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EP1966762A2 - Diagnostic medical a croisement de temps et de modalites - Google Patents

Diagnostic medical a croisement de temps et de modalites

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Publication number
EP1966762A2
EP1966762A2 EP06848195A EP06848195A EP1966762A2 EP 1966762 A2 EP1966762 A2 EP 1966762A2 EP 06848195 A EP06848195 A EP 06848195A EP 06848195 A EP06848195 A EP 06848195A EP 1966762 A2 EP1966762 A2 EP 1966762A2
Authority
EP
European Patent Office
Prior art keywords
image
images
cross
time
medical
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.)
Withdrawn
Application number
EP06848195A
Other languages
German (de)
English (en)
Inventor
Shoupu Chen
Zhimin Huo
Lawrence Allen Ray
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.)
Carestream Health Inc
Original Assignee
Carestream Health Inc
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Filing date
Publication date
Application filed by Carestream Health Inc filed Critical Carestream Health Inc
Priority claimed from US11/616,320 external-priority patent/US20070237372A1/en
Publication of EP1966762A2 publication Critical patent/EP1966762A2/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/14—Transformations for image registration, e.g. adjusting or mapping for alignment of images
    • G06T3/147—Transformations for image registration, e.g. adjusting or mapping for alignment of images using affine transformations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/38—Registration of image sequences
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • G06F2218/16—Classification; Matching by matching signal segments
    • G06F2218/20—Classification; Matching by matching signal segments by applying autoregressive analysis
    • 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
    • G06T2207/30068—Mammography; Breast
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G06V2201/032—Recognition of patterns in medical or anatomical images of protuberances, polyps nodules, etc.

Definitions

  • the present invention relates to a digital image processing method for image analysis and, in particular, to cross-time and cross-modality inspection of tissues of different properties (for example, abnormal and normal tissues) in medical image.
  • Multi-dimensional image analysis can be used in applications such as automatic quantification of changes (anatomical or functional) in serial image volume scans of body parts, foreign objects localization, consistent diagnostic rendering, and the like.
  • different medical imaging modalities produce images providing different views of human body function and anatomy that have the potential of enhancing diagnostic accuracy dramatically with the help of the right medical image processing software and visualization tools.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET Positron emission tomography
  • SPECT single photon emission computed tomography
  • CT and MRI images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MRL Modalities such as MRI and CT usually provide a stack of images for certain body parts.
  • Daw describes a method of medical image processing and visualization.
  • analysis indicators are provided in the upper left hand corner of the display providing a view indication of the results and status of any computer analysis being performed or that has been performed on the data.
  • Daw's system does not provide a function to automatically detect and differentiate image areas corresponding to materials (tissues) being imaged that have different time response to contrast enhancing agent. Applicants note that such a function is particularly useful in diagnosing malignant and benign breast tumors using MRI contrast enhanced images.
  • U.S. Patent No. 6,353,803 (Degani), incorporated herein by reference, is directed to an apparatus and method for monitoring a system in which a fluid flows and which is characterized by a change in the system with time in space. A preselected place in the system is monitored to collect data at two or more time points correlated to a system event. The data is indicative of a system parameter that varies with time as a function of at least two variables related to system wash-in and wash-out behavior.
  • the present invention provides a method for image analysis and, in particular, for cross-time and cross-modality inspection of tissues of different properties (for example, abnormal and normal tissues) in medical image.
  • An object of the present invention is to provide a method for cross- time and cross-modality inspection of tissues of different properties (for example, abnormal and normal tissues) in medical images.
  • the present invention provides a image processing/pattern recognition method for cross-time and cross-modality inspection of tissues of different properties (for example, abnormal and normal tissues) in medical images.
  • the method includes the steps of optionally classifying tissue properties in cross- time medical image sequences; performing cross-time cross-modality image mapping; and performing interactive cross-time cross-modality medical image inspection.
  • a method of cross- time inspection of tissues of different properties in cross-time medical image sequences includes the steps of: acquiring a plurality of medical image (e.g. MRI images before and after the injection of contrast enhancement agent) cross-time sequences; performing intra-registration of the plurality of medical image cross-time sequences with respect to spatial coordinates; performing inter-registration of the plurality of medical image cross- time sequences with respect to spatial coordinates; classifying tissues of different properties for the registered plurality of medical image cross-time sequences; and presenting the classification results for cross-time inspection.
  • a method for automatic abnormal tissue detection and differentiation using contrast enhanced MRI images augmented with other physical or non-physical factors e.g., MRI images before and after the injection of contrast enhancement agent
  • the method includes the steps of acquiring a plurality of MRI breast image sets; aligning the plurality of MRI breast images with respect to spatial coordinates; differencing the plurality of MRI breast image sets with a reference MRI image set, producing a plurality of difference image sets; segmenting the plurality of difference image sets, producing a plurality of MRI breast images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels, producing a plurality of dynamic system parameters; and classifying the plurality of system parameters augmented with other physical or non-physical factors into different classes.
