AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging

Intra-cardiac Echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing real-time, high-resolution views from within the heart. Despite its advantages, effective manipulation of the ICE catheter requires significant expertise, which can lead to inconsistent outcomes, particularly among less experienced operators. To address this challenge, we propose an AI-driven closed-loop view guidance system with human-in-the-loop feedback, designed to assist users in navigating ICE imaging without requiring specialized knowledge. Our method models the relative position and orientation vectors between arbitrary views and clinically defined ICE views in a spatial coordinate system, guiding users on how to manipulate the ICE catheter to transition from the current view to the desired view over time. Operating in a closed-loop configuration, the system continuously predicts and updates the necessary catheter manipulations, ensuring seamless integration into existing clinical workflows. The effectiveness of the proposed system is demonstrated through a simulation-based evaluation, achieving an 89% success rate with the 6532 test dataset, highlighting its potential to improve the accuracy and efficiency of ICE imaging procedures.

Breast Ultrasound Report Generation using LangChain

Breast ultrasound (BUS) is a critical diagnostic tool in the field of breast imaging, aiding in the early detection and characterization of breast abnormalities. Interpreting breast ultrasound images commonly involves creating comprehensive medical reports, containing vital information to promptly assess the patient's condition. However, the ultrasound imaging system necessitates capturing multiple images of various parts to compile a single report, presenting a time-consuming challenge. To address this problem, we propose the integration of multiple image analysis tools through a LangChain using Large Language Models (LLM), into the breast reporting process. Through a combination of designated tools and text generation through LangChain, our method can accurately extract relevant features from ultrasound images, interpret them in a clinical context, and produce comprehensive and standardized reports. This approach not only reduces the burden on radiologists and healthcare professionals but also enhances the consistency and quality of reports. The extensive experiments shows that each tools involved in the proposed method can offer qualitatively and quantitatively significant results. Furthermore, clinical evaluation on the generated reports demonstrates that the proposed method can make report in clinically meaningful way.

Improving Medical Speech-to-Text Accuracy with Vision-Language Pre-training Model

Automatic Speech Recognition (ASR) is a technology that converts spoken words into text, facilitating interaction between humans and machines. One of the most common applications of ASR is Speech-To-Text (STT) technology, which simplifies user workflows by transcribing spoken words into text. In the medical field, STT has the potential to significantly reduce the workload of clinicians who rely on typists to transcribe their voice recordings. However, developing an STT model for the medical domain is challenging due to the lack of sufficient speech and text datasets. To address this issue, we propose a medical-domain text correction method that modifies the output text of a general STT system using the Vision Language Pre-training (VLP) method. VLP combines textual and visual information to correct text based on image knowledge. Our extensive experiments demonstrate that the proposed method offers quantitatively and clinically significant improvements in STT performance in the medical field. We further show that multi-modal understanding of image and text information outperforms single-modal understanding using only text information.

Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning

Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applications due to the phase aberration artefact caused by variations in the speed of sound (SoS) within body parts. To address this, here we propose a novel self-supervised 3D CNN that enables phase aberration robust plane-wave imaging. Instead of aiming at estimating the SoS distribution as in conventional methods, our approach is unique in that the network is trained in a self-supervised manner to robustly generate a high-quality image from various phase aberrated images by modeling the variation in the speed of sound as stochastic. Experimental results using real measurements from tissue-mimicking phantom and \textit{in vivo} scans confirmed that the proposed method can significantly reduce the phase aberration artifacts and improve the visual quality of deep scans.

Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN

In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propose a novel unsupervised deep learning approach to improve 3-D US image quality. In particular, using {\em unmatched} high-quality 2-D US images as a reference, we trained a recently proposed switchable CycleGAN architecture so that every mapping plane in 3-D US can learn the image quality of 2-D US images. Thanks to the switchable architecture, our network can also provide real-time control of image enhancement level based on user preference, which is ideal for a user-centric scanner setup. Extensive experiments with clinical evaluation confirm that our method offers significantly improved image quality as well user-friendly flexibility.

Unsupervised Missing Cone Deep Learning in Optical Diffraction Tomography

Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles. Although the distribution of RI index is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along axial direction compared to the horizontal imaging plane. To solve this issue, here we present a novel unsupervised deep learning framework, which learns the probability distribution of missing projection views through optimal transport driven cycleGAN. Experimental results show that missing cone artifact in ODT can be significantly resolved by the proposed method.

Switchable Deep Beamformer

Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be combined with the beamforming. Unfortunately, in the current technology, a separate beamformer should be trained and stored for each application, demanding significant scanner resources. To address this problem, here we propose a {\em switchable} deep beamformer that can produce various types of output such as DAS, speckle removal, deconvolution, etc., using a single network with a simple switch. In particular, the switch is implemented through Adaptive Instance Normalization (AdaIN) layers, so that various output can be generated by merely changing the AdaIN code. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed methods for various applications.

OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN

Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. Classic approaches for solving these problems are usually model-based iterative approaches that have been developed specifically for each type of artifact, which are often computationally intensive. Recently, deep learning approaches have been proposed as computationally efficient and high performance alternatives. Unfortunately, in the current deep learning approaches, a dedicated neural network should be trained with matched training data for each specific artifact type. This poses a fundamental limitation in the practical use of deep learning for US, since large number of models should be stored to deal with various US image artifacts. Inspired by the recent success of multi-domain image transfer, here we propose a novel, unsupervised, deep learning approach in which a single neural network can be used to deal with different types of US artifacts simply by changing a mask vector that switches between different target domains. Our algorithm is rigorously derived using an optimal transport (OT) theory for cascaded probability measures. Experimental results using phantom and in vivo data demonstrate that the proposed method can generate high quality image by removing distinct artifacts, which are comparable to those obtained by separately trained multiple neural networks.

Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal

Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfortunately, it often suffers from poor visual quality from various origins, such as speckle noises, blurring, multi-line acquisition (MLA), limited RF channels, small number of view angles for the case of plane wave imaging, etc. Classical methods to deal with these problems include image-domain signal processing approaches using various adaptive filtering and model-based approaches. Recently, deep learning approaches have been successfully used for ultrasound imaging field. However, one of the limitations of these approaches is that paired high quality images for supervised training are difficult to obtain in many practical applications. In this paper, inspired by the recent theory of unsupervised learning using optimal transport driven cycleGAN (OT-cycleGAN), we investigate applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Experimental results for various tasks such as deconvolution, speckle removal, limited data artifact removal, etc. confirmed that our unsupervised learning method provides comparable results to supervised learning for many practical applications.

Adaptive and Compressive Beamforming using Deep Learning for Medical Ultrasound

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast to noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here we propose a deep learning-based end-to-end beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or sub-sampled radio-frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high quality ultrasound images using a single beamformer. The origin of such adaptivity is also theoretically analyzed. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.