CA2220177A1 - Automatic border delineation and dimensioning of regions using contrast enhanced imaging - Google Patents
Automatic border delineation and dimensioning of regions using contrast enhanced imaging Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
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- 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
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
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- 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
- G06T2207/10132—Ultrasound image
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- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
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- 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
- G06T2207/30048—Heart; Cardiac
Abstract
The present invention is a novel system and method for automatically identifying borders of regions of interest within an image of a patient's organ or tissue. The system generates images - before, during and after the administration of a contrast agent. Once the set of images have been taken, the system begins automatic processing of the images. The steps of the processing include the identification of baseline image frames, identification of baseline intensities for each given pixel in the ROI, baseline subtraction on a per-pixel basis, determining a probability of signal-to-noise ratio for each pixel, and thresholding each pixel to determine if a pixel belongs to an area inside the border region or an area outside the border region. To exactly determine which pixels that are at the border, the method refines the set by locally minimizing a total cost function that relates a low value to points typically found on a contrast enhanced image. The border of the region of interest is thereby determined.
Description
W O96/38815 PCT/US96/0~7 AUTOMATIC BORDER DEL~EATION AND DIMENSIONING OF REGIONS
USING CONTRAST ENElANCED IMAGING
FIELD OF THE IN~ENTION
The present invention relates in general to a method for processing ultrasound images of a patient's organs and tissue and, in particular, to a method for cl~ ;..g borders and ~im~n~ioning regions ofthe organs and tissues ofthe patient in such images.
BACKGROI~ND OF THE INVENTION
In mto-lif.~l diagnostic im~ ing, it is important to image regions of interest (ROIs) within a patient and analyze these images to provide effective diagnosis of potential disease conditions. A ~-ecç~c~y colllpollent ofthis diagnosis is the ability to dis-,lhllillaLe b~lweell various structures of the patient's tissues - incl~--ling, but not limited to, organs, tumors, vessels and the like - to identify the particular ROI for diagnosis.
The problems of structure i(lPntifi~ tion are exacell,aled in cases where the ROI is located in a tissue or organ that is moving significantly during the course of im~ping One organ that experiences a good deal of movement during im~ging is the heart. Several im~ ing modalities are currently used. For . ., ~ .lc, it is known to use single photon emission computed tomography ("SPECT"), positron emission tomography ("PET"), 2 o computed tomography ("CT"), m~gn~tic resonance im~ging ("MRI"), angiography and ultrasound. An overview ofthese dif~lell~ modalities is provided in: Cardiac Tm~in~ - A
Companion to Braunwald's Heart Disease, edited by Melvin L. Marcus, Heinrich R.
Schelbert, David J. Skorton, and Gerald L. Wolf (W. B. Saunders Co., Phil~clell hia, 1991).
W O96/38815 PCTrUS96/08257 One modality that has found particular lleefillne.ee is contrast f nh~nced ultrasound im~gin~ Briefly, this technique utilizes ultrasonic im~ginp~ which is based on the principle that waves of sound energy can be focused upon a "region of interest" ("ROI") and rP.flected in such a way as to produce an image thereof. The ultrasonic tr~ne~ f r ~ 5 utilized is placed on a body surface overlying the area to be im~ge-l, and sound waves are directed toward that area. The tr~n~duc~r detects rf flected sound waves and the ~t~h~d scanner tr~n~l~tf ~e the data into video images.
When ultrasonic energy is L~ l ed through a subsl~lce, the amount of energy reflected depf-ntle upon the frequency ofthe Ll~ ",;xx;Qn and the acoustic plopelLies of 10 the sul,sL~lce. Ch~nges in the substance's acoustic plopt;lLies (e.g. variance in the acoustic impedance) are most p.~..,;,lf ~l at the int~.rf~ces of di~e.~llL acoustic df neifif,s and colllp.es~;l,ilities, such as liquid-solid or liquid-gas. Consequently, when ultrasonic energy is dilc;~iLed through tissue, organ structures generate sound reflection signals for detection by the ultrasonic scanner. These signals can be intf n.eifiç d by the proper use of 15 a contrast agent.
There are several types of contrast agents in~ inE~ liquid emulsions, solids, encapsulated fluids and those which employ the use of gas. The latter agents are of particular importance because of their efflciency as a reflector of ultrasound. Resonant gas bubbles scatter sound a thousand times more efficiently than a solid particle of the same size. These types of agents include free bubbles of gas as well as those which are f n~pS~ ted by a shell m~t~ri~l Contrast çnh~n~ed images have the pl ~pel Ly that their presence in a particular ROI produce a contrast visually recognizable from surrounding regions that are not -W O96/38815 PCTrUS9''Q8~7 s--ffil~ed with the agent. One ~Y~mple of this type of im~gingis myocardial contrast echocardiography ("MCE"). In MCE, an intravascular injection of a contrast agentwashes into the patient's heart while, ~im~ neously, ultrasound waves are directed to and reflected from the heart - thereby producing a sequence of echocardiographic images.
In the field of echocardiography, important diagnostic measures include: (1 ) analysis of regional wall motion; and (2) the d~Le~ inn of the ejection fraction.
Abnormal systolic function is a diagnostic indication of cardiac di~ç~e; and measurements of the ejection fraction and regional wall motion are most useful in detec.ting chronic i~çh~mi~ The ejection fraction is a global measure of systolic function, while regional wall motion is a local measure.
The "ejection fraction" ("EF") is a widely used measure ofthe contractile ability of the ventricle. EF is defined as the ratio of the total ventricular stroke volume ("SV") to the end-tli~trlic ventricular volume ("EDV"). In equation form, we have:
SV EDV-ESV
EF - --EDV EDV
where ESV is the end-systolic ventricular volume.
Accurate dele-lllinaLion of EF and wall motion, however, is based on a precise identification of certain heart structures of the patient, such as the left ventricle and the c 2 o endocardial border in the left ventricle. Currently, identification of the endocardial border is made from non-contrast ~nh~n~.ed images. Endocardial borders in these non-contrast ~nh~n~.ed images are either m~m-~lly traced by trained echocardiographers or determined W O96/38815 PCTrUS96/08257 by image processing methods tailored specifically for non-contrast ~nh~nced images.
Such an image processing method is described in A Second-~eneration Computer-based Ed~e Detection Al~orithm for Short-axis. Two--limen~ nal Echocardiographic Images:
Accuracy and Improvement in Interobserver Variability. by Geiser et al. and published in 5 Vol. 3, No. 2, March-April l990 issue of the Journal of the American Society of Echocardiography (pps 79-90).
In Geiser et al.'s method, the co~ ed image processing starts with a human operator selecting three image frames from a cardiac cycle: the opening end-diastolic frame, the end-systolic frame, and the closing end-diastolic frame. Once selected, the 10 operator defines the endocardial and epicardial borders on each of the three selected frames. After the borders are defined for the first three frames, they are refined and the borders in the other frames from other points within the cardiac cycle are ~ o...~;c~lly determined by Geiser et al.'s process.
The disadvantage with Geiser et al.'s process for identification of the endocardiu...
5 is that it is performed without contrast ~nh~n~..om~nt ofthe heart's image. Without contrast enh~ncP.ment several im~ging problems occur. For example, the fibers within the myocardium create more or less bac1~c~tt~r depending upon their orientation relative to the inf.i~nt ultrasound beam - fibers that are parallel to the beam scatter less, so in these regions it is more difficult to di~rel~ iate the endocardium from the hypoechoic chamber 2 o region. These regions occur in the lateral regions of the image. Merely increasing the gain is not a ~ti.cf~ctQry solution, because many instruments have gain dependent lateral resolution, so that the proper identification of the border is adversely affected.
W O96/3881S PCTrUS~6/0~7 One way to avoid this diffficulty is to image with contrast enhancennP.nt The use of contrast agents, such as ALBUNEX~ (a registered trademark of Molecular Biosystems, Inc.), in echocardiograms has enh~nced the image resolution of patient heart structures By adding contrast agent into the heart's cha~l~ber, the ~h~ ber initially becomPs greatly illllmin~ted in co.l.p~-ison to the myocardium (in~ in~ the endocardium). Later, once the agent has washed out of the chamber, the myocardium remains ilhlmin~ted relative to the chamber due to the perfusion of agent into the myocardium tissue. In either case, the border region between the myoc~diu... and the ch~llbel is greatly di~ele~ tP,d - even in the lateral regions where the problem of 10 di~le~ l;nn without contrast ~nh~ncP.mPnt is g-c;~le~L without co--l-~sL.
~ ltho--gh the use of contrast agents has aided in the di~-enliation of the endocardium border, the typical method of border d~linps~tionlelllallls a manual process of "eyeb~lling" the border by a trained cardiologist. However, there are still problems with manual methods of border identification.
Speçific~lly a single frame of echocardiographic image data is selected during the time of appl oAi-.-aLe m~ x;~ ll l l ventricular op~cifiç~tion by the contrast agent A trained - echocardiographer then m~ml~lly traces, in the echocardiographer's best jl~dgmPnt, what appears to be the endocardial border in that single frame. The echocardiographer's j~dgmPnt is based on the perceived differences in the texture of the bri~htne~ in the 2 o image. For those frames where contrast agents have perfused into the myocardium while agent is still in the left ventricle chamber, the difference in texture may be less appa.elll.
Hence, this manual process leaves much to chance in accurately dete- ll.inh.g the endocardium border.
W O 96/38815 PCTrUS9"~Q~7 Additionally, in the single chosen frame, the ventricle may not be completely opacified - while all areas of the left ventricle may be op~ifiecl at some point during the injection of contrast agent, it is not likely that all areas are ~imlllt~neously opacified. For ex~mrle, ~tt~ml~tion and the effects of shadowing may produce an image whereby one 5 region of the left ventricle is at lll~illlUlll brightne~ while, in other regions, no contrast is observed at all.
Either of these problems may cause a border region of the left ventricle to be difflcult to identify, leading to uncel Laillly in the diagnosis process. Specifically, hll~ulope identification of the border region during the end-diastole or end-systole might lead to 10 either an over or under estim~tion of the motion of the ventricle. If the ejection fraction or regional wall motion are over-~ le-l the cardiologist might rule out a suspicion of i~ch.qmi~, when it is in fact present. On the other hand, if the ejection fraction or regional wall motion are under-e,Yl;"l~1lerl~ then the cardiologist might suspect i.~hemi~ where none is present and sent the patient on to a more ~ ellsi~e diagnostic procedure (e.g.
