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Method for the Quantitation of Myocardial Perfusion During Myocardial Contrast Two-Dimensional Echocardiography
Method for the Quantitation of Myocardial Perfusion During Myocardial Contrast Two-Dimensional Echocardiography Ananda R. Jayaweera, PhD, Thomas L. Matthew, MD,' Jiri Sklenar, PhD, William D. Spotnitz, MD, Denny D. Watson, PhD, and Sanjiv Kaul, MD,b Charlottesville, Va.
This article describes the hardware and software components of two systems designed for quantitative analysis of data obtained during myocardial contrast two-dimensional echocardiography. One system is meant for off-line analysis of data, whereas the other is designed for on-line analysis, especially in the operating room. The algorithms used for data transfer, selection of appropriate frames, data alignment, derivation of time-intensity plots, and curve-fitting and parameter generation are described in some detail. It is hoped that this information will be of use to others who work in the field of myocardial perfusion imaging. (JAM Soc EcHo 1990;3:91-8.)
The assessment of myocardial perfusion is pos sible by use of myocardial contrast two-dimensional echocardiography. 1 -3 This technique involves the intra-aortic or intracoronary injection of micro bubbles of air, which behave like red blood cells within the microcirculation. 4 Parameters of the time-intensity curves obtained from the myocardium by use of this technique have been shown to corre late with regional myocardial blood flow. 1-3 The conditions under which myocardial contrast two dimensional echocardiography is performed dictate the parameters of the time-intensity curves that cor relate best with regional myocardial blood flow. For example, if contrast is injected into the coronary artery in a beating heart, the width of the time intensity curve correlates best with myocardial blood flow. 1 If, on the other hand, vasodilation is induced From the Department of Medicine, Division of Cardiology, and the Department of Surgery, Division of Thoracic and Cardiovas cular Surgery, University of Virginia School of Medicine. Supported by a grant-in-aid from the Virginia Affiliate of the American Heart Association, Glen Allen, Virginia, and a grant in-aid from the National Center of the American Heart Associa tion, Dallas, Texas. Reprint requests: Sanjiv Kaul, MD, Box 158, Division of Car diology, University of Virginia School of Medicine, Charlottes ville, VA 22908. 'Recipient of a Ford Foundation fellowship grant. bRecipient of the Clinical Investigator Award (K08-HL01833) and the FIRST Award (R29-HL38345) of the National Institutes of Health, Bethesda, Maryland. 27/l/17162
by injection of dipyridamole, by which changes are induced in both myocardial blood flow and myocar dial blood volume, then the area under the curve correlates best withblood flow. 2 Similarly, the area under the curve and the initial slope of the curve correlate best with blood flow when contrast is in jected into an arrested heart by way of a cross clamped aorta during cardioplegia delivery. 3 The purpose of this article is to describe the al gorithms used to analyze data obtained during myo cardial contrast echocardiography. The steps in volved in the analysis of the data include the follow ing: (l) transfer of images into the computer, (2) identification of frames used for analysis, (3) align ment of images, (4) derivation of time-intensity plots, and (5) derivation of parameters. Each of these will be described in enough technical detail to allow users to be able to implement the same algorithms on their own systems or to design similar systems.
TRANSFER OF IMAGES TO THE COMPUTER
Images can be transferred to the computer either in real-time or subsequent to their acquisition. Either all images or only selected images with contrast en hancement can be transferred to the computer. Ideally, a digital echocardiographic system with a broad dynamic range and at least 256 levels of gray would be desirable for data acquisition. Because such a system is not commercially available, we transfer 91
92
Journal of the American Society of Echocardiography
Jayaweera et a!.
OFF- LINE CONTROLLER
IMAGE PRINTER
ANALYSIS MONITOR
SYSTEM IMAGE 01 SPLAY
VIDEO CAMERA
DMA INTERFACE TO MICROVAX
3500
DIGITIZER
TABLET
Figure 1
VCR
TAPE RIP
Kontron off-line image processing system.
analog data from the video output port of the echo cardiographic system either directly to the computer or we store the data on videotape for subsequent transfer to the computer. Most of our data in the beating heart are acquired either in the cardiac catheterization laboratory or in the experimental laboratory, and we prefer to store the data on videotape for subsequent analysis. In the operating room, however, because surgical decisions have to be made immediately, we prefer to transfer data on-line to a computer for immediate analysis. Off-line Transfer
Figure 2 Custom-designed on-line analysis system for use in the operating room.
