US20110262022A1 - System and method for processing images - Google Patents

System and method for processing images Download PDF

Info

Publication number
US20110262022A1
US20110262022A1 US13/122,379 US200913122379A US2011262022A1 US 20110262022 A1 US20110262022 A1 US 20110262022A1 US 200913122379 A US200913122379 A US 200913122379A US 2011262022 A1 US2011262022 A1 US 2011262022A1
Authority
US
United States
Prior art keywords
tilde over
temporal
time separated
separated images
selecting
Prior art date
Legal status (The legal status 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 status listed.)
Abandoned
Application number
US13/122,379
Other languages
English (en)
Inventor
Ting Yim Lee
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Western Ontario
Original Assignee
Individual
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
Application filed by Individual filed Critical Individual
Priority to US13/122,379 priority Critical patent/US20110262022A1/en
Assigned to THE UNIVERSITY OF WESTERN ONTARIO reassignment THE UNIVERSITY OF WESTERN ONTARIO ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LEE, TING YING
Assigned to THE UNIVERSITY OF WESTERN ONTARIO reassignment THE UNIVERSITY OF WESTERN ONTARIO CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNOR'S NAME PREVIOUSLY RECORDED ON REEL 026613 FRAME 0188. ASSIGNOR(S) HEREBY CONFIRMS THE TO RE-RECORD ASSIGNMENT TO CORRECT THE ASSIGNOR'S NAME FROM LEE, TING YING TO LEE, TING YIM. Assignors: LEE, TING YIM
Publication of US20110262022A1 publication Critical patent/US20110262022A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • 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
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • 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
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/037—Emission tomography
    • 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
    • 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
    • A61B6/50—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
    • 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
    • A61B6/507—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 determination of haemodynamic parameters, e.g. perfusion 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
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]