15 angiography or nuclear im~ging) or an expensive and invasive thel~t;uLic procedure (e.g.
angioplasty).
Thus, it is desirable to develop a method for the accurate icl~ntific~tion of the borders of patient tissues? such as the endocardial border of the heart.
It is, therefore, an object of the present invention to provide a method for such 2 0 accurate border identification.
It is another object of the present invention to provide an improved method of diagnosis of ejection fraction and regional wall motion.
W O96/38815 PCTrUS96108257 SIJMMARY OF THE INVENTION
Other features and advantages ofthe present invention will be a~pal~l,L from the following description of the pl ~r~ d embo-lim~nt~, and from the claims.
The present invention is a novel system and method for automatically idellLirying 5 borders of regions of interest within an image of a patient's organ or tissue. Initially, the Opc;l ~Lol . of the system identifies a given set of images that will be taken for the system to a~1tom~tically analyze. For example, if the organ in question is the heart, then the set of images selected for analysis will usually be images that are taken at the same point in the cardiac cycle.
Once the criteria for image set inc1usion is d~;Lelll.illed (e.g. images from the same point in the cardiac cycle), the system begins to generate images - before, during and after the ~ Lion of a collLl ~sL agent. Once the set of images have been taken, the system begins its ~-1tom~tic proces~ing Broadly, the steps of the processing include the itl~ntific.~fion of baseline image frames, id~ntific~tiQn of baseline ;..lç.~;l;es for each given 15 pixel in the ROI, b~cç1ine subtraction on a per-pixel basis, deL~llllinillg a probability of signal-to-noise ratio for each pixel, and thresholding each pixel to determine if a pixel belongs to an area inside the border region or an area outside the border region. To exactly determine which pixels are at the border, the method refines the set by locally Illil~;lll;,;llg a total cost function that relates a low value to points typically found on a 2 0 contrast enh~nced image. The border of the region of interest is thereby determined.
For a full underst~n~in~ of the present invention, reference should now be made to the following det~iled description ofthe pl~r~lled embo~1im~nt~ ofthe invention and to the accolnl)~lyillg drawings.
W O96138815 PCTrUS96/08257 BRIEF DESCRIPTION OF THE DRAWINGS
The file of this patent collL~ins at least one drawing executed in color. Copies of this patent with color drawings will be provided by the Patent and Trademark Offlce upon request and payment of the necç~ y fee.
Figure l depicts the manner in which ultrasound images are taken of a patient's heart by an ultrasound image processor that is used in accordance with the principles of the present invention.
Figure 2 is a high level block diagram of one embodiment of an image processor unit that is used in accordance with the principles of the present invention.
Figures 3-7 depict a flow chart of the prese"Lly claimed border deline~tiQn method.
Figures 8(A) and 8(B) depicts how the present system may select c~n~ te heart chamber border pixels.
DETAILED DESCRIPTION OF THE INVENTION
Although the present invention encomp~es general methods for the im~ging and diagnosis of any patient tissues or organs capable of being im~ged, the present description will be given from the standpoint of im~ging the human heart. In many ways, the problems involved with im~ging the human heart for purposes of border delin~tion and dimensioning are more difficult than with other organs.
One reason is that the regions on both sides of the border may be contrast ~nh~nced However, the chief reason is motion. The human heart, in the course of normal function, moves a great deal. As most border d~line~tion methods require a W O96/38815 PCTrUS96/08257 number of images (some having the heart perfused with a contrast agent) to accurately dt;Le~ ine the border, the movement of the heart tissue from frame-to-frame presents a problem when correlating the parts of heart tissue - especially when tissues do not n~cessQrily occupy the same pixel position in ~ c;nL frames. The present description of 5 the method for im~ging the heart may then be simplified in order to image other patient organs and tissues that do not experience such ~lifficlllti~s. Thus, the present invention should not be limited to merely for imQging the human heart; but encompQ~s~s all tissues capable of being imQ~ed Likewise, the present description is based upon ~-~mini~tration of a contrast agent 10 used with ultrasound imQging methodology. Again, the present invention should not be limited to merely ultrasound; but also ~n~.o.~ Qc~e5 other methodologies that may (or may not) use a contrast agent that is uniquely suited to that particular methodology.
Ultrasound methodology is described in greater detail in co-pending and co-~ipn~d patent application Serial Number 08/428,723 entitled "A METHOD FOR PROCESSING
15 REAL-TIME CONTRAST ENHANCED ULTRASONIC IMAGES", filed on April 25, 1995 by Levene et al., and herein incorporated by reference.
Ultrasound imQgin~ systems are well known in the art. Typical systems are mQmlf~.tllred by, for ~x~mplç7 Hewlett Packard Company; Acuson, Inc.; Toshiba America Medical Systems, Inc.; and Advanced Technology Laboratories. These systems 2 o are employed for tWo-r1im~n~i~mQl imQging Another type of imQging system is based on three-dimensional imQgin~ An example ofthis type of system is m~mlfQctllred by, for example, TomTec Tm~ginp~ Systems, Inc. The present invention may be employed with either two-dimensional or three--1im~n~ional imQging systems.
W O96138815 PCT~US96/08257 Likewise, ultrasound contrast agents are also well-known in the art. They indude, but are not limited to liquid emulsions, solids; encapsulated fluids, encapsulated bioco",p~l;ble gases and combin~tinn~ thereof. Fluorinated liquids and gases are especially useful in contrast compositions. The gaseous agents are of particular 5 importance because of their efficiency as a reflector of ultrasound. Resonant gas bubbles scatter sound a thousand times more efflciently than a solid particle of the same size.
These types of agents include free bubbles of gas as well as those which are encapsulated by a shell material. The contrast agent may be ~llmini.~tPred via any of the known routes.
These routes inr~ de~ but are not limited to intravenous (IV), intr~mll~clll~r (IM), 10 h~ll~Lelial (IA), and intracardiac (IC).
It is appreciated that any tissue or organ that receives a flow of blood may have images processed in the manner ofthe invention. These tissues/organs may inrl.lclP, but are not limited to the kidneys, liver, brain, testes, mllqrleC, and heart.
The angles and direction used to obtain views of the organs during im~ging are 5 well known in the art. For most organs, the various views used are only derived from the planes of the organ,.as there is not a problem with lungs or ribs dP.fining an acoustic window. The~;ru~;, the views are termed sagittal, transverse, and longit~lt1in~1 When im~ging the heart, there are three orthogonal planes, the long axis, the short axis, and the four chamber axis. There are also apical, parasternal, subcostal, or 2 o suprasternal acoustic windows. The common names for the views that are derived from these are the parasternal short axis, apical long axis, parasternal long axis, suprasternal long axis, subcostal short axis, subcostal four chamber, apical two chamber, and apical four chamber. Short axis views may bisect the heart at dirrelenL planes, at the level of the W O96/38815 PCT/US~610~7 mitral valve, at the level of the papillary mu,eçles, or at the level of the apex, for example.
Lastly, the apical four chamber view with the tr~ned~lcçr slightly tilted gives the five ch~llbel view, where the aorta is viell~li7ed with the usual four chambers. For a further ~ description of these various views, see Echocardiography, 5th edition, edited by Harvey F~ig~nb~llm (Lea & Febiger, Phil~-lçlrhi~, 1994).
Referring now to Figure 1, a cut-away view of patient 30 att~chçd to echocardiographic tr~neduc~r 36 is shown. A tr~neducçr is placed on the patient,proxim~tç to heart muscle 32. Images may alternatively be acquired transthoracically or transesoph~gç~lly. An injection (34) of contrast agent is made into the patient's vein so l o that the contrast agent reaches the heart and interacts with the ultrasound waves gen~ led by tr~ned~lc~r 36. Sound waves reflected and detected at tr~neducçr 36 are sent as input into image processing system 38.
As the contrast agent enters into various heart regions, image processing system38 detects an increased amplitude in the reflected ultrasound waves, which is chara~L~ ed by a brip;htçning ofthe image. Tissue areas that do not brighten when expected may in-lic~te a disease cnntlitic n in the area (e.g. poor or no circulation, - presence of Lll~ullll~ls, necrosis or the like).
Referring now to Figure 2, an embodiment, in block diagram form, of image processing system 38 is depicte~l Image processing system 38 comprises diagnostic ultrasound scanner 40, optional analog-to-digital converter 42, image processor 44, digital-to-analog converter 56, and color monitor 58. Ultrasound scanner 40 encomp~ esçs any means of rar1i~ting ultrasound waves to the region of interest and detecting the reflected waves. Scanner 40 could comprise trane(l~lc~r 36 and a means of CA 02220177 1997-ll-27 W O96/3881~ PCTruS9~0Q~7 producing electrical signals in accordance with the reflected waves detecter1 It will be appreciated that such scanners are well known in the art.
The electrical signals generated by scanner 40 could either be digital or analog. If the signals are digital, then the current embodiment could input those signals into image 5 processor 44 directly. Otherwise, an optional A/D collvt;l ~er 42 could be used to convert the analog signals.
Image processor 44 takes these digital signals and processes them to provide video images as output. The current embodiment of image processor 44 comprises a central processing unit 46, trackball 48 for user-supplied input of precl~finecl regions of interest, keyboard 50, and lllC:Illoly 52. Memory 52 may be large enough to retain several video images and store the border d~line~tit n method 54 of the present invention. CPU
44 thus analyzes the video images accoldh~ to stored border d~linP~ti~n method 54.
After a given video image is processed by image processor 44, the video image is output in digital form to D/A converter 56. D/A converter thereby supplies color monitor 15 58 with an analog signal capable of rendering on the l-loniLol. It will be appl~ciated that the present invention could ~ltern~tively use a digital color monitQr, in which case D/A
- converter 56 would be optional.
