particularly concerned with medical and industrial uses of radiation imaging and especially with X-ray imaging.
  • the present invention has the objective of employing radiation-generated images from a monitored process in the direct control of the monitored process. It has the further objective of reducing the exposure to imaging radiation encountered by equipment or plant operators and, in the case of medical applications, to patients.
  • a method for an industrial or medical process which employs radiation imaging to monitor progress of the process, in which a combination of parameters derived from the image is used to calculate output signals to enhance control of the process.
  • the invention offers the benefit that by feeding derived and calculated data back to the process a very high degree of control can instantaneously be achieved.
  • the invention thus provides that the parameters of the imaging system can be controlled and altered dynamically based on information contained in and extracted from the images themselves.
  • the invention offers the advantage that significant dose reduction is achievable compared with conventional X-ray imaging, thereby providing for optimisation of the minimum patient dose while maintaining diagnostic quality.
  • the parameters to be taken into account in the control calculations can include not only major "high level” parameters such as the intensity and duration of the applied radiation, movement of the target being monitored, composition and physical state of the target, but also secondary "low-level” parameters such as the absolute position of the target and its position relative to other elements.
  • Other possible low level parameters include the absolute and relative velocity of the target, the uniformity of the target, and the image texture, pixel intensity and pixel noise.
  • the invention offers the advantage that calculations based on certain parameters monitored by the image permit the construction of other relevant parameters, for example the movement and composition of the target.
  • parameters derived from the image can be combined with other process data, for example ambient temperature and pressure observed by point sensors, to achieve further precision of control.
  • the data from the image and any related parameters can be converted into process control by appropriate image manipulation apparatus.
  • suitable algorithms include rule based logic, fuzzy logic, neural networks and other linear or non-linear combinations.
  • Algorithms can also be provided for cross-correlation of different parameters. A particular benefit is that cross checking parameters, even low level parameters, can ensure that the input data is robust. Similarly cross checking of outputs is beneficial in ensuring that that they are consistent.
  • Feedback of monitored data allows the use of algorithms to test predicted response against actual response and thereby permit control of oscillations or unexpected results. It may however be desirable to delay a control adjustment to ensure that the monitored response is real and not simply caused by image noise.
  • Adaptive manipulation of input data may be of advantage to ensure the return of the most significant data for the particular control application.
  • Images may be segmented to identify specific classes of input signal. Images may usefully provide functional data (for example in localised MRI spectroscopy, ultrasound Doppler signals and X-ray diffraction) . Sensors can be dynamically adjusted in response to ambient conditions, even across the surface of the sensor (i.e. to accommodate wide dynamic range signals) .
  • Images within the scope of the invention can incorporate stereo and 3D sound, colour overlay to indicate spatially localised parameter values, overlay of images with physical models of processes, overlay of multiple image data sets and production of statistical representations of data (e.g. 3D texture maps) .
  • the entire set of monitored parameters from the image and elsewhere can be analysed and manipulated to study an entire process with a view to achieving their efficiency improvements .
  • the invention is especially suitable for use in X-ray imaging using adaptive image processing. It is of particular benefit for medical X-ray fluoroscopic procedures such as interventional neuroradiology, cardiology and peripheral vascular angiography, and is hereinafter described mainly with reference to these procedures. It is however also applicable with advantage across a wide range of other medical procedures and industrial tasks.
  • X-ray fluoroscopy is a commonly used procedure for guiding interventional procedures within the body, or for visualising the structure/function of internal organs in the body. It is characterised by the use of X-ray imaging at video rate (normally 6 to 30 frames per second) .
  • an X-ray imaging system for fluoroscopy comprises an
  • X-ray irradiation unit for example an X-ray tube and generator, collimator assembly, beam filter(s) and light beam diaphragm
  • an imaging chain for example, an X-ray image intensifier, lens system with optical iris, video camera, image processor and monitors
  • the images are observed by one or more specialist clinicians.
  • the present invention combines a conventional X-ray imaging system with a new data processing apparatus (a "black box") which dynamically and automatically controls the operation of the X-ray imaging system based on the image data itself.
  • a new data processing apparatus a "black box” which dynamically and automatically controls the operation of the X-ray imaging system based on the image data itself.
