Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography

Adaptive filtering of temporally varying X-ray image sequences acquired during endovascular interventions can improve the visual tracking of catheters by radiologists. Existing techniques blur the important parts of image sequences, such as catheter tips, anatomical structures and organs; and they may introduce trailing artifacts. To address this concern, an adaptive filtering process is presented to apply temporal filtering in regions without motion and spatial filtering in regions with motion. The adaptive filtering process is a multi-step procedure. First a normalized motion mask that describes the differences between two successive frames is generated. Secondly each frame is spatially filtered using the specific motion mask to specify different types of filtering in each region. Third an IIR filter is then used to combine the spatially filtered image with the previous output image; the motion mask thus serves as a weighted input mask to determine how much spatial and temporal filtering should be applied. This method results in improving both the stationary and moving fields. The visibility of static anatomical structures and organs increases, while the motion of the catheter tip and motion of anatomical structures and organs remain unblurred and visible during interventional procedures

Real-Time Quantum Noise Suppression In Very Low-Dose Fluoroscopy

Fluoroscopy provides real-time X-ray screening of patient's organs and of various radiopaque objects, which make it an invaluable tool for many interventional procedures. For this reason, the number of fluoroscopy screenings has experienced a consistent growth in the last decades. However, this trend has raised many concerns about the increase in X-ray exposure, as even low-dose procedures turned out to be not as safe as they were considered, thus demanding a rigorous monitoring of the X-ray dose delivered to the patients and to the exposed medical staff. In this context, the use of very low-dose protocols would be extremely beneficial. Nonetheless, this would result in very noisy images, which need to be suitably denoised in real-time to support interventional procedures. Simple smoothing filters tend to produce blurring effects that undermines the visibility of object boundaries, which is essential for the human eye to understand the imaged scene. Therefore, some denoising strategies embed noise statistics-based criteria to improve their denoising performances. This dissertation focuses on the Noise Variance Conditioned Average (NVCA) algorithm, which takes advantage of the a priori knowledge of quantum noise statistics to perform noise reduction while preserving the edges and has already outperformed many state-of-the-art methods in the denoising of images corrupted by quantum noise, while also being suitable for real-time hardware implementation. Different issues are addressed that currently limit the actual use of very low-dose protocols in clinical practice, e.g. the evaluation of actual performances of denoising algorithms in very low-dose conditions, the optimization of tuning parameters to obtain the best denoising performances, the design of an index to properly measure the quality of X-ray images, and the assessment of an a priori noise characterization approach to account for time-varying noise statistics due to changes of X-ray tube settings. An improved NVCA algorithm is also presented, along with its real-time hardware implementation on a Field Programmable Gate Array (FPGA). The novel algorithm provides more efficient noise reduction performances also for low-contrast moving objects, thus relaxing the trade-off between noise reduction and edge preservation, while providing a further reduction of hardware complexity, which allows for low usage of logic resources also on small FPGA platforms. The results presented in this dissertation provide the means for future studies aimed at embedding the NVCA algorithm in commercial fluoroscopic devices to accomplish real-time denoising of very low-dose X-ray images, which would foster their actual use in clinical practice

Radiation Safety during Interventional Procedures:

__Abstract__ In interventional cardiology revascularization procedures are on the increase and as such the x-ray radiation dose, used per procedure. The recent introduction of new technologies, e.g. the drug eluting stent, has led to a treatment shift. An increasing number of patients with multi-vessel disease, smaller coronary arteries and diabetes, who previously underwent surgery, now undergo a percutaneous coronary intervention. The worldwide increase in percutaneous coronary interventions is described in Chapter 1. While a coronary intervention can be lifesaving, there is no limit to either the investigation or the radiation time. Therefore this result in an increase in the x-ray radiation dose to the patient, and a higher scattered radiation dose to the operator. Thus increasing both the possibility of skin damage to the patient and consequences to the operator

Exploiting Temporal Image Information in Minimally Invasive Surgery

Minimally invasive procedures rely on medical imaging instead of the surgeons direct vision. While preoperative images can be used for surgical planning and navigation, once the surgeon arrives at the target site real-time intraoperative imaging is needed. However, acquiring and interpreting these images can be challenging and much of the rich temporal information present in these images is not visible. The goal of this thesis is to improve image guidance for minimally invasive surgery in two main areas. First, by showing how high-quality ultrasound video can be obtained by integrating an ultrasound transducer directly into delivery devices for beating heart valve surgery. Secondly, by extracting hidden temporal information through video processing methods to help the surgeon localize important anatomical structures. Prototypes of delivery tools, with integrated ultrasound imaging, were developed for both transcatheter aortic valve implantation and mitral valve repair. These tools provided an on-site view that shows the tool-tissue interactions during valve repair. Additionally, augmented reality environments were used to add more anatomical context that aids in navigation and in interpreting the on-site video. Other procedures can be improved by extracting hidden temporal information from the intraoperative video. In ultrasound guided epidural injections, dural pulsation provides a cue in finding a clear trajectory to the epidural space. By processing the video using extended Kalman filtering, subtle pulsations were automatically detected and visualized in real-time. A statistical framework for analyzing periodicity was developed based on dynamic linear modelling. In addition to detecting dural pulsation in lumbar spine ultrasound, this approach was used to image tissue perfusion in natural video and generate ventilation maps from free-breathing magnetic resonance imaging. A second statistical method, based on spectral analysis of pixel intensity values, allowed blood flow to be detected directly from high-frequency B-mode ultrasound video. Finally, pulsatile cues in endoscopic video were enhanced through Eulerian video magnification to help localize critical vasculature. This approach shows particular promise in identifying the basilar artery in endoscopic third ventriculostomy and the prostatic artery in nerve-sparing prostatectomy. A real-time implementation was developed which processed full-resolution stereoscopic video on the da Vinci Surgical System

