Cardiac ultrasound simulation for autonomous ultrasound navigation

Ultrasound is well-established as an imaging modality for diagnostic and interventional purposes. However, the image quality varies with operator skills as acquiring and interpreting ultrasound images requires extensive training due to the imaging artefacts, the range of acquisition parameters and the variability of patient anatomies. Automating the image acquisition task could improve acquisition reproducibility and quality but training such an algorithm requires large amounts of navigation data, not saved in routine examinations. Thus, we propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions, such that this pipeline can later be used by learning algorithms for navigation. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized volumetric data representation and GPU-accelerated Monte Carlo path tracing to generate view-dependent and patient-specific ultrasound images. We extensively validate the correctness of our pipeline with a phantom experiment, where structures' sizes, contrast and speckle noise properties are assessed. Furthermore, we demonstrate its usability to train neural networks for navigation in an echocardiography view classification experiment by generating synthetic images from more than 1000 patients. Networks pre-trained with our simulations achieve significantly superior performance in settings where large real datasets are not available, especially for under-represented classes. The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated.Comment: 24 pages, 10 figures, 5 table

Proceedings Virtual Imaging Trials in Medicine 2024

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday

High-level synthesis design of scalable ultrafast ultrasound beamformer with single FPGA

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of the proposed beamformer was compared with the software beamformer on the Verasonics Vantage system for both phantom imaging and in vivo imaging of a mouse brain

Dynamic Image Processing for Guidance of Off-pump Beating Heart Mitral Valve Repair

Compared to conventional open heart procedures, minimally invasive off-pump beating heart mitral valve repair aims to deliver equivalent treatment for mitral regurgitation with reduced trauma and side effects. However, minimally invasive approaches are often limited by the lack of a direct view to surgical targets and/or tools, a challenge that is compounded by potential movement of the target during the cardiac cycle. For this reason, sophisticated image guidance systems are required in achieving procedural efficiency and therapeutic success. The development of such guidance systems is associated with many challenges. For example, the system should be able to provide high quality visualization of both cardiac anatomy and motion, as well as augmenting it with virtual models of tracked tools and targets. It should have the capability of integrating pre-operative images to the intra-operative scenario through registration techniques. The computation speed must be sufficiently fast to capture the rapid cardiac motion. Meanwhile, the system should be cost effective and easily integrated into standard clinical workflow. This thesis develops image processing techniques to address these challenges, aiming to achieve a safe and efficient guidance system for off-pump beating heart mitral valve repair. These techniques can be divided into two categories, using 3D and 2D image data respectively. When 3D images are accessible, a rapid multi-modal registration approach is proposed to link the pre-operative CT images to the intra-operative ultrasound images. The ultrasound images are used to display the real time cardiac motion, enhanced by CT data serving as high quality 3D context with annotated features. I also developed a method to generate synthetic dynamic CT images, aiming to replace real dynamic CT data in such a guidance system to reduce the radiation dose applied to the patients. When only 2D images are available, an approach is developed to track the feature of interest, i.e. the mitral annulus, based on bi-plane ultrasound images and a magnetic tracking system. The concept of modern GPU-based parallel computing is employed in most of these approaches to accelerate the computation in order to capture the rapid cardiac motion with desired accuracy. Validation experiments were performed on phantom, animal and human data. The overall accuracy of registration and feature tracking with respect to the mitral annulus was about 2-3mm with computation time of 60-400ms per frame, sufficient for one update per cardiac cycle. It was also demonstrated in the results that the synthetic CT images can provide very similar anatomical representations and registration accuracy compared to that of the real dynamic CT images. These results suggest that the approaches developed in the thesis have good potential for a safer and more effective guidance system for off-pump beating heart mitral valve repair

COMPARISON OF A PATIENT-SPECIFIC COMPUTED TOMOGRAPHY ORGAN DOSE SOFTWARE WITH COMMERCIAL PHANTOM-BASED TOOLS

Computed Tomography imaging is an important diagnostic tool but carries some risk due to radiation dose used to form the image. Currently, CT scanners report a measure of radiation dose for each scan that reflects the radiation emitted by the scanner, not the radiation dose absorbed by the patient. The radiation dose absorbed by organs, known as organ dose, is a more relevant metric that is important for risk assessment and CT protocol optimization. Tools for rapid organ-dose estimation are available but are limited to using general patient models. These publicly available tools are unable to model patient-specific anatomy and positioning within the scanner. To address these limitations, the Personalized Rapid Estimator of Dose in Computed Tomography (PREDICT) dosimetry tool was recently developed. This study validated the organ doses estimated by ‘PREDICT’ with ground truth values. The patient-specific PREDICT performance was also compared to two publicly available phantom-based methods: VirtualDose and NCICT. The PREDICT tool demonstrated lower organ dose errors compared to the phantom-based methods, demonstrating the benefit of patient-specific modeling. This study also developed a method to extract the walls of cavity organs, such as the bladder and the intestines, and quantified the effect of organ wall extraction on organ dose. The study found that the exogenous material within the cavity organ can affect organ dose estimate, therefore demonstrating the importance of boundary wall extraction in dosimetry tools such as PREDICT

REAL-TIME ELASTOGRAPHY SYSTEMS

Ultrasound elastography is a technique that is often used to detect cancerous tumors and monitor ablation therapy by detecting changes in the stiffness of the underlying tissue. This technique is a computationally expensive due to the extensive searching between two raw ultrasound images, that are called radio frequency images. This thesis explores various methods to accelerate the computation required for the elastography technique to allow use during surgery. This thesis is divided into three parts. We begin by exploring acceleration techniques, including multithreading techniques, asynchronous computing, and acceleration of the graphics processing unit (GPU). Elastography algorithms are often affected by out-of-plane motion due to several external factors, such as hand tremors and incorrect palpation motion, amongst others. In this thesis, we implemented an end-to-end system that integrates an external tracker system to detect the in-plane motion of two radio frequency (RF) data slices. This in-plane detection helps to reduce de-correlated RF slices and produces a consistent elastography output. We also explore the integration of a da Vinci Surgical Robot to provide stable palpation motion during the surgery. The external tracker system suffers from interference due to ferromagnetic materials present in the operation theater in the case of an electromagnetic tracker, while optical and camera-based tracking systems are restricted due to human, object and patient interference in the path of sight and complete or partial occlusion of the tracking sensors. Additionally, these systems must be calibrated to give the position of the tracked objects with respect to the trackers. Although calibration and trackers are helpful for inter-modality registration, we focus on a tracker-less method to determine the in-plane motion of two RF slices. Our technique divides the two input RF images into regions of interest and performs elastography on RF lines that encapsulate those regions of interest. Finally, we implemented the world’s first known five-dimensional ultrasound system. We built the five-dimensional ultrasound system by combining a 3D B-mode volume and a 3D elastography volume visualized over time. A user controlled multi-dimensional transfer function is used to differentiate between the 3D B-mode and the 3D elastography volume