Medical diagnostic ultrasound system and method for region of interest determination

A method and system for determining the location of a region of interest throughout a sequence of images is provided. The system, in response to user input or automatically, identifies a region of interest associated with anatomy represented in an image. The data associated with the region of interest is compared with data for other or subsequent images. A maximum degree of correlation between the data associated with the region of interest and data for the subsequent image is determined. A translation and/or rotation associated with the maximum correlation determines the position of a region of interest designator in the subsequent image. The process may be repeated for a plurality of images. The same process may be used to determine the position of the designator in previous images.

BACKGROUND OF THE INVENTION
 This invention relates to a medical diagnostic ultrasound system and method
 for determining the position of a region of interest. In particular, a
 region of interest in determined in one or more images as a function of a
 region of interest in another image.
 A region of interest may identify particular anatomy in an ultrasound
 image. As subsequent images are generated, the representation of the
 anatomy in the image may move. Ultrasound imaging of anatomy is rarely
 static due to respiration motion, organ motion, transducer motion and
 other sources of motion. To designate the same anatomy in a plurality of
 images, the user may manually adjust the position of the region of
 interest in each image during a review of the images. However, proper
 placement of the region of interest to identify anatomy in real time is
 not provided, and manual adjustment may be time consuming.
 BRIEF SUMMARY
 The present invention is defined by the following claims, and nothing in
 this section should be taken as a limitation on those claims. By way of
 introduction, the preferred embodiment described below includes a method
 and system for determining the location of a region of interest throughout
 a sequence of images. The system, in response to user input or
 automatically, identifies a region of interest associated with anatomy
 represented in an image. The data associated with the region of interest
 is compared with data for other or subsequent images. A maximum
 correlation between the data associated with the region of interest and
 data for the subsequent image is determined. A translation and/or rotation
 associated with the maximum correlation determines the position of a
 region of interest designator in the subsequent image. The same process
 may be used to determine the position of the designator in previous
 images.
 In one aspect, a method for determining a region of interest with an
 ultrasound system is provided. A first region of interest associated with
 a first set of data is identified from a first frame of data. The first
 set of data is correlated with a second frame of data. A second region of
 interest in the second frame of data is determined as a function of the
 correlation. An image that is a function of the second frame of data is
 displayed, and the second region of interest is designated in the image.
 In a second aspect, the first and second frames of data are provided on a
 display with associated designations of the region of interest. A
 processor performs the correlation and determination of the position of
 the regions of interest.
 In a third aspect, a method for positioning a region of interest in a
 sequence of images with an ultrasound system is provided. A sequence of
 images are displayed. Each image comprises a region of interest
 designator. The region of interest designator in each one of the sequence
 of images is positioned as a function of a correlation value.
 Further aspects and advantages of the preferred embodiments are discussed
 below.

DETAILED DESCRIPTION OF THE INVENTION
 To determine the position of a region of interest in each image, an
 anatomical feature, speckle or another feature within a first image is
 identified. Preferably, an edge or other dominant feature related to an
 atomical structure represented in the image is identified. The position of
 the feature in subsequent images is estimated by correlating the data
 associated with the identified feature in the first image with data
 associated with subsequent images. The data corresponding to the region of
 interest is translated and/or rotated to various positions relative to the
 data used for subsequent images. A translation and/or rotation associated
 with the highest degree of correlation determines the position of the
 identified feature in each of the subsequent images. A region of interest
 designator is positioned in each image as a function of the translation
 and/or rotation associated with the highest degree of correlation (i.e.,
 to surround or label the identified feature).
 Referring to FIG. 1, a block diagram of an ultrasound system for
 determining the position of a region of interest is generally shown at 20.
 The ultrasound system 20 includes a transducer 22, a beamformer 24, a data
 processor 26, a scan converter 28, a filter 29, a display 30, a control
 processor 32, and a user interface 38. The ultrasound system 20 may
 comprise an 128XP.RTM., Aspen.TM. or Sequoia.RTM. ultrasound system from
 Acuson Corporation. Other systems may be used, such as systems adapted to
 process ultrasound data (e.g. the Aegis.RTM. system from Acuson
 Corporation) or systems made by other manufacturers.
