Medical diagnostic ultrasound system and method for improved flow or movement detection with multiple clutter filters

A method and system for flow or movement detection is provided. More than one clutter filter is used. Each clutter filter's magnitude versus frequency response is optimized differently. Estimates of the flow or movement are generated from the data output by each of the clutter filters. Using selection or combination of the resulting estimates, the best attributes of each filter are used for imaging.

BACKGROUND
 This invention relates to a medical diagnostic ultrasound system and method
 for imaging blood flow or tissue movement. In particular, clutter
 filtering for flow or movement imaging is provided.
 Ultrasound systems image blood flow and tissue movement using correlation
 or Doppler techniques. The flow or movement is represented by one or more
 of various estimated parameters, such as energy, velocity and/or variance.
 Prior to estimation of these parameters, a clutter filter may suppress
 undesired signals, such as associated with reflections of ultrasonic
 energy from stationary or slowly moving tissue.
 The clutter filter response is typically selected as a compromise between
 low flow or movement sensitivity, clutter suppression, and the number of
 taps for the clutter filter. A large number of taps allows filters with a
 sharper magnitude versus frequency rolloff, more efficiently isolating
 desired signals from undesired signals (i.e. better differentiation
 between flow or movement and clutter). The large number of taps allows for
 better sensitivity to low velocity flow or movement. The large number of
 taps also allows for filtering with a flat frequency passband, providing
 more desirable sensitivity over a larger range of velocities.
 However, more line firings are required for more taps. A larger number of
 line may reduce the frame rate for imaging.
 U.S. Pat. No. 5,249,578 to Karp et al. teaches processing data with
 different finite impulse response filters where the different filters are
 defined as having reversed coefficients. The magnitude versus frequency
 response of these two filters is the same. By using these two filters, a
 greater number of independent samples may reduce the variance of velocity
 estimation.
 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 flow or movement detection. More than one clutter filter is
 used. Each clutter filter is optimized differently. Estimates of the flow
 or movement are generated from the data output by each of the clutter
 filters. Using selection or combination of the resulting estimates, the
 best attributes of each filter are used for imaging.
 In one aspect, a medical diagnostic ultrasound system for estimating flow
 or movement is provided. The system includes first and second clutter
 filters. The first clutter filter is characterized by a magnitude versus
 frequency response different than the second clutter filter. A combiner is
 operatively connected to receive first and second estimates. The first and
 second estimates are responsive to the first and second clutter filters,
 respectively. The combiner is operable to combine the first and second
 estimates. In other aspects, the combiner is replaced with a selector. The
 selector is operable to select one of the first and second estimates.
 In another aspect, a medical diagnostic ultrasound method for estimating
 flow or movement is provided. The method includes the steps of filtering
 with a first clutter filter and filtering with a second clutter filter
 where the first clutter filter is characterized by a magnitude versus
 frequency response different than the second clutter filter. First and
 second estimates are estimated from data from the first and second clutter
 filters, respectively. The first and second estimates are combined, or one
 of the first and second estimates is selected.

DETAILED DESCRIPTION OF THE INVENTION
 The medical ultrasonic diagnostic systems and methods described herein may
 provide for improved imaging of blood flow or tissue movement. Two clutter
 filters with different magnitude versus frequency responses filter data
 prior to estimation. Preferably, the same data is input to both filters.
 Estimates are generated from the output of each clutter filter. The best
 estimate is selected or multiple estimates are combined. The selected or
 combined estimates are used to generate an image with increased low flow
 or movement sensitivity, increased high flow or movement sensitivity
 and/or increased frame rate. The systems and methods use separate paths or
 sequentially use a same path for the clutter filtering.
 Referring to FIG. 1, one preferred system for estimating flow or movement
 is shown at 90. The system 90 includes a comer turning memory 100, two
 different clutter filtering paths 102 and 104, a selection block 150, a
 spatial filter 160, a temporal filter 170, a CINE memory 180, a scan
 converter 190, a color mapping processor 191 and a display 192. More or
 fewer components may be used. The components may also be placed in a
 different order for processing. The system 90 may comprise any of various
 ultrasound systems, such as the 128XP, Aspen or Sequoia ultrasound systems
 manufactured by Acuson Corporation. Ultrasound systems manufactured by
 others may also be used.