Having described a current embodiment of the present invention, the border ine7~tion method ofthe present invention will now be described. Figures 3-7 are flowcharts describing the border d~lin~tion method as currently embodied. The method starts at step l 00 with the operator selecting a point of interest in the cardiac cycle where the set of images to be processed will always occur. The same point in the cycle is primarily used to image the heart at the same point in its contraction and to reduce the W O96/3881~ PCTrUS9''08?~7 amount of heart distortion and drift from frame-to-frame because the heart is presumably in the same place at the same point in the cardiac cycle.
Of all the point in the cardiac cycle, the most frequently used are the end-systolic and the end~ etolic points. These points are particularly useful in im~gin~ the heart because they represent the point of m~ximllm contraction and m~ximllm relaxation of the heart in the cardiac cycle. These cardiac points are useful because they are used to measure the contractile ability ofthe heart, i.e., the ejection fraction ofthe heart.
Once having dçri~led the pomt (or points) of the cardiac cycle to capture images, grey scale (not contrast-~-nh~nred) ultrasound im~ ing is started at step 102. As images are being ge~ led~ a decieion is made as to wht;Lhel to process the current image. If the image is at tne point of interest in the cardiac cycle, then the image is processed at steps 104 thru 108. Other-wise, it is not processed. Noncontrast ~nh~nred im~gin~ is continued until a sufficient number of initial b~e~line images are taken at step 110. These initial images, together with later images taken after the contrast agent has "washed out", form the basis of the entirety of the baseline images.
Once the requisite number of initial baseline frames have been taken, then a contrast agent is ~mini~t~red to the patient at step 114 and "washes into" the chambers of the heart, first, then slowly perfuses into the tissues of the heart muscles themselves.
The images are then captured at the selected point(s) in the cardiac cycle until the contrast 2 0 agent is no longer present in the heart's chamber at steps 11 6 thru 122. This could be determined by selecting a "trigger" region of interest (T-ROI) that is used to identify whether the contrast agent is the heart chamber. A most advantageous T-ROI to be W O96/3881~ PCTrUS96/082S7 selected would be somewhere in the heart chamber because the heart chamber receives the contrast agent prior to perfusion in the heart muscle.
After the contrast agent has "washed out" of the heart, several post-contrast, baseline image frames are taken, and the ultrasound im~ ing is tel " ,; .~ l at steps 124 e and 126. It should be appreciated that the step of obtaining the post-contrast baseline values could be omitted in order for real time processing impl~."~ l;t n After obtaining baseline frames, image motion correction is performed to improve the quality of the images at step 128. This may be done either m~n--~lly or in an ~ltom~ted f~ehion If done m~n--~lly, for ~x~mple7 the operator would intlic~te on each image to what extent 1 o and in what direction one image would need to move to register with a reference image.
Such a manual method is described in "Digital Subtraction Myocardial Contrast Echocardiography: Design and Application of a New Analysis Program for Myocardial Perfusion Tm~g~ng " M. T-T~lm~nn et al., J. Am. Soc. Echocardiogr. 7:355-362 (1994).
Examples of auLolllaled methods are described in, for example, "Q--~ntifi~tion of Images Obtained During Myocardial Contrast Echocardiography, " A.R. Jayaweera et al., Echocardiography 11 :385-396 (1994) and "Color Coding of Digitized Echocardiograms:
Description of a New Technique and Application in Detecfing and Correcting for Cardiac Translation," J.R. Bates et al., J. Am. Soc. Echocardiogr. 7:363-369 (1994).
After motion correction is performed, the operator then preselects a general 2 o region of interest on a given frame in order to give the process in an initial region for which to locate the border thereof at step 130. This may be acct-mpli~he~l by having the operator circle the region of interest with a light pen on an interactive video screen or by drawing with a mouse or using keys. This selected region is used only to restrict the CA 02220l77 l997-ll-27 W O 96/38815 PCTrUS9"~2~7 search area for the endocal diUnl border in order to reduce the processing time. A
properly selected region should include the left ventricle surrounded by myocardial tissue.
The analysis then begins on each pixel within the ROI.
The set of true baseline frames are selecte.d from the set of initial, pre-contrast frames and the set of post-contrast frames. Steps 134, 136, and 138 depict three di~lt;l,l ways in which this set may be formed. First, the operator could m~ml~lly select all of the baseline frames. Second, the operator id~ntifiçs an area clearly within the left ventricle and the mean pixel h~ltll~iLy is c~lc~ ted as a function of time. The opel ~or can then identify b~ n~ frames from a plot of h,le lsiLy versus time.
Lastly, the system could aulo~ ;c~lly dt;l~lll~ine the baseline for each pixel starting at step 138. A linear legl~s~ion is pel~lllled on all ofthe data points and the standard deviation ofthe fit is calculated at steps 140 and 142. The analysis may be performed with a varying number of frames at the beEinnin3~ and a varying number of frames at the end of the sequence, until the best fit is determined. It will be apl)reciaLed that such regression analysis is well known to those skilled in the art.
After the linear l ~l es~ion analysis has been performed, the standard deviation of the pixel hlLensily is calclll~te~1 For any given pixel, the data points over time are cc,lllpal ed against the computed standard deviation in step 144. If the pixel intensity is within the standard deviation for a putative ba~linç value, then the pixel data point is 2 o considered a baseline value.
Otherwise, the pixel data point is outside the standard deviation, and the data point is removed from any further consideration at step 146. The linear leglession W O 96138815 PCT~US96108257 analysis is then re-ç~lc~ te~l~inrl~ ing the standard deviation. This defines an iterative process for each pixel over time.
After all the baseline pixel data have been identified, then the pixels of the ch~mkt-r are deLe"nillcd, at step 148. By clearly idelllirying the pixels ofthe r~ .be~-~ the method 5 may then discard these pixels from further consideration in dP-linP~ting the border pixels.
The first step to accompli~hing this goal is baseline subtraction. For each pixel in the ROI, another linear regression analysis is performed on the baseline pixel intensity over time at step 152. This provides a linear best-fit curve having a derived slope and hlLel-;e~L at step 154. For each non-baseline frame occurring at a given time, tl, the 10 baseline intensity is derived from the linear curve as OC~.iullhlg for that particular time.
The b~c~linp value is then subtracted from the non-b~e~line pixel inLel,~iLy at step 156.
Once this estim~ted b~ line intensity is subtracted from the observed pixel intensity, the signal derived solely from the contrast, S;, is determined. In in.~t~nces where ~tt~ml~tion causes shadowing in the image, the observed pixel intensity may have 5 decreased to an extent to be less than the e~ .f ed baseline intensity. In such a case, Sj is taken to be zero.
For each non-baseline or contrast frame, k, a composite signal-to-noise ratio (S/N)k is deLellllined from the signal, Sk, and the signals from the temporally ~ c.~nt heart cycles, Sk l and Sk+l. A peak signal may arise from spurious noise, so that the 2 o signals are weighted acco~ding to the equation:
Wl Sk-l W2 Sk + W3= ~ Sk+l 2 W O96/38815 PCTrUS96/08257 where [ is the calculated standard deviation of the baseline data and w; ( j = 1,2,3) are the weights of the signals. It will be appreciated that more than more than three lFnrls could be used to form the signal-to-noise ratio.
The purpose of the weighting terms in the ç~ls~ tion of the signal-to-noise ratio is to reduce the inflllçnce of noise by p~lr~ g a smoothing within a small time region. It is diffic~llt to d~;Lellllille, a priori, what the optimal values for the w~oightin~ terms will be in these e~lc--l~tions. The optimal values can be deLelll~led by a "receiver opel~Lillg characteristic" (ROC) analysis where, for each variation of the w~i~htin~ factors, the sensitivity and specificity is determined by colllp&lison to a "gold standard" method (e.g.
where the opinion of a group of human experts form the gold standard in a given case). It will be appreciated that the methods of ROC analysis are well known to those in the art of biomedical analysis. An exposition of ROC analysis is provided in RECEIVER
OPERATING CHARACTERISTIC CURVES: A BASIC UNDERSTANDING. by Vining et al., published in RadioGraphics, Vol. 12, No. 6 (November 1992), and herein incorporated by l~;rt;lence.
The signal-to-noise ratio is then treated as a standardized, normal variable and the probability of obtaining the observed (S/N)k from random noise flnct~-~tions, P[(S/N)k]
may be c~lç~ tçcl as follows:
~ P[(N )k~ =2 ~ e ~ 2 3 W O96/38815 PCTrUS~G;~
As will be appreciated, as the signal-to-noise ratio increases, the probability that the signal results from random noise decreases. For each pixel, that probability is cletçrmin~d for each non-baseline frame and the minimllm probability for that pixel is taken at step 16Z.
In order to determine which pixels are then in the heart chamber, the m~imllm signal-to-noise ratio over the non-baseline frames is determined. Because there is a greater degree of briPh~ g in the ventricle than the myocardium r~s--lting from contrast agent ~nh~ncemlont, a probability threshold may be established rli~fin~li~hing the two regions, with probabilities above the threshold idenlirying pixels in the myocal diun- and 0 probabilities below the threshold identifying pixels in the left ventricle. This co~ison is accompli.ched at step 164 and continues until all the pixels in the ROI have been analyzed.
After every pixel in the ROI has been dete--.li..ed as part of the heart chamber or not, it is now possible to ~et~rmine among all the pixels not in the heart chamber, which are border pixels. This can be done by any suitable technique which is known in the art.
5 For example, see "A Novel Algc,.iLh... for the Edge Detection and Edge Fnh~n~m~nt of Medical Images," I. Crooks et al., Me~ Phys. 20:993-998 (1993) and "Multilevel Nonlinear Filters for Edge Detection and Noise Suppression, " H. Hwang et al., IEEE
Trans on Signal Processing 42:249-258 (1994).
In a pl ~r~ d embodiment, cost w~iPhting is used. In that case, if a small area 2 o within the chamber near the border is mi~ ifie-l, an edge detection method will have the chamber area smaller than it should be. The cost function for those points might be high so that the border is still correctly placed.
W O 96/38815 PCTrUS~6/~Q~7 To aid in this final dete- ...inalion, a binary image is made, with pixels above the bri~htening threshold given an i..Lel-si~y of zero and pixels below the threshold an intensity of one. The center of mass of the ventricle pixels, (xl, yl), is then determined at step 172 and referred to as the center ofthe left ventricle:
x, = _. ~ xi 4 m j=, ' m ~ Y' S
10 where m is the number of ventricle pixels.
