Accelerated multi-snapshot free-breathing B1+ 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 B1+ mapping method that allows for the investigation of spatial homogeneity of the transmitted radiofrequency field in the myocardium at 3.0T. Three joint approaches are used to adapt the dual refocusing echo acquisition mode (DREAM) sequence for cardiac applications: (1) electrocardiograph triggering; (2) a multi-snapshot undersampling scheme, which relies on the Golden Ratio, to accelerate the acquisition; and (3) motion-compensation based on low-resolution images acquired in each snapshot. STUDY TYPE: Prospective. PHANTOM/SUBJECTS: Eurospin II T05 system, torso phantom, and five healthy volunteers. FIELD STRENGTH/SEQUENCE: 3.0T/DREAM. ASSESSMENT: The proposed method was compared with the Bloch-Siegert shift (BSS) method and validated against the standard DREAM sequence. Cardiac B1+ maps were obtained in free-breathing and breath-hold as a proof of concept of the in vivo performance of the proposed method. STATISTICAL TESTS: Mean and standard deviation (SD) values were analyzed for six standard regions of interest within the myocardium. Repeatability was assessed in terms of SD and coefficient of variation. RESULTS: Phantom results indicated low deviation from the BSS method (mean difference = 3%). Equivalent B1+ distributions for free-breathing and breath-hold in vivo experiments demonstrated the motion robustness of this method with good repeatability (SD < 0.05). The amount of B1+ variations was found to be 26% over the myocardium within a short axis slice. DATA CONCLUSION: The feasibility of a cardiac B1+ mapping method with high spatial resolution in a reduced scan time per trigger was demonstrated. The free-breathing characteristic could be beneficial to determine shim components for multi-channel systems, currently limited to two for a single breath-hold. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:499-507.

Impact of denoising on precision and accuracy of saturation-recovery-based myocardial T1 mapping.

PURPOSE: To evaluate the impact of a novel postprocessing denoising technique on accuracy and precision in myocardial T1 mapping. MATERIALS AND METHODS: This study introduces a fast and robust denoising method developed for magnetic resonance T1 mapping. The technique imposes edge-preserving regularity and exploits the co-occurence of spatial gradients in the acquired T1 -weighted images. The proposed approach was assessed in simulations, ex vivo data and in vivo imaging on a cohort of 16 healthy volunteers (12 males, average age 39 ± 8 years, 62 ± 9 bpm) both in pre- and postcontrast injection. The method was evaluated in myocardial T1 mapping at 3T with a saturation-recovery technique that is accurate but sensitive to noise. ROIs in the myocardium and left-ventricle blood pool were analyzed by an experienced reader. Mean T1 values and standard deviation were extracted and compared in all studies. RESULTS: Simulations on synthetic phantom showed signal-to-noise ratio and sharpness improvement with the proposed method in comparison with conventional denoising. In vivo results demonstrated that our method preserves accuracy, as no difference in mean T1 values was observed in the myocardium (precontrast: 1433/1426 msec, 95%CI: [-40.7, 55.9], p = 0.75, postcontrast: 766/759 msec, 95%CI: [-60.7, 77.2], p = 0.8). Meanwhile, precision was improved with standard deviations of T1 values being significantly decreased (precontrast: 223/151 msec, 95%CI: [27.3, 116.5], p = 0.003, postcontrast: 176/135 msec, 95%CI: [5.5, 77.1], p = 0.03). CONCLUSION: The proposed denoising method preserves accuracy and improves precision in myocardial T1 mapping, with the potential to offer better map visualization and analysis. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1377-1388.

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. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

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. Such models provide dynamically adaptable bedside patient monitoring instead of conventional threshold-based monitoring. In the second module, we use the abnormal data together with the learned Gaussian models to estimate patients' risk level by predicting their in-hospital mortality and remaining length of stay in ICU ward. We show that prediction models with anomaly-encoded data have better performance than those with raw patient measurements, and they are comparable with state-of-art prediction models.