  • a method for automatic material classification includes the steps of: acquiring a plurality of image sets of an object sequentially in time; aligning the plurality of image sets with respect to spatial coordinates; differencing the plurality of image sets with a reference image set to produce a plurality of difference image sets; segmenting the plurality of difference image sets to produce a plurality of images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels of the plurality of images to produce a plurality of dynamic system parameters; and classifying the plurality of system parameters into different classes.
  • a method for abnormal tissue detection using contrast enhanced MRI images includes the steps of: acquiring a plurality of MRI breast image sets sequentially in time; aligning the plurality of MRI breast image sets with respect to spatial coordinates; differencing the plurality of MRl breast image sets with a reference MRI image set to produce a plurality of difference image sets; segmenting the plurality of difference image sets to produce a plurality of MRI breast image sets with segmented intensity pixels; applying a dynamic system identification to the segmented intensity pixels of the plurality of MRl breast image sets to produce a plurality of dynamic system parameters; and classifying the plurality of system parameters into different classes to detect abnormal tissue.
  • FIG. 1 is a graph illustrating dynamic contrast uptake properties (curves) for different breast tissues.
  • FIG. 2 is a schematic diagram of an image processing system useful in practicing the method in accordance with present invention.
  • FIG. 3 is a flowchart illustrating a method of cross-time and cross- modality inspection of medical images in accordance with the present invention.
  • FIG. 4 is a flowchart illustrating one embodiment of the cross-time tissue property inspection method in accordance with the present invention.
  • FIG. 5 is a flowchart illustrating a method of image registration in accordance with the present invention.
  • FIG. 6 is a graph illustrating image registration concept.
  • FIG. 7 is a graph illustrating two cross-time image sequences.
  • FIG. 8 is a flow chart illustrating one embodiment of the automatic abnormal tissue detection method in accordance with the present invention.
  • FIG. 9 is a graph illustrating dynamic contrast uptake properties (curves) for malignant and benign tumor tissues.
  • FIG. 10 is a schematic diagram illustrating the concept of step function response and system identification.
  • FIG. 11 is a flowchart illustrating a method of system identification in accordance with the present invention.
  • FIG. 12 is a graph illustrating a method of cross-time tissue property inspection visualization presentations of the present invention.
  • FIG. 13 is a graph illustrating tissues with different properties in medical images.
  • FIGS. 14A-14E show a collection of graphs illustrating steps of 3D image volume projections of the present invention.
  • FIG. 15 is a graph illustrating a slice with a cloud of pixels.
  • FIG. 16 is a collection of medical 3D volume projections.
  • FIG. 17 is a graph illustrating one embodiment of cross-time and cross-modality medical image inspection in accordance with the present invention.
  • X— ray mammography has limited specificity and sensitivity. It is reported that 5%- 15% of cancers are missed using X-ray mammograms.
  • MRI mammography as an alternative imaging method, has a high sensitivity for tumors larger than 3mm. It is known that malignant breast tumors begin to grow their own blood supply network once they reach a certain size; this is the way the cancer can continue to grow.
  • a contrast agent injected into the bloodstream can provide information about blood supply to the breast tissues; the agent "lights up" a tumor by highlighting its blood vessel network.
  • a contrast agent for MRI is Gadolinium or gadodiamide, and provides contrast between normal tissue and abnormal tissue in the brain and body. Gadolinium looks clear like water and is non-radioactive. After it is injected into a vein, Gadolinium accumulates in the abnormal tissue that may be affecting the body or head. Gadolinium causes these abnormal areas to become bright (enhanced) on the MRI.
  • Gadolinium is then cleared from the body by the kidneys. Gadolinium allows the MRI to define abnormal tissue with greater clarity. Tumors enhance after Gadolinium is given. The exact size of the tumor and location is important in treatment planning and follow up. Gadolinium is also helpful in finding small tumors by making them bright and easy to see.
  • Dynamic contrast enhanced MRI is used for breast cancer imaging; in particular for those situations that have an inconclusive diagnosis based on x- ray mammography.
  • the MRI study can involve intravenous injection of a contrast agent (typically gadopentetate dimeglumine) immediately prior to acquiring a set of Tl -weighted MR volumes with a temporal resolution of around a minute.
  • a contrast agent typically gadopentetate dimeglumine
  • FIG. 2 shows an image processing system 10 useful in practicing the method in accordance with the present invention.
  • System 10 includes a digital MRl image source 100, for example, an MRI scanner, a digital image storage device (such as a compact disk drive), or the like.
  • the digital image from digital MRI image source 100 is provided to an image processor 102, for example, a programmable personal computer, or digital image processing work station such as a Sun Sparc workstation.
  • Image processor 102 can be connected to a display 104 (such as a CRT display or other monitor), an operator interface such as a keyboard 106, and a mouse 108 or other known input device.
  • Image processor 102 is also connected to computer readable storage medium 107.
  • Image processor 102 transmits processed digital images to an output device 109.