Definitions

  • the present invention relates generally to image processing and, more particularly, to a system and method for processing image data obtained by using a dynamic imaging technique
  • Medical imaging encompasses techniques and processes used to create images of the human body for clinical purposes, including medical procedures for diagnosing or monitoring disease.
  • Medical imaging technology has grown to encompass many image recording techniques including electron microscopy, fluoroscopy, magnetic resonance imaging (MRI), nuclear medicine, photoacoustic imaging, positron emission tomography (PET), projection radiography, thermography, computed tomography (CT), and ultrasound.
  • Medical imaging can incorporate the use of compounds referred to as contrast agents or contrast materials to improve the visibility of internal bodily structures in an image.
  • Dynamic medical imaging involves an acquisition process that takes many “snapshots” of the organ/region/body of interest over time in order to capture a time-varying behaviour, for example, distribution of a contrast agent and hence capture of a specific biological state (disease, condition, physiological phenomenon, etc.). As the speed and digital nature of medical imaging evolves, this acquisition data can have tremendous temporal resolution and can result in large quantities of data.
  • Medical imaging technologies have been used widely to improve diagnosis and care for such conditions as cancer, heart disease, brain disorders, and cardiovascular conditions. Most estimates conclude that millions of lives have been saved or dramatically improved as a result of these medical imaging technologies. However, the risk of radiation exposure from such medical imaging technologies for patients must be considered.
  • the increasing use of CT in medical diagnosis has highlighted concern about the increased cancer risk to exposed patients because of the larger radiation doses delivered in CT than the more common, conventional x-ray imaging procedures.
  • the effective dose of a CT Stroke series consisting of: 1) a non-enhanced CT scan to rule out hemorrhage; 2) a CT angiography to localize the occlusion causing the stroke; and 3) a two-phase CT Perfusion protocol to define the ischemic region with blood flow and blood volume and predict hemorrhagic transformation (HT) with blood-brain barrier permeability surface area product (BBB-PS); is 10 mSv, of which 4.9 mSv is contributed by the two-phase CT Perfusion protocol.
  • HT hemorrhagic transformation
  • BBB-PS blood-brain barrier permeability surface area product
  • the CT Stroke series is predicted to induce 80 and 131 additional cancers for every exposed 100,000 male and female acute ischemic stroke (AIS) patients, respectively, with the two-phase CT Perfusion protocol alone predicted to cause half of the additional cancers (Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII. The National Academys Press, Washington D.C., 2006).
  • a method of processing a plurality of time separated images comprising: selecting a plurality of imaging units in each image; determining a temporal difference for each imaging unit; and selecting temporal differences above a threshold limit.
  • a system for processing a plurality of time separated images comprising: an interface for receiving a plurality of time separated images; and a processor for selecting a plurality of imaging units in each image, determining a temporal difference for each imaging unit, and selecting temporal differences above a threshold limit.
  • a computer readable medium embodying a computer program for processing a plurality of time separated images, the computer program comprising: computer program code for selecting a plurality of imaging units in each image; computer program code for determining a temporal difference for each imaging unit; and computer program code for selecting temporal differences above a threshold limit.
  • FIG. 1 is a flow chart of an image processing method
  • FIG. 2 schematically illustrates the positioning of imaging units in a plurality of time separated images
  • FIGS. 3A to 3E show brain blood flow maps from CT images processed with a statistical filtering technique
  • FIG. 4 graphically shows blood flow Figure of Merit calculated for the images shown in FIGS. 3A to 3D using the regions outlined in FIG. 3E ;
  • FIG. 5 is a diagram of a system for implementing the image processing method shown in FIG. 1 .
  • Dynamic medical imaging involves an acquisition process that takes many “snapshots” of an organ/region/body of interest (i.e. a target region) over time in order to capture a time-varying behaviour, for example uptake and/or wash out of a contrast agent.
  • This acquisition process results in the production of a plurality of time separated images.
  • the method and system described herein involves processing of such time separated images.
  • the plurality of time separated images may be obtained from dynamic medical imaging of a patient with or without the use of an injected contrast agent.
  • a contrast agent can selectively increase the contrast of the target region in an image, for example on the basis of the target region's structure or physiological state. For example, one may inject into a patient a compound which has a biophysical, molecular, genetic or cellular affinity for a particular organ, disease, state or physiological process.