The envelope (or border) of the ventricle pixels is now dclc~ ..-med from the binary image. The ventricular pixels are searched to find the points that have the minimllm and ... ,.x;.. -.. y value and the .. ;l-;.. ~. and m~imllm x value - thus, d~fining a maximum of four points. It should be app~ cciated that the orientation of the images is not important.
At each of these four locations, there may be one or more points; it is most convenient to pick a location with only one point, but is not neces~ry In the case of all four locations with multiple points, any one of the points at any of the locations will suffice as the reference point of step 178. The point is id~ntified as the first point belonging to the border and it serves as the starting point of the envelope tracing method.
W O96/3881~ PCTrUS96/Q8~7 ENVELOPE TRACING MET~OD
Generally speaking, the envelope is traced by determining which a-ljact-.nt point, among all the ~djac~nt points of the reference point is most likely to be a border point.
This process continues with the most l~cellLly selected ~dj~stsnt point as the new reference point and continues until the border is completely traced.
For the i(l~ntifiç~tion of the next border point, the starting point is referred to as the reference point. The angle, 1, ofthe reference point, (X2, Y2), is d~lellllh~ed as follows:
= tan~' ( Y2 - Y, ) 0 X2-X, 6 From the reference point, a set of potential ~a~ c~.nt border points" are established by putting out radial lines from the reference point. Figures 8A and 8B depict the selection of c~n(~ te border points in the myocal diulll. Figure 8A shows a color 5 picture of a heart chamber (colored red in the Figure) surrounded by the dark myocardium. Figure 8B shows an enlarged view of the region in Figure 8A that is bordered by the white box. As depicted in Figure 8B, as the method of the present invention advances, the border is gradually and autom~tiç~lly filled out (as depicted as the white solid curve). The last border point s~lected is depicted as the white circle. From this last border point, the radial lines are sent out to help determine the next border point.
A c~ntlid~te border point is found along each radial line, with the ventricular pixel nearest to the reference point chosen. Radial lines are radiated out over l 80 degrees, from l to l CA 02220l77 l997-ll-27 W O96/38815 PCTrUS96/082S7 + 180 degrees. The cost function is then calculated for each c~n~ te point. If the cost of all points is above a threshold cost, then the angular range of radial lines is increased.
The c~nt~ te point with the lowest cost is chosen as the ~ c-ont border point and becomes the le~lence point as the tracing continues until the border forms a closed loop.
The cost function may have global and local factors. Global factors, for example, may ~mph~i7e a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors ~mph~ e regional border characteristics. The cost factors are independent and weighted as follows:
1 0 Cj = ~I wi cij 7 where Cj is the total cost assoclated with c~n~litl~te pixel j; cjj is the cost factor i for c~n~ te pixel j; and wi is the w.?ighting factor for cost factor i. As with the w~ip;hting factor mentioned above for the signal-to-noise probability computations, these weight 5 may also be determined by the well known methods of receiver opel~ling characteristics.
Individual cost factors may, for e~ lc, include the following (which correspond to steps 180,182, and 184):
1. Contour definition.
The distance between aClj~c~nt border points is inversely proportional to how well the contour is defined - large distances between points will make the endocardial border appear jagged. For the c~n~li(1~te point, this cost factor, cl, is given as:
W O96/38815 22 PCTrUS96/08257 2 + (y Y )Z I
2 + (y y ~2 1 8 where (xc,yc) is the ~n-1itl~te point; (xr,yr) is the reference point; and (xR,yR) is the previous reference point.
2. Border Sh~ness.
The m~gnitu~e of the first derivative, or gradient, of the pixel hlLellsi~y about the c~nrlitl~te point is a measure of the change from ventricular pixels to myocardial pixels about this point. The m~gnitude of the gradient, G~p5), may be dt;Lelll~ed using the Sobel operators as defined as follows:
G(p5)~ = (p,+ 2P8 + Pg)-(p, + 2P2 + P3) G(p5)y = (P3 + 2P6 + p9) -(p~ + 2 p4+ p7) 10 G(p5) = ~IG(P5)~2+ G(p5)y2 11 where p5 iS the c~n~1id~te point and pl through pg are the neighboring pixels in a matrix format. The cost factor, c2, for the c~n~ te point is:
C2 = G' ,if G2 ~ ~
=lO,ifG2=O 12 CA 02220177 1997-ll-27 W O 96/38815 23 PCTrUS~6/~Y~7 where Gl is the m~gnit~lde of the gradient for the reference point and G2 is the m~gnit~lde of the gradient for the c~n~ te point.
USING CONTRAST ENElANCED IMAGING
FIELD OF THE IN~ENTION
The present invention relates in general to a method for processing ultrasound images of a patient's organs and tissue and, in particular, to a method for cl~ ;..g borders and ~im~n~ioning regions ofthe organs and tissues ofthe patient in such images.
BACKGROI~ND OF THE INVENTION
In mto-lif.~l diagnostic im~ ing, it is important to image regions of interest (ROIs) within a patient and analyze these images to provide effective diagnosis of potential disease conditions. A ~-ecç~c~y colllpollent ofthis diagnosis is the ability to dis-,lhllillaLe b~lweell various structures of the patient's tissues - incl~--ling, but not limited to, organs, tumors, vessels and the like - to identify the particular ROI for diagnosis.
The problems of structure i(lPntifi~ tion are exacell,aled in cases where the ROI is located in a tissue or organ that is moving significantly during the course of im~ping One organ that experiences a good deal of movement during im~ging is the heart. Several im~ ing modalities are currently used. For . ., ~ .lc, it is known to use single photon emission computed tomography ("SPECT"), positron emission tomography ("PET"), 2 o computed tomography ("CT"), m~gn~tic resonance im~ging ("MRI"), angiography and ultrasound. An overview ofthese dif~lell~ modalities is provided in: Cardiac Tm~in~ - A
Companion to Braunwald's Heart Disease, edited by Melvin L. Marcus, Heinrich R.
Schelbert, David J. Skorton, and Gerald L. Wolf (W. B. Saunders Co., Phil~clell hia, 1991).
W O96/38815 PCTrUS96/08257 One modality that has found particular lleefillne.ee is contrast f nh~nced ultrasound im~gin~ Briefly, this technique utilizes ultrasonic im~ginp~ which is based on the principle that waves of sound energy can be focused upon a "region of interest" ("ROI") and rP.flected in such a way as to produce an image thereof. The ultrasonic tr~ne~ f r ~ 5 utilized is placed on a body surface overlying the area to be im~ge-l, and sound waves are directed toward that area. The tr~n~duc~r detects rf flected sound waves and the ~t~h~d scanner tr~n~l~tf ~e the data into video images.
When ultrasonic energy is L~ l ed through a subsl~lce, the amount of energy reflected depf-ntle upon the frequency ofthe Ll~ ",;xx;Qn and the acoustic plopelLies of 10 the sul,sL~lce. Ch~nges in the substance's acoustic plopt;lLies (e.g. variance in the acoustic impedance) are most p.~..,;,lf ~l at the int~.rf~ces of di~e.~llL acoustic df neifif,s and colllp.es~;l,ilities, such as liquid-solid or liquid-gas. Consequently, when ultrasonic energy is dilc;~iLed through tissue, organ structures generate sound reflection signals for detection by the ultrasonic scanner. These signals can be intf n.eifiç d by the proper use of 15 a contrast agent.
There are several types of contrast agents in~ inE~ liquid emulsions, solids, encapsulated fluids and those which employ the use of gas. The latter agents are of particular importance because of their efflciency as a reflector of ultrasound. Resonant gas bubbles scatter sound a thousand times more efficiently than a solid particle of the same size. These types of agents include free bubbles of gas as well as those which are f n~pS~ ted by a shell m~t~ri~l Contrast çnh~n~ed images have the pl ~pel Ly that their presence in a particular ROI produce a contrast visually recognizable from surrounding regions that are not -W O96/38815 PCTrUS9''Q8~7 s--ffil~ed with the agent. One ~Y~mple of this type of im~gingis myocardial contrast echocardiography ("MCE"). In MCE, an intravascular injection of a contrast agentwashes into the patient's heart while, ~im~ neously, ultrasound waves are directed to and reflected from the heart - thereby producing a sequence of echocardiographic images.
In the field of echocardiography, important diagnostic measures include: (1 ) analysis of regional wall motion; and (2) the d~Le~ inn of the ejection fraction.
Abnormal systolic function is a diagnostic indication of cardiac di~ç~e; and measurements of the ejection fraction and regional wall motion are most useful in detec.ting chronic i~çh~mi~ The ejection fraction is a global measure of systolic function, while regional wall motion is a local measure.
The "ejection fraction" ("EF") is a widely used measure ofthe contractile ability of the ventricle. EF is defined as the ratio of the total ventricular stroke volume ("SV") to the end-tli~trlic ventricular volume ("EDV"). In equation form, we have:
SV EDV-ESV
EF - --EDV EDV
where ESV is the end-systolic ventricular volume.
Accurate dele-lllinaLion of EF and wall motion, however, is based on a precise identification of certain heart structures of the patient, such as the left ventricle and the c 2 o endocardial border in the left ventricle. Currently, identification of the endocardial border is made from non-contrast ~nh~n~.ed images. Endocardial borders in these non-contrast ~nh~n~.ed images are either m~m-~lly traced by trained echocardiographers or determined W O96/38815 PCTrUS96/08257 by image processing methods tailored specifically for non-contrast ~nh~nced images.
Such an image processing method is described in A Second-~eneration Computer-based Ed~e Detection Al~orithm for Short-axis. Two--limen~ nal Echocardiographic Images:
Accuracy and Improvement in Interobserver Variability. by Geiser et al. and published in 5 Vol. 3, No. 2, March-April l990 issue of the Journal of the American Society of Echocardiography (pps 79-90).
In Geiser et al.'s method, the co~ ed image processing starts with a human operator selecting three image frames from a cardiac cycle: the opening end-diastolic frame, the end-systolic frame, and the closing end-diastolic frame. Once selected, the 10 operator defines the endocardial and epicardial borders on each of the three selected frames. After the borders are defined for the first three frames, they are refined and the borders in the other frames from other points within the cardiac cycle are ~ o...~;c~lly determined by Geiser et al.'s process.