  • Output device 109 can comprise a hard copy printer, a long-term image storage device, a connection to another processor, an image telecommunication device connected, for example, to the Internet, or the like.
  • the present invention comprises a computer program product for detecting abnormal tissues in a digital MRI image in accordance with the method described.
  • the computer program of the present invention can be utilized by any well-known computer system, such as the personal computer shown in Figure 2.
  • other types of computer systems can be used to execute the computer program of the present invention.
  • the method of the present invention can be executed in the computer contained in a digital MRI machine or a PACS (picture archiving communication system). Consequently, the computer system will not be discussed in further detail herein.
  • the computer program product of the present invention can make use of image manipulation algorithms and processes that are well known.
  • Figure 3 generally illustrates a method of cross-time cross-modality medical image inspection of tissues of different properties in medical images. More particularly, Figure 3 shows a flow chart illustrating one embodiment of the method of cross-time cross-modality medical image inspection of tissues of different properties.
  • a plurality of multimodal medical images goes through a series of processes. These processes perform specific functionalities including acquiring medical images of one modality, acquiring medical images of another modality 1204, cross-modality image mapping 1206, optionally classifying tissue properties in cross-time medical image sequences 1202, and performing interactive cross-time cross-modality inspection 1208.
  • step 1202 may or may not be acquired with contrast enhancement agent administrated. If a medical image sequence is acquired without contrast enhancement agent administrated, no classification of tissue properties needs to be performed in step 1202.
  • Figure 4 is a flow chart illustrating one embodiment of the method of the cross-time inspection of tissues of different properties in medical images of the present invention.
  • a plurality of medical image cross-time sequences goes through a series of processes 802.
  • Each of these processes performs a specific functionality such as intra-sequence registration 804, inter-sequence registration 806, dynamic curve classification 808, and visualization and diagnosis 810.
  • FIG. 5 there is shown a flow chart of the method of a generic image registration process.
  • the intent of image registration is to determine a mapping between the coordinates in one space (a two dimensional image) and those in another (another two dimensional image), such that points in the two spaces that correspond to the same feature point of an object are mapped to each other.
  • the process of determining a mapping between the coordinates of two images provides a horizontal displacement map and a vertical displacement map of corresponding points in the two images.
  • the vertical and horizontal displacement maps are then used to deform one of the involved images to minimize the misalignment between the two.
  • the two images involved in registration process are referred to as a source image 1020 and a reference image 1022.
  • x and y are the horizontal and vertical coordinates of the image coordinate system, and t is the image index (image 1, image 2, etc.).
  • t is the image index (image 1, image 2, etc.).
  • the image (or image pixel) is also indexed as /(/ ' ,/) where i and j are strictly integers and parameter t is ignored for simplicity.
  • the column index i runs from 0 to w — 1 .
  • the row index j runs from 0 to h — I .
  • the registration process is to find an optimal transformation function ⁇ , + ⁇ (x,,j' / ) (see step 1002) such that
  • the transformation matrix is comprised of two parts, a rotation
  • the transformation function ⁇ is either a global function or a local function.
  • a global function ⁇ transforms every pixel in an image in a same way.
  • a local function ⁇ transforms each pixel in an image differently based on the location of the pixel.
  • the transformation function ⁇ could be a global function or a local function or a combination of the two.
  • the transformation function ⁇ generates two displacement maps (step 1004), XiUj) , and Y(Uj) , which contain the information that could bring pixels in the source image to new positions that align with the corresponding pixel positions in the reference image.
  • the source image is to be spatially corrected in step 1008 and become a registered source image 1024.
  • the column index i runs from 0 to w— l
  • the row index j runs from 0 to h — 1 .
  • FIG. 6 An exemplary result of misalignment correction is shown in Figure 6. Shown in this figure is a source image 1102, and a reference image 1106. There are varying vertical misalignments between source image 1102 and reference image 1106. By applying the steps shown in Figure 5 to these two images, a vertical misalignment corrected source image is obtained, shown in Figure 6 as image 1104.
  • the registration algorithm used in computing the image transformation function ⁇ could be a rigid registration algorithm, a non-rigid registration algorithm, or a combination of the two.
  • Those skilled in the art understand that there are numerous registration algorithms that can carry out the task of finding the transformation function ⁇ that generates the needed displacement maps for the correction of the misalignment in two relevant images. Exemplary registration algorithms can be found in "Medical Visualization with ITK", by Lydia Ng, et al. at http://www.itk.org.
  • spatially correcting an image with a displacement map could be realized by using any suitable image interpolation algorithms (see for example, "Robot Vision” by Berthold Klaus Paul Horn, The MIT Press Cambridge, Massachusetts.)
  • Box 1000 will be used in the following description of the present invention of cross- time inspection of tissues with different properties.
  • Exemplary MRI image sequences for an object are depicted in Figure 7.
  • An MRI image sequence 704 includes an exemplary collection of MRI slice sets 706, 708 and 710 for the same object (e.g., the breast).
  • Each MRI slice set includes a number of slices that are images (cross- sections) of the object (the breast).
