Parameter Analysis on Sensitivity Encoding (SENSE) Algorithm for Parallel Imaging of Magnetic Resonance Imaging

Author(s):  
Fitria Ariani ◽  
Basari Basari
2009 ◽  
Vol 60 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Patricia Noël ◽  
Roland Bammer ◽  
Caroline Reinhold ◽  
Masoom A. Haider

Objective To familiarize the reader with the fundamental concepts of partial parallel imaging (PPI); to review the technical aspects of PPI including calibration scan, coil geometry, and field of view (FOV); and to illustrate artifacts related to parallel imaging and describe solutions to minimize their negative impact. Results PPI has led to a significant advance in body magnetic resonance imaging by reducing the time required to generate an image without loss of spatial resolution. Although PPI can improve image quality, it is not free of artifacts, which can result in significant image degradation. Knowledge of these artifacts and how to minimize their effect is important to optimize the use of parallel imaging for specific body magnetic resonance imaging applications. Conclusions The reader will be introduced to the fundamental principles of PPI. Common imaging characteristics of PPI artifacts will be displayed with an emphasis on those seen with image-based methods, the principles behind their generation presented, and measures to minimize their negative impact will be proposed.


2021 ◽  
Author(s):  
Mehran Shams Kondori

Recent advances in ultra-high field magnetic resonance imaging have addressed substantial technological challenges in both hardware and software. These challenges, including transmit field inhomogeneity, primarily are due to the onset of far-field effects at the resonance frequencies at 7Tesla and 10.5Tesla MRI which becomes more demanding at higher field strengths. The advent of parallel imaging techniques in reception (multi-channel radio-frequency arrays), transmission (parallel transmit or pTx), and reconstruction (especially using deep learning models) has been an effort to address such challenges. Here, the most recent notable advances in MRI in both hardware and software fronts and their implications for human brain neuroscience applications are reviewed.


F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1636 ◽  
Author(s):  
Karthik Seetharam ◽  
Stamatios Lerakis

Over the last 15 years, cardiovascular magnetic resonance (CMR) imaging has progressively evolved to become an indispensable tool in cardiology. It is a non-invasive technique that enables objective and functional assessment of myocardial tissue. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of T1 and T2 parametric mapping to explore tissue characteristics. The emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. Significant progress in computer processing capabilities and cloud infrastructure has supported the growth of artificial intelligence in CMR imaging. In this review article, we describe recent advances in T1/T2 mapping, myocardial strain, and artificial intelligence in CMR imaging.


2009 ◽  
Vol 27 (1) ◽  
pp. 48-54 ◽  
Author(s):  
Tobias Breyer ◽  
Matthias Echternach ◽  
Susan Arndt ◽  
Bernhard Richter ◽  
Oliver Speck ◽  
...  

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