  • Such contrast agents are selected to have a property that provides enhanced information to a given imaging technique by altering imaging conditions, for example by altering image contrast, to reflect the behaviour of the compound in the body.
  • Contrast agents for many imaging techniques are well-known. In some cases contrast enhancement does not rely on an injected contrast agent, for example the use of “black blood” or “white blood” in magnetic resonance imaging (MRI) where specific pulse sequences are used to change the magnetic saturation of the blood and thus its appearance in the image, or tagged MRI sequences which alter the magnetic behaviour of a particular tissue or fluid.
  • An injected contrast agent, or a tissue or fluid with altered behaviour may all be regarded as “imaging agents”.
  • the plurality of time separated images are not limited to any particular imaging technique and include, for example, dynamic medical imaging using magnetic resonance imaging (MRI), computed tomography (CT), nuclear medicine (NM) or positron emission tomography (PET).
  • MRI magnetic resonance imaging
  • CT computed tomography
  • NM nuclear medicine
  • PET positron emission tomography
  • the plurality of time separated images are also not limited to particular image types. For example, grayscale or colour images may be processed.
  • An “imaging unit” may be any desired unit for separating an image into equivalent portions including, for example, a pixel, a plurality of pixels, a fraction of a pixel, a voxel, a plurality of voxels, a fraction of a voxel etc.
  • the imaging unit data is represented by a value, for example a digital value, and can thus be quantified and measured.
  • image intensity is measured for corresponding imaging units in the time separated images.
  • a temporal difference can be determined with respect to contrast concentration.
  • the temporal difference of contrast concentration can be represented as a plot of contrast enhancement as a function of time.
  • FIG. 1 Two alternatives for determining temporal differences of contrast concentration and selecting temporal differences are shown in FIG. 1 .
  • a temporal difference is determined for each imaging unit (step 115 ).
  • the determined temporal difference or representations thereof are then ranked according to degree of observed temporal difference (step 120 ).
  • Temporal differences above a threshold limit are then selected (step 125 ).
  • a processed image is then constructed on the basis of the selected temporal differences (step 150 ).
  • the temporal differences are not ranked. Rather, the temporal data for imaging units are analyzed to select temporal differences above a threshold limit using statistical techniques, and more particularly blind signal separation statistical techniques.
  • a covariance matrix of the co-variations of all pairs of contrast enhancement plots in relation to a mean curve is established (step 130 ) and analyzed with a blind signal separation statistical technique (step 135 ), such as with a principal components analysis being applied to the covariance matrix.
  • Eigenvector/eigenvalue pairs are calculated on the basis of the statistical analysis (step 140 ).
  • Resulting eigenvector/eigenvalue pairs above a threshold limit are then selected on the basis of their eigenvalues (step 145 ).
  • a processed image is then constructed using the selected eigenvectors, with different weightings of the selected eigenvectors being used to construct individual imaging units (step 160 ).
  • the plurality of time separated images may optionally be registered to correlate corresponding locations in the time separated images to each other. Registration of the plurality of time separated images is only needed to correct for gross misregistration. Otherwise, slight movement between the time separated images may be tolerated and may even be removed by the processing method described herein.
  • the plurality of time separated images may be preprocessed so that the time separated images are represented with quantifiable values or codes.
  • the time separated images may be represented by digital values using known digitization techniques. If the imaging data of the plurality of time separated images is provided in a digital form, then a digitization step is not necessary.
  • FIG. 2 shows a series of six chronologically ordered time separated images, t 1 to t 6 .
  • a 4 ⁇ 6 array of imaging units is marked on each time separated image.
  • five (5) imaging units, IU 1 to IU 5 have been selected.
  • imaging unit IU 1 is selected at the same position of the array (i.e. column 1 , row 2 ) for each of the time separated images.
  • each of imaging units IU 2 to IU 5 is selected in corresponding positions of the imaging unit array throughout the series of time separated images.
  • values for each imaging unit at its corresponding position throughout the time separated images can be plotted as a function of time to obtain a temporal curve.
  • the plotting step is illustrated by the arrows linking the corresponding positions of imaging units IU 1 , IU 2 and IU 5 .
  • the temporal curve for each imaging unit can then be analyzed to determine a temporal difference. For example, a mean curve for all of the temporal curves obtained can be calculated. The mean curve is then subtracted from each of the temporal curves to obtain a temporal difference curve for each selected imaging unit.
