Machine-Learning-Based Multi-Modal Force Estimation for Steerable Ablation Catheters.

Catheter-based cardiac ablation is a minimally invasive procedure for treating atrial fibrillation (AF). Electrophysiologists perform the procedure under image guidance during which the contact force between the heart tissue and the catheter tip determines the quality of lesions created. This paper describes a novel multi-modal contact force estimator based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

Separable Tendon-Driven Robotic Manipulator with a Long, Flexible, Passive Proximal Section.

This work tackles practical issues which arise when using a tendon-driven robotic manipulator (TDRM) with a long, flexible, passive proximal section in medical applications. Tendon-driven devices are preferred in medicine for their improved outcomes via minimally invasive procedures, but TDRMs come with unique challenges such as sterilization and reuse, simultaneous control of tendons, hysteresis in the tendon-sheath mechanism, and unmodeled effects of the proximal section shape. A separable TDRM which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable.

Inner Ear Breaches from Vestibular Schwannoma Surgery: Revisiting the Incidence of Otologic Injury from Retrosigmoid and Middle Cranial Fossa Approaches.

Objective: To assess the rate of iatrogenic injury to the inner ear in vestibular schwannoma resections.

Study Design: Retrospective case review.

Setting: Multiple academic tertiary care hospitals.

Iatrogenic inner ear dehiscence associated with lateral skull base surgery: a systematic analysis of drilling injuries and their causal factors.

Purpose: Drilling injuries of the inner ear are an underreported complication of lateral skull base (LSB) surgery. Inner ear breaches can cause hearing loss, vestibular dysfunction, and third window phenomenon. This study aims to elucidate primary factors causing iatrogenic inner ear dehiscences (IED) in 9 patients who presented to a tertiary care center with postoperative symptoms of IED following LSB surgery for vestibular schwannoma, endolymphatic sac tumor, Meniere's disease, paraganglioma jugulare, and vagal schwannoma.

EMDQ-SLAM: Real-time High-resolution Reconstruction of Soft Tissue Surface from Stereo Laparoscopy Videos.

We propose a novel stereo laparoscopy video-based non-rigid SLAM method called EMDQ-SLAM, which can incrementally reconstruct thee-dimensional (3D) models of soft tissue surfaces in real-time and preserve high-resolution color textures. EMDQ-SLAM uses the expectation maximization and dual quaternion (EMDQ) algorithm combined with SURF features to track the camera motion and estimate tissue deformation between video frames. To overcome the problem of accumulative errors over time, we have integrated a g2o-based graph optimization method that combines the EMDQ mismatch removal and as-rigid-as-possible (ARAP) smoothing methods.

EMDQ: Removal of Image Feature Mismatches in Real-Time.

This paper proposes a novel method for removing image feature mismatches in real-time that can handle both rigid and smooth deforming environments. Image distortion, parallax and object deformation may cause the pixel coordinates of feature matches to have non-rigid deformations, which cannot be represented using a single analytical rigid transformation. To solve this problem, we propose an algorithm based on the re-weighting and 1-point RANSAC strategy (R1P-RNSC), which operates under the assumption that a non-rigid deformation can be approximately represented by multiple rigid transformations.

F3RNet: full-resolution residual registration network for deformable image registration.

Purpose: Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream high-to-low, low-to-high network structure and can achieve satisfactory overall registration results.

A novel dual-network architecture for mixed-supervised medical image segmentation.

In medical image segmentation tasks, deep learning-based models usually require densely and precisely annotated datasets to train, which are time-consuming and expensive to prepare. One possible solution is to train with the mixed-supervised dataset, where only a part of data is densely annotated with segmentation map and the rest is annotated with some weak form, such as bounding box. In this paper, we propose a novel network architecture called Mixed-Supervised Dual-Network (MSDN), which consists of two separate networks for the segmentation and detection tasks respectively, and a series of connection modules between the layers of the two networks.

Real-Time Nonrigid Mosaicking of Laparoscopy Images.

The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time.

Mixed-Supervised Dual-Network for Medical Image Segmentation.

Deep learning based medical image segmentation models usually require large datasets with high-quality dense segmentations to train, which are very time-consuming and expensive to prepare. One way to tackle this difficulty is using the mixed-supervised learning framework, where only a part of data is densely annotated with segmentation label and the rest is weakly labeled with bounding boxes. The model is trained jointly in a multi-task learning setting.

MR Imaging of the Extracranial Facial Nerve with the CISS Sequence.

Background And Purpose: MR imaging is not routinely used to image the extracranial facial nerve. The purpose of this study was to determine the extent to which this nerve can be visualized with a CISS sequence and to determine the feasibility of using that sequence for locating the nerve relative to tumor.

Materials And Methods: Thirty-two facial nerves in 16 healthy subjects and 4 facial nerves in 4 subjects with parotid gland tumors were imaged with an axial CISS sequence protocol that included 0.