 Based on control signals from the control processor 32, the beamformer 24
 provides excitation signals to the transducer 22. The transducer 22
 comprises one of a one-dimensional, two dimensional or 1.5 D transducer.
 Various elements of the transducer 22 generate focused acoustical
 waveforms in response to the excitation signals. Based on the control
 instructions, a region of a patient is scanned in any of various formats,
 such as curved linear, trapezoidal, sector, Vector.RTM., or linear. Other
 formats may be used, including formats associated with pulsed wave or
 continuous wave transmissions or M-mode imaging. Echo signals responsive
 to the transmitted acoustic waveforms are received by the various elements
 of the transducer 22. In response, the transducer 22 generates echo
 signals. The beamformer 24 receives the echo signals and generates
 in-phase and quadrature data or radio frequency (RF) data. The beamformer
 24 may isolate echo signal information at a fundamental transmit frequency
 band or at a harmonic of the fundamental frequency.
 The isolated in-phase and quadrature or RF data is provided to the data
 processor 26. The data processor 26 comprises a B-mode processor, a
 Doppler processor, or both the B-mode and the Doppler processor. The
 B-mode processor envelope detects the in-phase and quadrature data and log
 compresses the result to generate intensity data. The intensity data may
 be used for B-mode or M-mode imaging. The Doppler processor generates
 energy, velocity, or variance data or data representing combinations
 thereof from the in-phase and quadrature data. The data output from the
 Doppler processor may be filtered to represent tissue motion, blood flow
 or combinations thereof. Furthermore, the Doppler processor may output
 data for a spectral strip display (i.e., data representing a range of
 frequencies and associated energies as a function of time for one or more
 locations within the patient).
 Data generated by the data processor 26 is scan converted by the scan
 converter 28 through interpolation or other processes. The scan converter
 28 reformats the data from the polar coordinate pattern associated with
 the scan format to a Cartesian coordinate pattern for display. The scan
 converted data is provided to the filter 29.
 The filter 29 comprises a digital signal processor, a general processor
 programmed with software or another filtering device. Preferably, a
 two-dimensional filter with separable axial and azimuthal FIR filters is
 used. As shown, the filter 29 receives information output by the scan
 converter 28. In alternative embodiments, the filter 29 is between or
 within other components of the system 20, such as filtering the output of
 the image processor 26 prior to scan conversion. In one embodiment, the
 filter 29 is implemented by the control processor 32 or another processor.
 The filter 29 filters data for correlation calculations. The filter 29 may
 also filter data used for generating images on the display 30. In
 alternative embodiments, the filter 29 is by-passed or not included in the
 system 20.
 The filter 29 spatially filters the ultrasound data output by the scan
 converter 28. The filter 29 preferably applies low pass filtering to
 remove fine detail related to pixel quantization. The filter 29 may also
 high pass filter to remove DC and low frequency variations in the data. In
 alternative embodiments, the filter 29 comprises a band pass filter.
 Preferably, the filtering avoids blurring or otherwise reducing edges of
 anatomy identifiable by the transitions between spatially adjacent data.
 The output of the filter 29 is provided to the display 30 for generation
 of an image and/or to a processor for correlation calculations.
 The user interface 38 comprises one of a keyboard, dedicated keys, buttons
 selectable on the display 30, a trackball, a mouse, other user input
 devices and combinations thereof. The user interface 38 provides user
 input information to the control processor 32 for operating the system 20.
 As discussed below, the user interface 38 may also be used in conjunction
 with the control processor 32 to designate one or more regions of interest
 on an image.
 The control processor 32 comprises a digital signal processor, a general
 processor, or a combination of processors or other processor. The control
 processor 32 controls the operation of various components of the
 ultrasound system 20. In one embodiment, the control processor 32 also
 processes ultrasound data for determining the position of regions of
 interest in a sequence of images. As used herein, ultrasound data includes
 data at intermediate stages within or data input or output from any one or
 more of the beamformer 24, the data processor 26, the scan converter 28 or
 the display 30. For example, the control processor has access to data
 output by the data processor 26 or data output by the scan converter 28.
 Preferably, the ultrasound data comprises scan converted data. In
 alternative embodiments, other ultrasound data is used for correlation
 analysis to determine the position of the regions of interest.
 Alternatively or additionally, the ultrasound data is transferred to a
 remote processor 34, either directly, such as over a network, or
 indirectly, such as on a removable storage medium. The remote processor 34
 comprises a personal computer, a workstation, a motion processor, or other
 processor for determining the position of regions of interest. For
 example, the remote processor 34 comprises an AEGIS.RTM. workstation
 system from Acuson Corporation. In yet other alternative embodiments,
 processors within any of the beamformer 24, data processor 26, scan
 converter 28 or display 30 determine a correlation between a set of
 ultrasound data and a frame of ultrasound data.
 A memory 36 is associated with the processor for determining the position
 of regions of interest. The memory 36 is directly connected to the
 relevant processor or is remote from the processor. The memory 36
 comprises a RAM device, VCR, solid state memory, disk drive or other
 memory device for storing frames of data.
 The ultrasound system 20 generates frames of data, each frame of data
 corresponding to an ultrasound image. As discussed above, each frame of
 data is in any of various formats, such as the sector format shown in
 FIGS. 2A-2D. Each datum (i.e., pixel) in a frame of data represents a
 unique spatial location. For example, a frame of data includes a plurality
 of B-mode intensities. Each B-mode intensity is associated with an
 azimuthally positioned scan line and a range along the scan line. The
 position in the axial and azimuthal dimensions comprises location data. In
 alternative embodiments, a frame of data and associated image corresponds
 to M-mode or spectral Doppler data at a particular time.
 Preferably, the frames of data are acquired sequentially without any
 intervening acquisitions. In alternative embodiments, one or more frames
 of data are acquired in between acquisition of frames of data used for
 determining the position of a region of interest.
 A sequence of frames of data are acquired and used to generate a sequence
 of images. Referring to FIG. 2A, each image 40 includes a region of
 interest designator 42. In alternative embodiments, only one or fewer than
 all of the images 40 in the sequence include the designator 42. The region
 of interest designator 42 identifies anatomic structure represented by the
 image 40. The region of interest designator 42 comprises an outline box of
 any various geometric shapes, such as the sector shape shown. The region
 of interest designator may comprise closed shapes, a set of closed shapes,
 a single point or gate, a line, a label or other designators of a
 particular portion of the image 40.
 Referring to FIG. 2C, the region of interest designator 42 also comprises
 an outline box, but is an outline of an anatomical structure 44. A region
 of interest designator 46 associated with a gate, such as a PW gate, is
 also shown. More, fewer or different regions of interest and associated
 regions of interest designators may be used on any image 40.
 Referring to FIG. 3, a flow chart representing the steps performed for
 determining the position of a region of interest designator is shown. A
 region of interest within an image is identified at step 60. In step 62,
 the region of subsequent images that corresponds to the identified region
 of interest is determined using translation and correlation. As used
 herein, translation broadly includes linear translation, rotation and
 translation with rotation. Regions of interest and associated designators
 in the subsequent images are positioned in step 64 as a function of the
 correlation.
 The region of interest and associated region of interest designator 42 are
 identified on one image, such as the first image in a sequence. Later
 images or more than one image may be used for identifying the region of
 interest.
 In one embodiment, the system 20 automatically determines the region of
 interest. For example, the control processor 32 or another processor
 applies an algorithm to data associated with the image, such as detected
 or scan converted ultrasound data. In one embodiment, the algorithm
 identifies edges associated with structure boundaries based on intensity
 or movement differences. For example, the region of interest or associated
 anatomical feature is preferably identified using one of various metrics,
 such as gradient, entropy or texture content. For gradient based
 identification, the location or locations associated with a frame of data
 corresponding to the maximum gradient or change in ultrasound data
 amplitude is selected as the feature. For example, a location or locations
 associated with an edge of an imaged structure is selected. Other
 selection processes may be used, including selecting a plurality of
 locations associated with gradient values above a threshold.
 In one embodiment, the ultrasound data is convolved with one or more
 kernels or matrices to generate the gradient data. For example, azimuthal
 and axial gradient values are determined by using an azimuthal kernel and
 an axial kernel. In two different embodiments, the kernels are given by
 [1, -1] and [1, 2, 0, -2, -1]. Any of various kernals may be used,
 including kernels comprising matrices of any size, such as 3 by 3, 9 by 9
 or 9 by 12. Furthermore, the kernel may be derived from a Gaussian or
 Laplacian of a Gaussian or other functions. Other techniques to identify
 gradient or change information may be used, such as filters or look-up
 tables.
 To convolve the X or azimuthal gradient, the kernel is aligned with the
 ultrasound datum of interest within the frame of data, such as aligning
 the upper left corner of the matrix with the datum. Using the neighboring
 ultrasound data and the datum of interest, an output gradient is
 determined by convolution with the kernel. The kernel is repositioned for
 the next datum of interest. The process is repeated until an X gradient is
 determined for all or a subset of all the locations represented by the
 ultrasound data. Likewise, the Y or axial gradient is determined.
 For each location within the frame of data, the gradient magnitude is
 determined from the X and Y gradients. In one embodiment, the gradient
 magnitude is equal to the absolute value of the X gradient plus the
 absolute value of the Y gradient. In alternative embodiments, the true
 magnitude, the square root of the X gradient squared plus the Y gradient
 squared, is determined. Other gradient magnitude calculations may be used.
 In other embodiments, the system 20 identifies the region of interest using
 one of other algorithms. For example, a threshold is applied to the
 ultrasound data to identify locations within the region of interest. As
 another example, artificial intelligence techniques using matched filters
 to identify a texture shape may be used. The system 20 may adjust any of
 the regions of interest identified in response user input.
 In alternative embodiments, the user designates the region of interest. In
 response to input from the user interface, the system identifies the
 region of interest. For example, the user traces the region of interest in
 a displayed image using a trackball or mouse. The system 20 determines the
 spatial location of the user trace relative to the frame of data used to
 create the image. Other user input may be used to designate a region of
 interest, such as the placement of a cursor for a PW gate or placement and
 sizing of a two-dimensional designator.
 The system 20 determines a set of data from the frame of data used to
 generate the image that corresponds to the identified region of interest.
 The set of data comprises a sub-set of the frame of data. For a PW gate,
 the set of data may comprise a set of one datum. The set of data may
 correspond to a plurality of regions of interest, such as where the user
 identifies two or more regions of interest in an image that are to be
 tracked in subsequent images.
 In one embodiment, the set of data is expanded to include data from
 locations adjacent or near the identified region of interest. Expansion
 may allow for more accurate correlation by including additional structural
 information within the set of data. The expansion may include any grouping
 of locations, such as including five additional spatial locations added to
 each side of the region of interest. In one embodiment, the expansion
 includes the addition of spatially separated data, such as adding only
 every other location for an additional range from one or more sides of the
 region of interest. In alternative embodiments, the set of data is
 reduced, such as by selecting every other datum or other sub-set of the
 set of data corresponding to the region of interest or by selecting data
 within the set of data corresponding to the region of interest as a
 function of metric information (e.g., identifying locations adjacent the
 maximum gradient with gradient values above a threshold). The set of data
 may be both expanded by adding additional locations and reduced by
 selecting certain ones of the expanded set of data.
 If the region of interest and corresponding set of data represents a PW
 gate, the set of data is preferably expanded. A region of locations
 surrounding the PW gate location is included in the set of data, such as a
 5 by 5 or other sized and/or shaped region. For a vessel, the preferred
 region is a square shape that includes the vessel walls and is at least 10
 by 10 pixels in size.