  • the marked array and the selection of imaging units IU 1 to IU 5 is for illustration purposes only, and that any number of imaging units may be selected and analyzed as desired.
  • the determination of temporal differences by subtraction of a mean curve from the temporal curves for imaging units is for illustration only, and other methods of analyzing the temporal curves, for example using derivatives and/or statistical techniques are contemplated.
  • n dynamic images Given a series of n dynamic (i.e. time separated) images from a CT Perfusion study, the n dynamic images can be represented compactly as a matrix:
  • ⁇ tilde over (X) ⁇ [ ⁇ tilde over (x) ⁇ 1 T ⁇ tilde over (x) ⁇ 2 T . . . ⁇ tilde over (x) ⁇ p ⁇ 1 T ⁇ tilde over (x) ⁇ p T ] T
  • the (i,j) th element of ⁇ tilde over (X) ⁇ is the j th element x ij of ⁇ tilde over (x) ⁇ i .
  • Each ⁇ tilde over (x) ⁇ i , i 1, . . .
  • p can also be interpreted as repeated observations of a single time vs. enhancement curve ⁇ tilde over ( ⁇ ) ⁇ (a random process consisting of n random variables, ⁇ 1 , ⁇ 2 , . . . , ⁇ n-1 , ⁇ n ).
  • One approach to removing noise from the n dynamic images is to find an ‘expansion’ of the p ⁇ n matrix, ⁇ tilde over (X) ⁇ , and then truncate the expansion by certain pre-determined criteria.
  • a common expansion is the singular value decomposition of ⁇ tilde over (X) ⁇ :
  • ⁇ is a p ⁇ p orthogonal matrix
  • ⁇ tilde over (V) ⁇ is a n ⁇ n orthogonal matrix
  • ⁇ tilde over (L) ⁇ is a p ⁇ n diagonal matrix with singular values of ⁇ tilde over (X) ⁇ as its diagonal elements.
  • l 1 , . . . ,l n be the singular values of ⁇ tilde over (X) ⁇ (it is assumed without loss of generality that ⁇ tilde over (X) ⁇ is of full column rank, n and p>n). Then:
  • x by definition is a n ⁇ 1 vector (the sample mean time vs. enhancement curve) and the j th component of x , according to Equation (3), is:
  • n is typically 40- 60.
  • ⁇ is a given n ⁇ 1 vector of coefficients.
  • ⁇ is also a zero mean random variable.
  • the sample variance of ⁇ is given by:
  • ⁇ is the eigenvalue of ⁇ tilde over (S) ⁇ and ⁇ is the corresponding eigenvector
  • ⁇ is the eigenvector of ⁇ tilde over (S) ⁇ that has the highest eigenvalue.
  • the sample covariance of ⁇ 1 and ⁇ 2 is:
  • ⁇ 2 is once more an eigenvalue of ⁇ tilde over (S) ⁇ , and ⁇ 2 the corresponding eigenvector.
  • ⁇ 2 T ⁇ tilde over (S) ⁇ 2 ⁇ 2 or the the sample variance of ⁇ 2 is ⁇ 2 .
  • ⁇ 2 is the second largest eigenvalue and ⁇ 2 the corresponding eigenvector.
  • the vectors of coefficients ⁇ 3 , ⁇ 4 , . . . , ⁇ n are the eigenvectors of ⁇ tilde over (S) ⁇ corresponding to ⁇ 3 , ⁇ 4 , . . . , ⁇ n , the third, fourth largest, . . . , and the smallest eigenvalue.
  • the sample variances of the principal components are also given by the eigenvalues of ⁇ tilde over (S) ⁇ .
  • ⁇ tilde over (Z) ⁇ to be the p ⁇ n matrix of the scores of observations on principal components.
  • the i th row consists of the scores of the i th observation of ⁇ tilde over ( ⁇ ) ⁇ (i.e. ⁇ tilde over (y) ⁇ i ) on all principal components ⁇ 1 , ⁇ 2 , . . . , ⁇ n-1 , ⁇ n , that is:
  • ⁇ is a n ⁇ n matrix whose columns are the eigenvectors of ⁇ tilde over (S) ⁇ and is orthogonal.
  • the first principal component, ⁇ 1 , maximized is expressed by:
  • ⁇ tilde over (x) ⁇ 1 ⁇ x is the deviation of the time vs. enhancement curve (TEC) from the i th pixel (pixel block) from the mean of TECs from all pixels (pixel blocks).
  • TEC time vs. enhancement curve
  • the eigenvectors: ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , . . . ⁇ n represent the first, second, third, fourth, . . . , and least dominant time vs. enhancement behaviour in the set of TECs from all pixels.
  • the loadings of the eigenvectors ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , . . . , ⁇ n in the TEC from the i th pixel (pixel block) are:
  • the i th column of ⁇ tilde over (Z) ⁇ therefore, gives the loading map of the eigenvector ⁇ i which corresponds to the i th largest eigenvalue ⁇ i .
  • Equation (1) the p ⁇ n matrix ⁇ tilde over (Y) ⁇ can be expanded by singular value decomposition as:
  • ⁇ is a p ⁇ p orthogonal matrix
  • ⁇ tilde over (V) ⁇ is a n ⁇ n orthogonal matrix
  • ⁇ tilde over (L) ⁇ is a p ⁇ n diagonal matrix with singular values of ⁇ tilde over (Y) ⁇ as its diagonal elements.
  • l 1 , . . . ,l n be the singular values of ⁇ tilde over (Y) ⁇ (it is assumed without loss of generality that ⁇ tilde over (Y) ⁇ is of full column rank, n and p>n).
  • ⁇ tilde over (L) ⁇ ⁇ tilde over (l) ⁇ 1 . . . ⁇ tilde over (l) ⁇ i . . ⁇ tilde over (l) ⁇ n ⁇
  • Equation (5a) is:
  • c* is set to:
  • ⁇ is a p ⁇ p orthogonal matrix
  • ⁇ tilde over (V) ⁇ is a n ⁇ n orthogonal matrix
  • ⁇ tilde over (L) ⁇ is a p ⁇ n diagonal matrix with singular values of ⁇ tilde over (Y) ⁇ , l 1 , . . . ,l n , as its diagonal elements; it is also assumed without loss of generality that ⁇ tilde over (Y) ⁇ is of full column rank, n and p>n.
  • Equation (8) also affords a very simple geometric interpretation as explained in the following.
  • Equation (10) suggests that the TEC for the i th pixel (pixel block) is a weighted summation of r eigenvectors with the weights given by the loading of the i th pixel TEC on the eigenvectors.
  • the eigenvectors ⁇ 1 , ⁇ 2 , . . . , ⁇ r ⁇ 1 , ⁇ r can be regarded as forming an orthonormal basis in n dimensional space.
  • the projection of ⁇ tilde over (y) ⁇ i on ⁇ i is ⁇ tilde over (y) ⁇ i T ⁇ i and ⁇ tilde over (y) ⁇ i can be reconstituted as