  • the improved system comprises an "imaging computer” that interprets the video X-ray image and uses information derived from the images to drive such parameters as the position of the X-ray collimators, the X-ray pulse duration, the X-ray pulse frequency, the X-ray tube voltage and the X-ray tube current. Additionally, the imaging computer can automatically adjust the displayed image by using colour, by stitching together live images with static or partially illuminated background images and by using temporal and/or spatial filtering. The imaging computer achieves this by extracting low level parameters from each image such as catheter tip position, local or global movement vectors, noise metrics and contrast/brightness data. The low level data are input to a predictive algorithm to ensure appropriate optimisation of the X-ray system for the next video frame.
  • the system can advantageously be programmed to allow an operator to enter fixed parameters of an individual field of activity, for example: the type of study (cardiac or neurological); whether the patient is on a moving couch; whether the main collimator is fixed or mobile; whether a contrast agent is being employed; whether the operator requires subtracted data, road map, live image etc. ; the position of any reference/mask subtraction image; whether colour is to be used in the image and if so which colours.
  • the type of study cardiac or neurological
  • the main collimator is fixed or mobile
  • a contrast agent is being employed
  • the operator requires subtracted data, road map, live image etc.
  • the position of any reference/mask subtraction image whether colour is to be used in the image and if so which colours.
  • Information drawn from each X-ray image in the fluoroscopy sequence is used to predict how the X-ray imaging system should be optimised for the next X-ray exposure.
  • the result is thus image driven, giving dynamic optimisation and control of the entire X-ray imaging chain.
  • the signals used to control the X-ray system are derived from adaptive (spatially and temporally variant) analysis of the X-ray images produced by the imaging chain. This approach maintains or enhances the clinical efficiency, namely the ability of the clinical team to perform their required task.
  • the invention thus provides that all the chosen system parameters may be controlled together, and not just a subset.
  • a particular advance is the extraction of a set of low level parameters and their use together in a predictive optimisation algorithm.
  • a particular benefit of the invention in its application to fluoroscopy is its capability to minimise the X-ray dosage to reduce potential detriment to the patient and to the clinicians conducting the procedure.
  • a typical X-ray imaging system that uses control according to the invention in order to achieve radiation dose reduction combines a "black box" multi-processor data processing system with an X-ray generating apparatus and X-ray imaging apparatus.
  • the X-ray generating apparatus may operate in a continuous output mode or in a pulsed fluoroscopy mode (i.e. using a pulsed X-ray beam with its intensity modulated as a series of discrete pulses) . It includes X-ray attenuating diaphragms (collimators) that may be moved independently under computer control.
  • the system should incorporate variable filters that can also be manipulated under computer control.
  • the X-ray imaging chain should produce images with as little persistence and lag as possible so that the appropriate dynamic performance can be generated by the data processing system based on the information contained in the image sequence itself.
  • examination of the statistical values of the parameters collected over an entire sequence can provide a comparison with other sequences, for example in assessing individual operator performance. Further, the sum of collected data can determine where the major dose delivery is occurring, thereby enabling improved dosing to be planned and implemented.
  • Low level parameters may be extracted from the fluoroscopy image sequences for use in real-time optimisation of the X-ray imaging system, with the particular aim of reducing patient dose.
  • Low-level parameters in fluoroscopy include the position of a catheter tip, the position of a catheter guide wire, the location of vessels containing contrast agent and the position of the X-ray collimators. Further relevant information may include local movement vectors for extracting anatomical motion (e.g. rate of flow of contrast agent in a particular vessel) , global motion vectors (e.g. change in patient position with respect to the X-ray source) and image statistical properties (e.g. brightness, signal-to-noise ratio, noise-power spectrum and image contrast) .
  • anatomical motion e.g. rate of flow of contrast agent in a particular vessel
  • global motion vectors e.g. change in patient position with respect to the X-ray source
  • image statistical properties e.g. brightness, signal-to-noise ratio, noise-power spectrum and image contrast
  • frame-by-frame evaluation of these parameters is required.
  • such methods will have relatively low accuracy and/or reliability. Therefore, in order to ensure efficient feature extraction, the frame-by-frame methods are mirrored by other, potentially more accurate and sophisticated but slower, methods to check/re- align the frame-by- frame methods. This ensures overall system reliability.
  • the invention thus provides for low level parameters to be extracted from X-ray images for the purposes of dynamic optimisation of the X-ray system. Further, these parameters may result from quick but non-robust methods operating within a single video frame or from sophisticated methods that update less frequently. Parallel computation may be employed to achieve sufficient speed for image analysis.
