Robert Rohling

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This faculty member is currently not looking for graduate students or Postdoctoral Fellows. Please do not contact the faculty member with any such requests.


Research Interests


Relevant Thesis-Based Degree Programs

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I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.

Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

A multimodal approach for placenta characterization: towards an objective and effective pregnancy screening system (2022)

Recent developments in the understanding of the role of the placenta as the ‘master regulator’ of the intra-uterine environment underline the importance of placental evaluation in pregnancy screening. The placenta plays a major role in the pathogenesis of many pregnancy complications such as preeclampsia (PE) and intrauterine growth restriction (IUGR). These complications are leading causes of maternal and perinatal mortality and morbidity, affecting as many as 34% of all pregnancies. Antenatal monitoring of placentas using non-invasive, real time imaging techniquescould potentially identify sensitive biomarkers of placental health and thereby offer opportunity for early intervention enabling improved perinatal outcomes. The overarching objective of this thesis is to develop an effective and objective pregnancy screening system. To attain this objective, the first part of the thesis focused on the theoretical development of several user- and system-independentQuantitative Ultrasound (QUS) algorithms. Different approaches were adopted for improved QUS estimation by addressing the fundamental precision-resolution trade-off and effect of tissue heterogeneity. Particularly, we proposed a prior-based regularization technique, the prior being derived from ultrasound physics, and a deep learning based approach, predictUS. In the second part of the thesis, a multimodal placental imaging study, including the ultrasound, MRI, and histopathology data from 47 placentas ex vivo, was designed and conducted in collaboration with a multi-disciplinary team. The dataset from this study was utilized for the validation of the proposed QUS algorithms. Specifically, we analyzed the efficacy of different QUS parameters, includingattenuation coefficient, backscatter coefficient, scatterer diameter, elasticity, and viscosity, to detect placenta-mediated diseases and clinical outcomes.

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Gaze tracking for human-ultrasound machine interaction and medical image understanding (2022)

Despite the rapid advancement in medical imaging technologies, health care systems in Canada as well as in many other countries still cannot provide patients with sufficient medical imaging resources due to the ever-growing demands, lack of imaging devices, and fully trained medical practitioners. What makes the situation worse are persistent and prevalent work-related injury situations for medical practitioners, especially sonographers. To help these problems, the overall objective of this thesis is to improve the user interface design for ultrasound machines and contribute to automated medical imaging-based diagnosis with the help of the gaze tracking technology.Multi-themed study and research were conducted to achieve the objectives. On one hand, for the interface design improvement, we started by analyzing the characteristics of gaze signals through statistical modelling. Then we surveyed among sonographers to understand their daily usage of the ultrasound machines. Also, we analyzed different kinds of ultrasound machines to understand common design patterns and suboptimal control logic. To improve the suboptimal control logic and observed usage difficulties on ultrasound machines, we designed and implemented a gaze tracking contingent ultrasound machine. For the validation of our design ideas and machine effectiveness, comparative user studies were conducted. Additionally, we enhanced the user experience by solving the gaze tracker accuracy deterioration problem during normal usage of the devices by proposing a disruption-free auto-recalibration method. On the other hand, for the automated medical image diagnosis, with the integration of the gaze tracking dataset, we proposed deep learning methods that not only can provide a label that predicts which kind of abnormality is observed in the medical image, but also some reasoning that can guide humans to understand how the prediction is made. Multi-task learning methods and neural network attention mechanisms were used for enhanced automated diagnostic performance and better interpretability of the deep learning models.Our study has demonstrated the usefulness of integrating gaze tracking with human-computer interaction and image understanding in the medical field. Future work can be done to further processing and quantify gaze tracking data.

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Developing surgical navigation tools for minimally invasive surgery using ultrasound, structured light, tissue tracking and augmented reality. (2020)

Surgeons and their patients would benefit if, during an operation, a surgeon could inexpensively, safely and non-invasively peer beneath the surface of the organ s/he was operating on. Peering below the surface would allow the surgeon to see blood vessels, tumours and other important structures. Furthermore, it would allow them to better plan their surgery and avoid damaging important structures with their tools. Giving surgeons the ability to peer beneath the surface and better formulate their surgical plan is the goal of image guided surgery research and the focus of this thesis. In this thesis accurate 3D models of cancer tumour phantoms are generated and displayed to the surgeon. This is achieved via the development of: an ultrasound calibration technique (Chapter 2); the augmented reality ultrasound navigation system (ARUNS) (Chapter 3); a miniature projector for surgery called the Pico Lantern (Chapter 4); and the Projector-based Augmented Reality Intracorporeal System (PARIS)(Chapter 5). The ultimate goal is to improve surgical navigation which will help surgeons be more accurate and reduce the amount of healthy tissue they excise during operations.The ultrasound calibration technique improved ultrasound-based pinhead point reconstruction accuracy from 3.1mm to 1.3mm. The Pico Lantern and the PARIS were developed to improve surface reconstruction and to improve the realism of the augmented reality in surgery. The Pico Lantern is a miniature projector for surface reconstruction, augmented reality and guidance in laparoscopic surgery. The PARIS was tested by two surgeons in a user study of 32 simulated kidney cancer surgeries. Compared to using a laparoscopic ultrasound transducer alone, when using the PARIS, surgeons found the surgical navigation more intuitive and they had a better spatial understanding of the underlying anatomy. Furthermore, positive margin rates decreased and there was a statistically significant reduction in the amount of healthy tissue excised. Key conclusions are that wide baseline ultrasound calibration is effective, simple guidance cues are important in augmented reality in surgery and that projected light in surgery is a viable strategy for surface reconstruction and augmented reality.