  • ⁇ i 1 r ⁇ ( y ⁇ 1 T ⁇ a ⁇ j ) ⁇ a ⁇ j T .
  • ⁇ i 1 r ⁇ a ⁇ i ⁇ a ⁇ i T
  • ⁇ tilde over (X) ⁇ * [ ⁇ tilde over (y) ⁇ 1 *+ x . . . ⁇ tilde over (y) ⁇ i *+ x . . . ⁇ tilde over (y) ⁇ p *+ x ].
  • PCA principal component analysis
  • the PCA method will now be summarized based on a CT Perfusion study that generates a plurality of time separated images that capture the passage of contrast through the brain. Corresponding to each pixel in the series of images, a curve is generated that represents the contrast concentration in that pixel as a function of time. A difference curve, equal to the difference of the curve from the mean of all pixel curves is also generated. A covariance matrix of the co-variations of all pairs of difference curves is calculated and represents all possible variations about the mean curve. The principal component analysis method analyzes the covariance matrix to find common temporal patterns among the curves by finding linear combinations of the difference curves that represent most of the variations about the mean curve.
  • the PCA method is tested versus the traditional filtered back-projection (FBP) method in radiation dose reduction for CT Perfusion applications.
  • FBP filtered back-projection
  • FIG. 3 shows brain blood flow maps of the pig calculated using the J-W model with CT Perfusion images obtained using (A) 190 mA and FBP reconstruction; (B) 100 mA, FBP reconstruction and PCA; (C) 70 mA, FBP reconstruction and PCA; and, (D) 50 mA, FBP reconstruction and PCA.
  • FIG. 3(E) shows regions of interest (3 outlined circles) used for calculation of Figure of Merit of the blood flow maps shown in FIG. 4 . The regions are superimposed on a CT image which shows a coronal section through the brain of the pig. The dark areas are the skull and jaw bones.
  • FIG. 4 shows that in terms of standard deviation/mean of blood flow (Figure of Merit) in the three regions shown in FIG. 3(E) : 70 mA with PCA was better than 190 mA with FBP, while 50 mA PCA was worse than 190 mA with FBP (p ⁇ 0.05).
  • the image quality of the blood flow maps in FIG. 3 also reflects this progression. Therefore, the PCA method is able to reduce radiation dose required to obtain images in CT Perfusion studies and in maintaining the quality of derived blood flow maps compared to the use of FBP alone. More specifically, the PCA method achieved approximately three fold reduction in radiation dose compared to the use of FBP alone.
  • the principal component analysis methodology described herein has been implemented in a computer executable software application using C++.
  • the time required to process ninety-five (95) 512 ⁇ 512 CT brain images is less than two (2) minutes on a general purpose computing device such as for example a personal computer (PC).
  • PC personal computer
  • An input 510 receives a plurality of time separated images, which can be previously stored, received directly from an imaging device, or communicated over a wired or wireless communication link from a remote location.
  • a general purpose computing device 505 receives the time separated images from the input and executes a software application thereby to process the time separated images as described above.
  • the general purpose computing device 505 interfaces with the user through a display 520 , a keyboard 515 and/or a mouse or other pointing device 525 .
  • Results for example the constructed processed images, can be presented on display 520 and/or output to any suitable output device 530 , for example, a printer, a storage device, or a communication device for communicating the results to a remote location.
  • the software application described herein may run as a stand-alone application or may be incorporated into other available applications to provide enhanced functionality to those applications.
  • the software application may include program modules including routines, programs, object components, data structures etc. and may be embodied as computer readable program code stored on a computer readable medium.
  • the computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of computer readable media include for example read-only memory, random-access memory, CD-ROMs, magnetic tape and optical data storage devices.
  • the computer readable program code can also be distributed over a network including coupled computer systems so that the computer readable program code is stored and executed in a distributed fashion.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Physics & Mathematics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • General Health & Medical Sciences (AREA)
  • Radiology & Medical Imaging (AREA)
  • Public Health (AREA)
  • Pathology (AREA)
  • Veterinary Medicine (AREA)
  • Animal Behavior & Ethology (AREA)
  • Surgery (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Biomedical Technology (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Optics & Photonics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • General Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Vascular Medicine (AREA)
  • Data Mining & Analysis (AREA)
  • Pulmonology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Image Processing (AREA)
US13/122,379 2008-10-02 2009-10-02 System and method for processing images Abandoned US20110262022A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US13/122,379 US20110262022A1 (en) 2008-10-02 2009-10-02 System and method for processing images

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US10220608P 2008-10-02 2008-10-02
PCT/CA2009/001397 WO2010037233A1 (en) 2008-10-02 2009-10-02 System and method for processing images
US13/122,379 US20110262022A1 (en) 2008-10-02 2009-10-02 System and method for processing images

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2009/001397 A-371-Of-International WO2010037233A1 (en) 2008-10-02 2009-10-02 System and method for processing images

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US14/039,619 Continuation US8965086B2 (en) 2008-10-02 2013-09-27 System and method for processing images

Publications (1)

Publication Number Publication Date
US20110262022A1 true US20110262022A1 (en) 2011-10-27

Family

ID=42073000

Family Applications (4)

Application Number Title Priority Date Filing Date
US13/122,379 Abandoned US20110262022A1 (en) 2008-10-02 2009-10-02 System and method for processing images
US14/039,619 Expired - Fee Related US8965086B2 (en) 2008-10-02 2013-09-27 System and method for processing images
US14/628,731 Abandoned US20150230771A1 (en) 2008-10-02 2015-02-23 System and method for processing images
US15/338,000 Abandoned US20170109886A1 (en) 2008-10-02 2016-10-28 System and method for processing images

Family Applications After (3)

Application Number Title Priority Date Filing Date
US14/039,619 Expired - Fee Related US8965086B2 (en) 2008-10-02 2013-09-27 System and method for processing images
US14/628,731 Abandoned US20150230771A1 (en) 2008-10-02 2015-02-23 System and method for processing images
US15/338,000 Abandoned US20170109886A1 (en) 2008-10-02 2016-10-28 System and method for processing images

Country Status (4)

Country Link
US (4) US20110262022A1 (zh)
EP (1) EP2332122A4 (zh)
CN (1) CN102282587B (zh)
WO (1) WO2010037233A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130089252A1 (en) * 2010-06-21 2013-04-11 Koninklijke Philips Electronics N.V. Method and system for noise reduction in low dose computed tomography
US20150206287A1 (en) * 2013-10-25 2015-07-23 Acamar Corporation Denoising Raw Image Data Using Content Adaptive Orthonormal Transformation with Cycle Spinning