The disadvantage with Geiser et al.'s process for identification of the endocardiu...
5 is that it is performed without contrast ~nh~n~..om~nt ofthe heart's image. Without contrast enh~ncP.ment several im~ging problems occur. For example, the fibers within the myocardium create more or less bac1~c~tt~r depending upon their orientation relative to the inf.i~nt ultrasound beam - fibers that are parallel to the beam scatter less, so in these regions it is more difficult to di~rel~ iate the endocardium from the hypoechoic chamber 2 o region. These regions occur in the lateral regions of the image. Merely increasing the gain is not a ~ti.cf~ctQry solution, because many instruments have gain dependent lateral resolution, so that the proper identification of the border is adversely affected.
W O96/3881S PCTrUS~6/0~7 One way to avoid this diffficulty is to image with contrast enhancennP.nt The use of contrast agents, such as ALBUNEX~ (a registered trademark of Molecular Biosystems, Inc.), in echocardiograms has enh~nced the image resolution of patient heart structures By adding contrast agent into the heart's cha~l~ber, the ~h~ ber initially becomPs greatly illllmin~ted in co.l.p~-ison to the myocardium (in~ in~ the endocardium). Later, once the agent has washed out of the chamber, the myocardium remains ilhlmin~ted relative to the chamber due to the perfusion of agent into the myocardium tissue. In either case, the border region between the myoc~diu... and the ch~llbel is greatly di~ele~ tP,d - even in the lateral regions where the problem of 10 di~le~ l;nn without contrast ~nh~ncP.mPnt is g-c;~le~L without co--l-~sL.
~ ltho--gh the use of contrast agents has aided in the di~-enliation of the endocardium border, the typical method of border d~linps~tionlelllallls a manual process of "eyeb~lling" the border by a trained cardiologist. However, there are still problems with manual methods of border identification.
Speçific~lly a single frame of echocardiographic image data is selected during the time of appl oAi-.-aLe m~ x;~ ll l l ventricular op~cifiç~tion by the contrast agent A trained - echocardiographer then m~ml~lly traces, in the echocardiographer's best jl~dgmPnt, what appears to be the endocardial border in that single frame. The echocardiographer's j~dgmPnt is based on the perceived differences in the texture of the bri~htne~ in the 2 o image. For those frames where contrast agents have perfused into the myocardium while agent is still in the left ventricle chamber, the difference in texture may be less appa.elll.
Hence, this manual process leaves much to chance in accurately dete- ll.inh.g the endocardium border.
W O 96/38815 PCTrUS9"~Q~7 Additionally, in the single chosen frame, the ventricle may not be completely opacified - while all areas of the left ventricle may be op~ifiecl at some point during the injection of contrast agent, it is not likely that all areas are ~imlllt~neously opacified. For ex~mrle, ~tt~ml~tion and the effects of shadowing may produce an image whereby one 5 region of the left ventricle is at lll~illlUlll brightne~ while, in other regions, no contrast is observed at all.
Either of these problems may cause a border region of the left ventricle to be difflcult to identify, leading to uncel Laillly in the diagnosis process. Specifically, hll~ulope identification of the border region during the end-diastole or end-systole might lead to 10 either an over or under estim~tion of the motion of the ventricle. If the ejection fraction or regional wall motion are over-~ le-l the cardiologist might rule out a suspicion of i~ch.qmi~, when it is in fact present. On the other hand, if the ejection fraction or regional wall motion are under-e,Yl;"l~1lerl~ then the cardiologist might suspect i.~hemi~ where none is present and sent the patient on to a more ~ ellsi~e diagnostic procedure (e.g.
15 angiography or nuclear im~ging) or an expensive and invasive thel~t;uLic procedure (e.g.
angioplasty).
Thus, it is desirable to develop a method for the accurate icl~ntific~tion of the borders of patient tissues? such as the endocardial border of the heart.
It is, therefore, an object of the present invention to provide a method for such 2 0 accurate border identification.
It is another object of the present invention to provide an improved method of diagnosis of ejection fraction and regional wall motion.
W O96/38815 PCTrUS96108257 SIJMMARY OF THE INVENTION
Other features and advantages ofthe present invention will be a~pal~l,L from the following description of the pl ~r~ d embo-lim~nt~, and from the claims.
The present invention is a novel system and method for automatically idellLirying 5 borders of regions of interest within an image of a patient's organ or tissue. Initially, the Opc;l ~Lol . of the system identifies a given set of images that will be taken for the system to a~1tom~tically analyze. For example, if the organ in question is the heart, then the set of images selected for analysis will usually be images that are taken at the same point in the cardiac cycle.
Once the criteria for image set inc1usion is d~;Lelll.illed (e.g. images from the same point in the cardiac cycle), the system begins to generate images - before, during and after the ~ Lion of a collLl ~sL agent. Once the set of images have been taken, the system begins its ~-1tom~tic proces~ing Broadly, the steps of the processing include the itl~ntific.~fion of baseline image frames, id~ntific~tiQn of baseline ;..lç.~;l;es for each given 15 pixel in the ROI, b~cç1ine subtraction on a per-pixel basis, deL~llllinillg a probability of signal-to-noise ratio for each pixel, and thresholding each pixel to determine if a pixel belongs to an area inside the border region or an area outside the border region. To exactly determine which pixels are at the border, the method refines the set by locally Illil~;lll;,;llg a total cost function that relates a low value to points typically found on a 2 0 contrast enh~nced image. The border of the region of interest is thereby determined.
For a full underst~n~in~ of the present invention, reference should now be made to the following det~iled description ofthe pl~r~lled embo~1im~nt~ ofthe invention and to the accolnl)~lyillg drawings.
W O96138815 PCTrUS96/08257 BRIEF DESCRIPTION OF THE DRAWINGS
The file of this patent collL~ins at least one drawing executed in color. Copies of this patent with color drawings will be provided by the Patent and Trademark Offlce upon request and payment of the necç~ y fee.
Figure l depicts the manner in which ultrasound images are taken of a patient's heart by an ultrasound image processor that is used in accordance with the principles of the present invention.
Figure 2 is a high level block diagram of one embodiment of an image processor unit that is used in accordance with the principles of the present invention.
Figures 3-7 depict a flow chart of the prese"Lly claimed border deline~tiQn method.
Figures 8(A) and 8(B) depicts how the present system may select c~n~ te heart chamber border pixels.
DETAILED DESCRIPTION OF THE INVENTION
Although the present invention encomp~es general methods for the im~ging and diagnosis of any patient tissues or organs capable of being im~ged, the present description will be given from the standpoint of im~ging the human heart. In many ways, the problems involved with im~ging the human heart for purposes of border delin~tion and dimensioning are more difficult than with other organs.
One reason is that the regions on both sides of the border may be contrast ~nh~nced However, the chief reason is motion. The human heart, in the course of normal function, moves a great deal. As most border d~line~tion methods require a W O96/38815 PCTrUS96/08257 number of images (some having the heart perfused with a contrast agent) to accurately dt;Le~ ine the border, the movement of the heart tissue from frame-to-frame presents a problem when correlating the parts of heart tissue - especially when tissues do not n~cessQrily occupy the same pixel position in ~ c;nL frames. The present description of 5 the method for im~ging the heart may then be simplified in order to image other patient organs and tissues that do not experience such ~lifficlllti~s. Thus, the present invention should not be limited to merely for imQging the human heart; but encompQ~s~s all tissues capable of being imQ~ed Likewise, the present description is based upon ~-~mini~tration of a contrast agent 10 used with ultrasound imQging methodology. Again, the present invention should not be limited to merely ultrasound; but also ~n~.o.~ Qc~e5 other methodologies that may (or may not) use a contrast agent that is uniquely suited to that particular methodology.
Ultrasound methodology is described in greater detail in co-pending and co-~ipn~d patent application Serial Number 08/428,723 entitled "A METHOD FOR PROCESSING
15 REAL-TIME CONTRAST ENHANCED ULTRASONIC IMAGES", filed on April 25, 1995 by Levene et al., and herein incorporated by reference.
Ultrasound imQgin~ systems are well known in the art. Typical systems are mQmlf~.tllred by, for ~x~mplç7 Hewlett Packard Company; Acuson, Inc.; Toshiba America Medical Systems, Inc.; and Advanced Technology Laboratories. These systems 2 o are employed for tWo-r1im~n~i~mQl imQging Another type of imQging system is based on three-dimensional imQgin~ An example ofthis type of system is m~mlfQctllred by, for example, TomTec Tm~ginp~ Systems, Inc. The present invention may be employed with either two-dimensional or three--1im~n~ional imQging systems.
W O96138815 PCT~US96/08257 Likewise, ultrasound contrast agents are also well-known in the art. They indude, but are not limited to liquid emulsions, solids; encapsulated fluids, encapsulated bioco",p~l;ble gases and combin~tinn~ thereof. Fluorinated liquids and gases are especially useful in contrast compositions. The gaseous agents are of particular 5 importance because of their efficiency as a reflector of ultrasound. Resonant gas bubbles scatter sound a thousand times more efflciently than a solid particle of the same size.
These types of agents include free bubbles of gas as well as those which are encapsulated by a shell material. The contrast agent may be ~llmini.~tPred via any of the known routes.
These routes inr~ de~ but are not limited to intravenous (IV), intr~mll~clll~r (IM), 10 h~ll~Lelial (IA), and intracardiac (IC).
It is appreciated that any tissue or organ that receives a flow of blood may have images processed in the manner ofthe invention. These tissues/organs may inrl.lclP, but are not limited to the kidneys, liver, brain, testes, mllqrleC, and heart.
The angles and direction used to obtain views of the organs during im~ging are 5 well known in the art. For most organs, the various views used are only derived from the planes of the organ,.as there is not a problem with lungs or ribs dP.fining an acoustic window. The~;ru~;, the views are termed sagittal, transverse, and longit~lt1in~1 When im~ging the heart, there are three orthogonal planes, the long axis, the short axis, and the four chamber axis. There are also apical, parasternal, subcostal, or 2 o suprasternal acoustic windows. The common names for the views that are derived from these are the parasternal short axis, apical long axis, parasternal long axis, suprasternal long axis, subcostal short axis, subcostal four chamber, apical two chamber, and apical four chamber. Short axis views may bisect the heart at dirrelenL planes, at the level of the W O96/38815 PCT/US~610~7 mitral valve, at the level of the papillary mu,eçles, or at the level of the apex, for example.
