scholarly journals An Automated Method for Quality Control in MRI Systems: Methods and Considerations

2020 ◽  
Vol 6 (10) ◽  
pp. 111
Author(s):  
Angeliki C. Epistatou ◽  
Ioannis A. Tsalafoutas ◽  
Konstantinos K. Delibasis
Keyword(s):  
Magnetic Resonance Imaging ◽  
Quality Control ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Selection Criterion ◽  
Test Results ◽  
Resonance Imaging ◽  
Phantom Image ◽  
Automated Method ◽  
Magnetic Resonance Imaging Mri

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

RFID-Based Real-Time Navigation for Interventional Magnetic Resonance Imaging: Development and Evaluation of a Novel Tracking System

2017 ◽  
Vol 11 (3) ◽  
Author(s):  
Felix Güttler ◽  
Andreas Heinrich ◽  
Peter Krauß ◽  
Jonathan Guntermann ◽  
Maximilian de Bucourt ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Spatial Resolution ◽  
Radio Frequency Identification ◽  
Signal To Noise Ratio ◽  
Tracking System ◽  
Single Shot ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Steady State Precession

The purpose of this study was to evaluate the suitability of a novel radio-frequency identification (RFID)-based tracking system for intraoperative magnetic resonance imaging (MRI). A RFID tracking system was modified to fulfill MRI-compatibility and tested according to ASTM and NEMA. The influence of the RFID tracking system on MRI was analyzed in a phantom study using a half-Fourier acquisition single-shot turbospin echo (HASTE) and true fast imaging with steady-state precession sequence (TrueFISP) sequence. The RFID antenna was gradually moved closer to the isocenter of the MR scanner from 90 to 210 cm to investigate the influence of the distance. Furthermore, the RF was gradually changed between 865 and 869 MHz for a distance of 90 cm, 150 cm, and 210 cm to the isocenter of the magnet to investigate the influence of the frequency. The specific spatial resolution was measured with and without a permanent line of sight (LOS). After the modification of the reader, no significant change of the signal-to-noise ratio (SNR) could be observed with increasing distance of the RFID tracking system to the isocenter of the MR scanner. Also, different radio frequencies of the RFID tracking system did not influence the SNR of the MR-images significantly. The specific spatial resolution deviated on average by 8.97 ± 7.33 mm with LOS and 11.23 ± 12.03 mm without LOS from the reference system. The RFID tracking system had no relevant influence on the MR-image quality. RFID tracking solved the LOS problem. However, the spatial accuracy of the RFID tracking system has to be improved for medical usage.

scholarly journals LAB–QA2GO: A free, easy-to-use toolbox for the quality assessment of magnetic resonance imaging data

2019 ◽  
Author(s):  
Christoph Vogelbacher ◽  
Miriam H. A. Bopp ◽  
Verena Schuster ◽  
Peer Herholz ◽  
Andreas Jansen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Virtual Machine ◽  
Signal To Noise Ratio ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Data Set ◽  
Magnetic Resonance Imaging Mri ◽  
Movement Parameters ◽  
Set Up

AbstractImage characteristics of magnetic resonance imaging (MRI) data (e.g. signal-to-noise ratio, SNR) may change over the course of a study. To monitor these changes a quality assurance (QA) protocol is necessary. QA can be realized both by performing regular phantom measurements and by controlling the human MRI datasets (e.g. noise detection in structural or movement parameters in functional datasets). Several QA tools for the assessment of MRI data quality have been developed. Many of them are freely available. This allows in principle the flexible set-up of a QA protocol specifically adapted to the aims of one’s own study.However, setup and maintenance of these tools bind time, in particular since the installation and operation often require a fair amount of technical knowledge. In this article we present a light-weighted virtual machine, named LAB-QA2GO, which provides scripts for fully automated QA analyses of phantom and human datasets. This virtual machine is ready for analysis by starting it the first time. With minimal configuration in the guided web-interface the first analysis can start within 10 minutes, while adapting to local phantoms and needs is easily possible. The usability and scope of LAB–QA2GO is illustrated using a data set from the QA protocol of our lab. With LAB–QA2GO we hope to provide an easy-to-use toolbox that is able to calculate QA statistics without high effort.

Development of a Device-to-Image Registration Free Needle Guide for Magnetic Resonance Imaging-Guided Targeted Prostate Biopsy

2020 ◽  
Vol 14 (4) ◽  
Author(s):  
Pankaj Kulkarni ◽  
Sakura Sikander ◽  
Pradipta Biswas ◽  
Sumit Laha ◽  
Heather Cornnell ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Image Registration ◽  
Prostate Biopsy ◽  
Signal To Noise Ratio ◽  
Clinical Environment ◽  
Resonance Imaging ◽  
Transperineal Prostate Biopsy ◽  
Mri Guided ◽  
Magnetic Resonance Imaging Mri

Abstract Significant research has been done in the past decade for the development of magnetic resonance imaging (MRI) guided needle guide (NG) systems for prostate intervention. Most of these systems have been restricted to application in the lab environment with lack of progress toward clinical application. Bulky and complex designs can be attributed to this practice. These systems also demand complex technical setup and usage procedures, which require extra technical personnel during the intervention in addition to specialized training for physicians. Moreover, “device-to-image” registration, essential for accurate and precise targeting, further complicates the overall process while increasing total time for intervention. In order to address these limitations, a simplified, MRI-guided, transperineal prostate biopsy NG system was designed and developed for rapid adoption into the clinical environment. The system consists of a NG device and a software toolkit. It does not require any special intraprocedural technical expertise or dedicated training. Also, to simplify and shorten total procedure time, the device uses the unique concept of “fixed coordinate device” eliminating the need for any device-to-image registration making it clinically friendly. To verify the NG design along with the registration free feature, image quality tests and agar phantom-based targeting experiments were performed under the guidance of 3T MRI scanner. The imaging tests resulted in a distortion of less than 1% in presence of the device and an average change of 1.3% in signal-to-noise ratio. For targeting experiments, maximum in-plane error distance of 3.8 mm with a mean of 2.2 mm and standard deviation of 0.8 mm was observed. The results show that an MRI-compatible simplified intervention device without the need of device-to-image registration is technically feasible.

Selective Non-Excitation of Water or Fat Protons in Magnetic Resonance Imaging

1987 ◽  
Vol 42 (12) ◽  
pp. 1391-1395
Author(s):  
M. Braun ◽  
W. I. Jung ◽  
O. Lutz ◽  
R. Oeschey
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Fat Distribution ◽  
Whole Body ◽  
Pulse Excitation ◽  
Resonance Imaging ◽  
Spectral Separation ◽  
Magnetic Resonance Imaging Mri ◽  
Test Objects

Nuclear magnetic resonance imaging (MRI) of water and fat protons has been performed with a 1.5 T whole body imager. The highly selective excitation, necessary for the discrimination of the two proton species, has been achieved by different four and five pulse excitation schemes which had to be adapted to the needs of MRI and completed to imaging sequences. Their ability to produce well separated water and fat distribution images of test objects is demonstrated. The special features of the method such as signal-to-noise ratio, insensitivity to rf-field inhomogeneities, ease of implementation and data handling are discussed and compared to existing spectral separation techniques.

scholarly journals Demonstration and Experimental Validation of Plastic-Encased Resonant Ultrasonic Piezoelectric Actuator for Magnetic Resonance Imaging-Guided Surgical Robots

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Paulo A. W. G. Carvalho ◽  
Christopher J. Nycz ◽  
Katie Y. Gandomi ◽  
Gregory S. Fischer
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Surgical Instruments ◽  
Resonance Imaging ◽  
Surgical Robots ◽  
Additional Signal ◽  
High Metal Content ◽  
Piezoelectric Elements ◽  
Magnetic Resonance Imaging Mri

Abstract Intra-operative medical imaging based on magnetic resonance imaging (MRI) coupled with robotic manipulation of surgical instruments enables precise feedback-driven procedures. Electrically powered nonferromagnetic motors based on piezoelectric elements have shown to be well suited for MRI robots. However, even avoiding ferrous materials, the high metal content on commercially available motors still cause distortions to the magnetic fields. We construct semicustom piezoelectric actuators wherein the quantity of conductive material is minimized and demonstrate that the distortion issues can be partly addressed through substituting several of these components for plastic equivalents, while maintaining motor functionality. Distortion was measured by assessing the root-mean-squared (RMS) change in position of 49 centroid points in a 12.5 mm square grid of a gelatin-filled phantom. The metal motor caused a distortion of up to 4.91 mm versus 0.55 mm for the plastic motor. An additional signal-to-noise-ratio (SNR) drop between motor off and motor spinning of approximately 20% was not statistically different for metal versus plastic (p = 0.36).

scholarly journals Autonomous Magnetic Resonance Imaging

2020 ◽  
Author(s):  
Keerthi Sravan Ravi ◽  
Sairam Geethanath
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Pulse Sequences ◽  
User Interaction ◽  
Time Constraints ◽  
Acquisition Time ◽  
Resonance Imaging ◽  
Image Reconstructions ◽  
Magnetic Resonance Imaging Mri

AbstractAccess to Magnetic Resonance Imaging (MRI) across developing countries from being prohibitive to scarcely available. For example, eleven countries in Africa have no scanners. One critical limitation is the absence of skilled manpower required for MRI usage. Some of these challenges can be mitigated using autonomous MRI (AMRI) operation. In this work, we demonstrate AMRI to simplify MRI workflow by separating the required intelligence and user interaction from the acquisition hardware. AMRI consists of three components: user node, cloud and scanner. The user node voice interacts with the user and presents the image reconstructions at the end of the AMRI exam. The cloud generates pulse sequences and performs image reconstructions while the scanner acquires the raw data. An AMRI exam is a custom brain screen protocol comprising of one T1-, T2- and T2*-weighted exams. A neural network is trained to incorporate Intelligent Slice Planning (ISP) at the start of the AMRI exam. A Look Up Table was designed to perform intelligent protocolling by optimising for contrast value while satisfying signal to noise ratio and acquisition time constraints. Data were acquired from four healthy volunteers for three experiments with different acquisition time constraints to demonstrate standard and self-administered AMRI. The source code is available online. AMRI achieved an average SNR of 22.86 ± 0.89 dB across all experiments with similar contrast. Experiment #3 (33.66% shorter table time than experiment #1) yielded a SNR of 21.84 ± 6.36 dB compared to 23.48 ± 7.95 dB for experiment #1. AMRI can potentially enable multiple scenarios to facilitate rapid prototyping and research and streamline radiological workflow. We believe we have demonstrated the first Autonomous MRI of the brain.

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