MRI: How to perform a pediatric scan

2013 ◽  
Vol 54 (9) ◽  
pp. 991-997 ◽  
Author(s):  
Øystein E Olsen
Keyword(s):  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Acquisition Time ◽  
Artifact Reduction ◽  
Resonance Imaging ◽  
Basic Principles ◽  
Low Snr ◽  
Patient Preparation ◽  
Magnetic Resonance Imaging Mri

Magnetic resonance imaging (MRI) is rich in diagnostic information but requires optimization for use in children. The main problems are motion artifacts and poor signal-to-noise ratio (SNR). SNR is proportional to voxel volume, which must therefore not be too small, however, usually needs to be reduced compared to adult imaging to account for the finer anatomy of the child. The use of multi-channel coils with element sizes appropriate for the anatomy of interest ensures optimal baseline SNR. Longer acquisition time increases SNR (with a square-root factor), but the flip-side is that this allows more motion artifacts. Attention to patient preparation and to techniques for motion artifact reduction is therefore crucial, and the most important principles are discussed. Low SNR may in part be compensated by optimizing the image contrast by weighting (tissue and lesions T1 and T2 may differ from adults) and by using contrast agents. It is also powerful to combine different image contrasts during postprocessing. The basic principles are discussed, followed by an example scan protocol.

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.

Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques

2021 ◽  
Vol 16 ◽  
Author(s):  
Penta Anil Kumar ◽  
R. Gunasundari ◽  
R. Aarthi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Optimization Technique ◽  
Motion Artifacts ◽  
Reconstruction Technique ◽  
Resonance Imaging ◽  
Research Gaps ◽  
Conventional Mri ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri

Background: Magnetic Resonance Imaging (MRI) plays an important role in the field of medical diagnostic imaging as it poses non-invasive acquisition and high soft-tissue contrast. However, the huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterpretation of data, and may cause uncomfortable to the patient. Thus, the main goal of MRI research is to accelerate data acquisition processing without affecting the quality of the image. Introduction: This paper presents a survey based on distinct conventional MRI reconstruction methodologies. In addition, a novel MRI reconstruction strategy is proposed based on weighted Compressive Sensing (CS), Penalty-aided minimization function, and Meta-heuristic optimization technique. Methods: An illustrative analysis is done concerning adapted methods, datasets used, execution tools, performance measures, and values of evaluation metrics. Moreover, the issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to obtain improved contribution for devising significant MRI reconstruction techniques. Results: The proposed method will reduce conventional aliasing artifacts problems, may attain lower Mean Square Error (MSE), higher Peak Signal-to-Noise Ratio (PSNR), and Structural SIMilarity (SSIM) index. Conclusion: The issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to devising an improved significant MRI reconstruction technique.

scholarly journals Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

2020 ◽  
Vol 30 (11) ◽  
pp. 5923-5932
Author(s):  
M.-L. Kromrey ◽  
D. Tamada ◽  
H. Johno ◽  
S. Funayama ◽  
N. Nagata ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motion Artifact ◽  
Motion Artifacts ◽  
Arterial Phase ◽  
Artifact Reduction ◽  
Gadoxetate Disodium ◽  
Mr Images ◽  
Liver Mri ◽  
Lesion Conspicuity

Abstract Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

scholarly journals Evaluation of Nonradiative Clinical Imaging Techniques for the Longitudinal Assessment of Tumour Growth in Murine CT26 Colon Carcinoma

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Johanne Seguin ◽  
Bich-Thuy Doan ◽  
Heldmuth Latorre Ossa ◽  
Lauriane Jugé ◽  
Jean-Luc Gennisson ◽  
...  
Keyword(s):  
Colon Carcinoma ◽  
Tumour Growth ◽  
Imaging Techniques ◽  
Acquisition Time ◽  
Longitudinal Assessment ◽  
Resonance Imaging ◽  
Orthotopic Model ◽  
Short Acquisition Time ◽  
Magnetic Resonance Imaging Mri ◽  
Good Delineation

