diff --git a/Artifacts.html b/Artifacts.html index 6d0349c..856de7e 100644 --- a/Artifacts.html +++ b/Artifacts.html @@ -423,6 +423,13 @@

Contents

  • Artifact Comparison
  • Displacement Artifacts
  • Motion and Flow Artifacts
    • +
  • Artifact Examples +
  • Simulations of Artifacts
    • @@ -478,9 +485,9 @@

    Artifact Comparison

    Aliasing

    -

    Sequence - FOV too small

    +

    Sequence - FOV too small, or parallel imaging failed

    Signal folds across image

    -

    Increase FOV, swap PE/FE

    +

    Increase FOV, swap PE/FE, reacquire sensitivity maps (parallel imaging)

    Phase encoding

    Gibbs Ringing/Truncation

    @@ -553,24 +560,46 @@

    Displacement Artifacts \[\Delta_{FE} = \frac{\Delta f}{RBW} FOV_{FE}\]

    where \(\Delta f\) is the frequency shift, \(RBW\) is the receiver bandwidth, and \(FOV_{FE}\) is the field of view in the frequency encoding direction.

    -

    During EPI, there is typically a much larger shift in the phase encoding direction that depends on the echo spacing, \(t_{esp}\). For simplicity, I convert the echo spacing into a “phase encoding bandwidth”, \(BW_{PE} = 1/t_{esp}\):

    +

    During EPI, there is typically a much larger shift in the phase encoding direction that depends on how much phase accumulates across k-space. This depends on the echo spacing, \(t_{esp}\), and well as how many k-space lines are covered in adjacent echoes, defined here as \(N_{steps}\). To characterize this, we can define a “phase encoding bandwidth”, \(BW_{PE} = N_{steps}/t_{esp}\), and the displacement will be

    -\[\Delta_{PE} = \frac{\Delta f}{BW_{PE} N_{interleaves}} FOV_{PE}\]
    -

    where \(N_{interleaves}\) is the number of interleaves that can be used to reduce the displacement.

    +\[\Delta_{PE} = \frac{\Delta f}{BW_{PE}} FOV_{PE}\] +

    \(N_{steps}\) will depend on whether there is any interleaving, and whether parallel imaging is used to skip lines. For example, EPI without acceleration in a single-shot is \(N_{steps} = 1\), 2 interleaves would have \(N_{steps} = 2\), while single-shot with \(R=2\) parallel imaging acceleration would have \(N_{steps} = 2\), and 2 interleaves with \(R=2\) parallel imaging would have \(N_{steps} = 4\).

    Finally, there will also be a displacement of the slice selection, and this will be

    -\[\Delta_{SS} = \frac{\Delta f}{BW_{rf}} \Delta_z\]
    +\[\Delta_{SS} = \frac{\Delta f}{BW_{rf}} \Delta z\]

    where \(BW_{rf}\) is the slice select pulse bandwidth.

    Motion and Flow Artifacts#

    Motion, including flow, can result in artifacts in MRI if it leads to inconsistency in the data.

    -

    Periodic motion such as breathing, heart beating, and pulsatile flow will lead to distinct ghosting artifacts in the phase encoding direction. The location of ghosting artifacts in sequential phase encoding, will be at predictable intervals in the phase encoding direction:

    +

    Periodic motion such as breathing, heart beating, and pulsatile flow will lead to distinct ghosting artifacts in the phase encoding direction. The location of ghosting artifacts in sequential phase encoding without parallel imaging will be at predictable intervals in the phase encoding direction:

    \[\Delta_{PE} = \frac{TR}{T_{motion}} FOV_{PE}\]

    where \(T_{motion}\) is the period of the motion (e.g. \(T_{motion} = 1 s\) for a heart rate of 60 beats per minute ).

    Incoherent or more random motion such as irregular breathing, arrhytthmias, coughing, bulk motion will lead to more diffuse ghosting artifacts.

    +
    +

    Artifact Examples#

    +
    +

    Aliasing#

    +

    Axial abdomen image with aliasing

    +
    +
    +

    Motion#

    +

    Axial abdomen image with motion

    +

    Coronal brain image with motion

    +

    Axial brain image with motion

    +
    +
    +

    Image Displacement/Distortion#

    +

    Axial brian EPI with distortion

    +

    Axial brian EPI with fat displacement

    +
    +
    +

    T2*#

    +

    Axial brain image with T2* artifact

    +
    +

    Simulations of Artifacts#

    @@ -823,6 +852,13 @@

    Simulations of ArtifactsArtifact Comparison
  • Displacement Artifacts
  • Motion and Flow Artifacts
    • +
  • Artifact Examples +
  • Simulations of Artifacts

    • diff --git a/Key MRI Concepts.html b/Key MRI Concepts.html index d34da20..0849527 100644 --- a/Key MRI Concepts.html +++ b/Key MRI Concepts.html @@ -423,11 +423,11 @@

    Contents

  • MRI System
  • MRI Experiment
  • MR Contrasts
    • -
  • In vivo spin physics
    • -
  • In vivo contrasts
    • +
  • In Vivo Spin Physics
    • +
  • In Vivo Contrasts
  • RF Pulses
  • Spatial Encoding
    • -
  • Image Characeristics
    • +
  • Image Characteristics
  • FT Imaging Sequence
  • Fast Imaging Pulse Sequences
  • Accelerated Imaging Methods
    • @@ -453,16 +453,20 @@

