ULTRASOUND SIGNAL PROCESSING AND MODELING FOR

AUTOMATED MEDICAL MONITORING

Cara A. CampbellNDE Laboratory, College of William and Mary

Advisor: Dr. Mark Hinders

Abstract

Our research focuses on the use of ultrasound formedical monitoring applications. Two projects thatwe discuss below are ultrasonic bladder disten-tion monitoring and emboli removal from cardiopul-monary bypass circuits. In the first topic, ultrasoundsignal processing methods are used to relate raw ul-trasound signals to bladder fullness. Clinical testshave been completed to collect relevant bladder dis-tention data. Further clinical data must be collectedto develop adequate bladder fullness algorithms. Thesecond topic involves the implementation of compli-cated acoustic force models in order to optimize ul-trasonic emboli removal in the operating room. Ourmodeling results show that experimental optimiza-tion of a removal system can be based on a simpleinviscid fluid model. The techniques we have devel-oped can be applied to various other applications.Acoustic processing of algal biofuels is one examplethat is briefly discussed.

Introduction

Automated medical monitoring using ultrasound sig-nal processing can be used in situations where a care-taker or doctor is unavailable. My research involvesacoustics modeling to help design medical monitor-ing devices, as well as creating signal processing al-gorithms to make automated monitoring possible. Inthis paper I will discuss the need for automated med-ical monitoring and then I will describe two projectsthat I currently am working on: removal of microem-boli from the blood stream and bladder fullness mon-itoring.

Telemedicine

Telemedicine systems around the world are im-proving patient care and virtual access to medical

specialists. The Indian Space Research Organiza-tion has connected nearly 80 rural hospitals to 22specialty hospitals through satellites, allowing morethan 25,000 patients in rural areas to receive tele-consultations [1]. In rural Germany, specialists haveused videoconference systems in 15 minute telecon-sultations to examine 153 patients and their CTscans [2]. In the United States surgeons have usedtelemedicine to allow distant expert surgeons to viewultrasound images and advise on-site surgeons in realtime [3]. These results have shown that telemedicineis a promising method in evaluating patient healthand that patients are open to the idea of telemedicine.

Space Medicine

NASA regards medical illness and trauma as highrisk for potential impact on mission and crew, espe-cially for extended duration missions to the moon,Mars, and beyond [4]. Ultrasound is the only medi-cal imaging technology currently available for medicaldiagnosis in space and there are no plans to imple-ment other medical imaging devices in space becauseultrasound has advantages over other imaging tech-niques such as X-ray or MRI [5]. It is not affected bythe space environment and does not expose the crewto radiation, scans can easily be performed by non-physicians, and modern ultrasound systems are small,very light-weight, and real time. A multipurposeHDI-5000 Ultrasound System (ATL/Philips, Both-well, WA) has been used aboard the ISS to exam-ine the ocular system, shoulder, and abdomen. Crewmembers were given approximately 3-6 hours of train-ing with the ultrasound apparatus. The crew mem-ber performing the ultrasound was guided throughthe procedure remotely by a physician at mission con-trol, with only a 2 second delay in communication. Inall of the trials it was found that non-physician crewmembers were capable of acquiring high quality ultra-sound images that could be used for diagnosis [6], [7].

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Although ultrasound has been demonstrated inspace, remote guiding by an ultrasound expert maynot work beyond low earth orbit. For example, it cantake 4 to 20 minutes for communication to travel fromthe Earth to Mars (depending on where the plan-ets are in their orbit). Real-time remote guidance ofmedical ultrasound is not realistic at these distances.As manned space exploration advances beyond lowearth orbit it will be necessary to develop much bet-ter systems to monitor crew health. Ultrasound sys-tems must be developed that do not require an expertto guide imaging. Our research focuses on computerprocessing of ultrasound signals to create artificial in-telligence algorithms that can interpret data withouthuman experts. By processing ultrasound signals inthis way the computer can yield the relevant medicalinformation. We are currently working on ultrasoundmodeling and signal processing for various applica-tions, as discussed below.

