Evidence map›Paper›PMID 30483988›Full record

ArticleJournal of digital imaging2019

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

Jose D Velazco Garcia, Nikhil V Navkar, Dawei Gui, Cristina M Morales, Eftychios G Christoforou, Alpay Ozcan, Julien Abinahed, Abdulla Al-Ansari, Andrew Webb, Ioannis Seimenis and 1 more

Abstract read
In one paragraph

Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jose D Velazco GarciaDepartment of Computer Science, University of Houston, Houston, TX, USA.
Nikhil V NavkarDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Dawei GuiGE Healthcare, Milwaukee, WI, USA.
Cristina M MoralesDepartment of Computer Science, University of Houston, Houston, TX, USA.
Eftychios G ChristoforouDepartment of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus.
Alpay OzcanDepartment of Biomedical Device Technologies, Acıbadem Mehmet Ali Aydınlar University, Istanbul, Turkey.
Julien AbinahedDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Abdulla Al-AnsariDepartment of Surgery, Hamad Medical Corporation, Doha, Qatar.
Andrew WebbC.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands.
Ioannis SeimenisDepartment of Medicine, Democritus University of Thrace, Alexandropoli, Greece.
Nikolaos V TsekosDepartment of Computer Science, University of Houston, Houston, TX, USA. nvtsekos@central.uh.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Imaging, Three-DimensionalMagnetic Resonance Imaging, InterventionalAbdomenComputer SimulationContrast MediaHumansImage Processing, Computer-AssistedRoboticsContrast MediaControl of MRI-compatible robotDynamic three-dimensional reconstruction and visualizationMRI-guided interventionsOblique multi-slice imaging

Identifiers

PMID30483988
PMCPMC6499858

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.