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range of possible effects and the typical clinical requirement Passive Ultrasound
for only a specific subset of these at any one instance under- Passive acoustic mapping (PAM) methods are intended to de-
scores the need for careful monitoring during treatment. De- tect, localize, and quantify cavitation activity based on analysis
spite the myriad bubble behaviors and resulting bioeffects, of bubble-scattered sound (for a review of the current art, see
very few are noninvasively detectible in vivo. For example, Haworth et al., 2017; Gray and Coussios, 2018). Unlike active-
light generation during inertial cavitation (sonolumines- imaging modalities, PAM images may be computed at any
cence) produced under controlled laboratory conditions time during atherapeutic ultrasound exposure and are decou-
may be detectible one meter away, but in soft tissue, both the pled from restrictions on timing, pulse length, or bandwidth
relatively weak light production and its rapid absorption ren- imposed by the means of cavitation generation. Because of this
ders in vivo measurement effectively impossible. Magnetic timing independence, PAM methods yield fundamentally dif-
resonance (MR) techniques, known for generating three- ferent (and arguably more relevant) information about micro-
dimensional anatomic images, may also be used to measure bubble activity. For example, features in the passively received
temperature elevation, with clinically achievable resolution spectrum such as half-integer harmonics (Figure 1) may be
on the order of 1°C, 1 s, and 1 mm (Rieke and Pauly, 2008). used to distinguish cavitation types from each other and from
However, because these techniques are generally agnostic to nonlinearities in the system being monitored and therefore
the cause(s) of heating, they cannot mechanistically identify may indicate the local therapeutic effects being applied.
bubble contributions to temperature elevation nor can they The processes for PAM imeee toimetien ere iliuetieted in
generally indicate nonthermal actions of bubble activity On Fi ee 2 S“ Gee e sin ie bubble ioeeted et osition it i_edi_
the basis of high-resolution availability of bubble-specific re- tg“ d   t. t t (ROD P ii t‘
sponse cues, active and passive ultrasonic methods appear 3 65 sour.‘ W1 m 2‘ reg.mn 0 m fires ’s“C as a llmor
best suited for noninvasive eiiniceimonitotin undergoing therapeutic ultrasound exposure. Under ideal
g" conditions, the bubble-emitted field propagates spherically,
Active Ultrasound where it may be detected by an array of receivers, commonly
The enhanced acoustic-scattering strength of a bubble excit- 3 ‘3°hvehtl°hel hdhdheld dldghdslle "‘“"“Y Pleeed 01" ‘he 5ldh-
ed near resonance (Ainslie and Leiglitom 2011) is exploitable PAM methods estimate the location of the bubble based on
with diagnostic systems that emit and receive ultrasound relative delays between array elements due to the curvature
pulses; bubbles can be identified as regions with an elevated of lhe reeelved Weve h'°ht5‘ Thls eldhds lh Cdhlldst ‘'3 eon‘
backscatter intensity relative the low-contrast scattering that vehllmlel “five lhelh°d5 lh“ l°‘3“ll7'e tdlgels dslhg ‘he lllhe
is characteristic of soft tissues and biological liquids. Intra- delay lletweeh “ellve llahslhlssloh and eeh° l’e‘3ePll°h-
venous introduction of microbubbles may therefore sub- After filtering raw array data to remove uriwarited signals
star-tially l-"“P1'°ve ‘he dl"‘Sl"°5‘l‘3 Visibility df l’l°°d vessels (such as the fundamental frequency of the active ultrasound
(where microbubbles are typically confined) by increasing that created the cavitation), PAM algorithms essentially run a
holh eCh° ampllhlde find handwldih (Smde and C0“55l°5- series of spatial and temporal signal similarity tests consisting
20l0)- Slmllal 1'e5°l“ll°1" e‘PPl'°e“3lllhg lo l-dh h“‘Y he Cll1"l' of two basic steps. First, the array data are steered to a point
Cally feasible with “super-resolution” methods employing (ac) in the ROI by adding ti.rne shifts to compensate for the path
microbubbles exposed to a high-frame rate sequence of low- ]er,gth berweer, x arid each array elerrieritr Ifthe source aemauy
amplitude s0“nd exposures (Couture et al., 2018), yielding was located at the steering location (2: : x), then all the array
lh vlvd mlerdvdsedldr l-‘hdgee Wllh 3 level °l Clem-l lhi“ W35 signals would be temporally aligned. Second, the steered data
Pl'evl°“5lY dhlhlhkdhle wlth dl“gh°-Slle “lll'“5°“hd- are combined, which in its simplest form involves summation.
Active iiitteeeimd tteiismiesioiis tei_i_iimi_eiiy interleaved with  more sophisticated forms, the  signal covariance ma-
tiieiepeiitie uiteesound piiiees have been iieed for bubble detee_ trix is scaledtby weight factors optunized to reduce interfer-
tioe end tteekiiie (Li et eie 2014i This tii_i_iii_ie eoiistmiiit meet“ ence from neighboring bubbles (Coviello et al., 2015).
that cavitation activity occurring during therapeutic ultrasound Regardless of the procedural details, the calculation typically
exposures (often thousands of cycles) would be missed, and yields an array-average signal power for each steering location
with it, information about the therapy process would remain in the ROI. This quantity is maximized when the array has been
unknown. This limitation is especially important if using solid steered to the source location, and the map has its best spatial
cavitation nuclei, which are essentially anechoic unless driven resolution when interference from other sources has been sup-
with a low-frequency excitation (e.g., therapy beam). pressed. The processing illustration in Figure 2 is in the time
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