    Background Material

    MR Spin Physics#

    -

    Resonance -$\(f = \bar{\gamma} \|\vec{B}\|\)$

    -

    Polarization and Net Magnetization -$\(\vec{M}(\vec{r},0) = +

    Resonance - nuclear spins in a magnetic field precess at a frequency proportional to the magnetic field strength

    +
    +\[f = \bar{\gamma} \|\vec{B}\|\]
    +

    Polarization - equilibrium magnetization

    +
    +\[M_0(\vec{r}) = \frac{N(\vec{r}) \bar{\gamma}^2 h^2 I_Z (I_Z +1) B_0}{3 k T}\]
    +

    Net Magnetization at Equilibrium

    +
    +\[\begin{split}\vec{M}(\vec{r},0) = \begin{bmatrix} 0 \\ 0 \\ M_0(\vec{r}) -\end{bmatrix}\)\( -\)\(M_0(\vec{r}) = \frac{N(\vec{r}) \bar{\gamma}^2 h^2 I_Z (I_Z +1) B_0}{3 k T}\)$

    +\end{bmatrix}\end{split}\]

    Excitation

    • Apply magnetic field at resonant frequency to rotate net magnetization out of alignment with static magnetic field

      • @@ -477,7 +481,12 @@

      MR Spin Physics#

      1. Main magnet - \(B_0\)

        2. -

      2. Radiofrequency (RF) coils, including a transmit RF coil - \(B_1^+(\vec{r},t)\) - and a receive RF coil - \(B_1^-(\vec{r},t)\)

        3. +

      3. Radiofrequency (RF) coils

        +
          +

        • transmit RF coil - \(B_1^+(\vec{r},t)\): provide homogeneous excitation

          • +

        • receive RF coil - \(B_1^-(\vec{r},t)\): detect signal with high sensitivity

          • +
        +

      4. Magnetic field gradient coils - \(\vec{G}(t)\)

    @@ -496,16 +505,25 @@

    MRI Experiment

    MR Contrasts#

    +

    Contrast weightings

    +

    spoiled GRE contrast

    \[S \propto M_0 \sin(\theta) \exp(-TE/T_2) \frac{1- \exp(-TR/T_1)}{1- \cos(\theta) \exp(-TR/T_1)}\]
    -

    Contrast weightings: T1w, T2w, PDw

    -

    Magnetization Preparation:

    -

    Inversion Recovery -$\(S_{IR} \propto M_0 \exp(-TE/T_2) (1 - 2\exp(-TI/T_1) + \exp(-TR/T_1) )\)$

    +

    Ernst angle - flip angle for maximum SNR

    +
    +\[\theta_{optimal} = \cos^{-1}(\exp(-TR/T_1))\]
    +

    Magnetization Preparation: +Inversion Recovery

    +
    +\[S_{IR} \propto M_0 \exp(-TE/T_2) (1 - 2\exp(-TI/T_1) + \exp(-TR/T_1) )\]
    -

    In vivo spin physics#

    +

    In Vivo Spin Physics#

    Magnetic susceptibility effects

    • magnetic susceptibility is inherent property of materials

      • @@ -519,7 +537,8 @@

      In vivo spin physics

    -

    In vivo contrasts#

    +

    In Vivo Contrasts#

    +

    Phase - chemical shift and off-resonance (e.g. magnetic susceptibility effects) create phase differences in MR signal

    T2*

    Contrast Agents

      -

    • Gd-based contrast agents - most common, primarily shortens \(T_1\)

      • -

    • iron-basec contrast agents - less common, shortens \(T_1\) but also can shorten \(T_2\)

      • +

    • Gadolinium (Gd)-based contrast agents - most common, primarily shortens \(T_1\)

      • +

    • Iron-based contrast agents - less common, shortens \(T_1\) but also can shorten \(T_2\)

    @@ -580,20 +599,23 @@

    Spatial Encoding -

    Image Characeristics#

    +
    +

    Image Characteristics#

    \[SNR \propto f_{seq}\ \mathrm{Voxel\ Volume}\ \sqrt{T_{meas}}\]
    \[ FOV = \frac{1}{\Delta k}\]
    \[ \delta = \frac{1}{2 k_{max}}\]
    +

    Scan Time

    +
    +\[ T_{scan} = \frac{ TR \cdot N_{PE,total} \cdot NEX}{ETL \cdot R}\]

    FT Imaging Sequence#

    -

    Typical acquisition uses frequency and phase encoding.

    +

    Typical acquisition uses frequency and phase encoding

    See Pulse Sequence for a typical 2D gradient-echo sequence

    -

    Can convert between sequence parameters (e.g. timings, gradient amplitudes) and the FOV, resolution and scan time

    +

    Can convert between sequence parameters (e.g. timings, gradient amplitudes) and the FOV, resolution and scan time, as well as predict relative SNR

    Fast Imaging Pulse Sequences#

    @@ -637,12 +659,12 @@

    Accelerated Imaging Methods

  • Why does it work? MRI data has typical patterns that can be predicted are represented by sparse coefficients

  • How does it work? Skip k-space data with a pseudo-random pattern. Define a sparsity domain

    • -

    Deep Learning Reconstructions

    +

    Deep Learning Reconstruction