Risk Estimation for ICU Patients with Personalized Anomaly-Encoded Bedside Patient Data.

We propose a novel framework to estimate intensive care unit patients' health risk continuously with anomaly-encoded patient data. This framework consists of two modules. In the first module, we use Gaussian process models to learn change trend and day-night circulation in temporal patient data and annotate abnormal data.

Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients.

Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients.

Accelerated multi-snapshot free-breathing mapping based on the dual refocusing echo acquisition mode technique (DREAM): An alternative to measure RF nonuniformity for cardiac MRI.

Background: Field inhomogeneities in MRI caused by interactions between the radiofrequency field and the patient anatomy can lead to artifacts and contrast variations, consequently degrading the overall image quality and thereby compromising diagnostic value of the images.

Purpose: To develop an efficient free-breathing and motion-robust mapping method that allows for the investigation of spatial homogeneity of the transmitted radiofrequency field in the myocardium at 3.0T.

Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning.

Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the through-plane direction, leading to poor image quality in that dimension. To overcome this problem, super-resolution (SR) techniques have been proposed to reconstruct a single isotropic 3D volume from multiple anisotropic acquisitions.

A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T mapping MRI.

Purpose: T mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm.

Methods: After nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps.

Impact of denoising on precision and accuracy of saturation-recovery-based myocardial T mapping.

Purpose: To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T mapping.

Materials And Methods: This study introduces a fast and robust denoising method developed for magnetic resonance T mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T -weighted images.

Effects of visual force feedback on robot-assisted surgical task performance.

Objective: Direct haptic (force or tactile) feedback is negligible in current surgical robotic systems. The relevance of haptic feedback in robot-assisted performances of surgical tasks is controversial. We studied the effects of visual force feedback, a haptic feedback surrogate, on tying surgical knots with fine sutures similar to those used in cardiovascular surgery.

Dynamic augmented reality for sensory substitution in robot-assisted surgical systems.

Teleoperated robot-assisted surgical systems provide surgeons with improved precision, dexterity, and visualization over traditional minimally invasive surgery. The addition of haptic (force and/or tactile) feedback has been proposed as a way to further enhance the performance of these systems. However, due to limitations in sensing and control technologies, implementing direct haptic feedback to the surgeon's hands remains impractical for clinical application.

DaVinci canvas: a telerobotic surgical system with integrated, robot-assisted, laparoscopic ultrasound capability.

We present daVinci Canvas: a telerobotic surgical system with integrated robot-assisted laparoscopic ultrasound capability. DaVinci Canvas consists of the integration of a rigid laparoscopic ultrasound probe with the daVinci robot, video tracking of ultrasound probe motions, endoscope and ultrasound calibration and registration, autonomous robot motions, and the display of registered 2D and 3D ultrasound images. Although we used laparoscopic liver cancer surgery as a focusing application, our broader aim was the development of a versatile system that would be useful for many procedures.

Scale-invariant registration of monocular endoscopic images to CT-scans for sinus surgery.

In this paper, we present a novel method for intra-operative registration directly from monocular endoscopic images. This technique has the potential to provide a more accurate surface registration at the surgical site than existing methods. It can operate autonomously from as few as two images and can be particularly useful in revision cases where surgical landmarks may be absent.