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. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.1

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

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. © 2019 The Author(s).1

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. © 2016 Optical Society of America.1

초음파 및 광학 기술 기반 정량적 다중모달 현미경 시스템 개발

Multimodal Imaging system and Analysis, Optical Imaging Technique, Quantitative Phase Imaging, Acoustic tweezer, Fluorescent Imaging System1. INTRODUCTION 1 1.1 Cancer Cell Characterization for Elucidation of Biological Phenomena 1 1.2 Conventional Methods of Estimation of Invasion Potentials 4 1.3 Academic Significance and Hypothesis 7 1.4 Overview of Thesis 10 2. THEORETICAL BACKGROUND 12 2.1 Ultrasound Transducer 12 2.1.1 Piezoelectric Materials 13 2.1.2 Matching Layer 16 2.1.3 Backing Layer 18 2.1.4 Single-element Ultrasound Transducer 18 2.2 Acoustic Trapping Technique 20 2.2.1 Various Type of Acoustic Trapping 20 2.2.2 Basic Principle of the Acoustic Tweezer Technique 20 2.2.3 Application of Acoustic Tweezer Technique 22 2.2.4 What is Differ from Optical Tweezers 23 2.3 Fluorescence Imaging 25 2.3.1 Basic Principle of the Fluorescence Imaging 25 2.3.2 Fluorescence Imaging and Calcium Ion Imaging in Bioscience 27 2.4 Quantitative Phase Imaging 30 2.4.1 What is Differ from the Bright-field Microscope 30 2.4.2 Basic Principle of Quantitative Phase Imaging 31 2.4.3 Introduction of Full-field QPI and Imaging Sensitivity 32 2.4.4 Strength Points of Quantitative Phase Imaging 34 3. Acoustic Trapping Technique for Studying Calcium Response of a Suspended Breast Cancer Cell: Determination of its Invasion Potentials 36 3.1 Introduction 36 3.2 Materials and Methods 39 3.2.1 Single-beam Acoustic Trapping System 39 3.2.2 Press-focused Single-crystal Ultrasound Transducer 40 3.2.3 Cell Preparation 42 3.2.4 Monitoring of Intracellular Calcium Elevation of Breast Cancer Cells While Acoustic Trapping 43 3.2.5 Cell Viability Test 44 3.3 Results 45 3.3.1 Calcium Response of a Trapped Cell to Acoustic Trapping Force at Different Voltages 45 3.3.2 Quantitative Analysis of Calcium Responses of MDAMB-231 and MCF-7 Cells to Acoustic Trapping Forces 47 3.3.3 Changes in Cell Viability due to Acoustic Trapping 49 3.4 Discussion 52 3.5 Conclusion 54 4. Fully-Automatic Deep Learning-Based Analysis for Determination of Invasiveness of Breast Cancer Cells in an Acoustic Trap 56 4.1 Introduction 56 4.1.1 Motivation 56 4.1.2 Contributions 58 4.1.3 Related Works 59 4.2 Materials and Methods 60 4.2.1 High-frequency Ultrasound Single beam Acoustic Trapping System with Deep learning-based Calcium Image Analysis 60 4.2.2 High-frequency Single Element Focused Ultrasound Transducer 62 4.2.3 Multi-scale and Multi-channel Deep Learning Network (MM-Net) for Segmentation of Fluorescent Cells 63 4.2.4 Automatic Calcium Analysis Algorithm for a Trapped Single Cell in Time-lapse Fluorescence Images 69 4.2.5 Cell Preparation and Experimental Setup 73 4.3 Results 74 4.3.1 Automatic Cell Segmentation 74 4.3.2 Automatic Calcium Analysis of a Trapped Cell in Acoustic Beam 76 4.3.3 Quantitative Analysis of Calcium Responses of the MDA-MB-231 and MCF-7 Cells to Different Acoustic Trapping Forces 79 4.4 Discussion 81 4.5 Conclusion 85 5. Development of a Cell Elastographic Microscope System based on Quantitative Phase Imaging and Acoustic Trapping Techniques for Characterizing Cancer Cells 86 5.1 Introduction 86 5.2 Materials and Methods 90 5.2.1 System Configuration of Cell Elastographic Microscope 90 5.2.2 High-Frequency Ring-shaped Ultrasound Transducer 93 5.2.3 Quantitative Phase Image Reconstruction and Image Processing 94 5.2.4 Experimental Set-up for Quantitative Measurement of Mechanical Properties of a Cell 96 5.2.5 Tissue Mimicking Elastic Phantom Fabrication 97 5.2.6 Cell Preparation 98 5.3 Results 99 5.3.1 Assessment of Optical Performance of the Quantitative Phase Imaging System 99 5.3.2 Measurement of Deformation Rate of Elastic Gelatin Phantoms under Varied Acoustic Pressure 101 5.3.3 Quantitative Comparison of Transverse Deformation Rate of MDA-MB-231 and MCF-7 cells 102 5.3.4 Measurement of Thickness Change in MDA-MB-231and MCF-7 cells during Acoustic Trapping 104 5.3.5 Comparison of Representative Cell Thickness Changes of Both Cells under 1.24 and 3.18MPa 107 5.3.6 Quantitative Analysis of Displacement of MDA-MB-231 and MCF-7 cells due to Acoustic Trapping Force 108 5.4 Discussion 109 5.5 Conclusion 112 6. CONCLUSION 114 BIBLIOGRAPHY 118DoctordCollectio