We use the Mipron medical image processing system (Kontron Electronics, Eching, West Ger many) .1•2 This system is built around a Z80A micro processor (Zilog Inc., Cupertino, California). It is configured with a clock speed of4 MHz, 64 kilobytes of random access memory (RAM), a 20-megabyte hard disk, a monochrome text-graphics monitor, a video red-green-blue/black-and-white monitor, a high-resolution digitizer tablet, and a high-speed di rect memory access (DMA) interface for connection to a host computer, which in our case is a V AXstation 3500 (Digital Equipment Corporation, Maynard, Massachusetts). The host computer allows for an ideal environment for program development and overcomes the limitations of the Zilog controller. The new Mipron system now available is built around
Volume 3 Number 2 March-April 1990
Quantitation of myocardial perfusion 93
Figure 3 Method for determination of end-diastolic frames. Endocardial outline is defined in one end-diastolic image by the observer.
an AT-based system with a clock speed of either 16 or 20 MHz, which overcomes most of the limitations of the Zilog host. The system is connected ·to a mi croprogrammable image-array processor with a speed of 10 million instructions per second and 16 megabytes of dynamic video RAM (Figure 1). Soft ware for image acquisition and analysis are developed in FORTRAN with use offunctions and subroutines written in microcode artd supplied by Kontron. The image size can be adjusted over a wide range. With an image size of 256 x 256, 240 images can be dig itized and stored in real time at a rate of 30 frames per second. All images have a grey scale of 256. The cost of this entire system (including the host com puter), is approximately $180,000, which is largely related to the cost of the video RAM chips. On-line Transfer
We have custom designed a system around an 80386 microprocessor based IBM-PC-compatible micro computer (system 310, Dell Computer Corporation, Austin, Texas). The system is configured with a clock speed of 20 MHz, a 40-megabyte hard disk, 640 kilobytes of RAM, and a video graphic-array color computer monitor. Analog to digital conversion of the echocardiographic video images is performed by an internal frame grabber built on a standard 16-bit IBM-PC-expansion card (model PCVISIONPLUS, Imaging Technology, Woburn, Massachusetts). The
frame grabber has a resolution of 256 x 240 pixels with each pixel capable of displaying 256 shades of gray. Digital images are displayed by use ofan analog color monitor (model PVM-127lQ, Sony Corpo ration, Park Ridge, New Jersey). Video intensity measurements are made within the frame grabber in real time without transferring the image to RAM. Measurements can be performed within up to six 9 X 9 pixel regions of interest. The average pixel intensities in the regions of interest and the time at which these measurements are taken are written to an ASCII file. The system is arranged vertically on a mobile com puter cart (model PC AnthroCart, Anthro Technol ogy Furniture, Portland, Oregon). The computer cart is bolted to a steel deck platform dolly with 8-inch pneumatic wheels (model 952llOP, Global Equipment Company, Hempstead, New York). This mobile arrangement permits the image processing system to be moved into the operating room for on line analysis of video images (Figure 2). The cost of this system is approximately $10,000.
IDENTIFICATION OF FRAMES USED FOR ANALYSIS
For analysis of data acquired from a beating heart we have conventionally used every end-diastolic
94
Journal of the American Society of Echocardiography
Jayawecra et al.
100
75
>
1 H
~ 50 w 1 zH 25
0
50
25
75
100
FRAME NUMBER
Video intensity plots obtained from within the region of interest shown in Figure 3. Points with the lowest videointensity denote end-diastolic frames. These frames are then automatically placed next to each other in video memory.
Figure 4
frame. If the washout of contrast is slow enough to require longer data acquisition, the acquisition speed is reduced to 15 frames per second. We have not used a gating algorithm to determine end-diastolic frames. Rather, we acquire all frames and then select the end-diastolic frames within the video memory. We do this by first defining a region of interest approximately equal to the endocardial outline of an end-diastolic echocardiographic image (Figure 3). The average pixel intensity in this region of interest is calculated for every frame in the sequence. An ex ample of the variation of average pixel intensity with frame number is shown in Figure 4. The end-diastolic frames are those with minimum average pixel inten sity values. This automatic method agrees well with observer-selected end-diastolic frames (images with the largest end-diastolic area in each cardiac cycle). Although we have used this method specifically on short-axis views, it can be modified to detect end diastolic frames in any other view. Identification of frames is not required in the arrested heart during cardiac operations. We use every frame for data analysis. ALIGNMENT OF IMAGES
In the case of the arrested heart, image alignment is
not necessary. In the beating heart, however, trans lation between end-diastolic frames can occur be
cause of respiration. To minimize translation, we ask patients to hold their breath during image acquisi tion. Because the transit of microbubbles through the myocardium may take as little as 5 to l 0 seconds, respiratory gating would not be effective. We use the cross-correlation method for image alignment. 5 This is a very computer-intensive approach. However, we have programmed the array processor to perform this step and, depending on the degree of translation, l 0 images can be aligned within l, to 2 minutes. A region of interest is defined in a reference frame that is approximately equal to the area within the epicardium, and a rectangular area is defined around the region of interest (Figure 5). It is within this rectangular area that the computer performs its "search" for the same region of interest within the other frames. The size of this area is determined by observing the end-diastolic frames in a cine-loop for mat and determining the degree of translation. The less the translation, the smaller the area, and the faster the alignment. Let pi denote the pixel intensity at a particular point i within the region of interest in the reference frame, and let qi denote the pixel intensity at the same location in another frame that has to be aligned to the reference frame (Figure 6). If we plot qi against pi for all the points in the two frames, and if the two frames are identical, then the correlation coefficient (r) will be equal to l with use of the following equation:
Volume 3 Number 2 March-Aprill990
Quantitation of myocardial perfusion 95
Figure 5 Method for image alignment. Circle denotes the region of interest that needs to be aligned with similar regions of interest in other frames. Rectangle denotes area within which the search will be made to find the maximal correlation between one image and another.
