MRI Analysis in R: An Educational Guide

Introduction to Magnetic Resonance Imaging (MRI)

Magnetic Resonance Imaging (MRI) is a pivotal medical imaging technique using nuclear magnetic resonance for producing detailed internal body structures, particularly effective for visualizing soft tissues with superior clarity compared to X-rays or CT scans. This modality is extensively applied in diagnosing a wide range of conditions, notably in brain imaging for detecting tumors, strokes, and other neurological conditions.

MRI involves a patient lying inside a large magnet, where radio waves target the body. The MRI sensors detect the energy emitted from the body and convert this data into images. Unlike methods involving ionizing radiation, MRI’s safety profile allows for repeated usage. However, its strong magnetic field may be contraindicated in patients with specific metal implants, and some may find the lengthy, motionless procedure challenging.

In the field of neuroscience, MRI’s non-invasive and detailed imaging capabilities are indispensable for accurately distinguishing brain tissues and detecting abnormalities, playing a critical role in diagnosing and monitoring neurological diseases like multiple sclerosis and Alzheimer’s.

MRI Data Analysis Using R Programming

In this section, we focus on visualizing and analyzing MRI data using R programming.

Data Format:
MRI images are commonly available in NIFTI format, with file extensions such as .nii or .nii.gz (compressed). NIFTI files are compatible with various neuroimaging analysis software.

Key R Packages:

• oro.nifti: Essential for loading and manipulating NIFTI objects.
• neurobase: Extends oro.nifti capabilities, offering additional imaging functions.

Loading MRI Data in R:

# Loading the oro.nifti and neurobase packages
library(oro.nifti)
library(neurobase)

# Reading a NIFTI file
mri_img = readnii("training01_01_mri_img.nii.gz")

Visualizing MRI Data:

• Three different planes:

  ortho2(mri_img)

  The neurobase::ortho2 function displays nifti objects in 3 different planes.

• Lightbox View:

  image(mri_img, useRaster= TRUE)

  This function from oro.nifti provides a lightbox view, showcasing all slices of an MRI image.

• Viewing Specific Slices:

  oro.nifti::slice(mri_img, z = c(60, 80))

  Viewing specific slices is vital for detailed examination of particular brain regions.

Analyzing Voxel Value Distributions:

In MRI imaging, voxels (short for “volumetric pixels”) function similarly to pixels in 2D images but they represent the smallest distinguishable three-dimensional units of the scanned volume.

Voxel values in MRI data can be analyzed to understand the distribution of different tissue types.

• Density Plot:

  plot(density(mri_img))

  This plot helps in understanding the distribution of voxel intensities.

• Histogram:

  hist(mri_img)

  Histograms are useful for visualizing the frequency distribution of voxel intensities.

Segmenting Brain Regions (ROIs):
A critical aspect of MRI analysis in neuroscience is the segmentation of the brain into biologically significant regions of interest (ROIs). This includes tissue segmentation, identifying deep gray matter structures, and segmenting pathology such as multiple sclerosis lesions or tumors.

Further Reading and References:

For more detailed information and advanced techniques in MRI analysis using R, the following resources are recommended:

1. The basics of MRI interpretation – This article provides a systematic approach to MRI interpretation, which is crucial for understanding MRI images.
2. Free Interactive Course on Magnetic Resonance Imagin – This comprehensive online course is designed to explain how magnetic resonance imaging works in a simple way. It covers a wide range of topics, including nuclear spin, MRI instrumentation and safety, NMR signal and MRI contrast, spatial encoding in MRI, MRI image formation, sequences, improving MRI contrast, image quality, and artifacts.
3. Neuroimaging Analysis within R – This is a great tutorial for using R to do MRI analysis.

MRI Image Processing and Statistical Analysis in R

Preprocessing MRI Images

Preprocessing is a critical step in MRI image analysis, pivotal for ensuring accuracy and reliability of results. This process involves several key steps:

1. Artifact Correction and Noise Reduction: Addressing artifacts from patient motion and equipment errors, as well as reducing noise, is essential for obtaining clear images.
2. Standardization of Images: Standardizing images compensates for physiological factors and differences in scanning protocols, allowing for consistent comparison across different scans and subjects.
3. Spatial Normalization and Brain Segmentation: These processes align images to a common space and separate brain tissues, respectively, essential for accurate analysis.
4. Adjustment for Confounders: Correcting for various confounders ensures that the results of the analysis are not skewed by external factors.

Preprocessing enhances image quality, interpretability, and increases the statistical power of analyses. It lays the groundwork for advanced analyses, including functional MRI studies and machine learning approaches.

