A system for endoscopic mechanically scanned localized proton MR and light-induced fluorescence emission spectroscopies

Radiolog

Manipulator-driven selection of semi-active MR-visible markers

Background: A method for the identification of semi-active fiducial magnetic resonance (MR) markers is presented based on selectively optically tuning and detuning them.Methods: Four inductively coupled solenoid coils with photoresistors were connected to light sources. A microcontroller timed the optical tuning/detuning of coils and image collection. The markers were tested on an MR manipulator linking the microcontroller to the manipulator control to visibly select the marker subset according to the actuated joint.Results: In closed-loop control, the average and maximum were 0.76 degrees +/- 0.41 degrees and 1.18 degrees errors for a rotational joint, and 0.87 mm +/- 0.26 mm and 1.13 mm for the prismatic joint.Conclusions: This technique is suitable for MR-compatible actuated devices that use semi-active MR-compatible markers.Radiolog

A Deep Learning Approach to Upscaling "Low-Quality" MR Images: An In Silico Comparison Study Based on the UNet Framework

MR scans of low-gamma X-nuclei, low-concentration metabolites, or standard imaging at very low field entail a challenging tradeoff between resolution, signal-to-noise, and acquisition duration. Deep learning (DL) techniques, such as UNets, can potentially be used to improve such "low-quality" (LQ) images. We investigate three UNets for upscaling LQ MRI: dense (DUNet), robust (RUNet), and anisotropic (AUNet). These were evaluated for two acquisition scenarios. In the same-subject High-Quality Complementary Priors (HQCP) scenario, an LQ and a high quality (HQ) image are collected and both LQ and HQ were inputs to the UNets. In the No Complementary Priors (NoCP) scenario, only the LQ images are collected and used as the sole input to the UNets. To address the lack of same-subject LQ and HQ images, we added data from the OASIS-1 database. The UNets were tested in upscaling 1/8, 1/4, and 1/2 undersampled images for both scenarios. As manifested by non-statically significant differences of matrices, also supported by subjective observation, the three UNets upscaled images equally well. This was in contrast to mixed effects statistics that clearly illustrated significant differences. Observations suggest that the detailed architecture of these UNets may not play a critical role. As expected, HQCP substantially improves upscaling with any of the UNets. The outcomes support the notion that DL methods may have merit as an integral part of integrated holistic approaches in advancing special MRI acquisitions; however, primary attention should be paid to the foundational step of such approaches, i.e., the actual data collected

A Platform Integrating Acquisition, Reconstruction, Visualization, and Manipulator Control Modules for MRI-Guided Interventions

This work presents a platform that integrates a customized MRI data acquisition scheme with reconstruction and three-dimensional (3D) visualization modules along with a module for controlling an MRI-compatible robotic device to facilitate the performance of robot-assisted, MRI-guided interventional procedures. Using dynamically-acquired MRI data, the computational framework of the platform generates and updates a 3D model representing the area of the procedure (AoP). To image structures of interest in the AoP that do not reside inside the same or parallel slices, the MRI acquisition scheme was modified to collect a multi-slice set of intraoblique to each other slices; which are termed composing slices. Moreover, this approach interleaves the collection of the composing slices so the same k-space segments of all slices are collected during similar time instances. This time matching of the k-space segments results in spatial matching of the imaged objects in the individual composing slices. The composing slices were used to generate and update the 3D model of the AoP. The MRI acquisition scheme was evaluated with computer simulations and experimental studies. Computer simulations demonstrated that k-space segmentation and time-matched interleaved acquisition of these segments provide spatial matching of the structures imaged with composing slices. Experimental studies used the platform to image the maneuvering of an MRI-compatible manipulator that carried tubing filled with MRI contrast agent. In vivo experimental studies to image the abdomen and contrast enhanced heart on free-breathing subjects without cardiac triggering demonstrated spatial matching of imaged anatomies in the composing planes. The described interventional MRI framework could assist in performing real-time MRI-guided interventions