A.
B.
256,----------------------.
256,----------------------.
190
190
180
180
y
y
Reference Image
Image to be Aligned
Figure 6 Illustration of method of image alignment between two consecutive end-diastolic images; x, andy, represent the same point within the two frames that, before image alignment, are shifted in both the x and y axes and rotated in terms of each other. A, Reference image. B, Depicts positions of both x, and y, relative to each other.
r =
JI,p,2"2,q,2
If the two frames are not aligned then the value of the correlation coefficient will be less than l. In such a case, the frame to be aligned is translated and rotated one step at a time within the search area relative to the reference frame, and the correlation
coefficient between all the points within the region of interest is recal~ulated at every position. The po sition at which the correlation coefficient is closest to 1 is taken as the best aligned position. The image is then translated and rotated to that position. We consider images to be well aligned when the r values between pixels within the two images are 2:0.90. The translation and rotation of the image occurs only
Journal of the American Society of Echocardiography
Jayaweera et a!.
96
Figure 7 Depicts three regions of interest placed for deriving time-intensity plots. The region on the top is placed over the left anterior descending arterial bed, the one on the left is placed over the right coronary arterial bed, and one on the right is placed over the left circumflex arterial bed.
within the rectangular search area rather than the entire 256 x 256 matrix, thus reducing the com putational time. The amount of rotation required for alignment of end-diastolic frames from the same car diac cycle is usually :52°.
A
DERIVATION OF TIME-INTENSI1Y PLOTS 0~--------~------~--------~------~
10
0
20
30
40
Time in seconds
B 100
€
CBF(TMBF) in ml/min(ml/s/min) 61(1.18) 91(1.64) - 106(1.77)- -· 131(2 03)--
80
~Q.) 60 .:: ·c:Q.) 40
....
;; ""
20 0
0
5
10
15
20
25
Time in seconds
Figure 8 A, Time-intensity plots obtained from a beating heart at four different flow rates during an intracoronary injection of contrast. B, Same data after background sub traction and curve-fitting by use ofthe gamma-variate func tion. CBF, Coronary blood flow; TMBF, transmural blood flow.
Once the end-diastolic frames have been aligned, re gions of interest are placed over any one of the aligned images to derive the time-intensity plots us ing all the images. In the case of the arrested heart, regions ofinterest are placed before contrast injection and then the computer calculates average video in tensity in subsequent frames. Because there are three major vessels that supply the myocardium, we like to place three large regions of interest--one over each vascular bed (Figure 7). The larger the region of interest, the less is the noise observed in the time intensity plots. 1•6 The regions of interest in the beat ing hearts are at least 500 pixels each. In the arrested heart we use smaller regions ofinterest to allow faster sampling rates, but for each value on the plot, we average pixel intensities in regions of interest over four successive frames to reduce noise. The average pixel intensities and the correspond ing time are written to an ASCII file. The pixel in tensities are plotted against time to obtain time intensity curves (Figure 8,A). The intensity levels in
Volume 3 Number 2 March-April 1990
Quantitation of myocardial perfusion
97
60
50
~
40
H
UJ
z
w
~ 30
H
0
w
0
~ 20
10
5
10
20
15
TIME
25
(sec)
Figure 9 Time-intensity curves obtained from an arrested heart during cardioplegia delivery and after fitting a general exponential function after background subtraction.
each region of interest before the appearance of con trast is subtracted from these intensities, and the time-intensity curves are replotted (Figure 8, B). The background-subtracted time-intensity plots are used to derive parameters of myocardial blood flow. DERIVATION OF PARAMETERS
Curve-fitting and derivation of parameters is done by use ofRS/ 1 (Bolt, Beraneck, and Newman, Cam bridge, Massachusetts). Figure 8, A shows time intensity plots derived from a beating heart. The ap pearance and washout of contrast can be character ized by a gamma-variate function 1•7 : y =A·
t ·
e<-n·t>
in which A is a scaling factor, a is a parameter that is inversely proportional to the curve width, and t denotes time. From this equation, the area under the curve (A/a 2 ) and curve amplitude (Alae) can be derived. 2 Unlike the beating heart, the washout of contrast is much longer in the arrested heart, which is related to the "stickiness" of the microbubbles to the vas cular endothelium in the presence of cardioplegia (Figure 9). 8 Therefore, instead of a gamma-variate function, a general exponential function is used 3 •9 : Y =ex. (e-b·t _ e_'.')
Unlike the gamma-variate function, the general exponential function separately characterizes the contrast appearance and washout by its two terms. We have slightly modified the equation we used previously3 •9 to ensure that the intensity at time 0 is
0. The initial slope of the curve, slope of the curve at 1 second, peak curve amplitude, and area under the curve can be derived in the following manner. The slope of the curve is given by the following: dy/dt
=
ex· (- b · e-b '
+ c · e<-c
')
Therefore, the initial slope of the curve is: a(c - b), and the slope of the curve at 1 second is as follows: cx(c · e-' - b · e-b)
The slope of the curve is zero at time t = ln(c/b)/(c - b). Therefore, the peak amplitude of the curve is given by the following: amp = ex. [e(-b·ln[c/b]/[c- b])
-
e<-c·ln[c/b]/[c- b])]
The area under the curve from time 0 to time t is given by the following:
I t
y · dt
= ex ·
0
I
I t
e-b ·' · dt - ex · e-c ' · dt
=
[1 - e-b ']·alb- [1 - e-' '] · cx/c
SUMMARY In this article we have described the hardware and software components of two systems designed for quantitative analysis of data obtained during myo cardial contrast two-dimensional echocardiography. One system is used for the off-line analysis of data acquired from the beating heart, whereas the other is used for the on-line analysis ofdata generated from cardioplegia-arrested hearts in the operating room. The algorithms used for data transfer, selection of
98
Journal of the American Society of Echocardiography
Jayaweera et a!.
appropriate frames, data alignment, derivation of time-intensity plots, and curve-fitting and parameter generation have been described in some detail. It is hoped that this article will be useful to others who work in the field of myocardial perfusion imaging. We would like to thank Mr. Craig Harding for execution of the artwork.
4.
5. 6.
REFERENCES
l. Kaul S, Kelly P, Oliner JD, Glasheen WP, Keller MW, Watson D D. Assessment of regional myocardial blood flow with myo cardial contrast two-dimensional echocardiography. JAm Coli Cardia! 1989;13:468-82. 2. Keller MW, Glasheen W, Smucker ML, Burwell LR, Watson DD, Kaul S. Myocardial contrast echocardiography in humans. II. Assessment of coronary blood flow reserve. J Am Coli Car dioll988;12:925-34. 3. Ketler MW, Spotnitz DD, MatthewTL, Glasheen WP, Watson DD, Kaul S. Quantitative intraoperative myocardial contrast
7. 8.
9.
echocardiography: implications for preventing perioperative infarction [Abstract]. Circulation 1988;78(suppl II):II-567. Keller MW, Segal SS, Kaul S, Duling BR. The behavior of sonicated albumin microbubbles in the microcirculation: a ba sis for their use during myocardial contrast echocardiography. Circ Res 1989;65:458-67. Kaul S, Boucher CA, Newell JB, et a!. Determination of the quantitative thallium imaging variables that optimize detection of coronary artery disease. JAm Coli Cardioll986;7:527-37. Reisner SA, Shapiro JR, Amico AF, Meltzer RS. Repro ducibility of washout curves derived from myocardial contrast echo [Abstract]. JAm Coli Cardioi1989;13:115A. Thompson MK, Starmer CF, Whorten RE, Mcintosh MD. Indicator transit time considered as a gamma variate. Circ Res 1964;14:502-14. Keller MW, Kaul S, Spotnitz WD, Duling BR. Perfusion with cardioplegia solution changes microbubble rheology: impli cations for intraoperative myocardial contrast echocardiogra phy [Abstract]. Circulation. 1989;80(suppl II):II-371. Spotnitz WD, Ketler MW, Watson DD, Nolan SP, Kaul S. Success of internal mammary bypass grafting can be assessed intraoperatively using myocardial contrast echocardiography. JAm Coli Cardiol1988;12:196-20l.
This article describes the hardware and software components of two systems designed for quantitative analysis of data obtained during myocardial contrast two-dimensional echocardiography. One system is meant for off-line analysis of data, whereas the other is designed for on-line analysis, especially in the operating room. The algorithms used for data transfer, selection of appropriate frames, data alignment, derivation of time-intensity plots, and curve-fitting and parameter generation are described in some detail. It is hoped that this information will be of use to others who work in the field of myocardial perfusion imaging. (JAM Soc EcHo 1990;3:91-8.)
The assessment of myocardial perfusion is pos sible by use of myocardial contrast two-dimensional echocardiography. 1 -3 This technique involves the intra-aortic or intracoronary injection of micro bubbles of air, which behave like red blood cells within the microcirculation. 4 Parameters of the time-intensity curves obtained from the myocardium by use of this technique have been shown to corre late with regional myocardial blood flow. 1-3 The conditions under which myocardial contrast two dimensional echocardiography is performed dictate the parameters of the time-intensity curves that cor relate best with regional myocardial blood flow. For example, if contrast is injected into the coronary artery in a beating heart, the width of the time intensity curve correlates best with myocardial blood flow. 1 If, on the other hand, vasodilation is induced From the Department of Medicine, Division of Cardiology, and the Department of Surgery, Division of Thoracic and Cardiovas cular Surgery, University of Virginia School of Medicine. Supported by a grant-in-aid from the Virginia Affiliate of the American Heart Association, Glen Allen, Virginia, and a grant in-aid from the National Center of the American Heart Associa tion, Dallas, Texas. Reprint requests: Sanjiv Kaul, MD, Box 158, Division of Car diology, University of Virginia School of Medicine, Charlottes ville, VA 22908. 'Recipient of a Ford Foundation fellowship grant. bRecipient of the Clinical Investigator Award (K08-HL01833) and the FIRST Award (R29-HL38345) of the National Institutes of Health, Bethesda, Maryland. 27/l/17162
by injection of dipyridamole, by which changes are induced in both myocardial blood flow and myocar dial blood volume, then the area under the curve correlates best withblood flow. 2 Similarly, the area under the curve and the initial slope of the curve correlate best with blood flow when contrast is in jected into an arrested heart by way of a cross clamped aorta during cardioplegia delivery. 3 The purpose of this article is to describe the al gorithms used to analyze data obtained during myo cardial contrast echocardiography. The steps in volved in the analysis of the data include the follow ing: (l) transfer of images into the computer, (2) identification of frames used for analysis, (3) align ment of images, (4) derivation of time-intensity plots, and (5) derivation of parameters. Each of these will be described in enough technical detail to allow users to be able to implement the same algorithms on their own systems or to design similar systems.
TRANSFER OF IMAGES TO THE COMPUTER
Images can be transferred to the computer either in real-time or subsequent to their acquisition. Either all images or only selected images with contrast en hancement can be transferred to the computer. Ideally, a digital echocardiographic system with a broad dynamic range and at least 256 levels of gray would be desirable for data acquisition. Because such a system is not commercially available, we transfer 91
92
Journal of the American Society of Echocardiography
Jayaweera et a!.
OFF- LINE CONTROLLER
IMAGE PRINTER
ANALYSIS MONITOR
SYSTEM IMAGE 01 SPLAY
VIDEO CAMERA
DMA INTERFACE TO MICROVAX
3500
DIGITIZER
TABLET
Figure 1
VCR
TAPE RIP
Kontron off-line image processing system.
analog data from the video output port of the echo cardiographic system either directly to the computer or we store the data on videotape for subsequent transfer to the computer. Most of our data in the beating heart are acquired either in the cardiac catheterization laboratory or in the experimental laboratory, and we prefer to store the data on videotape for subsequent analysis. In the operating room, however, because surgical decisions have to be made immediately, we prefer to transfer data on-line to a computer for immediate analysis. Off-line Transfer
Figure 2 Custom-designed on-line analysis system for use in the operating room.
We use the Mipron medical image processing system (Kontron Electronics, Eching, West Ger many) .1•2 This system is built around a Z80A micro processor (Zilog Inc., Cupertino, California). It is configured with a clock speed of4 MHz, 64 kilobytes of random access memory (RAM), a 20-megabyte hard disk, a monochrome text-graphics monitor, a video red-green-blue/black-and-white monitor, a high-resolution digitizer tablet, and a high-speed di rect memory access (DMA) interface for connection to a host computer, which in our case is a V AXstation 3500 (Digital Equipment Corporation, Maynard, Massachusetts). The host computer allows for an ideal environment for program development and overcomes the limitations of the Zilog controller. The new Mipron system now available is built around
Volume 3 Number 2 March-April 1990
Quantitation of myocardial perfusion 93
Figure 3 Method for determination of end-diastolic frames. Endocardial outline is defined in one end-diastolic image by the observer.
an AT-based system with a clock speed of either 16 or 20 MHz, which overcomes most of the limitations of the Zilog host. The system is connected ·to a mi croprogrammable image-array processor with a speed of 10 million instructions per second and 16 megabytes of dynamic video RAM (Figure 1). Soft ware for image acquisition and analysis are developed in FORTRAN with use offunctions and subroutines written in microcode artd supplied by Kontron. The image size can be adjusted over a wide range. With an image size of 256 x 256, 240 images can be dig itized and stored in real time at a rate of 30 frames per second. All images have a grey scale of 256. The cost of this entire system (including the host com puter), is approximately $180,000, which is largely related to the cost of the video RAM chips. On-line Transfer
We have custom designed a system around an 80386 microprocessor based IBM-PC-compatible micro computer (system 310, Dell Computer Corporation, Austin, Texas). The system is configured with a clock speed of 20 MHz, a 40-megabyte hard disk, 640 kilobytes of RAM, and a video graphic-array color computer monitor. Analog to digital conversion of the echocardiographic video images is performed by an internal frame grabber built on a standard 16-bit IBM-PC-expansion card (model PCVISIONPLUS, Imaging Technology, Woburn, Massachusetts). The
frame grabber has a resolution of 256 x 240 pixels with each pixel capable of displaying 256 shades of gray. Digital images are displayed by use ofan analog color monitor (model PVM-127lQ, Sony Corpo ration, Park Ridge, New Jersey). Video intensity measurements are made within the frame grabber in real time without transferring the image to RAM. Measurements can be performed within up to six 9 X 9 pixel regions of interest. The average pixel intensities in the regions of interest and the time at which these measurements are taken are written to an ASCII file. The system is arranged vertically on a mobile com puter cart (model PC AnthroCart, Anthro Technol ogy Furniture, Portland, Oregon). The computer cart is bolted to a steel deck platform dolly with 8-inch pneumatic wheels (model 952llOP, Global Equipment Company, Hempstead, New York). This mobile arrangement permits the image processing system to be moved into the operating room for on line analysis of video images (Figure 2). The cost of this system is approximately $10,000.
IDENTIFICATION OF FRAMES USED FOR ANALYSIS
For analysis of data acquired from a beating heart we have conventionally used every end-diastolic
94
Journal of the American Society of Echocardiography
Jayawecra et al.
100
75
>
1 H
~ 50 w 1 zH 25
0
50
25
75
100
FRAME NUMBER
Video intensity plots obtained from within the region of interest shown in Figure 3. Points with the lowest videointensity denote end-diastolic frames. These frames are then automatically placed next to each other in video memory.
Figure 4
frame. If the washout of contrast is slow enough to require longer data acquisition, the acquisition speed is reduced to 15 frames per second. We have not used a gating algorithm to determine end-diastolic frames. Rather, we acquire all frames and then select the end-diastolic frames within the video memory. We do this by first defining a region of interest approximately equal to the endocardial outline of an end-diastolic echocardiographic image (Figure 3). The average pixel intensity in this region of interest is calculated for every frame in the sequence. An ex ample of the variation of average pixel intensity with frame number is shown in Figure 4. The end-diastolic frames are those with minimum average pixel inten sity values. This automatic method agrees well with observer-selected end-diastolic frames (images with the largest end-diastolic area in each cardiac cycle). Although we have used this method specifically on short-axis views, it can be modified to detect end diastolic frames in any other view. Identification of frames is not required in the arrested heart during cardiac operations. We use every frame for data analysis. ALIGNMENT OF IMAGES
In the case of the arrested heart, image alignment is
not necessary. In the beating heart, however, trans lation between end-diastolic frames can occur be
cause of respiration. To minimize translation, we ask patients to hold their breath during image acquisi tion. Because the transit of microbubbles through the myocardium may take as little as 5 to l 0 seconds, respiratory gating would not be effective. We use the cross-correlation method for image alignment. 5 This is a very computer-intensive approach. However, we have programmed the array processor to perform this step and, depending on the degree of translation, l 0 images can be aligned within l, to 2 minutes. A region of interest is defined in a reference frame that is approximately equal to the area within the epicardium, and a rectangular area is defined around the region of interest (Figure 5). It is within this rectangular area that the computer performs its "search" for the same region of interest within the other frames. The size of this area is determined by observing the end-diastolic frames in a cine-loop for mat and determining the degree of translation. The less the translation, the smaller the area, and the faster the alignment. Let pi denote the pixel intensity at a particular point i within the region of interest in the reference frame, and let qi denote the pixel intensity at the same location in another frame that has to be aligned to the reference frame (Figure 6). If we plot qi against pi for all the points in the two frames, and if the two frames are identical, then the correlation coefficient (r) will be equal to l with use of the following equation:
Volume 3 Number 2 March-Aprill990
Quantitation of myocardial perfusion 95
Figure 5 Method for image alignment. Circle denotes the region of interest that needs to be aligned with similar regions of interest in other frames. Rectangle denotes area within which the search will be made to find the maximal correlation between one image and another.
A.
B.
256,----------------------.
256,----------------------.
190
190
180
180
y
y
Reference Image
Image to be Aligned
Figure 6 Illustration of method of image alignment between two consecutive end-diastolic images; x, andy, represent the same point within the two frames that, before image alignment, are shifted in both the x and y axes and rotated in terms of each other. A, Reference image. B, Depicts positions of both x, and y, relative to each other.
r =
JI,p,2"2,q,2
If the two frames are not aligned then the value of the correlation coefficient will be less than l. In such a case, the frame to be aligned is translated and rotated one step at a time within the search area relative to the reference frame, and the correlation
coefficient between all the points within the region of interest is recal~ulated at every position. The po sition at which the correlation coefficient is closest to 1 is taken as the best aligned position. The image is then translated and rotated to that position. We consider images to be well aligned when the r values between pixels within the two images are 2:0.90. The translation and rotation of the image occurs only
Journal of the American Society of Echocardiography
Jayaweera et a!.
96
Figure 7 Depicts three regions of interest placed for deriving time-intensity plots. The region on the top is placed over the left anterior descending arterial bed, the one on the left is placed over the right coronary arterial bed, and one on the right is placed over the left circumflex arterial bed.
within the rectangular search area rather than the entire 256 x 256 matrix, thus reducing the com putational time. The amount of rotation required for alignment of end-diastolic frames from the same car diac cycle is usually :52°.
A
DERIVATION OF TIME-INTENSI1Y PLOTS 0~--------~------~--------~------~
10
0
20
30
40
Time in seconds
B 100
€
CBF(TMBF) in ml/min(ml/s/min) 61(1.18) 91(1.64) - 106(1.77)- -· 131(2 03)--
80
~Q.) 60 .:: ·c:Q.) 40
....
;; ""
20 0
0
5
10
15
20
25
Time in seconds
Figure 8 A, Time-intensity plots obtained from a beating heart at four different flow rates during an intracoronary injection of contrast. B, Same data after background sub traction and curve-fitting by use ofthe gamma-variate func tion. CBF, Coronary blood flow; TMBF, transmural blood flow.
Once the end-diastolic frames have been aligned, re gions of interest are placed over any one of the aligned images to derive the time-intensity plots us ing all the images. In the case of the arrested heart, regions ofinterest are placed before contrast injection and then the computer calculates average video in tensity in subsequent frames. Because there are three major vessels that supply the myocardium, we like to place three large regions of interest--one over each vascular bed (Figure 7). The larger the region of interest, the less is the noise observed in the time intensity plots. 1•6 The regions of interest in the beat ing hearts are at least 500 pixels each. In the arrested heart we use smaller regions ofinterest to allow faster sampling rates, but for each value on the plot, we average pixel intensities in regions of interest over four successive frames to reduce noise. The average pixel intensities and the correspond ing time are written to an ASCII file. The pixel in tensities are plotted against time to obtain time intensity curves (Figure 8,A). The intensity levels in
Volume 3 Number 2 March-April 1990
Quantitation of myocardial perfusion
97
60
50
~
40
H
UJ
z
w
~ 30
H
0
w
0
~ 20
10
5
10
20
15
TIME
25
(sec)
Figure 9 Time-intensity curves obtained from an arrested heart during cardioplegia delivery and after fitting a general exponential function after background subtraction.