Bladder Distention Monitor

The goal of this project is to develop an ultrasounddevice that can measure bladder fullness, based ona NASA Langley technology but updated with newgeneration electronics and signal processing algo-rithms [8]. The bladder distention monitor uses asingle broad beam ultrasonic transducer that sendsand receives signals. The monitor will be designedto be worn underneath the clothing against the skinand is non-invasive. The signals are processed andrelated to bladder distention via optimized real-timealgorithms. The monitor can be worn by those suf-fering from urinary incontinence (UI) and will use adistinct tone to warn the wearer of the need to urinate10-15 minutes before bladder contraction.

Figure 1: Initial prototype of the bladder distentionmonitor and an ultrasound image of the human uri-nary bladder [9].

UI affects approximately 25 million adults in theUnited States [10]. It is experienced by around 53

percent of homebound adults age 65 and older andis often a key factor in transitioning to long-termcare [11]. UI can also be a deciding factor in thelevel independence available to mentally handicappedindividuals. Approximately 37 percent of mentallychallenged children have difficulty developing toilet-ing skills by adulthood [12]. Furthermore, in recentstudies it was estimated that the annual cost of man-aging UI through long-term care is around 19.5 billiondollars [13]. The ultrasound bladder monitor will bea new, improved and cost-effective method for moni-toring UI.

The signal processing aspect of this project requirescreating an algorithm to directly relate the ultra-sound signal to bladder distention. The design of thedevice will allow a transducer to send pulses of an ul-trasonic beam into the bladder. The ultrasound waveinteracts with the bladder wall and is reflected backto the transducer. The returning signal is detectedand the echoes will show a pattern that is relatedto the movement of the bladder as it expands. Thissystem will not create an image from the ultrasoundsignal, but rather, the RF waveform echo signal willbe processed to represent a level of bladder fullness.

In preliminary work on this project we created aphantom abdomen that included a bladder, uterus,rectum, pelvic bones, and abdominal fat. The phan-tom was scanned in an ultrasound immersion tank,as shown in figure 2. These initial scans allowed us toestablish a proof of principle for ultrasonically moni-toring bladder distention changes.

Figure 2: Phantom during an ultrasonic scan.

Once we completed the phantom scans, we movedon to collect clinical data. We performed two roundsof clinical tests at the nursing department of Old Do-

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minion University. The clinical tests involved a totalof 60 subjects of various sizes and ages. We firstcollected ultrasound signals when a subject’s bladderwas empty, and then collected signals as the bladderfilled due to liquid consumption. The clinical setupwas small and easily portable, involving only a lap-top, an ultrasonic transducer, and a small nanopulserdevice to drive the transducer (as shown in figure 3). Idesigned a simple Labview data acquisition programto control the nanopulser and to record raw ultra-sound signals.

Figure 3: The image shows the full data collectionsetup.

In the first round of testing we used small one inchwide flat transducers to send and receive ultrasoundsignals. An example of a result from the first roundof testing is shown in figure 4. After the first round oftesting we determined that most of the data did notcontain adequate information to relate the signals tobladder distention. This result is because the trans-ducer shape created a collimated directional beam.Therefore, if subjects held the transducers at any an-gle to the abdomen (which is common due to abdom-inal shape), then the beam was not actually directedtowards the bladder.

Figure 4: Example of results from the first roundof testing at ODU. The image shows ultrasonic sig-nals recorded from one subject with an empty bladder(green), and with a full bladder (pink).

Figure 5: The divergent beam transducer used in thesecond round of clinical tests.

In the second round of clinical tests we used smalla divergent beam transducer, as pictured on the leftside of figure 5. The divergent beam transducer waschosen to help decrease the directionality issue thatwe encountered in the first round of testing. A diver-gent transducer creates a broader beam width andtherefore has a greater chance of interacting withthe bladder while the patients hold the transduceragainst their abdomen. An example of a result fromthe second round of testing is shown in figure 6. Thefigure shows data that has undergone a wavelet fin-gerprint transform. The wavelet transform method

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can allow for the detection of patterns that may bedifficult to find in the raw data.