Collaborative tracking for MRI-guided robotic intervention on the beating heart

Magnetic Resonance Imaging (MRI)-guided robotic interventions for aortic valve repair promise to dramatically reduce time and cost of operations when compared to endoscopically guided (EG) procedures. A challenging issue is real-time and robust tracking of anatomical landmark points. The interventional tool should be constantly adjusted via a closed feedback control loop to avoid harming these points while valve repair is taking place in the beating heart. A Bayesian network of particle filter trackers proves capable to produce real-time, yet robust behavior. The algorithm is extremely flexible and general--more sophisticated behaviors can be produced by simply increasing the cardinality of the tracking network. Experimental results on 16 MRI cine sequences highlight the promise of the method

A Holographic Augmented Reality Interface for Visualizing of MRI Data and Planning of Neurosurgical Procedures

The recent introduction of wireless head-mounted displays (HMD) promises to enhance 3D image visualization by immersing the user into 3D morphology. This work introduces a prototype holographic augmented reality (HAR) interface for the 3D visualization of magnetic resonance imaging (MRI) data for the purpose of planning neurosurgical procedures. The computational platform generates a HAR scene that fuses pre-operative MRI sets, segmented anatomical structures, and a tubular tool for planning an access path to the targeted pathology. The operator can manipulate the presented images and segmented structures and perform path-planning using voice and gestures. On-the-fly, the software uses defined forbidden-regions to prevent the operator from harming vital structures. In silico studies using the platform with a HoloLens HMD assessed its functionality and the computational load and memory for different tasks. A preliminary qualitative evaluation revealed that holographic visualization of high-resolution 3D MRI data offers an intuitive and interactive perspective of the complex brain vasculature and anatomical structures. This initial work suggests that immersive experiences may be an unparalleled tool for planning neurosurgical procedures.Neuro Imaging Researc

A Holographic Augmented Reality Interface for Visualizing of MRI Data and Planning of Neurosurgical Procedures

The recent introduction of wireless head-mounted displays (HMD) promises to enhance 3D image visualization by immersing the user into 3D morphology. This work introduces a prototype holographic augmented reality (HAR) interface for the 3D visualization of magnetic resonance imaging (MRI) data for the purpose of planning neurosurgical procedures. The computational platform generates a HAR scene that fuses pre-operative MRI sets, segmented anatomical structures, and a tubular tool for planning an access path to the targeted pathology. The operator can manipulate the presented images and segmented structures and perform path-planning using voice and gestures. On-the-fly, the software uses defined forbidden-regions to prevent the operator from harming vital structures. In silico studies using the platform with a HoloLens HMD assessed its functionality and the computational load and memory for different tasks. A preliminary qualitative evaluation revealed that holographic visualization of high-resolution 3D MRI data offers an intuitive and interactive perspective of the complex brain vasculature and anatomical structures. This initial work suggests that immersive experiences may be an unparalleled tool for planning neurosurgical procedures. © 2021, Society for Imaging Informatics in Medicine

A MR compatible mechatronic system to facilitate magic angle experiments in vivo

When imaging tendons and cartilage in a MRI scanner, an increase in signal intensity is observed when they are oriented at 55 degrees with respect to Bo (the “magic angle”). There is a clear clinical importance for considering this effect as part of the diagnosis of orthopaedic and other injury. Experimental studies of this phenomenon have been made harder by practical difficulties of tissue positioning and orientation in the confined environment of cylindrical scanners. An MRI compatible mechatronic system has been developed to position a variety of limbs inside the field of view of the scanner, to be used as a diagnostic and research tool. It is actuated with a novel pneumatic motor comprised of a heavily geared down air turbine, and is controlled in a closed loop using standard optical encoders. MR compatibility is demonstrated as well as the results of preliminary trials used to image the Achilles tendon of human volunteers at different orientations. A 4 to 13 fold increase in signal at the tendon is observed at the magic angle