each region of interest before the appearance of con trast is subtracted from these intensities, and the time-intensity curves are replotted (Figure 8, B). The background-subtracted time-intensity plots are used to derive parameters of myocardial blood flow. DERIVATION OF PARAMETERS
Curve-fitting and derivation of parameters is done by use ofRS/ 1 (Bolt, Beraneck, and Newman, Cam bridge, Massachusetts). Figure 8, A shows time intensity plots derived from a beating heart. The ap pearance and washout of contrast can be character ized by a gamma-variate function 1•7 : y =A·
t ·
e<-n·t>
in which A is a scaling factor, a is a parameter that is inversely proportional to the curve width, and t denotes time. From this equation, the area under the curve (A/a 2 ) and curve amplitude (Alae) can be derived. 2 Unlike the beating heart, the washout of contrast is much longer in the arrested heart, which is related to the "stickiness" of the microbubbles to the vas cular endothelium in the presence of cardioplegia (Figure 9). 8 Therefore, instead of a gamma-variate function, a general exponential function is used 3 •9 : Y =ex. (e-b·t _ e_'.')
Unlike the gamma-variate function, the general exponential function separately characterizes the contrast appearance and washout by its two terms. We have slightly modified the equation we used previously3 •9 to ensure that the intensity at time 0 is
0. The initial slope of the curve, slope of the curve at 1 second, peak curve amplitude, and area under the curve can be derived in the following manner. The slope of the curve is given by the following: dy/dt
=
ex· (- b · e-b '
+ c · e<-c
')
Therefore, the initial slope of the curve is: a(c - b), and the slope of the curve at 1 second is as follows: cx(c · e-' - b · e-b)
The slope of the curve is zero at time t = ln(c/b)/(c - b). Therefore, the peak amplitude of the curve is given by the following: amp = ex. [e(-b·ln[c/b]/[c- b])
-
e<-c·ln[c/b]/[c- b])]
The area under the curve from time 0 to time t is given by the following:
I t
y · dt
= ex ·
0
I
I t
e-b ·' · dt - ex · e-c ' · dt
=
[1 - e-b ']·alb- [1 - e-' '] · cx/c
SUMMARY In this article we have described the hardware and software components of two systems designed for quantitative analysis of data obtained during myo cardial contrast two-dimensional echocardiography. One system is used for the off-line analysis of data acquired from the beating heart, whereas the other is used for the on-line analysis ofdata generated from cardioplegia-arrested hearts in the operating room. The algorithms used for data transfer, selection of
98
Journal of the American Society of Echocardiography
Jayaweera et a!.
appropriate frames, data alignment, derivation of time-intensity plots, and curve-fitting and parameter generation have been described in some detail. It is hoped that this article will be useful to others who work in the field of myocardial perfusion imaging. We would like to thank Mr. Craig Harding for execution of the artwork.
4.
5. 6.
REFERENCES
l. Kaul S, Kelly P, Oliner JD, Glasheen WP, Keller MW, Watson D D. Assessment of regional myocardial blood flow with myo cardial contrast two-dimensional echocardiography. JAm Coli Cardia! 1989;13:468-82. 2. Keller MW, Glasheen W, Smucker ML, Burwell LR, Watson DD, Kaul S. Myocardial contrast echocardiography in humans. II. Assessment of coronary blood flow reserve. J Am Coli Car dioll988;12:925-34. 3. Ketler MW, Spotnitz DD, MatthewTL, Glasheen WP, Watson DD, Kaul S. Quantitative intraoperative myocardial contrast
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echocardiography: implications for preventing perioperative infarction [Abstract]. Circulation 1988;78(suppl II):II-567. Keller MW, Segal SS, Kaul S, Duling BR. The behavior of sonicated albumin microbubbles in the microcirculation: a ba sis for their use during myocardial contrast echocardiography. Circ Res 1989;65:458-67. Kaul S, Boucher CA, Newell JB, et a!. Determination of the quantitative thallium imaging variables that optimize detection of coronary artery disease. JAm Coli Cardioll986;7:527-37. Reisner SA, Shapiro JR, Amico AF, Meltzer RS. Repro ducibility of washout curves derived from myocardial contrast echo [Abstract]. JAm Coli Cardioi1989;13:115A. Thompson MK, Starmer CF, Whorten RE, Mcintosh MD. Indicator transit time considered as a gamma variate. Circ Res 1964;14:502-14. Keller MW, Kaul S, Spotnitz WD, Duling BR. Perfusion with cardioplegia solution changes microbubble rheology: impli cations for intraoperative myocardial contrast echocardiogra phy [Abstract]. Circulation. 1989;80(suppl II):II-371. Spotnitz WD, Ketler MW, Watson DD, Nolan SP, Kaul S. Success of internal mammary bypass grafting can be assessed intraoperatively using myocardial contrast echocardiography. JAm Coli Cardiol1988;12:196-20l.