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Machine learning in ultrasound-guided spinal anesthesia (2020)

Acute and chronic pain treatment with topically or orally administered agents alone is often insufficient and requires injection of analgesic or anesthetic agents directly at the nociceptive site.Examples of target site injections include epidural or facet joint blocks in treatment of acute labour pain or chronic back pain, respectively.In this thesis, machine learning models and algorithms are proposed that aim to facilitate spinal injections through automatic identification of the anatomy in 2D and 3D ultrasound (US).In particular, the proposed techniques detect the anatomical landmarks of the needle target in paramedian and transverse planes for lumbar epidural and facet joint injections.Such methods can be used to identify the correct needle puncture site before injection.A local-directional feature extraction method is first proposed in order to recognize the patterns of the US echoes from the vertebrae in paramedian planes.Later, a supervised convolutional model is proposed to localize the needle target for epidural anesthesia in the paramedian US images.The model uses a combination of hand-engineered local-directional features and convolutional feature maps that are automatically learned from the images.In the transverse plane, a deep classifier is designed to identify the symmetry in the image, and accordingly, classify the midline from off-center images.Later, a conditional generative model along with an adaptive data augmentation algorithm is proposed, which synthesize transverse US images based on the performance of the classifier to improve its accuracy.Finally, an unsupervised model is proposed that learns the variations of the data without the need for labels.Unsupervised learning from US images is valuable because there is a major cost associated with data annotation, which can be avoided by unsupervised learning.The above mentioned methods were tested on US images collected in vivo and demonstrated promising performance to be a useful guide for injections.Moreover, the proposed systems can be utilized as training tools to familiarize the novices with the spine anatomy in US.

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Adaptive ultrasound imaging to improve the visualization of spine and associated structures (2018)

Visualizing vertebrae or other bone structures clearly in ultrasound imaging is important for many clinical applications such as ultrasound-guided spinal needle injections and scoliosis detection. Another growing research topic is fusing ultrasound with other imaging modalities to get the benefit from each modality. In such approaches, tissue with strong interfaces, such as bones, are typically extracted and used as the feature for registration. Among those applications, the spine is of particular interest in this thesis. Although such ultrasound applications are promising, clear visualization of spine structures in ultrasound imaging is difficult due to factors such as specular reflection, off-axis energy and reverberation artifacts. The received channel ultrasound data from the spine are often tilted even after delay correction, resulting in signal cancellation during the beamforming process. Conventional beamformers are not designed to tackle this issue. In this thesis, we propose three beamforming methods dedicated to improve the visualization of spine structures. These methods include an adaptive beamforming method which utilizes the accumulated phase change across the receive aperture as the beamforming weight. Then, we propose a log-Gabor based directional filtering method to regulate the tilted channel data back to the beamforming direction to avoid bone signal cancellation. Finally, we present a closed-loop beamforming method which feeds back the location of the spine to the beamforming process so that backscattered bone signals can be aligned prior-to the beamforming. Field II simulation, phantom and in vivo results confirm significant contrast improvement of spinal structures compared with the conventional delay-and-sum beamforming and other adaptive beamforming methods.

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Design, modeling and fabrication of polymer-based Capacitive Micromachined Ultrasonic Transducers (polyCMUTs) (2018)

Ultrasound imaging is the most widely used medical imaging modality in the world. Modern ultrasound systems still rely on the same piezoelectric-based technology since their creation in the 1930s. Despite their mature technology, they are expensive to fabricate, difficult to create 2D arrays and cannot be miniaturized. Capacitive Micromachined Ultrasonic Transducers (CMUTs) are considered the replacement of piezoelectric transducers given their high bandwidth, ease of integration with electronics and miniaturization. The main focus of this dissertation involves the simulation, fabrication and characterization of polymer-based CMUTs (polyCMUTs). A new fabrication process involving inexpensive polymer materials and minimum fabrication steps was developed. The fabrication procedure allows the creation of biocompatible ultrasound chips in a few hours and with costs well below $100 USD, having a performance comparable to current commercial devices.The fabricated polyCMUTs exhibit a phenomenon termed “pre-biasing”, which allowed the operation of polyCMUTs as passive devices (no external power needed). The first B-mode ultrasound image in the world created using polyCMUTs is also presented. As a future plan, the development of a low-cost wearable ultrasound health monitoring system is conceived.

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Image-based enhancement and tracking of an epidural needle in ultrasound using time-series analysis (2017)

Accurate placement of the needle to the target spot is crucial to the safety, efficiency and success of any ultrasound (US) guided procedures, e.g. epidurals. Real-time single-operator US guidance of epidurals is currently impractical with conventional 2D US and a standard needle. A novel 3D US imaging technology (3DUS+Epiguide) has been developed by our group to provide such capability, which comprises thick-slice rendering and a custom needle guide, Epiguide. This system aims to facilitate a single-operator real-time midline epidural needle insertion by visualizing the needle progression toward the epidural space. The visibility of the needle in US-guided procedures is, however, limited by dispersion of the needle's echoes away from the transducer. This makes needle visibility an ongoing challenge in US-guided procedures, e.g. epidurals.The aim of this thesis is to provide a software-based clinically-suitable solution to enhance needle visibility in US. In particular, we are interested in difficult cases, where the needle is invisible (i.e. minimal visibility) in an US image. To this end, we have developed frameworks to extract signatures from the needle using time-series analysis. We demonstrate that nearly invisible changes in motion dynamics of the needle can be revealed through spatio-temporal processing of the standard US image sequences. The extracted needle features are used to detect, track and localize a hand-held needle in a machine learning-based framework. Clinical, animal and phantom studies are designed to evaluate: 3DUS+Epiguide's capability to identify the needle puncture site for a midline insertion in the lumbar spine, and the capability of the proposed detection frameworks in localizing a needle within clinical acceptance. Methods are evaluated on the data from studies conducted at BC Women's Hospital and Jack Bell Research Facility, and are compared to the gold standard (GS). Results demonstrate that compared to the state-of-the-art needle detection methods using a finely-tuned Hough Transform, with 14% success rate, our proposed method is 100% successful in detecting the trajectory within the GS's close proximity (success: angular error
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Ultrasound elastography for intra-operative use and renal tissue imaging (2017)