Figure 6: Example of results from the second roundof testing at ODU. The image shows the wavelet fin-gerprint transform of ultrasonic signals recorded fromone subject as the bladder expanded.

Future Work

After the two rounds of clinical testing describedabove we concluded that further clinical tests are re-quired to develop bladder distention algorithms. Theresults of the collected data demonstrated that thereare changes in the abdominal region as the bladderfills. However, in the future we hope to perform clini-cal tests in a urodynamics laboratory where the blad-der position can be determined prior to raw data col-lection. By first determining bladder position we cancollect data which we are confident contains informa-tion for monitoring urinary bladder changes.

Gaseous Microemboli

The relationship between increased embolic load tothe brain and neurocognitive deficits are well doc-umented, and are a concern in high-altitude flight,space travel, deep water diving, and open heartsurgery. Arterial line filters are now used to stopemboli in extracorporeal circuits from passing backinto the bloodstream. However, small emboli andsometimes large emboli pass through these filters; es-pecially when the filters are overloaded [14]. It isimportant to monitor emboli load pre-filter becausea warning of increased load allows the medical teamto eliminate emboli sources. The EDAC QUANTI-FIER (Luna Innovations Inc., Roanoke VA, USA)uses broadband ultrasound pulses to detect and trackemboli [15]. The EDAC uses motion tracking algo-rithms identify the signals of individual emboli. In

addition, the EDAC has been proven to accurately es-timate the size of emboli using the backscatter echoesfrom emboli [16].

In this project, we are extending the EDACs capa-bilities by adding the ability to remove gaseous mi-croemboli from the extracorporeal circuit. It is there-fore necessary to precisely know the behavior of ra-diation force as a function of ultrasound frequency inorder to optimize the removal process. For example,if there are fairly broad resonance peaks, it may bepossible to increase the magnitude of radiation forcewhile keeping frequency low. In the section below Iwill briefly discuss the equations describing acousticradiation force.Acoustic Radiation Force

Acoustic radiation force is the force exerted uponan object by an incident sound wave. Over the pastcentury numerous authors have calculated acousticradiation force. There are various approaches for cal-culating the acoustic radiation force exerted by an in-cident plane progressive wave on a sphere immersedin a fluid. The derivation of radiation force is brieflydiscussed below.

We begin by writing conservation of mass and con-servation of momentum in a fluid as [17]

∂ρ

∂t= −∇ · (ρ~v)

∂(ρ~v)

∂t= ∇σ − ρ(~v · ∇)~v − ~v(∇ · ρ~v)

(1)

where ρ is density, σ is the stress tensor and ~v is ve-locity. Conservation of momentum was written usingthe substantive derivative

ρ∂(~v)

∂t=d~v

dt− ρ(~v · ∇)~v

= ∇σ − ρ(~v · ∇)~v .(2)

In its general form, the stress tensor can be writtenas

σik = −pδik + η

(∂vi∂xk

+∂vk∂xi− 2

3

∂vj∂xj

δik

)+ ξ

∂vj∂xj

δik

(3)where p is pressure, η is viscosity, and ξ is bulk vis-cosity.

Next we need to write an expression for velocity.We begin by writing the linearized vector wave equa-tion (

∇2 +K2)~v −

(1− K2

k2

)∇(∇ · ~v) = 0 (4)

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in which ~v is the perturbation in the fluid velocity dueto the acoustic field, K is the transverse wavenumberand k is the longitudinal wavenumber. Via Helmholtzdecomposition we can write velocity in terms of ascalar and a vector potential

~v = ∇φ+∇× ~Ψ (5)

where φ is the scalar velocity potential and Ψ is thevector velocity potential. The velocity potentials sat-isfy the following conditions

(∇2 + k2)φ = 0 (∇2 +K2)~Ψ = 0 . (6)

Finally, we write an equation for force acting on avolume in a fluid:

F =

∮σ dA

=

∫∇σ dV .

(7)

In the case of a freely suspended fluid or solidelastic sphere in an inviscid fluid we set viscosityand bulk viscosity equal zero in equation (3) so thatσik = −pδik and radiation force becomes [18], [19]

F =

∫−pδik dA

= −2πρ1|A|2∞∑

n=0

(n+ 1)(αn + αn+1+

2αnαn+1 + 2βnβn+1)

(8)

where A is the incident wave amplitude and

αn =−G2

n

G2n +H2

n

, (9)

βn =−GnHn

G2n +H2

n

, (10)

in which

Gn = (Ln − n)jn(k1a) + (k1a)jn+1(k1a) , (11)

Hn = (Ln − n)nn(k1a) + (k1a)nn+1(k1a) . (12)

where the compressional wavenumber in the sur-rounding fluid is k1 = ω/c1 , a is the sphere radius,and nn(x) is the spherical Bessel function of the 2ndkind. Ln is shown below for a fluid sphere:

Ln =ρ1ρ2

(k2a)[njn(k2a)− (k2a)jn+1(k2a)]

jn(k2a)(13)

where ρ2 is the density of the sphere, k2 is the com-pressional wavenumber in the sphere.

In the case of a solid elastic sphere shear wavesare created inside the sphere, and the function Ln isequal to

Ln =1

2

ρ1ρ2

(K2a)2(An −Bn)

(Dn − En)(14)

in which

An =njn(k2a)− (k2a)jn+1(k2a)

(n− 1)jn(k2a)− (k2a)jn+1(k2a), (15)

Bn =2n(n+ 1)jn(K2a)

[2n2 − (K2a)2 − 2]jn(K2a) + 2(K2a)jn+1(K2a),

(16)

Dn =[(K2a)2/2− n(n− 1)]jn(k2a)− 2(k2a)jn+1(k2a)

(n− 1)jn(k2a)− (k2a)jn+1(k2a),

(17)

En =2n(n+ 1)[(1− n)jn(K2a) + (K2a)jn+1(K2a)]

[2n2 − (K2a)2 − 2]jn(K2a) + 2(K2a)jn+1(K2a).

(18)In these equations the longitudinal and transversewavenumbers in the sphere are

k2a =c1c2

(k1a) K2a =c1C2

(k1a) (19)

where c2 is the compressional sound velocity in thesphere and C2 is the shear wave speed in the sphere.

When the problem is expanded to include viscosityin the surrounding fluid, the complexity of the radi-ation force equation increases dramatically. Due tothe complication of the expression, in this paper wewill only state the starting point below [20]:

F = 〈∫σ1nda〉+

∫〈σ2〉nds (20)

where σ1 is the first order stress term, σ2 is the secondorder stress term, and the brackets denote the timeaverage of the function enclosed. By plugging smallvariations in ρ, p, and v, up to second order, into theNavier-Stokes equation and the conservation of massequation, equation 20 becomes:

F = 〈∫

(σ2 − ρov1l v1k)nda〉 . (21)

We are currently numerically implementing boththe inviscid and viscous equations using Matlab. Pre-liminary results are shown below.

Figures 7 - 8 show radiation force versus ka for thematerials that are relevant to CPB circuits, namely

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air and lipid emboli in blood. During bypass surgerythe body is cooled to approximately 37◦C. The ma-terial properties used for these plots correspond tothis temperature.

Figure 7: Radiation force versus ka for an air bubblein blood. The solid lines show the range of radiationforce found using the inviscid model for the range ofmaterial properties listed in table 1. The dotted linesshow radiation force found using the viscous modelfor the range of material properties.

Figure 8: Radiation force versus ka for a lipid spherein blood. The solid lines show the range of radiationforce found using the inviscid model for the range ofmaterial properties listed in table 1. The dotted linesshow radiation force found using the viscous modelfor the range of material properties.

Table 1: CPB Material Properties Range

Material Density Speed of Sound Viscosity Bulk Viscosity(g/cm3) (cm/s) (g/cms) (g/cms)

Air 0.001174− 0.0013 32, 100− 35, 000 0.00018482− 0.00018673 0.00012− 0.00019

Blood 1.060− 1.2508 154, 000− 160.000 0.0225− 0.0319 0.024− 0.034

Lipid 0.89− 0.924 143, 000− 147, 000 1− 16 2.5 · 106 − 40 · 106

References: [21], [22], [23], [24], [25]

As seen in the figures above, when viscosity ofthe scatterer is small both methods give very simi-lar results. As expected, when viscosity of the scat-terer increases these two methods no longer yield thesame results. Furthermore, these preliminary radia-tion force plots show that there are no broad reso-nance peaks that can be used to get a large force ata low frequency.Future Work

We have shown that both viscous and inviscidacoustic force models can be implemented for realworld applications. The next step in acoustic forcemodeling is to experimentally verify the complicatedviscous model. We are not aware of any publicationsto date that verify the viscous model. We are cur-rently collaborating with Los Alamos National Lab-

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oratory to complete comparisons of the analytical re-sults to experiment. Over the next six months we ex-pect to receive experimental acoustic force data fromLANL which can be directly compared to our super-computer modeling results.

Acoustic force modeling has many applications.Now that we have successfully implemented the mod-els for the emboli application, we are looking intoother projects for acoustic modeling. We have re-cently become involved in a project that is focusedon creating biofuels from wild grown algae. Acous-tic forces may be used in algal processing prior tobiofuel conversion for the purpose of precise sortingof algal cells based on their content. We are justgetting started on applying the acoustic force mod-els to the algae biofuels application. In addition tothe models described above, we are implementing 3-dimensional acoustic finite integration simulations toinvestigate the affects of acoustic force on multiplescatterers (such as multiple algal cells). An exampleof a 2-dimensional image from the 3D AFIT simula-tion results is shown in figure 9.

Figure 9: Example of multiple scattering from anacoustic finite integration simulation.

Conclusion

The primary issue in telemedicine is that it still re-quires lengthy consultations by specialists, which areexpensive enough that many people in rural areascannot afford them. The type of algorithms thatwe are developing for the projects described abovecan make health care more affordable and accessible.By creating specialized computer programs to mapout level of suspicion for particular health problems,

only patients who show significant risk will need to beseen by (virtual) specialists. Rather than proposingto solve the general problem of computerized ultra-sound interpretation, we have described two currentprojects which we have had underway at William andMary recently. These collaborations all involve indus-trial and academic partners with the goal of commer-cializing the ultrasound systems described, as well asclinical partners. Our research focuses on those as-pects related to complicated acoustic modeling andultrasound signal processing algorithm developmentnecessary to automate medical devices. In particular,the goal of my research is to accurately model the in-teraction of ultrasound with anatomical structures ofinterest and to develop signal processing algorithmsto extract features from ultrasound RF waveforms.To accomplish this goal we will continue to collabo-rate with clinical partners to obtain additional humandata sets of ultrasound signals. I am analyzing thesesignals and comparing them to mathematical mod-els created using Matlab. Furthermore, I will usethe data from patients to determine key features increating the LOS algorithms. These techniques areoften common from one application to the next, andwill begin to lay the groundwork for self-diagnosingultrasound systems on spacecraft. Additionally, thetechniques have applications in non-medical basedprojects, such as green aviation with algal biofuels.

Acknowledgements

Thanks to Ted Lynch and Kevin Rudd for their con-versations and help regarding acoustics and AFITsimulations. Thanks also to John Companion andKaren Karlowicz for assistance in the bladder mon-itor project. Additionally, this work could not havebeen completed without the SciClone computing clus-ter at the College of William and Mary.

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