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103403702A (zh) * 2010-12-24 2013-11-20 澳大利亚国立大学 动态多维图像数据的重构
JP6730980B2 (ja) * 2014-08-14 2020-07-29 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 液体プールを検出し識別するための音響流
CN105809670B (zh) * 2016-02-29 2019-07-19 上海联影医疗科技有限公司 灌注分析方法
WO2017192629A1 (en) * 2016-05-02 2017-11-09 The Regents Of The University Of California System and method for estimating perfusion parameters using medical imaging
JP7086630B2 (ja) * 2018-02-09 2022-06-20 キヤノン株式会社 情報処理装置、情報処理方法、及びプログラム
CN112035818B (zh) * 2020-09-23 2023-08-18 南京航空航天大学 一种基于物理加密辐射成像身份认证系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040005083A1 (en) * 2002-03-26 2004-01-08 Kikuo Fujimura Real-time eye detection and tracking under various light conditions
US20040101156A1 (en) * 2002-11-22 2004-05-27 Dhiraj Kacker Image ranking for imaging products and services
US20040167395A1 (en) * 2003-01-15 2004-08-26 Mirada Solutions Limited, British Body Corporate Dynamic medical imaging
US20060083407A1 (en) * 2004-10-15 2006-04-20 Klaus Zimmermann Method for motion estimation
US20070183629A1 (en) * 2006-02-09 2007-08-09 Porikli Fatih M Method for tracking objects in videos using covariance matrices
US20080146897A1 (en) * 2004-12-07 2008-06-19 Research Foundation Of City University Of New York Optical tomography using independent component analysis for detection and localization of targets in turbid media

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB9314499D0 (en) * 1993-07-12 1993-08-25 Nycomed Imaging As Method
EP1171028B1 (en) * 1999-03-26 2005-11-16 Leif Ostergaard System for determining haemodynamic indices by use of tomographic data
US7187794B2 (en) 2001-10-18 2007-03-06 Research Foundation Of State University Of New York Noise treatment of low-dose computed tomography projections and images
US7078897B2 (en) * 2002-01-16 2006-07-18 Washington University Magnetic resonance method and system for quantification of anisotropic diffusion
FR2848093B1 (fr) * 2002-12-06 2005-12-30 Ge Med Sys Global Tech Co Llc Procede de detection du cycle cardiaque a partir d'angiogramme de vaisseaux coronaires
US20040218794A1 (en) 2003-05-01 2004-11-04 Yi-Hsuan Kao Method for processing perfusion images
US20050113680A1 (en) * 2003-10-29 2005-05-26 Yoshihiro Ikeda Cerebral ischemia diagnosis assisting apparatus, X-ray computer tomography apparatus, and apparatus for aiding diagnosis and treatment of acute cerebral infarct
GB0326381D0 (en) * 2003-11-12 2003-12-17 Inst Of Cancer Res The A method and means for image processing
WO2005120353A1 (en) * 2004-06-14 2005-12-22 Canon Kabushiki Kaisha Image processing device and method
US7668359B2 (en) 2004-06-28 2010-02-23 Siemens Meidcal Solutions USA, Inc. Automatic detection of regions (such as, e.g., renal regions, including, e.g., kidney regions) in dynamic imaging studies
JP4437071B2 (ja) 2004-12-24 2010-03-24 パナソニック株式会社 運転支援装置
US20080292194A1 (en) * 2005-04-27 2008-11-27 Mark Schmidt Method and System for Automatic Detection and Segmentation of Tumors and Associated Edema (Swelling) in Magnetic Resonance (Mri) Images
US20080100473A1 (en) * 2006-10-25 2008-05-01 Siemens Corporate Research, Inc. Spatial-temporal Image Analysis in Vehicle Detection Systems

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040005083A1 (en) * 2002-03-26 2004-01-08 Kikuo Fujimura Real-time eye detection and tracking under various light conditions
US20040101156A1 (en) * 2002-11-22 2004-05-27 Dhiraj Kacker Image ranking for imaging products and services
US20040167395A1 (en) * 2003-01-15 2004-08-26 Mirada Solutions Limited, British Body Corporate Dynamic medical imaging
US20060083407A1 (en) * 2004-10-15 2006-04-20 Klaus Zimmermann Method for motion estimation
US20080146897A1 (en) * 2004-12-07 2008-06-19 Research Foundation Of City University Of New York Optical tomography using independent component analysis for detection and localization of targets in turbid media
US20070183629A1 (en) * 2006-02-09 2007-08-09 Porikli Fatih M Method for tracking objects in videos using covariance matrices

