Advanced Image Reconstruction for Limited View Cone-Beam CT

In a standard CT acquisition, a high number of projections is obtained around the sample, generally covering an angular span of 360º. However, complexities may arise in some clinical scenarios such as surgery and emergency rooms or Intensive Care Units (ICUs) when the accessibility to the patient is limited due to the monitoring equipment attached. X-ray systems used in these cases are usually C-arms that only enable the acquisition of planar images within a limited angular range. Obtaining 3D images in these scenarios could be extremely interesting for diagnosis or image guided surgery. This would be based on the acquisition of a small number of projections within a limited angular span. Reconstruction of these limited-view data with conventional algorithms such as FDK result in streak artifacts and shape distortion deteriorating the image quality. In order to reduce these artifacts, advanced reconstruction methods can be used to compensate the lack of data by the incorporation of prior information. This bachelor thesis is framed on one of the lines of research carried out by the Biomedical Imaging and Instrumentation group from the Bioengineering and Aerospace Department of Universidad Carlos III de Madrid working jointly with the Hospital General Universitario Gregorio Marañón through its Instituto de Investigación Sanitaria. This line of research is carried out in collaboration with the company SEDECAL, which enables the direct transfer to the industry. Previous work showed that a new iterative reconstruction method proposed by the group, SCoLD, is able to restore the altered contour of the object, suppress greatly the streak artifacts and recover to some extend the image quality by restricting the space of search with a surface constraint. However, the evaluation was only carried out using a simulated mask that described the shape of the object obtained by thresholding a previous CT image of the sample, which is generally not available in real scenarios. The general objective of this thesis is the designing of a complete workflow to implement SCoLD in real scenarios. For that purpose, the 3D scanner Artec Eva was chosen to acquire the surface information of the sample, which was then transformed to be usable as prior information for SCoLD method. The evaluation done in a rodent study showed high similarity between the mask obtained from real data and the ideal mask obtained from a CT. Distortions in shape and streak artifacts in the limited-view FDK reconstruction were greatly reduced when using the real mask with the SCoLD reconstruction and the image quality was highly improved demonstrating the feasibility of the proposal.Grado en Ingeniería Biomédica (Plan 2010

Registration-based multi-orientation tomography

We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and exp

Image-Guided Interventions Using Cone-Beam CT: Improving Image Quality with Motion Compensation and Task-Based Modeling

Cone-beam CT (CBCT) is an increasingly important modality for intraoperative 3D imaging in interventional radiology (IR). However, CBCT exhibits several factors that diminish image quality — notably, the major challenges of patient motion and detectability of low-contrast structures — which motivate the work undertaken in this thesis. A 3D–2D registration method is presented to compensate for rigid patient motion. The method is fiducial-free, works naturally within standard clinical workflow, and is applicable to image-guided interventions in locally rigid anatomy, such as the head and pelvis. A second method is presented to address the challenge of deformable motion, presenting a 3D autofocus concept that is purely image-based and does not require additional fiducials, tracking hardware, or prior images. The proposed method is intended to improve interventional CBCT in scenarios where patient motion may not be sufficiently managed by immobilization and breath-hold, such as the prostate, liver, and lungs. Furthermore, the work aims to improve the detectability of low-contrast structures by computing source–detector trajectories that are optimal to a particular imaging task. The approach is applicable to CBCT systems with the capability for general source–detector positioning, as with a robotic C-arm. A “task-driven” analytical framework is introduced, various objective functions and optimization methods are described, and the method is investigated via simulation and phantom experiments and translated to task-driven source–detector trajectories on a clinical robotic C-arm to demonstrate the potential for improved image quality in intraoperative CBCT. Overall, the work demonstrates how novel optimization-based imaging techniques can address major challenges to CBCT image quality

IMPROVED IMAGE QUALITY IN CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED INTERVENTIONS

In the past few decades, cone-beam computed tomography (CBCT) emerged as a rapidly developing imaging modality that provides single rotation 3D volumetric reconstruction with sub-millimeter spatial resolution. Compared to the conventional multi-detector CT (MDCT), CBCT exhibited a number of characteristics that are well suited to applications in image-guided interventions, including improved mechanical simplicity, higher portability, and lower cost. Although the current generation of CBCT has shown strong promise for high-resolution and high-contrast imaging (e.g., visualization of bone structures and surgical instrumentation), it is often believed that CBCT yields inferior contrast resolution compared to MDCT and is not suitable for soft-tissue imaging. Aiming at expanding the utility of CBCT in image-guided interventions, this dissertation concerns the development of advanced imaging systems and algorithms to tackle the challenges of soft-tissue contrast resolution. The presented material includes work encompassing: (i) a comprehensive simulation platform to generate realistic CBCT projections (e.g., as training data for deep learning approaches); (ii) a new projection domain statistical noise model to improve the noise-resolution tradeoff in model-based iterative reconstruction (MBIR); (iii) a novel method to avoid CBCT metal artifacts by optimization of the source-detector orbit; (iv) an integrated software pipeline to correct various forms of CBCT artifacts (i.e., lag, glare, scatter, beam hardening, patient motion, and truncation); (v) a new 3D reconstruction method that only reconstructs the difference image from the image prior for use in CBCT neuro-angiography; and (vi) a novel method for 3D image reconstruction (DL-Recon) that combines deep learning (DL)-based image synthesis network with physics-based models based on Bayesian estimation of the statical uncertainty of the neural network. Specific clinical challenges were investigated in monitoring patients in the neurological critical care unit (NCCU) and advancing intraoperative soft-tissue imaging capability in image-guided spinal and intracranial neurosurgery. The results show that the methods proposed in this work substantially improved soft-tissue contrast in CBCT. The thesis demonstrates that advanced imaging approaches based on accurate system models, novel artifact reduction methods, and emerging 3D image reconstruction algorithms can effectively tackle current challenges in soft-tissue contrast resolution and expand the application of CBCT in image-guided interventions