The kidney is a vital organ within the human body and improvements in the ability to characterize the kidney tissue can create benefits for patients with kidney tumors and for kidney transplant recipients. Often, changes in tissue health or development of cancer are manifested in changes in tissue structure that affect tissue elastic properties. For example, the cancerous tissue of renal cell carcinoma is stiffer than healthy kidney tissue, and the development of fibrosis, which impairs kidney function, also causes the tissue to become stiffer over time. These changes can be imaged with ultrasound elastography, a technique for quantitatively assessing tissue elasticity. If proven effective, elastography tissue characterization can replace biopsy.The ultrasound elastography method used in this thesis, called Shear Wave Absolute Vibro-Elastography, or SWAVE, measures the wavelength of induced steady-state multi-frequency mechanical shear waves to calculate tissue elasticity. SWAVE can employ standard ultrasound transducers that image the kidney though the skin above the organ, or custom miniaturized transducers that are placed directly on the surface of the organ during surgery. The accuracy of SWAVE is vastly improved by the use of 3D ultrasound data. We propose and evaluate 3D SWAVE imaging based on the use of a tracked intra-operative ultrasound transducer designed for use with the da Vinci Robot. Different tracking methods are evaluated for future intra-operative use. Elasticity images of tissue phantoms are obtained using interpolated 3D tissue displacement data acquired with the da Vinci robot and the intra-operative transducer. The use of tracked ultrasound transducer opens the way for introducing registered preoperative imaging, including elastography, to improve surgical guidance. Different methods of characterizing kidney tissue using SWAVE imaging are examined. The elastic and viscous properties are estimated kidney tissue ex-vivo. The effect of arterial pressure on the measured kidney elasticity is characterized. It was found that increasing input pressure increases the measured elasticity. Finally, ultrasound and ultrasound elastography are applied to kidney transplant recipients in-vivo to assess the level of fibrosis development. A preliminary study indicates that it is possible to transmit shear waves into the transplanted kidney and measure the elastic properties of the kidney tissue.

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Three Dimensional Ultrasound Elasticity Imaging (2016)

Changes in tissue elasticity are correlated with certain pathological changes, such as localized stiffening of malignant tumours or diffuse stiffening of liver fibrosis or placenta dysfunction. Elastography is a field of medical imaging that characterizes the mechanical properties of tissue, such as elasticity and viscosity. The elastography process involves deforming the tissue, measuring the tissue motion using an imaging technique such as ultrasound or magnetic resonance imaging (MRI), and solving the equations of motion. Ultrasound is well suited for elastography, however, it presents challenges such as anisotropic measurement accuracy and providing two dimensional (2D) measurements rather than three dimensional (3D). This thesis focuses on overcoming some of these limitations by improving upon methods of imaging absolute elasticity using 3D ultrasound. In this thesis, techniques are developed for 3D ultrasound acquired from transducers fitted with a motor to sweep the image plane, however many of the techniques can be applied to other forms of 3D acquisition such as matrix arrays. First, a flexible framework for 3D ultrasound elastography system is developed. The system allows for comparison and in depth analysis of errors in current state of the art 3D ultrasound shear wave absolute vibro-elastography (SWAVE). The SWAVE system is then used to measure the viscoelastic properties of placentas, which could be clinically valuable in diagnosing preeclampsia and fetal growth restriction. A novel 3D ultrasound calibration technique is developed which estimates the transducer motor parameters for accurate determination of location and orientation of every data sample, as well as for enabling position tracking of a 3D ultrasound transducer so multiple volumes can be combined. Another calibration technique using assumed motor parameters is developed, and an improvement to an existing N-wire method is presented. The SWAVE research system is extended to measure shear wave motion vectors with a new acquisition scheme to create synchronous volumes of ultrasound data. Regularization based on tissue incompressibility is used to reduce noise in the motion measurements. Lastly, multiple ultrasound volumes from different angles are combined for measurement of the full motion vector, and demonstrating accurate reconstructions of elasticity are feasible using the techniques developed in this thesis.

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Dynamic Elastography with Finite Element-Based Inversion (2015)

Tissue stiffness is often correlated to its pathological state and can be used as a basis for initial recognition of many tissue abnormalities. The term elastography refers to the class of medical imaging techniques that non-invasively measure the viscoelastic properties of soft tissue. Elastography involves deforming the tissue using an exciter, measuring the deformations using an imaging technique such as ultrasound (US) or magnetic resonance imaging (MRI), and then calculating the tissue elasticity distribution by solving an inverse problem. The focus of this thesis is on the inverse problem. Specifically, this thesis studies the inverse problem of elastography using direct finite element methods under the condition of applying continuous harmonic excitation to measure the absolute value of the elasticity. First, the “mixed-FEM” inversion technique that solves for both the shear modulus and the pressure is considered. Different regularization techniques are investigated for this method. New sparsity and strain-based regularization techniques, which improve the accuracy, robustness to noise, and speed of the reconstruction, are developed. A comparison of the iterative and direct FEM techniques is performed using simulations. The results show the superiority of the direct method over the iterative method. In order to reduce the number of unknowns, the pressure parameters are removed using the curl operator in a new curl-based direct FEM technique (c-FEM). In this technique, unlike in all previous curl-based methods, the local homogeneity assumption is not used. One of the main observations of this thesis is the importance of the deleterious effect of the tissue homogeneity assumption on the reconstruction results for regions with large variations in the elasticity of the region. A new simplified direct FEM technique without the homogeneity assumption (shear-FEM) is also developed for cases where only partial displacement data is available, such as in US elastography. It is shown that using multi-frequency excitations in both c-FEM and shear-FEM techniques is beneficial by providing multiple measurements of the shear waves and reducing the problem of low-amplitude nodes. To conclude the thesis, the methods developed in this thesis, plus two other established reconstruction algorithms, are compared using simulations, phantoms, ex-vivo and in-vivo data.

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New Methods for Calibration and Tool Tracking in Ultrasound-Guided Interventions (2015)

Ultrasound is a safe, portable, inexpensive and real-time modality that can produce 2D and 3D images. It is a valuable intra-operative imaging modality to guide surgeons aiming to achieve higher accuracy of the intervention and improve patient outcomes. In all the clinical applications that use tracked ultrasound, one main challenge is to precisely locate the ultrasound image pixels with respect to a tracking sensor on the transducer. This process is called spatial calibration and the objective is to determine the spatial transformation between the ultrasound image coordinates and a coordinate system defined by the tracking sensor on the transducer housing. Another issue in ultrasound guided interventions is that tracking surgical tools (for example an epidural needle) usually requires expensive, large optical trackers or low accuracy magnetic trackers and there is a need for a low-cost, easy-to-use and accurate solution. In this thesis, for the first problem I have proposed two novel complementary methods for ultrasound calibration that provide ease of use and high accuracy. These methods are based on my differential technique which enables high measurement accuracy. I developed a closed-form formulation that makes it possible to achieve high accuracy with using a low number of images. For the second problem, I developed a method to track surgical tools (epidural needles in particular) using a single camera mounted on the ultrasound transducer to facilitate ultrasound guided interventions. The first proposed ultrasound calibration method achieved an accuracy of 0.09 ± 0.39 mm. The second method with a much simpler phantom yet achieved similar accuracy compared to the N-wire method. The proposed needle tracking method showed high accuracy of 0.94 ± 0.46 mm.

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Speckle Tracking for 3D Freehand Ultrasound Reconstruction (2014)

The idea of full six degree-of-freedom tracking of ultrasound images solely based on speckle information has been a long term research goal. It would eliminate the need for any additional tracking hardware and reduces cost and complexity of ultrasound imaging system, while providing the benefits of three-dimensional imaging.Despite its significant promise, speckle tracking has proven challenging due to several reasons including the dependency on a rare kind of speckle pattern in real tissue, underestimation in the presence of coherency or specular reflection, ultrasound beam profile spatial variations, need for RF (Radio Frequency) data, and artifacts produced by out-of-plane rotation. So, there is a need to improve the utility of freehand ultrasound in clinics by developing techniques to tackle these challenges and evaluate the applicability of the proposed methods for clinical use.We introduce a model-fitting method of speckle tracking based on the Rician Inverse Gaussian (RiIG) distribution. We derive a closed-form solution of the correlation coefficient of such a model, necessary for speckle tracking. In this manner, it is possible to separate the effect of the coherent and the non-coherent part of each patch. We show that this will increase the accuracy of the out-of-plane motion estimation.We also propose a regression-based model to compensate for the spatial changes of the beam profile.Although RiIG model fitting increases the accuracy, it is only applicable on ultrasound sampled RF data and computationally expensive. We propose a new framework to extract speckle/noise directly from B-mode images and perform speckle tracking on the extracted noise. To this end, we investigate and develop Non-Local Means (NLM) denoising algorithm based on a prior noise formation model.Finally, in order to increase the accuracy of the 6-DoF transform estimation, we propose a new iterative NLM denoising filter for the previously introduced RiIG model based on a new NLM similarity measure definition. The local estimation of the displacements are aggregated using Stein’s Unbiased Risk Estimate (SURE) over the entire image. The proposed filter-based speckle tracking algorithm has been evaluated in a set of ex vivo and in vivo experiments.

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Statistical Models of the Spine for Image Analysis and Image-guided Interventions (2014)

The blind placement of an epidural needle is among the most difficult regional anesthetic techniques. The challenge is to insert the needle in the midline plane of the spine and to avoid overshooting the needle into the spinal cord. Prepuncture 2D ultrasound scanning has been introduced as a reliable tool to localize the target and facilitate epidural needle placement. Ideally, real-time ultrasound should be used during needle insertion to monitor the progress of needle towards the target epidural space. However, several issues inhibit the use of standard 2D ultrasound, including the obstruction of the puncture site by the ultrasound probe, low visibility of the target in ultrasound images of the midline plane, and increased pain due to a longer needle trajectory. An alternative is to use 3D ultrasound imaging, where the needle and target could be visible within the same reslice of a 3D volume; however, novice ultrasound users (i.e., many anesthesiologists) have difficulty interpreting ultrasound images of the spine and identifying the target epidural space. In this thesis, I propose techniques that are utilized for augmentation of 3D ultrasound images with a model of the vertebral column. Such models can be pre-operatively generated by extracting the vertebrae from various imaging modalities such as Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). However, these images may not be obtainable (such as in obstetrics), or involve ionizing radiation. Hence, the use of Statistical Shape Models (SSM) of the vertebrae is a reasonable alternative to pre-operative images. My techniques include construction of a statistical model of vertebrae and its registration to ultrasound images. The model is validated against CT images of 56 patients by evaluating the registration accuracy. The feasibility of the model is also demonstrated via registration to 64 in vivo ultrasound volumes.

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Exploring multiple-mode vibrations of capacitive micromachined ultrasonic transducers (CMUTS) (2013)

