Calendar

Mar
25
Thu
Thesis Proposal: Blake Dewey
Mar 25 @ 3:00 pm
Thesis Proposal: Blake Dewey

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Harmonization of Structural MRI for Consistent Image Analysis

Abstract: Magnetic resonance imaging (MRI) is a flexible, non-invasive medical imaging modality that uses strong magnetic fields and radio-frequency pulses to produce images with excellent contrast in the soft tissues of the body. MRI is commonly used in diagnosis and monitoring of many conditions, but is especially useful in disorders of the central nervous system, such as multiple sclerosis (MS), where the brain and spinal cord are heavily involved. An MRI scan normally contains a number of imaging volumes, where different pulse sequence parameters are selected to highlight different tissue properties. These volumes can then be used together to provide complimentary information about the imaged area. Flexible design of the imaging system allows for a variety of questions to be answered during a single scanning session, but also comes with a cost. As there are many parameters to define when designing an imaging sequence, there is no common standard that is widely used. These differences lead to variability in image appearance between manufacturers, imaging centers, and even individual scanners. As an example, a commonly acquired MR volume is a T1-weighted image, where differences in a specific magnetic property (longitudinal relaxation time or T1) is highlighted. However, this general effect can be achieved with a myriad of different pulse sequences even before the individual parameters are considered. This is perhaps most apparent in the difference between T1-weighted images with and without a preparatory inversion pulse, where images with an inversion pulse tend to have a much clearer contrast between grey and white matter in the brain. With the advent of advanced machine learning methods, variations such as the example above create a large problem, as accurate methods become closely tied to the data used to train them and any variation in inputs can have unknown effects on output quality. This problem sets the stage for image harmonization, where synthetic “harmonized” images are produced after acquisition to provide consistent inputs to image analysis routines.

This thesis aims to develop harmonization strategies for structural brain MR images that will allow for the synthesis of harmonized images from differing inputs. These images can then be used downstream in automated analysis pipelines, most commonly whole-brain segmentation for volumetric analysis. Recently, deep learning-based techniques have been shown to be excellent candidates in the realm of image synthesis and can be readily incorporated in harmonization tasks. However, this is complicated, as training data (especially in multi-site settings) is rarely available. This work will approach these problems by covering three main topics:

  1. Development of a supervised harmonization technique for structural MRI that utilizes overlapping subjects scanned using multiple protocols.
  2. Development of a semi-supervised learning strategy that exploits existing multi-contrast MRI information within a single scan session to perform the harmonization task without overlapping subjects.
  3. Demonstration of the feasibility of MRI harmonization from the viewpoint of clinical research through validation and investigation in real-world data samples.

Committee Members

  • Jerry L. Prince, Department of Electrical and Computer Engineering
  • Vishal M. Patel, Department of Electrical and Computer Engineering
  • Muyinatu A. Lediju Bell, Department of Electrical and Computer Engineering
  • Peter C.M. van Zijl, Department of Radiology and Radiological Sciences
  • Peter A. Calabresi, Department of Neurology
Thesis Proposal: Shoujing Guo
Mar 25 @ 3:00 pm
Thesis Proposal: Shoujing Guo

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Intraoperative Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty

Abstract: Deep anterior lamellar keratoplasty (DALK) is a highly challenging procedure requiring micron accuracy to guide a “big bubble” needle into the stroma of the cornea down to Descemet’s Membrane (DM). It has important advantages over Penetrating keratoplasty (PK) including lower rejection rate, less endothelial cell loss, and increased graft survival. Currently, this procedure relies heavily on the visualization through a surgical microscope, the surgeon’s own surgical experience, and tactile feel to determine the relative position of the needle and DM. Optical coherence tomography (OCT) is a well-established, non-invasive optical imaging technology that can provide high-speed, high-resolution, three-dimension images of biological samples. Since it was first demonstrated in 1991, OCT has emerged as a leading technology for ophthalmic visualization, especially for retinal structures, and has been widely applied in ophthalmic surgery and research. Common-path (CP) OCT systems use single A-scan image to deduce the tissue layer information and can be operated at a much higher speed. This synergizes well with handheld tools and automated surgical systems which require fast response time. CP-OCT has been integrated into a wide range of microsurgical tools for procedures such as epiretinal membrane peeling and subretinal injection.

In this proposal, the common-path swept-source OCT system (CP-SSOCT) is proposed to guide DALK procedures. The OCT distal sensor integrated needle and OCT guided micro-control ocular surgical system (AUTO-DALK) will be designed and evaluated. This device will allow for the autonomous insertion of a needle for pneumo-dissection based on the depth-sensing results from the OCT system. An earlier prototype of AUTO-DALK was tested on the ex-vivo porcine cornea including the comparison of expert manual needle insertion. The result showed the precision and consistency of the needle placement were increased, which could lead to better visual outcomes and fewer complications. Future work will include improving the overall design for in-vivo testing and clinical use, advanced convolutional neural network based tracking, and system validation on larger sample size.