A Novel Mixed Reality Navigation System for Laparoscopy Surgery.

Objective: To design and validate a novel mixed reality head-mounted display for intraoperative surgical navigation.

Design: A mixed reality navigation for laparoscopic surgery (MRNLS) system using a head mounted display (HMD) was developed to integrate the displays from a laparoscope, navigation system, and diagnostic imaging to provide context-specific information to the surgeon. Further, an immersive auditory feedback was also provided to the user.

MR Imaging of the Facial Nerve through the Temporal Bone at 3T with a Noncontrast Ultrashort Echo Time Sequence.

The pointwise encoding time reduction with radial acquisition (PETRA) ultrashort echo time MR imaging sequence at 3T enables visualization of the facial nerve from the brain stem, through the temporal bone, to the stylomastoid foramen without intravenous contrast. Use of the PETRA sequence, or other ultrashort echo time sequences, should be considered in the MR imaging evaluation of certain skull base tumors and perhaps other facial nerve and temporal bone pathologies.

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

Purpose: In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects.

Methods: In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme.

Real-time probe tracking using EM-optical sensor for MRI-guided cryoablation.

Background: A method of real-time, accurate probe tracking at the entrance of the MRI bore is developed, which, fused with pre-procedural MR images, will enable clinicians to perform cryoablation efficiently in a large workspace with image guidance.

Methods: Electromagnetic (EM) tracking coupled with optical tracking is used to track the probe. EM tracking is achieved with an MRI-safe EM sensor working under the scanner's magnetic field to compensate the line-of-sight issue of optical tracking.

Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity.

Objective: The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type.

Materials And Methods: The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups.

Jugulodigastric lymph node size by age on CT in an adult cancer-free population.

Purpose: Evaluate jugulodigastric node size in cancer-free patients.

Methods: Retrospective review of 234 neck CT angiograms was performed. Known neoplasm, inflammation or poor imaging were excluded.

Computer-aided heterogeneity analysis in breast MR imaging assessment of ductal carcinoma in situ: Correlating histologic grade and receptor status.

Purpose: To identify breast MR imaging biomarkers to predict histologic grade and receptor status of ductal carcinoma in situ (DCIS).

Materials And Methods: Informed consent was waived in this Health Insurance Portability and Accountability Act-compliant Institutional Review Board-approved study. Case inclusion was conducted from 7332 consecutive breast MR studies from January 1, 2009, to December 31, 2012.

Feasibility of Intraoperative Breast MRI and the Role of Prone Versus Supine Positioning in Surgical Planning for Breast-Conserving Surgery.

We assessed the feasibility of supine intraoperative MRI (iMRI) during breast-conserving surgery (BCS), enrolling 15 patients in our phase I trial between 2012 and 2014. Patients received diagnostic prone MRI, BCS, pre-excisional supine iMRI, and postexcisional supine iMRI. Feasibility was assessed based on safety, sterility, duration, and image-quality.

Intraoperative Supine Breast MR Imaging to Quantify Tumor Deformation and Detection of Residual Breast Cancer: Preliminary Results.

Purpose To use intraoperative supine magnetic resonance (MR) imaging to quantify breast tumor deformation and displacement secondary to the change in patient positioning from imaging (prone) to surgery (supine) and to evaluate residual tumor immediately after breast-conserving surgery (BCS). Materials and Methods Fifteen women gave informed written consent to participate in this prospective HIPAA-compliant, institutional review board-approved study between April 2012 and November 2014. Twelve patients underwent lumpectomy and postsurgical intraoperative supine MR imaging.

Image-guided video assisted thoracoscopic surgery (iVATS) - phase I-II clinical trial.

Purpose: To facilitate localization and resection of small lung nodules, we developed a prospective clinical trial (ClinicalTrials.gov number NCT01847209) for a novel surgical approach which combines placement of fiducials using intra-operative C-arm computed tomography (CT) guidance with standard thoracoscopic resection technique using image-guided video-assisted thoracoscopic surgery (iVATS).

Methods: Pretrial training was performed in a porcine model using C-arm CT and needle guidance software.

Design and development of a mobile image overlay system for needle interventions.

Previously, a static and adjustable image overlay systems were proposed for aiding needle interventions. The system was either fixed to a scanner or mounted over a large articulated counterbalanced arm. Certain drawbacks associated with these systems limited the clinical translation.

Multimodal imaging for improved diagnosis and treatment of cancers.

The authors review methods for image-guided diagnosis and therapy that increase precision in the detection, characterization, and localization of many forms of cancer to achieve optimal target definition and complete resection or ablation. A new model of translational, clinical, image-guided therapy research is presented, and the Advanced Multimodality Image-Guided Operating (AMIGO) suite is described. AMIGO was conceived and designed to allow for the full integration of imaging in cancer diagnosis and treatment.