Focal Spot, Summer/Fall 2004

https://digitalcommons.wustl.edu/focal_spot_archives/1097/thumbnail.jp

Focal Spot, Spring/Summer 1985

https://digitalcommons.wustl.edu/focal_spot_archives/1040/thumbnail.jp

Augmented Reality and Artificial Intelligence in Image-Guided and Robot-Assisted Interventions

In minimally invasive orthopedic procedures, the surgeon places wires, screws, and surgical implants through the muscles and bony structures under image guidance. These interventions require alignment of the pre- and intra-operative patient data, the intra-operative scanner, surgical instruments, and the patient. Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. State of the art approaches often support the surgeon by using external navigation systems or ill-conditioned image-based registration methods that both have certain drawbacks. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. Consequently, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. This dissertation investigates the applications of AR, artificial intelligence, and robotics in interventional medicine. Our solutions were applied in a broad spectrum of problems for various tasks, namely improving imaging and acquisition, image computing and analytics for registration and image understanding, and enhancing the interventional visualization. The benefits of these approaches were also discovered in robot-assisted interventions. We revealed how exemplary workflows are redefined via AR by taking full advantage of head-mounted displays when entirely co-registered with the imaging systems and the environment at all times. The proposed AR landscape is enabled by co-localizing the users and the imaging devices via the operating room environment and exploiting all involved frustums to move spatial information between different bodies. The system's awareness of the geometric and physical characteristics of X-ray imaging allows the exploration of different human-machine interfaces. We also leveraged the principles governing image formation and combined it with deep learning and RGBD sensing to fuse images and reconstruct interventional data. We hope that our holistic approaches towards improving the interface of surgery and enhancing the usability of interventional imaging, not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications

Patient-Specific Polyvinyl Alcohol Phantoms for Applications in Minimally Invasive Surgery

In biomedical engineering, phantoms are physical models of known geometric and material composition that are used to replicate biological tissues. Phantoms are vital tools in the testing and development of novel minimally invasive devices, as they can simulate the conditions in which devices will be used. Clinically, phantoms are also highly useful as training tools for minimally invasive procedures, such as those performed in regional anaesthesia, and for patient-specific surgical planning. Despite their widespread utility, there are many limitations with current phantoms and their fabrication methods. Commercial phantoms are often prohibitively expensive and may not be compatible with certain imaging modalities, such as ultrasound. Much of the phantom literature is complicated or hard to follow, making it difficult for researchers to produce their own models and it is highly challenging to create anatomically realistic phantoms that replicate real patient pathologies. Therefore, the aim of this work is to address some of the challenges with current phantoms. Novel fabrication methods and frameworks are presented to enable the creation of phantoms that are suitable for use in both the development of novel devices and as clinical training tools, for applications in minimally invasive surgery. This includes regional anaesthesia, brain tumour resection, and percutaneous coronary interventions. In such procedures, imaging is of key importance, and the phantoms developed are demonstrated to be compatible across a range of modalities, including ultrasound, computed tomography, MRI, and photoacoustic imaging

Department of Radiology-Annual Executive Summary Report-July 1, 1989 to June 30, 1990

Department of Radiology Chairman, Vice Chairman 1 Divisions and Directors 1 Committees and Chairmen 1 Department Full Time Faculty 2 Faculty with Secondary Appointments 3 Adjunct Faculty 3 Radiology Residents and Fellows 4 Department Organization Charts 5 State of the Department 7 Teaching Programs Introduction 17 A. Teaching Programs for Medical Students 17 B. Residency Program 18 C. Training Programs for Fellows 19 D. Continuing Medical Education (CME) Programs . 20 Radiology Grand Rounds 21 Radiology Research Conferences 23 Publications Publications 25 Abstracts 34 Presentations Scientific Presentations 43 Exhibits and Poster Presentations 66 Honors, Editorial Activities, Service for National or Regional Radiology Organizations 69 Appendix A: Funded Researc