Lastly, the apical four chamber view with the tr~ned~lcçr slightly tilted gives the five ch~llbel view, where the aorta is viell~li7ed with the usual four chambers. For a further ~ description of these various views, see Echocardiography, 5th edition, edited by Harvey F~ig~nb~llm (Lea & Febiger, Phil~-lçlrhi~, 1994).
Referring now to Figure 1, a cut-away view of patient 30 att~chçd to echocardiographic tr~neduc~r 36 is shown. A tr~neducçr is placed on the patient,proxim~tç to heart muscle 32. Images may alternatively be acquired transthoracically or transesoph~gç~lly. An injection (34) of contrast agent is made into the patient's vein so l o that the contrast agent reaches the heart and interacts with the ultrasound waves gen~ led by tr~ned~lc~r 36. Sound waves reflected and detected at tr~neducçr 36 are sent as input into image processing system 38.
As the contrast agent enters into various heart regions, image processing system38 detects an increased amplitude in the reflected ultrasound waves, which is chara~L~ ed by a brip;htçning ofthe image. Tissue areas that do not brighten when expected may in-lic~te a disease cnntlitic n in the area (e.g. poor or no circulation, - presence of Lll~ullll~ls, necrosis or the like).
Referring now to Figure 2, an embodiment, in block diagram form, of image processing system 38 is depicte~l Image processing system 38 comprises diagnostic ultrasound scanner 40, optional analog-to-digital converter 42, image processor 44, digital-to-analog converter 56, and color monitor 58. Ultrasound scanner 40 encomp~ esçs any means of rar1i~ting ultrasound waves to the region of interest and detecting the reflected waves. Scanner 40 could comprise trane(l~lc~r 36 and a means of CA 02220177 1997-ll-27 W O96/3881~ PCTruS9~0Q~7 producing electrical signals in accordance with the reflected waves detecter1 It will be appreciated that such scanners are well known in the art.
The electrical signals generated by scanner 40 could either be digital or analog. If the signals are digital, then the current embodiment could input those signals into image 5 processor 44 directly. Otherwise, an optional A/D collvt;l ~er 42 could be used to convert the analog signals.
Image processor 44 takes these digital signals and processes them to provide video images as output. The current embodiment of image processor 44 comprises a central processing unit 46, trackball 48 for user-supplied input of precl~finecl regions of interest, keyboard 50, and lllC:Illoly 52. Memory 52 may be large enough to retain several video images and store the border d~line~tit n method 54 of the present invention. CPU
44 thus analyzes the video images accoldh~ to stored border d~linP~ti~n method 54.
After a given video image is processed by image processor 44, the video image is output in digital form to D/A converter 56. D/A converter thereby supplies color monitor 15 58 with an analog signal capable of rendering on the l-loniLol. It will be appl~ciated that the present invention could ~ltern~tively use a digital color monitQr, in which case D/A
- converter 56 would be optional.
Having described a current embodiment of the present invention, the border ine7~tion method ofthe present invention will now be described. Figures 3-7 are flowcharts describing the border d~lin~tion method as currently embodied. The method starts at step l 00 with the operator selecting a point of interest in the cardiac cycle where the set of images to be processed will always occur. The same point in the cycle is primarily used to image the heart at the same point in its contraction and to reduce the W O96/3881~ PCTrUS9''08?~7 amount of heart distortion and drift from frame-to-frame because the heart is presumably in the same place at the same point in the cardiac cycle.
Of all the point in the cardiac cycle, the most frequently used are the end-systolic and the end~ etolic points. These points are particularly useful in im~gin~ the heart because they represent the point of m~ximllm contraction and m~ximllm relaxation of the heart in the cardiac cycle. These cardiac points are useful because they are used to measure the contractile ability ofthe heart, i.e., the ejection fraction ofthe heart.
Once having dçri~led the pomt (or points) of the cardiac cycle to capture images, grey scale (not contrast-~-nh~nred) ultrasound im~ ing is started at step 102. As images are being ge~ led~ a decieion is made as to wht;Lhel to process the current image. If the image is at tne point of interest in the cardiac cycle, then the image is processed at steps 104 thru 108. Other-wise, it is not processed. Noncontrast ~nh~nred im~gin~ is continued until a sufficient number of initial b~e~line images are taken at step 110. These initial images, together with later images taken after the contrast agent has "washed out", form the basis of the entirety of the baseline images.
Once the requisite number of initial baseline frames have been taken, then a contrast agent is ~mini~t~red to the patient at step 114 and "washes into" the chambers of the heart, first, then slowly perfuses into the tissues of the heart muscles themselves.
The images are then captured at the selected point(s) in the cardiac cycle until the contrast 2 0 agent is no longer present in the heart's chamber at steps 11 6 thru 122. This could be determined by selecting a "trigger" region of interest (T-ROI) that is used to identify whether the contrast agent is the heart chamber. A most advantageous T-ROI to be W O96/3881~ PCTrUS96/082S7 selected would be somewhere in the heart chamber because the heart chamber receives the contrast agent prior to perfusion in the heart muscle.
After the contrast agent has "washed out" of the heart, several post-contrast, baseline image frames are taken, and the ultrasound im~ ing is tel " ,; .~ l at steps 124 e and 126. It should be appreciated that the step of obtaining the post-contrast baseline values could be omitted in order for real time processing impl~."~ l;t n After obtaining baseline frames, image motion correction is performed to improve the quality of the images at step 128. This may be done either m~n--~lly or in an ~ltom~ted f~ehion If done m~n--~lly, for ~x~mple7 the operator would intlic~te on each image to what extent 1 o and in what direction one image would need to move to register with a reference image.
Such a manual method is described in "Digital Subtraction Myocardial Contrast Echocardiography: Design and Application of a New Analysis Program for Myocardial Perfusion Tm~g~ng " M. T-T~lm~nn et al., J. Am. Soc. Echocardiogr. 7:355-362 (1994).
Examples of auLolllaled methods are described in, for example, "Q--~ntifi~tion of Images Obtained During Myocardial Contrast Echocardiography, " A.R. Jayaweera et al., Echocardiography 11 :385-396 (1994) and "Color Coding of Digitized Echocardiograms:
Description of a New Technique and Application in Detecfing and Correcting for Cardiac Translation," J.R. Bates et al., J. Am. Soc. Echocardiogr. 7:363-369 (1994).
After motion correction is performed, the operator then preselects a general 2 o region of interest on a given frame in order to give the process in an initial region for which to locate the border thereof at step 130. This may be acct-mpli~he~l by having the operator circle the region of interest with a light pen on an interactive video screen or by drawing with a mouse or using keys. This selected region is used only to restrict the CA 02220l77 l997-ll-27 W O 96/38815 PCTrUS9"~2~7 search area for the endocal diUnl border in order to reduce the processing time. A
properly selected region should include the left ventricle surrounded by myocardial tissue.
The analysis then begins on each pixel within the ROI.
The set of true baseline frames are selecte.d from the set of initial, pre-contrast frames and the set of post-contrast frames. Steps 134, 136, and 138 depict three di~lt;l,l ways in which this set may be formed. First, the operator could m~ml~lly select all of the baseline frames. Second, the operator id~ntifiçs an area clearly within the left ventricle and the mean pixel h~ltll~iLy is c~lc~ ted as a function of time. The opel ~or can then identify b~ n~ frames from a plot of h,le lsiLy versus time.
Lastly, the system could aulo~ ;c~lly dt;l~lll~ine the baseline for each pixel starting at step 138. A linear legl~s~ion is pel~lllled on all ofthe data points and the standard deviation ofthe fit is calculated at steps 140 and 142. The analysis may be performed with a varying number of frames at the beEinnin3~ and a varying number of frames at the end of the sequence, until the best fit is determined. It will be apl)reciaLed that such regression analysis is well known to those skilled in the art.
After the linear l ~l es~ion analysis has been performed, the standard deviation of the pixel hlLensily is calclll~te~1 For any given pixel, the data points over time are cc,lllpal ed against the computed standard deviation in step 144. If the pixel intensity is within the standard deviation for a putative ba~linç value, then the pixel data point is 2 o considered a baseline value.
Otherwise, the pixel data point is outside the standard deviation, and the data point is removed from any further consideration at step 146. The linear leglession W O 96138815 PCT~US96108257 analysis is then re-ç~lc~ te~l~inrl~ ing the standard deviation. This defines an iterative process for each pixel over time.
After all the baseline pixel data have been identified, then the pixels of the ch~mkt-r are deLe"nillcd, at step 148. By clearly idelllirying the pixels ofthe r~ .be~-~ the method 5 may then discard these pixels from further consideration in dP-linP~ting the border pixels.
The first step to accompli~hing this goal is baseline subtraction. For each pixel in the ROI, another linear regression analysis is performed on the baseline pixel intensity over time at step 152. This provides a linear best-fit curve having a derived slope and hlLel-;e~L at step 154. For each non-baseline frame occurring at a given time, tl, the 10 baseline intensity is derived from the linear curve as OC~.iullhlg for that particular time.
The b~c~linp value is then subtracted from the non-b~e~line pixel inLel,~iLy at step 156.
Once this estim~ted b~ line intensity is subtracted from the observed pixel intensity, the signal derived solely from the contrast, S;, is determined. In in.~t~nces where ~tt~ml~tion causes shadowing in the image, the observed pixel intensity may have 5 decreased to an extent to be less than the e~ .f ed baseline intensity. In such a case, Sj is taken to be zero.
For each non-baseline or contrast frame, k, a composite signal-to-noise ratio (S/N)k is deLellllined from the signal, Sk, and the signals from the temporally ~ c.~nt heart cycles, Sk l and Sk+l. A peak signal may arise from spurious noise, so that the 2 o signals are weighted acco~ding to the equation:
Wl Sk-l W2 Sk + W3= ~ Sk+l 2 W O96/38815 PCTrUS96/08257 where [ is the calculated standard deviation of the baseline data and w; ( j = 1,2,3) are the weights of the signals. It will be appreciated that more than more than three lFnrls could be used to form the signal-to-noise ratio.