  • Exemplary slices shown in Figure 7 are a slice (image) 712 for set 706, a slice (image) 714 for set 708, and a slice (image) 716 for set 710.
  • MRI slice sets are taken at different times to capture functional changes of the object in time space when contrast enhancement agent is administrated.
  • Exemplary time gaps between the MRl slice sets could be 1 minute, 2 minutes, and the like.
  • sequence 704 For cross-time inspection of tissues with different properties, besides sequence 704, one or more sequences of MRI image for the same object (the breast) are needed.
  • An exemplary MRI sequence 724 is such a sequence.
  • Sequence 724 is captured at a different time from sequence 704. Exemplary time gap between sequence 724 and sequence 704 could be several months.
  • sequence 724 includes an exemplary collection of MRI slice sets 726, 728 and 730 for the same object (the breast).
  • Each MRI slice set contains a number of slices that are images (cross-sections) of the object (the breast).
  • Exemplary slices are a slice (image) 732 for set 726, a slice (image) 734 for set 728, and a slice (image) 736 for set 730.
  • MRI slice sets are taken at different time to capture functional changes of the object in time space. Exemplary time gap between the MRI slice sets could be 1 minute, 2 minutes, or the like.
  • An intra-sequence registration (804) is defined as registering slices
  • images of the same cross-section of an object within a sequence of MRI image sets. For example, slices (images) 712, 714, and 716 for sequence 704, and slices (images) 732, 734, and 736 for sequence 724.
  • intra-sequence registration is discussed in the context of the method of tissue property inspection of a set of images, which acts as an independent entity.
  • the need of intra-sequence registration occurs since during the process of capturing MRI images, due the inevitable object (breast, for example) motion, images (for example, 712, 714 and 716) for the same cross- section of the object present misalignment. This misalignment can cause errors in the process of tissue property inspection.
  • FIG 8 is a flow chart illustrating one embodiment of the automatic abnormal tissue detection method of the present invention. Note that the flow chart illustrated in Figure 8 serves as an independent entity that constitutes a self- contained process. Therefore, the flow chart illustrated in Figure 8 is not interpreted as an expansion of step 808. Rather, step 808 and step 804 are explained using the steps shown in the flow chart in Figure 8.
  • a plurality of MRI breast images sets acquired before and after contrast agent injection go through a series of processes. Each of these processes performs a specific functionality such as alignment, subtraction, segmentation, system identification, and classification.
  • abnormal tissue detection tasks are accomplished by a means of dynamic system parameter classification.
  • a first step 202 (related to step 802 of Figure 4 and step 1202 of Figure 3) is employed for acquiring a plurality of MRI breast image sets before and after an injection of contrast enhancement agent at one time.
  • step 202 repeats to acquire another plurality of MRI breast image sets before and after an injection of contrast enhancement agent at another time.
  • medical image sequences obtained (step 202) at different times can contain only one set image slices in each sequence (e.g. 706 for sequence 704, 726 for sequence 726) without the injection of contract enhancement agent.
  • step 1202 performs acquiring medical image sequences and the classifying tissue properties in cross-time medical image sequences.
  • step 1202 performs the acquisition of medical image sequences; accordingly, steps 804 and 808 will be skipped.
  • steps 804 and 808 will be skipped.
  • i o (x,y,z) Denote i o (x,y,z) as a set of MRI image for a breast with a number of images (slices) in a spatial order before an injection of contrast agent, where ZG. [1,..JS] is the spatial order index, S is the number of images in the set, x and y are the horizontal and vertical indices respectively for an image where x e [1,...X] and y e [1,...7] .
  • a plurality of MRI image sets is acquired with the same number (S) of images of the same breast for each set in the same spatial order z .
  • the plurality of MRI image sets is taken with a temporal resolution, for example, of around one minute.
  • This MRI image sets can be expressed by I k (x, y, z) where k is the temporal order index and ke [1,...K]; K is the number of sets.
  • a step 204 (also step 804 intra-sequence registration), with a reference set of MRI images with respect to spatial coordinates ⁇ ,y .
  • the reference set of MRI image is the set of MRI images, / 0 ⁇ x, y, z) , taken before the injection of the contrast agent.
  • the alignment process ensures that pixels belong to a same tissue region of the breast have the same x, ⁇ coordinates in all the K sets of images.
  • i k (x,y, ⁇ ) is input to terminal A (1032)
  • I 0 (x, y, z) is input to terminal B (1034)
  • the registered image of I k ⁇ x,y,z) is obtained at output terminal D (1038).
  • An exemplary method employable to realize the alignment function, align(A, B) is a non-rigid registration that aligns terminal A with terminal B and is widely used in medical imaging and remote sensing fields.
  • the registration process has been discussed previously.
  • step 206 in Figure 8 carries out differencing the plurality of MRI breast image sets, I k (x, y,z) , ks [1,..X] with a reference MRI image set to produce a plurality of difference image sets, ⁇ l k (x, y, z), k e [1,...K].
  • the set of MRI images, I 0 (x, y, z) is selected as intensity reference images.
  • the differencing process is executed as:
  • An exemplary value of T is an empirical value 10.
  • the segmentation process in step 208 segments the images in the plurality of MRI breast image sets, I k (x, y, ⁇ ) , according to the non-zero pixels in the mask images, M k (x, y, ⁇ ) , to obtain segmented intensity pixels in the images of the plurality of MRI breast image sets.
  • the segmentation operation can be expressed as:
  • stage of generating mask images can be omitted and the segmentation process can be realized by executing:
  • Step 210 of Figure 8 is a dynamic system identification step, which is described with reference to Figures 9 and 10.
  • Figure 9 there is shown a chart that is a replica to the chart shown in Figure 1 except that Figure 9 includes the insertions of a step function, /(O , curve 302 and the removal of the normal and fat tissue curves.
  • Pixels that belong to normal and fat tissues are set to zeros in images S k ( ⁇ ,y,z) in the segmentation step 208.
  • the remaining pixels in images S k ⁇ x, y, z) belong to either malignant or benign tissues. It is difficult to differentiate malignant tissue from benign tissue by solely assessing the pixels brightness (intensity) in a static form, that is, in individual images.
  • the brightness changes present a distinction between these two types of tissues.
  • the brightness (contrast) curve 304, m(t) of the malignant tissue rises quickly above the step function curve 302 and then asymptotically approaches the step function curve 302; while the brightness (contrast) curve 306, b(t) , of the benign tissue rises slowly underneath the step function curve 302 and then asymptotically approaches the step function curve, /(0 , 302.
  • the brightness (contrast) curve 304, m(t) resembles a step response of an underdamped dynamic system
  • the brightness (contrast) curve 306, b(t) resembles a step response of an overdamped dynamic system.
  • FIG. 10 An exemplary generic approach to identifying a dynamic system behavior is generally depicted in Figure 10.
  • a step function 402 is used as an excitation.
  • a response 406 to the step function 402 from the dynamic system 404 is fed to a system identification step 408 in order to estimate dynamic parameters of system 404.
  • system modeling of dynamic system identification 210 can be accomplished at step 212.
  • An exemplary realization of dynamic system modeling 212 is shown in Figure 11 where it is shown an ARX (autoregressive) model 500 (refer to "System identification Toolbox", by Lennart Ljung, The Math Works).
  • a general ARX model can be expressed as the equation:
  • G(q) (506) and H(q) (504) are the system transfer functions, as shown in Figure 11 , u(t) (502) is the excitation, ⁇ (t) (508) is the disturbance, and y(t) (510) is the system output. It is known that the transfer functions G(q) (506) and H(q) (504) can be specified in terms of rational functions of q ⁇ l and specify the numerator and denominator coefficients in the forms:
  • t Q is the data sampling starting time and N 1 is the number of samples.
  • u(t) is a step function.
  • ⁇ m and ⁇ b the corresponding solutions.
  • the computation of ⁇ realizes the step of dynamic system identification 210 (also step 408 of Figure 10).
  • a supervised learning step 218 is provided.
  • a supervised learning is defined as a learning process in which the exemplar set consists of pairs of inputs and desired outputs.
  • the exemplar inputs are ⁇ m and ⁇ b (or the known curves)
  • the exemplar desired outputs are indicators O n , and O b for malignant and benign tumors respectively.
  • step 218 receives M sample breast MRI dynamic curves with known characteristics (benign or malignant) from step 216.
  • An exemplary value for M could be 100.
  • M 111 curves belong to malignant tumors and M b curves belong to benign tumors.
  • Exemplary values for M 1n and M b could be 50 and 50.
  • M b coefficient vectors denote ⁇ ..M b
  • These learned coefficient vectors ⁇ m ' and ⁇ b ' are used to train a classifier that in turn is used to classify a dynamic contrast curve in a detection or diagnosis process.
  • step 220 To increase the specificity (accuracy in differentiating benign tumors from malignant tumors) other factors (step 220) can be incorporated into the training (learning) and classification process. It is known that factors such as the speed of administration of the contrast agent, timing of contrast administration with imaging, acquisition time and slice thickness (refer to "Contrast-enhanced breast MRI: factors affecting sensitivity and specificity", by CW. Piccoli, Eur. Radiol. 7 (Suppl. 5), S281-S288 (1997)).
  • the above described method of tissue property inspection of a set of images (also steps 804 and 808) is applied to all the cross-time image sequences such 704 and 724 for cross-time tissue property inspection. It is understood that in the present invention, the cross-time image sequences are subject to the steps of intra-registration and inter-registration before entering step 808.
  • One exemplary execution procedure of the steps of intra-registration and inter-registration for the sequences is applying intra-registration to sequence 704 first, then applying inter- registration to sequences 704 and 724.
  • sequences 704 and 724 are exchangeable.
  • intra-registration sequence 704 for this particular exemplary execution procedure select arbitrarily a set of images as the reference image set, e.g. set 706. Images of set 706 are then input to terminal B (1034 of Figure 5), other image sets (e.g., 708 and 710) are input to terminal A (1032 of Figure 5).
  • images of sequence 724 are input to terminal A (1032 of Figure 5), images of sequence 704 are input to terminal B (1034 of Figure 5) and the
  • the vector p y [ ⁇ , a, ⁇ , ⁇ , ⁇ ] is traditionally called feature vector in computer vision literature.
  • 9 ⁇ rf represents a domain
  • d is the domain dimension.
  • the data format in Equation (11) is used in supervised leaning step 218 as well as in classification step 214.
  • the data vector p y can be constructed in a different manner and augmented with different physical or non-physical numerical elements (factors) other than the ones aforementioned.
  • classifiers There are known types of classifiers that can be used to accomplish the task of differentiating malignant tumors from benign tumors with the use of dynamic contrast curves along with other physical or non-physical factors.
  • An exemplary classifier is an SVM (support vector machine) (refer to "A tutorial on Support Vector Machines for Pattern Recognition", by C. Burges, Data Mining and Knowledge Discovery, 2(2), 1-47, 1998, Kluwer Academic Publisher, Boston, with information available at the website http://ava.technion.ac.il/karniel/CMCC/SVM-tutorial.pdf).
  • An example case of an SVM classifier would be training and classification of data representing two classes that are separable by a hyper-plane.
  • the goal of training the SVM is to determine the free parameter w and ⁇ .
  • a scaling can always be applied to the scale of W and ⁇ such that all the data obey the paired inequalities:
  • Equation (13) can be solved by minimizing a Lagrangian function
  • step 810 visualization tools are employed for medical professionals to examine concerned regions of the object (regions of interest in the images) for better diagnosis.
  • One embodiment of such visualization facility is illustrated in Figure 12.
  • FIG. 12 There is shown in Figure 12 a computer monitor screen 900 (which can correspond with display 104 in Figure 2) in communication with an image processor (which can correspond with image processor 102 of Figure 2) adapted to practice the method steps described.
  • an image processor which can correspond with image processor 102 of Figure 2
  • slice 712 is the first image of T k e [l,2,3j across three sets (706, 708 and 710) at spatial location 1 ;
  • slice 732 is the first image of e [l,2,3] across three sets (726,
  • Breast images 902 and 912 are shown in slices 712 and 732, respectively. Breast images 902 and 912 are the images of a same cross-section of a breast.
  • a medical professional navigates the image (for example, by moving a computer mouse 108 or other user interface) to move an indicator 906 over a location 908 in slice 712. Simultaneously, a ghost indicator 916 appears at the same spatial location 918 in slice 732 (i.e., same spatial location as 908 in slice 712).
  • a user can also move indicator 916 (as a user interface) over location 918 in slice 732, and simultaneously, ghost mouse 906 appears at the same spatial location 908 in slice 712 as 918 in slice 732.
  • indicator 916 as a user interface
  • ghost mouse 906 appears at the same spatial location 908 in slice 712 as 918 in slice 732.
  • two dynamic curves (solid curve 924 and dashed curve 926) appear in a chart 922 on display 900.
  • Exemplary curves 924 and 926 reflect different tissue properties for the same spot of a breast at two different times. For example, image sequence containing slice 712 can be taken 6 months prior to capturing the sequence containing slice 732. The medical professional can move the mouse to other locations to examine the change of the tissue properties over a period of time (e.g., 6 months). With this visualization facility, disease progression can be readily analyzed.
  • tissue properties can be represented by other means in addition to illustrated dynamic curve plots 924 and 926.
  • tissue properties can be represented by colored angiogenesis maps.
  • multiple cross-time image sequences can be processed by the method of the current invention and multiple dynamic curves can be displayed simultaneously for medical diagnosis.
  • FIG. 13 there is shown an exemplary breast angiogenesis map 1300 that includes a suspicious tumor region 1302 and other tissue regions.
  • region 1302 is a region of interest (ROI) for further inspection (e.g. quantitative analysis), and the remaining other regions are considered to be non-ROIs.
  • ROI region of interest
  • breast angiogenesis map 1300 will be used to describe the process of cross-time, cross-modality inspection of the present invention.
  • PET imaging modalities
  • CT CT
  • US and the like
  • signal (information) formats and/or to other diseases.
  • X— ray mammography has limited specificity and sensitivity.
  • MRI mammography as an alternative imaging method, has a sensitivity for tumors larger than a certain size. It can be beneficial for medical practitioners and researchers to examine both X-ray mammography and MRI images to gain complementary information. For example, micro- calcifications best captured by conventional X-ray images.
  • step 1202 acquires cross-time MRI image sequences as one modality
  • step 1204 acquires cross-time X-ray mammograms as another modality.
  • Fig. 7 along with the cross-time MRI sequences 704 and 724, two exemplary X-ray mammographic images 705 and 725 taken, respectively, at about the same time instances when sequences 704 and 724 are collected. These two mammographic images are to be used in steps 1206 and 1208 for cross-modality analysis.
