WO2008034182A1 - Procédé et système de traitement automatisé d'image - perfusion en un seul clic - Google Patents

Procédé et système de traitement automatisé d'image - perfusion en un seul clic Download PDF

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Publication number
WO2008034182A1
WO2008034182A1 PCT/AU2007/001388 AU2007001388W WO2008034182A1 WO 2008034182 A1 WO2008034182 A1 WO 2008034182A1 AU 2007001388 W AU2007001388 W AU 2007001388W WO 2008034182 A1 WO2008034182 A1 WO 2008034182A1
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WIPO (PCT)
Prior art keywords
aif
vof
user
image data
profile
Prior art date
Application number
PCT/AU2007/001388
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English (en)
Inventor
Qing Yang
Original Assignee
Apollo Medical Imaging Technology Pty Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from AU2006905183A external-priority patent/AU2006905183A0/en
Application filed by Apollo Medical Imaging Technology Pty Ltd filed Critical Apollo Medical Imaging Technology Pty Ltd
Publication of WO2008034182A1 publication Critical patent/WO2008034182A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0263—Measuring blood flow using NMR
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0275—Measuring blood flow using tracers, e.g. dye dilution
    • 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
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4058—Detecting, measuring or recording for evaluating the nervous system for evaluating the central nervous system
    • A61B5/4064—Evaluating the brain
    • 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
    • G06T2207/30016—Brain
    • 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
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion

Definitions

  • This invention relates to a method and system -for automated image processing of medical imaging data, and more particularly relates to a method and system for automated processing of dynamic perfusion imaging data of a subject in order to obtain desired imaging maps of functional information by a single user action.
  • the process of measuring blood flow within a body of a subject non-invasively is useful in diagnosing and treating the subject. This is particularly the case where a part of a subject or patient, such as a tissue or organ, suffers from diseases due, for example, to cancer or malfunction. Determining perfusion indices including the blood flow through such a tissue or organ can provide important information to a physician in order to determine an appropriate treatment regime for the patient.
  • CT computerised tomography
  • MRI magnetic resonance imaging
  • NM nuclear medicine
  • PET positron emission tomography
  • the temporal profile of the image intensity in a pixel or region of interest (ROI) reflects the characteristics of the contrast agent and hence the blood passing through the tissue.
  • a typical method of obtaining quantitative perfusion indices involves several steps including: (a) loading the dynamic imaging data into a computer memory;
  • the present invention seeks an automated post-processing method to substantially overcome or at least ameliorate, any one or more of the above-mentioned disadvantages associated with, for example, user controls for motion correction, selection of the appropriate AIF and setting the baseline time points or any other process associated with obtaining quantitative perfusion indices.
  • the automated process can be initiated by the user with a single action of a button.
  • a method for automated processing of image data of a subject by a user comprising the steps of:
  • the method may further include the step of applying motion correction after loading the data to compensate for motion artefacts due to the movement over time of, for example, a tissue or organ.
  • the step of determining a global arterial input function (AIF) may be performed by searching through all pixels of imaging slices and identifying a major feeding blood vessel such as an artery.
  • the step of determining the baseline may include time points before the arrival of the contrast agent on an AIF profile.
  • the AIF may be identified by pixels having the properties of early contrast arrival with high and narrow peak profile of contrast enhancement within an arterial first-pass period.
  • the method may further comprise the step of determining a venous output function (VOF) from a draining vein where a selected artery has partial voluming, such as in the brain, the vein being larger than the artery.
  • the VOF can be used to scale up the AIF profile to minimize a partial voluming effect (PVE) from the AIF.
  • the type of image may be any one of CT, MRI, NM or PET.
  • the single action that the user undertakes may be the mouse click of a control button on a computer interface or a key stroke on a keyboard of a computer.
  • the method may further include the feature of measuring the tissue profile and extracting a tissue impulse residue function (IRF) from the AIF and tissue profile by applying a selected model using deconvolution.
  • the perfusion indices calculated may include blood flow, blood volume, mean transit time, arterial delay time and permeability related parameters using the IRF.
  • the perfusion indices may also include dynamic angiography by subtracting the mean baseline image from the dynamic contrast enhanced images.
  • a computer program means for instructing a computer processor, in response to a user undertaking a single action on the processor to perform the steps (a) through to (e) according to the first aspect.
  • Figure 1 is a block diagram showing a communication network including a number of scanners linked to a data storage system and a processing system;
  • Figure 2 shows a database interface on the data storage system or the processing system
  • Figure 3 is a flow diagram showing steps performed by a computer program to enable the automated processing of image data and display of results.
  • the present invention is particularly applicable to CT, MRI, NM and PET imaging systems.
  • Raw data and/or images collected by a scan such as from a CT scanner 20, MRI scanner 25, NM scanner 30 or PET scanner 35 are forwarded to a data storage system 40 in the form of a Picture Archiving Communications System (PACS) in Figure 1.
  • a computer program operating on a processor 50 in the form of a computer, is used to retrieve or receive the various images or raw data from any one of the scanners 20, 25, 30 or 35 or from the data storage system 40.
  • the program then processes those images to provide an improved data set for a clinician to use, particularly in relation to perfusion indices including blood flow, blood volume, mean transit time, arterial delay time, permeability related parameters, etc.
  • the computer program need not reside on computer 50, but may reside in a console computer linked to any one of the scanners 20, 25, 30 or 35. Alternatively the program may reside in a workstation (stand-alone or in a system) or in the data storage system 40.
  • Medical image data is usually accessed on the workstation (or processor 50) via a typical database interface 200 as shown in Figure 2.
  • the user can browse the database and select the image dataset for processing by highlighting or viewing it.
  • a user can select a dataset of series number 220 under patient name 202, a patient ID 204 or a Study ID 206, etc.
  • the highlighted dataset 208 is for a CT perfusion of a stroke patient, as shown at 212.
  • Different control buttons can be provided on the database interface panel for different processing models associated with different organs (such as brain, kidney, liver or other body organs) and different types of image data (such as CT, MRI, NM or PET).
  • the automated image processing method can be executed by a mouse click on one control button 210 for the desired function.
  • the computer program will instruct the computer processor 50 or workstation to load the image data for the CT stroke dataset 208 into a memory of the computer processor.
  • the processor 50 may apply motion correction to the data if it is applicable by software configuration to a particular organ, such as the brain.
  • the automatic motion correction can be applied to the whole image of the subject and need not define a specific Region of Interest (ROI).
  • ROI Region of Interest
  • the processor 50 detects a global AIF from a feeding blood vessel by searching through all pixels of imaging slices and identifying a major feeding blood vessel such as an artery. Thereafter, the processor 50 detects or determines a baseline, including time points before the arrival of a contrast agent on the AIF profile.
  • the baseline is the image data without contrast enhancement.