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130089252A1 (en) * 2010-06-21 2013-04-11 Koninklijke Philips Electronics N.V. Method and system for noise reduction in low dose computed tomography
US9189832B2 (en) * 2010-06-21 2015-11-17 Koninklijke Philips N.V. Method and system for noise reduction in low dose computed tomography
US20150206287A1 (en) * 2013-10-25 2015-07-23 Acamar Corporation Denoising Raw Image Data Using Content Adaptive Orthonormal Transformation with Cycle Spinning

Also Published As

Publication number Publication date
EP2332122A4 (en) 2013-11-20
EP2332122A1 (en) 2011-06-15
CN102282587A (zh) 2011-12-14
CN102282587B (zh) 2015-11-25
US8965086B2 (en) 2015-02-24
US20150230771A1 (en) 2015-08-20
US20170109886A1 (en) 2017-04-20
WO2010037233A1 (en) 2010-04-08
US20140044334A1 (en) 2014-02-13

Similar Documents

Publication Publication Date Title
US8965086B2 (en) System and method for processing images
US10997725B2 (en) Image processing method, image processing apparatus, and computer-program product
US20180160935A1 (en) Medical image display apparatus
US7283652B2 (en) Method and system for measuring disease relevant tissue changes
US20200311932A1 (en) Systems and Methods for Synthetic Medical Image Generation
EP1958161B1 (en) Method and system of multivariate analysis on normalized volume-wise data in the sinogram domain for improved quality in positron emission tomography studies
Khader et al. Medical diffusion: denoising diffusion probabilistic models for 3D medical image generation
Tapiovaara et al. SNR and noise measurements for medical imaging: I. A practical approach based on statistical decision theory
US9299146B2 (en) Image processing device, image processing method and image processing program
Butler et al. Processing of spectral X-ray data with principal components analysis
KR20100105576A (ko) 영상 분석
US20090074279A1 (en) Method and system of multivariate analysis on slice-wise data of reference structure normalized images for improved quality in positron emission tomography studies
US8526701B2 (en) Image analysis method and system
He et al. Downsampled imaging geometric modeling for accurate CT reconstruction via deep learning
US7668359B2 (en) Automatic detection of regions (such as, e.g., renal regions, including, e.g., kidney regions) in dynamic imaging studies
US8150133B2 (en) System and method for automatic registration of 4D (3D plus time) renal perfusion MRI data
Lei et al. Estimating standard-dose PET from low-dose PET with deep learning
Vargas-Cardona et al. Tensor decomposition processes for interpolation of diffusion magnetic resonance imaging
Raj et al. Recovery of the spatially-variant deformations in dual-panel PET reconstructions using deep-learning
Park 10 Spatial-domain model observers for optimizing tomosynthesis
US10332252B2 (en) Slope constrained cubic interpolation
Lee et al. Robust extraction of input function from H/sub 2//sup 15/O dynamic myocardial positron emission tomography using independent component analysis
Tichý et al. Estimation of input function from dynamic PET brain data using Bayesian blind source separation
Sun et al. A method for detecting interstructural atrophy correlation in MRI brain images
Chen et al. Temporal and spatial compression of dynamic positron emission tomography in sinogram domain

Legal Events

Date Code Title Description
AS Assignment

Owner name: THE UNIVERSITY OF WESTERN ONTARIO, CANADA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:LEE, TING YING;REEL/FRAME:026613/0188

Effective date: 20110622

AS Assignment

Owner name: THE UNIVERSITY OF WESTERN ONTARIO, CANADA

Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNOR'S NAME PREVIOUSLY RECORDED ON REEL 026613 FRAME 0188. ASSIGNOR(S) HEREBY CONFIRMS THE TO RE-RECORD ASSIGNMENT TO CORRECT THE ASSIGNOR'S NAME FROM LEE, TING YING TO LEE, TING YIM;ASSIGNOR:LEE, TING YIM;REEL/FRAME:026863/0760

Effective date: 20110622

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION