Accelerated imaging of metallic implants using model‐based nonlinear reconstruction

Xinwei Shi; Evan Levine; Hans Weber; Brian A. Hargreaves

doi:10.1002/mrm.27536

Accelerated imaging of metallic implants using model‐based nonlinear reconstruction

Magnetic Resonance in Medicine ◽

10.1002/mrm.27536 ◽

2018 ◽

Vol 81 (4) ◽

pp. 2247-2263 ◽

Cited By ~ 1

Author(s):

Xinwei Shi ◽

Evan Levine ◽

Hans Weber ◽

Brian A. Hargreaves

Keyword(s):

Model Based ◽

Metallic Implants ◽

Accelerated Imaging

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Metallic Implants in MRI – Hazards and Imaging Artifacts

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/a-1460-8566 ◽

2021 ◽

Author(s):

Eva Peschke ◽

Patricia Ulloa ◽

Olav Jansen ◽

Jan-Bernd Hoevener

Keyword(s):

Tissue Imaging ◽

Slice Thickness ◽

New Developments ◽

Metallic Implants ◽

Scan Time ◽

Key Points ◽

Accelerated Imaging ◽

Imaging Artifacts ◽

Magnetic Resonance Imaging Mri ◽

The Cost

Background Magnetic resonance imaging (MRI) is an examination method for noninvasive soft tissue imaging without the use of ionizing radiation. Metallic implants, however, may pose a risk for the patient and often result in imaging artifacts. Due to the increasing number of implants, reducing these artifacts has become an important goal. In this review, we describe the risks associated with implants and provide the background on how metal-induced artifacts are formed. We review the literature on methods on how to reduce artifacts and summarize our findings. Method The literature was searched using PubMed and the keywords “MRI metal artifact reduction”, “metallic implants” and “MRI artefacts/artifacts”. Results and Conclusion The MRI compatibility of implants has to be evaluated individually. To reduce artifacts, two general approaches were found: a) parameter optimization in standard sequences (echo time, slice thickness, bandwidth) and b) specialized sequences, such as VAT, OMAR, WARP, SEMAC and MAVRIC. These methods reduced artifacts and improved image quality, albeit at the cost of a (sometimes significantly) prolonged scan time. New developments in accelerated imaging will likely shorten the scan time of these methods significantly, such that routine use may become feasible. Key Points: Citation Format

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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