Background and Objectives. To determine the most appropriate technique for tumour followup in experimental therapeutics, we compared ultrasound (US) and magnetic resonance imaging (MRI) to characterize ectopic and orthotopic colon carcinoma models. Methods. CT26 tumours were implanted subcutaneously (s.c.) in Balb/c mice for the ectopic model or into the caecum for the orthotopic model. Tumours were evaluated by histology, spectrofluorescence, MRI, and US. Results. Histology of CT26 tumour showed homogeneously dispersed cancer cells and blood vessels. The visualization of the vascular network using labelled albumin showed that CT26 tumours were highly vascularized and disorganized. MRI allowed high-resolution and accurate 3D tumour measurements and provided additional anatomical and functional information. Noninvasive US imaging allowed good delineation of tumours despite an hypoechogenic signal. Monitoring of tumour growth with US could be accomplished as early as 5 days after implantation with a shorter acquisition time (<5 min) compared to MRI. Conclusion. MRI and US afforded excellent noninvasive imaging techniques to accurately follow tumour growth of ectopic and orthotopic CT26 tumours. These two techniques can be appropriately used for tumour treatment followup, with a preference for US imaging, due to its short acquisition time and simplicity of use.

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.

Abbreviated Breast Magnetic Resonance Imaging (MRI): First Postcontrast Subtracted Images and Maximum-Intensity Projection—A Novel Approach to Breast Cancer Screening With MRI

2014 ◽  
Vol 32 (22) ◽  
pp. 2304-2310 ◽  
Author(s):  
Christiane K. Kuhl ◽  
Simone Schrading ◽  
Kevin Strobel ◽  
Hans H. Schild ◽  
Ralf-Dieter Hilgers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Reading Time ◽  
Breast Magnetic Resonance Imaging ◽  
Maximum Intensity Projection ◽  
Maximum Intensity ◽  
Acquisition Time ◽  
Resonance Imaging ◽  
Breast Magnetic Resonance ◽  
Magnetic Resonance Imaging Mri

Purpose We investigated whether an abbreviated protocol (AP), consisting of only one pre- and one postcontrast acquisition and their derived images (first postcontrast subtracted [FAST] and maximum-intensity projection [MIP] images), was suitable for breast magnetic resonance imaging (MRI) screening. Methods We conducted a prospective observational reader study in 443 women at mildly to moderately increased risk who underwent 606 screening MRIs. Eligible women had normal or benign digital mammograms and, for those with heterogeneously dense or extremely dense breasts (n = 427), normal or benign ultrasounds. Expert radiologists reviewed the MIP image first to search for significant enhancement and then reviewed the complete AP (consisting of MIP and FAST images and optionally their nonsubtracted source images) to characterize enhancement and establish a diagnosis. Only thereafter was the regular full diagnostic protocol (FDP) analyzed. Results MRI acquisition time for FDP was 17 minutes, versus 3 minutes for the AP. Average time to read the single MIP and complete AP was 2.8 and 28 seconds, respectively. Eleven breast cancers (four ductal carcinomas in situ and seven invasive cancers; all T1N0 intermediate or high grade) were diagnosed, for an additional cancer yield of 18.2 per 1,000. MIP readings were positive in 10 (90.9%) of 11 cancers and allowed establishment of the absence of breast cancer, with a negative predictive value (NPV) of 99.8% (418 of 419). Interpretation of the complete AP, as with the FDP, allowed diagnosis of all cancers (11 [100%] of 11). Specificity and positive predictive value (PPV) of AP versus FDP were equivalent (94.3% v 93.9% and 24.4% v 23.4%, respectively). Conclusion An MRI acquisition time of 3 minutes and an expert radiologist MIP image reading time of 3 seconds are sufficient to establish the absence of breast cancer, with an NPV of 99.8%. With a reading time < 30 seconds for the complete AP, diagnostic accuracy was equivalent to that of the FDP and resulted in an additional cancer yield of 18.2 per 1,000.

scholarly journals Automated Multimodal image fusion for brain tumor detection

2021 ◽  
Vol 3 (1) ◽  
pp. 68-82
Author(s):  
Harpreet Kaur ◽  
◽  
Deepika Koundal ◽  
Virendar Kadyan ◽  
Navneet Kaur ◽  
...  
Keyword(s):  
Image Fusion ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Visual Image ◽  
Fusion Rule ◽  
Resonance Imaging ◽  
Signal To Noise ◽  
Multiple Images ◽  
Fused Image ◽  
Magnetic Resonance Imaging Mri

