Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging

The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI’s performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform

Cardiovascular and Thoracic Imaging: Trends, Perspectives and Prospects

Radiology is evolving at a fast pace, and the specific field of cardiovascular and thoracic imaging is no stranger to that trend. While it could, at first, seem unusual to gather these two specialties in a common Issue, the very fact that many of us are trained and exercise in both is more than a hint to the common grounds these fields are sharing. From the ever-increasing role of artificial intelligence in the reconstruction, segmentation, and analysis of images to the quest of functionality derived from anatomy, their interplay is big, and one innovation developed with the former in mind could prove useful for the latter. If the coronavirus disease 2019 (COVID-19) pandemic has shed light on the decisive diagnostic role of chest CT and, to a lesser extent, cardiac MR, one must not forget the major advances and extensive researches made possible in other areas by these techniques in the past years. With this Issue, we aim at encouraging and wish to bring to light state-of-the-art reviews, novel original researches, and ongoing discussions on the multiple aspects of cardiovascular and chest imaging

Intracardiac Ultrasound Guided Systems for Transcatheter Cardiac Interventions

Transcatheter cardiac interventions are characterized by their percutaneous nature, increased patient safety, and low hospitalization times. Transcatheter procedures involve two major stages: navigation towards the target site and the positioning of tools to deliver the therapy, during which the interventionalists face the challenge of visualizing the anatomy and the relative position of the tools such as a guidewire. Fluoroscopic and transesophageal ultrasound (TEE) imaging are the most used techniques in cardiac procedures; however, they possess the disadvantage of radiation exposure and suboptimal imaging. This work explores the potential of intracardiac ultrasound (ICE) within an image guidance system (IGS) to facilitate the two stages of cardiac interventions. First, a novel 2.5D side-firing, conical Foresight ICE probe (Conavi Medical Inc., Toronto) is characterized, calibrated, and tracked using an electromagnetic sensor. The results indicate an acceptable tracking accuracy within some limitations. Next, an IGS is developed for navigating the vessels without fluoroscopy. A forward-looking, tracked ICE probe is used to reconstruct the vessel on a phantom which mimics the ultrasound imaging of an animal vena cava. Deep learning methods are employed to segment the complex vessel geometry from ICE imaging for the first time. The ICE-reconstructed vessel showed a clinically acceptable range of accuracy. Finally, a guidance system was developed to facilitate the positioning of tools during a tricuspid valve repair. The designed system potentially facilitates the positioning of the TriClip at the coaptation gap by pre-mapping the corresponding site of regurgitation in 3D tracking space

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

imaged-based tip force estimation on steerable intracardiac catheters using learning-based methods

Minimally invasive surgery has turned into the most commonly used approach to treat cardiovascular diseases during the surgical procedure; it is hypothesized that the absence of haptic (tactile) feedback and force presented to surgeons is a restricting factor. The use of ablation catheters with the integrated sensor at the tip results in high cost and noise complications. In this thesis, two sensor-less methods are proposed to estimate the force at the intracardiac catheter’s tip. Force estimation at the catheter tip is of great importance because insufficient force in ablation treatment may result in incomplete treatment and excessive force leads to damaging the heart chamber. Besides, adding the sensor to intracardiac catheters adds complexity to their structures. This thesis is categorized into two sensor-less approaches: 1- Learning-Based Force Estimation for Intracardiac Ablation Catheters, 2- A Deep-Learning Force Estimator System for Intracardiac Catheters. The first proposed method estimates catheter-tissue contact force by learning the deflected shape of the catheter tip section image. A regression model is developed based on predictor variables of tip curvature coefficients and knob actuation. The learning-based approach achieved force predictions in close agreement with experimental contact force measurements. The second approach proposes a deep learning method to estimate the contact forces directly from the catheter’s image tip. A convolutional neural network extracts the catheter’s deflection through input images and translates them into the corresponding forces. The ResNet graph was implemented as the architecture of the proposed model to perform a regression. The model can estimate catheter-tissue contact force based on the input images without utilizing any feature extraction or pre-processing. Thus, it can estimate the force value regardless of the tip displacement and deflection shape. The evaluation results show that the proposed method can elicit a robust model from the specified data set and approximate the force with appropriate accuracy

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Efficient sampling strategies for x-ray micro computed tomography with an intensity-modulated beam

The term "cycloidal CT" refers to a family of efficient sampling strategies that can be applied to x-ray micro-computed tomography (CT) systems which operate with an intensity-modulated beam. Such a beam can be employed to provide access to a phase contrast channel and high spatial resolutions (a few um). Phase contrast can offer better image contrast of samples which have traditionally been "invisible” to x-rays due to their weak attenuation, and high resolutions help view crucial details in samples. Cycloidal sampling strategies provide images more quickly than the gold standard in the field ("dithering”). I conceived and compared four practical implementation strategies for cycloidal CT, three of which are "flyscans” (the sample moves continuously). Flyscans acquire images of similar resolution to dithering with no overheads, reducing acquisition time to exposure time. I also developed a "knife-edge” position tracking method which tracks subpixel motions of the sample stage. This information can be used to facilitate, automate, and improve the reconstruction of cycloidal data. I analysed the effects of different levels of dose on the signal-to-noise ratio (SNR) of an image acquired with cycloidal CT. The results show that cycloidal images yield the same SNR as dithered images with less dose, although a more extensive study is required. Finally, I explored the potential of using cycloidal CT for intraoperative specimen imaging and tissue engineering. My results are encouraging for tissue engineering; for intraoperative imaging, the cycloidal images did not show comparable resolution to the dithered images, although that is possibly linked to issues with the dataset. Overall, my work has provided a benchmark for the implementation and application of cycloidal CT for the first time. Besides a summary of my research, this thesis is meant to be a comprehensive guide for facilitating uptake of cycloidal CT within the scientific community and beyond