Committee Members

Jin U. Kang (adviser), Department of Electrical and Computer Engineering

Israel Gannot, Department of Electrical and Computer Engineering

Xingde Li, Department of Biomedical Engineering

Apr
1
Thu
Thesis Proposal: Alycen Wiacek
Apr 1 @ 3:00 pm
Thesis Proposal: Alycen Wiacek

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Coherence-based learning from raw ultrasound data for breast mass diagnosis

Abstract: Breast cancer is the most prevalent cancer among women in the United States, with approximately one in eight women being diagnosed in their lifetimes. Imaging modalities such as mammography, MRI, and ultrasound are employed to non-invasively visualize breast masses in order to determine the need for a biopsy. However, each of these methods results in a significant number of patients requiring biopsies of benign masses. Ultrasound in particular is praised for its low cost, painlessness, and portability, yet the false positive rate of breast ultrasound can be as high as 93% depending on the type of mass in question. Most commonly, diagnosis is performed using the brightness-mode (B-mode) image present on most clinical ultrasound scanners, which transitions naturally to the use of B-mode images for segmentation and classification of breast masses. Ultimately, segmentation and classification of breast masses can be summarized as analysis of a grayscale image. While this approach has been successful, information is lost during the B-mode image formation process.

An alternative approach to the lossy process of information extraction from B-mode images is to leverage features (e.g., spatial coherence) of backscattered ultrasound waves to determine the content of a breast mass. I will first describe my contributions to improve the diagnostic quality of breast ultrasound images by leveraging spatial coherence information. Next, I will present my deep learning approach to overcome limitations with real-time implementation of coherence-based imaging techniques. Finally, I will present a new method to learn the high-dimensional features encoded within backscattered ultrasound waves in order to differentiate benign from malignant breast masses.

Committee Members

  • Muyinatu Bell, Department of Electrical and Computer Engineering
  • Vishal Patel, Department of Electrical and Computer Engineering
  • Najim Dehak, Department of Electrical and Computer Engineering
Apr
8
Thu
Thesis Proposal: Arlene Chiu
Apr 8 @ 3:00 pm
Thesis Proposal: Arlene Chiu

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Engineering Colloidal Quantum-Confined Nanomaterials for Multi-junction Solar Cell Applications

Abstract: Current single junction solar cell technologies are rapidly approaching their theoretical limits of approximately 33% power conversion efficiency. Semiconductor nanoparticles such as colloidal quantum dots (CQDs) are of interest for photovoltaic applications due to their infrared absorption, size-tunable optical properties and low-cost solution processability. Lead sulfide (PbS) CQDs offer the potential to increase solar cell efficiencies via multi-junction architectures due to these properties. This project aims to develop new strategies for implementing PbS CQDs as a material for multi-junction architectures to improve solar cell efficiencies and expand potential applications.

The first phase of the proposed research begins with developing a better-performing single junction PbS CQD solar cell by improving the performance-limiting hole transport layer HTL) in these devices. We will employ two methods to improve and replace this layer. First, we will use sulfur infusion via electron beam evaporation to alter the stoichiometry of the standard HTL. We also plan to completely replace the standard HTL with 2D nanoflakes of tungsten diselenide, an atomically-thin semiconducting transition metal dichalcogenide. The second phase of the reserach involves developing a PbS CQD multi-junction solar cell, including a novel recombination layer. The third phase of the research involves developing a hybrid multi-junction strategy in which PbS CQD films employing photonic band engineering for spectral selectivity serve as the infrared cell and other materials serve as the visible cell. The ultimate goal of these three research phases is to use photonic and materials engineering to improve efficiency and flexibility in CQD-based multi-junction solar cells to meet the demand for affordable, sustainable solar energy.

Committee Members

  • Susanna Thon, Department of Electrical and Computer Engineering
  • Jacob Khurgin, Department of Electrical and Computer Engineering
  • Amy Foster, Department of Electrical and Computer Engineering
Apr
15
Thu
Thesis Proposal: Sanjukta Nandi Bose
Apr 15 @ 3:00 pm
Thesis Proposal: Sanjukta Nandi Bose

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Early prediction of adverse clinical events and optimal intervention in ICUs

Abstract: Personalized healthcare is a rapidly evolving research area with tremendous potential for optimizing patient care strategies and improving patient outcomes. Traditionally, clinical decision making relies on assessment and intervention based on the collective experience of physicians. Using big-data analytics techniques, we can now harness data-driven models to enable early prediction of patients at risk of adverse clinical events. These predictive models can provide timely analytical information to physicians facilitating early therapeutic intervention and efficient management of patients in intensive care units (ICUs).