 In operation, the comer turning memory 100 stores data associated with
 multiple transmissions along a same scan line. The data comprises radio
 frequency or in-phase and quadrature (I/Q) data. The data is filtered by
 the clutter filtering paths 102 and 104. The filtered data is used to
 estimate parameters representing flow or movement, such as energy,
 velocity and/or variance, at each spatial location or range along the scan
 line for a given time. Each clutter filtering path 102 and 104 generates
 separate estimates for the same spatial locations. The selection block 150
 selects estimates from one of the two clutter filtering paths 102 and 104.
 The selected estimates are spatially filtered by spatial filter 160 and
 temporally filtered by the temporal filter 170. The filtered estimates are
 stored in the CINE memory for later review. The scan converter 190
 re-formats the estimates for display. The color mapping processor 191
 determines colors for display to represent the estimates and combines with
 any B-mode images. Based on the colors, an image representing flow or
 movement is generated on the display
 Each clutter filtering path 102, 104 includes a clutter filter 110, 120, an
 estimator 111, 121, and a discriminator 115, 125. In alternative
 embodiments, the discriminator 115, 125 is not provided. Additional
 components may be included within the clutter filtering paths 102, 104,
 including components represented in other portions of the system 90 (e.g.
 temporal or spatial filters 160 or 170). In an alternative embodiment, one
 clutter filter path 102 has different components than the other clutter
 filter path 104.
 The clutter filters 110 and 120 preferably comprise finite impulse response
 filters (FIR), infinite impulse response filters (IIR) or combinations
 thereof. The clutter filters 110 and 120 are either static or
 programmable. In one embodiment, the clutter filters 110 and 120 comprise
 digital signal processors. Other known or yet to be developed clutter
 filters may be used, such as processors or dedicated hardware. The same or
 different type of hardware may be used for each of the clutter filters 110
 and 120. Preferably, the clutter filters 110 and 120 comprise programmable
 FIR filters.
 The clutter filters 110 and 120 have a different magnitude versus frequency
 response. For example, different cut-off frequencies are used, different
 rolloffs are used (e.g. sharper or more gradual rolloff for one clutter
 filter), and/or different passband shapes are used (e.g. more flat or
 different magnitude versus frequency notches).
 The clutter filters 110, 120, in particular the coefficients and the number
 of taps defining the magnitude versus frequency response, are selected as
 a function of the imaging application and experimentation. Referring to
 FIG. 6, the frequency response for two preferred clutter filters 110,120
 is shown at 600 and 602, respectively. The log magnitude response 600, 602
 of each clutter filter 110, 120 is shown. The sampling frequency is 1
 divided by the pulse repetition interval (PRI). These two magnitude versus
 frequency responses 600 and 602 are preferred for each path 102 and 104
 (FIG. 1), respectively, for general imaging applications, such as kidney
 or liver imaging with a center frequency from 2-5 MHz and PRIs ranging
 from 320 to 2400 microseconds. Other center frequencies and PRIs with the
 same or different clutter filters 110, 120 may be used.
 One magnitude versus frequency response 600 is responsive to an 8 tap
 clutter filter with the filter coefficients of [0.201, -0.332, -0.154,
 0.266, 0.315, -0.147, -0.361, 0.213]. The other magnitude versus frequency
 response is responsive to a 5 tap clutter filter with the filter
 coefficients of [0.342, -0.863, 0.600, 0.059, -0.137]. More or fewer taps
 and/or different coefficients may be used.
 Both magnitude versus frequency responses have similar stopband
 performance, such as in the range of frequencies below about 0.05 times
 that of the sample frequency (1/PRI). One magnitude versus frequency
 response 600 has a higher magnitude below about 0.31 than the other
 magnitude versus frequency response 602. Above this frequency, the
 magnitude of the one magnitude versus frequency response 600 is degraded
 since the coefficients and number of taps have been selected to obtain a
 higher response at the lower frequencies with a sharper rolloff. The
 clutter filter 110 associated with the one magnitude versus frequency
 response 600 provides data for better low flow or movement velocity
 sensitivity. The clutter filter 120 associated with the other magnitude
 versus frequency response 602 provides data for better higher velocity
 estimation. This clutter filter 120 produces a stronger output for
 frequencies above 0.31 but has a more gradual rolloff. By combining or
 selecting from the parameters estimated from both clutter filters 110,
 120, better overall performance for imaging flow or movement is provided.