  • X-ray mammographic images 705 and 725 are projections of a three dimensional object (e.g., breast), while image sequences 704 and 724 are composed of two dimensional slices that are images of cross sections of the three dimensional object (breast).
  • image sequences 704 and 724 are composed of two dimensional slices that are images of cross sections of the three dimensional object (breast).
  • step 1206 of Figure 12 maps data (images) of one modality of higher dimensionality (MRI sequences, 704 and 724) to that of another modality of a lower dimensionality (X-ray images, 705 and 725).
  • mapping process (step 1206) of one modality of higher dimensionality to that of another modality of a lower dimensionality is described with reference to the graphs shown in Figures 14A-14E.
  • FIG 14A mere is shown an exemplary set of MRJ slices 1402 similar to the image sets such as 706 or 726 in Figure 7.
  • set 1402 has three slices 1403, 1404, and 1405 with breast images 1406, 1407 and 1408, respectively.
  • three dimensional medical imaging devices produces image slices wherein a distance between neighboring pixels in a slice is often smaller than the center-to-center slice separation. Therefore, the voxel dimensions are generally not isotropic, which is not desirable in most medical image analysis applications.
  • a step is thus taken in the present invention to perform slice interpolation to make the acquired image set (such as set 1402) be isotropic or sufficiently close to isotropic so that cross-modality mapping can be effectively performed.
  • included in the present invention is a slice interpolation method that generates an arbitrary number of new slices between two existing slices so that the property of isotropic can be obtained.
  • the formula of slice interpolation can be expressed as:
  • I x _ ⁇ ⁇ i — > j) and I ⁇ (j ⁇ i) are two intermediate slices that generate / int .
  • Slices I ⁇ _ ⁇ ⁇ i ⁇ j) and I ⁇ (j — > i) are obtained through the method of pseudo-cross-registration of two original neighboring slices l ⁇ i) and /(j).
  • the coefficients ⁇ and ⁇ — ⁇ control the amount of contributions of two intermediate slices toward the interpolated slice.
  • the roles of the subscripts ⁇ and 1 — ⁇ will be understood in the discussion of partial displacement maps and pseudo registration process below.
  • slice 1413 is an exemplary / int
  • slice 1403 is an exemplary /(/)
  • slice 1404 is an exemplary l ⁇ j).
  • the method of pseudo-cross-registration of two slices for slice interpolation of the present invention is now described.
  • the transformation function ⁇ generates two displacement maps, X(i, j) , and Y(i,j) , which include the information that could bring pixels in the source image to new positions that align with the corresponding pixel positions in the reference image.
  • partial displacement maps X ⁇ (i, j) and Y ⁇ (i, j) are introduced.
  • the partial displacement maps will bring pixels in the source image (slice), I(x) , to new positions that are somewhere between the source image pixels and the corresponding pixel positions in the reference image I(y) .
  • the partial displacement maps X ⁇ (/, j) and Y ⁇ (i, j) are computed with a pre-determined factor ⁇ of a particular value as:
  • Y ⁇ (iJ) ⁇ Y(i,j)
  • X a (iJ) ⁇ X(i,j) where 0 ⁇ a ⁇ 1.
  • the generated partial displacement maps are then used to deform the source image to obtain intermediate image (slice) I a (x — > y) computed as:
  • An interpolated breast image 1415 in slice set 1412 illustrates an interpolated breast with a size halfway between the sizes of 1406 and 1407.
  • slice set 1412 there is another interpolated slice 1414 between the original slices 1404 and 1405.
  • slice set 1412 includes an adequate number of interpolated slices between each pair of original slices so that the isotropic voxel requirement is satisfied.
  • slice set 1412 represents a three- dimensional MRI volume of an object (breast) that needs to be mapped to a lower dimension (2D) space in order to be examined together with the object (breast) representation (X-ray) in the two dimensional space.
  • a mapping from a higher dimension representation to a lower dimension representation involves a projection from a view of interest.
  • the commonly accepted views are Cranio-Caudal (CC) view and Medio-Lateral (ML) view for X-ray mammography.
  • MRl volume e.g., interpolated slice set 1412
  • arbitrary views including the CC and ML views
  • axis 1417 is substantially parallel with a top or bottom edge of the slices and ideally passes through the center of the volume.
  • axis 1417 passes through the center of the actual object (breast) volume since in general the center of the object volume does not necessarily coincide with the center of the slice volume. The method to find the rotation center (object center) will be discussed later.
  • one exemplary method of rotating the slice volume around axis 1417 is by re-slicing the volume of slice set 1412 wherein the resultant slices (e.g. 1423, 1424 and 1425 of set 1422) are perpendicular to axis 1417.
  • Rotating individual slices 1423, 1424 and 1425 is substantially equivalent to rotating slice set 1412.
  • These new slices (1423, 1424 and 1425) intersect with slices 1403, 1413, 1404, 1414 and 1405.
  • Breast Images such as 1406, 1415, 1407 and 1408 become lines in 1423, 1424 and 1425.