Capacitive Micromachined Ultrasonic Transducers (CMUTs) are considered advantageous over piezoelectric transducers for ultrasound imaging for the high bandwidth, ease of integration with electronics and miniaturization. Research efforts over the past two decades have been focusing on manufacturing and system integration of CMUTs to achieve comparable and better performance than the piezoelectric counterparts, while the uniqueness of the CMUT structure and physics is barely exploited.This thesis explores the complex behavior of CMUTs from a mode superposition perspective, and demonstrates imaging applications using CMUTs' multi-modal operation. The operation of CMUTs is first analytically modeled as a coupled electro-mechano-acoustical system using plate vibration theory. As the simplest case, the first symmetric and asymmetric modes of vibration can be excited simultaneously via asymmetric electrostatic actuation, resulting in a vibration profile with a shifted center. Finite element modeling (FEM) is used to verify the theoretical calculation, and an equivalent circuit consisting of two sub-circuits for the symmetric and asymmetric vibration modes is built to show the possibility of fast simulation of complex CMUT array behavior. Experimental characterization of fabricated CMUT chips show that asymmetric vibration can be achieved with multi-electrode CMUTs.Two imaging applications using the multi-modal operation of CMUTs are proposed. The first concept, tiltable transducers, explores the benefits of orienting each transducer element toward the focal point to concentrate the acoustic energy and reduce grating lobes and side lobes. Imaging simulation shows the grating lobes can be reduced by 20dB while the main lobe energy is preserved. FEM simulation demonstrates that CMUTs capable of asymmetric vibration can be a viable candidate as tiltable transducers with careful design of the cell dimension and central frequency. The second imaging application takes advantage of the ringing response of a CMUT to off-axis acoustic sources to achieve super-resolution imaging with low computational cost. The differential responses across all CMUT cells form a more decorrelated pattern than the regular average responses, which leads to better estimation performance of the proposed super-resolution algorithm. While only preliminary experimental results for the proposed applications are presented, the multi-modal operation concept shows potential in improving several aspects of ultrasound imaging.

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A Wave Equation Approach to Ultrasound Elastography (2010)

Tissue elastography is a field of medical imaging which deals with obtaining images of tissue mechanical properties, in particular tissue elasticity. Every elastography imaging system has three major components: an exciter, an imaging system, and an image processing software module. The principle of operation of these systems is that softer and harder tissues react differently to a mechanical excitation. The imaging system captures images of the tissue as the mechanical exciter deforms the tissue. The image processing software module then computes the tissue motion from the acquired images, and solves an inverse problem to obtain tissue elasticity from tissue displacements. In ultrasound elastography all three components of the displacement are not available, and the measurement is carried out only on the imaging plane. The problem of inversion using partial measurements is an inherently challenging problem. To solve this problem the phase speed is estimated in the first place. This thesis studies the process of estimating tissue elasticity from tissue displacements, using the phase speed estimation. It is shown that, in general, the elasticity cannot be inferred from the phase speed, but under certain boundary and excitation conditions, a relationship exists between the phase speed and the elasticity. However the relationship depends on the boundary and excitation conditions. Based on the developed results, a novel rheometry method is proposed for visco-elastic characterization of tissue samples. To increase the resolution of the elasticity images it is of interest to use higher frequencies of excitation. However the inherent low frame rate of ultrasound systems posed a limitation. A novel high-frame-rate ultrasound system is introduced which is capable of tracking motions of up to 500Hz. The high-frame-rate system is used in conjunction with the inversion algorithms to form an elastography system. The performance of the system is tested experimentally on tissue mimicking material having hard and soft inclusions.

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Instrumentation and ultrasound for epidural anesthesia (2010)

Lumbar epidural anesthesia is used for alleviating the pain of labor and for surgery. Here, a catheter is threaded through a Tuohy needle that is traditionally inserted using the loss-of-resistance technique to confirm entry into the epidural space. This research begins with a study of the loss-of-resistance through instrumentation. Sensors measure 1)the force applied at the plunger by the anesthesiologist, 2)the pressure at the needle tip and 3)the position of the plunger relative to the syringe. The “feel” in different tissues is quantified for porcine subjects ex vivo and human subjects in vivo.A vertebra counting protocol is developed to identify the desired vertebral interspaces. Ultrasound is then used to measure anatomical distances such as the distance between the skin and ligamentum flavum and surrogate measures compared to the actual needle insertion depth. Good correlation is only found between skin-to-ligamentum flavum and the actual needle insertion depth.Next, a real-time in-plane ultrasound technique is developed with a needle guide fixing the needle trajectory to the ultrasound transducer. This allows the anesthesiologist to guide the insertion of the epidural needle as an “aim-and-insert” method. In 18 of 19 subjects, the procedure was successfully performed.The key limitation of ultrasound in this application is the image quality that inhibits interpretation of the images. A median-based spatial compounding with warping is performed to align the anatomical features of different beam-steered images and combine them to obtain a single enhanced image. This method is tested on image sets of phantoms and lumbar anatomy of 23 human subjects and shows a significant improvement in noise reduction and clarity.Another limitation is the interpretation of ultrasounds of the spinal anatomy requires understanding of ultrasound. An automatic detection algorithm is developed based on the experienced sonographer’s method of detecting the ligamentum flavum in ultrasounds. This novel method is tested on ultrasounds of the lumbar anatomy in 20 human subjects and shows the method successfully detects the ligamentum flavum in 34 of 39 cases. The main conclusion is that specialized ultrasound tools and protocols are needed to accomodate the range of patients and levels of experience of practitioners.

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On the identification of mechanical properties of viscoelastic materials (2009)

Commonly used medical imaging techniques can render many properties of the anatomy or function, but are still limited in their ability to remotely measure tissue mechanical properties such as elasticity and viscosity. A remote and objective palpation function would help physicians in locating possible tumors or malignancies. The branch of medical imaging that characterizes tissues mechanical properties in a non-invasive manner has enjoyed increasing interest in the past two decades. The basic principle is to apply an excitation, such as tissue compression, to a region of interest and measure the resulting tissue deformation. Tissue mechanical properties can then be inferred from the observed deformation at multiple locations in the region, and the properties can be displayed as an image. If the excitation is dynamic, the deformation is considered as a motion field that varies in time and location over the region of interest. Ultrasound is particularly well suited for measuring motion fields due to its ability to image in real-time, low cost, low risk and ease-of-accessibility. The focus of this thesis is the estimation of the viscoelastic parameters such as Young's modulus, viscosity and relaxation-time. For this purpose, a motion estimation method is proposed to measure axial tissue displacements from the peak of the ultrasound radio frequency signals. The displacements can be further processed to identify the mechanical properties. Two methods were developed: the first one is based on a one dimensional Voigt's model of soft tissue and the second one is based on a finite element model. In the first method, a single frequency or wide-band excitation is applied to the tissue and the local relaxation-time is recovered from the phase difference between the strains or displacements. In this method, the elasticity can also be reconstructed from the magnitudes of the spectra. In the second approach, a novel dynamic finite element model is proposed for the incompressible soft materials. An inverse problem of viscoelasticity is solved iteratively to reconstruct the viscosity and elasticity based on a two or three dimensional model. The theoretical aspect of compressional elastography and longitudinal wave propagation is investigated. It is shown to be feasible to apply dynamic or transient compressional excitation to recover the dynamic properties of soft tissue.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Machine learning systems for obstetric ultrasonography (2021)

Prenatal screening and ultrasound-guided epidurals are two common applications of ultrasound imaging in obstetrics that help with disease prevention and pain relief. While urban settings typically provide the expertise to perform these procedures, rural and underserved settings suffer from a lack of ultrasound equipment, training, and expertise that precludes a similar quality of care. This thesis seeks to address gaps in equipment and expertise by presenting machine learning systems for automating the analysis of conventional ultrasound images without the use of specialized equipment. The first system is a method for automatic segmentation of the placenta from 2D ultrasound sweeps acquired during first-trimester prenatal screening. We analyzed the performance and speed of four different deep learning architectures for spatiotemporal segmentation applied to 133 ultrasound sweeps from a diverse patient population. Compared to manual segmentations, the top-performing architecture achieved a Dice coefficient of 92.11 +/- 7.5 % and was able to segment at a rate of 100 frames per second. The second system is a method for 2D ultrasound image augmentation for improved interpretability during ultrasound-guided epidurals. This system relies on registering a 3D statistical shape model of the lumbar vertebrae constructed from computerized tomography scans of the lumbar spine to automatically classified and segmented 2D ultrasound images. The classification and segmentation of ultrasound images achieved an accuracy of 90% and a mean Dice coefficient of 74.9 +/- 4.9%, respectively. The registration to the segmented regions was evaluated on 43 ultrasound images, and the achieved a root mean squared error of 1.4 +/- 0.3 mm when compared to the ground truth. We showcase the ability of machine learning systems to automate ultrasound image analysis in common obstetric applications. The results show the potential for these systems to be further developed in a translational research setting.

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Automatic localization and labelling of spine vertebrae in MR images using deep learning (2020)

Magnetic Resonance (MR) and Computed Topography (CT) are the most common modalities for spine imaging. Localization and identification of vertebrae is an essential first step in examining these volumes for diagnosis, surgical planning and management of patients with disc or vertebra pathologies and conditions. With large volumes of spinal scans acquired at imaging centres, development of a computerized solution for spine labelling has received attention from several research groups, as it can help save radiologists time and clicks. It can also expedite the imaging-dependent pre- and post-operation procedures. Nonetheless, automatic spine labelling in CT and MR is non-trivial and has proven challenging. This is due to: 1) limited and variable field-of-view (FOV); 2) variability in imaging parameters and resolution; 3) variability in shape, size and appearance of the spinal anatomies, especially in the presence of various pathologies or implants; 4) the repetitive nature of the spine and similar appearance of the vertebrae; and particularly for learning-based solutions, 5) dependence on expert annotations. In this thesis, learning-based approaches that perform simultaneous identification and localization of vertebrae are introduced. The principal goal is to design a supervised spine labelling approach that requires minimal manual annotations, and can perform both identification and localization tasks within a unified framework. We achieved an identification rate of 89.76%.

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Automated analysis of the placenta in ultrasound (2019)

The placenta is an organ that serves as an interface for macromolecule exchange between a mother and fetus. Revealing symptoms of placenta disease are usually presented later on in the pregnancy with limited treatment options. Access to specialists of placental disease is also limited, particularly in regions distant from urban centres. There is a need for an accessible method to screen patients for risk of placenta disease early. Ultrasound provides a non-invasive imaging modality capable of visualizing the placenta commonly used in obstetric clinics. However, ultrasound images contain unique artifacts that are difficult to interpret with visualinspection. This thesis presents a pipeline of three ultrasound processing methods developed to aid placenta ultrasound analysis. The first is a method to detect ultrasound acoustic shadow artifacts that obscure anatomy. Radiofrequency signals and pixel entropy were analyzed to identify acoustic shadow in images. A clinical study was performed to obtain ultrasound scans from 37 subjects to evaluate performance. A Dice coefficient of 0.90±0.07 for radiofrequency-based and 0.87±0.08 for pixel entropy-based techniques was achieved when compared to manual shadow detection. The second is a method to segment the placenta in images preprocessed by shadow detection using a convolutional neural network. Performance was evaluated on data from 1364 fetal ultrasound images from 247 patients. A Dice coefficient of 0.92±0.04 was achieved when compared to manual segmentation. The third is a method to classify placenta appearance as either normal or abnormal. Images were preprocessed with the first two methods to provide a placenta-only image. A residual convolutional neural network was then used to classify the placenta appaearance. Performance was evaluated on 7831 fetal ultrasound images from 367 patients. Placenta classification achieved a sensitivity of 0.91 and a specificity of 0.87 when compared to classification by physicians. The methods demonstrate the capability of ultrasound physics analysis and machine learning methods in processing placenta ultrasound images. The results show the potential for developing a tool in the future to assist physicians in analyzing the placenta to screen for disease.

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Automated lumbar vertebral level identification using ultrasound (2017)

Spinal needle procedures require identification of the vertebral level for effectiveness and safety. E.g. in obstetric epidurals, the preferred target is between the third and fourth lumbar vertebra. The current clinical standard involves "blind" identification of the level through manual palpation, which only has a 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion. Ultrasound provides anatomical information to physicians, which is not obtainable via manual palpation. However, due to artifacts and the complex anatomy of the spine, ultrasound is not commonly used for pre-puncture planning.This thesis describes two machine learning based systems that can aid physicians to utilize ultrasound for lumbar level identification.The first system, LIT, is proposed to identify vertebrae, assigning them to their respective levels and tracking them in a sequence of ultrasound images in the paramedian plane. A deep sparse auto-encoder network learns to extract anatomical features from pre-processed ultrasound images. A feasibility study (n=15) evaluated performance.The second system, SLIDE, identifies vertebral levels from a sequence of ultrasound images in the transverse plane. The system uses a deep convolutional neural network (CNN) to classify transverse planes of the lower spine. In conjunction, a novel state-machine is developed to automatically identify vertebral levels as the transducer moves.A feasibility study (n=20) evaluated performance. The CNN achieves 88% accuracy in discriminating images from three planes of the spine. As a system, SLIDE successfully identifies all lumbar levels in 17 of 20 test scans, processed at real-time speed.A clinical study with 76 parturient patients was performed. The study compares level identification accuracy between manual palpation, versus SLIDE, with both compared to freehand ultrasound. SLIDE's level identification outperformed palpation with an odds ratio of nearly 3. A subset of recorded ultrasound (n=60) was labeled and used to retrain the CNN, improving classification accuracy to 93%.The systems showcase the utility of machine learning in spinal ultrasound analysis, with varied approaches to automatically identifying vertebral levels. The systems can be used to improve the accuracy of vertebral level identification compared to manual palpation alone.

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Intra-operative ultrasound-based augmented reality for laparoscopic surgical guidance (2017)

Laparoscopic partial nephrectomy involves the complete resection of a kidney tumour, while minimizing healthy tissue excised, and under a time constraint before irreparable kidney damage occurs. The surgeon must complete this operation in a reduced sensory environment with poor depth perception, limited field of view, and little or no haptic feedback. For endophytic tumours (grows inwards), this is particularly difficult. In order to assist the surgeon, augmented reality can provide intra-operative guidance. Intra-operative ultrasound is low cost, non-ionising, and real-time. This has tremendous potential to guide the surgeon. This thesis details the development of three intra-operative augmented reality systems from a single framework, with augmentations all based on intra-operative ultrasound. The systems were all developed on the da Vinci Surgical System, using it as a development and testing platform. All systems leverage a single fiducial marker called the Dynamic Augmented Reality Tracker which can track the local surface and create a tumour-centric paradigm. A 3D ultrasound volume is reconstructed using a tracked ultrasound transducer. A tumour model is then extracted via manual segmentation of the volume. The three systems were developed and evaluated in simulated robot-assisted partial nephrectomies. The first system shows the feasibility of providing continuous ultrasound-based guidance during excision and achieves a system error of 5.1 mm RMS. Improving on this, the second system demonstrates clinically acceptable system error of 2.5 ± 0.5 mm. The second system significantly reduced healthy tissue excised from an average of 30.6 ± 5.5 cm³ to 17.5 ± 2.4 cm³ (p
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Novel Stand-off Pads for Ultrasound-CT Registration and Elastography (2012)

Laparoscopic surgery has many advantages including reduced patient mor-bidity and improved recovery times, but has the drawback of a very limited eld of view. Thus improvements in image guidance in laparoscopic surgeryare highly desirable, especially in highly technical operations such as robot-ically assisted laparoscopic partial nephrectomies (RALPN).In RALPN, image guidance can be enhanced by bringing pre-operativecomputed tomography (CT) and intra-operative ultrasound images into thesurgeon's eld of view, depicting underlying anatomy. Multiple tracking andregistration steps with inherent trade-o s of accuracy and convenience arenormally required to display the images in the camera view. In this thesis, anew tracker-less method is developed for the step of registering pre-operative3D CT to pre-operative 3D ultrasound which uses a novel ducial stand-o pad. The pad contains ducial markers visible in both modalities which arematched to obtain the registration parameters. The ducial stand-o pad istested in a controlled phantom study to determine registration accuracy andin a small clinical study to determine clinical feasibility. The ducial stand-o pad is capable of similar registration accuracies to incumbent approacheswithout the need for external tracking equipment, and is easily integratedinto medical imaging protocols.A second enhancement of RALPN image guidance is the integration ofultrasound elastography to display mechanical properties of the tissue. Elas-tography has been in development for over two decades, but further improve-ments are required to improve quantitative estimations of tissue properties.In this thesis, a stand-o pad is used as a method of measuring ultrasoundtransducer contact force distributions, which will allow force measurementsto be used in solving for local tissue elasticities. Forces are obtained bymeasuring displacements in the stand-o pad and converting them to forcesusing a nite element model. The accuracy of displacement estimates istested, and the force computation process is validated. As well, a forcemeasurement system is implemented for use with a three-dimensional lineararray transducer. The results show that this is a feasible force measurementmethod, providing approximately 10% error in force measurements.

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Intra-operative "pick-up" ultrasound for guidance and registration to pre-operative imaging (2011)

The integration of ultrasound into robotic laparoscopic surgery can provide a surgeon with navigational guidance that could decrease operating times and increase surgeon confidence during complicated procedures such as partial nephrectomy. CT scans are taken for diagnosis before surgery and have a wide field-of-view and high resolution but do not provide real-time information for the surgeon during surgery. Ultrasound is an inexpensive, portable, non-invasive imaging modality, which has the potential to provide the surgeon with real-time information. With accurate registration between CT and ultrasound, a surgeon can be provided with a wide field-of-view of the patients underlying anatomy, that cannot be seen through the laparoscope.Organ motion within the abdomen between the time of diagnostic scanning and intra-operative imaging can affect the accuracy of image registration. CT scans of the patients’ kidneys in both the supine (diagnostic) and flank (surgical) positions were registered to determine the extent of kidney motion. The center of mass was observed to move between 10 and 75 mm resulting in a recommendation that diagnostic CT scans be performed with the patient in the potential surgical position when image registration will be performed.This thesis presents the design of a new intra-abdominal ultrasound transducer, which can be controlled directly by the operating surgeon throughout the duration of the procedure. Initial use of the transducer is targeted for robotic laparoscopic surgery, where the operating surgeon must rely on a patient-side assistant. Multiple tracking methods have been integrated into the transducer to allow 3D ultrasound volumes to be constructed from a set of 2D slices. These methods include tracking using electromagnetic sensors, optical markers and robotic kinematics. The vessels of the kidney serve as important landmarks during the surgical procedure and can also be used as features for CT to ultrasound registration. A registration method using automatic ultrasound vessel segmentation is proposed and tested in a phantom and human model. The root mean square error in the phantom was calculated to be 3.2 mm, which is comparable to other reported registration errors, while the error in the registration using the human model was 7.5 mm.

