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| FIXME: list authors' names and email addresses. | ||
| Nikhil Bhagwat [email protected] | ||
| Erin Dickie [email protected] |
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| --- | ||
| title: "Course Overview" | ||
| teaching: 30 | ||
| exercises: 0 | ||
| questions: | ||
| - "How are structural MR imaging analyses performed?" | ||
| objectives: | ||
| - "Overview of various (pre)processing tasks involved in a sMRI pipeline and subsequent statistical analyses" | ||
| keypoints: | ||
| - "sMRI preprocessing is a starting point to gain insight into brain anatomy and its variations" | ||
| --- | ||
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| ## You Are Here! | ||
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| Welcome to the **Structural Neuroimaging Analysis in Python** workshop! The primary goals of this workshop are: | ||
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| 1. Understand basics of strcutural MR image acquisition | ||
| 2. Familiarize with structural MR image (pre)processing pipeline | ||
| 3. Perform and visualize group-level neuroanatomical analyses | ||
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| ## The Central Objective | ||
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| This workshop is designed to teach you the basics of handling structural MR images in Python and familiarize you with common image processing tasks in an sMRI processing pipeline part of typical neuroanatomical analyses within case-control studies. | ||
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| All of this may sound complicated, but we'll explain things step-by-step in depth with practical examples as the course goes along. | ||
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| ## Topics Covered | ||
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| 1. sMRI Modalities | ||
| 2. sMRI Preprocessing (Part 1: image clean-up) | ||
| 3. sMRI Preprocessing (Part 2: spatial normalization) | ||
| 4. sMRI Quantification | ||
| 5. sMRI Quality-Control | ||
| 6. Statistical Analyses (Part 1: ROIs) | ||
| 7. Statistical Analyses (Part 2: voxels) | ||
| 8. Reproducibility Considerations | ||
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| In the next section we'll begin with looking at commonly used sMRI modalities in neuroanatomical studies! | ||
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| {% include links.md %} | ||
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| --- | ||
| title: "Introduction to structural MR image modalities" | ||
| teaching: 20 | ||
| exercises: 10 | ||
| questions: | ||
| - "How is structural MR image acquired?" | ||
| - "What anatomical features do different modalities capture?" | ||
| objectives: | ||
| - "Visualize and understand differnces in T1,T2,PD/FLAIR weighted images." | ||
| keypoints: | ||
| - "Different acquisition techniques will offer better quantification of specific brain tissues" | ||
| --- | ||
| ## You Are Here! | ||
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| ## Image acquisition | ||
| 1. The acquition starts with application of strong magnetic field B<sub>0</sub> (e.g. 1.5 or 3.0 Tesla > 10000x earth's magnetic field) which forces the hydrogen nuclei of the abundant water molecules in soft tissues in the body to align with the field. You can think of hydrogen nuclei as tiny magnets of their own. | ||
| 2. Then the scanner applies a RF pulse which tilts these nuclei from their alighment along B<sub>0</sub> and then _precess_ back to the alignment. The _precessing_ nuclei emit a signal, which is registered by the receiver coils in the scanner. | ||
| 3. This signal has two components: 1) _Longitudinal_ (z-axis along the scanner's magnetic field) 2) _Transverse_ (xy-plane orthogonal to the scanner's magnetic field). | ||
| 4. Initially the longitudinal signal is weak as most nuclei are tilted away from the z-axis. However this signal grows as nuclei realign. The _time constant_ that dictates the speed of re-alignment is denoted by _T1_. | ||
| 5. Initially the transverse signal is strong as most nuclei are in phase _coherence_. The signal decays as the nuclei dephase as they realign. This decay is denoted by the _T2 time constant_. | ||
| 7. The tissue specific differences in T1 and T2 relaxation times is what enables us to _see_ anatomy from image contrast. The final image contrast depends on when you _listen_ to the signal (design parameter: echo time (TE)) and how fast you repeat the _tilt-relax_ process i.e. RF pulse freuqency (design parameter: repetition time (TR)). | ||
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| ### T1 and T2 relaxation | ||
| Here we see signal from two different tissues as the nuclei are tilted and realigned. | ||
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| ### T1w, T2w, and PD acquisition | ||
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| | | TE short | TE ~ T2 of tissue of interest| | ||
| | :-------------: | :----------: | :-----------: | | ||