 Referring to FIG. 3, the region of subsequent images that corresponds to
 the identified region of interest is determined using translation and
 correlation in step 62. The control processor 32 or another processor uses
 the set of data for correlation calculations. The set of ultrasound data
 is correlated with ultrasound data in a second frame of ultrasound data.
 The set of ultrasound data is also separately correlated with ultrasound
 data in any subsequent or previous frames of ultrasound data, such as
 associated with a sequence of images. In alternative embodiments,
 different sets of ultrasound data are used for correlation with subsequent
 or previous frames of data. For example, the identified set of data from a
 first frame of data is correlated with a second frame of data to determine
 the position of the region of interest designator relative to the second
 frame of data. Data from the second frame of data corresponding to the
 determined region of interest designator is then used as the set of data
 for correlation with a third frame of data. The set of data from the first
 frame of data may be used to correlate with more than one subsequent or
 previous frames of data before being replaced.
 The motion of the anatomy associated with the region of interest between
 the two frames of ultrasound data is estimated as a function of the
 correlation in step 64. Based on the estimated motion, the position of the
 region of interest designator 42 relative to each of the frames of data is
 determined. The identified set of ultrasound data from the first frame of
 ultrasound data is placed in different relative positions to the second
 frame of ultrasound data and corresponding correlation values are
 calculated. Preferably, a cross-correlation or a similar method is used.
 Such techniques (which will be referred to herein generally as correlation
 techniques) have been used for tracking blood flow. In one embodiment, a
 sum of absolute differences (SAD) correlation technique is used.
 For each of a plurality of relative positions, a correlation value is
 determined. The identified set of data is translated and rotated to
 various positions to determine respective correlation values. If a
 particular translation and/or rotation results in a SAD that is close to
 zero, then it is probable that the location of the anatomy in the second
 frame of ultrasound data has been determined. The translation and rotation
 required to determine the location of the region of interest indicates the
 motion of the region between the two respective frames of ultrasound data.
 In alternative embodiments, the maximum of a cross-correlation is selected
 as indicating the location of the region of interest.
 Translation to different positions for determining correlation preferably
 corresponds to one or both of axial and azimuthal translation. In one
 embodiment, the selected ultrasound data and corresponding locations are
 translated in the azimuthal dimension by ten locations on each side of an
 origin point and in the axial dimension by ten locations from each side of
 the origin point. The search range may be a function of time between
 acoustic frames and the region of the body scanned. The set of ultrasound
 data is positioned relative to the second frame of ultrasound data at each
 possible integer location within the seven location range. Therefore, the
 selected locations and associated ultrasound data are translated to four
 hundred and forty one different positions. A combination of coarse
 sampling followed by fine sampling may be used.
 The origin point is selected (1) as a location associated with no
 translation, (2) as a location that is a function of a likely amount of
 translation based on previous amounts of translation or on the type of
 anatomy being imaged, (3) as a location that is selected arbitrarily, or
 (4) as a location selected using any other function.
 For each translation position, the selected ultrasound data is rotated by
 0.1 degrees over a -2 to 2 degree range. Therefore, for each translation
 position, 41 rotational positions are provided. For each correlation value
 associated with a different rotation, ultrasound data is interpolated
 within the second frame of data to align with the rotated set of
 ultrasound data and associated locations. In one embodiment, linear
 interpolation is used, but other interpolation or extrapolation methods
 may be provided. For each translation and rotation position, a correlation
 value is calculated. In this embodiment, 18,081 correlation values are
 determined.
 In another embodiment, correlation values are only determined for various
 translations without any rotation. In other embodiments, different ranges
 and numbers of positions within the ranges for translations and/or
 rotations are used, such as determining correlation values for every other
 location position within a translation range with the same or an unequal
 number of locations on each side of the origin point.
 In other alternative embodiments, past estimates of motion associated with
 the region of interest or the type of anatomy associated with the region
 of interest are used to determine the range of translations and rotations
 and the number of locations within a range on each side of the origin
 point. For example, if past estimates of motion show an average azimuthal
 translation of five pixels in one direction, the origin point is selected
 as a translation of five pixels in the direction of past motion.
 Additionally or alternatively, the previous estimates of motion are used
 to select a larger azimuthal translation range, such as twice the range of
 translation on one side of the original point. Furthermore, previous
 estimates of motion are used to identify an area for more dense
 determination of correlation values, such as determining a correlation
 value every second or third location position for locations representing
 motion that is opposite previous estimates. Using past estimates to
 estimate motion may include triggering to estimate cyclical movement
 associated with a heart or respiration cycle.
 After a correlation value is determined for each position associated with
 translation and/or rotation, the position of the region of interest in the
 second frame of ultrasound data is selected. The lowest SAD correlation,
 the highest cross-correlation or another correlation determines the
 selected position.
 Where each image in the sequence of images corresponds to imaging generally
 the same region of the patient, the region of interest may not move by
 large amounts from one image to the next. In one preferred embodiment,
 weights are applied to the various correlation values. Correlation values
 associated with smaller amounts of movement and/or rotation are emphasized
 over correlation values associated with larger amounts of movement. For
 example, SAD correlation values associated with smaller amounts of
 movement are multiplied by smaller values, and SAD correlation values
 associated with larger amounts of movement are multiplied by larger
 values. The least of the weighted correlation values is selected to
 determine the position of the region of interest. The weights are reversed
 for cross-correlation correlation values. Any of various weighting
 functions may be used, including linear and non-linear functions and
 functions limiting the amount of possible movement of the region of
 interest between images. If the selected correlation value is associated
 with movement beyond a threshold value, an error signal may be provided.
 Additionally or alternatively, the selected correlation value is compared
 to a threshold to avoid a region of interest that appears to randomly move
 between images. If the correlation value is above (i.e. cross-correlation)
 or below (i.e. SAD) the threshold, the correlation value is used as
 discussed above to determine the location of the region of interest
 designator 42. If the selected correlation value does not satisfy the
 threshold, then (1) an error signal is provided on the display, (2) the
 region of interest designator 42 is displayed in a previously determined
 position, (3) the region of interest designator 42 is positioned based on
 extrapolation from previously determined positions or (4) the region of
 interest designator 42 is not displayed.
 After the position of the region of interest is determined, the region of
 interest designator 42 may be displayed with the associated image on the
 display 30. Referring to FIG. 2B, an image 50 generated as a function of
 the second frame of ultrasound is shown. Based on the correlation, the
 region of interest designator 42 is displayed at a different position than
 for the first image 40. The anatomy 44 has correspondingly moved positions
 within the image 50. While a large translation to the right and a smaller
 but visible translation up are shown, the translation between any two
 images may or may not be visibly detectable. The translation throughout a
 sequence of images is more likely to be detected. Referring to FIG. 2D,
 the region of interest designators 46 and 42 are displayed with the
 corresponding anatomy. For the region of interest designator 42 associated
 with the anatomy outline, the edges of the region of interest designator
 42 may be re-determined for each subsequent image 50 where the anatomy may
 change dimensions or shape. The region of interest designator 42
 preferably is the same size as the originally identified region of
 interest prior to expansion for correlation determination. In alternative
 embodiments, the region of interest designator 42 represents the expanded
 region of interest.
 For M-mode images, the region of interest designator is repositioned along
 a line. The translation associated with determining the best correlation
 is performed only along the relevant scan line.
 In one embodiment for two dimensional region of interest designators, the
 region of interest designator 42 is the same size and shape as displayed
 throughout the sequence of images. In another embodiment, the region of
 interest designator 42 changes sizes, such as associated with matching the
 region of interest to anatomical structure. The region of interest
 designator 42 may also change sizes as a function of the selected
 correlation value. If the correlation value is associated with a high
 degree of correlation, the region of interest designator 42 may be kept
 the same size. If the correlation value is associated with a small degree
 of correlation, the region of interest designator 42 is enlarged to more
 likely include the anatomy of interest.