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Panorama ultrasound for navigation and guidence of epidural anesthesia (2011)

Epidural anesthesia is a common but challenging procedure in obstetrics and surgery, especially for the obese patient, and can result in complications such as dural puncture and nerve injury. Ultrasound has the potential to significantly improve epidural needle guidance, by being able to depict the spinal anatomy and the epidural space. An ultrasound guidance system is therefore proposed, using a transducer-mounted camera to create 3D panorama images of the spine relative to markings on the skin. Guidance will include depiction of the spinal anatomy, identification of individual vertebrae, and selection of a suitable puncture site, trajectory and depth of needle insertion. The camera tracks the transducer movement using a specialized strip of markers attached to the skin surface. This enables 6-DOF absolute position estimation of the transducer with respect to the patient over the full range of the spine. The 3D panorama image can then be resliced in various parasagittal planes to show either the target epidural spaces or the laminae. The overall accuracy of the panorama reconstruction is validated by measuring inter-feature distances of a phantom of steel beads against measurements obtained from an optical tracking system (Optotrak), resulting in an average error of 0.64 mm between camera and Optotrak. The algorithm is then tested in vivo by creating panorama images from human subjects (n=20), obtaining measurements for depth of insertion to the epidural space, intervertebral spacings, and registration of interspinous gaps to the skin, and validating these against independent measurements by an experienced sonographer. The results showed an average error of 1.69 mm (4.23%) for the depth measurements, average error of 4.44 mm (15.2%) for the interspinous distance measurements, and an average error of 6.65 mm for registering the interspinous gaps to the skin (corresponding to 18.5% of the interspinous distances). Tracking of ultrasound images with respect to the marker is implemented in real time and visualized using the 3D Slicer software package.

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Dual-transducer ultrasound for elastography (2010)

Medical imaging techniques provide valuable information about the internal anatomy of the body. Commonly used techniques can render many properties of the anatomy and its function, but they are limited in their ability to measure tissue mechanical properties such as elasticity. Over the past two decades there has been growing interest in developing methods of noninvasively characterizing mechanical properties of tissues; a field commonly referred to as elastography. Tissues are known to exhibit changes in mechanical properties in response to pathology. As a result, elastography has the particular potential to help physicians diagnose and locate cancerous tumors and other malignancies.The principle of operation of elastography systems is to apply an excitation to the tissue, such as a compression, and to measure the resulting tissuemotion as it deforms. The tissue elasticity can then be inferred from the motion estimates by solving the inverse problem. Tissue motion is typically measured with ultrasound because it is fast, safe, and relatively inexpensive.The point spread function of an ultrasound beam is anisotropic, resulting in poorer quality motion estimates in two of the three spatial directions. This thesis investigates a new method of estimating tissue motion by employing two ultrasound transducers with different view angles. The goal of using these two transducers is to create a plane of high quality 2D motion estimates. Simulations and experimental results on tissue mimicking phantomsshow that the method outperforms other commonly used 2D motion estimation methods. For example, in a tissue deformation simulation, the dual transducer method produced lower root mean square measurement error by a factor of 10 compared to a single transducer technique, and a factor of 3compared to a single transducer with angular compounding.A simple wire-based method of aligning the transducers into a coincident scan plane is initially developed. Later, a novel wedge-based phantom is designed for aligning the two transducers. Calibration results demonstrate improved alignment with the wedge phantom. Manual alignment is found to be repeatable with mean alignment errors under 1 degree in rotation and 1 mm in translation for all degrees of freedom after six independent trials.

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Registration of 3D Ultrasound to Computed Tomography Images of the Kidney (2010)

The integration of 3D computed tomography (CT) and ultrasound (US) isof considerable interest because it can potentially improve many minimallyinvasive procedures such as robot-assisted laparoscopic partial nephrectomy.Partial nephrectomy patients often receive preoperative CT angiography fordiagnosis. The 3D CT image is of high quality and has a large field of view.Intraoperatively, dynamic real-time images are acquired using ultrasound.While US is real-time and safe for frequent imaging, the images captured arenoisy and only provide a limited perspective. Providing accurate registrationbetween the two modalities would enhance navigation and image guidancefor the surgeon because it can bring the pre-operative CT into a currentview of the patient provided by US.The challenging aspect of this registration problem is that US and CTproduce very different images. Thus, a recurring strategy is to use preprocessing techniques to highlight the similar elements between the images.The registration technique presented here goes further by dynamically simulating an US image from the CT, and registering the simulated image tothe actual US. This is validated on US and CT volumes of porcine phantom data. Validation on realistic phantoms remains an ongoing problem inthe development of registration methods. A detailed protocol is presentedhere for constructing tissue phantoms that incorporate contrast agent intothe tissue such that the kidneys appear representative of in vivo humanCT angiography. Registration with 3D CT is performed successfully on thereconstructed 3D US volumes, and the mean TREs ranged from 1.8 to 3.5mm. In addition, the simulation-based algorithm was revised to considerthe shape of the US beam by using pre-scan converted US data. The corresponding CT image is iteratively interpolated along the direction of theUS beam during simulation. The mean TREs resulting from registering thepre-scan US data and CT data were between 1.4 to 2.6 mm. The resultsshow that both methods yield similar results and are promising for clinicalapplication. Finally, the method is tested on a set of in vivo CT and USimages of a partial nephrectomy patient, and the registration results arediscussed.

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