  • Low level parameters extracted from medical fluoroscopy image sequences may be combined by high-level algorithms for real-time optimisation of X-ray imaging systems with the aim of reducing patient dose.
  • High level algorithms include linear rule based logic, neural network based methods, fuzzy logic and statistically based algorithms.
  • Two outputs are required from these algorithms: hardware optimisation and image display optimisation.
  • the hardware algorithm should control tube voltage, tube current, X-ray pulse frequency, X-ray pulse duration and collimator aperture.
  • the image display optimisation should control spatio-temporal filtering, the use of colour, histogram equalisation and unsharp masking methods.
  • the invention further provides for low level inputs to be fed to control algorithms to undertake optimisation for system control and optimisation for image display. Further, the invention permits partitioning of these algorithms into parallel computing systems.
  • the data processing apparatus comprises a computing system programmed with algorithms required to implement the appropriate control and dose reduction strategies.
  • Each algorithm is best suited to a particular type of procedure (e.g. cardiology, barium fluoroscopy etc) and the data processing apparatus therefore needs to contain multiple algorithms, including one for each type of procedure.
  • Each algorithm is based on observation of the approach taken to the clinical task by the human observers performing the procedure, since it is the human perception of the quality of the image sequence that is the main criterion used for minimisation of the delivered dose.
  • a patient undergoing treatment is typically on a movable couch beneath a stationary X-ray irradiation unit. Movement of the couch effects global motion of the patient with respect to the irradiation unit. During this period of motion, the human visual system is unable to perceive fine spatial details because they are moving too fast. In order to maintain reasonable statistical accuracy in the displayed pixel values it is therefore preferable to reduce beam current and consequently coarsen the pixellation of the displayed image. Further, because the processing apparatus can detect the magnitude of movement from one frame to the next, it can use this information to re-register into the displayed image one or more "good" frames taken when the patient was static before the couch movement. This can minimise the perceived effects of image lag on the image intensifier.
  • the processing apparatus can detect the acceleration of the couch, and hence determine how long the movement is likely to continue. This information may be used to modulate the pulse rate of the X-ray beam, hence allowing frames to be dropped where couch movement is greatest and the quality of the recorded image is naturally lowest (due to image lag and image smearing during a given X-ray exposure). Compared with a conventional system the combination of these three procedures allows a reduction in dose of at least an order of magnitude during the couch movement.
  • the attention of the clinical operator is generally focussed on the site at the tip of the catheter. Therefore, by using dynamic collimation of the X-ray beam, it is possible to track the region around the tip of the catheter with high X-ray exposures (hence good feature visibility) , while delivering zero or low dose to peripheral regions of the image.
  • This requires the displayed image to be constructed from the "live" catheter image plus a background image constructed from image data acquired with a previous full frame exposure or from multiple frames acquired at low-dose that are re-registered to fit around the "live” catheter image. Additionally, it is possible to modulate the frame rate of the high dose localised catheter image based on the rate of movement of the catheter within the image.
  • the added noise can add to the visibility of features in the background region. This occurs since the detection of low contrast features by the human visual system can be enhanced by small temporal changes in image brightness within the static feature.
  • Suitable shapes of filter include linear wedge, exponential or parabolic profiles. With a wedge-shaped filter X-ray exposures are greatest at the (thin) tip of the wedge, and lowest in the thick regions of the wedge. By using two or more wedge-shaped filters, it is possible to reduce the dose significantly in the peripheral regions of the image, while maintaining full dose levels in the critical diagnostic regions of the image. Movement of the filters can be controlled by a suitable algorithm.
  • Adaptive image processing techniques are preferably used to perform temporal averaging with re-registered image frames in low-dose background regions while unmodified image data is displayed in high dose regions.
  • An electronics system may be used to apply algorithms for real-time optimisation of X-ray fluoroscopy systems for minimisation of patient dose.
  • Such systems generally require parallel electronics designs and are required to provide means for image acquisition, image display, low level parameter extraction, high level algorithm implementation and external X- ray system control.
  • the system according to the invention may be constructed using a number of technologies including multi-processor commodity component systems (based on PC/DSP/PGA approaches) , beowolf class supercomputers or SIMD/MIMD supercomputers, custom gate arrays, custom integrated circuits, custom processors and/or three- dimensional interconnect solutions.