A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T1 mapping MRI.

PURPOSE: T1 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. In addition, a region-based initialization is proposed to improve the initial guess of the T1 value. The algorithm was validated with cardiac T1 mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T1 relaxation with 2 and 3 parameters were tested. RESULTS: The vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of T1 values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T1. CONCLUSIONS: We have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T1 mapping with SR sequences.

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. Previously, local regularization techniques such as total variation have been applied to limit noise amplification while preserving sharp edges and small features in the images. In this paper, inspired by the recent progress in patch-based reconstruction, we propose a novel isotropic 3D reconstruction scheme that integrates non-local and self-similarity information from 3D patch neighborhoods. By grouping 3D patches with similar structures, we enforce the natural sparsity of MR images, which can be expressed by a low-rank structure, leading to robust image reconstruction with high signal-to-noise ratio efficiency. An Augmented Lagrangian formulation of the problem is proposed to efficiently decompose the optimization into a low-rank volume denoising and a SR reconstruction. Experimental results in simulations, brain imaging and clinical cardiac MRI, demonstrate that the proposed joint SR and self-similarity learning framework outperforms current state-of-the-art methods. The proposed reconstruction of isotropic 3D volumes may be particularly useful for cardiac applications, such as myocardial infarction scar assessment by late gadolinium enhancement MRI.

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. A by-product of video registration is an estimate of the local surface structure of the anatomy, thus providing the opportunity to dynamically update anatomical models as the surgery progresses. Our approach is based on a previously presented method [Burschka, D., Hager, G.D., 2004. V-GPS (SLAM):--Vision-based inertial system for mobile robots. In: Proceedings of ICRA, 409-415] for reconstruction of a scaled 3D model of the environment from unknown camera motion. We use this scaled reconstruction as input to a PCA-based algorithm that registers the reconstructed data to the CT data and recovers the scale and pose parameters of the camera in the coordinate frame of the CT scan. The result is used in an ICP registration step to refine the registration estimates. The details of our approach and the experimental results with a phantom of a human skull and a head of a pig cadaver are presented in this paper.

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. METHODS: By using a modified da Vinci robotic system (Intuitive Surgical, Inc, Sunnyvale, Calif) equipped with force-sensing instrument tips and real-time visual force feedback overlays in the console image, 10 surgeons each tied 10 knots with and 10 knots without visual force feedback. Four surgeons had significant prior da Vinci experience, and the remaining 6 surgeons did not. Performance parameters, including suture breakage and secure knots, peak and standard deviation of applied forces, and completion times using 5-0 silk sutures, were recorded. Chi-square and Student t test analyses determined the differences between groups. RESULTS: Among surgeon subjects with robotic experience, no differences in measured performance parameters were found between robot-assisted knot ties executed with and without visual force feedback. Among surgeons without robotic experience, however, visual force feedback was associated with lower suture breakage rates, peak applied forces, and standard deviations of applied forces. Visual force feedback did not impart differences in knot completion times or loose knots for either surgeon group. CONCLUSIONS: Visual force feedback resulted in reduced suture breakage, lower forces, and decreased force inconsistencies among novice robotic surgeons, although elapsed time and knot quality were unaffected. In contrast, visual force feedback did not affect these metrics among surgeons experienced with the da Vinci system. These results suggest that visual force feedback primarily benefits novice robot-assisted surgeons, with diminishing benefits among experienced surgeons.

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. A new, intuitive augmented reality system for presentation of force information through sensory substitution has been developed and evaluated. The augmented reality system consists of force-sensing robotic instruments, a kinematic tool tracker, and a graphic display that overlays a visual representation of force levels on top of the moving instrument tips. The system is integrated with the da Vinci Surgical System (Intuitive Surgical, Inc.) and tested by several users in a phantom knot tying task. The augmented reality system decreases the number of broken sutures, decreases the number of loose knots, and results in more consistent application of forces.

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.