The purpose of the weighting terms in the ç~ls~ tion of the signal-to-noise ratio is to reduce the inflllçnce of noise by p~lr~ g a smoothing within a small time region. It is diffic~llt to d~;Lellllille, a priori, what the optimal values for the w~oightin~ terms will be in these e~lc--l~tions. The optimal values can be deLelll~led by a "receiver opel~Lillg characteristic" (ROC) analysis where, for each variation of the w~i~htin~ factors, the sensitivity and specificity is determined by colllp&lison to a "gold standard" method (e.g.
where the opinion of a group of human experts form the gold standard in a given case). It will be appreciated that the methods of ROC analysis are well known to those in the art of biomedical analysis. An exposition of ROC analysis is provided in RECEIVER
OPERATING CHARACTERISTIC CURVES: A BASIC UNDERSTANDING. by Vining et al., published in RadioGraphics, Vol. 12, No. 6 (November 1992), and herein incorporated by l~;rt;lence.
The signal-to-noise ratio is then treated as a standardized, normal variable and the probability of obtaining the observed (S/N)k from random noise flnct~-~tions, P[(S/N)k]
may be c~lç~ tçcl as follows:
~ P[(N )k~ =2 ~ e ~ 2 3 W O96/38815 PCTrUS~G;~
As will be appreciated, as the signal-to-noise ratio increases, the probability that the signal results from random noise decreases. For each pixel, that probability is cletçrmin~d for each non-baseline frame and the minimllm probability for that pixel is taken at step 16Z.
In order to determine which pixels are then in the heart chamber, the m~imllm signal-to-noise ratio over the non-baseline frames is determined. Because there is a greater degree of briPh~ g in the ventricle than the myocardium r~s--lting from contrast agent ~nh~ncemlont, a probability threshold may be established rli~fin~li~hing the two regions, with probabilities above the threshold idenlirying pixels in the myocal diun- and 0 probabilities below the threshold identifying pixels in the left ventricle. This co~ison is accompli.ched at step 164 and continues until all the pixels in the ROI have been analyzed.
After every pixel in the ROI has been dete--.li..ed as part of the heart chamber or not, it is now possible to ~et~rmine among all the pixels not in the heart chamber, which are border pixels. This can be done by any suitable technique which is known in the art.
5 For example, see "A Novel Algc,.iLh... for the Edge Detection and Edge Fnh~n~m~nt of Medical Images," I. Crooks et al., Me~ Phys. 20:993-998 (1993) and "Multilevel Nonlinear Filters for Edge Detection and Noise Suppression, " H. Hwang et al., IEEE
Trans on Signal Processing 42:249-258 (1994).
In a pl ~r~ d embodiment, cost w~iPhting is used. In that case, if a small area 2 o within the chamber near the border is mi~ ifie-l, an edge detection method will have the chamber area smaller than it should be. The cost function for those points might be high so that the border is still correctly placed.
W O 96/38815 PCTrUS~6/~Q~7 To aid in this final dete- ...inalion, a binary image is made, with pixels above the bri~htening threshold given an i..Lel-si~y of zero and pixels below the threshold an intensity of one. The center of mass of the ventricle pixels, (xl, yl), is then determined at step 172 and referred to as the center ofthe left ventricle:
x, = _. ~ xi 4 m j=, ' m ~ Y' S
10 where m is the number of ventricle pixels.
The envelope (or border) of the ventricle pixels is now dclc~ ..-med from the binary image. The ventricular pixels are searched to find the points that have the minimllm and ... ,.x;.. -.. y value and the .. ;l-;.. ~. and m~imllm x value - thus, d~fining a maximum of four points. It should be app~ cciated that the orientation of the images is not important.
At each of these four locations, there may be one or more points; it is most convenient to pick a location with only one point, but is not neces~ry In the case of all four locations with multiple points, any one of the points at any of the locations will suffice as the reference point of step 178. The point is id~ntified as the first point belonging to the border and it serves as the starting point of the envelope tracing method.
W O96/3881~ PCTrUS96/Q8~7 ENVELOPE TRACING MET~OD
Generally speaking, the envelope is traced by determining which a-ljact-.nt point, among all the ~djac~nt points of the reference point is most likely to be a border point.
This process continues with the most l~cellLly selected ~dj~stsnt point as the new reference point and continues until the border is completely traced.
For the i(l~ntifiç~tion of the next border point, the starting point is referred to as the reference point. The angle, 1, ofthe reference point, (X2, Y2), is d~lellllh~ed as follows:
= tan~' ( Y2 - Y, ) 0 X2-X, 6 From the reference point, a set of potential ~a~ c~.nt border points" are established by putting out radial lines from the reference point. Figures 8A and 8B depict the selection of c~n(~ te border points in the myocal diulll. Figure 8A shows a color 5 picture of a heart chamber (colored red in the Figure) surrounded by the dark myocardium. Figure 8B shows an enlarged view of the region in Figure 8A that is bordered by the white box. As depicted in Figure 8B, as the method of the present invention advances, the border is gradually and autom~tiç~lly filled out (as depicted as the white solid curve). The last border point s~lected is depicted as the white circle. From this last border point, the radial lines are sent out to help determine the next border point.
A c~ntlid~te border point is found along each radial line, with the ventricular pixel nearest to the reference point chosen. Radial lines are radiated out over l 80 degrees, from l to l CA 02220l77 l997-ll-27 W O96/38815 PCTrUS96/082S7 + 180 degrees. The cost function is then calculated for each c~n~ te point. If the cost of all points is above a threshold cost, then the angular range of radial lines is increased.
The c~nt~ te point with the lowest cost is chosen as the ~ c-ont border point and becomes the le~lence point as the tracing continues until the border forms a closed loop.
The cost function may have global and local factors. Global factors, for example, may ~mph~i7e a smoothness in the change of the area of the left ventricle over the cardiac cycle. Local factors ~mph~ e regional border characteristics. The cost factors are independent and weighted as follows:
1 0 Cj = ~I wi cij 7 where Cj is the total cost assoclated with c~n~litl~te pixel j; cjj is the cost factor i for c~n~ te pixel j; and wi is the w.?ighting factor for cost factor i. As with the w~ip;hting factor mentioned above for the signal-to-noise probability computations, these weight 5 may also be determined by the well known methods of receiver opel~ling characteristics.
Individual cost factors may, for e~ lc, include the following (which correspond to steps 180,182, and 184):
1. Contour definition.
The distance between aClj~c~nt border points is inversely proportional to how well the contour is defined - large distances between points will make the endocardial border appear jagged. For the c~n~li(1~te point, this cost factor, cl, is given as:
W O96/38815 22 PCTrUS96/08257 2 + (y Y )Z I
2 + (y y ~2 1 8 where (xc,yc) is the ~n-1itl~te point; (xr,yr) is the reference point; and (xR,yR) is the previous reference point.
2. Border Sh~ness.
The m~gnitu~e of the first derivative, or gradient, of the pixel hlLellsi~y about the c~nrlitl~te point is a measure of the change from ventricular pixels to myocardial pixels about this point. The m~gnitude of the gradient, G~p5), may be dt;Lelll~ed using the Sobel operators as defined as follows:
G(p5)~ = (p,+ 2P8 + Pg)-(p, + 2P2 + P3) G(p5)y = (P3 + 2P6 + p9) -(p~ + 2 p4+ p7) 10 G(p5) = ~IG(P5)~2+ G(p5)y2 11 where p5 iS the c~n~1id~te point and pl through pg are the neighboring pixels in a matrix format. The cost factor, c2, for the c~n~ te point is:
C2 = G' ,if G2 ~ ~
=lO,ifG2=O 12 CA 02220177 1997-ll-27 W O 96/38815 23 PCTrUS~6/~Y~7 where Gl is the m~gnit~lde of the gradient for the reference point and G2 is the m~gnit~lde of the gradient for the c~n~ te point.
3. Contour Regularity.
~ 5 The angle of the gradient of the pixel intensity about the border should be slowly rh~nging for a smooth contour. The cost factor, c3, for the c~n-iitl~te point is given as:
C3 = I + 1~-1 sin(~r ~ ~c ~ ~ 13 where >r iS the angle of the gradient at the reference point; >c is the gradient angle for the c~n-lir1~te. point, and > is the angle bt;Lweell a line from the reference point to the center ofthe ventricle and the c~nrlid~te point. The angle of the gradient is given by:
~7 = tan~'(G(Ps)y/G(p5)~)) 14 It will be appreciated that although only three cost functions are herein ~ C~ etl many other di~t;lellL cost functions may be employed to identify potential border point.
Thus, the present invention should not be limited to the use of these particular cost functions. Indeed, the present invention encomp~s~ any cost method that aids in the W O96/38815 PCTrUS96/08257 automatic dc~ ~in~Lion of a border point. Moreover, the present invention encompasses the use of any subcombination of cost functions described herein.
After the endocardial border is fully identified in this manner, a summary image may be pr~s~ntecl The background of the sllmm~ry image may consist of an average of 5 the baseline frames. Superimposed upon this background is the border, which may be highlighted in a dirre~lt;-lL color. A possible format to display the border is depicted in Figure 8B as the solid white border line. The border is thus shown as the continuous broad white band that encloses the left ventricle chamber.
There has thus been shown and described a novel system and method for the 10 d~line~tion of a border region of a patient tissue or organ which meets the objects and advantages sought. As stated above, many ~h~ , mo~lific~tion~, variations and other uses and applic~tic)n~ of the subject invention will, however, become appalt;llL to those skilled in the art after considering this specification and accolllpallyhlg drawings which disclose prer~lled embodiments thereof. All such çh~ngee, motlific~tiQn~, variations and 15 other uses and applications which do not depart from the spirit and scope of the invention are deemed to be covered by the invention which is limited only by the claims which follow.
~ 5 The angle of the gradient of the pixel intensity about the border should be slowly rh~nging for a smooth contour. The cost factor, c3, for the c~n-iitl~te point is given as:
C3 = I + 1~-1 sin(~r ~ ~c ~ ~ 13 where >r iS the angle of the gradient at the reference point; >c is the gradient angle for the c~n-lir1~te. point, and > is the angle bt;Lweell a line from the reference point to the center ofthe ventricle and the c~nrlid~te point. The angle of the gradient is given by:
~7 = tan~'(G(Ps)y/G(p5)~)) 14 It will be appreciated that although only three cost functions are herein ~ C~ etl many other di~t;lellL cost functions may be employed to identify potential border point.