 None, one or more of the clutter filters 110, 120 decimate to increase the
 low velocity sensitivity. The resulting velocity estimates are scaled by
 dividing by the decimation factor before selection or combination. In one
 embodiment, one clutter filter 110 decimates and the other clutter filter
 120 outputs data at the same rate as the data is input. The parameters
 estimated from data output by the decimating clutter filter 110 are
 combined with the parameters estimated from the data output by the non
 decimating clutter filter 120. This embodiment provides better overall
 sensitivity over a wider passband.
 In an alternative or addition to the various embodiments above, one or more
 of the clutter filters 110, 120 comprise complex clutter filters. For
 example, a complex filter has a passband substantially in positive
 frequencies while rejecting low frequency clutter and negative
 frequencies. The other clutter filter 120 has a passband characterized as
 a complex conjugate of the first filter. The complex conjugate filter has
 a passband in negative frequencies and rejects positive frequencies. Other
 complex filters and passbands may be used.
 Referring to FIG. 1, the output from each of the clutter filters 110, 120
 is provided to the two estimators 111, 121, respectively. The estimators
 111, 121 comprise digital signal processors, general processors, dedicated
 hardware, other known or yet to be developed devices for estimating flow
 or movement parameters, and combinations thereof. The estimators 111, 121
 estimate a set of parameters for each spatial location. In one embodiment,
 the set of parameters includes velocity, variance and energy. More, fewer
 or different parameters may be estimated. The parameters are obtained from
 auto-correlation processing, cross correlation processing, Doppler
 processing or other similar methods. The parameters are estimated as
 auto-correlation values, R0 and complex R1 or other parameters related to
 Doppler energy, velocity, and variance.
 The sets of estimates are output to the discriminators 115, 125. The
 discriminators 115, 125 comprise digital signal processors, general
 processors, dedicated hardware, RAM look-up tables or other devices for
 applying a threshold to the estimates. The discriminators 115, 125 set all
 or a subset of the estimates to zero where the energy estimate is below an
 energy threshold or the velocity estimate is below a velocity threshold.
 The thresholds are selected to reject noise and low level signals for
 energy estimates and to reject low velocity clutter from tissue motion for
 velocity estimates. The levels of these thresholds is set independently
 for each clutter filter path 102, 104. Preferably, the thresholds applied
 by each discriminator 115, 125 are the same. Different thresholds to
 reject different signals may be used, such as different thresholds for
 flow than for tissue movement. The velocity threshold levels are
 determined experimentally and typically range from velocities with Doppler
 frequencies of 0.05 to 0.20 times the sample frequency, depending on the
 clinical situation, operating frequency, and PRI. The energy threshold
 typically ranges from 6 to 12 dB above the system noise floor. In
 alternative embodiments, a threshold is not applied to one or both of
 energy and velocity estimates prior to combination. In yet other
 alternative embodiments, one type of parameter is used to set estimate
 values for another type of parameter, such as velocity thresholds for
 energy estimates.
 The sets of estimates output by the discriminators 115, 125 are input to
 the selector 150. The selector 150 comprises a digital signal processor, a
 general processor, dedicated hardware, a RAM look-up table or other
 devices for comparing and selecting estimates. Preferably, the selector
 150 selects the set of estimates for imaging that includes the highest
 velocity magnitude. Other parameters may be used for the selection, such
 as energy estimates. In alternative embodiments, each type of parameter or
 a subset is selected independently. For example, velocity is selected from
 one clutter filter path 102 and energy is selected from the other clutter
 filter path 104 for representing the same spatial location. In this
 example, variance is selected either independently or as a function of the
 energy or the velocity selection.
 Referring to FIG. 2, a block diagram of a preferred structure for the
 selector 150 is shown. The selector 150 comprises absolute value blocks
 151, 152, comparitors 153, 154, a comparison select multiplexer 155 and a
 estimate selection multiplexer 156. Preferably, the selector 150 comprises
 a single field programmable gate array. In alternative embodiments, these
 components comprise separate dedicated hardware or one or more of the
 components is implemented with a processor. For example, the absolute
 value blocks 151, 152 and the comparitors 153, 154 are implemented with a
 single processor.
 The absolute value blocks 151, 152 convert signed velocity estimates to
 velocity magnitude estimates for selection determination.
 One comparitor 153 compares the velocity magnitude from the two different
 clutter filter paths 102, 104 (FIG. 1). Another comparitor 154 compares
 the energy from the two different clutter filter paths 102, 104. The set
 of estimates associated with the highest velocity magnitude and the
 highest energy is identified by the comparitors 153, 154, respectively.
 The lowest values or values closest to a threshold may also or
 alternatively be used to identify the set of parameters. In alternative
 embodiments, one comparitor 153, 154 is provided for either of velocity,
 energy or variance comparison. In yet other alternative embodiments,
 different parameters are compared, such as variance.
 In the preferred embodiment, the velocity magnitude and energy comparison
 used for selecting estimates is selectable. Variance comparison may also
 be provided. The multiplexer 155 receives control input to select velocity
 magnitude or energy comparison. In response to the control input, the
 comparison selection multiplexer 155 outputs the identification of the set
 of parameters from the appropriate comparitor 153, 154.
 The estimate selection multiplexer 156 receives the identification
 information as a control input. In response to the comparison control
 input information, the estimate selection multiplexer 156 passes one set
 of estimates for further image processing. For example, velocity magnitude
 comparison is selected for estimate selection. The comparitor 153
 identifies the clutter filter path 102, 104 corresponding to the set of
 estimates with the highest velocity magnitude. The comparison selection
 multiplexer 155 passes the identification from the velocity comparitor
 153. The estimate selection multiplexer 156 passes the set of estimates
 corresponding to the identified clutter filter path 102, 104.
 In an alternative embodiment, a RAM look-up table is used for selection of
 the set of estimates. The look-up table is responsive to the parameters
 discussed above, such as velocity or energy, or multiple parameters. For
 example, the parameter set with the highest energy is selected unless the
 difference between the energies in each set is less than a threshold. If
 the energies are close, then the set of estimates corresponding to the
 lowest variance is selected. Other structures and comparison combinations
 may be used, including independent comparison of velocity, variance or
 energy or dependent comparison of any combination of velocity, variance
 and/or energy.
 FIG. 1 represents a system 90 for selecting from two parallel clutter
 filter paths 102, 104. Referring to FIG. 3, an alternative system 190 that
 uses sequential processing to obtain the sets of estimates using clutter
 filters with different magnitude versus frequency responses is shown. The
 system 190 comprises a clutter filter 210, a coefficient storage 210a, an
 estimator 211, a persistence filter 212, a discriminator 215, a threshold
 level storage 215a, a memory 216, and a selector 250.
 The corner turning memory 200 provides data to the single clutter filter
 210. The clutter filter preferably comprises a programmable FIR filter,
 but other programmable filters discussed above may be used. The
 coefficient storage 210a contains at least two different coefficient sets,
 such as the coefficients shown in FIG. 6. The coefficient set in use is
 selected depending on the sequential data pass-pass1 (the first pass) or
 pass2 (the second pass). On the first pass, data for one acoustic line of
 parameter estimates is read out of the corner turning memory 200 and
 processed by the clutter filter 210 programmed with coefficient set 1. The
 flow parameters velocity, variance, and energy are estimated by estimator
 211.
 The optional persistence filter 212 comprises a digital signal processor, a
 general processor or dedicated hardware including a memory for buffering
 data. The persistence filter 212 optionally performs frame based temporal
 persistence. Independent frame storage for each processing pass for each
 set of parameter estimates is provided for persisting. Each new set of
 estimates is persisted with previous sets of estimates from the same pass
 (e.g., pass 1 or pass 2).
 The estimates are passed to the discriminator 215 where a lower energy
 threshold and/or a velocity threshold are applied to the estimates. The
 levels of these thresholds is set independently for each processing pass
 by downloading different thresholds from the threshold storage block 215a.
 Preferably, the same thresholds are used for each pass. Other thresholds,
 such as discussed above, may be used.