  • Projecting slices 1423, 1424 and 1425 in direction 1419 results in a graph 1432 with dots shown in image 1433, as shown in Figure 14D.
  • Projecting slices 1423, 1424 and 1425 in direction 1421 results in an image 1434 with lines shown in graph 1432.
  • slice set 1412 can be rotated in a roll-pitch- yaw fashion about axes 1443, 1444 and 1445 (see graph 1442 of Figure 14E) before performing projections.
  • axes 1443, 1444 and 1445 see graph 1442 of Figure 14E
  • slice set 1412 can be rotated in a roll-pitch- yaw fashion about axes 1443, 1444 and 1445 (see graph 1442 of Figure 14E) before performing projections.
  • axes 1443, 1444 and 1445 see graph 1442 of Figure 14E
  • a center (O 1 , O 2 ) of cloud 1602 is computed by:
  • m pq J J c* elf (c ⁇ , c 2 )dc i dc 2
  • Figure 16 shows three projections of an MRI breast volume after slice interpolation.
  • Image 1533 is a projection along the direction 1419
  • image 1544 is a projection along directionl 421.
  • the mapping process registers the resultant projections (e.g. images 1533 and 1544) with images acquired directly in 2D space (such as X-ray mammograms 705).
  • the 3D volume involved in mapping can be the original unprocessed slices (such as slice set 706), or the 3D volume composed of angiogenesis images (such as map 1300).
  • projections of 3D volume (such as slice set 706 or 716) need to be registered with images (such as 705 or 725).
  • projections of 706 and 716 need to be registered to each other.
  • images 705 and 725 need to be registered to each other as well.
  • FIG. 17 a computer monitor screen 900 (which can correspond with display 104 in Figure 2) in communication with an image processor (102) that executes previously described steps.
  • Displayed on screen 900 are two representative cross-time 2D images (mammograms) 705 and 725 are.
  • image 705 is captured 6 months before image 725 is captured for the same object (a breast).
  • Images 705 and 725 are registered to each other after the acquisition.
  • displays on screen 900 are two cross-time MRI volume projections 1705 and 1725. Practical examples of projections were shown in Figure 16.
  • cross-time volume projections 1705 and 1725 are registered to each other. Moreover, they are registered with 705 and 725 as well.
  • a medical professional moves an indictor 1706 (such as a mouse provided through a user interface) over a location 1708 of breast 1702 in image 705.
  • an indictor 1706 such as a mouse provided through a user interface
  • a marker such as circle 1716 is displayed around the same spatial location 1718 of breast 1712 in image 725 as 1708 in image 705.
  • a circle 1726 appears around the same spatial location 1728 of breast 1722 in image 1705 as 1708 in image 705.
  • a circle 1736 appears around the same spatial location 1738 of breast 1732 in image 1705 as 1708 in image 705.
  • the medical practitioner can select a spot (location/region) of interest in any one of the images (slices) involved in cross-time cross-modality inspection, corresponding spots (regions) will be highlighted with a marker (such as a circle or a square or other form) in all the other images (slices) for pathological analysis.
  • a marker such as a circle or a square or other form
  • the method of cross-time cross-modality inspection of the present invention can be implemented in a stand-alone CAD (computer aid diagnosis) workstation, or in a PACS (picture archiving and communication system).
  • the inspection results can be transmitted through a secured network link or through secured wireless communication.
  • the subject matter of the present invention relates to digital image processing and computer vision technologies, which is understood to mean technologies that digitally process a digital image to recognize and thereby assign useful meaning to human understandable objects, attributes or conditions, and then to utilize the results obtained in the further processing of the digital image.
  • a computer program for performing the method of the present invention can be stored in a computer readable storage medium.
  • This medium may comprise, for example: magnetic storage media such as a magnetic disk (such as a hard drive or a floppy disk) or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; solid state electronic storage devices such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program.
  • the computer program for performing the method of the present invention may also be stored on computer readable storage medium that is connected to the image processor by way of the Internet or other communication medium. Those skilled in the art will readily recognize that the equivalent of such a computer program product may also be constructed in hardware.

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Abstract

La présente invention concerne un procédé d’inspection à croisement de temps et de modalités pour un diagnostic d’imagerie médicale. Un premier ensemble d’images médicales d’un sujet est obtenu, celui-ci étant capturé à une première période de temps par une première modalité. Un second ensemble d’images médicales du sujet est obtenu, celui-ci étant capturé à une seconde période de temps par une seconde modalité. Les premier et second ensembles sont chacun composés d’une pluralité d’images médicales. L’enregistrement des images est réalisé en mappant la pluralité d’images médicales des premier et second ensembles par rapport à des coordonnées spatiales prédéterminées. Un mappage d’images à croisement de temps des premier et second ensembles est réalisé. Des moyens sont proposés pour une analyse d’images médicales à croisement de temps.
EP06848195A 2005-12-29 2006-12-27 Diagnostic medical a croisement de temps et de modalites Withdrawn EP1966762A2 (fr)

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