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Related Concept Videos

Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Structural Joints: Fibrous Joints01:03

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
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Joints01:26

Joints

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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Parallel Resonance01:23

Parallel Resonance

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The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
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Related Experiment Video

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Contrast Enhanced Vessel Imaging using MicroCT
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Improving parallel imaging by jointly reconstructing multi-contrast data.

Berkin Bilgic1,2, Tae Hyung Kim3,4, Congyu Liao1,5

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA.

Magnetic Resonance in Medicine
|January 12, 2018
PubMed
Summary
This summary is machine-generated.

New parallel imaging methods, Joint Virtual Coil (JVC)-Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) and Joint (J-) LORAKS, accelerate MRI data acquisition. These techniques significantly reduce reconstruction error and improve image quality.

Keywords:
GRAPPALORAKSparallel imagingpartial Fouriersimultaneous multi-slicevirtual coil

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Area of Science:

  • Magnetic Resonance Imaging
  • Medical Imaging
  • Image Reconstruction

Background:

  • Parallel imaging techniques accelerate data acquisition by exploiting spatial encoding from receiver coils.
  • Current methods often face limitations in acceleration factors and reconstruction accuracy.
  • Exploiting complementary information across multiple images can further enhance parallel imaging performance.

Purpose of the Study:

  • To develop advanced parallel imaging techniques for highly accelerated Magnetic Resonance Imaging (MRI) data acquisition.
  • To integrate coil sensitivity encoding, image phase information, inter-image similarities, and k-space sampling strategies.
  • To improve reconstruction accuracy and reduce artifacts in accelerated MRI.

Main Methods:

  • Introduction of Joint Virtual Coil (JVC)-Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) for joint reconstruction of data with different contrasts.
  • Extension of the joint parallel imaging concept using Joint (J-) LORAKS formulation to incorporate limited support and smooth phase constraints.
  • Application of JVC-GRAPPA and J-LORAKS in 2D, 3D, and simultaneous multi-slice (SMS) acquisitions, including partial Fourier sampling and calibrationless reconstruction.

Main Results:

  • Demonstration of highly accelerated 2D, 3D, and SMS MRI acquisitions in vivo.
  • Achieved more than a 2-fold reduction in reconstruction error compared to conventional GRAPPA.
  • Showcased improved artifact and noise mitigation.

Conclusions:

  • JVC-GRAPPA leverages spatial encoding from phase and image similarity, utilizing diverse sampling patterns.
  • J-LORAKS achieves efficient k-space representation by treating multiple images as additional coils.
  • Both methods offer substantial improvements in artifact and noise reduction over standard single-contrast parallel imaging.