 Referring to FIG. 4, a flow chart of one preferred embodiment for
 positioning a region of interest is shown at 70. In step 72, first (n) and
 second (n+1) frames of scan converted ultrasound data are filtered, such
 as the band pass or high and low pass filtering discussed above.
 A region of interest and related parameters are identified in the first
 frame of data at step 74. A variable region of interest, ROI*, is set
 equal to the identified region of interest, and a scale factor is set to
 1. A feature score is calculated from the set of data associated with the
 region of interest. The feature score may provide an indication that the
 region of interest includes texture sufficient for correlation. For
 example, the feature score is the standard deviation in the set of data.
 As another example, the feature score is the mean value of the set of
 data. Other indications of texture associated with the region of interest
 may be used.
 In step 76, the region of interest used for correlation is enlarged. The
 region of interest is enlarged as a function of the feature score and a
 maximum scale factor. If the feature score is less than a threshold and
 the maximum scale factor has not yet been reached, the scale factor is
 increased. In alternative embodiments, the scale factor is increased if
 the feature score is between two thresholds (e.g., a feature score based
 on the mean) or greater than the threshold. Various scale factor increases
 may be used, such as multiplying the scale factor by 1.1 or another value.
 The variable region of interest is enlarged as a function of the scale
 factor, such as determining the area and center of the variable region of
 interest, multiplying the area by the scale factor and determining the
 locations within the enlarged region of interest covered by the resulting
 enlarged area centered at the same location. The variable region of
 interest is set equal to the enlarged region of interest, and the feature
 score is recomputed. Step 76 is repeated until the feature score meets the
 threshold or the maximum scale factor is reached. The maximum scale factor
 may be any one of various values, such as a value associated with
 enlargement by a factor of 2 or associated with an area that is a
 percentage of the full image.
 In step 78, correlation values associated with translating and rotating the
 variable region of interest to different positions relative to the second
 frame of data are determined. The centeroid of the variable region of
 interest is determined. The variable region of interest is rotated in
 angular steps of .theta. and translated in steps of r to various positions
 relative to the second frame of data. This function may be represented by:
 ROI*.sub.n+1 =(ROI*.sub.n -c)e.sup.i.theta. +c+r. The correlation values
 are calculated as discussed above, such as using the minimum sum of
 absolute differences. The calculation of correlation values may be
 represented as the cost function J: J.sub.r,.theta. =J(I.sub.n
 (ROI*.sub.n), I.sub.n+1 (ROI*.sub.n+1), r, .theta.), where I represents a
 filtered frame of data.
 In step 80, the minimum cost function is used to determine the position of
 the region of interest in the second frame of data. r and .theta. that
 minimize the cost function represent the translation and rotation of the
 centeroid from the first frame of data to the second frame of data. Steps
 72 through 80 may be repeated for subsequent frames of data.
 Alternatively, steps 72 and 78 through 80 are repeated for comparing the
 variable region of interest determined in steps 74 and 76 with multiple
 frames of data.
 Ultrasound data associated with the region of interest or the region of
 interest designator 42 is used for one or more of various purposes. The
 region of interest designator 42 may be displayed throughout a sequence of
 images to assist qualitative assessment of an anatomical region. For
 example, the region of interest designator 42 is used to provide
 convenient identification of a region during pharmacological intervention
 or contrast agent injection.
 The ultrasound data associated with the region of interest, whether or not
 the designator 42 is displayed, may be used for quantitative analysis. The
 ultrasound data comprise data from the second frame of data. The
 ultrasound data may correspond to an original region of interest before
 any expansion for correlation, the expanded region of interest, or a
 region of interest expanded or reduced for quantitative analysis. Various
 quantities may be calculated. For example, the mean signal intensity or
 Doppler signal intensity within the region of interest is calculated and
 displayed. Other quantities that are a function of the area within the
 region of interest, the translation and/or rotation of the region of
 interest, the location of the region of interest, data corresponding to
 the region of interest or other parameters of the region of interest may
 be calculated. As a sequence of images are displayed, the quantity is
 calculated for each or a sub-set of the images. A mean of the quantity or
 other statistical representation of the quantity may be determined and
 displayed. Additionally or alternatively, a plot of the quantity as a
 function of time or image number may be displayed or stored. Furthermore,
 a table of the quantity may be displayed or stored.
 Referring to FIG. 5, a flow chart representing one embodiment for deriving
 one or more quantities as a function of the region of interest tracking is
 shown. In step 84, a sequence of images are obtained, such as by acquiring
 the images in real time or by loading the images from a storage device or
 memory. In step 86, the initial region of interest is identified as
 discussed above. The first image may correspond to any one of the images
 within the sequence of images. More than one region may be identified. In
 step 88, the region of interest is tracked using correlation in other
 images, such as all other images within the sequence of images. Also as
 discussed above, the quantity or quantities associated with all, one or a
 sub-set of all the regions of interest are calculated for each of the
 images 90. The calculation may be performed in real time as the regions of
 interest within an image are determined or after the regions of interest
 for multiple or all the images are determined.
 The quantity or quantities are output in step 92. These derived indices may
 be output in one or more of various forms, such as a plot of the quantity
 output in real time with the display of the image sequence or as a table
 of the quantity output after multiple images have been displayed with or
 without the display of the images.
 The user may elect to adjust the region or regions of interest identified
 in the first image in step 94. After reviewing the quantity or quantities,
 the user may determine that more accurate or different regions of interest
 should be identified. If a change is desired, the user causes the system
 20 to move or rotate the identified region of interest in the first image,
 such as by manipulating a trackball. The process returns to step 88 for
 another determination of the quantity or quantities. The user may also
 select different quantities for calculation. The process would return to
 step 90 for calculation of different quantities. If different quantities
 or regions of interest are not desired, the process continues to step 96.
 In step 96, the user may delete any data used for calculation of one or
 more of the quantities. One or more images may be removed from the
 sequence, or one of a plurality of regions of interest may be removed.
 Alternatively, one or more of a plurality of regions of interest are
 removed from a sub-set, such as one or two, of the images. A sub-set of
 data within a region of interest may also be removed. In step 98, the
 output of the quantity or quantities is determined again as a function of
 the data remaining after step 96. The quantity or quantities may be output
 in the same or a different format, such as a plot, a table or a file of
 data.
 Another purpose for tracking the region of interest through a plurality of
 images is the alteration of imaging parameters. Imaging parameters may be
 altered as a function of the region of interest or the corresponding data.
 For example, the transmit and receive focal points are adjusted to
 correspond to the location of a PW gate region of interest. Other imaging
 parameters may be adjusted, such as the number of focal regions, the
 number of scan lines, the amount of filtering, the data filtered, the data
 processed to obtain Doppler information or data stored for later review.
 For imaging contrast agents, a region associated with transmissions for
 destroying contrast agents (i.e., high power, longer duration and/or
 greater bandwidth) may be altered as a function of the tracked region of
 interest. In this embodiment, other regions of the image are associated
 with transmissions providing less destruction of contrast agents.
 While the invention has been described above by reference to various
 embodiments, it will be understood that many changes and modifications can
 be made without departing from the scope of the invention. For example,
 one dimensional (i.e. M-mode), two-dimensional or three-dimensional
 regions of interest and associated designators may be tracked through a
 sequence of images. Different ultrasound systems, whether analog or
 digital, may be used to acquire and/or display the ultrasound data than
 are used to determine the location of the region of interest throughout
 the sequence of images.
 It is therefore intended that the foregoing detailed description be
 understood as an illustration of the presently preferred embodiments of
 the invention, and not as a definition of the invention. It is only the
 following claims, including all equivalents, that are intended to define
 the scope of this invention.