Thus, the present invention should not be limited to the use of these particular cost functions. Indeed, the present invention encomp~s~ any cost method that aids in the W O96/38815 PCTrUS96/08257 automatic dc~ ~in~Lion of a border point. Moreover, the present invention encompasses the use of any subcombination of cost functions described herein.
After the endocardial border is fully identified in this manner, a summary image may be pr~s~ntecl The background of the sllmm~ry image may consist of an average of 5 the baseline frames. Superimposed upon this background is the border, which may be highlighted in a dirre~lt;-lL color. A possible format to display the border is depicted in Figure 8B as the solid white border line. The border is thus shown as the continuous broad white band that encloses the left ventricle chamber.
There has thus been shown and described a novel system and method for the 10 d~line~tion of a border region of a patient tissue or organ which meets the objects and advantages sought. As stated above, many ~h~ , mo~lific~tion~, variations and other uses and applic~tic)n~ of the subject invention will, however, become appalt;llL to those skilled in the art after considering this specification and accolllpallyhlg drawings which disclose prer~lled embodiments thereof. All such çh~ngee, motlific~tiQn~, variations and 15 other uses and applications which do not depart from the spirit and scope of the invention are deemed to be covered by the invention which is limited only by the claims which follow.
Claims (20)
1. A method for automatically determining the border of a patient's tissue found in an operator-selected region of interest, said border determined from a set of contrast-enhanced, grey scale images, the steps of said method comprising:
A) obtaining a set of grey scale images of the patient's tissue, some of which contain contrast agent for image enhancement;
B) identifying a region of interest in which the patient tissue is located;
C) from the set of grey scale images collected from step (A), obtaining a baseline intensity value;
D) subtracting the baseline intensity value from the contrast enhanced images;
E) establishing a threshold based on signal to noise ratio F) establishing a reference point as a first border point in the region of interest;
G) from a set of candidate points adjacent to said reference point found in step (E), automatically selecting which candidate point is most likely to be a border point; and H) Substituting the selected candidate point in step (F) as the new reference point and continuing with step (F) until the entire border is determined.
A) obtaining a set of grey scale images of the patient's tissue, some of which contain contrast agent for image enhancement;
B) identifying a region of interest in which the patient tissue is located;
C) from the set of grey scale images collected from step (A), obtaining a baseline intensity value;
D) subtracting the baseline intensity value from the contrast enhanced images;
E) establishing a threshold based on signal to noise ratio F) establishing a reference point as a first border point in the region of interest;
G) from a set of candidate points adjacent to said reference point found in step (E), automatically selecting which candidate point is most likely to be a border point; and H) Substituting the selected candidate point in step (F) as the new reference point and continuing with step (F) until the entire border is determined.
2. The method as recited in claim 1 wherein said patient tissue is the endocardium.
3. The method as recited in claim 2 wherein step (A) further comprises:
(A)(i) selecting a point in the cardiac cycle at which to obtain a set grey scale images; and (A)(ii) obtaining a set of grey scale images, some of which contain contrast agent for image enhancement.
(A)(i) selecting a point in the cardiac cycle at which to obtain a set grey scale images; and (A)(ii) obtaining a set of grey scale images, some of which contain contrast agent for image enhancement.
4. The method as recited in claim 3 wherein step (A)(ii) further comprises:
(A)(ii)(a) obtaining a set of grey scale images prior to the introduction of contrast agent;
(A)(ii)(b) obtaining a set of grey scale images during the introduction of contrast agent; and (A)(ii)(c) obtaining a set of grey scale images after the contrast agent has been introduced.
(A)(ii)(a) obtaining a set of grey scale images prior to the introduction of contrast agent;
(A)(ii)(b) obtaining a set of grey scale images during the introduction of contrast agent; and (A)(ii)(c) obtaining a set of grey scale images after the contrast agent has been introduced.
5. The method as recited in claim 1 wherein step (A) further comprises:
(A)(i) obtaining a set of grey scale images of the patient's tissue, some of which contain contrast agent for image enhancement; and (A)(ii) correcting for motion of patient' tissue in the set of grey scale images obtained in step (A)(i).
(A)(i) obtaining a set of grey scale images of the patient's tissue, some of which contain contrast agent for image enhancement; and (A)(ii) correcting for motion of patient' tissue in the set of grey scale images obtained in step (A)(i).
6. The method as recited in claim 1 wherein the identifying step of step (B) is operator-selected.
7. The method as recited in claim 1 wherein the baseline intensity value of step (C) is obtained on a pixel-by-pixel basis.
8. The method as recited in claim 1 wherein the baseline intensity value of step (C) is obtained by an operator selecting baseline image frames by visual inspection.
9. The method as recited in claim 7 wherein the baseline intensity value of step (C) is obtained by an operator selecting baseline image frames from a graph of mean pixel intensity within the region of interest over time.
10. The method as recited in claim 7 wherein the baseline intensity value of step (C) is automatically obtained by an performing linear regression analysis on pixel intensity over time.
11. The method as recited in claim 2 wherein step (D) further comprises:
(D) (ii) subtracting the baseline intensity value from the contrast enhanced images on a pixel-by-pixel basis; and (D) (ii) determining, on a pixel-by-pixel basis, whether a given pixel is in the heart chamber;
(D) (iii) determining the center of mass of the heart chamber.
(D) (ii) subtracting the baseline intensity value from the contrast enhanced images on a pixel-by-pixel basis; and (D) (ii) determining, on a pixel-by-pixel basis, whether a given pixel is in the heart chamber;
(D) (iii) determining the center of mass of the heart chamber.
12. The method as recited in claim 11 wherein step (F) further comprises:
(F)(i) locating the set of points defined by the maximum and minimum x and y coordinates of the set of points in the heart chamber;
(F)(ii) picking one of the points located in step (F)(i) as a reference point.
(F)(i) locating the set of points defined by the maximum and minimum x and y coordinates of the set of points in the heart chamber;
(F)(ii) picking one of the points located in step (F)(i) as a reference point.
13. The method as recited in claim 2 wherein step (G) further comprises:
(G)(i) selecting a set of neighboring point to the reference point;
(G)(ii) calculating a cost function for each of the neighboring point selected in step (G)(i); and (G)(iii) selecting a new reference point likely to a border point based on the cost values generated in step (G)(ii).
(G)(i) selecting a set of neighboring point to the reference point;
(G)(ii) calculating a cost function for each of the neighboring point selected in step (G)(i); and (G)(iii) selecting a new reference point likely to a border point based on the cost values generated in step (G)(ii).
14. The method of claim 1 wherein end systole and end diastole points are used to determine regional wall motion.
15. The method of claim 1 wherein end systole and end diastole points are used to determine ejection fraction.
16. The method of claim 1 wherein end systole and end diastole points are used to determine fractional shortening.
17. The method of claim 1 wherein the imaging is performed from a view selected from the group consisting of sagittal, transverse, longitudinal, parasternal short axis, apical long axis, parasternal long axis, suprasternal long axis, subcostal short axis, subcostal four chamber, apical two chamber, and apical four chamber.
18. The method of claim 1 wherein the border delineates the left ventricle.
19. The method of claim 1 wherein the border delineates a venal thrombus.
20. The method of claim 1 wherein the processing is performed in real time.
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| JP3249946B2 (en) * | 1997-07-02 | 2002-01-28 | 裕美 都丸 | Semi-automatic renal region of interest setting method in renal scintigraphy |
| DE10041165B4 (en) * | 2000-08-21 | 2005-07-07 | Leica Microsystems Heidelberg Gmbh | Method and arrangement for controlling the analysis and adjustment processes of a microscope |
| JP4112368B2 (en) | 2001-02-13 | 2008-07-02 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Analysis of continuous data sets |
| JP2011507584A (en) * | 2007-12-20 | 2011-03-10 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | 3D reconstruction of body and body contour |
| US8200466B2 (en) | 2008-07-21 | 2012-06-12 | The Board Of Trustees Of The Leland Stanford Junior University | Method for tuning patient-specific cardiovascular simulations |
| US9405886B2 (en) | 2009-03-17 | 2016-08-02 | The Board Of Trustees Of The Leland Stanford Junior University | Method for determining cardiovascular information |
| US8315812B2 (en) | 2010-08-12 | 2012-11-20 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
| US8157742B2 (en) | 2010-08-12 | 2012-04-17 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
| US8548778B1 (en) | 2012-05-14 | 2013-10-01 | Heartflow, Inc. | Method and system for providing information from a patient-specific model of blood flow |
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|---|---|---|---|---|
| JPS62122635A (en) * | 1985-11-22 | 1987-06-03 | 株式会社東芝 | Radiation image treatment apparatus |
| JP3194741B2 (en) * | 1990-06-12 | 2001-08-06 | ユニバーシティ オブ フロリダ | Automatic digital image quantification system |
| US5239591A (en) * | 1991-07-03 | 1993-08-24 | U.S. Philips Corp. | Contour extraction in multi-phase, multi-slice cardiac mri studies by propagation of seed contours between images |
-
1996
- 1996-05-30 WO PCT/US1996/008257 patent/WO1996038815A1/en not_active Application Discontinuation
- 1996-05-30 EP EP96916909A patent/EP0829068A1/en not_active Withdrawn
- 1996-05-30 AU AU59629/96A patent/AU5962996A/en not_active Abandoned
- 1996-05-30 JP JP8536746A patent/JPH11506950A/en active Pending
- 1996-05-30 CA CA002220177A patent/CA2220177A1/en not_active Abandoned
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7630529B2 (en) | 2000-04-07 | 2009-12-08 | The General Hospital Corporation | Methods for digital bowel subtraction and polyp detection |
Also Published As
| Publication number | Publication date |
|---|---|
| EP0829068A1 (en) | 1998-03-18 |
| AU5962996A (en) | 1996-12-18 |
| WO1996038815A1 (en) | 1996-12-05 |
| JPH11506950A (en) | 1999-06-22 |
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| FZDE | Discontinued |