  • Algorithms are segmented to run on parallel systems e.g. calculation of each low level parameter is assigned to one or more processors, these algorithms being run in parallel to provide data for a concurrent high level algorithm, which in turn controls a parallel image display algorithm.
  • Use of pipelining and highly segmented memories is usually required in such systems.
  • the invention provides a method for construction of an image computer specifically for dose reduction in fluoroscopy.
  • the algorithm required for each class of clinical procedure will normally be different. For example, an algorithm designed for peripheral vascular procedures will not normally be successful for cardiac imaging. Further, there is additional information that is normally generated by the operator in terms of the type of display they would like to see. For example, the clinician may wish to see a live projection image or a subtraction image for visualisation of contrast agents.
  • the operator also provides input to the imaging system, for example in terms of mechanical movement of the patient couch or other equipment with respect to the patient.
  • These user generated input parameters are used by the optimisation algorithms alongside the low and high level parameters generated directly from the image.
  • This normally contains one or more buttons and one or more indicators and may include a text or graphic display.
  • buttons normally fulfils the function of system override. On pressing this button, the X-ray system returns to a normal, typically preset mode of operation. When the clinician releases the button, the automatic dose control system will either take over directly or pause for a preset time before resuming operation.
  • a further button or set of buttons may be used for technique selection, for example to select between cardiac or peripheral vascular studies. Alternatively, this information may be provided by a data link to the main X-ray system.
  • a further button or set of buttons may be used for algorithm control. For example target dose reduction levels may be requested as being "high or "normal” .
  • buttons may be used to control other aspects of system operation as required.
  • the dose control apparatus will include an electronic link to the X-ray diagnostic system through which the output control signals are propagated.
  • This electronic link will normally also contain inputs related to parameters such as couch movement or movement of the X-ray system relative to the patient. It may also contain input signals to indicate the injection of contrast agent.
  • Indicators may be provided to confirm the status of user specified input parameters, for example the class of the requested clinical technique.
  • a text or graphic display can be provided to indicate the actual setting of the control outputs.
  • the display may indicate, for example in either text or graphical form, the tube voltage, tube current, pulse rate and cumulative procedure dose.
  • Suitable statistical properties that may be generated automatically include, but are not restricted to, the value of all high and low level input parameters and the value of all control outputs. For example, it is possible to collate information regarding X-ray field size, instantaneous dose rate, time into the procedure. X- ray tube pulse rate, tube voltage and current, velocity of catheter, position of catheter tip with respect to landmarks and so on.
  • the database formed by this information may be structured such that data may be retrieved as a function of any one or more of these values.
  • Results from a search of the database may be structured in many ways including textual, graphical, tabular, numerical or image format. Colour may also be used to highlight specific data.
  • a control image or scatter plot may be generated from clinical image data to show the correlation between two or more variables, for example between dose rate and catheter tip velocity.
  • Colour can be applied to this image to indicate regions where, as an example, tube voltage increased above a certain limit, say 100 kNp.
  • colour can be applied in the image to indicate uncertainty in catheter tip position. A typical use of this information is to support algorithm testing.
  • a graph may be constructed of dose rate versus procedure time. Colour may be used to indicate for example catheter tip velocity, injection of contrast agent and/or X-ray tube pulse rate all on the same graph. It is then possible to analyse the data to observe such parameters as the fraction of the procedure for which high doses are being used, whether the catheter is moving substantially during this time and how frequently contrast agents are injected. This information is useful to the clinician in terms of review of their clinical practice.
  • models representing the collective performance of individual institutions may be compared against national models derived from data produced by sets of institutions. Such comparisons can result in improvements in clinical practice and national standards for clinical protocols.
  • national models may be compared against other national models to develop internationally acceptable clinical protocols.
  • Figure 1 is a schematic view of an imaging system according to the invention
  • Figure 2 is a diagram showing in simplified format the calculations which need to be performed within the system of Figure 1 ;
  • Figure 3 is a diagram showing in simplified format the structure of the data processing required in the system
  • Figure 4 is a diagram showing in simplified format a commodity computer cluster for the required data processing
  • Figure 5 is a diagram showing in simplified format a multiprocessor bespoke cluster for the required data processing
  • FIG. 6 is a schematic view of the data processing portion of the system.
  • an X-ray imaging chain feeds received signal data to an image processing apparatus which in turn controls an X-ray irradiation unit.