| | **TR ~ T1 of tissue of interest** | T1w | - | | ||
| | **TR long** | Proton Density (PD) | T2w | | ||
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| _Note_: More recently, the FLAIR (Fluid Attenuated Inversion Recovery) sequence has replaced the PD image. FLAIR images are T2-weighted with the CSF signal suppressed. | ||
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| ### T1 and T2 relaxation times for various tissues | ||
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| | | T1 (ms) | T2 (ms) | | ||
| | :-------------: | :----------: | :-----------: | | ||
| | Bones | 500 | 50 | | ||
| | CSF | 4000 | 500 | | ||
| | Grey Matter | 1300 | 110 | | ||
| | White Matter | 800 | 80 | | ||
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| ### T1w, T2w image contrasts | ||
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| | T1w | T2w | | ||
| | :-------------: | :-----------: | | ||
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| ### Applications per modality | ||
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| | Modality | Contrast Characteristics | Use Cases | | ||
| | :-------------: | :-----------: | :-----------: | | ||
| | T1w | Cerebrospinal fluid is dark | Quantifying anatomy _e.g. measure structrual volumes_ | | ||
| | T2w | CSF is light, but white matter is darker than with T1 | Identify pathologies related to lesions and tumors | | ||
| | PD | CSF is bright. Gray matter is brighter than white matter | Identify demyelination| | ||
| | FLAIR | Similar to T2 with the CSF signal suppressed| Identify demyelination | | ||
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| **Note: In this lesson, we have only talked about image contrast which is most relevent to sMRI image pipelines. The details of spatial encoding and k-space transforms are out of the scope.** | ||
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| {% include links.md %} | ||
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| --- | ||
| title: "Image preprocessing with smriprep (Part 1: image clean-up)" | ||
| teaching: 20 | ||
| exercises: 10 | ||
| questions: | ||
| - "What are the sources of noise and artifacts in MR images?" | ||
| - "How do we extract/mask the brain?" | ||
| objectives: | ||
| - "Visualize bias fields and motion artifacts" | ||
| - "Generate brain masks" | ||
| keypoints: | ||
| - "Presence of artifacts can lead to flawed analsis and incorrect findings" | ||
| --- | ||
| ## You Are Here! | ||
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| ## Image Clean-ups | ||
| ### Intensity normalization (a.k.a bias field correction; a.k.a. intensity inhomogeneity correction) | ||
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| - The bias field is a low-frequency spatially varying MRI artifact resulting from spatial inhomogeneity of the magnetic field, | ||
| variations in the sensitivity of the reception coil, and the interaction between the magnetic field and the human body. | ||
| - It causes a smooth signal intensity variation within tissue of the same physical properties. | ||
| - The bias field is dependent on the strength of the magnetic field. If it is not corrected for 1.5T or higher MR scanners, it can considerably affect downstram analyses. | ||
| - Commonly used tools | ||
| - [ANTs N4 bias correction](https://pubmed.ncbi.nlm.nih.gov/20378467/) (See figure below) | ||
| - [FSL FAST](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FAST) (_Note:FSL FAST is a multi-purpose segmentation tool that includes the bias field correction._) | ||
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| - ANTs N4 correction | ||
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| - Impact of correction (_source: [Despotović et al.](https://www.hindawi.com/journals/cmmm/2015/450341/)_) | ||
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| ### Brain extraction (a.k.a skull-stripping) | ||
| - Image contrasts from nonbrain tissues such as fat, skull, or neck can cause issues with downstream analyses starting with brain tissue segmentation. | ||
| - The brain extraction generates a mask that identifies brain voxels comprising grey-matter (GM), white-matter(WM), and Cerebrospinal fluid (CSF) of | ||
| the cerebral cortex and subcortical structures, including the brain stem and cerebellum. | ||
| - The scalp, dura matter, fat, skin, muscles, eyes, and bones are classified as nonbrain voxels. | ||
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| - Commonly used tools | ||
| - [antsBrainExtraction](https://nipype.readthedocs.io/en/latest/api/generated/nipype.interfaces.ants.html#brainextraction) | ||
| - [FSL brain extraction tool (BET)](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET) | ||
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| - ANTs Brain Extraction | ||
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| - FSL BET | ||
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| {% include links.md %} | ||
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