Non-invasive measurement of hemodynamic change during 8 MHz transcranial focused ultrasound stimulation using near-infrared spectroscopy.

BACKGROUND: Transcranial focused ultrasound (tFUS) attracts wide attention in neuroscience as an effective noninvasive approach to modulate brain circuits. In spite of this, the effects of tFUS on the brain is still unclear, and further investigation is needed. The present study proposes to use near-infrared spectroscopy (NIRS) to observe cerebral hemodynamic change caused by tFUS in a noninvasive manner. RESULTS: The results show a transient increase of oxyhemoglobin and decrease of deoxyhemoglobin concentration in the mouse model induced by ultrasound stimulation of the somatosensory cortex with a frequency of 8 MHz but not in sham. In addition, the amplitude of hemodynamics change can be related to the peak intensity of the acoustic wave. CONCLUSION: High frequency 8 MHz ultrasound was shown to induce hemodynamic changes measured using NIRS through the intact mouse head. The implementation of NIRS offers the possibility of investigating brain response noninvasively for different tFUS parameters through cerebral hemodynamic change.

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image.

The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this article, we propose a deep content-aware image prior (DCAIP) with a content-aware attention module (CAAM) for superior despeckling of ultrasound images without clean images. For the image prior, we developed a CAAM to deal with the content information in an input image. In this module, super-pixel pooling (SPP) is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The DCAIP consists of deep learning networks based on this attention module. The DCAIP is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall (PR) curve (AUPRC). The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems.

Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation.

Acoustic holography has been gaining attention for various applications, such as noncontact particle manipulation, noninvasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted, and even conventional acoustic hologram algorithms show limited performance in the fast and accurate generation of acoustic holograms, thus hindering the development of novel applications. We here propose a deep learning-based framework to achieve fast and accurate acoustic hologram generation. The framework has an autoencoder-like architecture; thus, the unsupervised training is realized without any ground truth. For the framework, we demonstrate a newly developed hologram generator network, the holographic ultrasound generation network (HU-Net), which is suitable for unsupervised learning of hologram generation, and a novel loss function that is devised for energy-efficient holograms. Furthermore, for considering various hologram devices (i.e., ultrasound transducers), we propose a physical constraint (PC) layer. Simulation and experimental studies were carried out for two different hologram devices, such as a 3-D printed lens, attached to a single element transducer, and a 2-D ultrasound array. The proposed framework was compared with the iterative angular spectrum approach (IASA) and the state-of-the-art (SOTA) iterative optimization method, Diff-PAT. In the simulation study, our framework showed a few hundred times faster generation speed, along with comparable or even better reconstruction quality, than those of IASA and Diff-PAT. In the experimental study, the framework was validated with 3-D printed lenses fabricated based on different methods, and the physical effect of the lenses on the reconstruction quality was discussed. The outcomes of the proposed framework in various cases (i.e., hologram generator networks, loss functions, and hologram devices) suggest that our framework may become a very useful alternative tool for other existing acoustic hologram applications, and it can expand novel medical applications.

Manipulation and Mechanical Deformation of Leukemia Cells by High-Frequency Ultrasound Single Beam.

Ultrasound single-beam acoustic tweezer system has attracted increasing attention in the field of biomechanics. Cell biomechanics play a pivotal role in leukemia cell functions. To better understand and compare the cell mechanics of the leukemia cells, herein, we fabricated an acoustic tweezer system in-house connected with a 50-MHz high-frequency cylinder ultrasound transducer. Selected leukemia cells (Jurkat, K562, and MV-411 cells) were cultured, trapped, and manipulated by high-frequency ultrasound single beam, which was transmitted from the ultrasound transducer without contacting any cells. The relative deformability of each leukemia cell was measured, characterized, and compared, and the leukemia cell (Jurkat cell) gaining the highest deformability was highlighted. Our results demonstrate that the high-frequency ultrasound single beam can be utilized to manipulate and characterize leukemia cells, which can be applied to study potential mechanisms in the immune system and cell biomechanics in other cell types.

Ultrasonic blood flowmeter with a novel Xero algorithm for a mechanical circulatory support system.