 The estimates from the first pass are stored in the memory 216. The memory
 216 preferably comprises a line FIFO buffer, but may comprise a RAM,
 buffer, or other memory devices.
 The process is repeated for the second pass. Preferably, the same data is
 output by the comer turning memory 200. A subset of data may be output to
 speed processing where some data is not needed due to the characteristics
 (e.g., number of taps) of the clutter filter. The clutter filter 210 is
 programmed with different coefficients from the memory 210a for the second
 pass. The second set of estimates is obtained, persisted, thresholded, and
 input to the selector 250 with the first set of estimates stored in the
 memory 216.
 As discussed above, estimates are selected for further processing and
 imaging by the selector 250. The selected estimates are output for further
 processing as discussed above for FIG. 1. The clutter filter coefficients,
 the thresholds, the persistence filter characteristics, the pass selector
 and any other controllable components are controlled by a controller.
 In another embodiment, the sets of estimates are combined for imaging.
 Referring to FIG. 4, one embodiment for combining the sets of estimates
 using sequential clutter filtering is shown at 300. Combining the sets of
 estimates may also be used with the parallel clutter filter paths 102, 104
 of FIG. 1. The system 300 includes a clutter filter and associated
 coefficient memory 310, an estimator 311 and a combiner 315. The clutter
 filter and associated coefficient memory 310 and the estimator 311 operate
 as described above.
 The combiner 315 comprises a persistence filter 312 and memory 313. In
 alternative embodiments, the combiner 315 comprises a RAM look-up table, a
 digital signal processor, a general processor, dedicated hardware or other
 devices for combining estimates. The persistence filter 312 passes the
 estimates to the memory 313 for the first pass. For the second pass, the
 persistence filter 312 combines the sets of estimates, one set from the
 memory 313 and the other set from the estimator 311.
 Any of various combination functions may be used, such as averaging,
 weighted averaging, linear combinations, and non-linear combinations.
 Different functions may be used for different types of estimates, such as
 one function for combining velocity estimates and a different function for
 combining energy estimates. Either energy or the log of the energy may be
 combined. In one embodiment, energy weighted combinations of velocity
 estimates is performed as disclosed in U.S. Pat. Nos. 5,609,155 and
 5,860,930, the disclosures of which are incorporated herein by reference.
 For example, the velocity output by the combiner 315 is represented as
 (E1*V1+E2*V2)/(E1+E2), where E1 and V1 are the energy and velocity of a
 first set and E2 and V2 are the energy and velocity of a second set.
 Referring to FIG. 5, an alternative embodiment of the combiner 315 is
 shown. The combiner 315 comprises a filter, digital signal processor,
 general processor, summers and multipliers, or other devices for averaging
 data. The combiner 315 sums the sets of estimates. In particular, the
 estimates for each type of estimate are summed together. The total for
 each type of estimate is multiplied by 1/2. The resulting averages are
 output for further processing and imaging. Preferably, the combination is
 performed after discrimination.
 Combining the estimates results in estimates optimized as a function of the
 attributes of two different clutter filters. The advantages of each
 individual clutter filter are provided without the disadvantages, such as
 degraded frame rate, of a single filter. If complex clutter filters are
 used with a combiner 315, the multiple complex clutter filter attributes
 produce combined parameter estimates which have a better signal to noise
 ratio over a wider bandwidth than the filters by themselves or by a real
 filter with a passband that is substantially similar to the combined
 passbands of the complex filters.
 In one embodiment, one or more clutter filters comprise adaptive clutter
 filters. For example, the adaptive clutter filters disclosed in U.S. Pat.
 Nos. 5,544,659 and 5,664,575, the disclosures of which are incorporated
 herein by reference, are used. Other adaptive clutter filters may be used,
 such as where the coefficients and/or number of taps are selected as a
 function of ultrasound data. Where both clutter filters are adaptive,
 different functions for adaptation may be used, resulting in different
 magnitude versus frequency responses.
 In further embodiments, more than two clutter filter paths or passes with a
 corresponding more than two different magnitude versus frequency responses
 are provided. For example, a collection of narrowband clutter filters
 boosts overall sensitivity over a greater range of desired Doppler
 frequencies than a single filter or two filters.
 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,
 other known or yet to be developed clutter filters, estimators, combiners,
 and selectors may be used.
 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.