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1. Ultrasound-InducedMembraneHyperpolarizationinMotorAxonsandMuscleFibersoftheCrayfishNeuromuscularJunction

   FeiyuanYua, WolfgangS.M€ullera (September 2023, Ultrasound in medicine biology)

   Objective: Focused ultrasound(FUS)canmodulateneuronalactivitybydepolarizationorhyperpolarization. Although FUS-evokeddepolarizationhasbeenstudiedextensively,themechanismsunderlyingFUS-evoked hyperpolarization (FUSH)havereceivedlittleattention.Inthestudydescribedhere,wedevelopedaprocedure using FUStoselectivelyhyperpolarizemotoraxonsincrayfish. Asapreviousstudyhadreportedthattheseaxons express mechano-andthermosensitivetwo-poredomainpotassium(K2P)channels,wetestedthehypothesisthat K2P channelsunderlieFUSH. Methods: Intracellular recordingsfromamotoraxonandamuscle fiber wereobtainedsimultaneouslyfromthe crayfish openerneuromuscularpreparation.FUSHwasexaminedwhileK2Pchannelactivitiesweremodulated by varyingtemperatureorbyK2Pchannelblockers. Results: FUSH intheaxonsdidnotexhibitacoherenttemperaturedependence,consistentwithpredictedK2P channel behavior,althoughchangesintherestingmembranepotentialofthesameaxonsindicatedwell-behaved K2P channeltemperaturedependence.Thesameconclusionwassupportedbypharmacologicaldata;namely, FUSH wasnotsuppressedbyK2Pchannelblockers.ComparisonbetweentheFUS-evokedresponsesrecordedin motor axonsandmuscle fibers revealedthatthelatterexhibitedverylittleFUSH,indicatingthattheFUSHwas specific totheaxons. Conclusion: It isnotlikelythatK2PchannelsaretheunderlyingmechanismforFUSHinmotoraxons.Alternative mechanisms suchassonophoreandaxon-specific potassiumchannelswereconsidered.Althoughthesonophore hypothesis couldaccountforelectrophysiologicalfeaturesofaxonalrecordings,itisnotconsistentwiththelack of FUSHinmuscle fibers. Anaxon-specific andmechanosensitivepotassiumchannelisalsoapossible explanation. 

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2. Design and Optimization of Ultrasound Phased Arrays for Large-Scale Ultrasound Neuromodulation

   https://doi.org/10.1109/TBCAS.2021.3133133  

   Ilham, Sheikh Jawad; Kashani, Zeinab; Kiani, Mehdi (January 2022, IEEE Transactions on Biomedical Circuits and Systems)
3. Ultrasound thermal monitoring with an external ultrasound source for customized bipolar RF ablation shapes

   https://doi.org/10.1007/s11548-018-1744-4  

   Kim, Younsu; Audigier, Chloé; Ziegle, Jens; Friebe, Michael; Boctor, Emad M. (June 2018, International Journal of Computer Assisted Radiology and Surgery)
4. Domain Adaptation for Ultrasound Beamforming

   https://doi.org/10.1007/978-3-030-59713-9_40  

   Tierney, Jaime; Luchies, Adam; Khan, Christopher; Byram, Brett; Berger, Matthew (September 2020, MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020)

   null (Ed.)

   Ultrasound B-Mode images are created from data obtained from each element in the transducer array in a process called beamforming. The beamforming goal is to enhance signals from specified spatial locations, while reducing signal from all other locations. On clinical systems, beamforming is accomplished with the delay-and-sum (DAS) algorithm. DAS is efficient but fails in patients with high noise levels, so various adaptive beamformers have been proposed. Recently, deep learning methods have been developed for this task. With deep learning methods, beamforming is typically framed as a regression problem, where clean, ground-truth data is known, and usually simulated. For in vivo data, however, it is extremely difficult to collect ground truth information, and deep networks trained on simulated data underperform when applied to in vivo data, due to domain shift between simulated and in vivo data. In this work, we show how to correct for domain shift by learning deep network beamformers that leverage both simulated data, and unlabeled in vivo data, via a novel domain adaption scheme. A challenge in our scenario is that domain shift exists both for noisy input, and clean output. We address this challenge by extending cycle-consistent generative adversarial networks, where we leverage maps between synthetic simulation and real in vivo domains to ensure that the learned beamformers capture the distribution of both noisy and clean in vivo data. We obtain consistent in vivo image quality improvements compared to existing beamforming techniques, when applying our approach to simulated anechoic cysts and in vivo liver data. 

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5. Blind-label subwavelength ultrasound imaging

   https://doi.org/10.1126/sciadv.ado2826  

   Lin, Jinuan; Ma, Chu (January 2025, Science Advances)

   There is a long-existing trade-off between the imaging resolution and penetration depth in acoustic imaging caused by the diffraction limit. Most existing approaches addressing this trade-off require controlled “labels,” i.e., metamaterials or contrast agents, to be deposited close to the objects and to either remain static or be tracked precisely during imaging. We propose a “blind-label” approach for acoustic subwavelength imaging. The blind labels are randomly distributed acoustic scatterers with deep-subwavelength sizes whose exact locations and trajectories are not necessary information in image reconstruction. The proposed method achieves the resolution of 0.24 wavelengths in ultrasound imaging experiments and 0.2 wavelengths in simulations, providing over 10 times improvement compared to the diffraction limit. We also elucidate the influence of scatterer size and concentration on imaging performance. The proposed “blind-label” approach relaxes the restrictions of existing acoustic subwavelength imaging technologies relying on controlled labels, therefore substantially improving the practicality of acoustic subwavelength imaging in biomedical ultrasound imaging, sonar, and nondestructive testing. 

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