In addition to early prediction, it is equally important to optimize intervention strategies for critically ill patients. One such urgent need is to optimally oxygenate COVID-19 patients diagnosed with acute respiratory distress syndrome (ARDS). Moderate to severe ARDS patients generally require mechanical ventilation to improve oxygen saturation and to reduce the risk of organ failure and death. The most common ventilator settings across all modes of mechanical ventilation are positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2). Increasing either of these settings is expected to increase oxygen saturation. However, prolonged ventilation of patients with high PEEP and FiO2 significantly increases the risk of ventilator associated lung injury. Therefore, an optimal strategy is required to improve patient outcomes.

This thesis presents two overarching aims: (1) early prediction of adverse events and (2) optimal intervention for mechanically ventilated patients. In contrast to fixed lead-time prediction models in prior work, our methodology proposes a new framework which hypothesizes the presence of a time-varying pre-event physiologic state that differentiates the target patients from the control group. We also present a unique approach to patient risk-stratification using unsupervised clustering technique that could enable identification of a high-risk group among all positive predicted cases with a positive predictive value of more than 93% when applied to multiple organ dysfunction prediction.

In the second aim, we propose a novel application of data-driven linear parameter varying systems to capture time-varying dynamics of oxygen saturation in response to ventilator settings with a changing physiological state of a patient and its comparison with linear time invariant models.  Most prior studies on closed loop ventilator control have used stepwise, rule-based procedures, fuzzy logic, and a combination of rule-based methods and proportional integral derivative (PID) controller for closed loop control of FiO2. Other studies have worked on control strategies based on ventilator measured variables and on various mathematical lung models. In contrast we design optimal closed-loop ventilator strategies that are model based. A simulation of optimal ventilation settings for maintaining desired oxygen saturation using feedback control of LPV systems is presented.

Committee Members

  • Raimond L. Winslow, Department of Biomedical Engineering
  • Sridevi V. Sarma, Department of Biomedical Engineering
  • Enrique Mallada, Department of Electrical Engineering
  • Melania M. Bembea, Department of Anesthesiology and Critical Care Medicine
Apr
29
Thu
Thesis Proposal: Michelle Graham
Apr 29 @ 3:00 pm
Thesis Proposal: Michelle Graham

Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.

Title: Photoacoustic imaging to detect major blood vessels and nerves during neurosurgery and head and neck surgery

Abstract: Real-time intraoperative guidance during minimally invasive neurosurgical and head and neck procedures is often limited to endoscopy, CT-guided image navigation, and electromyography, which are generally insufficient to locate major blood vessels and nerves hidden by tissue. Accidental damage to these hidden structures has incidence rates of 6.8% in surgeries to remove pituitary tumors (i.e., endonasal transsphenoidal surgery) and 3-4% in surgeries to remove parotid tumors (i.e., parotidectomy), often resulting in severe consequences, such as patient blindness, paralysis, and death. Photoacoustic imaging is a promising emerging imaging technique to provide real-time guidance of subsurface blood vessels and nerves during these surgeries.

Limited optical penetration through bone and the presence of acoustic clutter, reverberations, aberration, and attenuation can degrade photoacoustic image quality and potentially corrupt the usefulness of this promising intraoperative guidance technique. In order to mitigate image degradation, photoacoustic imaging system parameters may be adjusted and optimized to cater to the specific imaging environment. In particular, parameter adjustment can be categorized into the optimization of photoacoustic signal generation and the optimization of photoacoustic image formation (i.e., beamforming) and image display methods.

In this talk, I will describe my contributions to leverage amplitude- and coherence-based beamforming techniques to improve photoacoustic image display for the detection of blood vessels during endonasal transsphenoidal surgery. I will then present my contributions to the derivation of a novel photoacoustic spatial coherence theory, which provides a fundamental understanding critical to the optimization of coherence-based photoacoustic images. Finally, I will present a plan to translate this work from the visualization of blood vessels during neurosurgery to the visualization of nerves during head and neck surgery. Successful completion of this work will lay the foundation necessary to introduce novel, intraoperative, photoacoustic image guidance techniques that will eliminate the incidence of accidental injury to major blood vessels and nerves during minimally invasive surgeries.

Committee Members:

  • Muyinatu Bell, Department of Electrical and Computer Engineering
  • Xindge Li, Department of Biomedical Engineering
  • Jin Kang, Department of Electrical and Computer Engineering
Back to top