  • the AIF is identified by pixels having the properties of early contrast arrival with high and narrow peak profile of contrast enhancement within an arterial first-pass period.
  • the processor 50 further converts the signal intensity profile of the data to the contrast concentration profile depending on the type of image data loaded.
  • the processor 50 by configuration, may further detect a VOF from a draining vein in order to scale up the AIF to minimize a PVE from the AIF if it is applicable to a particular organ, such as of the brain. Thereafter the perfusion indices are calculated by applying a model of a region of interest of the subject, in this case the brain of the selected dataset 208. All of these are carried out by the processor as a result of the user clicking an icon or button on screen, using a mouse, or using a keyboard.
  • the results are then displayed on the processor 50 to the user.
  • the user may alternatively click on screen, using a mouse, or use a keyboard, to inspect any one or more of the detected baseline, AIF or VOF profiles after automatically detecting any the baseline, global AIF or VOF, allowing the user to ascertain if these are the correct baseline or AIF, VOF profiles to use for calculating the perfusion indices.
  • the method can be configured to apply a motion correction method depending on the specific function being executed via the mouse click of a control button. For example, by clicking on the button 210 designed for CT perfusion stroke model, a conventional 3D or 2D rigid body image registration method can be applied automatically. Since the movement of a patient's head during data acquisition is mostly within the axial imaging plane, the automatic image registration method can be further restricted to a 2D rigid body method.
  • the image registration method involves aligning the image of each time frame to a reference frame such as the first time frame.
  • the 2D rigid body alignment involves an iterative optimization of two translational parameters and one rotational parameter by minimizing a cost function such as that defined by the mean least squares of the differences between the aligned image and the reference image.
  • nonlinear motion compensation methods such as an image registration method involving nonlinear warping (United States Patent 5,359,513 Kano, et al.)
  • a virtual gating method such as discussed in International Patent
  • the process of selecting the AIF involves the program searching through all pixels and identifying the pixels of a feeding blood vessel such as an artery which has the properties of early contrast arrival with high and narrow peak profile of contrast enhancement within a typical arterial first-pass period.
  • the selected AIF is a global AIF which will be used in subsequent processing of perfusion maps for all pixels from all imaging slices.
  • the baseline time points can be set to include those time points before the contrast arrival on the AIF profile.
  • the automated method may be configured to further comprise determining a venous output function (VOF) from a draining vein where a selected artery has partial voluming, the vein being larger than the artery.
  • VIF venous output function
  • the VOF can be identified as the pixels with the maximum contrast enhancement peak, after the AIF first-pass time period, due to lack of partial voluming.
  • the AIF may then be scaled up for having the same peak height or the same first-pass bolus area as the VOF to minimize a partial voluming effect (PVE) from the AIF.
  • the first-pass AIF and VOF profiles can be obtained by fitting the profiles to gamma-variate function (GVF) profiles respectively to remove contrast recirculation effects.
  • VVF gamma-variate function
  • the method may pause after automatically detecting the AIF and VOF, allowing the user to inspect the detected baseline, AIF and VOF profiles.
  • the software configuration can involve modifying a parameter in a configuration file. During the pause, the user can simply click a "Processing" button to continue processing if satisfied with the detected baseline, AIF and VOF profiles. Otherwise the user may manually place a ROI covering an anatomical region containing an artery and then click a button labelled with "Auto AIF in ROI" to instruct the computer processor 50 to automatically find a new AIF within the ROI.
  • the user can place a different ROI covering another anatomical region covering a vein, and click on a button labelled with "Auto VOF in ROI” to enable the processor 50 to automatically find a new VOF within that ROI.
  • the user may further manually adjust the baseline time frames and/or apply additional motion correction before continuing the processing by clicking on the "Processing" button.
  • the method further involves the step of applying the functional perfusion model on a pixel-by-pixel basis for processing of various perfusion maps (as disclosed in International Patent Application No PCT/AU2004/000821 to the present applicant).
  • the perfusion maps may also include dynamic angiography by subtracting the mean baseline image from the dynamic contrast enhanced images.
  • the results are finally presented in colour display mode for easy visualization, analysis, reporting and archiving by the user.
  • Figure 3 there is shown a flow diagram 300 of the steps taken by a computer program loaded into memory of a computer processor 50.
  • Computer program code is written to perform the various steps and functions depicted in the flow diagram 300 whereby upon clicking the button 210 on the database interface of Figure 2, at step 302, the computer program loads the selected dataset into the memory of the computer processor 50 at step 304.
  • step 306 if it is applicable to a particular scan, such as of the brain, then motion correction can be applied by the computer program.
  • an arterial input function is detected from a feeding blood vessel and, if applicable, a venous output function is detected from a draining vein and used to scale up the arterial input function.
  • baseline time points are set before the arrival of the contrast agent.
  • the user is prompted to accept or refine any one or more of the detected AIF, VOF and baseline manually or semi-automatically.
  • the program converts the signal intensity profile of the data to the contrast concentration profile depending on the type of image scanned.
  • the tissue profile is measured and at step 316 the computer program extracts the tissue impulse residue function from the AIF and tissue profile by applying the selected model using deconvolution techniques.
  • the perfusion indices are calculated and at step 320 the results are then displayed of the whole process to the user.
  • This embodiment has been described using an example of a CT perfusion of a stroke patient.
  • the invention is equally applicable to other disease such as brain tumor or diseases in other body organs, not just of humans but animals as well, and using MRI or NM or PET scans.