  • the image processing apparatus however also feeds back processed data to the X-ray imaging chain such that the latter supplies to one or more clinical observer(s) not only information directly from the image but also the said information as enhanced by algorithms.
  • the clinical observers have control over the image processing apparatus but this control is effected with the enhanced information from the imaging chain.
  • Figure 3 summarises the structure of the required data processing. Regardless of the algorithm being used, the underlying processing required is similar.
  • a given algorithm is relatively straightforward to compute with a set of control outputs (diaphragm position, tube voltage, frame rate etc) and a set of optimisation inputs derived from the X-ray image data (catheter position, movement data, noise information etc) .
  • the algorithm itself may use one or more computational techniques such as rule based logic, fuzzy logic, neural networks or other linear on non-linear combination methods to combine these inputs to generate the outputs.
  • the calculations can be implemented extremely rapidly using modern computing methods.
  • inputs are combined in standard ways and compared against reference values determined by clinical trials.
  • multiple "low-level” inputs are combined into one "high- level” input, the values of the high level inputs being weighted to determine the appropriate control output.
  • the values of the weighting factors are normally determined through clinical trials.
  • control output 1 e.g. Ol, X-ray tube pulse rate
  • Ol x t X + y,Y
  • Xi and i are reference values for controlling Ol.
  • Different combinations of reference values and high level inputs will generally be used to determine the optimisation strategy for each of the other control outputs.
  • a second level check is preferably performed to ensure that conflicts between output control settings are eliminated. For example, if the noise high level input suggests that more X-ray photons are required to form an acceptable image, this can be achieved by increasing tube pulse rate (Ol), increasing tube voltage (O2), increasing tube current (O3) or undertaking more extensive temporal averaging in the displayed image (04) .
  • the appropriate strategy is determined by further linear combination of factors (e.g. select greatest of [aOl, bO2, cO3, dO4] where a, b, c and d are constant multipliers) .
  • increasing tube current is likely to be the preferred option over changing tube pulse rate, changing tube voltage or changing temporal averaging (and hence c > (a, b, d)) .
  • an iterative procedure is undertaken in which new values of the control outputs are selected and fed back into the algorithm to test the likely outcome in the next image frame. The process is repeated until the values converge to a suitable limit.
  • the output control values are constrained to prevent unacceptable fluctuations in system performance (e.g. rapid variations in image brightness or signal-to-noise ratio) .
  • the expected range of all values is determined through clinical testing. Generally, an iterative approach is adopted whereby the output control values are adjusted in order to bring the required input value back into range without driving a different input value out of range.
  • An optimisation algorithm may also be developed based on the neural network principle, whereby the network is trained by classifying the results of the algorithm as being clinically "acceptable” or “unacceptable” .
  • the algorithm requires input data that is derived from both the most recently acquired X-ray image, and also from the set of previous X-ray images. It is generally much more computationally demanding to acquire the information required by the control algorithm from the input image data than it is to implement the algorithm itself.
  • the input data is of "high value” , having been assembled from several “lower value” parameters.
  • the high value input "movement” can be constructed from several lower value data parameters including for example a map of local image movement vectors and a single global image movement vector.
  • the low level data used by a given algorithm has previously been subject to strong filtering specific to the design of the algorithm itself.
  • low level data is fed to the algorithm directly.
  • the location and direction of motion of the tip of the catheter are highly significant and together form a key component of the input data required to make a decision.
  • the accurate, but slow, method is used to correct and/or cross check the rapid but less accurate method on a periodic basis. This may be extended to multiple methods, each correcting/cross checking each other at suitable time periods.
  • a statistical check on the output of each method is used to determine confidence in the overall result for the parameter. If confidence falls below an acceptable value, optimisation of the X-ray system is normally automatically terminated and the entire X-ray system returned to standard dose operation as soon as possible. Once the results of the methods return into the confidence region, optimisation of the system can restart.
  • a quick but non- robust approach selects a small region of interest, say 30 x 30 pixels, around the catheter tip.
  • the data in the region of interest is subtracted from the data in the region of interest of the previous frame.
  • the subtracted data is compared on a pixel by pixel basis, and any pixels with a statistically significant value from the mean value (e.g. 2 ⁇ from the mean level) are assumed to be due to movement of the catheter. Therefore, using the sign as well as the magnitude of the subtraction data, the new catheter position can be rapidly updated.
  • the catheter tip position generated by the two methods is found to be inconsistent, then the confidence in this parameter is assumed to be reduced. This would occur, for example, if significant patient movement occurred between two image frames.
  • the control algorithm then evaluates whether to return to normal (i.e. non-optimised) operation for a short period of time until confidence in the parameter values is restored.
  • Low-level parameters that are usually important in medical X-ray fluoroscopy include a binary segmented map of vascular structure, the velocity of contrast flow in blood vessels, the structure and texture map of vessel diameters, local image statistics (to help locate image features), local image movement vectors, global motion vectors, and the position of anatomical landmarks in the image (for help with image registration) ,
  • the high level information used by the control algorithm may also be used to help image display.
  • the vascular structure determined as a low level data set during a previous injection of contrast agent can be displayed over the live image after re-registration for any patient movement. This can reduce the quantity of contrast agent injected into the patient while also minimising procedure time since the clinician can see, not simply remember, where the vascular structure is. Generally it is preferred to display the vascular structure as a series of line segments rather than true shapes to ease visibility of the underlying anatomical structures.
  • a further example is the use of adaptive temporal and spatial image processing.
  • Similar methods can be used for spatial smoothing; apply smoothing over regions of insignificant contrast, but do not apply smoothing to regions of significant contrast.
  • a further example is the use of frame summing, where images collected previously may be re-registered and summed with the current image to reduce effective photon noise, while maintaining image sharpness.
  • a further example is where reference images collected in the past may be used to back-fill new images where part of the new image is obscured by a collimator.
  • a further example is where the background image can be updated by regions of the new image which are not obscured by the collimator (i.e. those parts of the most recent image that contain good data) .
  • a further example is where adaptive contrast stretching is used to maximise visibility of features in the displayed image.
  • a particular method for example, is histogram equalisation.
  • a further example is where unsharp masking, e.g. superimposing a blurred image on a sharp image, is used to improve visibility of features in the displayed image.
  • a further example is where the true pixel values are scaled to brighten or darken a displayed image to achieve normal brightness. This is particularly useful in correcting the displayed brightness of pixels obscured by the partially attenuating collimator.
  • a further example is where random noise is added to the background/reference image in order to improve perceived quality and/or uniformity of the displayed composite background/live image.
  • a further example is where the displayed pixel dimension is altered depending on the value of localised low level parameters such as movement at the pixel level.
  • pixel sizes are increased (e.g. 2 x 2 or 3 x 3 pixel regions) by taking the average or median value to enhance signal-to-noise ratio, while pixels are displayed at the normal size in regions of significant movement or high contrast.
  • the data processing apparatus described above should be capable of sustaining high data rates with excellent compute capability. In general, this requires the use of parallel computing systems. In principle this is most likely to be achieved in one of the following ways:
  • FIG. 4 An example configuration for a commodity computer cluster is shown in figure 4 while an example multiprocessor bespoke system is described in figure 5.
  • Figures 4 and 5 are representative of other suitable configurations, although many other configurations are possible.
  • three-dimensional interconnects, programmable logic devices and multi-chip modules can all be used effectively for implementing parallel algorithms.
  • one (or more) processor(s) at each node can generate the low level data required by the optimisation algorithm while one (or more) other processor(s) can generate the new output image data.
  • the output image data is calculated on the segment of image data used previously to generate the low-level control information and hence network traffic is minimised.
  • a multi-processor bespoke system will typically copy the full image to a number of processors simultaneously through a high bandwidth backplane. Each processor then extracts a given high level parameter (e.g. movement, noise) from that image. The high-level data is then transferred to a master processor for implementing the system control algorithm. Alternatively, image data may be segmented and processed as described previously.
  • each processor is based on a high-end digital signal processor (DSP) that may itself contain eight or more processing units and multiple internal and external memory busses. Where appropriate, DSP and memory integrated circuits are combined with high performance programmable logic circuits to implement what might otherwise be computationally intensive tasks (e.g. binary segmentation and/or convolution) . In this way, real-time performance can be achieved within a reasonably sized package.
  • DSP digital signal processor
  • the dose reduction strategy described here may be implemented on standard or existing equipment by retrofitting suitable beam diaphragms and variable filters and adding an image data processing system.
  • a schematic of a possible data processing system is given in figure 6.
  • the existing system would use a pulsed X-ray tube, but significant dose reduction can still be achieved using a standard continuous output X- ray tube.

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Abstract

La présente invention concerne un procédé destiné à un processus industriel ou médical, et qui utilise l'imagerie des rayonnements pour surveiller sa progression. En l'occurrence, pour calculer les signaux de commande servant à renforcer la commande du processus, on utilise une combinaison de paramètres dérivés de l'image.
PCT/GB2003/003249 2002-07-20 2003-07-21 Gestion par l'image WO2004010381A1 (fr)

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US8885794B2 (en) 2003-04-25 2014-11-11 Rapiscan Systems, Inc. X-ray tomographic inspection system for the identification of specific target items
EP2002789A1 (fr) 2003-04-25 2008-12-17 CRX Limited Balayage à rayons X
US10901112B2 (en) 2003-04-25 2021-01-26 Rapiscan Systems, Inc. X-ray scanning system with stationary x-ray sources
US10591424B2 (en) 2003-04-25 2020-03-17 Rapiscan Systems, Inc. X-ray tomographic inspection systems for the identification of specific target items
US9618648B2 (en) 2003-04-25 2017-04-11 Rapiscan Systems, Inc. X-ray scanners
US11796711B2 (en) 2003-04-25 2023-10-24 Rapiscan Systems, Inc. Modular CT scanning system
US9442082B2 (en) 2003-04-25 2016-09-13 Rapiscan Systems, Inc. X-ray inspection system and method
US9675306B2 (en) 2003-04-25 2017-06-13 Rapiscan Systems, Inc. X-ray scanning system
US9020095B2 (en) 2003-04-25 2015-04-28 Rapiscan Systems, Inc. X-ray scanners
US8837669B2 (en) 2003-04-25 2014-09-16 Rapiscan Systems, Inc. X-ray scanning system
US9113839B2 (en) 2003-04-25 2015-08-25 Rapiscon Systems, Inc. X-ray inspection system and method
WO2006089557A1 (fr) * 2005-02-24 2006-08-31 Bang & Olufsen A/S Filtre de reduction adaptative du bruit et d’amelioration de la nettete pour images a affichage electronique
US8064718B2 (en) 2005-02-24 2011-11-22 Bang & Olufsen A/S Filter for adaptive noise reduction and sharpness enhancement for electronically displayed pictures
US9638646B2 (en) 2005-12-16 2017-05-02 Rapiscan Systems, Inc. X-ray scanners and X-ray sources therefor
US10976271B2 (en) 2005-12-16 2021-04-13 Rapiscan Systems, Inc. Stationary tomographic X-ray imaging systems for automatically sorting objects based on generated tomographic images
US10295483B2 (en) 2005-12-16 2019-05-21 Rapiscan Systems, Inc. Data collection, processing and storage systems for X-ray tomographic images
US9048061B2 (en) 2005-12-16 2015-06-02 Rapiscan Systems, Inc. X-ray scanners and X-ray sources therefor
DE102006037969A1 (de) * 2006-08-14 2008-02-28 Siemens Ag Verfahren zur rauschoptimierten Röntgenbildgebung unter Einsatz einer Subtraktionstechnik
US7796730B2 (en) 2007-05-24 2010-09-14 P-Cure, Ltd. Irradiation treatment apparatus and method
US7847275B2 (en) 2007-05-24 2010-12-07 Pcure Ltd. Method and apparatus for teletherapy positioning and validation
US8755489B2 (en) 2010-11-11 2014-06-17 P-Cure, Ltd. Teletherapy location and dose distribution control system and method
EP2840973B1 (fr) 2012-04-24 2017-06-28 Portavision Medical LLC Procédé et système d'imagerie mobile
US10363006B2 (en) 2012-04-24 2019-07-30 Terry L. Ancar Mobile imaging system and method
US10342505B2 (en) 2016-03-31 2019-07-09 General Electric Company System and method for adjusting a radiation dose during imaging of an object within a subject
EP3243437A3 (fr) * 2016-03-31 2018-01-03 General Electric Company Système et procédé de réglage de dose de rayonnement pendant l'imagerie d'un objet à l'intérieur d'un sujet
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US11013485B2 (en) 2018-10-09 2021-05-25 Siemens Healthcare Gmbh Method for dose reduction in an X-ray device taking account of a later display; imaging system; computer program; and data carrier
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