In medical domain, various multimodalities such as Computer tomography (CT) and Magnetic resonance imaging (MRI) are integrated into a resultant fused image. Image fusion (IF) is a method by which vital information can be preserved by extracting all important information from the multiple images into the resultant fused image. The analytical and visual image quality can be enhanced by the integration of different images. In this paper, a new algorithm has been proposed on the basis of guided filter with new fusion rule for the fusion of different imaging modalities such as MRI and Fluorodeoxyglucose images of brain for the detection of tumor. The performance of the proposed method has been evaluated and compared with state-of-the-art image fusion techniques using various qualitative as well as quantitative evaluation metrics. From the results, it has been observed that more information has achieved on edges and content visibility is also high as compared to the other techniques which makes it more suitable for real applications. The experimental results are evaluated on the basis of with-reference and without-references metric such as standard deviation, entropy, peak signal to noise ratio, mutual information etc.

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.

scholarly journals Simulation and Mitigation of the Wrap-Around Artifact in the MRI Image

2021 ◽  
Vol 15 ◽  
Author(s):  
Runze Hu ◽  
Rui Yang ◽  
Yutao Liu ◽  
Xiu Li
Keyword(s):  
Deep Neural Networks ◽  
Imaging Modality ◽  
Comprehensive Analysis ◽  
Perceptual Quality ◽  
Artifact Reduction ◽  
Resonance Imaging ◽  
Diagnostic Efficacy ◽  
Restoration Technique ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Image

Magnetic resonance imaging (MRI) is an essential clinical imaging modality for diagnosis and medical research, while various artifacts occur during the acquisition of MRI image, resulting in severe degradation of the perceptual quality and diagnostic efficacy. To tackle such challenges, this study deals with one of the most frequent artifact sources, namely the wrap-around artifact. In particular, given that the MRI data are limited and difficult to access, we first propose a method to simulate the wrap-around artifact on the artifact-free MRI image to increase the quantity of MRI data. Then, an image restoration technique, based on the deep neural networks, is proposed for wrap-around artifact reduction and overall perceptual quality improvement. This study presents a comprehensive analysis regarding both the occurrence of and reduction in the wrap-around artifact, with the aim of facilitating the detection and mitigation of MRI artifacts in clinical situations.

scholarly journals Implementation of a dedicated 1.5 T MR scanner for radiotherapy treatment planning featuring a novel high-channel coil setup for brain imaging in treatment position

2020 ◽  
Author(s):  
Veit Mengling ◽  
Christoph Bert ◽  
Rosalind Perrin ◽  
Siti Masitho ◽  
Thomas Weissmann ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Imaging ◽  
Treatment Planning ◽  
Signal To Noise Ratio ◽  
Motion Artifacts ◽  
Radiotherapy Treatment Planning ◽  
Tissue Contrast ◽  
Flexible Coil ◽  
Magnetic Resonance Imaging Mri

Abstract Purpose To share our experiences in implementing a dedicated magnetic resonance (MR) scanner for radiotherapy (RT) treatment planning using a novel coil setup for brain imaging in treatment position as well as to present developed core protocols with sequences specifically tuned for brain and prostate RT treatment planning. Materials and methods Our novel setup consists of two large 18-channel flexible coils and a specifically designed wooden mask holder mounted on a flat tabletop overlay, which allows patients to be measured in treatment position with mask immobilization. The signal-to-noise ratio (SNR) of this setup was compared to the vendor-provided flexible coil RT setup and the standard setup for diagnostic radiology. The occurrence of motion artifacts was quantified. To develop magnetic resonance imaging (MRI) protocols, we formulated site- and disease-specific clinical objectives. Results Our novel setup showed mean SNR of 163 ± 28 anteriorly, 104 ± 23 centrally, and 78 ± 14 posteriorly compared to 84 ± 8 and 102 ± 22 anteriorly, 68 ± 6 and 95 ± 20 centrally, and 56 ± 7 and 119 ± 23 posteriorly for the vendor-provided and diagnostic setup, respectively. All differences were significant (p > 0.05). Image quality of our novel setup was judged suitable for contouring by expert-based assessment. Motion artifacts were found in 8/60 patients in the diagnostic setup, whereas none were found for patients in the RT setup. Site-specific core protocols were designed to minimize distortions while optimizing tissue contrast and 3D resolution according to indication-specific objectives. Conclusion We present a novel setup for high-quality imaging in treatment position that allows use of several immobilization systems enabling MR-only workflows, which could reduce unnecessary dose and registration inaccuracies.

Sign in / Sign up

Export Citation Format

Share Document