ADVANCED INTRAVASCULAR MAGNETIC RESONANCE IMAGING WITH INTERACTION

Intravascular (IV) Magnetic Resonance Imaging (MRI) is a specialized class of interventional MRI (iMRI) techniques that acquire MRI images through blood vessels to guide, identify and/or treat pathologies inside the human body which are otherwise difficult to locate and treat precisely. Here, interactions based on real-time computations and feedback are explored to improve the accuracy and efficiency of IVMRI procedures. First, an IV MRI-guided high-intensity focused ultrasound (HIFU) ablation method is developed for targeting perivascular pathology with minimal injury to the vessel wall. To take advantage of real-time feedback, a software interface is developed for monitoring thermal dose with real-time MRI thermometry, and an MRI-guided ablation protocol developed and tested on muscle and liver tissue ex vivo. It is shown that, with cumulative thermal dose monitored with MRI thermometry, lesion location and dimensions can be estimated consistently, and desirable thermal lesions can be achieved in animals in vivo. Second, to achieve fully interactive IV MRI, high-resolution real-time 10 frames-per-second (fps) MRI endoscopy is developed as an advance over prior methods of MRI endoscopy. Intravascular transmit-receive MRI endoscopes are fabricated for highly under-sampled radial-projection MRI in a clinical 3Tesla MRI scanner. Iterative nonlinear reconstruction is accelerated using graphics processor units (GPU) to achieve true real-time endoscopy visualization at the scanner. The results of high-speed MRI endoscopy at 6-10 fps are consistent with fully-sampled MRI endoscopy and histology, with feasibility demonstrated in vivo in a large animal model. Last, a general framework for automatic imaging contrast tuning over MRI protocol parameters is explored. The framework reveals typical signal patterns over different protocol parameters from calibration imaging data and applies this knowledge to design efficient acquisition strategies and predicts contrasts under unacquired protocols. An external computer in real-time communication with the MRI console is utilized for online processing and controlling MRI acquisitions. This workflow enables machine learning for optimizing acquisition strategies in general, and provides a foundation for efficiently tuning MRI protocol parameters to perform interventional MRI in the highly varying and interactive environments commonly in play. This work is loosely inspired by prior research on extremely accelerated MRI relaxometry using the minimal-acquisition linear algebraic modeling (SLAM) method

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Differential geometry methods for biomedical image processing : from segmentation to 2D/3D registration

This thesis establishes a biomedical image analysis framework for the advanced visualization of biological structures. It consists of two important parts: 1) the segmentation of some structures of interest in 3D medical scans, and 2) the registration of patient-specific 3D models with 2D interventional images. Segmenting biological structures results in 3D computational models that are simple to visualize and that can be analyzed quantitatively. Registering a 3D model with interventional images permits to position the 3D model within the physical world. By combining the information from a 3D model and 2D interventional images, the proposed framework can improve the guidance of surgical intervention by reducing the ambiguities inherent to the interpretation of 2D images. Two specific segmentation problems are considered: 1) the segmentation of large structures with low frequency intensity nonuniformity, and 2) the detection of fine curvilinear structures. First, we directed our attention toward the segmentation of relatively large structures with low frequency intensity nonuniformity. Such structures are important in medical imaging since they are commonly encountered in MRI. Also, the nonuniform diffusion of the contrast agent in some other modalities, such as CTA, leads to structures of nonuniform appearance. A level-set method that uses a local-linear region model is defined, and applied to the challenging problem of segmenting brain tissues in MRI. The unique characteristics of the proposed method permit to account for important image nonuniformity implicitly. To the best of our knowledge, this is the first time a region-based level-set model has been used to perform the segmentation of real world MRI brain scans with convincing results. The second segmentation problem considered is the detection of fine curvilinear structures in 3D medical images. Detecting those structures is crucial since they can represent veins, arteries, bronchi or other important tissues. Unfortunately, most currently available curvilinear structure detection filters incur significant signal lost at bifurcations of two structures. This peculiarity limits the performance of all subsequent processes, whether it be understanding an angiography acquisition, computing an accurate tractography, or automatically classifying the image voxels. This thesis presents a new curvilinear structure detection filter that is robust to the presence of X- and Y-junctions. At the same time, it is conceptually simple and deterministic, and allows for an intuitive representation of the structure’s principal directions. Once a 3D computational model is available, it can be used to enhance surgical guidance. A 2D/3D non-rigid method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of the interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, which reduces the uncertainty inherent in 2D interventional images. A fully non-rigid registration model is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process. With a typical run time of less than 3 seconds, the algorithms are fast enough for interactive applications

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