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  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Hematology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physiology (AREA)
  • Cardiology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • Pathology (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

L'invention concerne un procédé destiné au traitement automatisé de données d'image d'un sujet, ledit traitement étant réalisé par un utilisateur. Ce procédé consiste à charger les données d'image dans une mémoire, détecter une fonction d'entrée artérielle globale (AIF) provenant d'un vaisseau sanguin alimentant, détecter une ligne de base avant l'arrivée d'un agent de contraste, convertir le profil d'intensité de signal des données d'image en profile de concentration d'agent de contraste en fonction du type des données d'images chargées, et calculer des indices de perfusion. L'utilisateur entreprend une seule action en vue d'effectuer les étapes précédentes et d'afficher les résultats des étapes.
PCT/AU2007/001388 2006-09-20 2007-09-20 Procédé et système de traitement automatisé d'image - perfusion en un seul clic WO2008034182A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AU2006905183A AU2006905183A0 (en) 2006-09-20 Method and System of Automated Image Processing - One Click Perfusion
AU2006905183 2006-09-20

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WO2008034182A1 true WO2008034182A1 (fr) 2008-03-27

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010109356A1 (fr) 2009-03-26 2010-09-30 Koninklijke Philips Electronics N.V. Imagerie de perfusion
US9600883B2 (en) 2009-04-13 2017-03-21 Koninklijke Philips N.V. Plausible reference curves for dynamic, contrast-enhanced imaging studies
US9715575B2 (en) 2011-03-29 2017-07-25 Koninklijke Philips N.V. Image acquisition and/or image related parameter recommender
CN114121236A (zh) * 2021-11-22 2022-03-01 上海商汤智能科技有限公司 图像检测方法及相关装置、电子设备和存储介质
US11883222B2 (en) 2019-01-29 2024-01-30 Andromeda Medical Imaging Inc. System and method for generating perfusion functional maps from temporally resolved helical computed tomographic images

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999049775A2 (fr) * 1998-03-30 1999-10-07 Echovision, Inc. Poste de travail d'echocardiographie
EP1016880A2 (fr) * 1998-12-31 2000-07-05 General Electric Company Procédé d'ajustement automatique d'échelle de velocité et fréquence de répétition d'impulsions pour spectrogrammes dans une sonde d'ultrasons à effet Doppler
WO2001035339A2 (fr) * 1999-10-29 2001-05-17 Cnr Consiglio Nazionale Delle Ricerche Analyse automatique de sequence temporelle d'images anatomiques
US20040218794A1 (en) * 2003-05-01 2004-11-04 Yi-Hsuan Kao Method for processing perfusion images
WO2004095371A1 (fr) * 2003-04-24 2004-11-04 Philips Intellectual Property & Standards Gmbh Delimitation de region en angiographie tomodensitometrique
DE10356275A1 (de) * 2003-11-28 2005-06-30 Siemens Ag Verfahren zur automatischen Segmentierung von phasenkodierten Flussbildern in der Magnetresonanztomographie
US20060025667A1 (en) * 2004-07-29 2006-02-02 Edward Ashton Method for tumor perfusion assessment in clinical trials using dynamic contrast enhanced MRI

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999049775A2 (fr) * 1998-03-30 1999-10-07 Echovision, Inc. Poste de travail d'echocardiographie
EP1016880A2 (fr) * 1998-12-31 2000-07-05 General Electric Company Procédé d'ajustement automatique d'échelle de velocité et fréquence de répétition d'impulsions pour spectrogrammes dans une sonde d'ultrasons à effet Doppler
WO2001035339A2 (fr) * 1999-10-29 2001-05-17 Cnr Consiglio Nazionale Delle Ricerche Analyse automatique de sequence temporelle d'images anatomiques
WO2004095371A1 (fr) * 2003-04-24 2004-11-04 Philips Intellectual Property & Standards Gmbh Delimitation de region en angiographie tomodensitometrique
US20040218794A1 (en) * 2003-05-01 2004-11-04 Yi-Hsuan Kao Method for processing perfusion images
DE10356275A1 (de) * 2003-11-28 2005-06-30 Siemens Ag Verfahren zur automatischen Segmentierung von phasenkodierten Flussbildern in der Magnetresonanztomographie
US20060025667A1 (en) * 2004-07-29 2006-02-02 Edward Ashton Method for tumor perfusion assessment in clinical trials using dynamic contrast enhanced MRI

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2010109356A1 (fr) 2009-03-26 2010-09-30 Koninklijke Philips Electronics N.V. Imagerie de perfusion
CN102362278A (zh) * 2009-03-26 2012-02-22 皇家飞利浦电子股份有限公司 灌注成像
US8965080B2 (en) 2009-03-26 2015-02-24 Koninklijke Philips N.V. Perfusion imaging
US9600883B2 (en) 2009-04-13 2017-03-21 Koninklijke Philips N.V. Plausible reference curves for dynamic, contrast-enhanced imaging studies
US9715575B2 (en) 2011-03-29 2017-07-25 Koninklijke Philips N.V. Image acquisition and/or image related parameter recommender
US11883222B2 (en) 2019-01-29 2024-01-30 Andromeda Medical Imaging Inc. System and method for generating perfusion functional maps from temporally resolved helical computed tomographic images
CN114121236A (zh) * 2021-11-22 2022-03-01 上海商汤智能科技有限公司 图像检测方法及相关装置、电子设备和存储介质

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