While preprocessing is critical, it is not the focus of this tutorial. For detailed preprocessing methods of MRI images in R, taking a look at this tutorial from John Muschelli and Kristin Linn is highly recommended.

For the purposes of this tutorial, we will assume preprocessing is already completed.

Statistical Analysis of MRI Images

Statistical analysis in MRI data encompasses a variety of techniques, each offering unique insights into brain structure and function:

1. Voxel-Based Analysis: Involves comparing individual voxels across subjects or conditions using statistical tests (e.g., t-tests, ANOVAs) to identify differences in brain structure or function.
2. Region of Interest (ROI) Analysis: Utilizes statistical methods to compare characteristics of predefined brain regions, aiding in the study of specific diseases or functions.
3. Pattern Recognition and Machine Learning: These advanced methods, including machine learning algorithms, help identify patterns in MRI data indicative of specific diseases or conditions.
4. Longitudinal Analysis: Statistical models are employed for studies tracking brain changes over time, essential in understanding disease progression or development.
5. Network Analysis: Focuses on analyzing brain connectivity, using statistical methods to understand complex brain networks and their disruption in neurological disorders.

Voxel-Based Morphometry

Voxel-based morphometry is a popular technique in MRI analysis. This process involves focusing on specific regions of interest (ROIs) in the brain and analyzing the volume of these regions. The volume of a region can be calculated by multiplying the number of voxels in the region with the volume of each voxel.

Volume of an ROI

While our primary focus isn’t on calculating the volume of a ROI, those interested can follow these steps to mask an ROI and calculate its volume. Note that these steps require specific software and libraries.

Prerequisites

Ensure you have the necessary libraries installed. Installation of ANTsR is more complex than typical R packages. You can find detailed installation instructions here.

Step-by-Step Guide

1. Load Libraries:

   library(ANTsR)
   library(oro.nifti)

2. Read the MRI Image (NIfTI format):

   mri_image <- niftiImageRead(&quot;path_to_your_mri_image.nii&quot;, reorient = FALSE)

3. Preprocess the Image:
   This includes normalization, noise reduction, etc., and is highly dependent on your data and requirements.
4. Image Segmentation:
   The technique will depend on the quality of your image and specific requirements. ANTsR provides various tools for this purpose.For example, using Atropos (a multi-class segmentation method):

   segmentation_results <- atropos(a = mri_image, m = '[3,1x1x1]', c = '[2,0]', i = 'kmeans[3]', x = 1)

5. Extract and Save Segmented Images:
   This depends on how segmentation labels are defined. For instance:

   csf <- segmentation_results$segmentation == 1
   gm <- segmentation_results$segmentation == 2
   wm <- segmentation_results$segmentation == 3
   
   niftiImageWrite(csf, "csf_segmented.nii")
   niftiImageWrite(gm, "gm_segmented.nii")
   niftiImageWrite(wm, "wm_segmented.nii")

6. Calculate the Volume of Each Voxel:

   vres = voxres(t1, units = "cm")
   vol_csf = csf * vres

An example of an statistical study: Voxel-Based Morphometry in Alzheimer’s Disease

Consider a study with 30 adults over 55 with Alzheimer’s and 30 controls. The study spans over 2 years, tracking hippocampal volume changes.

• Load the alzheimer_hippo_vol.csv using read.csv() command in R.
• In the dataset, condition is a factor with two levels: control and alzheimer.
• At the begining of the study, MRI images are recoreded and Hippocampus volumes are calculated. The volume is listed in the initial_vol column.
• After 2 years, MRI scans are taken again, and the difference in volume is listed in the loss column.

Statistical Analysis in R

Perform the following analyses using the loaded data:

1. Plotting Marginal Means of Diagnosis: Visualize the average hippocampal volume loss in Alzheimer’s patients compared to controls.

2. ANOVA Analysis: Conduct an ANOVA to evaluate the effect of Alzheimer’s on volume loss. This analysis will help determine if the volume loss is significantly different between Alzheimer’s patients and controls.

3. ANCOVA Challenge: As an advanced challenge, use ANCOVA to evaluate the effect of Alzheimer’s on volume loss while controlling for the linear association between volume loss and initial volume. This analysis accounts for the initial volume of the hippocampus, providing a more nuanced understanding of the disease’s impact.

This case study provides a practical application of MRI image processing and statistical analysis in R, demonstrating the power of these techniques in understanding complex neurological conditions like Alzheimer’s disease.

Files to download:

1. alzheimer_hippo_vol.csv