Ultra-fast Acoustic Tweezer System for Quantification of Deformation and Viscoelasticity of a Single Cell

고주파 음향 핀셋은 비접촉 생물 물리학적 도구로써 생물학적 기전 연구를 포함한 다양한 생물학 의학 분야에서 유용하게 사용되어왔다. 세포의 기계적 특성은 세포의 병리학적 특성을 나타내기 위한 핵심 지표 중 하나이다. 이 음향 핀셋은 세포 포획 시 세포 변형 정량화를 통해 단일 세포의 기계적 특성을 측정하는 데 있어서 비접촉 방식으로써 그 유용성을 보여 왔다. 본 논문에서는 세포의 변형뿐만 아니라, 세포의 점탄성도 측정 가능한 음향 핀셋 시스템을 개발하고, 개발한 음향 핀셋 시스템이 세포 점탄성 및 변형 측정에서의 그 가능성을 조사한다. 점탄성 측정이 가능한 음향 핀셋 시스템을 개발하기 위해서 27.8 MHz LiNbO3 단일소자 초음파 변환자를 개발하고, 세포 포획 시 시간에 따른 세포 변형률을 초고속으로 측정하기 위해 초고속 카메라를 음향 핀셋 시스템에 결합하였다. 그 개발한 시스템의 세포 점탄성 측정 가능성을 평가하기 위해서 다른 형태의 포획 힘에 따른 세포 변형의 위상 변화를 확인하였다. 이와 더불어 이 시스템을 이용해 전이성이 상이 한 유방암 세포의 기계적 특성을 측정하였다. 상기 실험을 통해 진폭 변조된 포획력 아래에서 세포가 포획된 동안 가해진 힘과 세포의 변형 사이 위상 지연이 초래됨을 확인하였고, 전이성이 다른 유방암 세포의 변형률에서 차이가 나는 것을 확인하였다. 이 결과는 개발한 초고속 초음파 핀셋 시스템을 이용해 포획된 세포 변형 위상 지연을 분석함으로써 세포의 점탄성을 정량화할 수 있음을 시사한다. High-frequency acoustic tweezers have been used as noncontact biophysical tools for various biomedical applications. Cell mechanics are an important indicator for representing the pathological properties of cells. Acoustic tweezers have shown their potential as a non-contact method for measuring the mechanics of a single cell through the quantification of cell deformation during acoustic trapping. In this study, we developed an ultra-fast acoustic tweezer system that can measure both cell deformation and viscoelasticity and then examined its potential for measuring cell viscoelasticity. To develop the acoustic tweezer system for measuring cell viscoelasticity, a 27.8 MHz highly focused lithium niobate (LiNbO3) single-element ultrasound transducer was developed. An ultra high-speed camera was integrated with the acoustic tweezer system to measure the cell deformation over time. To evaluate its potential for measuring cell viscoelasticity, cell deformation variations over time under different types of acoustic trapping forces were monitored. The system was also applied to measure the mechanics of breast cancer cells with different degrees of invasiveness. The phase delay between the applied amplitude-modulated force and cell deformation was found to change during acoustic trapping. Moreover, the deformation rates of breast cancer cells were found to differ for different degrees of invasiveness. These results indicate that the viscoelasticity of cells can be quantified by analyzing the cell deformation phase delay by using the developed ultrafast ultrasound tweezer system.FALS

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. © 2018 Elsevier B.V.1

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

Acoustic holography has been gaining attention for various applications such as non-contact particle manipulation, non-invasive 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 layer. Simulation and experimental studies were carried out for two different hologram devices such as a 3D printed lens, attached to a single element transducer, and a 2D ultrasound array. The proposed framework was compared with the iterative angular spectrum approach (IASA) and the state-of-the-art 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 3D-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, 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. IEEEFALS

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 paper, we propose a deep content-aware image prior with a content-aware attention module for superior despeckling of ultrasound images without clean images. For the image prior, we developed a content-aware attention module to deal with the content information in an input image. In this module, super-pixel pooling 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 deep content-aware image prior consists of deep learning networks based on this attention module. The deep content-aware image prior 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 curve. 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. IEEEFALS

Deep laser microscopy using optical clearing by ultrasound-induced gas bubbles

Although laser scanning microscopy is a pivotal imaging tool in biomedical research, optical scattering from tissue limits the depth of the imaging. To overcome this limitation, we propose a scheme called ultrasound-induced optical clearing microscopy, which makes use of temporary, localized optical clearing based on ultrasound-induced gas bubbles. In this method, bubbles are generated by high-intensity pulsed ultrasound at a desired depth and subsequently maintained by low-intensity continuous ultrasound during imaging. As a result, optical scattering and unwanted changes in the propagation direction of the incident photons are minimized in the bubble cloud, and thus the laser can be tightly focused at a deeper imaging plane. Through phantom and ex vivo experiments, we demonstrate that ultrasound-induced optical clearing microscopy is capable of increasing the imaging depth by a factor of six or more, while the resolution is similar to that of conventional laser scanning microscopy. Optical clearing based on ultrasound-induced gas bubbles offers new opportunities for deeper laser scanning microscopy of biological tissue. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.FALS