Mechanical circulatory support systems (MCSSs) are crucial devices for transplants in patients with heart failure. The blood flowing through the MCSS can be recirculated or even stagnated in the event of critical blood flow issues. To avoid emergencies due to abnormal changes in the flow, continuous changes of the flowrate should be measured with high accuracy and robustness. For better flowrate measurements, a more advanced ultrasonic blood flowmeter (UFM), which is a noninvasive measurement tool, is needed. In this paper, we propose a novel UFM sensor module using a novel algorithm (Xero) that can exploit the advantages of both conventional cross-correlation (Xcorr) and zero-crossing (Zero) algorithms, using only the zero-crossing-based algorithm. To ensure the capability of our own developed and optimized ultrasonic sensor module for MCSSs, the accuracy, robustness, and continuous monitoring performance of the proposed algorithm were compared to those of conventional algorithms after application to the developed sensor module. The results show that Xero is superior to other algorithms for flowrate measurements under different environments and offers an error rate of at least 0.92%, higher robustness for changing fluid temperatures than conventional algorithms, and sensitive responses to sudden changes in flowrates. Thus, the proposed UFM system with Xero has a great potential for flowrate measurements in MCSSs.

Forward-Looking Multimodal Endoscopic System Based on Optical Multispectral and High-Frequency Ultrasound Imaging Techniques for Tumor Detection.

We developed a forward-looking (FL) multimodal endoscopic system that offers color, spectral classified, high-frequency ultrasound (HFUS) B-mode, and integrated backscattering coefficient (IBC) images for tumor detection in situ. Examination of tumor distributions from the surface of the colon to deeper inside is essential for determining a treatment plan of cancer. For example, the submucosal invasion depth of tumors in addition to the tumor distributions on the colon surface is used as an indicator of whether the endoscopic dissection would be operated. Thus, we devised the FL multimodal endoscopic system to offer information on the tumor distribution from the surface to deep tissue with high accuracy. This system was evaluated with bilayer gelatin phantoms which have different properties at each layer of the phantom in a lateral direction. After evaluating the system with phantoms, it was employed to characterize forty human colon tissues excised from cancer patients. The proposed system could allow us to obtain highly resolved chemical, anatomical, and macro-molecular information on excised colon tissues including tumors, thus enhancing the detection of tumor distributions from the surface to deep tissue. These results suggest that the FL multimodal endoscopic system could be an innovative screening instrument for quantitative tumor characterization.

Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap.

A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency.

Gravitational field flow fractionation: Enhancing the resolution power by using an acoustic force field.

An acoustic field flow fractionation (FFF) device was developed to fractionate a micro-particle mixture on the basis of the particle diameter using an acoustic force field in a carrier liquid flow. In the acoustic FFF channel used in the device, ultrasound waves generated from piezoelectric transducers driven by a sinusoidal signal of 2.02 Mhz propagated into the carrier liquid flow and built up a quarter-wavelength ultrasound standing wave field across the channel height. It was experimentally demonstrated that the acoustic field with a pressure node plane at the bottom surface of the channel reduced the thickness of the particle diffusion layer in a stagnant liquid proportional to the applied voltage driving the piezoelectric transducer. In the size-dependent particle separation, the particle mixture flowing through the acoustic FFF channel experienced an acoustic radiation force in the gravitational direction. As a result, suppressing the diffusion of small particles, particles were transported along the bottom surface of the channel with the local velocity of the carrier liquid at the particle center. The developed acoustic FFF device successfully fractionated a fluorescent micro-particle mixture (1, 3, 5, and 10 µm diameter), whereas the 3 and 5 µm particles were not fractionated in the FFF device using only the gravitational force field due to the diffusion of 3 µm particles.

Acoustic Trapping Technique for Studying Calcium Response of a Suspended Breast Cancer Cell: Determination of Its Invasion Potentials.

A noncontact single-beam acoustic trapping technique has proven to be a promising tool for cell manipulation and characterization that provide essential knowledge for a variety of biomedical applications. Here, we investigated cell characteristics as to whether the calcium responses of suspended breast cancer cells to different acoustic trapping forces are related to their invasiveness. For this, we combined a single-beam acoustic trapping system with a 30-MHz press-focused lithium niobate ultrasound transducer and an epifluorescence microscope. Using the system, intracellular calcium changes of suspended MDA-MB-231 (highly invasive) and MCF-7 (weakly invasive) cells were monitored while trapping the cells at different acoustic pressures. The results showed that a single suspended breast cancer cell isolated by the acoustic microbeam behaved differently on the calcium elevation in response to changes in acoustic trapping force, depending on its invasiveness. In particular, the MDA-MB-231 cells exhibited higher calcium elevation than MCF-7 cells when each cell was trapped at low acoustic pressure. Based on these results, we believe that the single-beam acoustic trapping technique has high potential as an alternative tool for determining the degree of invasiveness of suspended breast cancer cells.

Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis.

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis.